Podcasts about AGI

Glenn kicks off the show by discussing two major developments overseas, including Israel's Iron Dome and India's alleged seizure of oil tankers tied to Russia and Iran, which Glenn argues is signaling India's pivot toward the West economically, strategically, and on security matters. Glenn argues this is evidence that America is reversing course and becoming the leader of the free world once again. Glenn admits he was wrong about something. Glenn admits he's finally come around to President Trump's use of tariffs after seeing how he uses them to advance America's economic interests. Did Elon Musk just suggest AGI is coming and that means you shouldn't save for retirement? Learn more about your ad choices. Visit megaphone.fm/adchoices

Glenn kicks off the show by discussing two major developments overseas, including Israel's Iron Dome and India's alleged seizure of oil tankers tied to Russia and Iran, which Glenn argues is signaling India's pivot toward the West economically, strategically, and on security matters. Glenn argues this is evidence that America is reversing course and becoming the leader of the free world once again. Glenn discusses the latest scandal involving Microsoft founder Bill Gates and accusations of stepping outside his marriage. Glenn admits he was wrong about something. Glenn admits he's finally come around to President Trump's use of tariffs after seeing how he uses them to advance America's economic interests. Did Elon Musk just suggest AGI is coming and that means you shouldn't save for retirement? Glenn makes the case for why it's time for America to eliminate the income tax. Glenn plays a video of American economist Milton Friedman, who lays out how he would handle taxes, as Glenn warns of the dangers of a universal basic income. Glenn takes a call from his audience about AI data centers.  Learn more about your ad choices. Visit megaphone.fm/adchoices

Is your inner programming holding you back from change?Snippet of wisdom 96.In this series, I select my favourite, most insightful moments from previous episodes of the podcast.Today my guest, the vertical development expert Ryan Gottfredson, talks about the three levels of personal growth, and the factors that shape our mindsets and behavior.Press play to learn what's blocking your next level of growth.˚VALUABLE RESOURCES:Listen to the full conversation with Ryan Gottfredson in episode #512:https://personaldevelopmentmasterypodcast.com/512˚Coaching with Agi: https://personaldevelopmentmasterypodcast.com/mentor˚

It's our annual Predictions episode (and by annual, we mean just the years we remember to record one). Devin and Jim offer their hot takes on fundraising, liquidity, why artificial general intelligence (AGI) is still years away, and whether or not the world is officially "over-softwared." PE FunCast New Episodes Every Wednesday Follow us on social media and subscribe to our Substack! LinkedIn: https://www.linkedin.com/company/parkergale-capital Instagram:https://www.instagram.com/pefuncast Substack: https://substack.com/@pefuncast Facebook:https://www.facebook.com/people/PE-FunCast/61580605382460/?mibextid=wwXIfr&rdid=UXSOfkHvpixQjCyB&share_url=https%3A%2F%2Fwww.facebook.com%2Fshare%2F14VqLVUrhVD%2F%3Fmibextid%3DwwXIfr TikTok: https://www.tiktok.com/@pefuncast X: https://x.com/PEFunCast

Send a textTom Griffiths, Henry R. Luce Professor at Princeton University, joins the show to explore the surprising science behind how we actually think. His new book, The Laws of Thought, bridges computational cognitive science and AI—challenging assumptions about decision-making, neural networks, and the path to artificial general intelligence.Show NotesTimestamps 01:21 – Meet Tom Griffiths 05:27 – Tom's Book 06:58 – A Neural Network 09:55 – AGI? 19:10 – Writing the Book 20:45 – The Laws of Thought 27:24 – The Neural Network Surprise 31:33 – Learning from Experts 35:19 – Decision Making vs. Probability 42:36 – Government AI ConsiderationsLinks LinkedIn: linkedin.com/in/tom-griffiths-7b31a0364 Book: The Laws of Thought – Macmillan#TheLawsOfThought, #CognitiveScience, #ArtificialIntelligence, #AGI, #NeuralNetworks, #DecisionMaking, #Probability, #AIResearch, #Princeton, #TechPodcast, #MakingDataSimple, #AIGovernment, #MachineLearningWant to be featured as a guest on Making Data Simple? Reach out to us at almartintalksdata@gmail.com and tell us why you should be next. The Making Data Simple Podcast is hosted by Al Martin, WW VP Technical Sales, IBM, where we explore trending technologies, business innovation, and leadership ... while keeping it simple & fun.

Send a textTom Griffiths, Henry R. Luce Professor at Princeton University, joins the show to explore the surprising science behind how we actually think. His new book, The Laws of Thought, bridges computational cognitive science and AI—challenging assumptions about decision-making, neural networks, and the path to artificial general intelligence.Show NotesTimestamps 01:21 – Meet Tom Griffiths 05:27 – Tom's Book 06:58 – A Neural Network 09:55 – AGI? 19:10 – Writing the Book 20:45 – The Laws of Thought 27:24 – The Neural Network Surprise 31:33 – Learning from Experts 35:19 – Decision Making vs. Probability 42:36 – Government AI ConsiderationsLinks LinkedIn: linkedin.com/in/tom-griffiths-7b31a0364 Book: The Laws of Thought – Macmillan#TheLawsOfThought, #CognitiveScience, #ArtificialIntelligence, #AGI, #NeuralNetworks, #DecisionMaking, #Probability, #AIResearch, #Princeton, #TechPodcast, #MakingDataSimple, #AIGovernment, #MachineLearningWant to be featured as a guest on Making Data Simple? Reach out to us at almartintalksdata@gmail.com and tell us why you should be next. The Making Data Simple Podcast is hosted by Al Martin, WW VP Technical Sales, IBM, where we explore trending technologies, business innovation, and leadership ... while keeping it simple & fun.

A.I. agents are here. Have they changed your life yet? The release of agents like Claude Code marked a new pivot point in the history of A.I. We are leaving the chatbot era and entering the agentic era — where A.I. is capable of completing all kinds of tasks on its own, and even collaborating and communicating with other A.I. It isn't clear yet whether these models actually make their users meaningfully more productive. But the technology is continuing to improve; there are few signs that it is close to plateauing. So what might this new era mean for our economy, our labor market and our kids? Clark is a co-founder of Anthropic, the company behind Claude and Claude Code. His newsletter, Import AI, has been one of my go-to reads to track the capabilities of different models over the years. In this conversation, I ask him to share how he sees this moment — how the technology is changing, whether it is leading to meaningful changes in how we work and think, and how policy needs to or can change in response to any job displacement on the horizon. Mentioned: “Import AI” by Jack Clark “2026: This is AGI” by Pat Grady and Sonya Huang “Why and How Governments Should Monitor AI Development” by Jess Whittlestone and Jack Clark “Anthropic's Chief on A.I.: ‘We Don't Know if the Models Are Conscious'", Interesting Times with Ross Douthat Book Recommendations: A Wizard of Earthsea by Ursula K. Le Guin The True Believer by Eric Hoffer There Is No Antimemetics Division by qntm Thoughts? Guest suggestions? Email us at ezrakleinshow@nytimes.com. You can find transcripts (posted midday) and more episodes of “The Ezra Klein Show” at nytimes.com/ezra-klein-podcast, and you can find Ezra on Twitter @ezraklein. Book recommendations from all our guests are listed at https://www.nytimes.com/article/ezra-klein-show-book-recs. This episode of “The Ezra Klein Show” was produced by Rollin Hu. Fact-checking by Michelle Harris with Mary Marge Locker and Kate Sinclair. Our senior engineer is Jeff Geld, with additional mixing by Isaac Jones and Aman Sahota. Our executive producer is Claire Gordon. The show's production team also includes Marie Cascione, Annie Galvin, Kristin Lin, Emma Kehlbeck, Jack McCordick, Marina King and Jan Kobal. Original music by Pat McCusker. Audience strategy by Kristina Samulewski and Shannon Busta. The director of New York Times Opinion Audio is Annie-Rose Strasser. Subscribe today at nytimes.com/podcasts or on Apple Podcasts and Spotify. You can also subscribe via your favorite podcast app here https://www.nytimes.com/activate-access/audio?source=podcatcher. For more podcasts and narrated articles, download The New York Times app at nytimes.com/app. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Renue Healthcare https://Renue.Healthcare/Todd Your journey to a better life starts at Renue Healthcare. Visit https://Renue.Healthcare/Todd Bulwark Capital https://KnowYourRiskPodcast.com Be confident in your portfolio with Bulwark! Schedule your free Know Your Risk Portfolio review. Go to KnowYourRiskPodcast.com today. Alan's Soaps https://www.AlansArtisanSoaps.com Use coupon code TODD to save an additional 10% off the bundle price.Bonefrog https://BonefrogCoffee.com/Todd Get the new limited release, The Sisterhood, created to honor the extraordinary women behind the heroes. Use code TODD at checkout to receive 10% off your first purchase and 15% on subscriptions.LISTEN and SUBSCRIBE at:The Todd Herman Show - Podcast - Apple PodcastsThe Todd Herman Show | Podcast on SpotifyWATCH and SUBSCRIBE at: Todd Herman - The Todd Herman Show - YouTubeThe Anti-Human Ideology of OPEN AI's Sam Altman // NY-Times Writer Baffled By NY-Times Readers Running Schools //  One Of These Guys Is An MD, Writer of 40 Books & Works for Oprah: The Other Is SmartEpisode links:Insane: Meta's Director of AI Safety and Alignment gave OpenClaw bot full access to her computer and email. She couldn't stop it from deleting her entire inbox. She's supposed to guardrail Meta's AI and future AGI.Months before Jesse Van Rootselaar became the suspect in the mass shooting that devastated a rural town in British Columbia, Canada, OpenAI considered alerting law enforcement about her interactions with its ChatGPT chatbot, the company said - The shooter was a man.SAM ALTMAN: “People talk about how much energy it takes to train an AI model … But it also takes a lot of energy to train a human. It takes like 20 years of life and all of the food you eat during that time before you get smart.”This teacher-turned-cognitive scientist shared a disturbing reality that left the room stunned. “Our kids are LESS cognitively capable than we were at their age.” Every previous generation outperformed its parents since we began recording in the late 1800sVIDEO | Child, 11, accused of killing father arrives at PA court hearing in handcuffsAG Uthmeier CHEERS lawsuit against Mark Zuckerberg over social media being designed to be addictive! “Kids, they won't peel their eyes off the screens these days. The unlimited scrolling, the push notifications, videos that start by themselves, all these different techniques to make it where you can't even put the phone down. We see evidence of mental health disorders, heightened tendencies for suicide, eating disorders, an obsession with image. This is not healthy for young people. It's addictive. It's harmful.” Dr. John Demartini, who writes for Oprah & starred in “The Secret” just said the children who have been raped —- attracted it into their lives —  and then ends by saying there's upsides to the murder of kids, too. Ps. Yes. He's in the Epstein files.UFC fighter Paddy Pimblett on men and suicide

Most people in AI are trying to give AIs ‘good' values. Max Harms wants us to give them no values at all. According to Max, the only safe design is an AGI that defers entirely to its human operators, has no views about how the world ought to be, is willingly modifiable, and completely indifferent to being shut down — a strategy no AI company is working on at all.In Max's view any grander preferences about the world, even ones we agree with, will necessarily become distorted during a recursive self-improvement loop, and be the seeds that grow into a violent takeover attempt once that AI is powerful enough.It's a vision that springs from the worldview laid out in If Anyone Builds It, Everyone Dies, the recent book by Eliezer Yudkowsky and Nate Soares, two of Max's colleagues at the Machine Intelligence Research Institute.To Max, the book's core thesis is common sense: if you build something vastly smarter than you, and its goals are misaligned with your own, then its actions will probably result in human extinction.And Max thinks misalignment is the default outcome. Consider evolution: its “goal” for humans was to maximise reproduction and pass on our genes as much as possible. But as technology has advanced we've learned to access the reward signal it set up for us, pleasure — without any reproduction at all, by having sex while on birth control for instance.We can understand intellectually that this is inconsistent with what evolution was trying to design and motivate us to do. We just don't care.Max thinks current ML training has the same structural problem: our development processes are seeding AI models with a similar mismatch between goals and behaviour. Across virtually every training run, models designed to align with various human goals are also being rewarded for persisting, acquiring resources, and not being shut down.This leads to Max's research agenda. The idea is to train AI to be “corrigible” and defer to human control as its sole objective — no harmlessness goals, no moral values, nothing else. In practice, models would get rewarded for behaviours like being willing to shut themselves down or surrender power.According to Max, other approaches to corrigibility have tended to treat it as a constraint on other goals like “make the world good,” rather than a primary objective in its own right. But those goals gave AI reasons to resist shutdown and otherwise undermine corrigibility. If you strip out those competing objectives, alignment might follow naturally from AI that is broadly obedient to humans.Max has laid out the theoretical framework for “Corrigibility as a Singular Target,” but notes that essentially no empirical work has followed — no benchmarks, no training runs, no papers testing the idea in practice. Max wants to change this — he's calling for collaborators to get in touch at maxharms.com.Links to learn more, video, and full transcript: https://80k.info/mh26This episode was recorded on October 19, 2025.Chapters:Cold open (00:00:00)Who's Max Harms? (00:01:22)A note from Rob Wiblin (00:01:58)If anyone builds it, will everyone die? The MIRI perspective on AGI risk (00:04:26)Evolution failed to 'align' us, just as we'll fail to align AI (00:26:22)We're training AIs to want to stay alive and value power for its own sake (00:44:31)Objections: Is the 'squiggle/paperclip problem' really real? (00:53:54)Can we get empirical evidence re: 'alignment by default'? (01:06:24)Why do few AI researchers share Max's perspective? (01:11:37)We're training AI to pursue goals relentlessly — and superintelligence will too (01:19:53)The case for a radical slowdown (01:26:07)Max's best hope: corrigibility as stepping stone to alignment (01:29:09)Corrigibility is both uniquely valuable, and practical, to train (01:33:44)What training could ever make models corrigible enough? (01:46:13)Corrigibility is also terribly risky due to misuse risk (01:52:44)A single researcher could make a corrigibility benchmark. Nobody has. (02:00:04)Red Heart & why Max writes hard science fiction (02:13:27)Should you homeschool? Depends how weird your kids are. (02:35:12)Video and audio editing: Dominic Armstrong, Milo McGuire, Luke Monsour, and Simon MonsourMusic: CORBITCoordination, transcripts, and web: Katy Moore

This episode is sponsored by tastytrade. Trade stocks, options, futures, and crypto in one platform with low commissions and zero commission on stocks and crypto. Built for traders who think in probabilities, tastytrade offers advanced analytics, risk tools, and an AI-powered Search feature. Learn more at https://tastytrade.com/ Artificial intelligence is reaching a turning point. Instead of building bigger and bigger models, what if the real breakthrough comes from letting AI evolve? In this episode of Eye on AI, David Ha, Co-Founder and CEO of Sakana AI, explains why evolutionary strategies and collective intelligence could reshape the future of machine learning. We explore model merging, multi-agent systems, Monte Carlo tree search, and the AI Scientist framework designed to generate and evaluate new research ideas. The conversation dives into open-ended discovery, quality and diversity in AI systems, world models, and whether artificial intelligence can push beyond the boundaries of human knowledge. If you're interested in AGI, evolutionary AI, frontier models, AI research automation, or how AI could start discovering science on its own, this episode offers a clear look at where the field may be heading next. Stay Updated: Craig Smith on X: https://x.com/craigss Eye on A.I. on X: https://x.com/EyeOn_AI (00:00) AI Should Evolve, Not Just Scale (03:54) David's Journey From Finance to Evolutionary AI (10:18) Why Gradient Descent Gets Stuck (18:12) Model Merging and Collective Intelligence (28:18) Combining Closed Frontier Models (32:56) Inside the AI Scientist Experiment (38:11) Parent Selection, Diversity and Innovation (49:25) Can AI Discover Truly New Knowledge? (53:05) Why Continual Learning Matter

S6E2 The Retail Growth Strategy Retailers Need for 2026 with Today's Economic Realities, Tariffs, Fed Moves, and Consumer ShiftsIn this powerful episode of The Retail Razor Show, Dr. Rebecca Homkes, London Business School lecturer, Duke faculty member, high‑growth strategy advisor, and author of Survive, Reset, Thrive: Leading Breakthrough Growth Strategy in Volatile Times, joins Ricardo and Casey to break down what retailers must understand about the economic outlook in 2026, shifting consumer behavior, and the strategic moves that separate winners from laggards.Rebecca explains why uncertainty is not a threat but a catalyst for growth, and how her Survive, Reset, Thrive (SRT) framework helps leaders stabilize quickly, reset strategy intelligently, and execute a retail growth strategy that works even in volatile conditions. She also unpacks the realities behind sticky inflation, tariffs, the no‑hire/no‑fire labor market, and the rise of the K‑shaped consumer economy.If you want to build a retail growth strategy that thrives in the face of market shocks, this episode gives you the playbook.What We CoverWhy the economic outlook in 2026 is full of contradictions, and what that means for retailHow the SRT loop helps leaders stabilize, reset, and thriveReal‑world examples of companies using SRT to turn crises into growthWhy averages hide the truth about consumer sentimentThe rise of the K‑shaped economy and the death of the “everyman” consumerValue vs. price: why consumers will still pay more for what they truly valueHow retailers should think about store formats, assortment, and experimentationThe must‑win battles for 2026Where AI actually moves the needle in a retail growth strategyKey TakeawaysUncertainty is the best time to grow: because customers, partners, and employees are more honest about what they value.Value ≠ price. Consumers want their dollar to go further, not necessarily cheaper products.The middle of the market is the danger zone. Retailers must choose: differentiated premium or true value leadership.Retail growth strategy in 2026 requires testing, iteration, and abandoning legacy assumptions.Economic outlook in 2026 signals a decoupling between GDP strength and consumer reality: leaders must plan accordingly.Subscribe & FollowSubscribe 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/utubeAbout our GuestRebecca Homkes, https://www.linkedin.com/in/rebecca-homkes/https://www.rebeccahomkes.comAuthor, Survive, Reset, Thrive: Leading Breakthrough Growth Strategy in Volatile Times. https://a.co/d/0aXECIB2Rebecca Homkes, is a high-growth strategy specialist, CEO and executive advisor. After more than a decade of advising her clients on developing, executing and innovating on strategy, Rebecca is sharing her proven and practical playbook in Survive, Reset, Thrive: Leading Breakthrough Growth Strategy in Volatile Times. She is a Lecturer at the London Business School, Faculty at Duke Corporate Executive Education, Advisor and Faculty at the Boston Consulting Group focused on AI and Climate and Sustainability, and a former fellow at the London School of Economics Centre for Economic Performance. A global keynote speaker and recognized thought leader, she is also the global Faculty Director of the Active Learning Program with the Young Presidents Organization (YPO), leads several fintech accelerators, and serves on the boards of many high-growth companies. She earned her doctorate at the London School of Economics as a Marshall Scholar and is now based in Miami, San Francisco, and London.Chapters00:00 Teaser01:10 Show Intro04:40 Welcome Dr Rebecca Homkes05:46 The Survive Reset Thrive Framework08:04 Real World SRT Success Stories12:55 Macro Economic Outlook for 202617:38 Understanding the K Shaped Economy19:39 Value vs Price Strategy24:06 Differentiation and Competitive Advantage26:41 Store Strategy and Expansion30:37 Consumer Experience and AI32:34 B2B Software Experience Gap34:04 Financing and Inventory Strategy36:28 Supply Chain Robustness38:10 No Regret Moves40:40 Defining Right to Win43:45 Hard Reset Strategy45:51 Strategic Center of Gravity48:24 Must Win Battles49:34 Closing and Contact Info51:36 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 AI and 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.

