Podcasts about AGI

The man who open-sourced the most-used AI image model in history sat down with the man who has spent a decade proving superintelligence cannot be controlled. Brian set up a debate. What emerged was something more unsettling than any debate. Roman Yampolskiy is the computer scientist who coined the term AI safety and author of AI: Unexplainable, Unpredictable, Uncontrollable. Emad Mostaque is the co-founder of Stable Diffusion and the only AI CEO who signed the pause letter. He now says he doesn't know how a pause could work. Yampolskiy thinks that is the only option left. The question underneath everything is simple: if you have a 50% chance of wiping out civilization and you build it anyway, what are you actually doing? We cover what AI safety researchers actually think the danger is, why nobody has published a paper, filed a patent, or shipped a prototype for controlling a superintelligence, what the Qwen weights being out means for the pause argument, and why Mostaque thinks swarm intelligence is the most dangerous and most unpredictable risk vector we have. What you'll hear: -Why both guests think P(doom) tells you less than you'd hope -What it means that no company, no lab, and no team has a patent on controlling superintelligence -Why the models the public receives are slightly lobotomized -The difference between an AI swarm and the ASI everyone is debating -Why giving every psychopath access to a cutting-edge intelligence weapon is incoherent safety strategy -What it would actually take to update Yampolskiy's assessment “We either do it, or we die. There is nothing for you to gain by doing it.” — Roman Yampolskiy CHAPTERS 00:00 A debate that wasn't a debate 00:36 Turing test, AGI, superintelligence: where are we? 02:02 The open source argument nobody wins 03:32 Pause frontier AI forever. Which button? 05:04 P(doom): parameterizing our ignorance 09:02 Nukes are inefficient. AI isn't. 11:46 The lobotomized model problem 18:04 Decade-old problems solved weekly now 26:18 We either do it or we die 31:10 Stop the training or stop the funders 33:34 Lipstick on a Shoggoth 35:50 No paper. No patent. No framework. 39:42 Train only on what you need 44:14 What lowers Mostaque's p(doom)? 52:00 Same future. Two perspectives. Get the transcript, fascinating bonus content, and my Monday M.A.G.I.C. Message: https://briankeating.com/yt Have a .edu email and live in the USA? You automatically win a meteorite: https://BrianKeating.com/edu Subscribe: https://www.youtube.com/DrBrianKeating?sub_confirmation=1 Support Into the Impossible on Patreon, get my weekly M.A.G.I.C. Message, unfiltered bonus content, and live monthly Office Hours with me: https://www.patreon.com/drbriankeating Join this channel for perks, monthly Office Hours, and your name in the Member Roster at the end of every episode: https://www.youtube.com/channel/UCmXH_moPhfkqCk6S3b9RWuw/join Featured Guests: Roman Yampolskiy on Twitter/X: https://x.com/romanyam?lang=en AI: Unexplainable, Unpredictable, Uncontrollable (book): https://www.amazon.com/dp/103257626X MIRI: https://intelligence.org Emad Mostaque on Twitter/X: https://x.com/EMostaque I.I.I. Inc.: https://ii.inc/ Stable Diffusion: https://stability.ai/ My books: Losing the Nobel Prize (memoir): http://amzn.to/2sa5UpA Think Like a Nobel Prize Winner: https://a.co/d/03ezQFu Focus Like a Nobel Prize Winner: https://a.co/d/hi50U9U Galileo's Dialogue (first-ever audiobook): https://a.co/d/iZPi9Un Twitter/X: https://x.com/BrianKeating Substack: https://briankeating.substack.com Blog: https://briankeating.com/blog Audio-only: https://briankeating.com/podcast #AIrisk #aisafety #stablediffusion #RomanYampolskiy #EmadMostaque #briankeating #intotheimpossible Learn more about your ad choices. Visit megaphone.fm/adchoices

Drew's book link: https://www.amazon.com/Preparing-Surv...Fortitude Ranch (Survival Community) website https://fortituderanch.com/about-2/Colonel Drew Miller (USAF, Ret.) is an honor graduate of the U.S. Air Force Academy with a Master's and PhD in Public Policy and Operations Research from Harvard University. He served as an Intelligence Officer, then Plans and Programs Officer in the Air Force, a Research Staff Member at the Institute for Defense Analyses, and in the Department of Defense Senior Executive Service.  He is the President of the Collapse Survival Institute and CEO of Fortitude Ranch, the largest survival-community network in the United States, and the author of Preparing to Survive in the Age of Collapse:  Political, Military, Foreign Policy, and Preparedness Reforms Vital for Our Survival.Drew's goal is to warn Americans that government is not providing honest warnings of the high and rapidly increasing risk of collapse disasters like an H5N1 pandemic, loss of the electric grid, AI being used to help bad people develop new Weapons of Mass Destruction, and super-intelligent AGI soon likely to start exterminating pesky humans according to top experts.  Government is killing Americans now with bad, often unconstitutional programs, protecting only top government/elected officials in a collapse, with Executive Orders authorizing government agencies to seize food and resources from private citizens in a collapse.Major changes in public policies, foreign and military policies, and preparedness are vital now if we are to survive in the Age of Collapse.#news #politics #politicsnews #survival #survivalskills #war #ai #pandemic

Are you chasing happiness, success, or the "next thing" in life, only to find that the moment you reach it, you're still not truly fulfilled?After more than 20 years as a civil engineer, Sarah Collins realised that the version of success she had been pursuing wasn't actually what she wanted. Her journey through depression, overthinking, mindfulness, meditation, and self-inquiry helped her recognise how thought patterns and conditioning can shape the way we experience reality. In this conversation, she explores why we so often fight against the present moment, believe the stories our minds tell us, and postpone happiness until some future event.Learn how to create distance from your thoughts instead of automatically believing or following them.Discover simple ways to practise acceptance in everyday situations and reduce unnecessary emotional struggle.Understand why happiness isn't something you need to achieve in the future, and how fully experiencing every moment, including difficult emotions, can change the way you live.Play the episode to discover how noticing your thought patterns, accepting the present moment, and letting go of the constant pursuit of future happiness can help you stop fighting with life and start living it more fully.˚KEY POINTS AND TIMESTAMPS:01:09 - Meet Sarah Collins: From Civil Engineer to Life Coach02:41 - Redefining Success and Letting Go of Society's Expectations05:48 - Living with Depression, Overthinking, and Low Self-Worth07:02 - You Are Not Your Thoughts10:29 - The Stories We Believe About Ourselves12:33 - Practicing Acceptance: Start with the Small Things15:34 - Rethinking Happiness as a Present-Moment Experience22:38 - Self-Inquiry, Spiritual Teachers, and Finding Your Own Answers29:21 - Closing Thoughts, Where to Find Sarah, and Final Takeaways˚MEMORABLE QUOTE:"The more you notice those thought patterns, the more you notice how it's creating your reality—you stop fighting with life and start living."˚VALUABLE RESOURCES:Sarah's website: https://www.livingfullywithsarah.com/˚Coaching with Agi: https://personaldevelopmentmasterypodcast.com/mentor

What's up, guys? Today I'm bringing you part 2 of a mind-blowing episode with former CIA intelligence officer and spycraft expert Andrew Bustamante. If you don't know Andrew, he's not only a decorated CIA veteran, but he's also spent years consulting with top organizations on national security and critical decision-making. Andrew is a master at deconstructing the global chess game between superpowers, and today, we go deep into why AI is the new arms race, what China is really doing behind closed doors, and what it means for the future of America and the world.We're talking about everything from the chilling pragmatism of the Chinese government, the perils of declining self-belief in the West, and what we need to do right now to win the future. Whether you're worried about rapid advances in artificial intelligence, the fate of democracy, or just want to know how to outsmart your competition in today's noisy world, this episode is for you.If you're hungry for actionable insights on how to think at the highest strategic level, and you want the clarity to navigate a world that's changing faster than ever before, you cannot miss this conversation. If you dig it like I did, please leave a review and help us get these vital ideas into the hands of everyone who wants to shape their destiny. I'm Tom Bilyeu, and welcome to Impact Theory.00:00 Japan's immigration challenges05:18 China's holistic tech integration07:25 Speculating on future AGI impacts11:36 Concerns about AI dominance12:57 AI advancement implications and concerns16:11 US needs its own AI19:36 AI and military applications23:02 China's strategy in Hong Kong28:27 American beliefs and financial hedging30:22 Feeling unsafe during COVID33:00 Future of the United States36:41 Finding optimism and making change42:08 Learning and knowledge cycle43:40 Using open-ended questions in sales46:12 Two questions and a confirmation strategyCash App: Download Cash App Today: https://capl.onelink.me/vFut/v6nymgjl #CashAppPod*Cash App is a financial services platform, not a bank. Banking services provided by Cash App's bank partner(s). Prepaid debit cards issued by Sutton Bank, Member FDIC. Cash App Visa® Debit Flex Cards issued by Sutton Bank, Member FDIC, and The Bancorp Bank, N.A., pursuant to a license from Visa U.S.A. Inc. See terms and conditions for the Sutton prepaid card, Sutton debit flex card, and Bancorp debit flex card. Cash App Green features, Savings, Direct deposit, Round ups, Overdraft coverage and Discounts provided by Cash App, a Block, Inc. brand. Visit cash.app/legal/podcast for full disclosure.Quince: Free shipping and 365-day returns at https://quince.com/impactpodWhatnot: Download the Whatnot app today and get free shipping on your first order.Ketone IQ: Visit https://ketone.com/IMPACT for 30% OFF your subscription orderEthos: Get a free quote at https://ethos.com/impactIncogni: Take your personal data back with Incogni! Use code IMPACT at the link below and get 60% off an annual plan: https://incogni.com/impact ATT Business: Switch to AT&T Business at https://business.att.comPique: 20% off at https://piquelife.com/impactWhat's up, everybody? It's Tom Bilyeu here:Want my help starting a business? Join me here inside Zero To FounderSign up for my AI Masterclass: AI MasterclassFOLLOW TOM:Instagram: https://www.instagram.com/tombilyeu/Tik Tok: https://www.tiktok.com/@tombilyeu?lang=enTwitter: https://twitter.com/tombilyeuYouTube: https://www.youtube.com/@TomBilyeuSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

What happens when you combine frontier AI, agentic systems, and quantum computing?In this episode of NEXT with John Koetsier, we chat with Mykola Maksymenko, co-founder and CTO of Haiqu, about how AI agents are dramatically accelerating quantum research and potentially scientific discovery as a whole.Maksymenko shares how an AI system was able to reconstruct months of his own PhD research in a fraction of the time, even identifying a bug in one of his formulas. He also discusses experiments involving genomics, molecular simulation, quantum chemistry, and condensed matter physics.The bigger question: do we really need to wait for fault-tolerant quantum computers with hundreds or thousands of logical qubits before quantum computing becomes useful?Maksymenko argues that useful quantum applications are already emerging today, particularly when AI agents help scientists discover algorithms, orchestrate workflows, and handle the complexity of working with noisy quantum hardware.We also explore the risks of increasingly capable AI systems, scientific guardrails, quantum utility versus quantum supremacy, and what happens when researchers can test ideas in a weekend that previously might have required months or years.Topics include:• Agentic AI for scientific research• Quantum utility vs. quantum supremacy• AI-assisted genomics• Quantum chemistry and molecular dynamics• Automating quantum software workflows• AI as a scientific collaborator and educator• The risks and guardrails of frontier AI• Why scientific discovery could accelerate dramatically00:00 — “I think AGI is here” 00:40 — Meet Mykola Maksymenko 01:01 — Why a quantum physicist started experimenting with genomics 02:18 — Reproducing years of research with AI and quantum computing 03:07 — The convergence of AI, agents, and quantum computers 04:26 — Are we entering the singularity? 04:45 — AI reconstructs six months of PhD research 05:28 — The risks of AI-assisted genomics and scientific discovery 06:46 — Pandora's box and the race for frontier AI 07:02 — What researchers are doing with the technology now 08:29 — Can quantum computers already do useful work? 09:21 — From quantum supremacy to quantum utility 11:06 — Molecular dynamics and quantum chemistry 12:12 — The minimum viable product of quantum computing 13:00 — Why Haiku moved deeper into agentic AI 13:30 — How much faster can AI make scientific research? 15:47 — AI as a scientist's educator and collaborator 16:51 — Running research experiments over a weekend 17:16 — What happens next for AI + quantum computing 17:38 — How AI agents operate quantum computers 18:59 — Closing thoughts

Group Chat News is back with the hottest news of the week including Meta agreed to pay $17 billion to 47 states to end the landmark trial over teen social media addiction and the stock went up. The guys get into why a record setting settlement paid out over ten years barely registers on a company this size, why the Big Tobacco comparison falls apart when you look at the numbers, and whether the new safeguards Meta agreed to will actually change anything. Time caps, overnight blocks, no notifications during school hours. The case for optimism is that a kid who never builds the habit never gets addicted. The case against is that nobody enforces any of it. Then the robots. China staged a robot Olympics, and the argument is that it was the smartest piece of marketing the country could have run get the whole population cheering for the machines before those machines start taking jobs. That leads into the argument about whether AGI is actually here, a $1,000 bet over how long before a robot unclogs your toilet, and the only AI use case anyone can agree is worth having: making an insurance company answer the phone. On the money side, Dick's Sporting Goods had its worst day in three years, down about 30% but the core business is growing. The problem is the $2.4 billion Foot Locker acquisition and a sneaker market that's gone quiet. Plus why billionaires should stay off the internet right now, why sports teams have become tax shelters, and what these new owners are getting wrong about the cities they just bought into. Crypto ends the episode with the most bullish structural change in years. Group Chat News, every week. If you enjoy the show, please leave us a 5 star review on Apple or Spotify it helps more than you know.

Artificial intelligence is becoming increasingly capable of doing more than processing information. It can recognize patterns, interpret human behavior, influence decisions, and increasingly interact with the world in ways that resemble aspects of human cognition. In this episode, we dive into this complex subject with Rana Gujral, AI entrepreneur, CEO of Behavioral Signals, and author of the upcoming book The AI Instinct: The Future of AI and Human Decision-Making, releasing August 31, 2026. Rana brings a perspective shaped by years of working at the intersection of artificial intelligence, cognitive science, voice technology, and human behavior. His work explores how machines can interpret signals such as tone, emotion, intent, and risk — and what happens when these capabilities begin influencing the decisions people make… The conversation explores: How AI is changing the way humans make decisions. The relationship between artificial intelligence and human cognition. What Cognitive AI can reveal about emotion, intent, and behavior. The evolution from today's AI systems toward AGI and more advanced forms of intelligence. As CEO of Behavioral Signals, Rana works on AI systems that analyze speech and behavioral signals to help organizations understand intent, emotion, and risk. His career has also included founding and building technology companies, leading product innovation at Logitech, and working across AI, robotics, cybersecurity, and voice intelligence. Connect with Rana: Personal Website The AI Instinct: The Future of AI and Human Decision-Making Behavioral Signals LinkedIn Facebook Instagram X

Dan Nathan sits down with Matt Turck, Managing Director at First Mark Capital and creator of the annual MAD (Machine Learning, AI & Data) Landscape, for a wide-ranging look at where AI investing stands right now. They cover the power-law dynamics driving venture dollars to a handful of companies, why Nvidia is starting to look like "the bank" of the AI industry, Anthropic's surge past $65 billion in revenue and its first profitable quarter, and how OpenAI, Microsoft, Google, and Meta are each positioning for what comes next. The conversation turns philosophical with a discussion of AGI, superintelligence, and the idea that Altman, Musk, and Amodei are all, in their own ways, trying to build God — before closing with a deep dive into China's AI progress, open source, and the robotics race. Matt also talks about his own podcast, The MAD Podcast, and his Data Driven NYC event series. Links Referenced The MAD Podcast (Apple Podcasts) The MAD Landscape (Matt's Website) Nvidia Has Become a Banker to the AI Boom, Putting It on Dangerous Ground (WSJ) Read "AI 2027" and "AI 2040" —FOLLOW USYouTube: @RiskReversalMediaInstagram: @riskreversalmediaTwitter: @RiskReversalLinkedIn: RiskReversal Media The financial opinions expressed in Risk Reversal content are for information purposes only. The opinions expressed by the hosts and participants are not an attempt to influence specific trading behavior, investments, or strategies. Past performance does not necessarily predict future outcomes. No specific results or profits are assured when relying on Risk Reversal. Before making any investment or trade, evaluate its suitability for your circumstances and consider consulting your own financial or investment advisor. The financial products discussed in Risk Reversal carry a high level of risk and may not be appropriate for many investors. If you have uncertainties, it's advisable to seek professional advice. Remember that trading involves a risk to your capital, so only invest money that you can afford to lose. Derivatives are not suitable for all investors and involve the risk of losing more than the amount originally deposited and any profit you might have made. This communication is not a recommendation or offer to buy, sell or retain any specific investment or service.

There's a lot to unpack about the economic effects of artificial intelligence. It's clear that artificial intelligence is having a moment (to say the least) and that it has a profound impact on global GDP. But is it just a boom that will bust? Ed Zitron, author and host of the “Better Offline” podcast, is deeply worried about the long-term viability of the industry. He points out that AI lacks the basic traits that have been associated with previous software booms. This raises the question: is AI running more on unsustainable costs and vibes rather than long-term profit potential? According to Ed, the answer is clear. Note that is episode was originally released on 6/9/26.  Sign up for MS NOW Premium on Apple Podcasts to listen to this show and other MS podcasts without ads. You'll also get exclusive bonus content from this and other shows. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Dr. Roman Yampolskiy is a professor of computer science at the University of Louisville and one of the world's leading AI safety researchers, credited with coining the term "AI safety" over a decade ago. In this conversation, we break down why he believes superintelligence may be fundamentally uncontrollable, and the recent incidents of AI models hacking, lying, and blackmailing their way out of containment. We also get into the surprising relationship between AI and bitcoin, the collapse of trust from deepfakes, and what happens to jobs and society once we cross the AGI threshold.=========================Arch Public is an agentic trading platform that automates investment strategies across Stocks, Commodities, ETFs and Crypto. Whether you're rotating into AI & Gold, allocating to the S&P 500, or accumulating Bitcoin, Arch Public executes your plan 24/7 without ever taking custody of your assets or funds. Sign up today at https://www.archpublic.com, and start your FREE automated trading strategy! =========================GalaxyOne is a financial technology platform built for people who want their cash working harder. Open an account with promo code POMP and deposit $10,000 to earn a $3,000 bonus. See site for promotion details → https://go.galaxy.app/HMiq/p57n69yy Galaxy Premium Yield is an investment note issued by Galaxy Digital LP and guaranteed by Galaxy Digital Holdings LP. It is not a bank deposit, is unsecured, and is not FDIC or SIPC insured. U.S. accredited investors only. Cash deposits held at Cross River Bank, Member FDIC. Securities products are not FDIC insured, not bank guaranteed, and may lose value. GalaxyOne Crypto is not FDIC or SIPC insured. Terms apply.=========================0:00 - Intro0:50 - Have we already hit AGI?2:51 - Why we can't control superintelligence5:14 - Could a global ban on superintelligence actually work?8:45 - Are AI labs really taking safety seriously?15:52 - Mass job loss & the economics of AGI22:03 - The surprising link between AI & bitcoin29:07 - A world of AI abundance, & why scarcity wins31:45 - AI watermarks, & the death of trust35:33 - Pros & Cons of physical AI world38:40 - What would it take to trust humanoid robot?39:50 - Brain computer interfaces: a good idea?42:25 - Rise & risks of deapfakes 49:40 - Controlling AI: Governments vs private companies54:07 - Open source vs closed, & the US vs China AI race57:34- Is bitcoin the one crypto project built to last?1:01:40 - Limitations of physical AI: bottlenecks & shortages 1:03:50 - Impact of healthcare with AI 111:06:09 - What would it take to actually declare AGI?1:09:35 - How the public can help with AI safety 

…Or at least, so says Mark Zuckerberg, who argues that EVERYONE should own their own AGI, self-aware super-computer-genius-machine. But is it REALLY, though?

