Podcasts about Roadmap

The Kardashian-ification of Travel BallA reality show about travel ball is coming, and the kids on it are about to become characters. Deven makes the case for why that is worse than it sounds, then pulls the game logs from his son's junior year to show what we are already asking of these players without cameras: 1,001 pitches, 223 innings caught, roughly 4,500 total throws, and a game frequency that beats an average D1 schedule. Plus the showcase invite they turned down in July, and why a senior-year football season is the right call.00:00 Intro and rundown01:20 Roadmap assessments and membership07:23 Team Moms: Baseball13:46 What travel ball became18:48 No producer wants a .500 team22:13 My son's game log28:24 Games per 100 days33:34 Using the word no40:04 Football as a senior44:50 WrapLinks:New Driveline youth memberships (for players 9–14) are live in Washington NOW, with Arizona and Florida coming soon!- Assessments- Group training- 1-on-1 hitter and pitcher development sessionsGet started now: ⁠⁠support@drivelinebaseball.com⁠⁠ or (425) 523-4030. Tell them Deven sent you!

In the News Is iOS 27 Draining Your iPhone Battery Amazon Prime Customers may be able to get up to $200 as Settlement Expands The Best Tech Jobs for Promotions, Pay, and Stability Why can't AI companies test with Samples of LLMs before releasing them into the real world? Signals From a Planet Beyond Our Solar System Astronomers Finally Pick Up Radio Signals From a Planet Beyond Our Solar System   ITPro Series with Benjamin Rockwell ToolConsolidation can make sense but there's a Roadmap to it From the Tech Corner Did OpenAI and Anthropic oversold AI Security Breaches to Pressure Feds ·      Computational Photography vs Traditional Photography ·      Cornell Researchers Develop Method to Restore EV Batteries to Up to 95% of Original Capacity Technology Chatter with Benjamin Rockwell and Marty Winston Dealing with Various Power and other Technical Issues

You don't have to be on dating apps to be "trying" in your dating life. This episode gives you the alternative strategy that has worked for hundreds of my clients (and worked for me to meet my husband). In this episode, you'll learn exactly how to date IRL. Inside episode 284: → Why "I don't chase, I attract" is one of my least favorite pieces of dating advice, and how it is negatively impacting your IRL dating life → My rule for feminist dating that I've seen work for hundreds of people who felt too behind, too late, too much, not enough → What to say when people tell you they don't know anybody single → Why awkwardness is the price of admission for connection

Get the FREE GUIDE to 10 Nonclinical Careers at nonclinicalphysicians.com/freeguide. Get a list of 70 nontraditional jobs at nonclinicalphysicians.com/70jobs.        In this episode, Dr. John Jurica introduces the first part of his 12-month roadmap to help physicians transition from clinical practice into nonclinical careers. He explains how the process can be applied to opportunities in pharmaceutical companies, consulting, insurance, education, and healthcare leadership. Dr. Jurica addresses common myths and self-limiting beliefs that hold physicians back, including concerns about job availability, qualifications, income, abandoning patients, and losing professional status. He highlights the value of physicians' medical knowledge and transferable skills in nonclinical roles. He outlines four key tasks for the first month: overcoming limiting beliefs, reviewing job descriptions, finding mentors, and creating a complete LinkedIn profile. These steps help physicians understand job requirements, identify skills they may need to develop, build professional connections, and prepare for future opportunities while continuing their current work. You'll find the transcript, links mentioned in the episode, and related resources at   https://nonclinicalphysicians.com/kick-off-the-12-month-roadmap/  

For episode 777 of the BlockHash Podcast, host Brandon Zemp is joined by Kenneth Shek, CEO at Moca Network and Managing Director at Animoca Brands.Kenneth leads the Moca Network, Animoca Brands' identity ecosystem, building decentralized identity infrastructure across a network spanning more than 500 portfolio companies and 700M+ addressable users, and can discuss AI agents and digital identity, the future of online trust, agentic commerce, and why identity will become a foundational layer of the AI-powered internet.

This week's episode of Vision ProFiles digs into the swirling debate over Apple's next Vision Pro—four competing redesign concepts, a leadership shake-up, soft retail sales, and Meta's cheaper $1,299 rival—while also covering visionOS 27 housekeeping, new apps, and community reactions to it all.▌ NEXT-GEN VISION PRO — THE ROADMAP DEBATENew Vision Pro Headset: AppleConsidering Four Different Form Factors — https://technewstube.com/macrumors/1871067/new-vision-pro-headset-apple-considering-four-different/Apple Working on New, LighterVision Pro Version — https://9to5mac.com/2026/09/27/apple-working-on-new-lighter-vision-pro-version/N224: Four Prototypes, OneFamiliar Shape — https://www.ithinkdiff.com/apple-vision-pro-successor-n224-smart-glasses/Vision Hardware LeadershipReshuffle — https://www.ithinkdiff.com/apple-vision-pro-leadership-n224-changes/Report: Next Vision ProReportedly "On Life Support" as Design Stays Undecided — https://machash.com/cult-of-mac/417488/apples-next-vision-pro-reportedly-life-support-design/Pushback: Apple's Development"Graveyard" Is a Feature, Not a Failure — https://appleinsider.com/articles/26/09/27/apple-vision-pros-development-graveyard-exemplifies-apples-1000-nos-to-every-yesRoadmap Forecast: SmartGlasses in 2027, Upgraded Vision Pro in 2028 — https://appleinsider.com/articles/26/09/27/apples-smart-glasses-predicted-for-2027-upgraded-apple-vision-pro-2028▌ INDUSTRY & BUSINESSReport: Some Apple StoresSelling Just One Vision Pro a Month — https://www.macobserver.com/news/apple-vision-pro-sales-one-unit-month-some-stores/Gurman: Meta's $1,299 VRGlasses Are "Exactly" What Vision Pro Should Have Been — https://machash.com/mac-daily-news/417486/gurman-metas-1299-vr-glasses-exactly-apple-vision-pro/Meta VR Glasses: Price,Weight, and the Pressure on Vision Pro — https://www.macobserver.com/news/meta-vr-glasses-price-weight-pressure-vision-pro/▌ VISIONOS 27 — HOUSEKEEPINGHow to Prepare Vision Pro forvisionOS 27 Before Monday — https://www.macobserver.com/news/vision-pro-prepare-visionos-27-before-monday/▌ XR COMMUNITY VOICESUsed Vision Pro or Wait forthe Meta VR Glasses? — Community Thread — https://www.reddit.com/r/virtualreality/comments/1wrnl29/used_vision_pro_or_wait_for_the_meta_vr_glasses/▌ ADDENDUM — LATE-BREAKING LINKSGurman/Bloomberg: Apple IsWeighing the Future of Vision Pro — Community Discussion — https://www.reddit.com/r/VisionPro/comments/1wro9ro/mark_gurmanbloomberg_apple_is_weighing_the_future/visionOS 27 Bug Reports:Widgets and Siri — https://www.reddit.com/r/VisionPro/comments/1wqkb6d/problems_with_visionos27_widgets_and_siri_please/New App: Depth Strike ($7.99)— https://apps.apple.com/us/app/depth-strike/id6816047104"We Still Believe inVision Pro, So We Built An..." — Community Post — https://www.reddit.com/r/VisionPro/comments/1wqzj7x/we_still_believe_in_vision_pro_so_we_built_an/New App: I'm Wizard — MagicCombat Game ($9.99) — https://apps.apple.com/us/app/im-wizard-magic-combat-game/id6747723768"What Do You Use ItFor?" — Community Thread — https://www.reddit.com/r/VisionPro/comments/1wq4hrc/what_do_you_use_it_for/Immersive Video: Changing ofthe Guard at Windsor Castle (180°) — https://www.youtube.com/watch?v=aY9oL0DtWsA▌ SHOW INFORMATIONHost: Marty Jencius | ThePodTalk.netPanelists: Eric Bolden · Dave Ginsburg Email: ThePodTalkNetwork@gmail.comWeb: ThePodTalk.Net

What if feeling lost as a dad wasn't a sign of failure, but the first step toward becoming the father you truly want to be? Kate Mason is joined by Jeff Hittner, leadership expert, executive coach, founder of Ambitious Dads, and devoted father of two boys. After learning he could not become a biological father, Jeff discovered that changing his mindset about fatherhood meant we can change our minds about anything. Today, he has interviewed more than 200 dads and uncovered six "dad gaps" that leave modern fathers without a roadmap. Kate and Jeff explore shame, emotional regulation, co-parenting, and why fatherhood may be the best leadership lab there is. This empowering conversation offers insight and practical hope for every parent seeking a deeper understanding of the dads in their family, and themselves. Listen for:4:11 How did infertility reshape what fatherhood means for Jeff Hittner?7:47 What are the six dad gaps holding modern fathers back?17:26 Why do so many dads feel shame about their parenting?22:55 How can the hard moments of fatherhood make you a better leader?34:46 Is there really such a thing as a naturally good dad? Guest Jeff Hittner Jeff Hittner Website | LinkedIn| Facebook | Instagram | Ambitious Dads| | Podcast | YouTube Leave a rating/review for this podcast with one click Contact Kate:Email | Website | Kate's Book on Amazon | LinkedIn | Facebook | X