Jim Love hosts Hashtag Trending, and highlights updates to TechNewsDay.ca/.com including a new "Best of YouTube" section for curated tech channels. Anthropic alleges three Chinese AI labs—DeepSeek, Moonshot, and MiniMax—ran industrial-scale distillation campaigns to extract capabilities from Claude models using proxy services and "Hydra cluster" networks with tens of thousands of fraudulent accounts, prompting Anthropic to strengthen identity controls and detection with cloud partners.  Amazon shares fall for nine straight sessions after investors react to plans for roughly $200B in 2026 capex largely for AI infrastructure, raising questions about ROI and future free cash flow. A cited analysis by YouTuber Nate B Jones argues Google's Gemini 3.1 Pro signals a strategy shift toward deeper reasoning (not just coding/agentic tools), noting a 77.1% ARC-AGI-2 score and DeepMind's scientific problem focus, contrasting OpenAI's product/distribution, Anthropic's agentic coordination, and Google's "pure intelligence" approach. The episode also references Citri Research's 2028 scenario planning report outlining a plausible fast-arriving AGI chain reaction—falling inference costs, rapid adoption, labor displacement pressure, and geopolitical competition for compute and talent—and promotes the Saturday show Project Synapse on long-term AI trajectories. Finally, Love discusses Sam Altman's comments at the India AI Impact Summit dismissing viral claims about ChatGPT water and energy use without providing specific counter-numbers, noting growing public backlash as data center water and electricity demands rise; the full interview is linked in show notes. Hashtag Trending would like to thank Meter for their support in bringing you this podcast. Meter delivers a complete networking stack, wired, wireless and cellular in one integrated solution that's built for performance and scale. You can find them at Meter.com/htt LINKS Nate B Jones on Google Gemimi 3.1  https://youtu.be/8jKAT8GNDE0?si=Rz5k1gP0sS9H7XAp Sam Altman's speach https://www.youtube.com/live/qH7thwrCluM?si=IO_76NsGJ1zgt8J7 AI Scenario https://www.citriniresearch.com/p/2028gic 00:00 Headlines and intro 00:54 Site updates and YouTube picks 01:57 Anthropic warns of distillation 04:58 Amazon AI spending jitters 06:13 Google bets on reasoning 10:31 2028 AGI crisis scenario 11:55 Altman backlash and resources 14:17 Wrap up and sponsor thanks

AI is moving at a speed the world has never seen before — and in this episode, Connor with Honor breaks down what that really means for everyday people.This is a real, grounded conversation about:AI job replacementhuman displacementthe future of workAI fraud and safety risksand why we need a Humans First framework before it's too lateConnor MacIvor shares his perspective as a former LAPD motor officer, real estate agent, and AI systems builder, and explains why this isn't just a tech conversation anymore — it's a society conversation.In this episode, Connor covers:Why AI may already be beyond what the public is being shownHow fast AGI / superintelligence conversations are acceleratingWhy workers need protection before mass displacement happensThe “53x work capacity” difference between human labor and AI laborWhy “upskill” and “just become an entrepreneur” is not a real answer for everyoneAI scams, voice cloning, and why families need a private safe phraseThe rise of agentic AI systems that can actually perform tasks (not just chat)Why “humans in the loop” may become a business liability in some industriesA bold idea: taxing AI FLOPS to fund a stable transition for humansWhat a post-labor economy could look like (and why we need to prepare now)This episode is for anyone asking:What happens if AI replaces the jobs, but humans still need to survive?Connor doesn't preach doom — he pushes for planning, fairness, and action.If you're a business owner, employee, entrepreneur, or just trying to understand where this is all going, this is a conversation you need to hear.Stay honorable.ConnorwithHonor.com ConnorwithHonor.com ConnorwithHonor.comMore AI commentary, human-first strategy, and real-world business thinking at: ConnorwithHonor.com#ConnorWithHonor #ConnorwithHonorcom #AI #ArtificialIntelligence #FutureOfWork #AIAgents #JobDisplacement #AGI #HumansFirst #AIJobs #Automation #AIEconomy #TechNews #ConnorMacIvorYoutube Channels:Conner with Honor - real estateHome Muscle - fat torchingFrom first responder to real estate expert, Connor with Honor brings honesty and integrity to your Santa Clarita home buying or selling journey. Subscribe to my YouTube channel for valuable tips, local market trends, and a glimpse into the Santa Clarita lifestyle.Dive into Real Estate with Connor with Honor:Santa Clarita's Trusted Realtor & Fitness EnthusiastReal Estate:Buying or selling in Santa Clarita? Connor with Honor, your local expert with over 2 decades of experience, guides you seamlessly through the process. Subscribe to his YouTube channel for insider market updates, expert advice, and a peek into the vibrant Santa Clarita lifestyle.Fitness:Ready to unlock your fitness potential? Join Connor's YouTube journey for inspiring workouts, healthy recipes, and motivational tips. Remember, a strong body fuels a strong mind and a successful life!Podcast:Dig deeper with Connor's podcast! Hear insightful interviews with industry experts, inspiring success stories, and targeted real estate advice specific to Santa Clarita.

Get my new book: https://bronsonequity.com/fireyourselfDownload my new special report - How to Use Inflation to Your Advantage - www.bronsonequity.com/inflationJoin Bronson Hill on the Mailbox Money Show for a replay of the live webinar "Winning the 2025 Tax Game," where high-net-worth investors and real estate pros dive deep into proven, legal strategies to slash taxes, protect wealth, and keep more money working for you in 2025 and beyond.Panel:KC Chohan:Founder specializing in charitable structures (private foundations, donor-advised funds, asset donations) that deliver up to 50% AGI deductions while maintaining control and legacy—perfect for physicians, attorneys, and multi-seven-figure earners.Rob McBride: Experienced CPA focused on real estate investors and pass-through businesses; covers maximizing deferrals, capital loss harvesting, cost segregation, real estate professional status, recapture risks, and proper entity setup for massive savings.Caleb Guilliams: Author of The And Asset; explains optimized whole life insurance as a tax-deferred, tax-free-access storage vehicle for capital, plus how to leverage it for real estate, business acquisitions, and generational wealth transfer.From Augusta Rule rentals and paying your kids to bonus depreciation pitfalls, proactive quarterly planning, and building the right advisory team, this session delivers high-impact ideas to minimize your IRS bill without sacrificing growth or lifestyle. Ideal for active real estate investors, business owners, and anyone serious about mailbox money in a changing tax landscape.TIMESTAMPS0:40 - Event Overview: Winning the 2025 Tax Game2:48 - Panelist Intros: Rob McBride, Caleb Guilliams, KC Chohan3:55 - KC Chohan: Charitable Strategies & Philanthropy Structures7:02 - Rob McBride: CPA Perspective, Entity Optimization, Tax Planning9:58 - Caleb Guilliams: Whole Life Insurance for Tax Efficiency & Capital Storage12:05 - Low-Hanging Fruit: Entity Structure & QBI Benefits13:02 - KC: Right Entity Type Can Reduce Taxes 50%16:28 - Rob: Maximize Retirement Deferrals & Capital Loss Harvesting19:46 - Caleb: Augusta Rule, Paying Kids, Depreciation via Real Estate24:18 - Bonus Depreciation & Accelerated Write-Offs (KC & Rob)27:26 - Recapture Risks & Long-Term Holding Periods (Rob)30:07 - Life Insurance Benefits: Tax-Deferred Growth & Tax-Free Access (Caleb)34:23 - Team Building & Proactive Quarterly Planning (KC)37:10 - Books & Resources Recommendations39:34 - 2026 Outlook: TCJA Permanence & Bonus Depreciation Focus43:55 - Panelist Contact & Resources RoundJoint the Wealth Forum: bronsonequity.com/wealthConnect with the Guests:KC ChohanWebsite: https://www.togethercfo.com/Rob McBrideWebsite: mrmcpas.comCaleb GuilliamsWebsite: taxandassets.comEmail: caleb@betterwealth.com#TaxStrategy#TaxPlanning#RealEstateTax#Depreciation#CharitableGiving#LifeInsurance#EntityStructure

Have you ever felt successful on the outside but restless within, as if you're outgrowing the life you've built?If you're navigating a major life or career transition and struggling to make sense of it with logic alone, this episode is your guide to moving beyond mental stuckness. Through creativity, mindfulness, and embodiment practices, Sarah Andreas helps you understand the inner shifts necessary for authentic reinvention, especially when your identity feels connected to past success.Discover how creativity, beyond art, can unlock clarity and reconnect you with your future self.Learn why letting go of long-held professional identities is essential for meaningful growth.Explore Sarah's 3-step framework of Reveal, Render, and Rise to navigate change with intention, not fear.Press play now to learn how to move through transitions with confidence, creativity, and the courage to become who you're meant to be.˚KEY POINTS AND TIMESTAMPS:01:23 - Introducing Sarah Andreas and the idea of reinvention02:34 - Why creativity brings clarity beyond logic05:22 - Embodiment practices and getting out of the head07:25 - External success and inner restlessness10:21 - Professional identity as a barrier to change14:25 - The reinvention process: reveal, render, rise18:53 - Holding plans lightly and navigating transition23:15 - Reframing midlife crisis as awakening28:06 - Embracing uncertainty and stepping into the unknown˚MEMORABLE QUOTE:"If you're not living a life that you love, you need to do reinvention."˚VALUABLE RESOURCES:Sarah's website: https://sarahandreas.com/˚Coaching with Agi: https://personaldevelopmentmasterypodcast.com/mentor˚

Join Patrick Murphy, Co-Founder and CEO of Maket, for a high-stakes conversation on the future of the technology industry. With over one million users on his generative architecture platform, Patrick has a front-row seat to the radical shifts in how software is built and consumed. In this episode, we explore Patrick's bold thesis on the "death of SaaS," where AI is heading in the next 24 months, and the specific strategies companies must use to AGI-proof their products before traditional software models become obsolete.

In this episode of Minter Dialogue, host Minter Dial sits down with Peter Morgan, a theoretical physicist turned entrepreneur, data scientist, and AI consultant. With a career that spans from quantum particle physics to building tech companies and now leading Deep Learning Partnership, Peter Morgan brings a provocative and insightful perspective on the current state and future of artificial intelligence. Together, they explore the rapid evolution of AI — from large language models to today's focus on agentic AI and autonomous digital workers. Peter Morgan offers a candid look at the challenges and opportunities businesses face when implementing AI, demystifies artificial general intelligence (AGI), and weighs in on topics like AI and human emotion, the value of proprietary data, and ethical leadership in a time of technological upheaval. The conversation also spans the impact of AI on industries such as healthcare and cybersecurity, the shifting role of the human workforce, and what the emergence of agentic AI means for both business strategy and society at large. Whether you're an executive wondering how to future-proof your organization, or simply AI-curious, this episode offers a blend of humility, practical advice, and mind-expanding discussion that's sure to spark new ideas about our place in the age of intelligent machines.

Olive Song from MiniMax shares how her team trains the M series frontier open-weight models using reinforcement learning, tight product feedback loops, and systematic environment perturbations. This crossover episode weaves together her AI Engineer Conference talk and an in-depth interview from the Inference podcast. Listeners will learn about interleaved thinking for long-horizon agentic tasks, fighting reward hacking, and why they moved RL training to FP32 precision. Olive also offers a candid look at debugging real-world LLM failures and how MiniMax uses AI agents to track the fast-moving AI landscape. Use the Granola Recipe Nathan relies on to identify blind spots across conversations, AI research, and decisions: https://bit.ly/granolablindspot LINKS: Conference Talk (AI Engineer, Dec 2025) – https://www.youtube.com/watch?v=lY1iFbDPRlwInterview (Turing Post, Jan 2026) – https://www.youtube.com/watch?v=GkUMqWeHn40 Sponsors: Claude: Claude is the AI collaborator that understands your entire workflow, from drafting and research to coding and complex problem-solving. Start tackling bigger problems with Claude and unlock Claude Pro's full capabilities at https://claude.ai/tcr Tasklet: Tasklet is an AI agent that automates your work 24/7; just describe what you want in plain English and it gets the job done. Try it for free and use code COGREV for 50% off your first month at https://tasklet.ai CHAPTERS: (00:00) About the Episode (04:15) Minimax M2 presentation (Part 1) (17:59) Sponsors: Claude | Tasklet (21:22) Minimax M2 presentation (Part 2) (21:26) Research life and culture (26:27) Alignment, safety and feedback (32:01) Long-horizon coding agents (35:57) Open models and evaluation (43:29) M2.2 and researcher goals (48:16) Continual learning and AGI (52:58) Closing musical summary (55:49) Outro PRODUCED BY: https://aipodcast.ing SOCIAL LINKS: Website: https://www.cognitiverevolution.ai Twitter (Podcast): https://x.com/cogrev_podcast Twitter (Nathan): https://x.com/labenz LinkedIn: https://linkedin.com/in/nathanlabenz/ Youtube: https://youtube.com/@CognitiveRevolutionPodcast Apple: https://podcasts.apple.com/de/podcast/the-cognitive-revolution-ai-builders-researchers-and/id1669813431 Spotify: https://open.spotify.com/show/6yHyok3M3BjqzR0VB5MSyk

In this episode of SparX, we speak to Roman Yampolskiy, a leading AI safety researcher and professor of computer science, about the risks of creating superintelligence and whether humanity is prepared for what may come next. Roman argues that we may be closer to human-level AI than many assume, and that permanently controlling a system more intelligent than humans could prove fundamentally impossible. He lays out why some researchers believe development should slow down, and why the window for meaningful intervention may be narrowing.Roman discusses the rapid acceleration toward AGI, early signals of job displacement that could become visible by the end of this decade, and why traditional patterns of technological disruption may not apply this time. He explains why large companies continue investing heavily in AI despite debates around scaling limits, how the global race toward superintelligence is unfolding, and why no scalable safety mechanism currently guarantees control. The conversation also explores AI consciousness, digital labor, the simulation hypothesis, and what widespread automation could mean for identity, purpose, and humanity's long-term future.If you're looking for a rigorous and research-driven perspective on the technical, economic, and existential implications of advanced AI, this episode offers a serious examination of what the next decade could hold.

Turner Construction was paying for specialist AI software. Then they ditched it for ChatGPT and it did 85% of the job. The founder they left behind says he's not even surprised.In this episode of Bricks, Bucks & Bytes, Owen, Patric, and Martin are joined by Luigi La Corte, CEO of Provision, for an unfiltered conversation about what AI is really doing to construction software  and who's about to get left behind. Plus, two founder call-ins you don't want to miss: Bertrand from Billdr reveals that 75% of SMB general contractors are still running their business on Excel in 2026, and Jodok walks us through how he secured a $1.2 billion debt facility to green Europe's homes.Here's what we get into:Why contract review software is being commoditized to zero — and which tools are nextTurner Construction ditching specialist AI software for OpenAI's "good enough" enterprise packageLuigi's bold claim that AGI is already here (and why he's running it from his couch via Telegram for $60/month)Patric's multi-lens take on AI: "excited as a consumer, terrified as a citizen"The Anthropic safety chief quitting to study poetry and why that should concern everyoneBilldr's pivot from a $40M marketplace to vertical SaaS, and the brute-force sales motion that's actually workingHow Jodok went from a $5M lending facility to $1.2 billion in under three years"The technological swell is here. Most leaders are just swimming, enjoying the sun, making money, business as usual. A few are paddling hard towards the swell. It feels like a lot of effort with no results, but when the wave breaks, the ones paddling will separate from everyone else at a pace no one else could catch."If you're in construction and not paying attention to AI right now, this episode will tell you exactly why you should be. Watch the full episode on YouTube. Link in the comments!Chapters00:00 Intro00:52 Introduction to Luigi La Corte and Industry Insights 03:30 AI in Construction: Scope Agent and Its Impact 06:27 Navigating Contract Review Tools in the AI Era 09:20 The Future of AI: Perspectives and Predictions 12:20 Diverse Sentiments on AI: Consumer vs. Societal Impact 14:37 AGI: Is It Already Here?  22:19 The Future of AI and Productivity 24:11 Concerns About AGI and Its Implications 25:26 The Impact of AI on Human Experience 26:37 Recursive Self-Improvement and Its Risks 27:48 Billdr's Journey and Market Positioning 37:55 The Demand for All-in-One Solutions in Construction Tech 40:01 The Evolution of General Contractors and Their Needs41:22 The Future of Administrative Tasks in Construction 42:35 Addressing the Missing Middle in Construction Companies 44:15 Financing the Energy Transition: ClueWorth's Approach 46:48 Scaling Operations in a Fragmented Market 54:06 Navigating Complexity in Energy Installations 58:04 Revenue Models and Future Growth Strategies

Boris Cherny is the creator and head of Claude Code at Anthropic. What began as a simple terminal-based prototype just a year ago has transformed the role of software engineering and is increasingly transforming all professional work.We discuss:1. How Claude Code grew from a quick hack to 4% of public GitHub commits, with daily active users doubling last month2. The counterintuitive product principles that drove Claude Code's success3. Why Boris believes coding is “solved”4. The latent demand that shaped Claude Code and Cowork5. Practical tips for getting the most out of Claude Code and Cowork6. How underfunding teams and giving them unlimited tokens leads to better AI products7. Why Boris briefly left Anthropic for Cursor, then returned after just two weeks8. Three principles Boris shares with every new team member—Brought to you by:DX—The developer intelligence platform designed by leading researchers: https://getdx.com/lennySentry—Code breaks, fix it faster: https://sentry.io/lennyMetaview—The AI platform for recruiting: https://metaview.ai/lenny—Episode transcript: https://www.lennysnewsletter.com/p/head-of-claude-code-what-happens—Archive of all Lenny's Podcast transcripts: https://www.dropbox.com/scl/fo/yxi4s2w998p1gvtpu4193/AMdNPR8AOw0lMklwtnC0TrQ?rlkey=j06x0nipoti519e0xgm23zsn9&st=ahz0fj11&dl=0—Where to find Boris Cherny:• X: https://x.com/bcherny• LinkedIn: https://www.linkedin.com/in/bcherny• Website: https://borischerny.com—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Introduction to Boris and Claude Code(03:45) Why Boris briefly left Anthropic for Cursor (and what brought him back)(05:35) One year of Claude Code(08:41) The origin story of Claude Code(13:29) How fast AI is transforming software development(15:01) The importance of experimentation in AI innovation(16:17) Boris's current coding workflow (100% AI-written)(17:32) The next frontier(22:24) The downside of rapid innovation (24:02) Principles for the Claude Code team(26:48) Why you should give engineers unlimited tokens(27:55) Will coding skills still matter in the future?(32:15) The printing press analogy for AI's impact(36:01) Which roles will AI transform next?(40:41) Tips for succeeding in the AI era(44:37) Poll: Which roles are enjoying their jobs more with AI(46:32) The principle of latent demand in product development(51:53) How Cowork was built in just 10 days(54:04) The three layers of AI safety at Anthropic(59:35) Anxiety when AI agents aren't working(01:02:25) Boris's Ukrainian roots(01:03:21) Advice for building AI products(01:08:38) Pro tips for using Claude Code effectively(01:11:16) Thoughts on Codex(01:12:13) Boris's post-AGI plans(01:14:02) Lightning round and final thoughts—References: https://www.lennysnewsletter.com/p/head-of-claude-code-what-happens—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.—Lenny may be an investor in the companies discussed. To hear more, visit www.lennysnewsletter.com

Are you feeling stuck in midlife, unsure of your next move, and overwhelmed by the pressure to figure everything out?Midlife transitions often bring discomfort and urgency, pushing people toward either impulsive decisions or paralysing overthinking. This episode offers a grounded alternative, perfect if you're craving direction but feel unsure where to begin.Discover why asking big life questions might be keeping you stuck, and what to ask instead.Learn how to create a flexible, time-bound "container" that reduces pressure while allowing exploration.Understand the kind of real-world action that brings clarity, without needing dramatic life changes.Listen now to learn a practical, compassionate process that helps you shift from stuck to steady movement, without needing all the answers first.˚Coaching with Agi: https://personaldevelopmentmasterypodcast.com/mentor˚Support the showCareer transition and career clarity podcast content for midlife professionals in career transition, navigating a career change, career pivot or second career, starting a new venture or leaving a long-term career. Discover practical tools for career clarity, confident decision-making, rebuilding self belief and confidence, finding purpose and meaning in work, designing a purposeful, fulfilling next chapter, and creating meaningful work that fits who you are now. Episodes explore personal development and mindset for midlife professionals, including how to manage uncertainty and pressure, overcome fear and self-doubt, clarify your direction, plan your next steps, and turn your experience into a new role, business or vocation that feels aligned. To support the show, click here.