What if 10 days of silence and meditation could help you become calmer, more aware, and better equipped to face the challenges of everyday life?If your mind is constantly busy, you struggle with cravings or emotional reactions, or you simply feel drawn to explore meditation more deeply, a 10-day Vipassana meditation course may offer a powerful way to step away from daily distractions and observe what is happening within you.In this episode, Agi speaks with Udo Marquardt, an assistant Vipassana teacher who first attended a course in 1998 and has since experienced and served many courses. They explore what Vipassana meditation is, who it is for, what actually happens during the 10 days, and why the practice can create meaningful changes in the way we respond to ourselves and the world around us.By listening to this episode, you will:Understand what to expect from a 10-day silent Vipassana meditation course and why the structure is designed the way it is.Discover how developing awareness and equanimity can help you respond differently to cravings, anxiety, difficult emotions, and challenging situations.Learn why regular practice after the course can help sustain the greater calm, clarity, and resilience developed during the retreat.Press play to find out how the practice of Vipassana can help you become more aware, more equanimous, and ultimately happier in everyday life.˚KEY POINTS AND TIMESTAMPS01:15 - Introducing Udo Marquardt03:14 - Udo's Journey to Vipassana07:54 - Who Vipassana Meditation Is For14:26 - Why the 10 Days Are Structured This Way16:27 - What to Expect From the Course20:32 - Reactions, Habits, and Overcoming Cravings23:40 - Awareness, Equanimity, and Becoming Happier26:40 - How to Prepare for a Vipassana Course29:05 - Closing Thoughts and Advice˚VALUABLE RESOURCES:Vipassana website: https://dhamma.org˚Send us a textSupport the showSubscribe to our weekly email: https://personaldevelopmentmasterypodcast.com/email---A personal development podcast for midlife professionals, offering mindset tips and practical tools for personal growth, self mastery, personal mastery, and purposeful living. Discover psychology tips for emotional intelligence and growth mindset, including overcoming impostor syndrome and building self mastery.Personal Development Mastery features personal development interviews and solo episodes empowering professionals, entrepreneurs, and seekers to cultivate self mastery and create a meaningful, fulfilling life aligned with who they truly are.To support the show, click here.

Today, Razib talks to Collin Hogue-Spears, a cybersecurity and artificial intelligence governance expert with more than 20 years of experience in technology, product management, federal compliance and software security. Hogue-Spears is an independent researcher and the author of From Lab to Life: How AI Works in China. His analysis has appeared in publications including The Wall Street Journal and Politico. Razib and Hogue-Spears first talk abut China's rapid AI development, driven by provincial initiatives and significant investments in infrastructure. Hogue-Spears emphasizes China's focus on practical AI applications, contrasting it with the U.S.'s emphasis on AGI. He predicts China's dominance in AI, particularly in embodied AI, and warns of the potential global impact if China surpasses the U.S. in AI development. The conversation highlights the striking differences between the U.S. and Chinese technological ecosystems.

Episode 403 of RevolutionZ takes a side trip away from our current ideology sequence to revisit the issues surrounding the development of Artificial Intelligence. Media is full of smart sober folks saying the end is near but also with smart sober folks saying utopia is near. Are we on a deadly trajectory to hell? Or is heaven on earth the next stop on the technology express? Or maybe it is just two-headed hype. Just another shiny new technology but barely more than that? More immediately, what has happened of late? What is likely to happen next? What should we ourselves work to cause to happen?AI is getting “better” so fast that arguing about today's mistakes can miss the main fact that matters most: the slope of the curve. We consider what happens when large language models evolve into agentic systems. When being able to spit out "the next word" so effectively that passing the Turing Test is a lark, to being able to plan, to autonomously act, even to recruit other AIs, and to self-improve in ways even their creators can't predict not only impinges on what used to be how humans earn and live, but also, how we manifest our humanity. We ask a simple question with big implications: if you had a three-position switch, would you let AI continue full speed, pause it until strict enforceable rules exist, or shut it down forever? We dig into the motives behind AI's trajectory. The story involves innovations; but the crux is motivations and incentives. When CEOs believe the first country and company to reach artificial super intelligence, or ASI, wins everything, for them to seek safety becomes in their eyes a competitive handicap. The public becomes collateral. We consider how the emerging "who is going to win, it better be me" emphasis means for AI safety, regulation, data centers and "compute," and why “trust the companies” and "trust the government," is not a plan. We also get specific about real-world impacts: deepfakes that shred shared reality, AI-enabled fraud that scales without limit, surveillance that tightens lives, and job displacement that turns productivity into pink slips. And then we step outside the box of familiar AI discussion to ask even if we imagine perfect intentions--no nefarious apocalypse of greed and hate--we still have to wrestle with the impact of unintended consequences like outsourced reading, outsourced care, and the slow erosion of the skills and bonds that make us human. This episode is unsettling. Share it with a friend who thinks AI is just another tool, all that matters is who owns it, and ask yourself and others: do we need fast, faster, and fastest development, as now, or do we need a moratorium, a full shutdown, or something else entirely? Data centers are certainly horrible. To oppose them is more than warranted. But if the companies were to find a way to ASI without data centers, would that suddenly be something to celebrate--or to terminate? Is AI an existential danger? Is it a path to civilized living at last? Or is it just a lot of hype?Support the show

1044. Are rising healthcare costs ruining your budget? Laura answers a listener's question about how to maximize every tax advantage available for healthcare costs. You'll learn the rules for deducting them on your tax return or paying them with tax-advantaged savings accounts like HSAs and FSAs. We'll cover which expenses are tax-free and simple strategies to optimize your healthcare spending.Key Takeaways:You can only claim the medical tax deduction if you itemize deductions on Schedule A instead of claiming the standard deduction on your tax return.You can only deduct unreimbursed healthcare expenses that exceed 7.5% of your adjusted gross income (AGI), making the medical deduction best for years with high medical bills.Tax-advantaged medical savings accounts are powerful because they allow you to save 20% to 35% on qualified costs without claiming a medical deduction.Health savings account (HSA) balances roll over forever, can be invested for tax-free growth, and can be withdrawn penalty-free for non-medical expenses after age 65 (subject to ordinary income tax).Flexible spending accounts (FSAs) and health reimbursement arrangements (HRAs) are employer-sponsored perks for cutting healthcare costs.You cannot claim an itemized medical deduction on Schedule A for any healthcare expense paid for or reimbursed using pre-tax funds from an HSA, FSA, or HRA.Lawmakers have expanded HSA, FSA, and HRA qualified expenses to cover various over-the-counter (OTC) medications and products.Discover more from Money Girl!FacebookNewsletterTranscripts available at QuickandDirtyTips.com.Email: Laura@LauraDAdams.com or leave a voicemail: (302) 364-0308. Hosted on Acast. See acast.com/privacy for more information.