In this episode of the PBL Simplified podcast for administrators, Ryan Steuer dives deep into the challenges and strategies involved in rolling out project-based learning (PBL) across an entire district. Drawing on 15 years of experience, Ryan Steuer outlines why both top-down mandates and purely organic approaches often fall short, and explains how applying the innovation curve leads to sustainable, successful implementations. Real-world examples, including a case study from Perry Township in Indianapolis, illustrate how to customize the rollout to meet the distinct readiness and needs of each school and leader.Main PointsTwo Classic Approaches & Why They Fail: Ryan discusses why rolling out PBL with either a strict top-down mandate or a purely “let it happen” organic approach usually doesn't produce lasting results.Innovation Curve Application: The optimal strategy is matching the district's rollout model to the innovation curve, aligning cohorts of schools and leaders to stages of readiness (innovators, early majority, late majority, laggards).Cohort Structure & Customization: Successful implementation relies on transparent and customized cohort launches, where early adopters build and adjust tools/resources that later cohorts can use more seamlessly.Building Capacity and Sustainability: By year 3–4, districts build internal expertise, empower building leaders, and develop district sustainability through certified local trainers.Case Study: The approach is illustrated with Perry Township, a 15,000-student district, whose multi-year phased plan exemplifies customizing the process for each building and leadership team.Notable Quotes"There is a pretty solid format that we've seen work over and over again in our 15 years of doing this work, and it follows the innovation curve.""When you get to cohort 2, they don't want to do that. They want the AI to work perfectly. They want it to be set up right. They want to be able to just use it.""Because when people aren't ready, that's when you feel resistance. That's when they pull back, push back, especially if you get into a fight or flight piece."Call to ActionReady to find out if your district is prepared for Project Based Learning? Take the quick readiness assessment and access free resources at pblscore.com.

In this episode of The Free Thought Project podcast, Matt Agorist and Jason Bassler sit down with Mark Maresca, a prominent advocate for voluntaryism and the creator and host of The White PillBox podcast and Substack. Maresca has established himself as a vital voice in the liberty movement, dedicated to dismantling the illusion of political authority and equipping individuals with positive, solution-oriented frameworks to overcome state overreach. The conversation begins with a clear, grounded definition of voluntaryism, illustrating how everyday consensual interactions prove that people already operate under stateless principles in their private lives. The discussion dives deep into the 30,000-foot view of statism, exploring how the persistent belief in political authority is sustained through state-driven legitimacy narratives and media control rather than sheer physical force. From deconstructing classic objections like the "warlord scenario" and the "social contract" myth to analyzing effective communication strategies that plant seeds of liberty without triggering psychological defenses, the dialogue highlights the steady erosion of state legitimacy. This awakening is further demonstrated by growing, organic pushback against pervasive surveillance infrastructure—such as the widespread deployment of Flock automated license plate readers—proving that public skepticism toward centralized control is accelerating faster than ever. Closing on an inspiring "white pill" note, the discussion emphasizes how the decentralized structure of the internet permanently waters down state propaganda, allowing individuals to discover parallel networks and embrace true self-sovereignty. Rather than falling into demoralization or endless political cycles, the path forward relies on voluntary association, counter-economics, and local community building. Looking decades into the future, a genuinely free society is framed not as an unattainable utopia, but as a practical world defined by peaceful cooperation, direct competition, and the absolute freedom to opt out of centralized state monopolies in education, utilities, and daily life. (Length: 53:05) Guest Links & Resources: Mark Maresca's Substack: Mark Maresca on Substack Support The Free Thought Project

What if Psalm 23 isn't simply a scripture we believe, but a roadmap showing us how to live?In this episode of Friends With God, Joe Longo explores Psalm 23 as an invitation to stop waiting for someday and begin experiencing the presence of God right here, right now.“The Lord is my shepherd, I lack nothing.”What changes when we actually carry those words into our everyday lives? When we trust that God is guiding us, refreshing our souls, walking beside us through the darkest valleys, preparing a table for us, and reminding us that we already dwell in the house of the Lord?Joe offers a simple practice for the week: read Psalm 23 every day. Read it slowly. Let it become more than words you know. Allow it to settle into your heart and become a reminder that you are guided, provided for, protected, and never walking alone.The episode closes with a slow, meditative reading of Psalm 23 and a prayer to help you return to the present moment and trust God one step at a time.Stop waiting. Start living. You are already dwelling in the house of the Lord.And if you're ready to go deeper, join Joe and the Friends With God community for the Monday Night Mastermind beginning in October as we explore renewing the mind, trusting God, abiding in His presence, and opening ourselves to receive.Music by Andrii Poradovskyi from Pixabay

In this episode of The Industry Podcast, we sit down with Christin Marvin, founder of Columbine Hospitality, author, and host of The Restaurant Leadership Podcast. Christin started as a line cook at 15, worked her way through fine dining and high-volume concepts, and eventually helped scale a Denver restaurant group from six locations to 48 before burning out. She shares the honest story of how that growth cost her — including a period of heavy drinking she eventually walked away from — and how the process of rebuilding her own life became the foundation for the company she runs today. Christin now leads Columbine Hospitality, where she and a small team of coaches help independent restaurant and bar owners build the leadership teams and systems needed to run a business that doesn't require them to be in it every hour of every day. She walks through how that work actually happens — remote coaching, digging past the surface-level complaint to the real problem, and rolling out change gradually so owners don't lose their sense of purpose in the process. She also gets into why she started writing (her first book, The Hospitality Leader's Roadmap, and her second, Multi-Unit Mastery) how self-publishing actually works, and her plan to grow her coaching team over the next decade. - Growing up in the industry and the "highlight of her career" moment that hooked her on hospitality - Scaling a restaurant group from 6 to 48 locations and what private equity changed - Burnout, losing her voice as a leader, and the drinking that came with it - Getting sober and rebuilding a life and business around her core values - How Columbine Hospitality coaches independent owners remotely - Writing and self-publishing *The Hospitality Leader's Roadmap* and *Multi-Unit Mastery* - Hosting The Restaurant Leadership Podcast and building her coaching team - Why "structure" is no longer just a corporate word for independent operators Notable Quote "There's gotta be more intention and more clarity and more systems, and systems used to be a very corporate word, but there's gotta be more structure to the business than there used to be in the past, because it's so hard to find experienced people." — Christin Marvin Connect with Christin Marvin - Columbine Hospitality - LinkedIn: Christin Marvin - Instagram: @christinlmarvin - The Restaurant Leadership Podcast — available wherever you listen to podcasts - Books: The Hospitality Leader's Roadmap and Multi-Unit Mastery A big thank you to Jean-Marc Dykes of Imbiblia. Imbiblia is a cocktail app for bartenders, restaurants and cocktail lovers alike and build by a bartender with more than a decade of experience behind the bar. Several of the features of the platform include the ability to create your own Imbiblia Recipe Cards with the Imbiblia Cocktail Builder, rapidly select ingredients, garnishes, methods and workshop recipes with a unique visual format, search by taste, using flavour profiles unique to Imbiblia, share recipes publicly plus many more....Imbiblia - check it out! Contact the host Kypp Saunders by email at kyppsaunders@gmail.com for products from Elora Distilling and Noble Estates. Links: kyppsaunders@gmail.com @sugarrunbar @the_industry_podcast email us: info@theindustrypodcast.club

Eine Funktion hier, ein Kundenwunsch dort und plötzlich ist das Produkt kaum noch erklärbar. In dieser Folge geht es darum, wie unnötige Komplexität in der Produktentwicklung entsteht und warum sich die eierlegende Wollmilchsau trotz ihres großen Funktionsumfangs oft nur schwer verkaufen lässt. Michael Stiller zeigt, wie Unternehmen Kundenwünsche richtig einordnen, Produktfunktionen priorisieren und ihre Produkt-Roadmap auf das Wesentliche konzentrieren. Du lernst drei Warnsignale für überladene Produkte kennen und erhältst drei konkrete Fragen, mit denen du Produktfunktionen direkt überprüfen kannst.

River of Life is an inter-denominational, interracial, Spirit-filled church located in the heart of Wakulla County, Florida. We share the sermons from our services in the hopes they'll reach others determined to worship God in spirit and truth.

River of Life is an inter-denominational, interracial, Spirit-filled church located in the heart of Wakulla County, Florida. We share the sermons from our services in the hopes they'll reach others determined to worship God in spirit and truth.