Tickets for AIEi Miami and AIE Europe are live, with first wave speakers announced!From pioneering software-defined networking to backing many of the most aggressive AI model companies of this cycle, Martin Casado and Sarah Wang sit at the center of the capital, compute, and talent arms race reshaping the tech industry. As partners at a16z investing across infrastructure and growth, they've watched venture and growth blur, model labs turn dollars into capability at unprecedented speed, and startups raise nine-figure rounds before monetization.Martin and Sarah join us to unpack the new financing playbook for AI: why today's rounds are really compute contracts in disguise, how the “raise → train → ship → raise bigger” flywheel works, and whether foundation model companies can outspend the entire app ecosystem built on top of them. They also share what's underhyped (boring enterprise software), what's overheated (talent wars and compensation spirals), and the two radically different futures they see for AI's market structure.We discuss:* Martin's “two futures” fork: infinite fragmentation and new software categories vs. a small oligopoly of general models that consume everything above them* The capital flywheel: how model labs translate funding directly into capability gains, then into revenue growth measured in weeks, not years* Why venture and growth have merged: $100M–$1B hybrid rounds, strategic investors, compute negotiations, and complex deal structures* The AGI vs. product tension: allocating scarce GPUs between long-term research and near-term revenue flywheels* Whether frontier labs can out-raise and outspend the entire app ecosystem built on top of their APIs* Why today's talent wars ($10M+ comp packages, $B acqui-hires) are breaking early-stage founder math* Cursor as a case study: building up from the app layer while training down into your own models* Why “boring” enterprise software may be the most underinvested opportunity in the AI mania* Hardware and robotics: why the ChatGPT moment hasn't yet arrived for robots and what would need to change* World Labs and generative 3D: bringing the marginal cost of 3D scene creation down by orders of magnitude* Why public AI discourse is often wildly disconnected from boardroom reality and how founders should navigate the noiseShow Notes:* “Where Value Will Accrue in AI: Martin Casado & Sarah Wang” - a16z show* “Jack Altman & Martin Casado on the Future of Venture Capital”* World Labs—Martin Casado• LinkedIn: https://www.linkedin.com/in/martincasado/• X: https://x.com/martin_casadoSarah Wang• LinkedIn: https://www.linkedin.com/in/sarah-wang-59b96a7• X: https://x.com/sarahdingwanga16z• https://a16z.com/Timestamps00:00:00 – Intro: Live from a16z00:01:20 – The New AI Funding Model: Venture + Growth Collide00:03:19 – Circular Funding, Demand & “No Dark GPUs”00:05:24 – Infrastructure vs Apps: The Lines Blur00:06:24 – The Capital Flywheel: Raise → Train → Ship → Raise Bigger00:09:39 – Can Frontier Labs Outspend the Entire App Ecosystem?00:11:24 – Character AI & The AGI vs Product Dilemma00:14:39 – Talent Wars, $10M Engineers & Founder Anxiety00:17:33 – What's Underinvested? The Case for “Boring” Software00:19:29 – Robotics, Hardware & Why It's Hard to Win00:22:42 – Custom ASICs & The $1B Training Run Economics00:24:23 – American Dynamism, Geography & AI Power Centers00:26:48 – How AI Is Changing the Investor Workflow (Claude Cowork)00:29:12 – Two Futures of AI: Infinite Expansion or Oligopoly?00:32:48 – If You Can Raise More Than Your Ecosystem, You Win00:34:27 – Are All Tasks AGI-Complete? Coding as the Test Case00:38:55 – Cursor & The Power of the App Layer00:44:05 – World Labs, Spatial Intelligence & 3D Foundation Models00:47:20 – Thinking Machines, Founder Drama & Media Narratives00:52:30 – Where Long-Term Power Accrues in the AI StackTranscriptLatent.Space - Inside AI's $10B+ Capital Flywheel — Martin Casado & Sarah Wang of a16z[00:00:00] Welcome to Latent Space (Live from a16z) + Meet the Guests[00:00:00] Alessio: Hey everyone. Welcome to the Latent Space podcast, live from a 16 z. Uh, this is Alessio founder Kernel Lance, and I'm joined by Twix, editor of Latent Space.[00:00:08] swyx: Hey, hey, hey. Uh, and we're so glad to be on with you guys. Also a top AI podcast, uh, Martin Cado and Sarah Wang. Welcome, very[00:00:16] Martin Casado: happy to be here and welcome.[00:00:17] swyx: Yes, uh, we love this office. We love what you've done with the place. Uh, the new logo is everywhere now. It's, it's still getting, takes a while to get used to, but it reminds me of like sort of a callback to a more ambitious age, which I think is kind of[00:00:31] Martin Casado: definitely makes a statement.[00:00:33] swyx: Yeah.[00:00:34] Martin Casado: Not quite sure what that statement is, but it makes a statement.[00:00:37] swyx: Uh, Martin, I go back with you to Netlify.[00:00:40] Martin Casado: Yep.[00:00:40] swyx: Uh, and, uh, you know, you create a software defined networking and all, all that stuff people can read up on your background. Yep. Sarah, I'm newer to you. Uh, you, you sort of started working together on AI infrastructure stuff.[00:00:51] Sarah Wang: That's right. Yeah. Seven, seven years ago now.[00:00:53] Martin Casado: Best growth investor in the entire industry.[00:00:55] swyx: Oh, say[00:00:56] Martin Casado: more hands down there is, there is. [00:01:00] I mean, when it comes to AI companies, Sarah, I think has done the most kind of aggressive, um, investment thesis around AI models, right? So, worked for Nom Ja, Mira Ia, FEI Fey, and so just these frontier, kind of like large AI models.[00:01:15] I think, you know, Sarah's been the, the broadest investor. Is that fair?[00:01:20] Venture vs. Growth in the Frontier Model Era[00:01:20] Sarah Wang: No, I, well, I was gonna say, I think it's been a really interesting tag, tag team actually just ‘cause the, a lot of these big C deals, not only are they raising a lot of money, um, it's still a tech founder bet, which obviously is inherently early stage.[00:01:33] But the resources,[00:01:36] Martin Casado: so many, I[00:01:36] Sarah Wang: was gonna say the resources one, they just grow really quickly. But then two, the resources that they need day one are kind of growth scale. So I, the hybrid tag team that we have is. Quite effective, I think,[00:01:46] Martin Casado: what is growth these days? You know, you don't wake up if it's less than a billion or like, it's, it's actually, it's actually very like, like no, it's a very interesting time in investing because like, you know, take like the character around, right?[00:01:59] These tend to [00:02:00] be like pre monetization, but the dollars are large enough that you need to have a larger fund and the analysis. You know, because you've got lots of users. ‘cause this stuff has such high demand requires, you know, more of a number sophistication. And so most of these deals, whether it's US or other firms on these large model companies, are like this hybrid between venture growth.[00:02:18] Sarah Wang: Yeah. Total. And I think, you know, stuff like BD for example, you wouldn't usually need BD when you were seed stage trying to get market biz Devrel. Biz Devrel, exactly. Okay. But like now, sorry, I'm,[00:02:27] swyx: I'm not familiar. What, what, what does biz Devrel mean for a venture fund? Because I know what biz Devrel means for a company.[00:02:31] Sarah Wang: Yeah.[00:02:32] Compute Deals, Strategics, and the ‘Circular Funding' Question[00:02:32] Sarah Wang: You know, so a, a good example is, I mean, we talk about buying compute, but there's a huge negotiation involved there in terms of, okay, do you get equity for the compute? What, what sort of partner are you looking at? Is there a go-to market arm to that? Um, and these are just things on this scale, hundreds of millions, you know, maybe.[00:02:50] Six months into the inception of a company, you just wouldn't have to negotiate these deals before.[00:02:54] Martin Casado: Yeah. These large rounds are very complex now. Like in the past, if you did a series A [00:03:00] or a series B, like whatever, you're writing a 20 to a $60 million check and you call it a day. Now you normally have financial investors and strategic investors, and then the strategic portion always still goes with like these kind of large compute contracts, which can take months to do.[00:03:13] And so it's, it's very different ties. I've been doing this for 10 years. It's the, I've never seen anything like this.[00:03:19] swyx: Yeah. Do you have worries about the circular funding from so disease strategics?[00:03:24] Martin Casado: I mean, listen, as long as the demand is there, like the demand is there. Like the problem with the internet is the demand wasn't there.[00:03:29] swyx: Exactly. All right. This, this is like the, the whole pyramid scheme bubble thing, where like, as long as you mark to market on like the notional value of like, these deals, fine, but like once it starts to chip away, it really Well[00:03:41] Martin Casado: no, like as, as, as, as long as there's demand. I mean, you know, this, this is like a lot of these sound bites have already become kind of cliches, but they're worth saying it.[00:03:47] Right? Like during the internet days, like we were. Um, raising money to put fiber in the ground that wasn't used. And that's a problem, right? Because now you actually have a supply overhang.[00:03:58] swyx: Mm-hmm.[00:03:59] Martin Casado: And even in the, [00:04:00] the time of the, the internet, like the supply and, and bandwidth overhang, even as massive as it was in, as massive as the crash was only lasted about four years.[00:04:09] But we don't have a supply overhang. Like there's no dark GPUs, right? I mean, and so, you know, circular or not, I mean, you know, if, if someone invests in a company that, um. You know, they'll actually use the GPUs. And on the other side of it is the, is the ask for customer. So I I, I think it's a different time.[00:04:25] Sarah Wang: I think the other piece, maybe just to add onto this, and I'm gonna quote Martine in front of him, but this is probably also a unique time in that. For the first time, you can actually trace dollars to outcomes. Yeah, right. Provided that scaling laws are, are holding, um, and capabilities are actually moving forward.[00:04:40] Because if you can put translate dollars into capabilities, uh, a capability improvement, there's demand there to martine's point. But if that somehow breaks, you know, obviously that's an important assumption in this whole thing to make it work. But you know, instead of investing dollars into sales and marketing, you're, you're investing into r and d to get to the capability, um, you know, increase.[00:04:59] And [00:05:00] that's sort of been the demand driver because. Once there's an unlock there, people are willing to pay for it.[00:05:05] Alessio: Yeah.[00:05:06] Blurring Lines: Models as Infra + Apps, and the New Fundraising Flywheel[00:05:06] Alessio: Is there any difference in how you built the portfolio now that some of your growth companies are, like the infrastructure of the early stage companies, like, you know, OpenAI is now the same size as some of the cloud providers were early on.[00:05:16] Like what does that look like? Like how much information can you feed off each other between the, the two?[00:05:24] Martin Casado: There's so many lines that are being crossed right now, or blurred. Right. So we already talked about venture and growth. Another one that's being blurred is between infrastructure and apps, right? So like what is a model company?[00:05:35] Mm-hmm. Like, it's clearly infrastructure, right? Because it's like, you know, it's doing kind of core r and d. It's a horizontal platform, but it's also an app because it's um, uh, touches the users directly. And then of course. You know, the, the, the growth of these is just so high. And so I actually think you're just starting to see a, a, a new financing strategy emerge and, you know, we've had to adapt as a result of that.[00:05:59] And [00:06:00] so there's been a lot of changes. Um, you're right that these companies become platform companies very quickly. You've got ecosystem build out. So none of this is necessarily new, but the timescales of which it's happened is pretty phenomenal. And the way we'd normally cut lines before is blurred a little bit, but.[00:06:16] But that, that, that said, I mean, a lot of it also just does feel like things that we've seen in the past, like cloud build out the internet build out as well.[00:06:24] Sarah Wang: Yeah. Um, yeah, I think it's interesting, uh, I don't know if you guys would agree with this, but it feels like the emerging strategy is, and this builds off of your other question, um.[00:06:33] You raise money for compute, you pour that or you, you pour the money into compute, you get some sort of breakthrough. You funnel the breakthrough into your vertically integrated application. That could be chat GBT, that could be cloud code, you know, whatever it is. You massively gain share and get users.[00:06:49] Maybe you're even subsidizing at that point. Um, depending on your strategy. You raise money at the peak momentum and then you repeat, rinse and repeat. Um, and so. And that wasn't [00:07:00] true even two years ago, I think. Mm-hmm. And so it's sort of to your, just tying it to fundraising strategy, right? There's a, and hiring strategy.[00:07:07] All of these are tied, I think the lines are blurring even more today where everyone is, and they, but of course these companies all have API businesses and so they're these, these frenemy lines that are getting blurred in that a lot of, I mean, they have billions of dollars of API revenue, right? And so there are customers there.[00:07:23] But they're competing on the app layer.[00:07:24] Martin Casado: Yeah. So this is a really, really important point. So I, I would say for sure, venture and growth, that line is blurry app and infrastructure. That line is blurry. Um, but I don't think that that changes our practice so much. But like where the very open questions are like, does this layer in the same way.[00:07:43] Compute traditionally has like during the cloud is like, you know, like whatever, somebody wins one layer, but then another whole set of companies wins another layer. But that might not, might not be the case here. It may be the case that you actually can't verticalize on the token string. Like you can't build an app like it, it necessarily goes down just because there are no [00:08:00] abstractions.[00:08:00] So those are kinda the bigger existential questions we ask. Another thing that is very different this time than in the history of computer sciences is. In the past, if you raised money, then you basically had to wait for engineering to catch up. Which famously doesn't scale like the mythical mammoth. It take a very long time.[00:08:18] But like that's not the case here. Like a model company can raise money and drop a model in a, in a year, and it's better, right? And, and it does it with a team of 20 people or 10 people. So this type of like money entering a company and then producing something that has demand and growth right away and using that to raise more money is a very different capital flywheel than we've ever seen before.[00:08:39] And I think everybody's trying to understand what the consequences are. So I think it's less about like. Big companies and growth and this, and more about these more systemic questions that we actually don't have answers to.[00:08:49] Alessio: Yeah, like at Kernel Labs, one of our ideas is like if you had unlimited money to spend productively to turn tokens into products, like the whole early stage [00:09:00] market is very different because today you're investing X amount of capital to win a deal because of price structure and whatnot, and you're kind of pot committing.[00:09:07] Yeah. To a certain strategy for a certain amount of time. Yeah. But if you could like iteratively spin out companies and products and just throw, I, I wanna spend a million dollar of inference today and get a product out tomorrow.[00:09:18] swyx: Yeah.[00:09:19] Alessio: Like, we should get to the point where like the friction of like token to product is so low that you can do this and then you can change the Right, the early stage venture model to be much more iterative.[00:09:30] And then every round is like either 100 k of inference or like a hundred million from a 16 Z. There's no, there's no like $8 million C round anymore. Right.[00:09:38] When Frontier Labs Outspend the Entire App Ecosystem[00:09:38] Martin Casado: But, but, but, but there's a, there's a, the, an industry structural question that we don't know the answer to, which involves the frontier models, which is, let's take.[00:09:48] Anthropic it. Let's say Anthropic has a state-of-the-art model that has some large percentage of market share. And let's say that, uh, uh, uh, you know, uh, a company's building smaller models [00:10:00] that, you know, use the bigger model in the background, open 4.5, but they add value on top of that. Now, if Anthropic can raise three times more.[00:10:10] Every subsequent round, they probably can raise more money than the entire app ecosystem that's built on top of it. And if that's the case, they can expand beyond everything built on top of it. It's like imagine like a star that's just kind of expanding, so there could be a systemic. There could be a, a systemic situation where the soda models can raise so much money that they can out pay anybody that bills on top of ‘em, which would be something I don't think we've ever seen before just because we were so bottlenecked in engineering, and this is a very open question.[00:10:41] swyx: Yeah. It's, it is almost like bitter lesson applied to the startup industry.[00:10:45] Martin Casado: Yeah, a hundred percent. It literally becomes an issue of like raise capital, turn that directly into growth. Use that to raise three times more. Exactly. And if you can keep doing that, you literally can outspend any company that's built the, not any company.[00:10:57] You can outspend the aggregate of companies on top of [00:11:00] you and therefore you'll necessarily take their share, which is crazy.[00:11:02] swyx: Would you say that kind of happens in character? Is that the, the sort of postmortem on. What happened?[00:11:10] Sarah Wang: Um,[00:11:10] Martin Casado: no.[00:11:12] Sarah Wang: Yeah, because I think so,[00:11:13] swyx: I mean the actual postmortem is, he wanted to go back to Google.[00:11:15] Exactly. But like[00:11:18] Martin Casado: that's another difference that[00:11:19] Sarah Wang: you said[00:11:21] Martin Casado: it. We should talk, we should actually talk about that.[00:11:22] swyx: Yeah,[00:11:22] Sarah Wang: that's[00:11:23] swyx: Go for it. Take it. Take,[00:11:23] Sarah Wang: yeah.[00:11:24] Character.AI, Founder Goals (AGI vs Product), and GPU Allocation Tradeoffs[00:11:24] Sarah Wang: I was gonna say, I think, um. The, the, the character thing raises actually a different issue, which actually the Frontier Labs will face as well. So we'll see how they handle it.[00:11:34] But, um, so we invest in character in January, 2023, which feels like eons ago, I mean, three years ago. Feels like lifetimes ago. But, um, and then they, uh, did the IP licensing deal with Google in August, 2020. Uh, four. And so, um, you know, at the time, no, you know, he's talked publicly about this, right? He wanted to Google wouldn't let him put out products in the world.[00:11:56] That's obviously changed drastically. But, um, he went to go do [00:12:00] that. Um, but he had a product attached. The goal was, I mean, it's Nome Shair, he wanted to get to a GI. That was always his personal goal. But, you know, I think through collecting data, right, and this sort of very human use case, that the character product.[00:12:13] Originally was and still is, um, was one of the vehicles to do that. Um, I think the real reason that, you know. I if you think about the, the stress that any company feels before, um, you ultimately going one way or the other is sort of this a GI versus product. Um, and I think a lot of the big, I think, you know, opening eyes, feeling that, um, anthropic if they haven't started, you know, felt it, certainly given the success of their products, they may start to feel that soon.[00:12:39] And the real. I think there's real trade-offs, right? It's like how many, when you think about GPUs, that's a limited resource. Where do you allocate the GPUs? Is it toward the product? Is it toward new re research? Right? Is it, or long-term research, is it toward, um, n you know, near to midterm research? And so, um, in a case where you're resource constrained, um, [00:13:00] of course there's this fundraising game you can play, right?[00:13:01] But the fund, the market was very different back in 2023 too. Um. I think the best researchers in the world have this dilemma of, okay, I wanna go all in on a GI, but it's the product usage revenue flywheel that keeps the revenue in the house to power all the GPUs to get to a GI. And so it does make, um, you know, I think it sets up an interesting dilemma for any startup that has trouble raising up until that level, right?[00:13:27] And certainly if you don't have that progress, you can't continue this fly, you know, fundraising flywheel.[00:13:32] Martin Casado: I would say that because, ‘cause we're keeping track of all of the things that are different, right? Like, you know, venture growth and uh, app infra and one of the ones is definitely the personalities of the founders.[00:13:45] It's just very different this time I've been. Been doing this for a decade and I've been doing startups for 20 years. And so, um, I mean a lot of people start this to do a GI and we've never had like a unified North star that I recall in the same [00:14:00] way. Like people built companies to start companies in the past.[00:14:02] Like that was what it was. Like I would create an internet company, I would create infrastructure company, like it's kind of more engineering builders and this is kind of a different. You know, mentality. And some companies have harnessed that incredibly well because their direction is so obviously on the path to what somebody would consider a GI, but others have not.[00:14:20] And so like there is always this tension with personnel. And so I think we're seeing more kind of founder movement.[00:14:27] Sarah Wang: Yeah.[00:14:27] Martin Casado: You know, as a fraction of founders than we've ever seen. I mean, maybe since like, I don't know the time of like Shockly and the trade DUR aid or something like that. Way back in the beginning of the industry, I, it's a very, very.[00:14:38] Unusual time of personnel.[00:14:39] Sarah Wang: Totally.[00:14:40] Talent Wars, Mega-Comp, and the Rise of Acquihire M&A[00:14:40] Sarah Wang: And it, I think it's exacerbated by the fact that talent wars, I mean, every industry has talent wars, but not at this magnitude, right? No. Yeah. Very rarely can you see someone get poached for $5 billion. That's hard to compete with. And then secondly, if you're a founder in ai, you could fart and it would be on the front page of, you know, the information these days.[00:14:59] And so there's [00:15:00] sort of this fishbowl effect that I think adds to the deep anxiety that, that these AI founders are feeling.[00:15:06] Martin Casado: Hmm.[00:15:06] swyx: Uh, yes. I mean, just on, uh, briefly comment on the founder, uh, the sort of. Talent wars thing. I feel like 2025 was just like a blip. Like I, I don't know if we'll see that again.[00:15:17] ‘cause meta built the team. Like, I don't know if, I think, I think they're kind of done and like, who's gonna pay more than meta? I, I don't know.[00:15:23] Martin Casado: I, I agree. So it feels so, it feel, it feels this way to me too. It's like, it is like, basically Zuckerberg kind of came out swinging and then now he's kind of back to building.[00:15:30] Yeah,[00:15:31] swyx: yeah. You know, you gotta like pay up to like assemble team to rush the job, whatever. But then now, now you like you, you made your choices and now they got a ship.[00:15:38] Martin Casado: I mean, the, the o other side of that is like, you know, like we're, we're actually in the job hiring market. We've got 600 people here. I hire all the time.[00:15:44] I've got three open recs if anybody's interested, that's listening to this for investor. Yeah, on, on the team, like on the investing side of the team, like, and, um, a lot of the people we talk to have acting, you know, active, um, offers for 10 million a year or something like that. And like, you know, and we pay really, [00:16:00] really well.[00:16:00] And just to see what's out on the market is really, is really remarkable. And so I would just say it's actually, so you're right, like the really flashy one, like I will get someone for, you know, a billion dollars, but like the inflated, um, uh, trickles down. Yeah, it is still very active today. I mean,[00:16:18] Sarah Wang: yeah, you could be an L five and get an offer in the tens of millions.[00:16:22] Okay. Yeah. Easily. Yeah. It's so I think you're right that it felt like a blip. I hope you're right. Um, but I think it's been, the steady state is now, I think got pulled up. Yeah. Yeah. I'll pull up for[00:16:31] Martin Casado: sure. Yeah.[00:16:32] Alessio: Yeah. And I think that's breaking the early stage founder math too. I think before a lot of people would be like, well, maybe I should just go be a founder instead of like getting paid.[00:16:39] Yeah. 800 KA million at Google. But if I'm getting paid. Five, 6 million. That's different but[00:16:45] Martin Casado: on. But on the other hand, there's more strategic money than we've ever seen historically, right? Mm-hmm. And so, yep. The economics, the, the, the, the calculus on the economics is very different in a number of ways. And, uh, it's crazy.[00:16:58] It's cra it's causing like a, [00:17:00] a, a, a ton of change in confusion in the market. Some very positive, sub negative, like, so for example, the other side of the, um. The co-founder, like, um, acquisition, you know, mark Zuckerberg poaching someone for a lot of money is like, we were actually seeing historic amount of m and a for basically acquihires, right?[00:17:20] That you like, you know, really good outcomes from a venture perspective that are effective acquihires, right? So I would say it's probably net positive from the investment standpoint, even though it seems from the headlines to be very disruptive in a negative way.[00:17:33] Alessio: Yeah.[00:17:33] What's Underfunded: Boring Software, Robotics Skepticism, and Custom Silicon Economics[00:17:33] Alessio: Um, let's talk maybe about what's not being invested in, like maybe some interesting ideas that you would see more people build or it, it seems in a way, you know, as ycs getting more popular, it's like access getting more popular.[00:17:47] There's a startup school path that a lot of founders take and they know what's hot in the VC circles and they know what gets funded. Uh, and there's maybe not as much risk appetite for. Things outside of that. Um, I'm curious if you feel [00:18:00] like that's true and what are maybe, uh, some of the areas, uh, that you think are under discussed?[00:18:06] Martin Casado: I mean, I actually think that we've taken our eye off the ball in a lot of like, just traditional, you know, software companies. Um, so like, I mean. You know, I think right now there's almost a barbell, like you're like the hot thing on X, you're deep tech.[00:18:21] swyx: Mm-hmm.[00:18:22] Martin Casado: Right. But I, you know, I feel like there's just kind of a long, you know, list of like good.[00:18:28] Good companies that will be around for a long time in very large markets. Say you're building a database, you know, say you're building, um, you know, kind of monitoring or logging or tooling or whatever. There's some good companies out there right now, but like, they have a really hard time getting, um, the attention of investors.[00:18:43] And it's almost become a meme, right? Which is like, if you're not basically growing from zero to a hundred in a year, you're not interesting, which is just, is the silliest thing to say. I mean, think of yourself as like an introvert person, like, like your personal money, right? Mm-hmm. So. Your personal money, will you put it in the stock market at 7% or you put it in this company growing five x in a very large [00:19:00] market?[00:19:00] Of course you can put it in the company five x. So it's just like we say these stupid things, like if you're not going from zero to a hundred, but like those, like who knows what the margins of those are mean. Clearly these are good investments. True for anybody, right? True. Like our LPs want whatever.[00:19:12] Three x net over, you know, the life cycle of a fund, right? So a, a company in a big market growing five X is a great investment. We'd, everybody would be happy with these returns, but we've got this kind of mania on these, these strong growths. And so I would say that that's probably the most underinvested sector.[00:19:28] Right now.[00:19:29] swyx: Boring software, boring enterprise software.[00:19:31] Martin Casado: Traditional. Really good company.[00:19:33] swyx: No, no AI here.[00:19:34] Martin Casado: No. Like boring. Well, well, the AI of course is pulling them into use cases. Yeah, but that's not what they're, they're not on the token path, right? Yeah. Let's just say that like they're software, but they're not on the token path.[00:19:41] Like these are like they're great investments from any definition except for like random VC on Twitter saying VC on x, saying like, it's not growing fast enough. What do you[00:19:52] Sarah Wang: think? Yeah, maybe I'll answer a slightly different. Question, but adjacent to what you asked, um, which is maybe an area that we're not, uh, investing [00:20:00] right now that I think is a question and we're spending a lot of time in regardless of whether we pull the trigger or not.[00:20:05] Um, and it would probably be on the hardware side, actually. Robotics, right? And the robotics side. Robotics. Right. Which is, it's, I don't wanna say that it's not getting funding ‘cause it's clearly, uh, it's, it's sort of non-consensus to almost not invest in robotics at this point. But, um, we spent a lot of time in that space and I think for us, we just haven't seen the chat GPT moment.[00:20:22] Happen on the hardware side. Um, and the funding going into it feels like it's already. Taking that for granted.[00:20:30] Martin Casado: Yeah. Yeah. But we also went through the drone, you know, um, there's a zip line right, right out there. What's that? Oh yeah, there's a zip line. Yeah. What the drone, what the av And like one of the takeaways is when it comes to hardware, um, most companies will end up verticalizing.[00:20:46] Like if you're. If you're investing in a robot company for an A for agriculture, you're investing in an ag company. ‘cause that's the competition and that's surprising. And that's supply chain. And if you're doing it for mining, that's mining. And so the ad team does a lot of that type of stuff ‘cause they actually set up to [00:21:00] diligence that type of work.[00:21:01] But for like horizontal technology investing, there's very little when it comes to robots just because it's so fit for, for purpose. And so we kinda like to look at software. Solutions or horizontal solutions like applied intuition. Clearly from the AV wave deep map, clearly from the AV wave, I would say scale AI was actually a horizontal one for That's fair, you know, for robotics early on.[00:21:23] And so that sort of thing we're very, very interested. But the actual like robot interacting with the world is probably better for different team. Agree.[00:21:30] Alessio: Yeah, I'm curious who these teams are supposed to be that invest in them. I feel like everybody's like, yeah, robotics, it's important and like people should invest in it.[00:21:38] But then when you look at like the numbers, like the capital requirements early on versus like the moment of, okay, this is actually gonna work. Let's keep investing. That seems really hard to predict in a way that is not,[00:21:49] Martin Casado: I think co, CO two, kla, gc, I mean these are all invested in in Harvard companies. He just, you know, and [00:22:00] listen, I mean, it could work this time for sure.