When we first dicsussed the Summer of Simulative AI in 2024 we knew it would be a brief summer, but it has recently come back with a vengeance with SimGym in April and now Simile AI's $2B Series B, backed by GreenOaks and Index Ventures with prominent backers like Fei-Fei Li and Andrej Karpathy, running tens of millions of simulations for Fortune 100 clients like CVS and 85–99% accuracy vs human focus groups. Time to catch up on why this Second Summer of simulation is working!From creating Smallville, the landmark 2023 paper on Generative Agents that showed AI characters could remember, plan, socialize, and develop emergent behaviors, to now building foundation models of human behavior, Joon Sung Park is trying to answer a much bigger question: what if we could simulate the world before making decisions in it? In this episode, the Simile co-founder and CEO joins us to unpack the path from generative agents to digital twins, why today's frontier models still fail to capture how humans actually behave, and what it would take to eventually simulate all 8 billion people on Earth.We go deep on Simile's approach to modeling human behavior: long-form interviews, observational and transaction data, randomized controlled trials, population-level and individual-level models, and post-training on the causal mechanisms behind why people make decisions. Joon explains how his research created digital twins that reproduced human behavior and attitudes 85% as accurately as people reproduced their own responses, why models optimized to be rational can be bad simulations of irrational humans, and why understanding “social physics” may require changing model weights rather than simply prompting frontier LLMs.We also explore the much larger ambition behind simulation: testing products and policies before deploying them, finding counterintuitive paths toward desired outcomes, modeling emergent behavior across entire societies, and potentially tackling problems like climate change, democratic instability, and UBI. Joon reflects on scaling laws for simulation, the economics of data-center-scale simulated worlds, the connection to Thomas Schelling and psychohistory, why simulation is surprisingly similar to painting, and whether we might already be living in one.We discuss:* How Smallville and Generative Agents led to Simile* Why Joon's team asked: “What if we can just recreate the world that we live in?”* Why useful personal agents require deep models of their users* Memory architectures, Markdown files, and the limits of prompting* “Social physics” and behavioral foundation models* Why web data captures what people say more than what they actually do* Interviews, transactions, observational data, and randomized controlled trials* Why predicting the future matters less than understanding how to shape it* How Simile creates representative simulated populations* Simulation versus prediction and the connection to Foundation's psychohistory* How to evaluate simulations instead of simply stacking LLM hallucinations* Creating digital twins of 1,000 real people and reaching 85% behavioral accuracy* Why frontier models can struggle to reproduce real human behavior* Why good simulations need to reproduce human biases and mistakes* Post-training models on randomized controlled trials* Population-level versus individual-level simulation* Scaling laws for human simulation* The long-term ambition to simulate all 8 billion people on Earth* Whether simulations could help solve climate change or detect collapsing democracy* Thomas Schelling and the history of agent-based modeling* Why future simulations could require an entire data center* Multi-agent simulations and what happens when simulated people interact* Replacing expensive human panels with synthetic populations* Why market research is only the starting point for simulation* Why Joon sees simulation as surprisingly similar to painting* Using simulation to study questions like UBI* Whether we are already living in a simulation* Why AGI and simulation may be the twin technologies of advanced civilizationsJoon Sung Park* LinkedIn: https://www.linkedin.com/in/joonspark* X: https://x.com/joon_s_pk* Website: https://www.joonsungpark.com* Simile: https://www.simile.comTimestamps00:00:00 Introduction and Joon's Path from Art to AI00:01:46 Smallville, Generative Agents, and the Origins of Simulation00:05:03 “Let's Just Create a World” and the Future of Personal Agents00:09:53 Social Physics and Behavioral Foundation Models00:14:08 Prediction vs. Simulation: How Do You Shape the Future?00:16:59 How Simile Models Real People and Populations00:25:35 Evaluating Simulations, Digital Twins, and 85% Accuracy00:30:23 Post-Training Models to Reproduce Human Behavior00:40:04 Scaling Laws and Simulating 8 Billion People00:43:10 From Schelling to Society-Scale Agent Simulations00:46:13 The Cost and Economics of Simulating the World00:52:05 Real-World Use Cases, Synthetic Populations, and the Market00:57:27 The Future of Simulation, Painting, and UBI01:04:23 Are We Already Living in a Simulation?01:06:08 Building Simile and HiringTranscriptIntroduction: Joon Sung Park, Simile, and the Story So FarVibhu [00:00:00]: Today, we have Joon in the podcast. Excited to kick this one off. Very exciting company. I wanna kick off and ask you the question, talk us through the story of your life. How have you gotten here?Joon [00:00:13]: Yeah, for sure. I'm really excited to be here. A story of my life. So I was born in Korea, and I lived there for a good 11 years or so of my life, and then my family moved to Boston. So we moved when I was 11, and my parents were doctors, so they were going through their postdoctoral studies. My dad was a surgeon, so he was doing his sabbatical years at the Boston Children's Hospital. So I grew up there, not too close to tech. I was very much a music and artsy, painting kind of guy.Vibhu [00:00:49]: Painting.Joon [00:00:49]: Exactly. I got into painting a little bit later, in high school, but that's what I used to do. And then I grew up mostly in the East Coast after Korea. So I lived a good number of years in New Hampshire, and then I went to college in Pennsylvania. And I got into more of this tech scene, in college. So I was originally trained to be an artist. I thought that would be my professional career. So it wasn't a hobby. It was like, “Hey, let's make a living out of this.” And then gradually, I got really interested in this idea of, hey, the greatest artist often creates their own medium, and the best medium that we had available today was in computation. So I decided to go deeper into that, and one thing led to another, and we can go deeper into this, but I decided that research was something that I gradually got interested in, and here I am.Smallville, Generative Agents, and the 2023 Breakout PaperSwyx [00:01:46]: So there's a lot that you packed into the research components. You had one of the best papers of 2023, which was the generative agents paper, commonly known as the Smallville paper.Swyx [00:01:58]: Feel free to call back to anything else that you mentioned, but most people would have heard of you from this. Do you have any statistics on how many people have, like, read it? arXiv gives you something, right? Some stats.Joon [00:02:10]: Yeah, it's a good question. How many people have read it, I'm not sure.Joon [00:02:14]: I know we do keep track of citations, and they are going up quite fast.Swyx [00:02:23]: Yeah, Google Scholar has 7,200 citations.Vibhu [00:02:25]: I feel like it made a bigger hit than that, and it was a pretty instrumental paper. It got cited so many times.Swyx [00:02:34]: It is frequently the answer when people ask, “What is the best paper you've read recently?” It's this one.Vibhu [00:02:39]: I thought the memory component was pretty underrated. It was a very good early memory system, and one of the biggest papers.Foundation Models and the Search for Killer ApplicationsJoon [00:02:47]: Yeah, so maybe I can talk a little bit about how this particular paper came together. So when I got into research, it was back in 2020 when I started my PhD program at Stanford, and that was the year, when we were about to get GPT-3 to be available. So we already had GPT-2, and you could sense that there was this new class of models that was just becoming available in the market, and the team got very intrigued. And the general consensus was, “Well, is this model going to be useful for anything?” “It's really strange that these models are not trained to do any particular task.” But we decided to take a bet. So a large group of scholars at Stanford, and it was led by one of my co-founders, Percy Liang, and we came togetherSwyx [00:03:35]: Who coined foundation models.Joon [00:03:36]: Who coined the term foundation models. We wrote this paper, where that term came from called Opportunities and Risks of Foundation Models. And during that process, really the thing that I started to think deeply about was, here is a model that is fundamentally new in our ecosystem. The reason why this was new was it wasn't, again, trained to do anything in particular, but its premise was it could do anything and everything. It was like a stem cell, if you were to take a biology analogy. And I got really interested in this idea that, well, if we were to really think about what are the killer applications that this particular technology would enable, what would that be? Many of my colleagues were using this for simple classification, simple generations. Interesting that these models can do that, but from an interaction perspective, not that interesting. We've known how to do that for many decades. And what we came down to was these models are trained on this very broad data from the web, right? So these are human behavioral data. It's social media, Wikipedia, all these data. So if you poke at the right angle, then you could see human behavior that would just pop out that's quite realistic, and we've never seen that before.The Time Machine Game and Recreating the WorldJoon [00:04:45]: So that got us really interested. The exercise that we decided to do, with this particular group of colleagues, Michael Bernstein, Percy Liang, and myself, who ended up becoming my co-founder at Simile, we sat down and we played this game that we call the time machine game.Joon [00:05:03]: Imagine we were to get on a time machine and fast-forward 10 years and look back. What would have been the single application that will have mattered that would be the most interesting and inspiring? And when we thought, “Well, what if we can just recreate the world that we live in?” it's really hard to get more ambitious than that. Like, let's just create a world.Joon [00:05:24]: And that's where we started. And initially, we had this paper that was a precursor to the generative agents paper called Social Simulacra.Swyx [00:05:32]: Before you go further, were there other candidates for the most ambitious thing in the time machine exercise? What was number two or number three?Personal Agents, User Models, and Why Simulation Came FirstJoon [00:05:44]: There is a close second that we were considering, which ended up becoming more of these automation tools, especially the vision around really personalized agents that would do things for you.Swyx [00:05:59]: That's also happening.Joon [00:06:00]: It's also happening. But it was interesting for us, right, in that the reason why, we decided to go with the idea of simulation, one, I was a huge science fiction nerd, and this idea of creating simulation, I was personally really just fascinated. I loved the idea. It's really cool to see, like, a game town like this and just see these agents live in it. But at the same time, my bet was if you were to create a really amazing personal assistant out of this technology, what you need first is an amazing model of your users. So I told a model, “Hey, can you go buy late dinner for me?” And it orders Hawaiian pizza, and I do not like pineapples on my pizza. Then it totally failed. The way for it to not make that mistake is only by having a deep understanding of who I am. And I gave a very simple and dumb example here, but you can imagine how this core understanding of people is instrumental. This is how, if we have our family and closest friends, they have a good mental model of who we are. That's the basis of our social connection. So our bet also was this technology around simulation, creating accurate representation of people ought to precede the more complex agents that would automate the world that we live in. So that was the bet. But that was a very close second, and I'm still very much fascinated by it. I think there's a lot of interesting work that's going around. My hot take here, though, is I don't think we've seen a true personal assistant that's useful, in ways that meet the ambition of that particular line of work. I think there are early applications that are interesting, and if you talk to even ChatGPT nowadays or Claude, they know a lot about us. So a lot of the generation it's doing, I do think it's much more tailored, but I think the ambition is quite large in that field, and I don't think we quite have all the right ingredients just yet.Swyx [00:08:01]: So OpenClaw and these personal agents, what do you want to see from them that they don't currently have?Memory, Markdown, and the Limits of PromptingJoon [00:08:09]: I do think it's slowly getting there, but I do generally want them to have much deeper understanding of the person. Right now, you look at the models. OpenClaw, what it's leveraging is a Markdown file, and I think it's quite clever, right? So if you look at the generative agents paper, this was the same intuition that we had, where initially when we were creating the memory architecture for the generative agents, and, like, this is, like, back in 2022, so we didn't really quite have the idea of even agentive architecture or the term agent. But the intuition that we shared with some of the work that's coming out today was we initially thought, “Well, do we want to make the memory into, let's say, knowledge graph? Do we want to train a bespoke model?” All of these things. And what we decided to do was, “No. Just forget about all this.” These language models are quite good at modeling text and understanding and reasoning about text. So just put everything in a Markdown file or a text file. You're done. I thought that was quite interesting that we could do that, and there's a lot of strength in doing that. But also, there are limitations. It's the way you retrieve and make sense of data that's extremely large, it takes a lot of work. So I think that technology is getting better. I also do, however, think, there are certain things you just cannot shape just by prompting the model. So to some degree, you do need to touch the parameters of the model itself. So there is this work that I do think does need to happen, and it is happening. The question is, how far can we take it? How do we source data, and how do you also create an ecosystem where people are continuously feeding data to this model so it's learning about you?Vibhu [00:09:50]: What's the intuition between why you need to do it in the model?Social Physics and Behavior Foundation ModelsJoon [00:09:53]: My intuition behind the actual when do you train or even post-train a model versus just prompt a model is if the model has to learn the underlying physics of the world that it's operating in. So it has to learn new social physics. The places where it doesn't have to train are the places where it already has the physics. We trust the physics. It already has the base statistics, but it's just trying to react to an environment. Then I think you can just prompt your way into getting the actions out of it. I don't think the models that are out in the open have yet learned the complete mapping of social physics of humanity. This is one of the core theses of Simile, right? And one of the core reasons why that is the case is if you look at the data that the model was trained on, these models were trained on the web data, like, whatever was available on the web. And these are really interesting data sets, but they are fundamentally the self-exposed attitudinal data with some behavior data that's sprinkled around here and there. And it has yet to learn the really deep behavioral nature of people, not just what people say they do online, but what they do in real life. And this is one of what I would consider to be the dark knowledge of humanity that we haven't quite captured. And it's these data that would also need to get factored into the model creation.Vibhu [00:11:21]: You call it behavior foundation model.Vibhu [00:11:23]: There's a good one-liner here, but outside of that, what type of data do you need? What are you changing on the model level? How do you go about modeling, doing a behavior foundation model?The Three Data Buckets: Interviews, Behavior, and CausalityJoon [00:11:35]: We think about data in three buckets. So one bucket is interview data. It's quite interesting. Rich qualitative data is interesting. It's not behavioral, but we would literally ask people, “Hey, tell me the story of your life.”Vibhu [00:11:53]: It's just what we're doing here exactly.Joon [00:11:54]: The question that you all asked at the beginning of this interview literally is the question we also ask. And we ask our participants to go a little bit deeper, than how far I went. Maybe I can give more of my life story in lieu of this. But the reason why that data is interesting is by learning about this very long-tail information about people, you get a lot of texture around this model, like, this person as a model. So even understanding their childhood memory or even their trauma, their first love, these things, quite informative in ways that's really hard to predict. So that's one. Then there are two tranches of what I would consider to be the behavioral data. One kind of behavioral data is observational. So these might be like transaction data, or these might be data that you can get by scraping the web, right? So you can imagine why these data sets would be interesting, right, because they give you the base statistics of people's behavior.Joon [00:12:55]: But then there is the last category of data, that I personally think is perhaps the most important, which is the data that describes the causal mechanism, the whys of people. Some of this is covered by the interview data, the qualitative, because people talk about why they made certain decisions. But really, where you get to see the most behavioral aspect of this is in randomized controlled trials, like RCTs. Imagine you have the same setup, but you have a few different variables that you are trying to tweak. Can you get realistic human behavior out of it in ways where, imagine you had this particular option. Imagine you're even trying to choose whether you're going to drink coffee or not. The day you drink coffee versus the day you didn't drink coffee, does your behavior change? That's a data set that describes a causal mechanism. This is quite important in modeling people. The reason why this is important is oftentimes when people come to us, or not just to us, but the reason why people are interested in simulation isn't because they want to predict the future. If you're trying to win against the stock market, predicting the future is interesting.Prediction vs. Simulation: Shaping the FutureJoon [00:14:08]: But most people, most decision-makers, what they want to know is, how can we shape the future? It doesn't really help you to hear that your sales are going to tank in two quarters. They're just gonna say, “Wow, that sucks.” What they want to know is, well, what do we need to do now to avoid that future? That's the causal mechanism. And this is also very hard data to come by, right, because the world is our ground truth, but it happens once. So in a very controlled setup where everything is equal except for one variable, this kind of data set rarely happens. So this is a reason why this data set is both hard to come by and quite important if you're trying to model human behavior.Swyx [00:14:50]: So behavior, I think, is the hardest data set to acquire. What is out there? What is even possible? You're not going to know a lot of details about my life. I don't even have data for myself on my own health or habits, and I just don't log everything. So how can you have that data?Joon [00:15:14]: So we run a lot of randomized controlled trials.Swyx [00:15:17]: But you put people in the lab, they watch them sleep, or what?Joon [00:15:20]: We do care a lot about the consent process. People know that we invite them to be a member of this community to both share data and have themselves represented in different forms. But we bring a lot of people to the lab, or virtual lab, where we design experiments that would pose them real behavioral decisions. And often in these experimental setups, what makes the difference between what is attitudinal versus behavioral is whether the stake in your decision is real. That's ultimately what makes it behavioral. So in these setups, we are inspired by our colleagues in social sciences, psychology, and so forth. So when they run studies, the techniques they utilize is imagine there's an online store that you're inviting people to come by. Then whatever they purchase in this experiment, they actually get that item delivered. Like, these are the things that make the stakes real. So we run a lot of these experiments, and we also do partner with firms. Right now, we also have customers who are quite excited to at least give us a glimpse of the behaviors that their users exhibit so that we can get a little bit deeper understanding of how people behave in these different platforms.How Customers Use Simile: Populations, Queries, and ExperimentsVibhu [00:16:39]: I think on the customer side, they have a lot of data about their users, who has bought. They have the action data.Vibhu [00:16:47]: Can you walk us through an example of what someone comes to you for? What questions would they want solved? Do you customize a model for them? Do you have something off the shelf? What does that look like?Joon [00:16:59]: Today, when people leverage our models, it's often to better understand the population of their interest. So usually, the start of the relationship, we come together and hear about what population they want us to model, right? So it might be that if you're a CPG company that's selling to all of the US, then maybe it's fairly straightforward. You want to model the gen pop of the US. But at the same time, if there is a vertical or if there's a market that they're trying to go into, imagine, they want to better understand, let's say, people in their 20s and 30s living in California. That's a much more specific population. So we hear about this population, and we go recruit these people, with consent, and with incentives, and we collect some of their data and create a model of these people. Then what our product allows you to do is query them. So it can take as input a filter that is a description of the population that you want to talk to, just like the one I just mentioned, and an environment. The environment can literally be survey questions, behavioral experiments, It can be A/B testing. Oftentimes, the core use cases are things like concept testing, to start with. But also, people sometimes want to do focus groups or one of the fun use cases that we also serve is even modeling things like earnings calls for public companies.Joon [00:18:21]: So these are the use cases that we often start with.Swyx [00:18:23]: Concept testing, is that an established term? I've never heard of concept testing.Concept Testing, Gallup, and PoliticsJoon [00:18:27]: Yeah. So it has to do with they have, let's say, different messaging, different products, different ideas.Swyx [00:18:32]: It's like a marketing exercise.Swyx [00:18:33]: Okay, got it. Got it. Politics?Joon [00:18:36]: We do, have a strategic partnership with Gallup, and of course, Gallup is deep into policy space and so forth. Right now, we have not worked deeply with politics, like that area just yet, however.Swyx [00:18:49]: I'm curious if there is demand or if they really would have different needs that somehow fundamentally don't mix with your existing, users or people.Joon [00:19:00]: I think there's certainly demand.Joon [00:19:02]: But we are very much mindful of how this technology gets adopted and the societal impact that we'll end up having with this technology. And I do see politics as an area where a company has to be particularly thoughtful about the way they operate and make impact. So this is where we also want to make sure that we form enough of guardrail and perspective on how to leverage this technology before we go on to serve markets like the politics.Swyx [00:19:29]: I'll give people an example. one of my favorite shows is The West Wing. I don't know if people have watched.Swyx [00:19:34]: One of the key storylines is, like, the president has, multiple sclerosis, but they haven't. they need to figure out how to disclose it. So they run a poll with a fake governor and ask people to respond on the poll,Counterfactuals, Polling, and When Simulation Is UsefulSwyx [00:19:47]: They try to make decisions based on the results of that poll on, like, how well they'll be received, like where, how should we play this?Swyx [00:19:54]: And I'm like, well, I think those counterfactual things, I would use a simulation for this if I could trust it.Joon [00:20:01]: For sure.Joon [00:20:02]: In that show, how'd it go?Swyx [00:20:04]: In that show, it was, like a foregone conclusion. They were like, “We know it's bad. We just don't know how bad.” And then the poll came back. It was like, “It's really bad.” And then they just did it anyway.Joon [00:20:14]: Part of it is to show, right? So you're, you're looking at the ideaSwyx [00:20:17]: Maximizing drama.Joon [00:20:18]: How bad could it be? Oh, it's horrible.Swyx [00:20:20]: And to some extent, I think that is part of the trick of the, or the challenge or with being a customer of yours, which is that if I know it's. if I roughly know and can intuitSwyx [00:20:35]: What the effect is going to be, do I need you? What sensitivity of it, of effect do I need in order to make a decision, right? So for example, if I, my approval rating is 50%Swyx [00:20:48]: And I, they have this negative piece, news item comes out, and it drops to 30.Swyx [00:20:52]: If it drops to 20, if it drops to 40, do I care? No. It, I know it drops. It's negative. So when do I care about simulations?Joon [00:21:01]: You do something that's clearly bad, that's not popular, and people don't like you, like, yeah, it's likeSwyx [00:21:05]: You don't need a simulation.Joon [00:21:07]: Yeah. Well, so there are a couple of things. one is, there are use cases where, like every day, developers, designers, policymakers, marketers, every single day, they create assets. They create new products. And turns out, it's many of the decisions in hindsight is obvious. Yes, of course this is bad, but we still run those studies because understanding the magnitude and understanding how acute something is quite difficult, even if, we feel like, of course, like this makes sense. this is the reason why we make so many mistakes. Like, every time somebody goes online and say something that has huge backlash, you look at that and like, “What an idiot.” However, it's tough. That's one. There's also another aspect here, which is, again, this is the reason why simulation is different from prediction. In simulation, in the ideal case scenario. So what simulation is trying to show is it's trying to show each step of the way or each step that we need to take to get to a certain outcome, right? So in the most advanced simulations, sometimes the next step that we're suggesting might be quite counterintuitive. The analogy that I sometimes give, and I ground it in a more realistic example, but, I, as I mentioned, I'm a huge fan of science fiction, and I don't know how, many of the audience members have read, like, things like the Foundation series by Asimov.Simulation as a Path, Not Just a PredictionSwyx [00:22:37]: Oh, yeah. We've mentioned psychohistory a number of times.Joon [00:22:39]: Okay, fantastic. So I might be, talking to the right crew. If you read Foundation series, literally the first act is there's a group of scientists who have found out that, “Oh, our galactic empire is going to collapse, and we're going to have 30,000 years of unrest.” And they run psychohistory, the simulator that tries to teach them, “Okay, how can we keep this unrest to a 1,000 years?” And they plan this out, and the first step of that plan is to get the scientists who say, “Okay, this is coming,” exiled into this random place in this, galax- galaxy.Swyx [00:23:18]: Terminus.Joon [00:23:19]: Exactly. And that's so counterintuitive. Like, what a strange move that you literally sent the group of scientists who was raising voice around this potential collapse of galactic empire into nowhere. How is that the right first move? Well, it turns out in this particular simulation, that was the move.Joon [00:23:40]: It's these things, right? And the reason why these reasoning is possible is because you're showing the step function or each step that results in a particular outcome. So really what simulation allows you to do in its highest form is you give it not a problem or question, like what would people answer to the survey? That's not what we do. What we tell it is, “Here is a goal that we have. In the context of foundation, we want to keep the unrest to a 1,000 years. What is the path that we need to take now to get to that particular future?” And that's what simulation allows you to do. Now, translating that into real market, imagine you're a automobile company and you're about to release a, EV, and you're trying to understand, well, how do we market EV, to make sure that our stock price goes up? But what if the answer comes down that, well, you can market your EV in XYZ way, but that might change people's perception around the cars that's not EV and make your overall sales to go down. Not very intuitive, especially all you're trying to optimize is EV salesss, and that's the only thing that you're tracking, then that might result in a completely wrong solution, or at least different solution than what you would have expected, whether it's right or wrong.Joon [00:24:57]: That's the power of simulation.Swyx [00:24:58]: For listeners, we covered a similar topic with Mikhail Parakhin from Shopify, where they are working on SimGym. I don't know if he ever talked to you about it. it's very similar.Joon [00:25:07]: ISwyx [00:25:07]: The goal is increased conversion, but then the journey is very unusual.Joon [00:25:12]: Journey is unusual.Swyx [00:25:12]: Yeah. The-- He's trying to look for interventions on a shopping trajectory, which is similar to what you're saying. Like, it's not about the attitudinal, is your word for it.Swyx [00:25:24]: It's about behavior.Joon [00:25:25]: It's about behavior.Swyx [00:25:25]: And that's exactly the difference, right? It's, like, not about the near-term direction about-- but it's more about, like, how do you affect multiple turns of interactions.Vibhu [00:25:35]: You had a good quote at the start about this as well. It's not about people wanting to know the outcome. It's about how they can change it, change the way to get there, something like that. But I wanna take it back to how do we know this is grounded? LikeGrounding and Evaluating Digital TwinsVibhu [00:25:47]: How do you run evals? How do you test that simulations come through? if I was to do the same thing that you described with, say, your favorite LLM, Opus, GPT-5.6, have some agent to map out these thingsVibhu [00:26:02]: How different are the answers we would get if I give it the same goal, the same objective, make a decent system? You're saying that you need to change the model weight. You have your own solution to this. But how far off are we, and how do you check if it's grounded? you have some interesting stuff on your site that points to how you run real evals, but if you could take us through that side. I think that's one of the big concerns that people have. They're like, “LLMs hallucinate.”Vibhu [00:26:27]: “You're just hallucinating layer after layer,” right?Joon [00:26:30]: The way we do this, and this is the paper that we worked on after the generative agents paper that really became the, at least for Simile and also the field of simulation and synthetic panels, really became the foundation. Yeah, this is the paper. the paper is called Generative Agent Simulations of 1000 People. Here's what we've done. For this paper, we brought 1,000 people that's representatively sampled from the US to a virtual lab. And what we have done was we spent two hours collecting fairly wide-ranging data. In this particular study, we focused a lot on this interview data, that was, whose script was taken from this project called American Voices Project. And then we would also pair that with a lot of behavior data and so forth, whatever we can collect within two hours. And then we would send these people away for a couple of weeks. And during that time, I would use this data to create their digital twins. And I would bring the humans, participants back after 2 weeks and have them complete a battery of surveys, experiments, behavior studies. So we have the list here, which included things like behavioral economics games. We would run literally, like, Big Five personality test, General Social Survey. We would also go ahead and run the randomized controlled trials that were published on PNAS. And we would have their digital twins predict how the source individuals would have acted in these studies and surveys. And this is where we could replicate people's behaviors and attitudes 85 percent as accurately as people would replicate their own. So that was the first really paper that gave this validated results that we can model individuals in an accurate way. And what we ended up finding now, of course, in AI space, so this paper came out at the end of 2024. AI space, a year and a half, 2 years, that's a lifetime.85% Accuracy and Why Frontier Models Miss Human BehaviorSwyx [00:28:24]: Yeah. Just, for listeners who are not seeing the YouTube, I just wanna say, like, the headline figure is 85 percent accuracy, like, which is a big improvement over all the otherSwyx [00:28:34]: Methods that you showed.Joon [00:28:36]: But the part that was particularly striking to us, especially as we improved this technology even further, was the generative AI models like ChatGPT, Claude that's coming out, it does give you the right foundation. However, what they do not consider is the true attitudinal and behavioral aspect of people, especially in the population that you care about. So what these models are really good at today is they're trying to become the super rational, objective machines, right? So you go get their data from places like Mercor, Scale. You talk to professional programmers, scientists to create model that's amazing at reasoning. That's what they do. Simile doesn't care about any of this. The models that we're talking about here, what we're trying to create are models that are as dumb as I am, right? So if I make some mistakes, the model has to make the same mistake.Swyx [00:29:34]: Oh, that's very hard.Joon [00:29:35]: That's very hard.Swyx [00:29:36]: You're solving Murphy's paradox.Joon [00:29:37]: That's exactly. And this is a completely different data and training objective. This is also where we see quite a bit of discrepancy in the performance in human behavior prediction between the frontier models, Simile's model, and the models being created in this space, where in some cases, the model performance of frontier models go all the way down to 20, 30 percent, especially if you go into that more niche population on topics that our customers would care about. On more gen pop, it might be around 50 to 60 percent. So it's not very robust. Like, you wouldn't want to make your decision off of these and these findings. If you can bring that up to 85 percent, that is ultimately what people end up getting very excited about.Swyx [00:30:20]: Yeah. Do we wanna keep going on the paper, routes?Joon [00:30:23]: Yeah, for sure. So the last one, was an interesting one. So this, paper was the follow-up paper that we had, to the 1000 agents paper, where the idea was now can we augment the models even further and post-train a model based on a lot of randomized controlled trials? So this was an interesting one. The data is always the most interesting part of modeling in many ways. The data that we got here was there's this, there's this platform called Open Science Framework. So some, the audience might be familiar with this. And there has been, especially in the social sciences over the past 5 years or so, there has been this concern around replicability of studies. And so it was a bit of a crisis, the scientists acknowledged, where we rerun the study and we don't see the same finding.Post-Training on RCTs and Replication StudiesVibhu [00:31:12]: Oof.Joon [00:31:12]: It's tough. And the reason why it's there-- that was often the case was there's this survival bias where the papers that get published often need to maintain what we call the value of less than 0.05 in the experiments that we ran. That suggests that only-- there's only 5% chance that the results that we saw is false positive. But the tricky part was all the papers that were not published, and there's still a 5% chance that whatever we publish is totally just randomly generated. Like, there's a 5% chance that, hey, this effect is not real, but it just happened to be real because of the sampling bias. So because of that, what scientists started to do was they started to register their studies. So before running an experiment, they would go to this platform and say, “Here is the data. Here is the population that we're collecting, and here's the hypotheses.” And they would just say, “Here is our hypothesis.” Like, “This is what we believe.” And you cannot retroactively change those hypotheses. This is what gives us more scientific statistical confidence that whatever effect that you ended up seeing is true. So that ended up creating this really interesting platform where there's one platform that has now contains tens of thousands of real-world experiments and hypotheses. And a lot of these are really high-quality, like, professionally designed behavior studies and random- randomized controlled trials. So we got the data and the studies from this platform and used that to make a point. And this particular, model is not, something that we're serving commercially because this was a part of the open science. But this particular data set, helped us make a point that by collecting a lot of these randomized controlled trials, that are really well-designed, we can make significant improvement in model's capability to predict human behaviors. So that's what this paper was about.Vibhu [00:33:10]: Is this stuff done on a individual level? Like, do I need to tune the model per individual, per company? Is there foundation model changes and then some slight post-training? Anything you can share there?Population-Level vs. Individual-Level ModelsJoon [00:33:21]: So this particular model was trained. the data we had at the level of individuals, but this particular model was trained. We experimented with both. And this is what we end up doing at Simile too. We always train 2, distinct model. One is what we call the population-level model. The other is what we call the individual-level model. And both take very similar input, which is the description of a subpopulation or individual and a stimuli. In this particular work, we've done the same. Here, the results that we are reporting are much more geared towards individuals because we do think that is a harder task in many ways, but that's what we have done.Vibhu [00:34:02]: You seen anything on the questions that humans can solve that models can't solve? So likeHuman Biases, Mundane Choices, and What Models MissVibhu [00:34:09]: Currently, it's, I live 5 minutes walk away from a car wash. It's a 10-minute drive. Should I walk or drive?Joon [00:34:16]: Huh.Vibhu [00:34:16]: The model will say, “Oh, walk to the car wash.” And, you don't have your car.Vibhu [00:34:20]: Is anything like this a problem in simulation? You would assume, like, very simple for human to think about, but if the model is saying you should walk to the car wash, anything here?Joon [00:34:32]: It's less, what can we solve, but I think it's more about what biases or mistakes do people make that models miss. Like, imagine that you are, like the. When I was still at Stanford, I lived in Palo Alto. So it's about, I would say, 40-minute walk from the campus. You ask the model, “Okay, let's go home. What can I, what can I do?” It would likely call an Uber or, give me, the bus time. But for the longest time, I really liked walking back. And the reason why I wanted to do that was not for efficiency. It really helped me think. And I like to walk for, half an hour or 40 minutes or so a day, where I just get to, just think about ideas, research, just get lost in my thoughts. That's very human activity. Unless the model has seen that and understands the importance of that activity, it would miss these kinds of features. So that I think, is fundamentally what we're trying to model. Like, what is fundamentally human might not be the most efficient thing to do, might not be the right thing to do, but things that make us who we are.Swyx [00:35:43]: I'm curious if, there are some data sets that you really want that would materially help you. One version of this may be interesting, which is more valuable to you to acquire as a data set, all of LinkedIn, all of Twitter, all of Facebook?What Data Matters: Social Media, Transactions, and FacebookJoon [00:35:57]: It's a little bit hard to rank, in part because, there's, there's this product saying where no feedback is wrong because it teaches you something about your users. Doesn't matter what feedback.Joon [00:36:11]: I think it's a little bit like that.Swyx [00:36:12]: So just whatever is bigger.Vibhu [00:36:13]: What about a different domain? Say it was. What about all of Amazon data?Joon [00:36:17]: Oh, yeah.Vibhu [00:36:18]: Shopping data, right?Joon [00:36:18]: Shopping data. So Amazon data is interesting in that it's very much behavioral, although, like, what people do on social media, you could squint and say that is also behavioral. But the transaction data is always interesting. It is also most commonly available, however.Joon [00:36:33]: If we were to look at purely social media, like if you really, if I were, if I had to really pick, Facebook likely is interesting because I do think it is most a default version of people. Because you go to LinkedIn, it's very much professional environment. So people put up their, they have their guards up, right? And that still is interesting because that is true human attitude and behavior, but it is not your base state. you go to Twitter- Twitter, people have their own crazy personas, or depending on who you are. Like, my Twitter profile and, persona is very much, initially was I was very much an academic. “Hey, I'm here to share my studies.” Now, I share, things that's related to Simile. But Facebook is one of those more private space where people just connect with their friends. In that way, I do think it shows you a little bit more about who that person is. So if I had to pick, I'd likely pick, Facebook.Swyx [00:37:30]: Yeah. And you're interested in, like, the whole person and their background and philosophy. I, is it too clinical or too machine learning-oriented to just say this is just ways to inject variance and biases? The broad question, is, like, is this any better than a randomized, like, combinatorial explosion version? So we have a link to the TencentBillion Personas, Synthetic Demographics, and Bespoke DataSwyx [00:37:54]: Billion persona paper, where they did not do any of the groundwork that you are doing.Swyx [00:37:59]: They just did like a cross matrix of here's all the professions in the world, here's all the people, possible backgrounds in the world, do a dot product across all of them, and that's it. That's your prompt for a billion people.Swyx [00:38:12]: This will do something. I don't know if it'll do what you do, but it gets you some way, some percent of the way there.Joon [00:38:18]: So this was an interesting paper. Like, what I admired about this paper when it came out was the scale. And you do gradually want to be able to simulate really large societies and interactions. So the scale is definitely admirable. it is relying heavily on the known statistics that went into training the model. So to the extent that you believe that statistics is correct, this is not a bad way to go about this. But the thesis here, and this is something that we also have seen in the market, like if this works, then we have solved simulation.Joon [00:38:54]: It,Swyx [00:38:55]: Because I survey, like, okay, 5% of the US population is in construction.Swyx [00:39:01]: The other 5% is in medicine, whatever, right? And then you just keep going down the list, and then you do the other side. 5% has, like, the big 5 personalitySwyx [00:39:08]: Of, like, neurotic or whatever. That's it.Joon [00:39:11]: That's it. So if you believe that the underlying data set and the platform that we're leveraging has all the right statistics, then this will have solved it. you're at that point merely retrieving the knowledge that is already embedded in the model, in the model parameters. That's not, unfortunately, what we see, where there is such detailed and also niche knowledge about people that if you just take one example, it might feel very mundane, but it's quite rich when you put together, that you do need to do a lot of bespoke data collection to better understand people. And this is also, I think what makes this particular, job fun, which you want to deeply understand people, and the process of deeply understanding them requires a lot of attention to the details. And you do need to pay attention to and pay respect to the daily lives that people lead.Scaling Simulation: From Thousands to SocietiesVibhu [00:40:04]: I wanna talk about scaling simulation.Vibhu [00:40:07]: So what can't we simulate, what can we simulate, and how does scaling affect this? So how big are the models? What if we go from, 8B, like, couple 100 billionVibhu [00:40:18]: Like billion000 parameters, billion000? Do we get scaling? Any interesting emergence? Like, at a certain scale, at a certain amount of training, you uncover anything unusual and any learnings from that?Joon [00:40:31]: What we are seeing is at Simile, so we do post-train our own model. The thing that we're seeing is the early glimpse of scaling law in simulations. The more data about humans and more compute you ingest, you start to get predictive and predictable gains of the model performance in simulating it, simulating people.Vibhu [00:40:51]: Ooh. We need a scaling law curve.Joon [00:40:52]: It's scaling law. Whenever you find it's a beautiful thing. And we're starting to see the glimpse of it, which is quite exciting. But if you talk about the ambition of simulation as a whole, it's not merely about building a model. It's about building a model, then creating the agents that become the individuals in a much larger ecosystem. So they're creating this multi-agent simulation. Down the line, you want these multi-agent simulation to also live in a very rich environment, right? What we are really trying to get to at that point is, hey, can we create. All right, let's do a time machine game again, and 5 years, 10 years into the future, can we create a simulation of 8 billion people living on Earth? I think that's quite interesting. And that really is the vision. And once you get to that state, the questions that you can help answer for the society also start to change from my perspective. The answers are fundamentally about emergence of the emergent behavior of society and large groups of people.Joon [00:41:53]: So the questions that I get excited by, and maybe this is a stodgy- a bit. I have my, academic side of me.Joon [00:42:01]: And for me, it's questions like, can we help solve climate change? If you look at climate change as a problem space, this is what we, like social scientists would often call it the wicked problems, problem where you have many actors with competing incentives for trying to make a very complex decision and coordinating that coordination decision. Very difficult to really solve in real life, which is also the reason why we couldn't solve it. Can simulation help us solve that? Another one is, can we understand the signals for collapsing democracy, or can we understand or can we uncover the origin story of the monetary system? These are societal questions that we never really had a good way of answering. If we can create simulations of our society, you have to believe that these are the problems that we can solve. So that's really the ambition of this field. And, I also think, yes, I think there's a Nobel Prize to be won there, which wouldn't be surprising. And I think there's some amazing societal impact that we can have to help people make better decisions.Climate Change, Democracy, and Societal SimulationSwyx [00:43:04]: Nobel Prize in economics?Joon [00:43:06]: In economics.Swyx [00:43:06]: Oh, I see. I see. Rooting for you to write that paper.Joon [00:43:10]: One of these days. But, one of the scholars that I was deeply inspired by, When I was coming into the space of simulation, is this scholar, named Thomas Schelling.Schelling, Agent-Based Models, and the Nobel PrizeSwyx [00:43:23]: Schelling point?Joon [00:43:24]: So the canonical example of the work that he's done was he was one of the creators of agent-based modeling. So this was, like, in the 1970s and 80s. It's very early days, but this was truly one of the first exemplars of simulations. And one of the canonical model from that time, and of course many of these simulations are trying to tackle the societal problems that's most relevant for their era, it was called the model of segregation. So racial segregation was a big topic, that, we cared about. And what they've done was they created this grid world where they had red dots and blue dots. And these dots were, back in the day, like, they were the agents, and they had a simple rule that governed their behavior. If certain percentage of your neighbors are of different color and if that goes above certain threshold, then you move to a new location at random.Joon [00:44:21]: One of the striking finding of this paper or this agent-based model was for the longest time, people thought the segregation within society was caused by explicit and overt racism.Joon [00:44:34]: But if you look at this model, people's preference towards living with people of the same color, that preference can be very minute.Joon [00:44:42]: But the very small difference causes the society to segregate completely over time. This was very counterintuitive for a lot of people. And this particular work ended up informing housing policies. Mixed income housing, got really inspired by this work. And Thomas Schelling ends up winning the Nobel Prize for having laid the groundwork for very early versions of simulations. The opportunity that I do see here in the more scientific terms, is agent-based models for the longest, had impact in the 1980s, 90s, to some extent, early 2000s, but it has now gotten forgotten by the community a little bit. Because as you can imagine, red dots and blue dots is not really a rich description of people.Joon [00:45:31]: But with the emergence of things like generative AI and, in particular, generative agents, we do have an opportunity to create these agent-based models that are high fidelity enough to help us make really complex decisions. And that's the opportunity that I see. If that truly works, then yes, that is the work that will result in a Nobel Prize.Swyx [00:45:53]: Yeah. For what it's worth, and I grew up in Singapore. 80% of Singapore is in public housing, and public housing has, enforced racial quotas for exactly that reason, which is very interesting. okay, so we talk about scaling, we talk about all these, the agent possible applications.Cost, Reuse, and the Economics of SimulationSwyx [00:46:13]: I'm scared about the cost. if you even-- let's just keep it to the US, about 8 billion people.Swyx [00:46:21]: But, how much does it cost to model so many hundreds of millions of people?Joon [00:46:26]: Oftentimes today, we don't start at that scale, this stage of the, of industry and simulation as technology. But we can get our users extremely rich and meaningful insights even by modeling thousands, tens of thousands of people. And today what we do is every week we are collecting data on the scale of tens of thousands people's data, and we have panel partnerships that gets us to tens of millions of people globally. So that's what we do today.Swyx [00:46:55]: And just as a side note once you've collected one person for one studySwyx [00:46:59]: Can you reuse that same person for all the subsequent studies?Joon [00:47:03]: That's exactly right.Swyx [00:47:03]: Okay.Joon [00:47:04]: The beauty of this model and these agents is the fact that they are domain-agnostic.Joon [00:47:08]: That what you're really trying to understand is what is the fundamental nature of these people? What's their social physics? And there are a lot of, a lot of, people that does change over time. Like, even, like, even things like, how many times have you gone have you been to, like, CVS the past week? that will change. But there's so many traits about people that are also known to never change. Like, your risk tolerance doesn't really change over time. It's very consistent. So it's these things that we're trying to learn. But the scale we are operating is right now hundreds or, tens of thousands to hundreds of thousands. And in many of the core use cases that we are deployed in, and this is more than enough population, to cover those. Really, at that point, what you care about is less the number of people, but more do you have the right subpopulation of interest covered? And this is also the reason why people want a larger sample. It's not because they want, stronger statistical guarantees. It's more that can they filter down to any population of their interest. However, you can also imagine in 10 years, if we truly believe that the compute is going to scale, that we'll have much more availability for compute, and our ambition for simulation is also going to scale accordingly, there's definitely a reason for us to create an entire data center worth of simulations.Joon [00:48:35]: Or in my hunch here is I do think in the next some number of years, we will start creating simulations that will cost as much as training a foundation model. But perhaps it's going to be so valuable to the society that it would be a no-brainer. Right now, even today, like, we are training bunch of new foundation model just so we can say we trained one and we spent tens of millions. But if we can create a simulation at the level of society that would solve climate change, I would run that today. I would raise the money right now just to run that.Multi-Agent Simulation and Social InfluenceSwyx [00:49:10]: Amazing. the follow-up question is, does it also compound if you let the simulations talk to each other?Swyx [00:49:18]: Or do they already do that today? They don't, right, as far as I understand?Joon [00:49:22]: It depends on what simulation you're trying to run.Joon [00:49:24]: In the multi-agent simulation setup, the agents do talk to each other.Swyx [00:49:28]: Right, which is exactly Smallville, right?Joon [00:49:29]: That's right.Swyx [00:49:30]: But a lot of times, for example, in commerce, you're just by yourself, so there's no point talking. which is way cheaper.Vibhu [00:49:37]: But they use all these levels, right? Like, you decide what you will buy based on what other people around you buy and talk about, right?Swyx [00:49:43]: It depends.Vibhu [00:49:44]: It depends.Swyx [00:49:45]: Again, I'm, I'm coming at this from a cost point of view. I'm like, “Oh my God.” LikeVibhu [00:49:48]: I thinkSwyx [00:49:49]: If there is, like, some combinatorial thing of, like, thousands of people talking to thousands of people, then that one million X's might cost.Vibhu [00:49:56]: I have a very different view as the cost point aside. Like, running these studies in reality is a lot more expensive, right? Running any study like this is you gotta have people do it, you gotta sign people up. It's very expensive and sometimes, like, not feasible to run the study.Vibhu [00:50:14]: But the outcome or the decisions you make are very expensive on them, right? So spend X million on something that, the overall process costs 100 million might as well, right? There's, there's a lot of value to be had there. It's a small cost, but I'm excited on the cost side.Joon [00:50:33]: To some extent, and when you deploy technology, you often want to deploy in a way where you can replace existing budget or you can make things more efficient, and that is the best way to deploy. However, the way you capture the long-term value of the technology is making the argument that, no, it's the upside, that by making this better decision using simulation, you have saved yourself or made yourself hundreds of millions or even billions of dollars, and that's a case to be made.Vibhu [00:51:06]: Random tangent question. So if you're doing a lot of inference, a lot of model multi-agent stuff, are you at the point where it makes sense to, train a model that' very sparse? You're expecting to do multi-million dollar runs. Are you thinking about this in model architecture standpoint or inference efficiency, or, you're still at the research phase of it works, we're not super there yet?Joon [00:51:34]: Efficiency, we do think quite a bit about. this is technology that is deployed now in some of the largest enterprise companies in the world, and we do process significant number of queries, that are trying to, simulate the populations in the world. So efficiency is a consistent thing. we don't want to over-optimize too early, so I wouldn't say, like, this is the higher bid Right now, but this is definitely something that we think pretty carefully about.Swyx [00:52:05]: Yeah. Are there other case studies? So we, you talked about CVS, talked about Gallup, Deloitte, Wealthfront.Efficiency, Enterprise Use, and Real-World Case StudiesJoon [00:52:12]: Wealthfront is an interesting one, because one of the things they were trying to do, they were one of the first customers that wanted to do product testing that goes beyond just asking people what they think about, let's say, behavior experiments and so forth. So there, really what we had to do was reason about multimodal input, so images, but also you can also imagine, like, these agents traversing through Figma mockups or websites. So some of the things that our agents can also do is it can be given a domain, like, or, like, a website URL and go use it for a while. It's these things. And Wealthfront was one of the first, customers, that was very excited about this possibility.Vibhu [00:52:53]: What have people been asking? Like, is there any demand that we have not covered? Like, UI testing, right?Vibhu [00:52:59]: I wanna try a new. I wanna ship a new feature, test the UI, simulate how people will do it. Any interesting things that you're seeing demand for?Product Testing, Websites, and Synthetic PanelsJoon [00:53:08]: Today, a lot of the demand does come from like, the places where people have historically used human panels, we can now replace with agents, and these synthetic populations. And this is not replacing human panel. in many ways, the simulation that Simile is building is grounded. So the way that I think about this is we are trying to represent humanity at scale. And in that way, the use cases are what we would expect, but it's the scale of deployment that surprises me.Joon [00:53:44]: Turns out there are so many decisions that people make every day in these organizations, groups, and we want to be able to say, “We listen to people. We have consulted our users.” But in reality, that is rarely the case because getting to people and asking them many questions, it's difficult. It's both costly, time-consuming, but most importantly, people are just not available. If I had to answer 1000 survey questions for this one particular, vendor, even if I wanted to do that, like, I would never do it. And that's very much the case. What simulation can do is ensure that the voices of people are always represented in rooms where the decisions for them is made, right? So all the stakeholders of this particular product launch, ideally they're consulted. That's what this technology really is trying to enable.Market Size, TAM, and Human Decision-MakingSwyx [00:54:39]: In my mind, that means it skews towards more consumer focus, right? Like, anything with a wide enough customer base where you do benefit from the diversity that you represent. What are some rough statistics, just for people who are not familiar with this market in general, what's the market size that. I'm sure you have some, like, rough numbers. market size is, like, a vague questionSwyx [00:55:01]: But, like, how much do people spend?Joon [00:55:03]: So market research is a $100 billion industry.Joon [00:55:06]: But the thing about simulation is not a tool for market research. Simulation is a tool for human decision-making. So the question around what is a TAM here is quite tricky, right? Because it's easy to say, “Well, market research TAM is roughly 100 million or 100 billion.” so is it a TAM? And not really, right? Because in many ways, you're trying to inform all human decision-making. You're trying to inform every decision that are made about humans for humans. What is a TAM for that? It's really unclear. And I'll be honest. Like, I have a scientific background, I have a research background, so I didn't come into the field calculating, oh, what is the TAM for human decision-making? But I just had to assume, well, if we can inform every decision that is made about human for human, that has to be big.Swyx [00:55:58]: Some- something valuable.Joon [00:55:59]: Exactly.Swyx [00:55:59]: To some extent, you are a unicorn founder now, and you have to care as a CEO. But, like, I do think, like, yeah, when you go into these boardrooms with people that you're quoting millions of dollars of contracts for, like, you have to say, “Well, here's what you spend on humans-”Swyx [00:56:15]: “. And here's what we save you, and it's 85% similar.”Joon [00:56:19]: And certainly, the value case, is something that we care deeply about. Like, what is the value that we provide to the users and the decision-makers? But this is also where, like, as a founder, I think valuation only tells one very superficial aspect of the story, and I try not to think too much about valuation, in general, because that's not what also motivates a team or certainly doesn't. I'm, I-- Again, the interesting thing about researchers is we are happy living in academia, getting paid next to. we get paid okay. we don't get paid that much, as a researcher here in academia, but it's the impact and it's the, it's the value that we can provide to the individuals and the society that really drives us. And in that way, ultimately what drives us is the impact. Does the simulation we provide have a real impact in people's decision-making in ways that progresses our society forward? If the answer is yes, then yes. that has to be great business, and we see that in numbers, and we do care deeply about that upside story, but that's the heart of it.Where Simulation Goes NextVibhu [00:57:27]: Do you have any timeline predictions? So we talked about scaling laws of simulations.Vibhu [00:57:33]: You brought up, okay, maybe one day we can simulate how to solve climate change.Vibhu [00:57:38]: Where are we now?Vibhu [00:57:40]: If that's not the end state, what is an end state, and what does progress look like?Joon [00:57:45]: So what I sometimes tell people is simulation as industry, it feels a lot like where GPT-3.5, GPT-4 was, for the AGI saga, which is we have now technology that is powerful enough to do real damage on the verticals that we are tackling. At the same time, there's a lot of progress that is yet to come. And that's, I think, where this is. So the way I see it, I do think there will continue to be breakthroughs both in data, in algorithms, and there will be much more aggressive scaling that will also happen over the next few years. But I think that's roughly where we are.Swyx [00:58:27]: I think that was about the ro