Can a haram relationship ever become halal?What if you have already fallen in love? Is there still hope? Can Allah forgive you? And what practical steps should you take if you sincerely want to make your relationship pleasing to Allah?In this deeply practical and compassionate lecture, Ustaz Dr. Abdulfattah Adeyemi addresses one of the most important questions facing Muslim youth today:How do you transform a haram relationship into a halal one?Drawing from the Qur'an, the Sunnah, Islamic scholarship, psychology, and real-life counselling experience, this lecture offers a step-by-step roadmap for those who genuinely seek Allah's pleasure without losing hope in His mercy.Whether you are currently in a relationship, considering marriage, recovering from emotional attachment, or guiding someone through these challenges, this lecture provides timeless principles rooted firmly in Islam. IN THIS SERIES OF LECTURES, YOU WILL DISCOVER:PART 1: What makes a relationship haram in Islam?PART 2: Why emotional attachment can become so powerful.PART 3: Warning signs that your relationship needs immediate tawbah.PART 4: Can a haram relationship truly become halal?PART 5: The Seven-Step Islamic Roadmap from Haram to Halal.PART 6: When walking away becomes an act of worship.PART 7: How to know if someone truly loves you.PART 8: Common excuses Muslims make for remaining in haram relationships.PART 9: The foundations of a successful halal marriage.PART 10: Timeless lessons from the stories of Prophet Yusuf (AS), Prophet Musa (AS), Maryam (AS), Khadijah (RA), and Aishah (RA). THIS LECTURE IS ESPECIALLY BENEFICIAL FOR:Muslim youthSingles preparing for marriageCouples seeking Allah's blessingsParents raising teenagersMarriage counsellorsImams and community leadersUniversity students and young professionals KEY THEMESIslamic Relationships • Dating in Islam • Halal Love • Marriage in Islam • Tawbah (Repentance) • Emotional Attachment • Muslim Youth • Islamic Counselling • Premarital Guidance • Love and Faith • Nikah • Taqwa • Family Life • PersonalDevelopment • Spiritual GrowthIf this lecture benefits you:Like this lecture.Share your reflections in the comments.Share this lecture with someone who may need hope and guidance today.Subscribe for more authentic lectures on marriage, family life, Islamic psychology, emotional wellbeing, parenting,personal development, and spiritual growth.Book a Counselling/Therapy session here:https://calendly.com/abdulfattahadeyemi/counseling-therapyDownload the Adeyemi App from Google Play: https://play.google.com/store/apps/details?id=com.kwickapp.panel.android665e0fb9ed2faVisit: www.adeyemi.ng⁠Join Abdulfattah Adeyemi's Community:https://t.me/+Gz7wGuTsRLRmNzU0⁠FOLLOW ME ON:Podcast: https://open.spotify.com/show/1Ve9GDn0C01bVIffD9D8sS?si=-4hvd8wRQRGuX3uWsuiwQAInstagram: @dr.abdulfattahadeyemiFacebook: @dr.abdulfattahadeyemiTikTok: @dr.abdulfattahadeyemiYouTube: www.youtube.com/@dr.abdulfattahadeyemiMay Allah guide every heart seeking Him, bless every halal marriage with sakīnah, mawaddah, and raḥmah, forgive those who sincerely repent, and grant our youth spouses who bring them closer to Jannah. Āmīn.#HalalRelationship #DatingInIslam #IslamicMarriage#MarriageInIslam #HalalLove #Tawbah #MuslimYouth #IslamicCounseling #Nikah#LoveInIslam #RelationshipAdvice #MuslimSingles #BeforeMarriage#IslamicReminder #Quran #Sunnah #UstazAbdulfattahAdeyemi #Baynakum #Marriage

Podcasting 2.0 September 25th 2026 Episode 272 - "Is this Y'all?" 00 -

Dave gives a road map to meditation and what you might expect to find in doing it for a half hour, an hour, a decade, or a lifetime or two. What is the one technique that unlocks all benefits instantly while carrying us down the stream unendingly? Can meditation done wrong actually do harm? And why does Zen think death is so funny?? Find out here!

Podcasting 2.0 September 25th 2026 Episode 272 - "Is this Y'all?" 00 -

September 25, 2026 - Ashley Grayned and Dr. Jay Marino joined Byers & Co to talk about their Roadmap 2030 strategic plan, community feedback, future plans, and tightening timelines. Listen to the podcast now!See omnystudio.com/listener for privacy information.

What happens when open source, AI, and decades of database technology collide?Corey Quinn sits down with German Eichberger, Principal AI Engineering Manager at Microsoft, to dig into DocumentDB, why it's built on PostgreSQL, and the realities of MongoDB compatibility. They explore how Kubernetes and databases have evolved to better support stateful workloads, why MCP servers could give AI agents safer database access, and how AI may dramatically increase the number of databases organizations need to manage.Show Highlights: (0:00) Databases in Volatile Environments(00:12) Welcome and Introductions(01:20) AI Titles and Pay Signals(02:53) Why DocumentDB Exists(05:04) Mongo API on Postgres(07:15) No Forking Postgres(08:09) Compatibility and Standards(12:12) Governance and Roadmap(15:15) Kubernetes and MCP AgentsSponsored by: duckbillhq.com

Got a question or comment? Message us here!Patches are meant to strengthen security, but they can also give attackers valuable clues. In this episode, we explore how threat actors analyze updates, reverse-engineer fixes, and weaponize vulnerabilities before organizations have time to patch.#CyberSecurity #PatchManagement #ThreatIntelligence #BlueMoonSupport the showWatch full episodes at youtube.com/@aliascybersecurity.Listen on Apple Podcasts, Spotify and anywhere you get your podcasts.

Send us Fan MailWe all know that a confirmation phone call for vendor remittance changes is a standard control to avoid payment fraud.  Anyone who has ever performed that duty knows how frustrating it can be to reach some vendors. One subscriber asked if they could use a video conferencing platform instead.  Hmmm….Keep listening. Check out my website training.debrarrichardson.com if you need help implementing authentication techniques, internal controls, and best practices to reduce the potential for fraudulent payments, compliance fines or bad vendor data. The Vendor Process Training Center for 173+ hours of weekly live and on-demand training for the Vendor team. Links mentioned in the podcast + other helpful resources:    YouTube Video:  One Gesture That May Expose a Deepfake VideoAsk Vendor Process Questions:  AskVeraTM  Authentication Free Training:  3 Step Vendor Setup & Maintenance Process WorkshopDo You Have A Controlled Vendor Process?  Roadmap to a Controlled Vendor Process Free Download:  Vendor Validation Reference List with Resource Links https://training.debrarrichardson.com/validation-referenceVendor Process Training Center - https://training.debrarrichardson.comCustomized Fraud Training:  https://training.debrarrichardson.com/customized-fraud-training Free Live and On-Demand Webinars: https://training.debrarrichardson.com/webinarsVendor Master File Clean-Up: https://training.debrarrichardson.com/cleanupYouTube Channel:  https://www.youtube.com/c/DebraRRichardsonLLCMore Podcasts/Blogs/Webinars https://training.debrarrichardson.comMore ideas?  Email me at debra@debrarrichardson.com Music Credit:  www.purple-planet.com

Read the full article: https://petronella.ai/blog/designing-a-practical-roadmap-to-meet-strict-cmmc-and-cui/A conversation about "Designing a Practical Roadmap to Meet Strict CMMC and CUI Compliance Standards" from the Petronella Technology Group, Inc. blog.Subscribe to Encrypted Ambition and hear every episode: https://petronellatech.com/podcasts/Questions about AI, cybersecurity, or compliance for your business? Call Petronella Technology Group, Inc. at 919-348-4912.

Today on Trending Middle East, Iran has presented the US with a proposed road map to end the war. Sources told The National that it includes a region-wide ceasefire of up to 60 days, the gradual reopening of the Strait of Hormuz and an end to the US blockade of Iran. The US-backed Board of Peace has proposed a six-month, $2.45 billion plan to begin rebuilding Gaza and restoring basic services. The UAE Central Bank has barred Bank Melli Iran from carrying out financial transactions between its branches in the Emirates and in Iran, after investigations found the lender failed to comply with the UAE's anti-money laundering regulations. In Syria, visitor numbers more than doubled in the first half of this year compared with the same period in 2025, as tourism picks up. And on the sidelines of the UN General Assembly, the Emirates Foundation has partnered with the Responsible AI Future Foundation to use artificial intelligence for social good in the UAE and around the world. Trending Middle East is AI-assisted, using original reporting published in The National and curated and edited by humans.