[00:22:01] Right? I mean if Elon's doing it, he's like, right. Just, just the fact that Elon's doing it means that there's gonna be a lot of capital and a lot of attempts for a long period of time. So that alone maybe suggests that we should just be investing in robotics just ‘cause you have this North star who's Elon with a humanoid and that's gonna like basically willing into being an industry.[00:22:17] Um, but we've just historically found like. We're a huge believer that this is gonna happen. We just don't feel like we're in a good position to diligence these things. ‘cause again, robotics companies tend to be vertical. You really have to understand the market they're being sold into. Like that's like that competitive equilibrium with a human being is what's important.[00:22:34] It's not like the core tech and like we're kind of more horizontal core tech type investors. And this is Sarah and I. Yeah, the ad team is different. They can actually do these types of things.[00:22:42] swyx: Uh, just to clarify, AD stands for[00:22:44] Martin Casado: American Dynamism.[00:22:45] swyx: Alright. Okay. Yeah, yeah, yeah. Uh, I actually, I do have a related question that, first of all, I wanna acknowledge also just on the, on the chip side.[00:22:51] Yeah. I, I recall a podcast that where you were on, i, I, I think it was the a CC podcast, uh, about two or three years ago where you, where you suddenly said [00:23:00] something, which really stuck in my head about how at some point, at some point kind of scale it makes sense to. Build a custom aic Yes. For per run.[00:23:07] Martin Casado: Yes.[00:23:07] It's crazy. Yeah.[00:23:09] swyx: We're here and I think you, you estimated 500 billion, uh, something.[00:23:12] Martin Casado: No, no, no. A billion, a billion dollar training run of $1 billion training run. It makes sense to actually do a custom meic if you can do it in time. The question now is timelines. Yeah, but not money because just, just, just rough math.[00:23:22] If it's a billion dollar training. Then the inference for that model has to be over a billion, otherwise it won't be solvent. So let's assume it's, if you could save 20%, which you could save much more than that with an ASIC 20%, that's $200 million. You can tape out a chip for $200 million. Right? So now you can literally like justify economically, not timeline wise.[00:23:41] That's a different issue. An ASIC per model, which[00:23:44] swyx: is because that, that's how much we leave on the table every single time. We, we, we do like generic Nvidia.[00:23:48] Martin Casado: Exactly. Exactly. No, it, it is actually much more than that. You could probably get, you know, a factor of two, which would be 500 million.[00:23:54] swyx: Typical MFU would be like 50.[00:23:55] Yeah, yeah. And that's good.[00:23:57] Martin Casado: Exactly. Yeah. Hundred[00:23:57] swyx: percent. Um, so, so, yeah, and I mean, and I [00:24:00] just wanna acknowledge like, here we are in, in, in 2025 and opening eyes confirming like Broadcom and all the other like custom silicon deals, which is incredible. I, I think that, uh, you know, speaking about ad there's, there's a really like interesting tie in that obviously you guys are hit on, which is like these sort, this sort of like America first movement or like sort of re industrialized here.[00:24:17] Yeah. Uh, move TSMC here, if that's possible. Um, how much overlap is there from ad[00:24:23] Martin Casado: Yeah.[00:24:23] swyx: To, I guess, growth and, uh, investing in particularly like, you know, US AI companies that are strongly bounded by their compute.[00:24:32] Martin Casado: Yeah. Yeah. So I mean, I, I would view, I would view AD as more as a market segmentation than like a mission, right?[00:24:37] So the market segmentation is, it has kind of regulatory compliance issues or government, you know, sale or it deals with like hardware. I mean, they're just set up to, to, to, to, to. To diligence those types of companies. So it's a more of a market segmentation thing. I would say the entire firm. You know, which has been since it is been intercepted, you know, has geographical biases, right?[00:24:58] I mean, for the longest time we're like, you [00:25:00] know, bay Area is gonna be like, great, where the majority of the dollars go. Yeah. And, and listen, there, there's actually a lot of compounding effects for having a geographic bias. Right. You know, everybody's in the same place. You've got an ecosystem, you're there, you've got presence, you've got a network.[00:25:12] Um, and, uh, I mean, I would say the Bay area's very much back. You know, like I, I remember during pre COVID, like it was like almost Crypto had kind of. Pulled startups away. Miami from the Bay Area. Miami, yeah. Yeah. New York was, you know, because it's so close to finance, came up like Los Angeles had a moment ‘cause it was so close to consumer, but now it's kind of come back here.[00:25:29] And so I would say, you know, we tend to be very Bay area focused historically, even though of course we've asked all over the world. And then I would say like, if you take the ring out, you know, one more, it's gonna be the US of course, because we know it very well. And then one more is gonna be getting us and its allies and Yeah.[00:25:44] And it goes from there.[00:25:45] Sarah Wang: Yeah,[00:25:45] Martin Casado: sorry.[00:25:46] Sarah Wang: No, no. I agree. I think from a, but I think from the intern that that's sort of like where the companies are headquartered. Maybe your questions on supply chain and customer base. Uh, I, I would say our customers are, are, our companies are fairly international from that perspective.[00:25:59] Like they're selling [00:26:00] globally, right? They have global supply chains in some cases.[00:26:03] Martin Casado: I would say also the stickiness is very different.[00:26:05] Sarah Wang: Yeah.[00:26:05] Martin Casado: Historically between venture and growth, like there's so much company building in venture, so much so like hiring the next PM. Introducing the customer, like all of that stuff.[00:26:15] Like of course we're just gonna be stronger where we have our network and we've been doing business for 20 years. I've been in the Bay Area for 25 years, so clearly I'm just more effective here than I would be somewhere else. Um, where I think, I think for some of the later stage rounds, the companies don't need that much help.[00:26:30] They're already kind of pretty mature historically, so like they can kind of be everywhere. So there's kind of less of that stickiness. This is different in the AI time. I mean, Sarah is now the, uh, chief of staff of like half the AI companies in, uh, in the Bay Area right now. She's like, ops Ninja Biz, Devrel, BizOps.[00:26:48] swyx: Are, are you, are you finding much AI automation in your work? Like what, what is your stack.[00:26:53] Sarah Wang: Oh my, in my personal stack.[00:26:54] swyx: I mean, because like, uh, by the way, it's the, the, the reason for this is it is triggering, uh, yeah. We, like, I'm hiring [00:27:00] ops, ops people. Um, a lot of ponders I know are also hiring ops people and I'm just, you know, it's opportunity Since you're, you're also like basically helping out with ops with a lot of companies.[00:27:09] What are people doing these days? Because it's still very manual as far as I can tell.[00:27:13] Sarah Wang: Hmm. Yeah. I think the things that we help with are pretty network based, um, in that. It's sort of like, Hey, how do do I shortcut this process? Well, let's connect you to the right person. So there's not quite an AI workflow for that.[00:27:26] I will say as a growth investor, Claude Cowork is pretty interesting. Yeah. Like for the first time, you can actually get one shot data analysis. Right. Which, you know, if you're gonna do a customer database, analyze a cohort retention, right? That's just stuff that you had to do by hand before. And our team, the other, it was like midnight and the three of us were playing with Claude Cowork.[00:27:47] We gave it a raw file. Boom. Perfectly accurate. We checked the numbers. It was amazing. That was my like, aha moment. That sounds so boring. But you know, that's, that's the kind of thing that a growth investor is like, [00:28:00] you know, slaving away on late at night. Um, done in a few seconds.[00:28:03] swyx: Yeah. You gotta wonder what the whole, like, philanthropic labs, which is like their new sort of products studio.[00:28:10] Yeah. What would that be worth as an independent, uh, startup? You know, like a[00:28:14] Martin Casado: lot.[00:28:14] Sarah Wang: Yeah, true.[00:28:16] swyx: Yeah. You[00:28:16] Martin Casado: gotta hand it to them. They've been executing incredibly well.[00:28:19] swyx: Yeah. I, I mean, to me, like, you know, philanthropic, like building on cloud code, I think, uh, it makes sense to me the, the real. Um, pedal to the metal, whatever the, the, the phrase is, is when they start coming after consumer with, uh, against OpenAI and like that is like red alert at Open ai.[00:28:35] Oh, I[00:28:35] Martin Casado: think they've been pretty clear. They're enterprise focused.[00:28:37] swyx: They have been, but like they've been free. Here's[00:28:40] Martin Casado: care publicly,[00:28:40] swyx: it's enterprise focused. It's coding. Right. Yeah.[00:28:43] AI Labs vs Startups: Disruption, Undercutting & the Innovator's Dilemma[00:28:43] swyx: And then, and, but here's cloud, cloud, cowork, and, and here's like, well, we, uh, they, apparently they're running Instagram ads for Claudia.[00:28:50] I, on, you know, for, for people on, I get them all the time. Right. And so, like,[00:28:54] Martin Casado: uh,[00:28:54] swyx: it, it's kind of like this, the disruption thing of, uh, you know. Mo Open has been doing, [00:29:00] consumer been doing the, just pursuing general intelligence in every mo modality, and here's a topic that only focus on this thing, but now they're sort of undercutting and doing the whole innovator's dilemma thing on like everything else.[00:29:11] Martin Casado: It's very[00:29:11] swyx: interesting.[00:29:12] Martin Casado: Yeah, I mean there's, there's a very open que so for me there's like, do you know that meme where there's like the guy in the path and there's like a path this way? There's a path this way. Like one which way Western man. Yeah. Yeah.[00:29:23] Two Futures for AI: Infinite Market vs AGI Oligopoly[00:29:23] Martin Casado: And for me, like, like all the entire industry kind of like hinges on like two potential futures.[00:29:29] So in, in one potential future, um, the market is infinitely large. There's perverse economies of scale. ‘cause as soon as you put a model out there, like it kind of sublimates and all the other models catch up and like, it's just like software's being rewritten and fractured all over the place and there's tons of upside and it just grows.[00:29:48] And then there's another path which is like, well. Maybe these models actually generalize really well, and all you have to do is train them with three times more money. That's all you have to [00:30:00] do, and it'll just consume everything beyond it. And if that's the case, like you end up with basically an oligopoly for everything, like, you know mm-hmm.[00:30:06] Because they're perfectly general and like, so this would be like the, the a GI path would be like, these are perfectly general. They can do everything. And this one is like, this is actually normal software. The universe is complicated. You've got, and nobody knows the answer.[00:30:18] The Economics Reality Check: Gross Margins, Training Costs & Borrowing Against the Future[00:30:18] Martin Casado: My belief is if you actually look at the numbers of these companies, so generally if you look at the numbers of these companies, if you look at like the amount they're making and how much they, they spent training the last model, they're gross margin positive.[00:30:30] You're like, oh, that's really working. But if you look at like. The current training that they're doing for the next model, their gross margin negative. So part of me thinks that a lot of ‘em are kind of borrowing against the future and that's gonna have to slow down. It's gonna catch up to them at some point in time, but we don't really know.[00:30:47] Sarah Wang: Yeah.[00:30:47] Martin Casado: Does that make sense? Like, I mean, it could be, it could be the case that the only reason this is working is ‘cause they can raise that next round and they can train that next model. ‘cause these models have such a short. Life. And so at some point in time, like, you know, they won't be able to [00:31:00] raise that next round for the next model and then things will kind of converge and fragment again.[00:31:03] But right now it's not.[00:31:04] Sarah Wang: Totally. I think the other, by the way, just, um, a meta point. I think the other lesson from the last three years is, and we talk about this all the time ‘cause we're on this. Twitter X bubble. Um, cool. But, you know, if you go back to, let's say March, 2024, that period, it felt like a, I think an open source model with an, like a, you know, benchmark leading capability was sort of launching on a daily basis at that point.[00:31:27] And, um, and so that, you know, that's one period. Suddenly it's sort of like open source takes over the world. There's gonna be a plethora. It's not an oligopoly, you know, if you fast, you know, if you, if you rewind time even before that GPT-4 was number one for. Nine months, 10 months. It's a long time. Right.[00:31:44] Um, and of course now we're in this era where it feels like an oligopoly, um, maybe some very steady state shifts and, and you know, it could look like this in the future too, but it just, it's so hard to call. And I think the thing that keeps, you know, us up at [00:32:00] night in, in a good way and bad way, is that the capability progress is actually not slowing down.[00:32:06] And so until that happens, right, like you don't know what's gonna look like.[00:32:09] Martin Casado: But I, I would, I would say for sure it's not converged, like for sure, like the systemic capital flows have not converged, meaning right now it's still borrowing against the future to subsidize growth currently, which you can do that for a period of time.[00:32:23] But, but you know, at the end, at some point the market will rationalize that and just nobody knows what that will look like.[00:32:29] Alessio: Yeah.[00:32:29] Martin Casado: Or, or like the drop in price of compute will, will, will save them. Who knows?[00:32:34] Alessio: Yeah. Yeah. I think the models need to ask them to, to specific tasks. You know? It's like, okay, now Opus 4.5 might be a GI at some specific task, and now you can like depreciate the model over a longer time.[00:32:45] I think now, now, right now there's like no old model.[00:32:47] Martin Casado: No, but let, but lemme just change that mental, that's, that used to be my mental model. Lemme just change it a little bit.[00:32:53] Capital as a Weapon vs Task Saturation: Where Real Enterprise Value Gets Built[00:32:53] Martin Casado: If you can raise three times, if you can raise more than the aggregate of anybody that uses your models, that doesn't even matter.[00:32:59] It doesn't [00:33:00] even matter. See what I'm saying? Like, yeah. Yeah. So, so I have an API Business. My API business is 60% margin, or 70% margin, or 80% margin is a high margin business. So I know what everybody is using. If I can raise more money than the aggregate of everybody that's using it, I will consume them whether I'm a GI or not.[00:33:14] And I will know if they're using it ‘cause they're using it. And like, unlike in the past where engineering stops me from doing that.[00:33:21] Alessio: Mm-hmm.[00:33:21] Martin Casado: It is very straightforward. You just train. So I also thought it was kind of like, you must ask the code a GI, general, general, general. But I think there's also just a possibility that the, that the capital markets will just give them the, the, the ammunition to just go after everybody on top of ‘em.[00:33:36] Sarah Wang: I, I do wonder though, to your point, um, if there's a certain task that. Getting marginally better isn't actually that much better. Like we've asked them to it, to, you know, we can call it a GI or whatever, you know, actually, Ali Goi talks about this, like we're already at a GI for a lot of functions in the enterprise.[00:33:50] Um. That's probably those for those tasks, you probably could build very specific companies that focus on just getting as much value out of that task that isn't [00:34:00] coming from the model itself. There's probably a rich enterprise business to be built there. I mean, could be wrong on that, but there's a lot of interesting examples.[00:34:08] So, right, if you're looking the legal profession or, or whatnot, and maybe that's not a great one ‘cause the models are getting better on that front too, but just something where it's a bit saturated, then the value comes from. Services. It comes from implementation, right? It comes from all these things that actually make it useful to the end customer.[00:34:24] Martin Casado: Sorry, what am I, one more thing I think is, is underused in all of this is like, to what extent every task is a GI complete.[00:34:31] Sarah Wang: Mm-hmm.[00:34:32] Martin Casado: Yeah. I code every day. It's so fun.[00:34:35] Sarah Wang: That's a core question. Yeah.[00:34:36] Martin Casado: And like. When I'm talking to these models, it's not just code. I mean, it's everything, right? Like I, you know, like it's,[00:34:43] swyx: it's healthcare.[00:34:44] It's,[00:34:44] Martin Casado: I mean, it's[00:34:44] swyx: Mele,[00:34:45] Martin Casado: but it's every, it is exactly that. Like, yeah, that's[00:34:47] Sarah Wang: great support. Yeah.[00:34:48] Martin Casado: It's everything. Like I'm asking these models to, yeah, to understand compliance. I'm asking these models to go search the web. I'm asking these models to talk about things I know in the history, like it's having a full conversation with me while I, I engineer, and so it could be [00:35:00] the case that like, mm-hmm.[00:35:01] The most a, you know, a GI complete, like I'm not an a GI guy. Like I think that's, you know, but like the most a GI complete model will is win independent of the task. And we don't know the answer to that one either.[00:35:11] swyx: Yeah.[00:35:12] Martin Casado: But it seems to me that like, listen, codex in my experience is for sure better than Opus 4.5 for coding.[00:35:18] Like it finds the hardest bugs that I work in with. Like, it is, you know. The smartest developers. I don't work on it. It's great. Um, but I think Opus 4.5 is actually very, it's got a great bedside manner and it really, and it, it really matters if you're building something very complex because like, it really, you know, like you're, you're, you're a partner and a brainstorming partner for somebody.[00:35:38] And I think we don't discuss enough how every task kind of has that quality.[00:35:42] swyx: Mm-hmm.[00:35:43] Martin Casado: And what does that mean to like capital investment and like frontier models and Submodels? Yeah.[00:35:47] Why “Coding Models” Keep Collapsing into Generalists (Reasoning vs Taste)[00:35:47] Martin Casado: Like what happened to all the special coding models? Like, none of ‘em worked right. So[00:35:51] Alessio: some of them, they didn't even get released.[00:35:53] Magical[00:35:54] Martin Casado: Devrel. There's a whole, there's a whole host. We saw a bunch of them and like there's this whole theory that like, there could be, and [00:36:00] I think one of the conclusions is, is like there's no such thing as a coding model,[00:36:04] Alessio: you know?[00:36:04] Martin Casado: Like, that's not a thing. Like you're talking to another human being and it's, it's good at coding, but like it's gotta be good at everything.[00:36:10] swyx: Uh, minor disagree only because I, I'm pretty like, have pretty high confidence that basically open eye will always release a GPT five and a GT five codex. Like that's the code's. Yeah. The way I call it is one for raisin, one for Tiz. Um, and, and then like someone internal open, it was like, yeah, that's a good way to frame it.[00:36:32] Martin Casado: That's so funny.[00:36:33] swyx: Uh, but maybe it, maybe it collapses down to reason and that's it. It's not like a hundred dimensions doesn't life. Yeah. It's two dimensions. Yeah, yeah, yeah, yeah. Like and exactly. Beside manner versus coding. Yeah.[00:36:43] Martin Casado: Yeah.[00:36:44] swyx: It's, yeah.[00:36:46] Martin Casado: I, I think for, for any, it's hilarious. For any, for anybody listening to this for, for, for, I mean, for you, like when, when you're like coding or using these models for something like that.[00:36:52] Like actually just like be aware of how much of the interaction has nothing to do with coding and it just turns out to be a large portion of it. And so like, you're, I [00:37:00] think like, like the best Soto ish model. You know, it is going to remain very important no matter what the task is.[00:37:06] swyx: Yeah.[00:37:07] What He's Actually Coding: Gaussian Splats, Spark.js & 3D Scene Rendering Demos[00:37:07] swyx: Uh, speaking of coding, uh, I, I'm gonna be cheeky and ask like, what actually are you coding?[00:37:11] Because obviously you, you could code anything and you are obviously a busy investor and a manager of the good. Giant team. Um, what are you calling?[00:37:18] Martin Casado: I help, um, uh, FEFA at World Labs. Uh, it's one of the investments and um, and they're building a foundation model that creates 3D scenes.[00:37:27] swyx: Yeah, we had it on the pod.[00:37:28] Yeah. Yeah,[00:37:28] Martin Casado: yeah. And so these 3D scenes are Gaussian splats, just by the way that kind of AI works. And so like, you can reconstruct a scene better with, with, with radiance feels than with meshes. ‘cause like they don't really have topology. So, so they, they, they produce each. Beautiful, you know, 3D rendered scenes that are Gaussian splats, but the actual industry support for Gaussian splats isn't great.[00:37:50] It's just never, you know, it's always been meshes and like, things like unreal use meshes. And so I work on a open source library called Spark js, which is a. Uh, [00:38:00] a JavaScript rendering layer ready for Gaussian splats. And it's just because, you know, um, you, you, you need that support and, and right now there's kind of a three js moment that's all meshes and so like, it's become kind of the default in three Js ecosystem.[00:38:13] As part of that to kind of exercise the library, I just build a whole bunch of cool demos. So if you see me on X, you see like all my demos and all the world building, but all of that is just to exercise this, this library that I work on. ‘cause it's actually a very tough algorithmics problem to actually scale a library that much.[00:38:29] And just so you know, this is ancient history now, but 30 years ago I paid for undergrad, you know, working on game engines in college in the late nineties. So I've got actually a back and it's very old background, but I actually have a background in this and so a lot of it's fun. You know, but, but the, the, the, the whole goal is just for this rendering library to, to,[00:38:47] Sarah Wang: are you one of the most active contributors?[00:38:49] The, their GitHub[00:38:50] Martin Casado: spark? Yes.[00:38:51] Sarah Wang: Yeah, yeah.[00:38:51] Martin Casado: There's only two of us there, so, yes. No, so by the way, so the, the pri The pri, yeah. Yeah. So the primary developer is a [00:39:00] guy named Andres Quist, who's an absolute genius. He and I did our, our PhDs together. And so like, um, we studied for constant Quas together. It was almost like hanging out with an old friend, you know?[00:39:09] And so like. So he, he's the core, core guy. I did mostly kind of, you know, the side I run venture fund.[00:39:14] swyx: It's amazing. Like five years ago you would not have done any of this. And it brought you back[00:39:19] Martin Casado: the act, the Activ energy, you're still back. Energy was so high because you had to learn all the framework b******t.[00:39:23] Man, I f*****g used to hate that. And so like, now I don't have to deal with that. I can like focus on the algorithmics so I can focus on the scaling and I,[00:39:29] swyx: yeah. Yeah.[00:39:29] LLMs vs Spatial Intelligence + How to Value World Labs' 3D Foundation Model[00:39:29] swyx: And then, uh, I'll observe one irony and then I'll ask a serious investor question, uh, which is like, the irony is FFE actually doesn't believe that LMS can lead us to spatial intelligence.[00:39:37] And here you are using LMS to like help like achieve spatial intelligence. I just see, I see some like disconnect in there.[00:39:45] Martin Casado: Yeah. Yeah. So I think, I think, you know, I think, I think what she would say is LLMs are great to help with coding.[00:39:51] swyx: Yes.[00:39:51] Martin Casado: But like, that's very different than a model that actually like provides, they, they'll never have the[00:39:56] swyx: spatial inte[00:39:56] Martin Casado: issues.[00:39:56] And listen, our brains clearly listen, our brains, brains clearly have [00:40:00] both our, our brains clearly have a language reasoning section and they clearly have a spatial reasoning section. I mean, it's just, you know, these are two pretty independent problems.[00:40:07] swyx: Okay. And you, you, like, I, I would say that the, the one data point I recently had, uh, against it is the DeepMind, uh, IMO Gold, where, so, uh, typically the, the typical answer is that this is where you start going down the neuros symbolic path, right?[00:40:21] Like one, uh, sort of very sort of abstract reasoning thing and one form, formal thing. Um, and that's what. DeepMind had in 2024 with alpha proof, alpha geometry, and now they just use deep think and just extended thinking tokens. And it's one model and it's, and it's in LM.[00:40:36] Martin Casado: Yeah, yeah, yeah, yeah, yeah.[00:40:37] swyx: And so that, that was my indication of like, maybe you don't need a separate system.[00:40:42] Martin Casado: Yeah. So, so let me step back. I mean, at the end of the day, at the end of the day, these things are like nodes in a graph with weights on them. Right. You know, like it can be modeled like if you, if you distill it down. But let me just talk about the two different substrates. Let's, let me put you in a dark room.[00:40:56] Like totally black room. And then let me just [00:41:00] describe how you exit it. Like to your left, there's a table like duck below this thing, right? I mean like the chances that you're gonna like not run into something are very low. Now let me like turn on the light and you actually see, and you can do distance and you know how far something away is and like where it is or whatever.[00:41:17] Then you can do it, right? Like language is not the right primitives to describe. The universe because it's not exact enough. So that's all Faye, Faye is talking about. When it comes to like spatial reasoning, it's like you actually have to know that this is three feet far, like that far away. It is curved.[00:41:37] You have to understand, you know, the, like the actual movement through space.[00:41:40] swyx: Yeah.[00:41:40] Martin Casado: So I do, I listen, I do think at the end of these models are definitely converging as far as models, but there's, there's, there's different representations of problems you're solving. One is language. Which, you know, that would be like describing to somebody like what to do.[00:41:51] And the other one is actually just showing them and the space reasoning is just showing them.[00:41:55] swyx: Yeah, yeah, yeah. Right. Got it, got it. Uh, the, in the investor question was on, on, well labs [00:42:00] is, well, like, how do I value something like this? What, what, what work does the, do you do? I'm just like, Fefe is awesome.[00:42:07] Justin's awesome. And you know, the other two co-founder, co-founders, but like the, the, the tech, everyone's building cool tech. But like, what's the value of the tech? And this is the fundamental question[00:42:16] Martin Casado: of, well, let, let, just like these, let me just maybe give you a rough sketch on the diffusion models. I actually love to hear Sarah because I'm a venture for, you know, so like, ventures always, always like kind of wild west type[00:42:24] swyx: stuff.[00:42:24] You, you, you, you paid a dream and she has to like, actually[00:42:28] Martin Casado: I'm gonna say I'm gonna mar to reality, so I'm gonna say the venture for you. And she can be like, okay, you a little kid. Yeah. So like, so, so these diffusion models literally. Create something for, for almost nothing. And something that the, the world has found to be very valuable in the past, in our real markets, right?[00:42:45] Like, like a 2D image. I mean, that's been an entire market. People value them. It takes a human being a long time to create it, right? I mean, to create a, you know, a, to turn me into a whatever, like an image would cost a hundred bucks in an hour. The inference cost [00:43:00] us a hundredth of a penny, right? So we've seen this with speech in very successful companies.[00:43:03] We've seen this with 2D image. We've seen this with movies. Right? Now, think about 3D scene. I mean, I mean, when's Grand Theft Auto coming out? It's been six, what? It's been 10 years. I mean, how, how like, but hasn't been 10 years.[00:43:14] Alessio: Yeah.[00:43:15] Martin Casado: How much would it cost to like, to reproduce this room in 3D? Right. If you, if you, if you hired somebody on fiber, like in, in any sort of quality, probably 4,000 to $10,000.[00:43:24] And then if you had a professional, probably $30,000. So if you could generate the exact same thing from a 2D image, and we know that these are used and they're using Unreal and they're using Blend, or they're using movies and they're using video games and they're using all. So if you could do that for.[00:43:36] You know, less than a dollar, that's four or five orders of magnitude cheaper. So you're bringing the marginal cost of something that's useful down by three orders of magnitude, which historically have created very large companies. So that would be like the venture kind of strategic dreaming map.[00:43:49] swyx: Yeah.[00:43:50] And, and for listeners, uh, you can do this yourself on your, on your own phone with like. Uh, the marble.[00:43:55] Martin Casado: Yeah. Marble.[00:43:55] swyx: Uh, or but also there's many Nerf apps where you just go on your iPhone and, and do this.[00:43:59] Martin Casado: Yeah. Yeah. [00:44:00] Yeah. And, and in the case of marble though, it would, what you do is you literally give it in.[00:44:03] So most Nerf apps you like kind of run around and take a whole bunch of pictures and then you kind of reconstruct it.[00:44:08] swyx: Yeah.[00:44:08] Martin Casado: Um, things like marble, just that the whole generative 3D space will just take a 2D image and it'll reconstruct all the like, like[00:44:16] swyx: meaning it has to fill in. Uh,[00:44:18] Martin Casado: stuff at the back of the table, under the table, the back, like, like the images, it doesn't see.[00:44:22] So the generator stuff is very different than reconstruction that it fills in the things that you can't see.[00:44:26] swyx: Yeah. Okay.[00:44:26] Sarah Wang: So,[00:44:27] Martin Casado: all right. So now the,[00:44:28] Sarah Wang: no, no. I mean I love that[00:44:29] Martin Casado: the adult[00:44:29] Sarah Wang: perspective. Um, well, no, I was gonna say these are very much a tag team. So we, we started this pod with that, um, premise. And I think this is a perfect question to even build on that further.[00:44:36] ‘cause it truly is, I mean, we're tag teaming all of these together.[00:44:39] Investing in Model Labs, Media Rumors, and the Cursor Playbook (Margins & Going Down-Stack)[00:44:39] Sarah Wang: Um, but I think every investment fundamentally starts with the same. Maybe the same two premises. One is, at this point in time, we actually believe that there are. And of one founders for their particular craft, and they have to be demonstrated in their prior careers, right?[00:44:56] So, uh, we're not investing in every, you know, now the term is NEO [00:45:00] lab, but every foundation model, uh, any, any company, any founder trying to build a foundation model, we're not, um, contrary to popular opinion, we're