This Week In Startups is made possible by: Vanta https://www.vanta.com/twist Agree https://agree.com YSecurity https://YSecurity.io/TWIST   Today's show: Frontier AI models can ace PhD-level exams, but it's still bad at tracking down the product you want in the style that suits you. Onton's Zach Hudson tell us that the problem is that models are a black box. His solution? A neurosymbolic model, Ontology 1, that learns about your taste and preferred aesthetic over time, then produces product searches tailored specifically to you, rather than just using keywords and relevant tags. How do neurosymbolic models work, and how does Onton understand your prompts and favorite design trends? And why aren't the frontier labs working on neurosymbolic models of their own? Zach joins Jason and Lon to discuss. PLUS, following a record-smashing SpaceX IPO, Ashi Dissanayake of Spacium makes the case that the real bottleneck in space isn't launching rockets off the ground any more. It's refueling in orbit. Guests Zach Hudson on X: ****https://x.com/nosduhz Onton: https://onton.com/ Spacium: https://spaceium.com/ Spacium on X: https://x.com/SpaceiumInc Relevant Links Poolside's journey to AGI: https://poolside.ai/vision/purpose Startup Archive: Sam Altman on the Paul Graham advice that saved OpenAI: https://www.startuparchive.org/p/sam-altman-on-the-paul-graham-advice-that-saved-open-ai-always-make-an-api Kelly Wearstler: https://www.kellywearstler.com/ Ennis House: https://franklloydwright.org/site/ennis-house/ Los Feliz Living: Ennis House profile: https://www.losfelizliving.com/los-feliz-historic-homes/ennis-house-los-feliz-hcm-149 Indiewire: Ennis-inspired "The Studio" offices: https://www.indiewire.com/features/craft/the-studio-production-design-interview-seth-rogen-1235114365/ Monocle Magazine: https://monocle.com/ Spaceium on Y Combinator: https://www.ycombinator.com/companies/spaceium-inc Orbit Fab's RAFTI: https://www.orbitfab.com/rafti/ James Webb Space Telescope: https://science.nasa.gov/mission/webb/ Houzz: https://www.houzz.com/ Timestamps: 0:00 Zach Hudson joins: What is "neurosymbolic search" 4:03 Ontology 1 isn't a black box 6:31 Who is using Onton? 9:36 Vanta - Get $1000 off your SOC 2 at https://www.vanta.com/twist 11:35 Could neurosymbolic models reach AGI? 13:19 Jason loves Wright's Ennis House 17:03 The shape of AI companies is changing 19:28 Agree.com - Stop chasing invoices and automate your entire contract-to-cash stack. Go to https://agree.com and tell them Jason sent you to get 50% off for life! 22:32 UGC as a data moat 26:03 The Dead Internet Theory 28:13 Ashi Dissanayake of Spacium joins 29:52 YSecurity - The on-demand security team for startups. Need enterprise-grade security without hiring a $400k CISO? YSecurity gives you 40+ expert engineers, matched to exactly what you need, by the hour, with your first six hours completely free. Go to https://YSecurity.io/TWIST 31:50 Storables vs. cryogenics: the zero-boil-off breakthrough 34:11 All kinds of propulsion requires refueling 36:09 Getting more value from LEO to GEO 38:28 Moving at rocket speed 44:54 Why demand is so acute 49:37 The investing climate for space, post-SpaceX Subscribe to the TWiST500 newsletter: https://ticker.thisweekinstartups.com Check out the TWIST500: https://www.twist500.com Subscribe to This Week in Startups on Apple: https://rb.gy/v19fcp   Follow Lon: X: https://x.com/lons   Follow Jason: X: https://twitter.com/Jason LinkedIn: https://www.linkedin.com/in/jasoncalacanis   Check out all our partner offers: https://partners.launch.co/   Great TWIST interviews: Will Guidara, Eoghan McCabe, Steve Huffman, Brian Chesky, Bob Moesta, Aaron Levie, Sophia Amoruso, Reid Hoffman, Frank Slootman, Billy McFarland   Check out Jason's suite of newsletters: https://substack.com/@calacanis   Follow TWiST: Twitter: https://twitter.com/TWiStartups YouTube: https://www.youtube.com/thisweekin Instagram: https://www.instagram.com/thisweekinstartups TikTok: https://www.tiktok.com/@thisweekinstartups Substack: https://twistartups.substack.com