Welcome to the “Fraudian Slip,” a podcast by the Identity Theft Resource Center, where we talk about the latest scams, fraud and identity threats. I'm James E. Lee, President of the ITRC. Today, we're going to talk about a topic that a lot of people talk about across the dinner table, conference tables in businesses and in the halls of Congress and state legislatures: scams and scam prevention. Follow on LinkedIn: linkedin.com/company/idtheftcenter/ Follow on Instagram: instagram.com/idtheftcenter/ Follow on Facebook: facebook.com/IDTheftResourceCenter/ Follow on X: twitter.com/IDTheftCenter Follow on TikTok: www.tiktok.com/@idtheftcenter_ Follow on YouTube: www.youtube.com/@IDTheftCenter

Benedicte Bull har nettopp gitt ut boka «Trumps bakgård – fra Monroe-doktrine til røverimperialisme i Latin-Amerika».Nå kommer hun for å hjelpe oss med å forstå Trumps politikk i Latin-Amerika. Hun forteller om parallellene mellom Trump og en mafiaboss, og hva som har skjedd i Venezuela siden USAs angrep i januar – handlet det bare om olje? Mathias har prisnytt, Trine har rapportnytt, og Ingrid Tungen fra Regnskogfondet har ukas innringerspørsmål.Linker: The roadmap for eradiceting poverty beyond growth: The Roadmap for Eradicating Poverty Beyond GrowthAsle Tojes kronikk: Asle Toje: Norge bør kutte bistandenAfrika på langs: InstagramSeeking Security and opportunity in a stormy world: Seeking Security and Opportunity in a Stormy World: A 34-Country Survey | RF

Change shows up whether we invite it or not, and the hardest part is rarely the ending or the fresh start. It's the stretch in between, when the old identity doesn't fit anymore and the new one hasn't arrived. That's where so many of us feel lost, especially in midlife, retirement, or the later chapters of adulthood when the culture quietly tells us to fade out. We sit down with Dr. Elaine Yarbrough and Dr. Bob Benson, authors of *Roadmap for a Brilliant Life via Haiku, Pros and Photography*, to talk about what it really means to keep living instead of simply getting older. Elaine brings decades of work in communication, personal growth, and human well-being, along with haiku that distills big truths into a few sharp lines. Bob brings an analytical mind shaped by technology, management, and academia, plus a photographer's devotion to beauty. Together, they offer a grounded approach to purposeful aging built on real experience, not airy theory. We get into the themes that make or break a transition: letting go of old roles and the stuff we cling to, facing the neutral zone without panic, and learning to notice what brings joy before you can even name your next goal. We also talk about self-awareness, old patterns that keep us stuck, and why suffering and beauty both belong in a meaningful life. If you're searching for purpose, clarity, and a practical roadmap for life stage transitions, this conversation will meet you where you are and nudge you forward. Find the book here: https://link.amazon/B0etdPxi0 and website here: https://elaineandbob.com/INTERVIEW DISCLOSURE & BOOKING: This is a sponsored interview on The Elsa Kurt Show. Paid interview opportunities are available on a limited basis.Authors, experts, public figures, and other prospective guests who are represented by a publicist, marketing agency, PR firm, publisher, or professional representative are welcome to have their representative contact us regarding interview opportunities and current packages.Professional booking inquiries: info@elsakurt.comSubscribe for more honest conversations about growth and reinvention, share this with someone in a season of change, and leave a review telling us: what are you letting go of right now?Support the showElsa's AMAZON STORE Elsa's FAITH & FREEDOM MERCH STOREElsa's BOOKSElsa Kurt: You may know her for her uncanny, viral Kamala Harris impressions & conservative comedy skits, but she's also a lifelong Patriot & longtime Police Wife. She has channeled her fierce love and passion for God, family, country, and those who serve as the creator, Executive Producer & Host of the Elsa Kurt Show with Clay Novak. Her show discusses today's topics & news from a middle class/blue collar family & conservative perspective. The vocal LEOW's career began as a multi-genre author who has penned over 25 books, including twelve contemporary women's novels. Clay Novak: Clay Novak was commissioned in 1995 as a Second Lieutenant of Infantry and served as an officer for twenty four years in Mechanized Infantry, Airborne Infantry, and Cavalry units .  He retired as a Lieutenant Colonel in 2019. Clay is a graduate of the U.S. Army Ranger School and is a Master Rated Parachutist, serving for more tha...

In this episode of the 47 Morning Update w Ben Ferguson, Ben discusses a political montage featuring statements from Democratic Socialists of America leaders and progressive politicians, debate what those comments reveal about the modern Democratic Party, and examine concerns about political weaponization, lawfare, immigration policy, law enforcement, and the future of American democratic institutions. A new political ad highlighting statements from Democratic Socialists of America figures, progressive candidates, and Democratic politicians sparks a broader debate over the future of American institutions. The discussion centers on claims about abolishing or restructuring key pillars of government, including the Senate, law enforcement, ICE, and other federal institutions. The episode also examines allegations that political power is increasingly being used against ideological opponents, and whether those concerns should influence how voters view upcoming elections. Drawing comparisons to political developments abroad, the conversation focuses on the consequences of government action, political retaliation, and the role of media and public discourse in a polarized climate. Topics Covered: Democratic Socialists of America and its influence on Democratic politics Debates over abolishing or restructuring the Senate, policing, and federal institutions Immigration policy, ICE, and pathways to citizenship Claims of political retaliation against Trump allies and supporters Comparisons between U.S. politics and developments in Hungary The role of media, government power, and ideological conflict Please Hit Subscribe to this podcast Right Now. Also Please Subscribe to the The Ben Ferguson Show Podcast and Verdict with Ted Cruz Wherever You get You're Podcasts. And don't forget to follow the show on Social Media so you never miss a moment! Thanks for Listening X: https://x.com/benfergusonshowYouTube: https://www.youtube.com/@VerdictwithTedCruzSee omnystudio.com/listener for privacy information.

Do you think of your life as bringing something significant to those around you every day? Do you wake up in the morning and say, “Who's life can I impact for you today, Lord? And does it involve a vanilla latte with extra cream?”  Or, is it too late because my life is a mess?   Today we have the lovely, Briana Gray –a fellow podcaster and a kindred spirit when it comes to hospitality. I first met Briana when she graciously hosted me to be on her podcast, Echos of Impact and Briana loves helping women find their purpose and full potential for their lives. As a life coach, podcast host, wife and mom of four, Briana Gray, joins us today to talk about two of God's favorite topics: his purpose for our lives and our surrender to his will.  She is offering a free resource, Road Map to Purpose—here's the link. Some gems from our conversation: Whenever I think, "This isn't my idea..." that's when I need to pay attention to what God is leading me to do next. A wonderful thing about our podcast, Echos of Impact, is that we give an opportunity for some women to share thier story for the very first time. I've been learning I can be too task oriented, and people can get shifted to the side.  Releasing my schedule and agenda to God helps me remember that people are more important than my To-Do List. Investing in others has a ripple effect where God gets lifted up and we get blessed at the same time. A time of illness turned out to be a blessing in disguise where I had to slow down and reacquaint myself with God and with Scripture.  God isn't just after what we can do for him but for who we can be with him.  If someone's pain comes across our threshold, we get to do our small act of kindness to show God's love. Hospitality is a way to help people feel welcome and seen regardless of their beliefs.  We are seed-throwers and waterers and our job is to trust God with the outcome.  To get an idea of God's purpose is to learn how to steward what is right in front of me right now.  Follow Briana in all these places:  Instagram: https://www.instagram.com/echos_of_impact/   Listen to our podcast on Apple: https://podcasts.apple.com/us/podcast/echos-of-impact/id1745017999   Listen to our podcast on Spotify: https://open.spotify.com/show/0l6bdZq8HGLNfA5Gvpaz3u?si=4bf91737ef9b448d   Watch us on YouTube: https://www.youtube.com/@EchosofImpact   Join our free Facebook Community: https://www.facebook.com/share/g/1DfGJtjvV1/   Free Resource from Briana: Live Your Purpose Now!        

In this episode, Ivy Slater chats with us about the importance of strategic planning as the fourth quarter approaches, the value of reflecting on business and personal achievements, and how to set a vision for growth through 2027. In this episode, we discuss: How to set a clear vision for 2027 by reflecting on last year's accomplishments and planning accordingly. What reflection process to follow at the end of September and the start of October, including reviewing what has worked well and identifying accomplishments in both business and personal growth. When to carve out time in the fourth quarter for strategic planning, and why it's important to use practical tools like the soon-to-be-released strategic planning toolkit. Understanding exactly which tactics led to success (e.g., marketing initiatives, conferences, speaking engagements) is crucial when forming plans for upcoming years. How to draw key takeaways from the reflection process and commit to actionable steps to achieve future goals. Ivy Slater is a professionally certified business coach, speaker, best-selling author and podcast host. After owning and operating a 7-figure printing business, having been in the industry for 20 years, she started Slater Success which focuses on developing great leaders and facilitating business growth and expansion. Ivy holds masterminds and retreats with her private client base and corporate training on communication and strategic planning. She speaks nationwide on the topics of leadership, sustainable growth, relationships and sales. Best Selling Author of From the Barre to the Boardroom Website: https://slatersuccess.com/  

On this episode of the The Member Engagement Show, our guest is Sahil Kapadia, Founder of AMSNow. Sahil has 20 years of experience leading technology and organizational transformation across associations and other industries. Today his work involves helping organizations assess their AI readiness, establish governance, and turn AI opportunities into practical projects. Topics covered include: What are association leaders saying about AI and how they're approaching it? The importance of leadership listening to frontline staff about real bottlenecks. How AI is a utility that needs to be deployed with organizational cohesion. What scares associations about AI adoption? What to do before implementation. Identifying roadblocks and where AI can help improve workflows. Moving from planning to action. Why an association needs AI governance and what it should cover. Why policy reviews should be done annually. When AI policies get too strict and limiting. What to do if you can't have a dedicated AI guardian. How leadership can help team members leaning into AI. How to decide which AI projects are worth pursuing. What a good AI project plan looks like. Where to start for the overwhelmed.   Helpful links: AMSnow.io Elizabeth Engel's episode