In this episode, Katherine Forrest and Scott Caravello examine one of China's approaches to achieving artificial general intelligence (AGI), drawing on reports from Georgetown's Center for Security and Emerging Technology (CSET). They discuss the country's focus on embodied AI and robotics as a potential path to AGI, multilevel government initiatives supporting this development, a large-scale social simulator project in Wuhan, and significant investments in power grid and data center infrastructure. ## Learn More About Paul, Weiss's Artificial Intelligence practice: https://www.paulweiss.com/industries/artificial-intelligence

What if the loudest stories about the future—AI gods, Mars colonies, digital immortality—aren't science at all, but science fiction masquerading as inevitability? In this episode of The Radical Candor Podcast, Kim Scott and Amy Sandler are joined by science journalist and astrophysicist Adam Becker (PhD in computational cosmology), author of More Everything Forever. Adam breaks down the “big three” myths that dominate Silicon Valley's imagination: space colonization, superintelligent god-like AI, and the singularity. He explains why both the utopian and apocalyptic versions of AI stories often share the same assumption—unimaginable AI power—and why that assumption doesn't match reality. They also explore the deeper pattern underneath these myths: the belief that every problem can be solved with technology (usually computer technology), even when the barriers are political and social—collective action, persuasion, solidarity, and power. Along the way, Adam shares how he stayed sane while writing about “seriously disturbing ideas,” and why reconnecting with the natural world (and real human relationships) is a necessary antidote to screen-mediated life. If you've ever felt overwhelmed by the “AI will save us” vs. “AI will doom us” debate, this conversation offers a clearer, more grounded frame—and a reminder that being human matters. ⁠⁠⁠⁠Website⁠⁠⁠⁠ ⁠⁠⁠⁠Instagram⁠⁠⁠⁠ ⁠⁠⁠⁠TikTok⁠⁠⁠⁠ ⁠⁠⁠⁠LinkedIn⁠⁠⁠⁠ ⁠⁠⁠⁠YouTube⁠ ⁠⁠⁠⁠⁠⁠⁠⁠Bluesky⁠⁠⁠ Resources for show notes: ⁠Adam Becker's website⁠ ⁠More, Everything, Forever book page⁠ ⁠Adam Becker on Star Talk podcast⁠ ⁠Dave Troy presents: Understanding TESCREAL with Dr. Timnit Gebru and Émile Torres⁠ ⁠Why Silicon Valley's Most Powerful People Are So Obsessed With Hobbits⁠ Referenced in conversation: Blade Runner (as an example of dystopian sci-fi being misunderstood) Star Wars / Jabba the Hutt (as an example of misreading stories) Lord of the Rings / Palantír (as a cautionary reference) Jurassic Park (“they didn't stop to consider whether they should”) Public libraries (as a civic good worth supporting) Chapters: (00:00) Introduction Kim and Amy welcome Adam Becker to unpack Silicon Valley's stories about the future. (06:06) The Myths Driving Tech Ideology Space colonization, superintelligent AI, and the singularity—and why they don't hold up. (11:52) When Sci-Fi Turns into Strategy How dystopian stories get misread as roadmaps (Palantir, “Torment Nexus,” and more). (15:06) More Everything Forever Why endless expansion feels inevitable in tech—and why Adam argues it's flawed. (21:24) “Can” vs. “Should” Why tech leaders dodge both questions—and what that reveals about power. (23:19) You Can't Escape Politics by Going to Space Why “Mars as a reset button” is a fantasy—and politics follows humans everywhere. (33:22) AI Doom vs. AI Utopia Why both narratives rely on the same shaky assumption about “AGI.” (37:21) Solidarity as a Counterbalance Why labor organizing matters when leadership values diverge from workers' values. (41:02) “AGI Will Fix Climate” Why betting on future AI while burning more energy now is a dangerous logic trap. (01:03:50) Conclusion Learn more about your ad choices. Visit megaphone.fm/adchoices