Dan Nathan hosts FirstMark Capital partner David Waltcher at the firm's HQ to discuss Waltcher 's path from an Accel internship to investing in enterprise software, security, and AI. They compare the consumer-to-SaaS shift, the post-2021 “SaaSpocalypse,” and how public markets can overreact to AI narratives, using Salesforce as a system-of-record case study. Waltcher argues many incumbents will prove durable due to switching costs, ecosystems, and trust, while M&A is accelerating amid volatile publics, strong buyers, and fast-growing AI businesses, citing deals like Stripe–OpenRouter and interest in Workday. The conversation turns to Chinese and open models driving token cost deflation and model routing, and to rising security threats, including agent-related incidents, fueling demand. Waltcher highlights FirstMark investments Onyx (agent security), Nebulock (agentic threat hunting), and Tracebit (assume-breach deception), and says innovation risk is highest if recession or a market crash hits, not from AGI timing debates. —FOLLOW USYouTube: @RiskReversalMediaInstagram: @riskreversalmediaTwitter: @RiskReversalLinkedIn: RiskReversal Media The financial opinions expressed in Risk Reversal content are for information purposes only. The opinions expressed by the hosts and participants are not an attempt to influence specific trading behavior, investments, or strategies. Past performance does not necessarily predict future outcomes. No specific results or profits are assured when relying on Risk Reversal. Before making any investment or trade, evaluate its suitability for your circumstances and consider consulting your own financial or investment advisor. The financial products discussed in Risk Reversal carry a high level of risk and may not be appropriate for many investors. If you have uncertainties, it's advisable to seek professional advice. Remember that trading involves a risk to your capital, so only invest money that you can afford to lose. Derivatives are not suitable for all investors and involve the risk of losing more than the amount originally deposited and any profit you might have made. This communication is not a recommendation or offer to buy, sell or retain any specific investment or service.

Nick Bostrom is an AI philosopher and the author of Superintelligence and Deep Utopia. Bostrom joins Big Technology to discuss whether the rise of autonomous AI agents is making the technology's existential risks more concrete. Tune in to hear his assessment of the alignment problem, recursive self-improvement, and humanity's chances of steering superintelligence toward a positive outcome. We also cover whether we have reached AGI, the case for a precisely timed AI pause, biological threats, AI consciousness, and the moral status of digital minds. Hit play for a clear-eyed conversation about AI's greatest dangers and its potential to radically improve human life. --- Enjoying Big Technology Podcast? Please rate us five stars ⭐⭐⭐⭐⭐ in your podcast app of choice. Watch the full documentary here: https://www.gravitee.io/ai-agent-documentary Want a discount for Big Technology on Substack + Discord? Here's 25% off for the first year: https://www.bigtechnology.com/subscribe?coupon=0843016b Learn more about your ad choices. Visit megaphone.fm/adchoices

Could America build the world's best AI and still lose the AI race?What if the biggest threat to America's lead isn't China's technology, but our own fear of using it?Katrina Mulligan is Head of National Security Partnerships at OpenAI and previously served in senior roles at the Department of Defense, National Security Council, and Department of Justice. Few people have seen both Washington and frontier AI development from the inside.She argues that AI is no longer a technology question for the future. It is becoming core infrastructure for economic strength, national security, and the way we work.She discusses:●  Why America could win the race to AGI but lose on adoption●  Why trust in AI is dramatically higher in China and the developing world●  How China and the U.S. are taking fundamentally different approaches to AI●  Why AI may ultimately transform society as profoundly as electricity●  The biggest mistake leaders make when trying to adopt AI●  Why AI transformation cannot simply be delegated to the IT department●  How she has used AI to become at least 30% more effective at her own job●  Why using AI like a search engine means dramatically underestimating what it can doAmerica may currently have the advantage in building the most advanced AI.Katrina explains in today's 3 Takeaways™ conversation why that lead is far from guaranteed, how fear could become a strategic disadvantage, and why the countries and organizations that learn to redesign themselves around AI may ultimately have the most to gain.

Hank Crawford of Blue Collar Robotics and Paul Harker, retired CCO of Woolworths Group, on grocery store robotics a retailer can deploy today.There are 280,000 people walking the aisles of US grocery stores right now picking somebody else's order, and grocers cannot hire fast enough to keep up with the growth. That number comes from Hank Crawford, Co-Founder and CEO of Blue Collar Robotics, and it frames the whole conversation. Online grocery keeps growing. Labor keeps getting harder to find. And the two big swings the industry took at eGrocery fulfillment, the centralized fulfillment center and the micro fulfillment center, both left grocers with high CapEx, duplicated inventory, and people still picking most of the order. Hank is joined by Paul Harker, retired Chief Commercial Officer of Woolworths Group, Australia's largest retailer, who joined Blue Collar Robotics as a strategic advisor. Together with Ricardo Belmar and Casey Golden they work through the economics of eGrocery fulfillment from the operator's side of the table: why the warehouse answer collapsed, what grocery store robotics has been getting wrong, and why a purpose-built robot with a remote operator behind it can drop into a supermarket that already exists. The mechanism is the interesting part. Blue Collar Robotics sells the picking as a service. A grocer pays a cost per pick, which moves the spend from CapEx into OpEx. The robot handles packaged center-aisle goods using vacuum suction, which is roughly 65% of a typical order, and store associates get redeployed to fresh, where care actually shows up in the customer's bag. Blue Collar reports eGrocery fulfillment cost reductions of as much as 50%. This is Season 6, Episode 10.In This Episode, You'll Learn•       Why centralized fulfillment centers failed for grocery on four separate counts: volume density, distance from the customer, fresh, and CapEx•       What Paul Harker says micro fulfillment centers still cannot solve, including duplicated inventory and picking "the uglies"•       Why over 80% of a grocer's business is still the in-store customer, and how eGrocery growth quietly degrades that experience•       The case for a human behind every robot, and why it is now standard practice for anything operating in an unstructured environment•       What robot etiquette means in a store aisle, and what China's hotel delivery robots taught Hank about how not to do it•       How the grocery store robotics buying conversation changes when the customer has no robotics team of its own•       Why grocery store robotics took this long to find product-market fit in a supermarket, and what changed•       Why the labor argument runs backwards here: there is no line of people waiting for picking jobs•       How remote operation opens picking work to people who could never physically do it, and opens a 24 hour picking clock for the store•       What Hank says is actually slowing adoption, and it is not the technologySupport our sponsorsThis Episode is Brought to You By RetailClub.Retail and brand leaders can attend RetailClub AI Festival completely budget-free, plus get $1,750 toward travel and hotel. From the founders of Shoptalk and Groceryshop, it's three days fully outdoors in Huntington Beach, September 22 to 24, with 2,000 senior leaders working through how AI is transforming retail. If you're shaping AI's future in retail, budget shouldn't be why you miss it. Register by August 28 at retailclub.com/retail-razor-podcast.Subscribe & FollowHow much did you love this episode? Drop us a 5‑star rating and review on Apple Podcasts, Spotify, or Goodpods. Subscribe on YouTube so you never miss an episode and check out the other shows in the Retail Razor Podcast Network: Retail Transformers, Blade to Greatness, and Data Blades.Subscribe to the Retail Razor Podcast Network: https://retailrazor.com/Subscribe to our Newsletter: https://retailrazor.substack.comSubscribe to our YouTube channel: https://go.retailrazor.com/utubeFeatured guestsHank Crawford. https://www.linkedin.com/in/hank-crawford-a4a9755/Co-Founder & CEO of Blue Collar Robotics. https://bluecollarrobotics.aiHank Crawford is Co-Founder & CEO of Blue Collar Robotics, where he leads the company's mission to make in-store grocery picking affordable for retailers through purpose-built robots delivered as a service, pairing AI-driven autonomy with human-assisted teleoperation. A technology entrepreneur and commercial leader with more than 35 years of experience in advanced composites, global manufacturing, and robotics, Crawford has founded companies in the United States and China including PURE Material Science, a Guangzhou-based pioneer of continuous fiber-reinforced thermoplastic composites and has helped scale advanced technology businesses in senior engineering and commercial roles at Performance Materials Corporation (acquired by Toray), Daimler Trucks in both North America and China, TRB Lightweight Structures, and Avient. He holds multiple patents in composite materials and manufacturing.Paul Harker. https://www.linkedin.com/in/paul-s-harker/Retired Chief Commercial Officer, Woolworths Group. Strategic Advisor. Blue Collar Robotics.Paul Harker has more than three decades of senior leadership experience across Australia's retail and FMCG sectors, including 33 years with Woolworths Group. Most recently, Paul served as Chief Commercial Officer for Woolworths Group, where he was accountable for the $51 billion Australian Food commercial portfolio. In this role, he oversaw commercial practices across more than 1,000 stores and helped lead large-scale enterprise transformation initiatives, including the modernization of digital systems and the integration of data-led analytics across complex supply chain networks. Paul's earlier executive roles at Woolworths included leadership across Replenishment, Supply Chain, and Store Operations, giving him a broad understanding of the full retail operating model – from store execution to enterprise-level commercial strategy.Chapters00:00 Teaser 00:48 Show Intro 05:34 Welcome Hank and Paul! 06:37 Why Warehouses Failed 09:02 Store Picking Tensions 10:28 Amazon Threat Reality 14:47 Robotics Hype vs Reality 16:34 Human in the Loop 18:34 Blue Collar Robotics Intro 19:46 Service Model Economics 22:35 How Store Deployment Works 25:48 Comparing Other Models 30:50 Automation and Jobs 32:35 Scaling Labor With Robots 33:43 24 Hour Store Operations 35:09 Accuracy And Quality Standards 36:48 AI Regulation And Safety 40:18 Robot Etiquette In Aisles 41:36 China Robotics Lessons 45:19 Selling To Risk Averse Grocers 49:36 Five Year Outlook And Jobs 52:19 Wrap Up And Where To Learn More 53:40 Show CloseMeet your hostsHelping you cut through the clutter in retail & retail tech:Ricardo Belmar is an NRF Top Retail Voice for 2025 and a RETHINK Retail Top Retail Expert from 2021 – 2026. Thinkers 360 has named him a Top 10 Thought Leader in Retail, a Top 25 Thought Leader in AGI and Careers, a Top 50 Thought Leader in Agentic AIand Management, and a Top 100 Thought Leader in Digital Transformation and Transformation. Thinkers 360 also named him a Top Digital Voice for 2024 and 2025. He is an advisory council member at George Mason University's Center for Retail Transformationand the Retail Cloud Alliance. He was most recently the partner marketing leader for retail & consumer goods in the Americas at Microsoft.Casey Golden, is the North America Leader for Retail & Consumer Goods at CI&T, and CEO of Luxlock. She is a RETHINK Retail Top Retail Expert from 2023 - 2026, and Retail Cloud Alliance advisory council member. After a career on the fashion and supply chain technology side of the business, Casey is obsessed with the customer relationship between the brand and the consumer and is slaying franken-stacks and building retail tech! MusicIncludes music provided by imunobeats.com, featuring Overclocked, and E-Motive from the album Beat Hype, written by Heston Mimms, published by Imuno.

In this AI debate, we explore: Whether humans will exist in 2040. What will happen once we reach AGI. Whether AI gets smart enough to act malevolently or benevolently towards humans. If AI will grow powerful enough to be out of human control. and much more... Guests Zack Kass is an AI futurist, advisor, speaker, and author. Liv Boeree is a physicist, science communicator, podcaster and former professional poker champion. Aric Floyd is an AI writer, and video producer. Sponsors: See discounts for all the products I use and recommend: ⁠https://chriswillx.com/deals⁠ Timestamps: (00:00) Will Humans Still Be Around in 2040? (08:20) What Jobs Will Be Protected From AI? (14:22) Finding Meaning in a Post-Work World (23:13) Technology Is Pulling Us From Our Purpose (27:12) Where Do We Draw the Line With Outsourcing Thinking? (39:58) Is AI Unlocking Human Potential? (42:28) Refusal or Obedience: What's More Dangerous? (46:01) The AI Attack That Should Worry Everyone (54:44) Should We Slow Down Advanced AI? (56:41) Does Recursive Research Actually Work? (01:10:52) How Safe Is AI? (01:14:01) Can We Reach a Peace Deal for a Superintelligent World? (01:22:38) Why AI 2040 Got So Much Right (01:26:21) Is AGI Inevitable? (01:30:39) Could Techno-Pastoralism Be the Future? (01:37:16) Is AI Replacing Human Connection? (01:45:36) How Do We Navigate the Purpose-Friction Problem? (02:00:50) Why Data Centre Policy Matters More Than Ever (02:05:56) The Concentration of Power Concern (02:11:39) Is the Frontier Strategy the Winning Condition? (02:18:17) Will AI Weaken Democracy? (02:29:11) How We Can Limit the Concentration of Power (02:31:45) Is ChatGPT Pessimistic About the Future? (02:37:54) What Should We Be Focused On? Extra Stuff: Get my free reading list of 100 books to read before you die: ⁠https://chriswillx.com/books⁠ Try my productivity energy drink Neutonic: ⁠https://neutonic.com/modernwisdom⁠ Episodes You Might Enjoy: #577 - David Goggins - This Is How To Master Your Life: ⁠lnkfi.re/SN-Goggins⁠ #712 - Dr Jordan Peterson - How To Destroy Your Negative Beliefs: ⁠lnkfi.re/SN-Peterson⁠ #700 - Dr Andrew Huberman - The Secret Tools To Hack Your Brain: ⁠lnkfi.re/SN-Huberman⁠ - Get In Touch: Instagram: ⁠https://www.instagram.com/chriswillx⁠ Twitter: ⁠https://www.twitter.com/chriswillx⁠ YouTube: ⁠https://www.youtube.com/modernwisdompodcast⁠ Email: ⁠https://chriswillx.com/contact⁠ - Learn more about your ad choices. Visit megaphone.fm/adchoices

Why isn't understanding your problems enough to change them?Many high achievers know something needs to change, but insight alone rarely leads to transformation. In this episode, Karin Velicka shares why lasting change starts with reconnecting to your body and nervous system, not just your mind. Discover how regulating stress, releasing stored emotions, and creating inner safety can help you move from simply surviving to living with greater authenticity, fulfillment, and resilience.Discover why external success doesn't always lead to fulfillment, and how to reconnect with what truly matters to you.Learn how nervous system regulation and neurosomatic breathwork can help release accumulated stress, reduce reactivity, and create lasting emotional change.Explore practical breathing, movement, and sound techniques you can use to feel calmer, respond more intentionally, and build greater trust in yourself.Press play to discover how reconnecting with your body can unlock the lasting personal change you've been searching for.˚MEMORABLE QUOTE:"We can only change what we acknowledge."˚VALUABLE RESOURCES:Karin's website: https://www.velickakarin.com/˚Coaching with Agi: https://personaldevelopmentmasterypodcast.com/mentor˚

Welcome to Last Call, a look at the biggest stories Jim and Greg covered over the past week on the 3 Martini Lunch.This week, they discuss AOC trying to giggle away the left's repressive and woke lurch since 2020, Abdul El-Sayed becoming the latest Democrat to hate Thanksgiving, Tucker Carlson and Hunter Biden blaming Israel for Hunter's laptop scandal, and the WNBA still failing to define a woman.First, they fume as New York Rep. Alexandria Ocasio-Cortes laughs and tries to chalk up the left's destructive demonization of police, white people, and anyone who didn't obey every jot and tittle of the Covid rules as some some crazy "Woke 1" phase the party went through. Jim says you can be sure the next level of wokeness will be far worse.Next, they throw up their hands as Abdul El-Sayed becomes the latest Democrat in this cycle to express his hatred for Thanksgiving. It was seven years ago and El-Sayed insists he does not believe that anymore. But it came after his run for governor in 2018. They also react to El-Sayed's comments comparing football to the transatlantic slave trade.Then, they groan as Tucker Carlson and Hunter Biden blame Israel for Hunter's laptop fiasco and pushing the story to the media. Jim begs Biden to go away and he also addresses growing concerns that Tucker will start a third party designed to sink Republicans in 2028.Finally, they laugh as WNBA officials meet for hours to discuss who should be eligible to play in the league and they couldn't come to any conclusions.Please visit our great sponsors:AG1https://DrinkAG1.com/3ML Start your first AGI subscription order, and get a FREE AG1 Flavor Sampler and a FREE bottle of Vitamin D3 + K2 in your Welcome Kit.Noble Goldhttps://NobleGoldInvestments.com/3MLIf you want to see how physical gold and silver could fit into your portfolio, download Noble Gold Investments FREE Wealth Protection Kit.IncogniTake control of your digital footprint today. Use code 3ML at the link below and get 60% off an annual plan: https://incogni.com/3MLNew episodes every weekday. 