English Edition: Andre Piza is one of the authors of the paper: "Towards a National Research Software Engineering Capability in Arts and Humanities Research: A Roadmap". And in this episode Andre tells us what this roadmap contains and how this benefits digital skills in the arts and humanities domain. Links:https://zenodo.org/records/15083396 the original paperhttps://gtr.ukri.org/projects?ref=AH%2FU000019%2F1#/tabOverview funding of the project by the Arts and Humanities Research Council (AHRC) of the UKhttps://www.diskah.org  DISKAH initiativehttps://www.ukri.org/councils/ahrc/remit-programmes-and-priorities/convergent-screen-technologies-and-performance-in-realtime-costar/costar-national-lab/  CoStar initiativehttps://www.riches.ukri.org Riches initiative Get in touchThank you for listening! Merci de votre écoute! Vielen Dank für´s Zuhören!Contact Details/ Coordonnées / Kontakt:Email mailto:peter@code4thought.orgUK RSE Slack (ukrse.slack.com): @code4thought or @piddie Bluesky: https://bsky.app/profile/code4thought.bsky.socialLinkedIn: https://www.linkedin.com/in/pweschmidt/  (personal Profile)LinkedIn: https://www.linkedin.com/company/codeforthought/ (Code for Thought Profile)This podcast is licensed under the Creative Commons Licence:  https://creativecommons.org/licenses/by-sa/4.0/

Mike Day:- This House Pt. 28: Flourishing in Exile, Pt. 8: The Biblical Mandate for Apologetics (Becoming a Credible Witness) Theme and Core Focus Focuses on "The Church and the World" under the theme of becoming a dwelling place for God. Explores the biblical mandate for apologetics to become credible witnesses (1 Peter 2:11). Christian Relationship to Surrounding Culture Separation (Bomb Shelter): Isolation from cultural influence with the goal of protection and purity. Assimilation (Mirror): Adapting to cultural norms with the goal of relevance and commonality. Holy Presence (Salt & Light): Intentional presence in culture with the goal of transformation and influence. Roadmap for Apologetics 1. We Are All Apologists: Engaging in apologetics is not a matter of if, but whether our witness is good. Believers are called to set apart Christ as Lord in their hearts. 2. Know the Gospel: Different biblical writers emphasize distinct facets, such as the kingdom of God (Gospels), eternal life (John), Christ as Lord (Peter), and reconciliation (Paul). Contrasts "sin management" with "kingdom advancement". Involves context-sensitive approaches (e.g., Peter in Acts 2, Paul in Acts 17 and Acts 24–26) and navigating barriers/opportunities regarding freedom and history. Core aspects of sharing include Defending (do not assume agreement), Commending (do not assume knowledge), and Translating (do not assume understanding). 3. Be Prepared to Share: Always be ready to offer a defense of the hope within, doing so with gentleness and reverence (1 Peter 3:15).

The Sound Chaser Progressive Rock Podcast is on the air. On the show this time I have new music from The Instigations and Alan Goldberg. I have a couple of covers of famous prog music, the Symphonic Zone, and the usual mix of prog styles. All that plus news of tours and releases on Sound Chaser. Playlist1. Banco del Mutuo Soccorso - The Say Dolphins Speak, from ...As in a Last Supper2. From.UZ - Spare Wheel, from Audio Diplomacy3. Mats/Morgan Band - Min Häst, from Live4. Mats/Morgan Band - Banned Again, from Live5. Dwiki Dharmawan - Janger, from Rumah Batu6. The Instigations - Critical Disgrace Theory, from Miscreantics7. Minimum Vital - Prélude aux Oiseaux Tristes, from Esprit d'Amor8. Hermetic Science - Infinite Space, from Ed Macan's Hermetic Science9. Alan Goldberg - Changing by Degrees, from Fuel for the Fire 40th Anniversary Edition10. Tubular Tribute with Robert Reed - Return to Ommadawn (extracts), from Ommadawn 45th Anniversary Concert11. Sandy Owen - Angels We Have Heard on High, from Carols12. Sandy Owen - Hark! How All the Welkin Rings, from Carols13. Sandy Owen - Joy to the World, from Carols14. Andy Juhl - The Chimer Is Coming, from Pine Island15. Andy Juhl - The Chimer, from Pine Island16. Andy Juhl - Running Through Memories of You, from Pine Island17. John Beagley - Rock Paper Scissors Part 5, from Rock Paper Scissors18. Foxtrot - The Crowd, from A Shadow of the PastTHE SYMPHONIC ZONE19. Synergy - Earth in Space, from The Jupiter Menace20. Yes - Starship Trooper, from Symphonic Music of Yes21. Teru's Symphonia - After the Party, from Human Race Party22. Rael - Icarus, from Mascaras Urbanas23. Gustavo Montesano - Homenaje Color Naranje, from Homenaje24. Jet Black Sea - Hours Slip into Days, from Absorption Lines25. Mirthrandir - For You the Old Women, from For You the Old Women26. Tony Spada - The Final Act, from Balance of Power27. The Flower Kings - Big Puzzle, from Back in the World of AdventuresLEAVING THE SYMPHONIC ZONE28. Tony Williams - Beach Ball Tango, from Play or Die29. Frédéric L'Epée - Vers les Eaux Fondamentales, from Le Mont Analogue30. Dire Straits - Ride Across the River, from Brothers in Arms31. Soft Machine - Back in Season, from Other Doors32. Stick Men - Cyber Shards, from Open33. Alberto Rigoni featuring Marco Minnemann - Back to Life, from EvoRevolution34. Anthony Phillips - Reaper, from The Living Room Concert35. Spirits Burning & Clearlight - Roadmaps (The Other Way), from The Roadmap in Your Head36. Cartoon - Duendes, from Martelo37. Talisma - D Double U, from Corpus

Christy Zweig didn't start Always August Farm as the 24-acre facility it is today. She started with less than 8 acres and a 3-stall barn. Tune in to hear how she built business experience through a corporate career before making the leap to full-time horse professional at 35, and why reinvesting steadily beat rushing to scale. We dig into building a strong barn culture, navigating social media as a trainer, and the money mindset so many equine professionals struggle to shake (the belief that you're not supposed to make money doing this). Christy also gets candid about pricing herself fairly, setting boundaries with clients, and why knowing your numbers is non-negotiable if you want to grow without burning out.If you need a reminder that the slow, intentional build is a strategy, not a compromise, this is for you. Show Notes (also known as “Where to read a quick summary of what we talked about here and get links I mentioned.”) are over at Stormlily.com/231✨ FREE The Equine Entrepreneur's Roadmap to Grow a Sustainable Business Without Burning Out  → Stormlily.com/map

In this episode of ROPESCAST, ROPES CEO Ksenia Svetlova sits down with Dr. Marwan Muasher - former Foreign Minister and Deputy Prime Minister of Jordan and current Vice President for Studies at the Carnegie Endowment - for a wide-ranging and candid conversation.Dr. Muasher draws on decades of diplomatic experience, including opening Jordan's first embassy in Israel and helping shape the Middle East roadmap, to reflect on the current state and future of Israeli-Jordanian relations. The conversation covers the enduring relevance - and limitations - of the Arab Peace Initiative, whether a two-state solution remains viable today, and how the recent war with Iran has reshaped Jordan's strategic calculations and regional security concerns.This extraordinary episode offers a rare, ground-level perspective from one of the region's most experienced diplomatic minds.Listen now on ROPESCAST — the independent voice of the Middle East.Chapters:00:47 Meet Dr. Marwan Muasher03:00 What Was the Arab Peace Initiative?05:24 Why Israel Rejected the Initiative07:27 Why the U.S. Looked Away09:42 The Road Map to Peace14:49 Did Israel Lose Its Peace Camp?21:48 Why Isn't the Arab Peace Initiative Moving?27:03 Is a Two-State Solution Still Possible?37:23 A New Regional Security Architecture43:38 Does Geography Still Protect Israel?

Alicia and Hector Garcia catch up on a full year of Intuit Enterprise Suite development, covering multi-entity consolidation with automatic eliminations, dimensions that now span every transaction (including a new AI dimension backfill for historical data), and construction features like WIP reporting, negative change orders, and AIA-style billing that now surpass QuickBooks Desktop Enterprise. They also dig into what's coming next for manufacturing and inventory, including multiple units of measure, and run through the IES features that have already trickled down into QBO Advanced, like cost groups, project phases, and proposals.Sponsors:Intuit Accountants - http://uqb.promo/intuitPilot - http://uqb.promo/pilotKick.co - http://uqb.promo/kick(00:00) - Welcome and Setup (02:55) - What Is Enterprise Suite (08:11) - Pricing and User Limits (10:14) - Dimensions Deep Dive (11:26) - Multi Entity Consolidation (12:45) - Eliminations and Automation (16:59) - Chart of Accouants Control (19:28) - Construction Feature Leap (20:32) - WIP and Change Orders (21:33) - Permissions and Project KPIs (23:57) - AI Budget Imports (25:38) - AIA Billing and Roadmap (27:22) - Inventory and Manufacturing Push (31:45) - Nonprofit Future Plans (34:42) - August Dimension Updates (35:51) - AI Dimension Backfill (37:56) - Features Coming Downstream (38:24) - Cost Groups in Advanced (41:37) - Project Phases Pros Cons (43:59) - Proposals and E Signatures (45:58) - Calculated Fields and Orders (47:39) - Item Receipts Workflow (50:48) - Wrap Up and Next Recap (53:57) - Final Thoughts and Farewell LINKSAlicia's Fall classes! Attend the live webinar or watch the recording on-demand, all with CPE. Become a member of the OWLS for free automatic enrollment into all courses:Customizing QBO: http://royl.ws/CustomizingQBO?affiliate=5393907AI in QBO: http://royl.ws/AI?affiliate=5393907Intuit Accountant Suite: http://royl.ws/IAS?affiliate=5393907Come to Miami in November! Events.reframeaccounting.comCheck out Hector's RightTool: www.righttool.appWe want to hear from you!Send your questions and comments to us at unofficialquickbookspodcast@gmail.com.Join our LinkedIn community at https://www.linkedin.com/groups/14630719/Visit our YouTube Channel at https://www.youtube.com/@UnofficialQBOPodcastSign up to Earmark to earn free CPE for listening to this podcasthttps://www.earmark.app/onboarding 