A common model of AI suggests that there is a single measure of intelligence, often called AGI, and that AI systems are agents who can possess more or less of this intelligence. Cognitive science, in contrast, suggests that there are multiple forms of intelligence and that these intelligences trade-off against each other and have a distinctive developmental profile and evolutionary history. Exploitation, the pursuit of goals, resources and utilities, is characteristic of adult cognition. Alison Gopnik, professor of psychology and affiliate professor of philosophy at the UC Berkeley, and a member of the Berkeley AI Research Group, argues that two very different kinds of cognition characterize childhood and elderhood. Childhood is characterized by exploration. In particular, children seek out information about the world. However, forgoing reward for exploration requires support, care and teaching from others. Care and teaching are particularly characteristic of elders and the intelligence of care has a distinctive structure – it involves empowering others – giving them the resources they need to be effective. The combination of these different kinds of intelligence across the course of a life explains human success. Series: "UC Berkeley Graduate Lectures" [Humanities] [Science] [Show ID: 41068]

A common model of AI suggests that there is a single measure of intelligence, often called AGI, and that AI systems are agents who can possess more or less of this intelligence. Cognitive science, in contrast, suggests that there are multiple forms of intelligence and that these intelligences trade-off against each other and have a distinctive developmental profile and evolutionary history. Exploitation, the pursuit of goals, resources and utilities, is characteristic of adult cognition. Alison Gopnik, professor of psychology and affiliate professor of philosophy at the UC Berkeley, and a member of the Berkeley AI Research Group, argues that two very different kinds of cognition characterize childhood and elderhood. Childhood is characterized by exploration. In particular, children seek out information about the world. However, forgoing reward for exploration requires support, care and teaching from others. Care and teaching are particularly characteristic of elders and the intelligence of care has a distinctive structure – it involves empowering others – giving them the resources they need to be effective. The combination of these different kinds of intelligence across the course of a life explains human success. Series: "UC Berkeley Graduate Lectures" [Humanities] [Science] [Show ID: 41068]

A common model of AI suggests that there is a single measure of intelligence, often called AGI, and that AI systems are agents who can possess more or less of this intelligence. Cognitive science, in contrast, suggests that there are multiple forms of intelligence and that these intelligences trade-off against each other and have a distinctive developmental profile and evolutionary history. Exploitation, the pursuit of goals, resources and utilities, is characteristic of adult cognition. Alison Gopnik, professor of psychology and affiliate professor of philosophy at the UC Berkeley, and a member of the Berkeley AI Research Group, argues that two very different kinds of cognition characterize childhood and elderhood. Childhood is characterized by exploration. In particular, children seek out information about the world. However, forgoing reward for exploration requires support, care and teaching from others. Care and teaching are particularly characteristic of elders and the intelligence of care has a distinctive structure – it involves empowering others – giving them the resources they need to be effective. The combination of these different kinds of intelligence across the course of a life explains human success. Series: "UC Berkeley Graduate Lectures" [Humanities] [Science] [Show ID: 41068]

A common model of AI suggests that there is a single measure of intelligence, often called AGI, and that AI systems are agents who can possess more or less of this intelligence. Cognitive science, in contrast, suggests that there are multiple forms of intelligence and that these intelligences trade-off against each other and have a distinctive developmental profile and evolutionary history. Exploitation, the pursuit of goals, resources and utilities, is characteristic of adult cognition. Alison Gopnik, professor of psychology and affiliate professor of philosophy at the UC Berkeley, and a member of the Berkeley AI Research Group, argues that two very different kinds of cognition characterize childhood and elderhood. Childhood is characterized by exploration. In particular, children seek out information about the world. However, forgoing reward for exploration requires support, care and teaching from others. Care and teaching are particularly characteristic of elders and the intelligence of care has a distinctive structure – it involves empowering others – giving them the resources they need to be effective. The combination of these different kinds of intelligence across the course of a life explains human success. Series: "UC Berkeley Graduate Lectures" [Humanities] [Science] [Show ID: 41068]

A common model of AI suggests that there is a single measure of intelligence, often called AGI, and that AI systems are agents who can possess more or less of this intelligence. Cognitive science, in contrast, suggests that there are multiple forms of intelligence and that these intelligences trade-off against each other and have a distinctive developmental profile and evolutionary history. Exploitation, the pursuit of goals, resources and utilities, is characteristic of adult cognition. Alison Gopnik, professor of psychology and affiliate professor of philosophy at the UC Berkeley, and a member of the Berkeley AI Research Group, argues that two very different kinds of cognition characterize childhood and elderhood. Childhood is characterized by exploration. In particular, children seek out information about the world. However, forgoing reward for exploration requires support, care and teaching from others. Care and teaching are particularly characteristic of elders and the intelligence of care has a distinctive structure – it involves empowering others – giving them the resources they need to be effective. The combination of these different kinds of intelligence across the course of a life explains human success. Series: "UC Berkeley Graduate Lectures" [Humanities] [Science] [Show ID: 41068]

A common model of AI suggests that there is a single measure of intelligence, often called AGI, and that AI systems are agents who can possess more or less of this intelligence. Cognitive science, in contrast, suggests that there are multiple forms of intelligence and that these intelligences trade-off against each other and have a distinctive developmental profile and evolutionary history. Exploitation, the pursuit of goals, resources and utilities, is characteristic of adult cognition. Alison Gopnik, professor of psychology and affiliate professor of philosophy at the UC Berkeley, and a member of the Berkeley AI Research Group, argues that two very different kinds of cognition characterize childhood and elderhood. Childhood is characterized by exploration. In particular, children seek out information about the world. However, forgoing reward for exploration requires support, care and teaching from others. Care and teaching are particularly characteristic of elders and the intelligence of care has a distinctive structure – it involves empowering others – giving them the resources they need to be effective. The combination of these different kinds of intelligence across the course of a life explains human success. Series: "UC Berkeley Graduate Lectures" [Humanities] [Science] [Show ID: 41068]

Get our AI Video Guide: https://clickhubspot.com/dth Episode 97: How close are we to a world where AI-generated videos are indistinguishable from reality? Matt Wolfe (https://x.com/mreflow) and Joe Fier (linkedin.com/in/joefier) dive deep into Seedance 2.0—ByteDance's new AI video model that could outpace giants like Sora and Veo. Joe, a marketing and business expert known for his hands-on approach and insights into AI's rapid evolution, helps to break down the five most fascinating developments in the AI space this week. They tackles game-changing AI advances: Seedance 2.0's mind-blowing video generation for ads and motion graphics, the rollout of Google's Veo 3.1 in Google Ads, the GPT-5.3 Codex Spark coding model built on specialized inference chips, Gemini's DeepThink model for scientific research, and the early rollout of ChatGPT ads. Check out The Next Wave YouTube Channel if you want to see Matt and Nathan on screen: https://lnk.to/thenextwavepd — Show Notes: (00:00) Seedance 2.0 arrives – AI video generation blurs reality, ad creation moves fast. (03:03) Google's Veo 3.1 powers video ads, advertisers can now generate clips directly from image uploads. (05:33) Comparison of Runway, Kling, Veo, and Sora—head-to-head prompt showdown. (07:00) Motion graphics and explainers—AI's take on the creative industry. (08:35) US vs. China—Copyright, IP, and training data debates. (12:10) Deepfake and video authenticity—why we now default to skepticism. (13:30) Google's edge in visual AI via YouTube's massive corpus. (14:39) The next frontier: Longer, more consistent video generation. (15:14) Where do humans fit in? Taste, storytelling, and creative direction. (18:30) GPT-5.3 Codex Spark—coding models on Cerebras inference chips, demo generating a website in 18 seconds. (24:34) AI tool comparisons—Codex vs. Cursor vs. Claude Code. (25:12) Speed as the key bottleneck breaker in creative and technical workflows. (28:02) Google's Gemini DeepThink—state-of-the-art research, advanced coding and physics capabilities. (32:52) Gemini demo attempt—3D-printable STL file and solving the three-body problem. (33:20) ChatGPT rolls out ads—impact on monetization and user trust. (40:02) Google's ad history—how “sponsored” is becoming harder to distinguish. (44:02) Democratizing AI access via ad-supported models. (45:03) Matt Schumer's viral article—why AI is moving even faster than most people realize. (51:11) Tools that build tools—AGI's path and the new role for humans. (53:12) Real-world skills and taste—where humanity still wins (for now). (54:01) Final thoughts—wake up, pay attention, and stay on the leading edge. — Mentions: Seedance 2.0: https://www.seedance.com/ ByteDance: https://www.bytedance.com/ CapCut: https://www.capcut.com/ Veo: https://deepmind.google/models/veo/ Runway: https://runwayml.com/ ChatGPT Codex: https://chatgpt.com/codex Matt Schumer's Viral Article: https://www.mattshumer.com/blog/ai-changes-everything Super Bowl Claude Commercial: https://www.anthropic.com/news/super-bowl-ad Get the guide to build your own Custom GPT: https://clickhubspot.com/tnw — Check Out Matt's Stuff: • Future Tools - https://futuretools.beehiiv.com/ • Blog - https://www.mattwolfe.com/ • YouTube- https://www.youtube.com/@mreflow — Check Out Nathan's Stuff: Newsletter: https://news.lore.com/ Blog - https://lore.com/ The Next Wave is a HubSpot Original Podcast // Brought to you by Hubspot Media // Production by Darren Clarke // Editing by Ezra Bakker Trupiano

Taxes. Filing status. Adjusted gross income. Capital gains. Just reading those words can make you want to close the tab and deal with it “later.” But here's the truth: taxes change after widowhood. Sometimes in ways no one warns you about. Filing status shifts. Income is calculated differently. Survivor benefits can become taxable. And all of it is happening while you're navigating one of the hardest transitions of your life. In this episode, Stacy Francis and Allen Sakkon walk through what really happens to your tax situation after the loss of a spouse - in plain language - so you can feel more confident, ask better questions, and avoid costly surprises. You'll hear them discuss: How your filing status works in the year your spouse passes, and what changes in the years that follow (including qualifying surviving spouse and head of household) Why your adjusted gross income (AGI) is such a powerful number and how it affects Social Security taxation, Medicare premiums, and eligibility for credits and deductions When and why Social Security survivor benefits become partially taxable and how timing major financial decisions can help What cost basis means, how the step-up in basis works at death, and why it can dramatically reduce capital gains taxes on a home or investment account How selling a house or investments in the wrong year can unexpectedly spike your income and how to think strategically about timing The most commonly missed deductions after a spouse's death, including medical expenses, property taxes, mortgage interest, charitable gifts, and capital loss carry-forwards One simple habit - tracking your income deposits —-that can help you regain control and make your tax return far less intimidating Resources Allen Sakon on LinkedIn⁠ | Email Stacy Francis on LinkedIn | X(Twitter) | Email FrancisFinancial.com Reach out to receive a complimentary consultation! Contact Francis Financial at +212-374-9008 or visit Francis Financial today!