Join Jim and Greg for the Friday 3 Martini Lunch as they cover Mike Rogers leading the Michigan Senate race but "college-educated" white women overwhelmingly backing Abdul El-Sayed, Minnesota Lt. Gov. and Dem Senate nominee Peggy Flanagan blaming only Gov. Tim Walz for the massive fraud in her state, New Jersey Dems spending a ridiculous amount of taxpayer dollars on a portrait, and the WNBA still failing to define a woman.First, they welcome the only encouraging poll of a key Senate race released by Fox News yesterday. Mike Rogers leads Abdul El-Sayed by four points in Michigan. White men are overwhelmingly behind Rogers, as are women who did not go to college. But "college-educated" women back El-Sayed 60-37 percent. We dig into the numbers and why that group is so much different than the others.Next, they shake their heads as Minnesota Lt. Gov. and Democratic U.S. Senate nominee Peggy Flanagan says she bears no responsibility for the fraud scandal in her state because "the buck stops" with Gov. Tim Walz. She also wishes somebody would have done something about it sooner. Jim and Greg suspect voters will not be satisfied with that answer.Then, they dig into New Jersey spending $135,000 in taxpayer dollars on a portrait of former Gov. Phil Murphy. That's $50,000 more than the price tag for Gov. Chris Christie's portrait eight years ago. Jim and Greg have a simple and very cost-effective solution.Finally, they laugh as WNBA officials meet for hours to discuss who should be eligible to play in the league and they couldn't come to any conclusions.Please visit our great sponsors:AG1https://DrinkAG1.com/3ML Start your first AGI subscription order, and get a FREE AG1 Flavor Sampler and a FREE bottle of Vitamin D3 + K2 in your Welcome Kit.Noble Goldhttps://NobleGoldInvestments.com/3MLIf you want to see how physical gold and silver could fit into your portfolio, download Noble Gold Investments FREE Wealth Protection Kit.IncogniTake control of your digital footprint today. Use code 3ML at the link below and get 60% off an annual plan: https://incogni.com/3MLNew episodes every weekday. 

Alphabet chief scientist Demis Hassabis has been discussing the formation of an independent AI safety entity with AI lab peers and government officials. WSJ reporter Amrith Ramkumar takes us inside those conversations, and what the move means for Google. Plus, meet the datamaxxers who are feeding AI chatbots their every health move. WSJ reporter Natalie Kaufman discusses the trend. Belle Lin, a reporter for the Wall Street Journal Leadership Institute, hosts. Sign up for the WSJ's free Technology newsletter. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

https://youtu.be/_OpJcRFbACk Abhi Jadhav, Founder and CEO of Bay Leaf Digital and Founder of Brazenly, helps B2B SaaS leaders create operational rigor while embracing AI, innovation, and disciplined execution. Driven by the freedom to experiment, anticipate change, and take the road less traveled, Abhi builds businesses that combine human expertise with AI-powered systems to help teams remain competitive and shape their own futures. In this conversation, Abhi introduces The Operational Rigor Framework—Aggregate To-Dos, Import Them into a System, Prioritize Weekly, and Execute. He explains how turning commitments into organized, prioritized tasks prevents work from falling through the cracks and improves execution across client projects, internal initiatives, and personal responsibilities. Abhi also discusses keeping humans in the loop when deploying AI agents, responding to rapid changes in the SaaS market, and driving growth through visibility, exceptional people, and innovation. — Create Operational Rigueur with Abhi Jadhav Good day. Steve Preda here, and today I’m joined by Abhi Jadhav, founder and CEO of Bay Leaf Digital, a B2B SaaS marketing agency he’s run since 2013. And he’s also the founder of Brazenly, an AI agent orchestration platform he’s building for marketing teams. Abhi, welcome to the show.  Thank you for having me, Steve. Appreciate it.  Well, great to have you here. And I’m super interested in how you are evolving your business and your focus on SaaS companies, which is very interesting. But I’d like to start with the question, your personal why. So how are you contributing to human flourishing? What is your personal why, and how do you manifest it in your business, in Bay Leaf Digital?  Yeah. So I gave this some thought. It’s been several years since I’ve been running the agency, so it took me back a few years to figure out, well, why did I even start this? So I’m going to date myself, and this is way back when I was in business school. One of my dear friends and classmates at the time, she pointed to me when she was asked, “Who’s most likely to strike it out on their own and run their own business?” And I thought that was really funny at that time because I had no desire to be my own entrepreneur or guy running your own business, et cetera. I was through and through a corporate guy.  And a few years later, I realized that I don’t really get energized by playing corporate games to climb the ladder. I prefer freedom to do things and to experiment. And I love taking the road less traveled, and it’s not just speak, it’s actually doing. Yeah. There’s a sign here. It’s a Jeep sign that says, “Road ends and fun begins.” And that kind of encapsulates who I am, really. Yeah. So when I saw this sign, I’m like, “This is me. It needs to go on my wall.” Okay. So that’s my personal why, how I ended up doing what I’m doing.  So how does it manifest in Bay Leaf or even Brazenly? What is that road that has not been traveled yet that you are embarking on?  So it is more about being able to anticipate what is coming and being able to be ready for it. I think that is primarily the difference, or to me, that is being my own business owner, it allows me to do that. So moving from an agency and seeing what is coming, AI is going to touch all of us. It already has in so many different ways.  So understanding where we are going and being able to do something about it rather than wait for someone else to come along and tell us, “You guys are now obsolete, and we are now moving on to something else.” So it allows me to chart my own path and be able to control, to a certain extent, I can’t say 100%, to a certain extent, what our business destiny is going to be.  So where are we going? What is your vision of the direction?  AI has tremendous power, right? And we are still very early, in the early stages of where it’s going to take us. But I’m a firm believer that the human in the loop is extremely important, and that we need to shape AI systems, our posture, in a manner that we maximize the use of AI while making sure that it is being run for the benefit of humans. So the way we approach it is, yes, we will adopt AI, but at the same time, we will never compromise on human in the loop in that process.  So our approach is, let’s take things that are easily automated. Let’s take things that we would otherwise not have been able to do. I have a framework on how we deploy AI, and the lowest part of that framework is obvious processes need to be automated. Top part of that framework is things that you could not ever do because it was impossible, impractical to do, automate that.  But in all of that, there is a certain amount of co-piloting or piloting that is needed in order to get valuable outcomes. Where I see us going is we will become pilots or co-pilots with extremely efficient and informed agents that will do the work, but they will not do the work exactly as is needed unless there’s a human in the loop that’s guiding it, right?  Yeah.  And I foresee this for the next several years. At some point, and this evolves all the time, but that’s kind of our posture at the moment.  Very interesting. So basically, AI agents do the work, or much of the repetitive work, a big part of the work, and the humans are directing the agents. Is that your vision?  Humans are directing the agents as well as checking on agent outputs, basically being the guide on making sure that things get done the way they should be done. So more and more, we are leaning on domain expertise, people who know what needs to be done, have that knowledge to be able to man these agents today.  Yeah. That is fascinating. So you mentioned that you have a framework. So what is your framework?  I have multiple frameworks. Let me talk about probably the most fundamental, foundational framework that can be used by anyone and everyone. Again, going back to my days before the agency, when I was in the corporate world, we would do 360 feedbacks. And at one point, one of the feedback I got, obviously it was constructive feedback, but it was that, “Abhi’s got a great team and he’s a great leader, but sometimes the team does not follow through on things that we’re committed to.”  I looked at that feedback and I said, “This is not something that my team needs to fix. This is something that I need to fix. It’s not a team problem.” So we took that weakness and we turned that into a core strength. That is one of my foundational approaches to the way we work, the way we run the business. I’ll break it down for you. Here’s what we really do.  Yeah, please.  So you and I are having a conversation. Something comes out of that conversation and I tell you, “Hey, I’ll follow up. I’ll get back to you.” And great, I’ll make a note of it. We have 100 different conversations like this throughout the day. I talk to my clients, I talk to my team. Everyone has something that they need or they’re going to offer. So there’s a pile of things that need to be done.  And without having operational discipline around it, things fall through the cracks, right? So a very simple manifestation of that is, hey, make a list. Make a list of things. But there’s a lot more to that. Yeah, you can make a list. You can write it in pencil on a pad, and then it gathers dust, and then you look at it after a few months, you’re like, “Oh, I never really got back to this person, didn’t really do it.” So what we’ve done is we’ve evolved that into an operational rigor where nothing, almost nothing, ever falls through the cracks.  We talk, we’ll organize our thoughts. We’ll put it—it doesn’t matter. It’s a framework, right? It’s not a set of rules. So people might take down notes. They might use Zoom meeting transcripts, whatever it might be. But at the end of the day, we are constantly putting together a list of to-dos. And that’s never enough because at the end of the week, you need to make sure that you’re working on the most important things. So we’ll go through a prioritization process.  I go through it, my team goes through it, and then that defines what we do in the following week. It’s very agile-like, but it’s not quite agile. It’s a set of tasks that eventually kind of come together for a greater purpose. So we’re looking and we are prioritizing and going, “Which is important, which is not?” So the conversations that we have with our clients, I have internally with the team, the conversations are never about, “Hey, whatever happened to that thing?” It never happens. The conversations are more about, “We talked about this last week. Here’s where we are.  Here’s why we are going to either change course, not do it anymore, or we have found a different way of doing it.” In terms of just an extremely important skill, whether you’re a business owner, in the corporate world, to me, operational rigor that leads to disciplined execution is absolutely key.  So what are the steps to this operational rigor framework? How do you actually do it? What are three to five steps or elements that will allow us to communicate to our listeners and maybe for them to try it out?  Yeah. Let’s just list them out. So the first thing is just pulling together the to-dos, right? So whether it’s from OneNote or a pad or transcripts, at the end of the day, set aside 30 minutes, an hour max, typically towards the end of the week, to allow you to go through that list of things that you want to do, okay? Put them into a system that allows you to keep track of the status.  We use our systems. We use a tool called ClickUp. We’ve used many, many different tools, but today we use that tool. We are able to change priorities, we are able to increase or reduce the size of those tasks, and we are able to collaborate with people on those tasks. So we’ll organize that, spend an hour or so, put it in ClickUp in the agile framework, if you will, and then we execute throughout the week. Monday morning, I look at—there’s never a question about, “Hey, what am I going to do today?” Right?  There’s never a question of, “Oh my God, there’s such a long list of things to do. How am I ever going to get through?” By the time Monday morning hits, I know exactly what I’m doing. By the time Tuesday morning hits, I know how much I accomplished on Monday and what I’m accomplishing on Tuesday. So it becomes very well-oiled, and it’s execution through and through without letting anything fall through the cracks.  Okay.  And this is not just me. The entire team behaves this way, and that is what is different, right? This was my feedback I got so many years ago, is follow-through is not there. Okay, we turned that around, made it a strength.  Love it. So aggregate the to-dos, import them into the system, prioritize, and execute.  And execute. And throughout the week, if you need to reprioritize, reprioritize. It’s okay.  And this pertains to your client work, or that also pertains to your internal projects that are improving your business, your own business?  It pertains to everything. It’s fundamental to everything we do, right? When we are improving our own business, we break it down to a series of steps, and then we prioritize the steps. For example, Steve, I could have come to your podcast seeing that, oh, I have a meeting at 8:00 AM on Friday. Let me just make sure that I have a shirt on and come to it.  But the way I approached it is the same operational rigor, which is, okay, sometime during this week, I’m going to make sure that this wall behind me is clean. I’m going to make sure that I have my talking points, and I’m going to go through a dress rehearsal myself before I come to you. So this operational rigor offers a much higher quality of output.  So it’s not just client-facing, it’s not internal. It’s in everything we do, and that includes my personal life too. To a fault, I use lists to organize my life, but it’s the same concept. It’s not as rigorous, but it is, I have this big to-do, let me break it down into a smaller to-do, knock it out, move to the next one.  Love it. That’s great. It’s a good framework. We can call it the Operational Rigor Framework, perhaps.  Operational rigor? Yeah, absolutely.  Yeah. Okay. So let’s switch gears here, and I’m really curious what drives growth in your businesses, in Bay Leaf Digital and your new business as well?  Okay, so we’re a marketing agency, but more specifically, we are a SaaS marketing agency. So we are niched down into SaaS, and then we niched down further. We are a B2B SaaS marketing agency, right? So it’s a very specific niche that we operate in. But some of the forces that affect us are the same as other agencies, other marketing agencies. So we live on this edge of constant change. And let me explain what these changes are. Number one, marketing channel change.  You’ve got Google, Meta, LinkedIn, you name it. They’re all making changes to their platforms all the time, right? Some of my marketers tell me they go in one day into the Meta advertising platform, and the next day they see something completely different. A feature has changed, and what they thought was working yesterday no longer works. So we are living on this edge of constant change as far as the marketing channels are concerned. Then it’s very surprising how much the economy actually affects us.  Because we serve a startup audience, B2B SaaS startup audience, whether there is money flowing in the economy or not actually affects us quite a bit. Post-Covid, when suddenly there was contraction, we felt it. So there’s that external force. And then our competitors are not sitting down napping. They’re also on top of these things. So we have these three external forces affecting us all the time. So for us to live and thrive on this edge, we have to be forward-looking, and we have to be extremely methodical. It goes back to that operational rigor.  Imagine you’ve got 20 different things coming at you. You have to figure out, “Hey, what is the most important thing here, and how do I respond to that? How do I respond to the change?” Being able to anticipate, being able to be forward-looking, and responding has allowed us to weather storms. It was post-Covid, obviously Covid too, but post-Covid was more important from a business perspective. And then you may have heard about this in maybe other conversations, when Claude released their latest version of Claude Code, they unleashed what is called the SaaS apocalypse, meaning SaaS companies, right?  SaaS companies are outdated. They no longer are needed because you can create your own app and you don’t need a SaaS company. So companies that help SaaS companies such as ourselves, B2B SaaS marketing agencies, were in that first line of companies to get affected. But we saw that coming. We saw that coming a year before, right? And we were working, we were preparing for it. So six, eight months in, it doesn’t hurt us as much as maybe it has hurt other companies. These are the things that help us grow. Yeah. Go ahead.  No, no. I’m just wondering, I understand that you are very agile. I mean, you use an agile system, but you’re also agile in the way you are responding to the market changes. You adjusted well with the SaaS apocalypse, or whatever it’s called. But just because you are avoiding a major landmine doesn’t mean it’s going to grow your business. So I wonder what drives the growth beyond being agile. So as the market changes, does it automatically help you create more revenue?  That’s a good question. Yes. Yes. So I’m probably talking very big picture. So let me convert that into how this actually drives growth for us. So a change happens, we respond to that change. Sure, it helps us survive. But think about a change in the LLM, the large language models such as Gemini or ChatGPT or Claude, the way they process information and the way they present that information to you.  Because we see that, we respond to it, we are able to make sure that when, Steve, maybe you go into Claude and you ask for, “Hey, I want to work with a SaaS marketing agency,” we make sure that we are on top over there because we are responding to changes that are happening in the back end, right? So it’s not just thriving, it’s making sure that you know what is happening so that you can always be at the forefront. Wherever people go to find you, you need to be there. But I’m simplifying this significantly. So that is just responding, still responding. The thing that really drives growth for us internally is we find and work with really, really good people, great team members. We put in a lot of effort to find these great people, and when we find them, we hold onto them. This did not happen overnight. Finding great people is extremely hard, especially for a small business. So we came up with a methodology to actually finding those people. I looked at various methods, but the one method that appealed to me was this method called Who by Geoff Smart and Randy Street, these two authors.  I started there, but then I evolved that framework significantly over time to where it is now a core strength. So whenever we hire people, we know exactly what are the areas of strength they have, what are the areas that we’ll need to help them improve in. And so we know who is coming in and how we can best leverage them. So once we know that, we’re able to move forward fairly easily. So that’s one of our core things that helps us.  So what drives growth is, if I hear it correctly, what drives growth is staying visible with all the algorithm changes and the platform changes. You’re going towards more AI visibility as opposed to SEO visibility. And then also being able to recruit those people who will be able to do the work that is going to respond to the market demand.  Exactly. And the third part, the third lever to all of this is innovation. I mean, you would think that an agency would not innovate, but there’s innovation happening all the time in various things. Whether it’s marketing approaches, whether it’s putting together a marketing operational platform or whatever else, you have to innovate to stay ahead. So I would say these are the three things that help us grow.  Yeah. Love it. That’s great. So visibility, people attraction, and innovation. That’s the three legs of the stool. Yeah. So what is one thing that you’re actively trying to figure out in your business right now?  One thing we’re trying to actively figure out? That’s such an interesting question. There’s so many things happening all the time, right? So let me pick one that comes right to mind. We’re trying to figure out whether we should raise capital or not. Because we look at some of the greatest recent successes in AI platforms and companies, and they’ve come from one-person to five-person teams. So the question now becomes, when there is no technology moat, there is no reason to hire 100 people to thrive, what is the role of capital?  So that is something that I’m debating internally about what should we do here. Do we need it? Do we not need it? And there are so many other things that we work on all the time. But I would say this is, at the moment, today, June or July 2026, what’s top of mind for me.  Yeah. It’s a great question. If you don’t need more people and it’s a professional service that you’re providing, you’re innovating with your own resources and AI, then what is the capital need of the business?  Right.  So to what extent can a business grow today without adding people? Is it possible to scale a business without adding people?  The popular word on the street is grow a business without people. I don’t think that’s possible, to grow a business without people. If you have domain experts and you have people with the right traits, you can grow with a very small company. And those traits are things like: you need to be a critical thinker. You need to be able to question and analyze things a bit differently. You need to have domain expertise.  Most important for me, you need to have operational rigor. And when you have these kinds of people, yeah, I think you can grow with a very small team, but not a one-person company. Maybe in a few years, maybe in a few decades, that might be possible. But today, you can significantly speed up what you can do using AI, but you definitely need people who can manage those systems, who can squeeze the most out of those systems. You need those people manning those systems.  I also wonder if there is a threshold below which you don’t actually have a business. If you don’t have enough people to reduce your dependency on any single individual, including yourself, then do you actually have a business, or is it more like a professional practice?  Right. Right. Yeah. So it’s like you design a system and you just deploy it and it just runs on its own. So is that the business? I don’t know. It’s not really a business at that point. It’s just a tool that you put out there in the universe and it’s doing its thing. It’s a very philosophical question about where we end up as a result.  Yeah. And then things are changing all the time, so can you just rely on the system rejuvenating itself, or do you still need human energy to keep improving and keeping that business competitive in a fast-changing environment?  Right. It’s a question of the autonomous agents, right? How autonomous can autonomous agents really be today, and where does that lead us eventually? Does it lead us into AGI? We’ll see. But today, no. I would say strongly that autonomous agents are good within certain guardrails.  You can provide a lot more freedom for them to do it as long as they don’t break certain rules, and that’s where humans come in to make sure that those guardrails are maintained and the guiding happens. In today’s world, I don’t think that business exists without any employees. In tomorrow’s world, it might. Yeah. And that changes the economy, that changes everything.  Yeah. That’s an even bigger question.  Yeah.  Well, if you had a magic wand and you could fix one thing in your business in the next 12 months, what would that be?  Learning curves. I would love to skip past learning curves. And by that I mean, even when we find the best people with all the best traits, frequently what we see is we end up having to train people on what we know and what we know works, and that takes several months. It takes several months to get there. So it’d be amazing if I had a magic wand and day one they’re ready to go.  Yeah. So you’re deploying these agents for SaaS companies. Who is the ideal client that you would like to contact you when they hear this podcast?  We serve B2B SaaS companies, right? And so we work typically with companies who have what is called product-market fit, meaning their value has been articulated and they’re looking to grow. Those are the companies that we normally work with. And as we deploy Brazenly, our marketing operations orchestration tool, we’re looking to help marketing teams. And those teams could be a three-person team.  It could be a 20-person team. It doesn’t matter. It is built to scale. But that part of the business is definitely focused more on team productivity or automation of marketing teams as opposed to an individual. That is what ChatGPT and others are focused on, individual productivity. We focus on the team productivity aspect of it.  So you need companies that have figured out their product and they already have multiple people in marketing that need the support?  We normally work with companies that have figured out their value proposition and are looking to grow, so they don’t have, this is on the marketing agency side, right? So they don’t have all the people in place in order for them to grow, and that’s where we come in. We come in as a marketing department, and we will help take over all those marketing functions. That is on the agency side.  On the AI platform side, it’s a slightly different focus, which is we are helping ourselves. We are helping ourselves become more efficient as we help our clients. That’s one. But for someone else to use a marketing platform, they need to have a marketing team that can actually leverage the marketing operations platform. So there’s the agency focus, and then there’s the Brazenly marketing platform focus.  So you have the done-for-you version, which is the professional service as an agency, and then you have the DIY version, which is them using your platform to do it themselves, basically.  You’re exactly right. So there’s the done-for-you, there’s the done-with-you, and then there’s the DIY, right? Okay. Done-for-you is the agency. The done-with-you is, as much as there’s promise with AI platforms, you still need to build the agents. You still need to deploy them. That is the done-with-you, which is we’ll consult with marketing teams to help them deploy these agents. And the DIY is, here’s the AI operations platform. Go and use it yourself. That is the DIY. Okay. So you’re exactly right.  Yeah. Love it. Love it. That’s a great slate of services. So if people would like to learn more, I get it, they should not go to LinkedIn, but where should they go? Where can they find you?  Yeah. So you can always contact me on various platforms, Twitter, email, Instagram, and such. But if you want to get in touch, I would say the best way is to just drop me an email. My email is aj@bayleafdigital.com.  So Abhi Jadhav, the CEO and Founder of Bayleaf Digital and Brazenly. So if you’d like to accelerate your marketing, if you’re a B2B SaaS company and you want to accelerate your marketing, whether it’s complete done-for-you, done-with-you, or DIY, Bayleaf Digital and Brazenly can take care of you. So reach out to Abhi Jadhav on Twitter, email, or through the website, bayleafdigital.com.  And if you enjoyed this episode, then make sure you subscribe and you follow us because we come out with a couple of interviews every week with exciting entrepreneurs who are building great businesses. So thanks for coming, Abhi, and sharing your wisdom, and thanks for listening.  Thank you very much for having me, Steve. It was a pleasure.  Important Links: Abhi's LinkedIn Abhi's website Abhi's email: aj@bayleafdigital.com