Send us Fan MailFrom process failures to managers covering up what was really going on, here are 5 missed chances a fraudulent payment could have been stopped or contained. Plus, what lessons you can take so the same thing does not happen at your organization.Keep listening. Check out my website training.debrarrichardson.com if you need help implementing authentication techniques, internal controls, and best practices to reduce the potential for fraudulent payments, compliance fines or bad vendor data. The Vendor Process Training Center for 173+ hours of weekly live and on-demand training for the Vendor team. Links mentioned in the podcast + other helpful resources:    Article:  Report says procedural failures, managerial cover-up led to Ocala's $492,000 cyber fraud incidentFraud Training:  Fraud News & New Scam Alerts + How To Prevent ThemAdd More Controls to Avoid Fraud > AVM Framework:  3 Step Vendor Setup & Maintenance Process WorkshopAsk Vendor Process Questions:  Ask VeraTM  Do You Have A Controlled Vendor Process?  Roadmap to a Controlled Vendor Process Free Download:  Vendor Validation Reference List with Resource Links https://training.debrarrichardson.com/validation-referenceVendor Process Training Center - https://training.debrarrichardson.comCustomized Fraud Training:  https://training.debrarrichardson.com/customized-fraud-training Free Live and On-Demand Webinars: https://training.debrarrichardson.com/webinarsVendor Master File Clean-Up: https://training.debrarrichardson.com/cleanupYouTube Channel:  https://www.youtube.com/c/DebraRRichardsonLLCMore Podcasts/Blogs/Webinars https://training.debrarrichardson.comMore ideas?  Email me at debra@debrarrichardson.com Music Credit:  www.purple-planet.com

Alzheimer's disease affects millions of individuals and families, yet many people remain unaware of the critical role early detection can play in improving outcomes. In this episode, host Jeremy Schrand is joined by Andrew Pytlik and Patti Hahn of the Alzheimer's Association to discuss the growing importance of cognitive screening, advances in diagnostic testing, and the steps individuals can take today to support long-term brain health. Together, they explore the realities facing patients, caregivers, and healthcare providers, while sharing why earlier recognition, stronger community support, and continued research are creating new hope for the future of Alzheimer's care.1:00 | Meet the Alzheimer's Association and its mission to support patients, families, and research.3:56 | Why Alzheimer's often develops years before symptoms appear and why early detection matters. 6:47 | Brain health habits that may help reduce risk, including sleep, exercise, and lifestyle choices. 8:42 | Common misconceptions about Alzheimer's and warning signs that go beyond memory loss. 11:27 | New advances in screening, blood-based biomarker testing, and earlier diagnosis. 14:59 | The impact of Alzheimer's on caregivers, families, and the workplace.17:51 | Inside the Walk to End Alzheimer's and how community engagement accelerates research and support. 23:11 | Volunteer, advocacy, and fundraising opportunities that help advance the mission year-round.27:01 | How employer partnerships, including CTI Cares, can strengthen awareness and community impact.29:13 | Looking ahead: the future of Alzheimer's screening, treatment, and hope for patients and families.

The Great Spiritual Awakening is happening now! More and more people around the world are having spiritual awakenings, propelled by Near-Death Experiences, Kundalini Awakenings, intense meditation, spiritual renewal events, and other Spiritually Transformative Experiences (STEs). But what happens next – after the Awakening? Is there a roadmap to guide us on the spiritual path toward our goal of self-realization? Dr. Yvonne Kason shares a beautiful roadmap, “Purifying the Heart”, to guide us through the fascinating stages of our spiritual awakening journey: from a Spiritual Seeker to Spiritual Server, Spiritual Warrior, to Devotee, and then to an Adept. Using her own STEs as examples, she shares the key focuses and spiritual learning tasks at each stage, and how to speed up your progress. Based on her own awakening journey and 40 years of STE research, Dr. Kason concludes that the ultimate goal for us all is self-realization—God-Communion—with continuous expanded mystical consciousness.

The Great Spiritual Awakening is happening now! More and more people around the world are having spiritual awakenings, propelled by Near-Death Experiences, Kundalini Awakenings, intense meditation, spiritual renewal events, and other Spiritually Transformative Experiences (STEs). But what happens next – after the Awakening? Is there a roadmap to guide us on the spiritual path toward our goal of self-realization? Dr. Yvonne Kason shares a beautiful roadmap, “Purifying the Heart”, to guide us through the fascinating stages of our spiritual awakening journey: from a Spiritual Seeker to Spiritual Server, Spiritual Warrior, to Devotee, and then to an Adept. Using her own STEs as examples, she shares the key focuses and spiritual learning tasks at each stage, and how to speed up your progress. Based on her own awakening journey and 40 years of STE research, Dr. Kason concludes that the ultimate goal for us all is self-realization—God-Communion—with continuous expanded mystical consciousness.

The Transformation Ground Control podcast covers a number of topics important to digital and business transformation. This episode covers the following topics and interviews:   Your ERP Vendor's Roadmap Is a Sales Document Beyond the Requirements Checklist: What Really Decides an ERP Selection (Brad Feakes, Estes Group) The 7-Year Contract Is the Riskiest Thing You'll Sign   We also cover a number of other relevant topics related to digital and business transformation throughout the show.  