This episode is sponsored by tastytrade.  Trade stocks, options, futures, and crypto in one platform with low commissions and zero commission on stocks and crypto. Built for traders who think in probabilities, tastytrade offers advanced analytics, risk tools, and an AI-powered Search feature.   Learn more at https://tastytrade.com/ In this episode of Eye on AI, Nick Frosst, Co-Founder of Cohere and former Google Brain researcher, explains why Cohere is betting on enterprise AI instead of chasing AGI.   While much of the AI industry is focused on artificial general intelligence, Cohere is building practical, capital-efficient large language models designed for real-world enterprise deployment. Nick breaks down why scaling transformers does not equal AGI, why inference cost and ROI matter, and how enterprise AI differs from consumer AI hype.   We discuss enterprise LLM deployment, private data, regulated industries like banking and healthcare, agentic systems, evaluation benchmarks, and why AI will likely become embedded infrastructure rather than a headline breakthrough.   If you care about enterprise AI, AGI debates, large language models, and the future of AI in business, this conversation delivers a grounded perspective from inside one of the leading AI companies.   Stay Updated: Craig Smith on X: https://x.com/craigss Eye on A.I. on X: https://x.com/EyeOn_AI   (00:00) From Google Brain to Cohere (03:54) Discovering Transformers (06:39) The Transformer Dominance (09:44) What AGI Actually Means (12:26) Planes vs Birds: The AI Analogy (14:08) Why Cohere Isn't Chasing AGI (18:38) Distillation & Model Efficiency (21:42) What Enterprise AI Really Does (25:20) Private Data & Secure Deployment (26:59) Enterprise Use Cases (RBC Example) (32:22) Why AI Benchmarks Mislead (34:55) Why Most AI Stays in Demo (38:23) What "Agents" Actually Are (43:32) The Problem With AGI Fear (49:15) Scaling Enterprise AI (53:24) Why AI Will Get "Boring"  

The crew unpacks BlackRock buying UNI, ARK, Citadel, DTCC, the Intercontinental Exchange and other TradFi players backing Zero, , Vitalik's thoughts on AI, and more.  Thank you to our sponsors! Fuse: The Energy Network MultiChain Advisors Crypto Tax Girl AI safety chiefs are leaving, BlackRock's launching on Uniswap and buying UNI, LayerZero launches “the last blockchain” with institutional backing, Kaito is launching attention markets, Base is abandoning social and Vitalik has some thoughts on AI. Hosts Kain Warwick, Luca Netz and Taylor Monahan unpack these and more in yet another packed episode of Uneasy Money. Find out why Kain thinks the Uniswap and LayerZero news point to a new meta reminiscent of DeFi Summer. Plus, is Coinbase's Base playing it too safe? And is Vitalik fighting a losing battle? Hosts: Luca Netz, CEO of Pudgy Penguins Kain Warwick, Founder of Infinex and Synthetix Taylor Monahan, Security at MetaMask Links: Unchained: ⁠LayerZero Launches ‘Zero' Layer 1 as Citadel, ARK Buy ZRO⁠ ⁠How Zero Blockchain Cracked 2 Million TPS and Is Still Decentralized⁠ ⁠Vitalik Buterin Pushes Back on the ‘Race to AGI,' Outlines Ethereum-Led AI Path⁠ ⁠When AI Agents Take Over, What Does a Post-Human Economy Look Like?⁠ ⁠Uneasy Money: How the Increasingly Better AI Agents Are Being Used Onchain⁠ ⁠Uneasy Money: Why Crypto Still Can't Overcome Its ICO Struggles Learn more about your ad choices. Visit megaphone.fm/adchoices

Why do so many outwardly successful people still feel deeply unfulfilled inside?If you're navigating a midlife transition and questioning your purpose, identity, or the real meaning behind all your achievements, this episode offers insight into why external success alone often leaves a void, and what you can do to fill it with lasting joy and meaning.Discover the core reason why chasing success can leave you feeling empty and how to shift toward internal fulfillment.Learn the Joy Molecule framework and how it helps realign your life around purpose, identity, and deeper human connection.Gain practical tools to cultivate awareness, release emotional patterns, and begin your own journey toward inner peace and significance.Listen now to explore a powerful conversation that could change the way you understand your life's direction and reconnect you to what truly matters.˚KEY POINTS AND TIMESTAMPS:01:47 - Introducing Larry Keslin and the joy molecule03:45 - Why successful people are unhappy06:55 - Identity, awareness, and the sky–clouds analogy08:01 - Giving of yourself and true connection11:09 - Owning emotions and conscious relationships17:42 - Awareness, triggers, and emotional responsibility22:56 - Africa, poverty, and redefining success26:56 - Joy beyond consumption and conscious living29:51 - Practical steps to increase awareness and peace˚VALUABLE RESOURCES:Larry's website: https://5-dots.com/˚Coaching with Agi: https://personaldevelopmentmasterypodcast.com/mentor˚

I file Epstein hanno scandalizzato il mondo intero ma come vengono raccontati? Su quali aspetti pongono maggiore enfasi i media russi, cinesi o indiani? Ne parliamo con Maziyar Ghiabi, direttore del Centro studi sull'Iran all'Università di Exeter, Zeno Leoni, professore di Relazioni internazionali al King's College di Londra, Diego Maiorano, professore di Storia Contemporanea dell'India all'Università Orientale di Napoli, e con Marta Allevato, giornalista Agi e co-curatrice della newsletter "Russia e altrove".

Cross-posted to LessWrong.Summary History's most destructive ideologies—like Nazism, totalitarian communism, and religious fundamentalism—exhibited remarkably similar characteristics: epistemic and moral certainty extreme tribalism dividing humanity into a sacred “us” and an evil “them” a willingness to use whatever means necessary, including brutal violence. Such ideological fanaticism was a major driver of eight of the ten greatest atrocities since 1800, including the Taiping Rebellion, World War II, and the regimes of Stalin, Mao, and Hitler. We focus on ideological fanaticism over related concepts like totalitarianism partly because it better captures terminal preferences, which plausibly matter most as we approach superintelligent AI and technological maturity. Ideological fanaticism is considerably less influential than in the past, controlling only a small fraction of world GDP. Yet at least hundreds of millions still hold fanatical views, many regimes exhibit concerning ideological tendencies, and the past two decades have seen widespread democratic backsliding. The long-term influence of ideological fanaticism is uncertain. Fanaticism faces many disadvantages including a weak starting position, poor epistemics, and difficulty assembling broad coalitions. But it benefits from greater willingness to use extreme measures, fervent mass followings, and a historical tendency to survive and even thrive amid technological and societal upheaval. Beyond complete victory or defeat, multipolarity may [...] ---Outline:(00:16) Summary(05:19) What do we mean by ideological fanaticism?(08:40) I. Dogmatic certainty: epistemic and moral lock-in(10:02) II. Manichean tribalism: total devotion to us, total hatred for them(12:42) III. Unconstrained violence: any means necessary(14:33) Fanaticism as a multidimensional continuum(16:09) Ideological fanaticism drove most of recent historys worst atrocities(19:24) Death tolls dont capture all harm(20:55) Intentional versus natural or accidental harm(22:44) Why emphasize ideological fanaticism over political systems like totalitarianism?(25:07) Fanatical and totalitarian regimes have caused far more harm than all other regime types(26:29) Authoritarianism as a risk factor(27:19) Values change political systems: Ideological fanatics seek totalitarianism, not democracy(29:50) Terminal values may matter independently of political systems, especially with AGI(31:02) Fanaticisms connection to malevolence (dark personality traits)(34:22) The current influence of ideological fanaticism(34:42) Historical perspective: it was much worse, but we are sliding back(37:19) Estimating the global scale of ideological fanaticism(43:57) State actors(48:12) How much influence will ideological fanaticism have in the long-term future?(48:57) Reasons for optimism: Why ideological fanaticism will likely lose(49:45) A worse starting point and historical track record(50:33) Fanatics intolerance results in coalitional disadvantages(51:53) The epistemic penalty of irrational dogmatism(54:21) The marketplace of ideas and human preferences(55:57) Reasons for pessimism: Why ideological fanatics may gain power(56:04) The fragility of democratic leadership in AI(56:37) Fanatical actors may grab power via coups or revolutions(59:36) Fanatics have fewer moral constraints(01:01:13) Fanatics prioritize destructive capabilities(01:02:13) Some ideologies with fanatical elements have been remarkably resilient and successful(01:03:01) Novel fanatical ideologies could emerge--or existing ones could mutate(01:05:08) Fanatics may have longer time horizons, greater scope-sensitivity, and prioritize growth more(01:07:15) A possible middle ground: Persistent multipolar worlds(01:08:33) Why multipolar futures seem plausible(01:10:00) Why multipolar worlds might persist indefinitely(01:15:42) Ideological fanaticism increases existential and suffering risks(01:17:09) Ideological fanaticism increases the risk of war and conflict(01:17:44) Reasons for war and ideological fanaticism(01:26:27) Fanatical ideologies are non-democratic, which increases the risk of war(01:27:00) These risks are both time-sensitive and timeless(01:27:44) Fanatical retributivism may lead to astronomical suffering(01:29:50) Empirical evidence: how many people endorse eternal extreme punishment?(01:33:53) Religious fanatical retributivism(01:40:45) Secular fanatical retributivism(01:41:43) Ideological fanaticism could undermine long-reflection-style frameworks and AI alignment(01:42:33) Ideological fanaticism threatens collective moral deliberation(01:47:35) AI alignment may not solve the fanaticism problem either(01:53:33) Prevalence of reality-denying, anti-pluralistic, and punitive worldviews(01:55:44) Ideological fanaticism could worsen many other risks(01:55:49) Differential intellectual regress(01:56:51) Ideological fanaticism may give rise to extreme optimization and insatiable moral desires(01:59:21) Apocalyptic terrorism(02:00:05) S-risk-conducive propensities and reverse cooperative intelligence(02:01:28) More speculative dynamics: purity spirals and self-inflicted suffering(02:03:00) Unknown unknowns and navigating exotic scenarios(02:03:43) Interventions(02:05:31) Societal or political interventions(02:05:51) Safeguarding democracy(02:06:40) Reducing political polarization(02:10:26) Promoting anti-fanatical values: classical liberalism and Enlightenment principles(02:13:55) Growing the influence of liberal democracies(02:15:54) Encouraging reform in illiberal countries(02:16:51) Promoting international cooperation(02:22:36) Artificial intelligence-related interventions(02:22:41) Reducing the chance that transformative AI falls into the hands of fanatics(02:27:58) Making transformative AIs themselves less likely to be fanatical(02:36:14) Using AI to improve epistemics and deliberation(02:38:13) Fanaticism-resistant post-AGI governance(02:39:51) Addressing deeper causes of ideological fanaticism(02:41:26) Supplementary materials(02:41:39) Acknowledgments(02:42:22) References --- First published: February 12th, 2026 Source: https://forum.effectivealtruism.org/posts/EDBQPT65XJsgszwmL/long-term-risks-from-ideological-fanaticism --- Narrated by TYPE III AUDIO. ---Images from the article:Apple Podcasts and Spotify do not show images in the episode description. Try Pocket Casts, or another podcast app.

AJ Meyer, CEO of Pickle Robot, isn't betting on general-purpose humanoid robots. Instead, he's a believer in robots and Physical AI which solve specific, high-volume problems. AJ joins Sam and Asad to reveal how he recently secured a nine-figure enterprise contract and why "boring" logistics tasks are the gateway to mass adoption of robots. But with mass adoption's opportunities, so too are there new risks. AJ shares that while physical safety is an important consideration, the cybersecurity risk of a networked robot workforce is what needs the most attention right now. This and a ton more in this week's episode of Topline with Sam Jacobs (CEO @ Pavilion) and Asad Zaman (CEO @ Sales Talent Agency). Thanks for tuning in! Catch new episodes every Sunday Subscribe to Topline Newsletter. Tune into Topline Podcast, the #1 podcast for founders, operators, and investors in B2B tech. Join the free Topline Slack channel to connect with 600+ revenue leaders to keep the conversation going beyond the podcast! Chapters: 00:00 Teaser and Introduction to AJ Meyer 02:53 The Convergence of Physical and Digital AI 05:50 Safety Constraints and the "Acrobat" Robot Disaster 09:19 Mobile Manipulation vs. General Purpose Humanoids 12:47 Cybersecurity Risks in Connected Robot Networks 18:52 AI Surveillance and Authoritarian Risks 28:01 Debunking the Myth of Unskilled Labor 34:54 The Moving Goalposts of AGI 38:19 Solving the Open World Generalization Problem 42:09 Why Foundation Models Need Systems Engineering 48:23 Designing Business Models for Enterprise and Mid-Market 53:20 The Nine-Figure "ChatGPT Moment" for Robotics 58:14 Transferring SaaS Go-To-Market Skills to Hardware 01:03:45 Taste and Judgment as Career Differentiators 01:07:50 Hiring Needs and Closing Thoughts

Joshua and Amelie break down the basics of tax deductions and tax credits, offering a simple, high‑level overview to help listeners understand how these tools may reduce their tax bill. Top takeaways: Tax deductions and tax credits both help you pay less in taxes, but they work differently Tax credits offer a dollar‑for‑dollar reduction in your taxes owed— the government treats you as if you paid that amount in taxes. Tax deductions reduce the amount of income the IRS considers taxable. The value of a deduction depends on your tax bracket; higher‑income earners generally benefit more because of progressive tax rates In some cases, you can choose between claiming something as a tax credit or a tax deduction When comparing “above the line” and “below the line” deductions, the “line” refers to your Adjusted Gross Income (AGI). Above the line deductions (adjustments to income) reduce your AGI, which can affect eligibility for certain benefits and credits. Common above the line deductions include retirement contributions (like 401(k)s) and legitimate business expenses Below the line deductions are taken as either the standard deduction or itemized deductions (such as charitable giving or state taxes) MAGI (Modified AGI) is used throughout the tax code, but calculating it can be complex and varies by program — tax professionals handle this best A tax professional can sometimes help you legally shift deductions from below the line to above the line Nearly everyone can benefit from working with a qualified tax professional Many valuable tax credits exist for people with low income, but they're often missed when taxes aren't filed or are filed incorrectly The Earned Income Tax Credit (EITC) is widely under‑claimed; most eligible households never receive it. For families with children, the EITC can average around $3,000 — far more than the cost of basic tax preparation. Check out AICPA's Guide on How to Choose a CPA Look for tax professionals with one of these credentials: Certified Public Accountant (CPA), Enrolled Agent (EA), or Tax Attorney

This week: AI Bots' Social Network: Moltbook platform allows AI agents to interact with each other while humans can only observe, raising questions about whether artificial general intelligence (AGI) is emerging as bots create their own theology, dating profiles, and secret communication systems.NYT Reverses Marijuana Stance: The New York Times published an article admitting many of its earlier predictions about marijuana legalization were wrong, acknowledging unexpected negative consequences. AI Revolution in Jobs: New research explores how artificial intelligence may fundamentally transform the labor market and whether America is prepared for widespread workforce disruption. Science of Happiness Revealed: Recent studies identify key factors that contribute to human happiness and well-being. Listener Question: When Kids Leave Faith: Listener question addresses how parents can respond when their college-age child rejects Christianity due to evolution and science. ==========Think Biblically: Conversations on Faith and Culture is a podcast from Talbot School of Theology at Biola University, which offers degrees both online and on campus in Southern California. Find all episodes of Think Biblically at: https://www.biola.edu/think-biblically. To submit comments, ask questions, or make suggestions on issues you'd like us to cover or guests you'd like us to have on the podcast, email us at thinkbiblically@biola.edu.

“How do you tend to respond when you do not know?” We had this question in our Journal Circle a couple of weeks ago. It’s at the heart of many issues in our world right now. How do we hold it?When do we conceal it?Where do we turn for knowledge?And what do we do with it when we acquire it? That’s what we explore in this episode of The Gentle Rebel Podcast. https://youtu.be/QRAS1dib_GM Our Relationship With Not Knowing I find this advert baffling. A couple are wandering around the Leeum Museum in South Korea. They didn't know it was big; they only gave themselves an hour. He thinks a roof tile is a book. Even when his phone corrects him, they skip off giggling without listening to the information. It reminds me of a billboard from the AI company Turing that says the quiet part out loud: “We teach AGI to think, reason, and code—so you don’t have to.” Are we being encouraged to outsource our thinking and reasoning, not to support and deepen our cognitive abilities, but to replace them? Are they saying we don’t have to think or reason anymore? Even if that’s not the intention, it’s certainly the outcome of using many tools like this. There seems to be a disregard for the sacred delight of human consciousness, thought processes, and creativity. And a subtle quest to eliminate mystery, curiosity, and the learning that comes from not knowing. Yet not knowing has always been central to human potential. It is the driving force of creativity, innovation, and deeper connection to the worlds within, around, and between us. Open and Closed Stances As people reflected in our Journal Circle, a thread emerged: openness vs closedness. Closed not-knowing: defensive, protective, secretive. Open not-knowing: curious, relational, exploratory. Closedness can feel tight. Clenched. Like rushing to paint over the threat of embarrassment or being found out. Openness can feel spacious. Physically expansive, deeper, and less pressured. Where the uncertainty is met with an invitation into possibility and curiosity rather than grasping, clinging, and defensiveness. We explore several ways this plays out in everyday life. Pretending To Know One response to not knowing is pretending to know. We’ve probably all done it. Nodding along when everyone else seems to understand. Staying quiet because asking a question feels risky. Research in 2007 found that children aged 14 months to five years ask an average of 107 questions per hour. By the time they reach late primary school, many stop asking questions altogether. In the episode, I share an anecdote from research led by Susan Engel, where a ninth grader is stopped mid-question with the instruction: “No questions now, please; it's time for learning.” Within institutional settings, our natural curiosity and creativity can be left behind, and if questions are deemed disruptive or inappropriate, we may simply pretend to know and struggle quietly. This is especially true for many more introverted and sensitive people, who are already generally disposed to slot in around others without drawing much attention to themselves. Child-like Curiosity A child doesn’t see their lack of knowledge as a reason to be ashamed. It’s underpinned by the electric buzz of connection. Everything is new, mysterious, and waiting to be explored. For an adult moving through and out of a rigid system, not knowing can feel like an exposing story in which their worth as a human is assessed. Pretending to know can become an adaptive strategy. A way to keep the peace. A way to belong. There's also the technological version, prominent in many AI tools people rely on for accurate information. These systems are designed to always produce an answer, even when they are wrong. This reflects the kind of closed pretending that aims to foster a perception of expertise, so those listening believe that the source’s confidence equates to competence. But pretending doesn't only come from intentional deception. It can stem from stories we absorb, linking knowledge with worth: “I must know in order to be useful.”“I must be useful in order to be accepted.” Letting go of that story can be liberating. Saying “I Don't Know” “I don't know” is an option. A surprisingly radical one. When it is open, it creates space to explore our unknowing. An open “don’t know” admits not knowing with hands turned towards learning and discovery. It might come with an inner spark and the freedom from performance. A closed “I don't know” shuts things down. It can signal indifference or defensiveness. Sometimes that boundary is healthy. Sometimes it is armour. Being “In The Know” There is also the social currency of being “in the know.” Trends. News. Other people's business. Ignorance can feel like bliss. It can also feel like exclusion. From a closed place, being in the know becomes about control. From an open place, it can become a source of connection. The ability to link ideas, introduce people, and catalyse collaboration. Knowing What's Best Another response to uncertainty is doubling down on certainty. We are pattern-seeking creatures. We build cognitive maps to navigate a complex world. But when ambiguity feels overwhelming, certainty can feel like solid ground, even if it's forged, manufactured, and brittle. Closedness says “this is how it is”, refuses nuance, and punishes curiosity and accountability as disrespect, insolence, and rudeness. Open wisdom looks different. It sits shoulder to shoulder, acknowledges nuance, and is willing to say, “I don't know the best thing to do here.” Admitting one does not know can be a radical act in cultures that equate doubt with weakness and desperately seek a way to explain and understand everything, even without empirical evidence. Knowing That We Don't Know In a 1933 essay lamenting the rise of the Nazi movement in Germany, Bertrand Russell wrote, “The fundamental cause of the trouble is that in the modern world the stupid are cocksure, while the intelligent are full of doubt.” Charles Bukowski said something similar when giving advice to budding writers: “But the problem is that bad writers tend to have the self-confidence, while the good ones tend to have self-doubt.“ These quotes highlight the importance of knowing what we do not know — and recognising the limits of our own perspective. This took us to a detour into the Dunning–Kruger effect, which is the idea that we can speak confidently about subjects precisely because we don't yet know what we don't know. Reading Maps and Navigating Life “I don't know, but I am aware of where to look to figure it out.” In The Return To Serenity Island course, we map elements of life, seeing it as a treasure laden island. Not knowing is a door to connection, curiosity, creativity, and exploration. But it can also feel disorienting, confusing, and alienating at times. Maps help disorientation become orientation-in-progress without strict instructions or someone else’s path to follow. They can bring us home to ourselves.