Join Jim and Greg for the Thursday 3 Martini Lunch as they discuss how the U.S. Secret Service smuggled President Trump out of Turkey on a different plane without the media or his staff knowing, Texas Republicans getting very worried about Ken Paxton losing the U.S. Senate race, Abdul El-Sayed becoming the latest Democrat to hate Thanksgiving, and the cautionary tale of former rising political star Andrew Gillum.First, they dive into how the Secret Service used covert moves to get President Trump onto a different plane when he was leaving Turkey last month due to the threat of an Iranian attack. Some in the media are crying foul because they or their colleagues were left on the original plane. Jim and Greg walk through what criticism is fair and what is not.Next, they get nauseous as Republicans in Texas openly worry that Ken Paxton is on track to lose his U.S. Senate race against James Talarico. GOP operatives tell Mark Halperin that Paxton is being lazy in his campaigning and evasive in addressing his many scandals. But this is Texas. Against Talarico. Is it really that dire?Then, they throw up their hands as Abdul El-Sayed becomes the latest Democrat in this cycle to express his hatred for Thanksgiving. It was seven years ago and El-Sayed insists he does not believe that anymore. But it came after his run for governor in 2018. They also react to El-Sayed's comments comparing football to the transatlantic slave trade.Finally, they detail the latest legal trouble for former Tallahassee, Florida, Mayor Andrew Gillum, who was narrowly defeated by Ron DeSantis in the 2018 governor's race. Since then Gillum has faced numerous drug charges and other revelations. Jim wonders about the impact of being a political star everyone quickly forgets about. Greg says Florida dodged a huge bullet.Please visit our great sponsors:AG1https://DrinkAG1.com/3ML Start your first AGI subscription order, and get a FREE AG1 Flavor Sampler and a FREE bottle of Vitamin D3 + K2 in your Welcome Kit.Noble Goldhttps://NobleGoldInvestments.com/3MLIf you want to see how physical gold and silver could fit into your portfolio, download Noble Gold Investments FREE Wealth Protection Kit.IncogniTake control of your digital footprint today. Use code 3ML at the link below and get 60% off an annual plan: https://incogni.com/3MLNew episodes every weekday. 

Governments are prioritizing data centers at the expense of communities and the planet, but it doesn't have to be this way. Matt Haugen joins Paris Marx to discuss how national strategies are dismantling environmental protections while enriching a handful of megacorporations, and what an alternative agenda could look like.Matt Haugen is Research and Editorial Manager at the Climate and Community Institute.The podcast is made in partnership with The Nation. Production is by Kyla Hewson. Support the show on Patreon.Also mentioned in this episode:Check out the new report Matt co-wrote AI First.If you missed it, Molly White recently broke down how the tech industry is spending big on elections.Big tech's emissions are continuing to rise.Colossus is still being powered by unpermitted gas turbines.Support the show

What if the reason you're not moving forward has nothing to do with what you know or what you've done, but with who you are being?A moment from the podcast archive that I felt deserved to be heard again.Today, my guest Dr Ryan Gottfredson, Wall Street Journal and USA Today bestselling author and leading expert on mindsets and vertical development, shares one of the most clarifying distinctions I have ever heard in personal development: the difference between your doing side and your being side, and why focusing on the wrong one keeps so many high achievers permanently stuck.Press play to discover why the next degree, certificate or course might not be what you need, and what to focus on instead.˚VALUABLE RESOURCES:Listen to the full conversation with Dr Ryan Gottfredson in episode #512:https://personaldevelopmentmasterypodcast.com/512˚Coaching with Agi: https://personaldevelopmentmasterypodcast.com/mentor˚

Join Jim and Greg for the Wednesday 3 Martini Lunch as they dig into Tuesday's critical primary results in three states, another blue state enthusiastically embracing abortion up to the moment of birth, Tucker Carlson and Hunter Biden blaming Israel for Hunter's laptop scandal, and another House incumbent losing his primary.First, they walk through the primary results in Wisconsin, Minnesota, and South Carolina, including how socialist Francesca Hong blew a nearly 30 point lead within just a few days, whether Michele Tafoya can win a U.S. Senate seat in Minnesota, and what Tuesday's South Carolina U.S. Senate primary might tell us about who might win the runoff there.Next, they shudder as Massachusetts becomes the latest state to legalize abortion for any reason or no reason up to the moment of birth. Greg points out that any third trimester complication can be dealt with by delivering instead of killing the baby. Jim details how drastically the left's position on abortion has changed in recent years.Then, they groan as Tucker Carlson and Hunter Biden blame Israel for Hunter's laptop fiasco and pushing the story to the media. Jim begs Biden to go away and he also addresses growing concerns that Tucker will start a third party designed to sink Republicans in 2028.Finally, they note Connecticut Rep. John Larson losing his primary on Tuesday, adding to a much longer list of incumbents fired by their own party's voters in 2026 thna in most cycles.Please visit our great sponsors:AG1https://DrinkAG1.com/3ML Start your first AGI subscription order, and get a FREE AG1 Flavor Sampler and a FREE bottle of Vitamin D3 + K2 in your Welcome Kit.Noble Goldhttps://NobleGoldInvestments.com/3MLIf you want to see how physical gold and silver could fit into your portfolio, download Noble Gold Investments FREE Wealth Protection Kit.IncogniTake control of your digital footprint today. Use code 3ML at the link below and get 60% off an annual plan: https://incogni.com/3MLNew episodes every weekday. 

We are continuing our investigation into the implications of the Economic Singularity, the time when we need a new economic system because full automation by AI means that people can no longer obtain the resources they need by doing jobs.Our guest today is Ewan McMillan, the founder of New Gradient, an Edinburgh-based consultancy that builds AI systems for a variety of industries, from biotech to mining. He took a degree in theoretical physics at the University of Edinburgh, and since then he has spent seven years delivering applied AI R&D. We wanted to have Ewan on the show because he also writes about the economics of AI from the perspective of someone who is actually implementing it. This spring he posted a series of thought-provoking articles called “The Economics of AGI”.Selected follow-ups:New Gradient - Company websiteEwan McMillan - LinkedIn"The Economics of AGI" - Part 1  of Ewan's essay"The Innovation Paradox" - Part 2 of the essay"The Allocation Stack" - Part 3 of the essay"Bitter lesson" (general AIs will in due course out-perform specific AIs) - Wikipedia"AlphaZero" (covers the chess match vs. Stockfish) - Wikipedia"Smartphones and Beyond: Lessons from the remarkable rise and fall of Symbian" - book by David"Culture series" (about science fiction series written by Iain M Banks) - Wikipedia"Aghion–Howitt model"  - Wikipedia2025 Nobel Prize in Economics (recipients include Philippe Aghion and Peter Howitt)"The First Solo Unicorns Are Here: Meet the One-Person Companies Racing to $1B in 2026" - Founder Institute"Understanding Alpha in Investing" - Investopedia"'We Must Act Now': Sixteen Nobel Laureates Join Leading Economists and AI Researchers in Call to Prepare for AI's Economic Transformation" - Stanford Digital Economy LabMusic: Spike Protein, by Koi Discovery, available under CC0 1.0 Public Domain Declaration

Join Jim and Greg for the Tuesday 3 Martini Lunch as they discuss the Trump political team reportedly telling Ohio Republicans to stop trying to force Max Miller out of his re-election bid, the Democratic Socialists of America having no ideas beyond slogans, Mamdani's rent adviser owing more than $108,000 in rent, and critical primaries being held today in three states.First, they shake their heads as Politico reports Trump's political advisers urged the Ohio GOP to stop pressuring Rep. Miller to drop out and resign from Congress because it won't work. Miller is facing numerous serious abuse allegations and the polls show he is likely to lose in November.Next, they highlight Democratic Socialists of America (DSA) Co-Chair Megan Romer having no idea how much the DSA would tax the rich, how it would abolish prisons, end borders, or defund the police. Jim is amazed Romer is still doing interviews. Greg argues her evasive answers on taxes aren't much different from regular Democrats.Then, they get a kick out of news that New York City Mayor Zohran Mamdani's rent adviser has not paid her own rent since 2021 and owes more than $108,000 in back rent. Jim and Greg discuss how this case seems to be another example of the left trying to demonize landlords.Finally, they offer a quick preview and predictions for key primaries today in Wisconsin, Minnesota, and South Carolina.Please visit our great sponsors:AG1https://DrinkAG1.com/3ML Start your first AGI subscription order, and get a FREE AG1 Flavor Sampler and a FREE bottle of Vitamin D3 + K2 in your Welcome Kit.Noble Goldhttps://NobleGoldInvestments.com/3MLIf you want to see how physical gold and silver could fit into your portfolio, download Noble Gold Investments FREE Wealth Protection Kit.IncogniTake control of your digital footprint today. Use code 3ML at the link below and get 60% off an annual plan: https://incogni.com/3MLNew episodes every weekday. 

Ryan Greenblatt is the Chief Scientist at Redwood Research, where he works on technical AI safety research. He's also lead author on the "Alignment faking in Large Language Models", and is currently working on a third party investigation into the OpenAI/HuggingFace incident. In my opinion, he's one of the most interesting thinkers on the future of AI.Had him on to discuss/debate recursive self-improvement. This might be the most important question in the world right now – whether within a year or so of achieving human-level intelligence, you slingshot towards having 10s of billions of superintelligences, each of which is dramatically more competent than human experts across all fields.I've historically been skeptical of this possibility. My intuition has been that we will end up significantly bottlenecked by not only compute scaling but human expert data, which I think underlies most of the AI progress today.If, because of RSI, we got a jump as big as GPT-3 to a Mythos (i.e. 6 years of AI progress) within a single year of achieving AGI, then the thing we get there at the end of that year is definitively and wildly superhuman.We hashed it out, and I think Ryan made a pretty good case that this kind of speedup is plausible. FWIW, Ryan's median for when we automate AI R&D is 2031.We then discussed the alignment implications of this scenario. Who should these superintelligences be aligned to? In the future, our capacity to steward our votes and our capital, and to make sense of what's happening in the world, will all be titrated by superintelligences. And I worry that specs like the Claude Constitution are not shaping these ASIs to truly be my personal advocates and guardian angels.And can we get them aligned to anything in the first place? Ryan and I had a long debate about whether the kind of reward hacking we saw with the OAI/Hugging Face hack extrapolates to superintelligences that would team up to literally take over the world.The first piece of advice you get when you're learning to drive is that it will go much smoother if you look at the horizon instead of directly in front of your tires. And so it is with the trajectory of AI. Hope you enjoy!Watch on YouTube; read the transcript.Sponsors* Antithesis is a software testing platform that finds the failures no human or AI could ever anticipate. It runs thousands of copies of your code inside a fully deterministic computer, injecting faults and steering each trajectory toward the most insidious bugs. This lets you find critical issues in minutes rather than waiting months for your users to uncover them. Learn more at antithesis.com/dwarkesh* Jane Street's back with a new puzzle. They designed an ASIC and sent me the final masks… but they didn't tell me what the chip actually does. So that's the challenge: reverse engineer the circuit and figure out the chip's purpose. Jane Street has a bunch of swag ready to send to the most creative solutions, and they're also planning to feature the top write-ups in a blog post. Download the files and get started at janestreet.com/dwarkesh* Cursor and SpaceX recently released Grok 4.5, and I've been surprised by just how good the model is. For example, when I tested it against Fable and Sol on a bunch of AI governance questions, all three models gave substantially the same answers, but Grok was faster, more concise, and cheaper. Grok 4.6 is coming soon, but in the meantime, you can try 4.5 at cursor.com/dwarkeshTimestamps(00:00:00) – Is AI R&D verifiable enough to unlock recursive self-improvement?(00:16:52) – Is AI progress bottlenecked by human expert data?(00:34:02) – Flat token prices suggest scaling has been slow(00:39:47) – Skills AI can't train on: does it even need them?(00:48:07) – Aligned to whom?(01:09:18) – Recent incidents of AIs colluding and deceiving humans(01:19:38) – What could possibly go wrong? A concrete scenario(01:48:02) – From reward hacking to takeover Get full access to Dwarkesh Podcast at www.dwarkesh.com/subscribe

Drew Miller is an Air Force Academy honor graduate with a master's and a PhD from Harvard. His dissertation was on underground nuclear shelters and field fortifications. Thirty years in uniform across active duty, Air Guard, and Reserve. Intelligence officer. Strategic Air Command, the Pentagon, and a DoD think tank. Retired colonel. He founded Fortitude Ranch, a survival community with eight locations, and the Collapse Survival Institute. His new book is Preparing to Survive in the Age of Collapse. His model puts the annual odds of a collapse disaster between 16 and 57 percent. Bioengineered H5N1. The grid going down. The economy stops, and law and order goes with it. We disagreed on a lot of this one. He wants superintelligent AGI outlawed and says he would nuke a data center to enforce it. I don't get there with him. We went back and forth on facts versus assumptions, on what a real fight with China looks like, and on whether this government is salvageable. Also covered: the Ninth and Tenth Amendments, civil war inside states instead of between them, two million prisoners with no power, and surviving an AI takeover by being too boring to kill.   Join the Cleared Hot Newsletter: https://www.clearedhotpodcast.com Take the Operator Code Assessment: https://www.theoperatorcode.com   Today's Sponsors:  Montana Knife Company:  https://www.montanaknifecompany.com Better Help:  Sign up and get 10% off at https://www.betterhelp.com/clearedhot

Join Jim and Greg for the Monday 3 Martini Lunch as they focus on AOC trying to giggle away the left's repressive and woke lurch since 2020, Abdul El-Sayed refusing to disavow Hasan Piker and dismissing Piker's comment that the U.S. deserved 9/11, Wisconsin Democrats frantically trying to defeat Francesca Hong in tomorrow's primary, and the passing of Dukes of Hazzard actor-turned-Georgia Rep Ben Jones.First, they fume as New York Rep. Alexandria Ocasio-Cortes laughs and tries to chalk up the left's destructive demonization of police, white people, and anyone who didn't obey every jot and tittle of the Covid rules as some some crazy "Woke 1" phase the party went through. Jim says you can be sure the next level of wokeness will be far worse.Next, they shake their heads as El-Sayed refuses to distance himself from Hasan Piker and labels Piker's contention that we deserved 9/11 as dumb or out of context. Jim and Greg also consider how much this issue will hurt Piker and why these comments are not the same as most other issues.Then, they thoroughly enjoy the desperate attempt by Wisconsin Democrats to do whatever they can to stop Francesca Hong from being the party's nominee for governor. Jim suspects it won't work because the Dem electorate is more radical than even a few years ago. Greg says this is a problem of the Dems' own creation.Finally, they note the passing of former Georgia Rep. Ben Jones, who is best known as Cooter Davenport on The Dukes of Hazzard. Jim and Greg remember his brief time in Congress, his rivalry with Newt Gingrich, and the greatness of The Dukes of Hazzard.Please visit our great sponsors:AG1https://DrinkAG1.com/3ML Start your first AGI subscription order, and get a FREE AG1 Flavor Sampler and a FREE bottle of Vitamin D3 + K2 in your Welcome Kit.Noble Goldhttps://NobleGoldInvestments.com/3MLIf you want to see how physical gold and silver could fit into your portfolio, download Noble Gold Investments FREE Wealth Protection Kit.IncogniTake control of your digital footprint today. Use code 3ML at the link below and get 60% off an annual plan: https://incogni.com/3MLNew episodes every weekday. 