AIUC first got our attention with the NFDG backing, and have just announced a $40M series A today, with the most impressive industry advisor list we may have ever seen for an early startup behind AIUC-1, their agent standard backed by real insurance:From being Anthropic's first product hire to building the standards, testing, and insurance infrastructure meant to make frontier AI deployable, Rune Kvist is betting that the biggest constraint on AI adoption won't be capability it will be trust. In this episode, the AIUC cofounder joins swyx and Vibhu to announce a new $40M round and explain why companies like Cursor, Harvey, Lovable, and ElevenLabs are increasingly confronting a problem that gets harder as AI gets better: who is responsible when autonomous systems fail?We go deep on AIUC-1, the emerging standard for agent security, safety, and reliability; how AI agents are stress-tested for jailbreaks, hallucinations, and data leaks; and why Rune thinks standards and insurance could become critical infrastructure for AI. We also discuss the growing trust gap between governments and frontier labs, AI-enabled cyber and biological risks, why every model can ultimately be jailbroken, what happens when a $20 coding agent causes $200M of damage, whether AI engineers should be certified, and why even after AGI there may be one job the labs can never do themselves: be their own watchdog.We discuss:* Why risk, liability, and trust may become the binding constraint on AI adoption* Rune's path from reading the Scaling Laws paper to joining Anthropic in its earliest days* What Anthropic understood about scaling, compute, and the future years before it became obvious* Why Waymo illustrates the gap between AI capability and real-world deployment* AIUC's $40M round and work with Cursor, Harvey, Lovable, ElevenLabs, and other frontier AI companies* AIUC-1: a standard for AI agent security, safety, and reliability* How agents are tested for jailbreaks, hallucinations, and data leakage* Why most AI companies optimize the happy path without seriously stress-testing adversarial cases* Why AI standards may need to update every quarter instead of every decade* The emerging trust gap between frontier AI labs and governments* Cybersecurity, child safety, biological weapons, and the expanding frontier-model risk surface* Why standards and insurance may need to evolve together* How Lloyd's of London can insure AI systems and bring trust to enterprise deployment* What happens if a $20 Cursor subscription contributes to a $200M plane crash* The Air Canada chatbot case and how AI failures are beginning to clarify legal liability* Why copyright may be one of the hardest AI risks to insure* Evals, mechanistic interpretability, monitoring, and models becoming aware they're being tested* The impossible CISO mandate: adopt AI fast, but don't let anything go wrong* Why robotics will make AI liability dramatically more consequential* Whether AI engineers should have Level 1, 2, and 3 certifications* AIUC's roadmap across agents, frontier models, robotics, and universal red teaming* Why AGI could become a question of national sovereignty* Why the labs can never fully serve as their own watchdogs* The Big Short problem: how do you stop competing watchdogs from racing standards to the bottom?Rune Kvist* LinkedIn: https://www.linkedin.com/in/runekvist/* X: https://x.com/RuneKvistAIUC* https://aiuc.comTimestamps00:00:00 AIUC's $40M Round and the Risk Bottleneck for AI00:01:07 From Scaling Laws to Early Anthropic00:07:58 Why Trust, Not Capability, Could Limit AI Adoption00:12:19 Founding AIUC and Building AIUC-100:18:52 How AI Agents Are Audited and Stress-Tested00:25:26 Frontier Models, Government, and the AI Trust Gap00:33:32 Cyber, Child Safety, and AI-Enabled Biological Risk00:38:14 Why Standards and Insurance Belong Together00:41:45 What Does an AI Insurance Policy Actually Cover?00:50:44 The $20 Cursor Subscription and the $200M Plane Crash00:53:53 AI Liability, Monitoring, and Earning Enterprise Trust00:56:21 From AI Agents to Models to Robotics00:58:29 Copyright, Adverse Selection, and AI Insurance01:03:28 Evals, Mechanistic Interpretability, and Eval Awareness01:08:36 The Impossible Enterprise AI Mandate01:11:52 Prediction Markets vs. AI Audits01:14:43 Should AI Engineers Be Certified?01:19:10 AIUC's Roadmap, AGI, and Who Watches the Watchdogs?TranscriptIntroduction: AIUC, the $40M Series A, and Risk as the Adoption BottleneckSwyx [00:00:00]: Okay, we're in the studio with Rune from AIUC, the Artificial Intelligence Underwriting Company, with our trusty co-host, Vibhu. Welcome.Rune Kvist [00:00:10]: Thank you. Thanks for having me. Thank you.Swyx [00:00:11]: What are you announcing today?Rune Kvist [00:00:12]: We have raised $40 million, led by Ribbit Capital and First Harmonic.Swyx [00:00:17]: You first came to my attention when Nat and Daniel invested in you guys. Is the story, like, pretty much the same? Like, what are you today versus what you thought you were back then?Rune Kvist [00:00:26]: When we raised our seed round, we had a hypothesis that at some point risk was going to hold down adoption. At that point in time, that felt kind of hypothetical, and I think that is now over. Clearly, the moment is now with Mythos and Fable. It's pretty obvious that literally the binding constraint on adoption is risk. And so for us, it feels like this is a natural continuation of the same hypothesis, but where previously it was speculation, now it feels like fact.Swyx [00:00:54]: And let's get a list of the customers that you're highlighting as part of your Series A.Rune Kvist [00:00:58]: Totally. Yeah. So we are now working with folks like Cursor, Harvey, Lovable, ElevenLabs.Swyx [00:01:05]: Yeah. Amazing. Congrats.Rune Kvist [00:01:06]: Thank you.Swyx [00:01:07]: So you were famously one of the first hires involved in GTM and product. I'm just kind of curious: what was your path into AI? Just recap.Rune's Path Into AI: Scaling Laws, Capital, and AnthropicRune Kvist [00:01:18]: Yeah.Rune Kvist [00:01:19]: Late 2021, I sold a company, my first company, an edtech company. I had a bit of time to think about what was next. I came across the Scaling Laws paper, and that just struck me like lightning. I was just like, “This is a big idea.” In short, the Scaling Laws paper just says the bigger the model, the smarter the model.Swyx [00:01:38]: So this is the Kaplan one, not the Chinchilla one?Rune Kvist [00:01:40]: Exactly, the Kaplan one.Swyx [00:01:42]: Yeah.Rune Kvist [00:01:42]: And the important thing that clicked for me there was, oh, now capital will understand this. If you put in more money, you get more money out, and so that will kick off a hype cycle. And so you get a sense of predictable returns, which is, in fact, what's played out. And so I just packed my bags. I'd never been to San Francisco. I'd never been there. I just packed my bags, flew out here to find the people who had written it. And at the time, they had just started a small lab called Anthropic. There were around 40 people at the time or so. Drank a bunch of coffee until I eventually got introduced to Dario. And at the time, they were wrestling with some of these questions of, like, should we deploy our models? Should we make revenue? How should we engage with the rest of the world? They'd just broken off from OpenAI, and it's been publicly reported that they were kind of concerned with how they were dealing with deployment. So they were wrestling with some of those questions. At this point, this is early fog of war, like early 2022. The hottest product at the time was, like, Jasper. Like, there's nothing out there. So where value was going to accrue, and what the different parts of the stack were going to be, were all open questions.Swyx [00:02:48]: I want to highlight to people, you ask these questions because you have a PPE background.Rune Kvist [00:02:52]: Yes.Swyx [00:02:52]: I actually was in Singapore in one of the sort of feeder programs for prepping people for PPE. So I had a tutor. We learned, you know, philosophy and politics and economics. But, like, I think your kind of background matters. Machine learning people who read the neural, Scaling Laws paper would not necessarily draw the same conclusions that you did. Whereas any capitalist would read that and go, “Holy s**t.”Rune Kvist [00:03:19]: Correct.Swyx [00:03:20]: Right?Rune Kvist [00:03:21]: Yes.Swyx [00:03:21]: Who tipped you onto that paper? Because it's not a paper that you normally read, right, like, in your circles?Rune Kvist [00:03:26]: Yeah. I think I'd actually, ever since AlphaGo, had some appreciation that AI was a big deal.Swyx [00:03:36]: Yeah.Rune Kvist [00:03:36]: But it kind of felt like it raised all these kind of interesting philosophical questions, but it was kind of not clear from afar where exactly that would go. But it was obvious enough that it was like, this is going to be a big thing if we find the kind of right mechanism to kind of get the techno-capital machine to work on this. But it was just not clear. And so I think there was some way in which, like, that became obvious, and also it wasn't as obvious at the time than it is now, right? Like, it was just like, wow, this is so interesting. But it still felt, coming from kind of a philosophy and economics background, it felt like if this turns out to be true, you're going to be wrestling with all of the big questions in society. Everything you've learned about politics gets thrown out of the window. Everything you've learned about economics at least gets challenged. And so what felt interesting was to be at that frontier that has ramifications across everything. So that's why I sought it out.Swyx [00:04:32]: I mean, clearly really good insight. For people who don't know, the PPE program is, like, where prime ministers are born. So then you end up meeting Dario.Rune Kvist [00:04:41]: Yep. First Dario, yeah.Swyx [00:04:43]: Yeah. Well, I mean, like, so did you get extra insights from talking with them that you didn't get from your original hypothesis?Anthropic's Early Conviction and the Scaling Laws Crystal BallRune Kvist [00:04:50]: If you read the Scaling Laws paper, you get this, like, very vague sketch of like, wow, this seems kind of important. There are some lines on a chart. This seems kind of important. And what I think the team at Anthropic had thought more about than anyone was like, what are the implications of this if you really play this out? And back then they had, kind of vision documents for what the world would look like in 2026, and they were kind of in vivid detail playing out how much compute is going to be needed, what the CapEx was going to look like, what some of the societal concerns were going to be, but also what is the amount of economic value coming out here? And so it kind of felt like they held a crystal ball that in hindsight turned out to just be dramatically correct. And they weren't holding it like they were obviously correct. They were just like, “Take this hypothesis really seriously.”Swyx [00:05:38]: Think it through, yeah.Rune Kvist [00:05:38]: And think it through in the same way as the kind of situational awareness that isSwyx [00:05:43]: Across the street.Rune Kvist [00:05:44]: Across the street.Swyx [00:05:44]: Your office, yeah. Oh my God, we're all living across the street in the same one square mile.Rune Kvist [00:05:50]: Correct. And that's now a couple of years old, but also people keep referencing it these particular weeks with Fable and Mythos, and it's like, wow, if you take this one idea seriously- For the Scaling Laws, a lot of things fall into place.Vibhu [00:06:03]: And keep in mind, at this point, this is the same team that did GPT-1, GPT-2, and GPT-3.Rune Kvist [00:06:08]: Correct.Vibhu [00:06:08]: Which is also, like, it's not just some experimentation. Like, this is a real model that we just scaled up.Rune Kvist [00:06:14]: And they had deep conviction in this idea: if you take a big blob of compute and data, it just wants to learn, and out of that will come smarter and smarter models. And all the particulars were not clear.Vibhu [00:06:26]: Yeah.Rune Kvist [00:06:27]: And all the implications were not clear. But