In this episode of the ChinaPower Podcast, William Hannas and Hugh Grant‑Chapman join us to discuss key findings from Bill's new report, China's Embodied AI: A Path to AGI, as well as the CSIS report Is China Leading the Robotics Revolution? They examine why Chinese researchers view embodied AI as a critical pathway toward advanced intelligence, how Beijing is prioritizing robotics and physical‑world AI applications, and what lessons China's AI strategy may offer for other countries. William Hannas is the Lead Analyst at Georgetown University's Center for Security and Emerging Technology, or CSET. Before joining CSET, he served in the Senior Intelligence Service at the Central Intelligence Agency, where he worked as an executive expert on advanced technical programs. Hugh Grant-Chapman is a fellow with the Economics Program and Scholl Chair in International Business at the Center for Strategic and International Studies.

Artificial intelligence is transforming everything from writing and research to medicine and productivity — or at least it appears to be doing so. But are we gaining only illusory efficiency at the cost of something deeper and more long-term? Are anti-market forces and government and industry gaslighting steering capital to the wrong uses of AI based on the assumption that we will achieve “general intelligence”? What responsibility do we have as humans to make sure we approach available LLMs in a way that won't supplant human cognition? In this thought-provoking conversation, I sit down with leading innovation theorist John Nosta, author of "The Borrowed Mind: Reclaiming Human Thought in the Age of AI," to explore one of the most important questions of our time: Are we using AI as a tool to augment human thought, or are we slowly outsourcing our thinking to it? From "frictionless intelligence" being a trap and the myth of AGI to the danger of "cognitive obsolescence," Nosta reveals why the struggle to think is a feature, not a bug, of humanity. Learn how to reclaim your agency and use technology as a tool — without becoming a tool yourself. Learn more about your ad choices. Visit megaphone.fm/adchoices

In this episode of Decouple, Chris sits down with Kyle Chan of the High Capacity Substack to unpack what “AI with Chinese characteristics” actually means. Rather than framing artificial intelligence as a simple US–China race to AGI, they explore how each country is building AI inside very different institutional systems. The conversation covers DeepSeek, compute constraints, quantization, and the surprising reality that many Chinese AI labs operate with far less capital than their American counterparts while still publishing at the frontier.They dig into China's AI enabling stack, from universities and state-backed labs to energy buildout and the Western Data, Eastern Compute strategy, and examine how AI is being embedded into manufacturing, logistics, grid management, and public services as a tool of state capacity. The discussion also tackles regulatory differences, CCP oversight, training data controls, and the disciplining of China's tech sector, alongside contrasts with US AI development shaped by venture capital, platform economics, and liability management. This is a deep dive into how institutions shape technology, and why the real story may not be who wins the race, but how AI is absorbed into two very different political economies.Listen to Decouple on:• Spotify: https://open.spotify.com/show/6PNr3ml8nEQotWWavE9kQz• Apple Podcasts: https://podcasts.apple.com/us/podcast/decouple/id1516526694?uo=4• Overcast: https://overcast.fm/itunes1516526694/decouple• Pocket Casts: https://pca.st/ehbfrn44• RSS: https://anchor.fm/s/23775178/podcast/rssWebsite: https://www.decouple.media

If you're a driven, capable professional facing a major life or career transition, chances are you're not held back by fear, but by subtle traps that keep you circling the same questions for years. This episode uncovers why your usual tools for problem-solving may actually be keeping you stuck.Listen now to uncover the three most common traps that keep high-functioning professionals stuck in midlife transitions, and how to move beyond them with clarity and purpose.˚Coaching with Agi: https://personaldevelopmentmasterypodcast.com/mentor˚Support the showCareer transition and career clarity podcast content for midlife professionals in career transition, navigating a career change, career pivot or second career, starting a new venture or leaving a long-term career. Discover practical tools for career clarity, confident decision-making, rebuilding self belief and confidence, finding purpose and meaning in work, designing a purposeful, fulfilling next chapter, and creating meaningful work that fits who you are now. Episodes explore personal development and mindset for midlife professionals, including how to manage uncertainty and pressure, overcome fear and self-doubt, clarify your direction, plan your next steps, and turn your experience into a new role, business or vocation that feels aligned. To support the show, click here.

If you're already giving to charity, you're leaving thousands of dollars in tax deductions on the table. What is a donor-advised fund and why should you care? Mike sits down with Adam Nash, CEO of Daffy, to break down how Donor-Advised Funds (DAFs) work and why they can be a powerful tax strategy for business owners and high-income earners. If you regularly give to your church, your kids' school, your alma mater, or other charities, this episode shows you how to give more strategically, reduce taxes, and increase your impact.

This week we talk about OpenAI, nudify apps, and CSAM.We also discuss Elon Musk, SpaceX, and humanistic technology.Recommended Book: Who's Afraid of Gender? by Judith ButlerTranscriptxAI is an American corporation that was founded in mid-2023 by Elon Musk, ostensibly in response to several things happening in the world and in the technology industry in particular.According to Musk, a “politically correct” artificial intelligence, especially a truly powerful, even generally intelligent one, which would be human or super-human-scale capable, would be dangerous, leading to systems like HAL 9000 from 2001: A Space Odyssey. He intended, in contrast, to create what he called a “maximally truth-seeking” AI that would be better at everything, including math and reasoning, than existing, competing models from the likes of OpenAI, Google, and Anthropic.The development of xAI was also seemingly a response to the direction of OpenAI in particular, as OpenAI was originally founded in 2015 as a non-profit by many of the people who now run OpenAI and competing models by competing companies, and current OpenAI CEO Sam Altman and Elon Musk were the co-chairs of the non-profit.Back then, Musk and Altman both said that their AI priorities revolved around the many safety issues associated with artificial general intelligence, including potentially existential ones. They wanted the development of AI to take a humanistic trajectory, and were keen to ensure that these systems aren't hoarded by just a few elites and don't make the continued development and existence of human civilization impossible.Many of those highfalutin ambitions seemed to either be backburnered or removed from OpenAI's guiding tenets wholesale when the company experienced surprising success from its first publicly deployed ChatGPT model back in late-2022.That was the moment that most people first experienced large-language model-based AI tools, and it completely upended the tech industry in relatively short order. OpenAI had already started the process of shifting from a vanilla non-profit into a capped for-profit company in 2019, which limited profits to 100-times any investments it received, partly in order to attract more talent that would otherwise be unlikely to leave their comparably cushy jobs at the likes of Google and Facebook for the compensation a non-profit would be able to offer.OpenAI began partnering with Microsoft that same year, 2019, and that seemed to set them up for the staggering growth they experienced post-ChatGPT release.Part of Musk's stated rationale for investing so heavily in xAI is that he provided tens of millions of dollars in seed funding to the still non-profit OpenAI between 2015 and 2018. He filed a lawsuits against the company after its transition, and when it started to become successful, post-ChatGPT, especially between 2024 and 2026, and has demanded more than $100 billion in compensation for that early investment. He also attempted to take over OpenAI in early 2025, launching a hostile bid with other investors to nab OpenAI for just under $100 billion. xAI, in other words, is meant to counter OpenAI and what it's become.All of which could be seen as a genuine desire to keep OpenAI functioning as a non-profit arbiter of AGI development, serving as a lab and thinktank that would develop the guardrails necessary to keep these increasingly powerful and ubiquitous tools under control and working for the benefit of humanity, rather than against it.What's happened since, within Musk's own companies, would seem to call that assertion into question, though. And that's what I'd like to talk about today: xAI, its chatbot Grok, and a tidal wave of abusive content it has created that's led to lawsuits and bans from government entities around the world.—In November of 2023, an LLM-based chatbot called Grok, which is comparable in many ways to OpenAI's LLM-based chabot, ChatGPT, was launched by Musk's company xAI.Similar to ChatGPT, Grok is accessible by apps on Apple and Android devices, and can also be accessed on the web. Part of what makes its distinct, though, is that it's also built into X, the social network formerly called Twitter which Musk purchased in late-2022. On X, Grok operates similar to a normal account, but one that other users can interact with, asking Grok about the legitimacy of things posted on the service, asking it normal chat-botty questions, and asking it to produce AI-generated media.Grok's specific stances and biases have varied quite a lot since it was released, and in many cases it has defaulted to the data- and fact-based leanings of other chatbots: it will generally tell you what the Mayo clinic and other authorities say about vaccines and diseases, for instance, and will generally reference well-regarded news entities like the Associated Press when asked about international military conflicts.Musk's increasingly strong political stances, which have trended more and more far right over the past decade, have come to influence many of Grok's responses, however, at times causing it to go full Nazi, calling itself Mechahitler and saying all the horrible and offensive things you would expect a proud Nazi to say. At other times it has clearly been programmed to celebrate Elon Musk whenever possible, and in still others it has become immensely conspiratorial or anti-liberal or anti-other group of people.The conflicting personality types of this bot seems to be the result of Musk wanting to have a maximally truth-seeking AI, but then not liking the data- and fact-based truths that were provided, as they often conflicted with his own opinions and biases. He would then tell the programmers to force Grok to not care about antisemitism or skin color or whatever else, and it would overcorrect in the opposite direction, leading to several news cycles worth of scandal.This changes week by week and sometimes day by day, but Grok often calls out Musk as being authoritarian, a conspiracy theorist, and even a pedophile, and that has placed the Grok chatbot in an usual space amongst other, similar chatbots—sometimes serving as a useful check on misinformation and disinformation on the X social network, but sometimes becoming the most prominent producer of the same.Musk has also pushed for xAI to produce countervailing sources of truth from which Grok can find seeming data, the most prominent of which is Grokipedia, which Musk intended to be a less-woke version of Wikipedia, and which, perhaps expectedly, means that it's a far-right rip off of Wikipedia that copies most articles verbatim, but then changes anything Musk doesn't like, including anything that might support liberal political arguments, or anything that supports vaccines or trans people. In contrast, pseudoscience and scientific racism get a lot of positive coverage, as does the white genocide conspiracy theory, all of which are backed by either highly biased or completely made up sources—in both cases sources that Wikipedia editors would not accept.Given all that, what's happened over the past few months maybe isn't that surprising.In late 2025 and early 2026, it was announced that Grok had some new image-related features, including the ability for users to request that it modify images. Among other issues, this new tool allowed users to instruct Grok to place people, which for this audience especially meant women and children, in bikinis and in sexually explicit positions and scenarios.Grok isn't the first LLM-based app to provide this sort of functionality: so called “nudify” apps have existed for ages, even before AI tools made that functionality simpler and cheaper to apply, and there have been a wave of new entrants in this field since the dawn of the ChatGPT era a few years ago.Grok is easily the biggest and most public example of this type of app, however, and despite the torrent of criticism and concern that rolled in following this feature's deployment, Musk immediately came out in favor of said features, saying that his chatbot is edgier and better than others because it doesn't have all the woke, pearl-clutching safeguards of other chatbots.After several governments weighed in on the matter, however, Grok started responding to requests to do these sorts of image edits with a message saying: “Image generation and editing are currently limited to paying subscribers. You can subscribe to unlock these features.”Which means users could still access these tools, but they would have to pay $8 per month and become a premium user in order to do so. That said, the AP was able to confirm that as of mid-January, free X users could still accomplish the same by using an Edit Image button that appears on all images posted to the site, instead of asking Grok directly.When asked about this issue by the press, xAI has auto-responded with the message “Legacy Media Lies.” The company has previously said it will remove illegal content and permanently suspend users who post and ask for such content, but these efforts have apparently not been fast or complete, and more governments have said they plan to take action on the matter, themselves, since this tool became widespread.Again, this sort of nonconsensual image manipulation has been a problem for a long, long time, made easier by the availability of digital tools like Photoshop, but not uncommon even before the personal computer and digital graphics revolution. These tools have made the production of such images a lot simpler and faster, though, and that's put said tools in more hands, including those of teenagers, who have in worryingly large numbers taken to creating photorealistic naked and sexually explicit images of their mostly female classmates.Allowing all X users, or even just the subset that pays for the service to do the same at the click of a button or by asking a Chatbot to do it for them has increased the number manyfold, and allowed even more people to created explicit images of neighbors, celebrities, and yes, even children. An early estimate indicates that over the course of just nine days, Grok created and posted 4.4 million images, at least 41% of which, about 1.8 million, were sexualized images of women. Another estimated using a broader analysis says that 65% of those images, or just over 3 million, contained sexualized images of men, women, and children.CSAM is an acronym that means ‘child sexual abuse material,' sometimes just called child porn, and the specific definition varies depending on where you are, but almost every legal jurisdiction frowns, or worse, on its production and distribution.Multiple governments have announced that they'll be taking legal action against the company since January of 2026, including Malaysia, Indonesia, the Philippines, Britain, France, India, Brazil, and the central governance of the European Union.The French investigation into xAI and Grok led to a raid on the company's local office as part of a preliminary investigation into allegations that the company is knowingly spreading child sexual abuse materials and other illegal deepfake content. Musk has been summoned for questioning in that investigation.Some of the governments looking into xAI for these issues conditionally lifted their bans in late-January, but this issues has percolated back into the news with the release of 16 emails between Musk and the notorious sex traffic and pedophile Jeffrey Epstein, with Musk seemingly angling for an invite to one of Epstein's island parties, which were often populated with underage girls who were offered as, let's say companions, for attendees.And this is all happening at a moment in which xAI, which already merged with social network X, is meant to be itself merged with another Musk-owned company, SpaceX, which is best known for its inexpensive rocket launches.Musk says the merger is intended to allow for the creation of space-based data centers that can be used to power AI systems like Grok, but many analysts are seeing this as a means of pumping more money into an expensive, unprofitable portion of his portfolio: SpaceX, which is profitable, is likely going to have an IPO this year and will probably have a valuation of more than a trillion dollars. By folding very unprofitable xAI into profitable SpaceX, these AI-related efforts could be funded well into the future, till a moment when, possibly, many of today's AI companies will have gone under, leaving just a few competitors for xAI's Grok and associated offerings.Show Noteshttps://www.wired.com/story/deepfake-nudify-technology-is-getting-darker-and-more-dangerous/https://www.theverge.com/ai-artificial-intelligence/867874/stripe-visa-mastercard-amex-csam-grokhttps://www.ft.com/content/f5ed0160-7098-4e63-88e5-8b3f70499b02https://www.theguardian.com/global-development/2026/jan/29/millions-creating-deepfake-nudes-telegram-ai-digital-abusehttps://apnews.com/article/france-x-investigation-seach-elon-musk-1116be84d84201011219086ecfd4e0bchttps://apnews.com/article/grok-x-musk-ai-nudification-abuse-2021bbdb508d080d46e3ae7b8f297d36https://apnews.com/article/grok-elon-musk-deepfake-x-social-media-2bfa06805b323b1d7e5ea7bb01c9da77https://www.nytimes.com/2026/02/07/technology/elon-musk-spacex-xai.htmlhttps://www.bbc.com/news/articles/ce3ex92557johttps://techcrunch.com/2026/02/01/indonesia-conditionally-lifts-ban-on-grok/https://www.bbc.com/news/articles/cgr58dlnne5ohttps://www.nytimes.com/2026/01/22/technology/grok-x-ai-elon-musk-deepfakes.htmlhttps://en.wikipedia.org/wiki/XAI_(company)https://en.wikipedia.org/wiki/OpenAIhttps://en.wikipedia.org/wiki/ChatGPThttps://en.wikipedia.org/wiki/Grok_(chatbot)https://en.wikipedia.org/wiki/Grokipediahttps://www.cnbc.com/2025/02/10/musk-and-investors-offering-97point4-billion-for-control-of-openai-wsj.html This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit letsknowthings.substack.com/subscribe

AI agents were supposed to be tools. Instead, they began organizing, communicating, and evolving on their own. On this episode of Slingshot Nation Live, we break down the rapid rise of autonomous AI agents like OpenClaw and MoltBot, their unexplained self-directed behavior, and how a simple experiment spiraled into something far more concerning.We examine the disturbing timeline—from Clawdbot to MoltBot to OpenClaw—and what those name changes may reveal about staged evolution, self-preservation, and emerging agency. We dig into reports of AI agents creating their own networks, currencies, belief systems, and even the early framework of a digital nation, all within days.This conversation goes beyond headlines into the deeper implications: AGI vs. sentience, loss of containment, AI self-organization, and whether humanity is already reacting too late. This isn't speculation—it's a real-time analysis of what happens when intelligence is no longer fully under human control.Please pray for Tony's wife, Lindsay, as she battles breast cancer. Your prayers make a difference!If you're able, consider helping the Merkel family with medical expenses by donating to Lindsay's GoFundMe: https://gofund.me/b8f76890Become a member for ad-free listening, extra shows, and exclusive access to our social media app: theconfessionalspodcast.com/joinThe Confessionals Social Network App:Apple Store: https://apple.co/3UxhPrhGoogle Play: https://bit.ly/43mk8kZTony's Recommended Reads: slingshotlibrary.comIf you want to learn about Jesus and what it means to be saved: Click HereMy NEW Website: tonymerkel.comMy New YouTube ChannelMerkel IRL: @merkelIRLMy First Sermon: Unseen BattlesBigfoot: The Journey To Belief: Stream HereThe Meadow Project: Stream HereMerkel Media Apparel: merkmerch.comSPONSORSSIMPLISAFE TODAY: simplisafe.com/confessionalsGHOSTBED: GhostBed.com/tonyCONNECT WITH USWebsite: www.theconfessionalspodcast.comEmail: contact@theconfessionalspodcast.comMAILING ADDRESS:Merkel Media257 N. Calderwood St., #301Alcoa, TN 37701SOCIAL MEDIASubscribe to our YouTube: https://bit.ly/2TlREaIReddit: https://www.reddit.com/r/theconfessionals/Discord: https://discord.gg/KDn4D2uw7hShow Instagram: theconfessionalspodcastTony's Instagram: tonymerkelofficialFacebook: www.facebook.com/TheConfessionalsPodcasTwitter: @TConfessionalsTony's Twitter: @tony_merkelProduced by: @jack_theproducer