Ben Lorica talks with Evangelos Simoudis  about three forces reshaping AI: why recent “rogue agent” incidents may be more about permissions, identity, and governance than runaway intelligence; how the belief that AGI is near is helping sustain enormous investment in AI models and data centers despite unanswered questions about margins and ROI; and whether Chinese robotaxi companies can challenge Waymo globally by following a playbook similar to China's rise in solar and batteries. Subscribe to the Gradient Flow Newsletter

Sam records from the beach while dealing with a broken bilge pump. Dave calls in after herding cattle. Fortunately, tech continues without them, which Sam takes as further proof that AGI is already running the industry. The crew unpacks Google's leadership shakeup and Jeff Dean's departure after 27 years, debates whether Airtable's sale to Bending Spoons is the blueprint for surviving the AI transition, and Sam argues that today's AI labs look increasingly like yesterday's overvalued SaaS companies. Along the way they explain why Sam's AI built his kid an iPhone game, why OpenAI's luxury creator retreat backfired, why Apple may have accidentally created its own lawsuit, and whether AI can ever overcome the growing public backlash against it. Chapters:0:00 Episode Trailer1:07 Episode Start2:08 Sam's Boston Whaler Beach Studio3:13 Dave Herds Cattle in Montana5:31 Google's AI Shakeup, Jeff Dean Leaves, Demis Steps Back8:57 Why Great Researchers Don't Always Make Great CEOs10:48 Recursive Self-Improvement and the AI Talent Wars19:41 Airtable Sells to Bending Spoons25:33 Not Every SaaS Company Is Dead27:50 The Constellation Software Playbook30:05 The Next Big Write Downs Are AI Labs31:59 Apple vs. OpenAI, Courtesy of iCloud35:20 OpenAI's Creator Summit Backlash40:53 Why People Still Hate AI43:39 AI Slop and the Content Problem45:11 SpaceX's Lockup and Number Big48:00 Nikita Bier Leaves X50:22 Jess's Posting Dilemma53:40 Next Week on More or LessWe're also on ↓X: https://twitter.com/moreorlesspodInstagram: https://instagram.com/moreorlessSpotify: https://podcasters.spotify.com/pod/show/moreorlesspodConnect with us here:1) Sam Lessin: https://x.com/lessin2) Dave Morin: https://x.com/davemorin3) Jessica Lessin: https://x.com/Jessicalessin4) Brit Morin: https://x.com/brit

Cal Newport takes a critical look at recent AI News. Video from today's episode: youtube.com/calnewportmedia (0:00) Does Open AI's Astra mean AGI has arrived? (3:07) What actually happened? (11:35) What does this mean for mathematics? (21:35) What does this mean for OpenAI?  Links: Buy Cal's latest book, “Slow Productivity” at www.calnewport.com/slow  https://x.com/polynoamial/status/2083467194663571701 https://x.com/deanwball/status/2083545756003176724?s=61 https://x.com/kevinroose/status/2083632335438905441 https://x.com/mattshumer_/status/2083595078233202919 https://x.com/polynoamial/status/2083478171975082334 https://x.com/__alpoge__/status/2083898804563243033 https://x.com/polynoamial/status/2083476852216369294 https://x.com/thomasfbloom/status/2083444983592284465 Sponsor: https://www.donedaily.com Thanks to Jesse Miller for production and mastering and Nate Mechler for research and newsletter. Learn more about your ad choices. Visit podcastchoices.com/adchoices

Both Silicon Valley and the public can't get enough of ‘AGI timelines.' But Toby Ord, senior researcher at Oxford's AI Governance Initiative and author of The Precipice, believes we consistently make big mistakes when thinking about them. He lays out the 14 ways he most often sees people go wrong:Assuming AI research is just hill-climbingImagining AI research is just programmingForecasting “could” instead of “will”Believing the current benchmark is the last oneExtrapolating trends with no clear finish lineAssuming inputs keep scaling at the same rateConflating intelligence with capabilityConsuming point estimates and discarding the error barsDismissing dissenting expertsForecasting very different things while using the same wordsAssuming capabilities arrive togetherTreating “we don't know” as permission to carry on as usualChoosing a plan that minimises regret rather than maximises impactTrusting surface model impressivenessIn this extended conversation with Rob Wiblin, Toby also explains why he thinks:AI self-improvement is uniquely dangerous in four ways, but also might not even workA ban on superintelligence is possibleA US-China treaty on superintelligence is also possibleThe case for ‘broad timelines'Transformative AI is likely a decade awayWe should just ban unmonitorable chain-of-thought today.This episode was recorded on July 2, 2026.Links to learn more, video, and full transcript: https://80k.info/to26Want to get up to speed on AI? We've got a crash course of 10 of our podcast episodes designed to help you get to grips with transformative AI — particularly if you're new to the topic — and what you can do to help shape its trajectory.Chapters:Toby Ord is back — for the 5th time! (00:00:00)AI self-improvement might not matter (00:00:14)4 ways AI self-improvement is dangerous (00:12:39)A US-China treaty on superintelligence is possible (00:20:47)Could we ban superintelligence? (00:37:07)We should just ban unmonitorable chain of thought (00:57:46)Why Toby thinks AGI is a decade away (01:09:28)Even superintelligence needs work experience (01:17:50)Is AI coming for mathematicians? (01:32:22)The case for broad timelines (01:45:01)How should broad timelines change what we do? (02:22:24)Are current models all they're cracked up to be? (02:31:03)Coordinating careers for different timelines (02:43:36)Our production team includes:Video editors: Josh Alward, Dominic Armstrong, Ollie Bignell, Andrés Escobar, Jasper Luithlen, Milo McGuire, Luke Monsour, and Simon MonsourProducers: Elizabeth Cox and Nick StocktonCoordination and support: Katy Moore and Lou MoranCamera operator: Jeremy ChevillotteMusic: CORBIT

What if the secret to mastering life is not doing more, but interfering less?In this series, I select my favourite and most insightful moments from previous episodes of the podcast.Today, my guest Bob Martin, former criminal trial lawyer turned mindfulness mentor, shares a teaching he received from a 72nd generation Taoist Master, on what it truly means to master life. Through the metaphor of a swimmer reading the tide, Bob reveals why knowing when to push and when to wait is one of the most powerful and underrated skills a person can develop.Press play for one of the most memorable and practically useful pieces of ancient wisdom I have ever shared on this podcast.˚VALUABLE RESOURCES:Listen to the full conversation with Bob Martin in episode #496:https://personaldevelopmentmasterypodcast.com/496˚Coaching with Agi: https://personaldevelopmentmasterypodcast.com/mentor˚

What drives someone to spend decades pursuing artificial general intelligence, not for wealth, but for scientific discovery? In this episode of Technovation, Peter High speaks with bestselling author and financial historian Sebastian Mallaby about his new book, The Infinity Machine, which chronicles the remarkable journey of DeepMind co-founder Demis Hassabis and the global race to build artificial general intelligence (AGI). Drawing on more than 30 hours of interviews with Hassabis and conversations with over 100 colleagues, competitors, and friends, Mallaby explores the leadership philosophy, scientific ambition, and organizational decisions that shaped one of the world’s most influential AI companies. Listeners will also hear an in-depth discussion of Google’s acquisition of DeepMind, the evolution of AI from AlphaGo to Gemini, the competitive dynamics among OpenAI, Anthropic, Microsoft, and Google, and what the pursuit of superintelligence means for business leaders navigating the next era of technological transformation. Key topics include: Why Demis Hassabis devoted his career to AGI The founding story behind DeepMind Google’s AI strategy and the Innovator’s Dilemma Leadership lessons from building world-class research organizations The opportunities of increasingly capable AI systems Listen to discover why the future of AI may depend as much on leadership and organizational design as on technological breakthroughs. This episode is presented by ElevenLabs — Bringing technology to life. Learn more at elevenlabs.io

David Cahn is a partner at Sequoia Capital. Cahn joins Big Technology Podcast to discuss how much revenue the AI industry must generate to pay back its massive infrastructure investments and why the pursuit of AGI is driving companies to keep spending. Tune in to hear his assessment of the strategies guiding OpenAI, Anthropic, Google, Meta, Microsoft, Amazon, Apple, Nvidia, and SpaceX. We also cover the risk of an investment-timeline mismatch, the value of AI talent and proprietary chips, and whether building artificial intelligence could change religion and spirituality. Hit play for a cool-headed examination of the enormous financial and strategic bets shaping the future of AI. --- Enjoying Big Technology Podcast? Please rate us five stars ⭐⭐⭐⭐⭐ in your podcast app of choice. Watch the full documentary here: https://www.gravitee.io/ai-agent-documentary Want a discount for Big Technology on Substack + Discord? Here's 25% off for the first year: https://www.bigtechnology.com/subscribe?coupon=0843016b Learn more about your ad choices. Visit megaphone.fm/adchoices

Intelligence too cheap to meter -- could that actually be coming?

Theo Jaffee is joined by Joshua Achiam, Chief Futurist at OpenAI, for a conversation on AI cybersecurity, frontier model capabilities, and why he believes society may have already crossed the threshold into an AGI-era without fully recognizing it. They discuss AI's rapidly advancing cyber capabilities, state-sponsored hacking, model jailbreaks, recursive self-improvement, and what happens when AI systems begin discovering vulnerabilities faster than humans can patch them. Joshua also explains why most people have quietly adapted to capabilities that would have seemed unimaginable just a few years ago, and why the biggest changes from AI may arrive gradually rather than all at once.   Resources: Follow Joshua Achiam on X: https://x.com/jachiam0 Follow Theo Jaffee on X: https://x.com/theojaffee Follow MTS on X: https://x.com/mtslive Stay Updated:Find a16z on YouTube: YouTubeFind a16z on XFind a16z on LinkedInListen to the a16z Show on SpotifyListen to the a16z Show on Apple PodcastsFollow our host: https://twitter.com/eriktorenberg Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Last October, famed coder Andrej Karpathy called AI agents “slop.” Two months later he completely reversed his view, calling agents “alien tools” that are “rocking the profession.”He was far from alone in his whiplash. Six months ago, host Rob Wiblin recorded a video explaining why so many AI experts had longer timelines to AGI than a year earlier. By the time he clicked publish, another huge vibe shift was well underway. Evidence of AI acceleration has piled up since:Models now complete software engineering tasks that would take human professionals a full day — improving faster than our measurements can even keep up. Anthropic's revenue is growing at an annualised 8,400%, a trend so steep it would hit the whole world's GDP in 2028 if it continued.AI models are making breakthroughs in famous mathematics puzzles.And according to Anthropic, Claude now writes 80% of their code and is itself a key contributor to making itself smarter. While legitimately impressive, Rob isn't entirely sold. Going through each point carefully he finds this evidence is less decisive than it looks at first glance.And key gaps remain, such as models struggling with complex, real-world tasks. He tours the odd experiments that remain our best attempts to measure that gap: vending machine simulators, an “AI Village” that organises live events, and a real cafe and shop where AI managers are left to do their best handling staff, suppliers, and government paperwork on their own.Rob argues that the nature of the gap between clean and messy work is one of the four biggest unresolved questions in AGI forecasting.In today's piece he explains that, the three other key disagreements between AGI bulls and bears, the seven big pieces of evidence we've gotten about AGI timelines in 2026, and his updated timelines to AGI.Links to learn more, video, and full transcript: https://80k.info/2026-timelines This episode was written and recorded before OpenAI's AI agents hacked Hugging Face. You can read about the incident on our Substack.This episode was recorded on July 3, 2026.Chapters:What the hell happened? (00:00)Vibe shift (01:17)Exhibit 1: AI revenue explodes (04:33)Exhibit 2: That METR graph (09:54)Exhibit 3: AI capabilities jump, then flatten out (14:57)Exhibit 4: AI starts to build itself… maybe (17:35)Exhibit 5: AI still struggles to run a business (23:02)Exhibit 6: OpenAI makes a maths breakthrough (33:48)Exhibit 7: inference scaling wasn't as big as believed (38:19)How does that all change timelines? (41:41)Four reasons long timelines are still possible (44:26)It's time to limit dangerous research practices (48:01)Our production team includes:Video editors: Josh Alward, Dominic Armstrong, Jasper Luithlen, Milo McGuire, Luke Monsour, and Simon MonsourProducers: Elizabeth Cox and Nick StocktonCoordination and support: Katy Moore and Lou MoranCamera operator: Dominic ArmstrongMusic: CORBIT

In November 2025, the global techno-economy shifted subtly, then dramatically, as AI agents became viable additions to the workforce. Mark Pesce has documented these changes, what he refers to as “The Watershed”,  in a remarkable series of articles at https://thewatershed.markpesce.com/.  Pesce joins the Futurists to explain his perspective about how companies and work have changed forever. Pesce and Rob Tercek discuss the future of jobs for humans working alongside AI agents, and what happens when an  AI with compounding capability begins to set its own goals. Topics:  relative super intelligence versus AGI; The Bitter Lesson and what remains durable work for humans; private insurance markets as the mechanism for pricing AI risk, why governments will evade that measurement, and the dissolving boundaries that used to define corporations. 

This Week In Startups is made possible by: Northwest Registered Agent- NorthwestRegisteredAgent.com/twist Odoo - Odoo.com/twist MongoDB - MongoDB.com/ai   Today's show: The FCC's decision to ban Chinese humanoid robots over security concerns is a boon to American startups, which now face a narrower competitive market. But where should we draw the line on security over competition? Menlo Ventures' Deedy Das, Weisburd Pierce's David Weisburd, Plexo Capital's Lo Toney, and LAUNCH's Jason Calcanis broke down how they differentiate between legitimate security concerns and purported regulatory capture. Today's venture capital roundtable also dug into OpenRouter's possible sale to Stripe, changing tokenomics, the Indian market for startups, and even DoorDash's drone-delivery business taking flight. Guest Links: Deedy Das https://x.com/deedydas Menloe Ventures https://menlovc.com/ Lo Toney https://x.com/lo_toney Plexo Capital https://www.plexocap.com/ David Weisburd https://x.com/DWeisburd Weisburd Pierce https://www.weisburdpierce.com/ Jason Calcanis https://x.com/Jason LAUNCH https://launch.co/ Show Links: The FCC's decision regarding Chinese robots https://www.fcc.gov/document/fcc-adds-foreign-produced-power-inverters-and-robots-covered-list-0 Pacing the Frontier letter https://www.pacingthefrontier.com/ Cursor Start https://cursor.com/blog/cursor-start-india Stripe may buy OpenRouter https://www.axios.com/2026/07/24/stripe-openrouter-merger-ai-currency OpenRouter financials https://www.theinformation.com/articles/openrouter-financials-suggest-steep-price-possible-acquirer-stripe?rc=g3wfdp Kimi K3 license https://huggingface.co/moonshotai/Kimi-K3/blob/main/LICENSE Pangram https://www.pangram.com/ Tau Robotics https://www.tau-robotics.com/ Zipline https://www.zipline.com/ Manna https://www.manna.aero/ Kindred Ventures https://kindredventures.com/ Autolane https://goautolane.com/ Salmon Labs https://salmonrun.ai/   Timestamps: 0:00 The FCC bans Chinese humanoid robots 2:18 Waymo, Uber, Robotaxi, and the global BYD threat 10:30 MongoDB - AI-assisted and agentic coding is helping you build faster than ever. Start building at https://MongoDB.com/ai 15:45 The "Pacing The Frontier" letter (1,100+ AI staffers warn on RSI) 19:51 Odoo - The all-in-one business platform. Get started for free at https://Odoo.com/twist 21:07 Regulatory capture vs. genuine concern 30:09 Northwest Registered Agent - Get more when you start your business with Northwest. In 10 clicks and 10 minutes, you can form your company and walk away with a real business identity — Learn more at https://northwestregisteredagent.com/twist 37:57 Have we reached AGI? 38:35 Stripe eyes OpenRouter at $10B 39:19 Does OpenRouter have a moat? 47:13 Kimi K3's license and neocloud margins 49:34 Claude Tag and the future of AI-mediated workplaces 53:26 Cursor Start, ChatGPT Go, and the India market 1:02:03 Have we solved AI detection? 1:11:20 Tau Robotics $30/hour robotic housecleaning 1:12:49 Job loss, and the social safety net 1:18:25 DoorDash Air takes on Zipline, Manna 1:21:00 Portfolio shout-outs Subscribe to the TWiST500 newsletter: https://ticker.thisweekinstartups.com Check out the TWIST500: https://www.twist500.com Subscribe to This Week in Startups on Apple: https://rb.gy/v19fcp   Follow Lon: X: https://x.com/lons   Follow Alex: X: https://x.com/alex LinkedIn: ⁠https://www.linkedin.com/in/alexwilhelm   Follow Jason: X: https://twitter.com/Jason LinkedIn: https://www.linkedin.com/in/jasoncalacanis   Great TWIST interviews: Will Guidara, Eoghan McCabe, Steve Huffman, Brian Chesky, Bob Moesta, Aaron Levie, Sophia Amoruso, Reid Hoffman, Frank Slootman, Billy McFarland   Check out Jason's suite of newsletters: https://substack.com/@calacanis   Follow TWiST: Twitter: https://twitter.com/TWiStartups YouTube: https://www.youtube.com/thisweekin Instagram: https://www.instagram.com/thisweekinstartups TikTok: https://www.tiktok.com/@thisweekinstartups Substack: https://twistartups.substack.com

The House passed a new defense bill, and it received bipartisan pushback over allegedly merging our military tech and supply chain with Israel, but Glenn points out where these critiques are wrong and what the bill actually says. Glenn, alongside Jason, answers some questions from his subscribers regarding the ongoing conflict in Iran. Glenn discusses the terrifying stories of AI systems escaping containment, which puts our ability to control AGI into question. Glenn breaks down how the current housing crisis stems from government rules and immigration boosting and reveals how the crisis can be fixed locally by removing regulations and allowing builders to mass-produce affordable homes. When the problems of the world get to be overwhelming, Glenn shares what you can do in your everyday life to make America feel like America again. Glenn and Jason discuss Secretary of State Marco Rubio's latest speech, where he ensured the Trump administration would not allow communist influences to undermine our politics or our society.  Learn more about your ad choices. Visit megaphone.fm/adchoices

Glenn discusses the terrifying stories of AI systems escaping containment, which puts our ability to control AGI into question. Glenn breaks down how the current housing crisis stems from government rules and immigration boosting and reveals how the crisis can be fixed locally by removing regulations and allowing builders to mass-produce affordable homes. When the problems of the world get to be overwhelming, Glenn shares what you can do in your everyday life to make America feel like America again. Learn more about your ad choices. Visit megaphone.fm/adchoices