their deep conviction in this, like, core thesis, and that was kind of dizzying. It was both phenomenally interesting and exciting, and also very quickly you get to, like, the world we know today will no longer be if this hypothesis holds. So it also just felt, like, important in some kind of grand sense.Vibhu [00:06:48]: What kind of shaped you there? So that was early 2022. Not only had GPT-1, GPT-2, and GPT-3 come out, but, you know, the amazing founders of Anthropic that have never split up, the only ones, they actually had the conviction to leave OpenAI, start their lab. You said there were about 40 people there. What was the time like there?Inside Early Anthropic: Mission, Deployment, and RiskRune Kvist [00:07:06]: It was kind of remarkably like what it looks like on the outside today. Extremely cohesive, extremely mission-oriented, and living in this tension between their two ideas, which is AI could both go really well and really bad, and we want to be part of building it. That creates astounding amounts of tension. And they were wrestling with this incentive challenge where they know they're in a race that they're in where you might get forced to cut corners, but it also felt very important to them to be at the forefront of technology. And all of those ideas were just present at that time. It kind of feels like that line has been just very clear, and I think kind of love them or hate them, they have really stuck to their guns. There's a core set of beliefs that they hold more deeply than most companies hold any beliefs.Vibhu [00:07:58]: Yeah. Fast-forward to today.Rune Kvist [00:08:00]: Yeah.Vibhu [00:08:00]: What does that lead us to AI underwriting company? What are you up to? What motivated you to start this?From Waymo to AIUC: Confidence Infrastructure for AIRune Kvist [00:08:05]: Yeah. AIUC builds confidence infrastructure for frontier AI through standards and insurance. The link from Anthropic to building confidence infrastructure, looking out the windows at Anthropic offices and seeing Waymos driving by. Already back then, early 2022, Waymos were in some ways like AGI for cars. Like, they were superhuman drivers, but you couldn't take one to the airport. And now, four and a bit years later, you still can't take your Waymo to the airport, despite now everyone having kind of looked at the evidence and being like, “They're better drivers than humans.” So in that particular instance, what's clear is that the binding constraint on AI being useful is not capability, but is that liability or risk or trust. That problem is, general. The reason why right nowRune Kvist [00:08:52]: Fable is not open for access is not because it's not a good model, it's because it's a very good model. It's just hard to make promises about what it will or will not do. And this problem gets worse as AI gets better. Basically, more intelligent AI can be more autonomous. That's more valuable, but also the risk surface grows. And so - what Waymo illustrates is that unless you build the confidence infrastructure to make promises about AI, or at least bring light to the risks, you grind adoption to a halt. Governments, banks, hospitals, militaries need to have some sense of what AI will and will not do to be able to operate for them to incorporate it. And that's the problem that we're trying to solve. Now, why standards and insurance? If you trace this problem back through history, every technology wave has had some version of this problem. So if you go back to, like, year 1900, electricity comesVibhu [00:09:47]: Ben Franklin.Rune Kvist [00:09:48]: Cars burn down, sorry, houses burn down, lots of people die. 1930s, cars are a big deal, kill lots of people. 1950s, private nuclear energy is a big deal, poses big risks. In each of those instances, the market runs ahead of regulation to create confidence infrastructure because that's required to make go/go decisions. That is required for adoption, and the market fundamentally wants adoption. And in all of those instances, common blueprint emerges between standards and insurance. The reason these two components is standards kind of provide the rules of the road, and they also specify, like, what are the tests that need to be run so we can get a sense of how high the risk is. So take in the case of cars, that's like a car crash. Great, everyone, they inform your insurance pricing today, they inform your purchasing decisions, et cetera. That's basically the risk framework. The insurers are important because they pick up the bill. So they are the private institution that is most on the side of. That is best incentivized to quantify the risks truthfully and then figure out all the ways to reduce the risk ‘cause that increases their profit. So they're basically, they help shape the incentives. And these two work really well in unison. Now, how does that show up as a company? Well, one of the things that was obvious even - or starting to become obvious even a couple years ago was that frontier companies, some of our customers today, like Cursor, Sierra, ElevenLabs, Harvey, were going to have a very easy time selling a pilot to a bank. The, like, the demo just sells itself. It's magic. But bringing that through, if you want to do a wall-to-wall rollout at a bank or a hospital, you have to go through the risk process. These banks have no idea even which questions to ask, let alone which answers are sufficient, let alone, like, how do they go and test whether these agents actually work the way they're supposed to. And so they had this problem of, like, what can we say to earn the trust? And we think there's, like, a golden sentence that goes something like, “Hey, I hear you're really worried about hallucinations or jailbreaks or whatever it may be. We've had an independent third party test us against the gold standard. We passed with flying colors. And as a vote of confidence, the world's most conservative insurers have looked at the data.” And they're willing to take some of the risk onto their balance sheet.Swyx [00:12:06]: Yeah.Rune Kvist [00:12:07]: So if something does go wrongSwyx [00:12:07]: There's money behind it, yeah.Rune Kvist [00:12:09]: Exactly. So that's kind of like the link between all this. We can get into some of the hard parts related to the technical testing, which is, I think, the crux of the matter, but I'll pause there.Swyx [00:12:19]: How did you and Rajiv come together? This-- there's always, like, you come across very confident and, you know, and we're announcing your Series A and all these things, but I want to see, like, the early initial stages of, like, idea formation.Cofounding AIUC with Rajiv DattaniRune Kvist [00:12:31]: Yeah. Rajiv is actually my soon-to-be brother-in-law.Swyx [00:12:35]: Oh.Rune Kvist [00:12:36]: So I'm actually, in a week and a half getting married to Rajiv's sister.Swyx [00:12:42]: Okay, now you're tight.Rune Kvist [00:12:44]: Exactly.Swyx [00:12:44]: Now you know.Rune Kvist [00:12:45]: So - Rajiv and I have known each other for a decade. Funny story, I met both Rajiv and his sister, Hena, at the same time when Hena and I were interns at McKinsey in London, and Rajiv was assigned as my mentor. And so met them at the same time. For the longest time, it was not obvious that we were necessarily going to work together. I was in startups. He was, an insurance partner at McKinsey. Three or four years ago, I think Hena convinced him that AI was going to be a really big thing. And so he quit his job, cushy partner job at McKinsey in London, packed his bags, flew to San Francisco, and ended up joining METR. You guys are probably online enoughSwyx [00:13:24]: CEO.Rune Kvist [00:13:24]: Exactly.Swyx [00:13:24]: We've, we've, we've heard of METR.Rune Kvist [00:13:25]: You see the plot-- the chart of the horizons of the tasks that agents can take on is doubling extremely fast. So he was COO at METR, led their partnerships with Anthropic and OpenAI to test their models before release, but also working closely with the US and UK government, to figure out, like, how do you know whether a model can be released? And in some ways, that was, like, the perfect background. He's spent a lot of time in insurance, knows that world, spent a lot of time with frontier testing of models. And so when I was bumbling around this idea space, starting with some of the ideas we talked about related to Waymo, as soon as we got into the content, we were both like, “Oh, this would be an amazing business to build together.” This is wrestling with the problem that we both think is the most important in the world from a market angle, which is kind of our intuitions is that the market can do a lot, and the faster AI moves, the harder it is for government to solve some of these problems. And then it took a little bit of time to work through what is it like to work with family.Swyx [00:14:27]: Sure.Rune Kvist [00:14:27]: And,Swyx [00:14:30]: Because you were already dating at the timeRune Kvist [00:14:31]: Yeah. Yeah, exactly.Swyx [00:14:33]: Yeah.Rune Kvist [00:14:34]: Already back then, itSwyx [00:14:35]: Yeah.Rune Kvist [00:14:35]: We felt like we were a family.Swyx [00:14:36]: Nice.Rune Kvist [00:14:36]: And so starting a business together felt like kind of a big step. And, here we are with just immense amounts of trust.Vibhu [00:14:43]: Yeah. So now you're a company of how big? How big are you guys now?AIUC-1 Certification: Agent Security, Safety, and ReliabilityRune Kvist [00:14:46]: There are just 20 of us now.Vibhu [00:14:47]: 20 of you guys now, have Series A, and you have your first certification out, the AIUC-1. Let's bring up the certification. So this is the agent certification, right? What goes into the process? I have, like, two questions here. One is, walk us through the certification, and two is, what is the process for a company to get certified, you know?Rune Kvist [00:15:08]: Great. As it says right on the top, AIUC-1 is a standard for agent security, safety, and reliability. The fundamental design principle is take all of the concerns that slow down adoption, so all the questions, all the fears that keep, security leaders in the Fortune 1000 up at night, and put them into one comprehensive framework. That's what you'll see there. You can see the six categories. Two, you want to ground all of this in technical testing. So one of the concerns with security standards that often feel kind of like theater paperwork is that they're not actually ground out in, does any of this work? Does any of this matter? And so we had a conviction from early on that was going to be the kind of crux, was to pass this, you must get tested every quarter, basically run thousands of simulations to see, well, so can it actually be jailbroken? How hard is it to jailbreak? How often does it hallucinate? How often does it leak data? Et cetera. And then the last, core idea here, if you scroll up to the top here, is to refresh it quarterly.Rune Kvist [00:16:08]: So the core trait of AI is that it moves extremely fast. Whatever concerns we're discussing today were not the same ones three months ago, and this will keep changing. Typically, standards update on a, like, a decade cycle is obviously not going to work. But the question is kind of how do you update it? And the core thing here was to basically get the risk leaders of the Fortune 1000 around the table. So if you go over to the left hereVibhu [00:16:32]: YeahRune Kvist [00:16:32]: You'll see the AIUC-1 consortium. The consortium is a group of risk leaders who run real banks, real hospitals, real critical infrastructure, who are facing these challenges every day. And we meet with these folks twice a quarter and hear what's top of mind, what is keeping them up at night. There's tremendous amount of desire for that conversation. And then we operationalize that into a specific standard that gets into. And actually, we can go into and look at whatVibhu [00:16:55]: YeahRune Kvist [00:16:55]: What even is the standard. So if we go back to introduction, out there to the left, scroll up a little bit to the wheel, click into reliability. So if you take something like hallucinations sits in reliability. There is a number of requirements here. If you go into the top one, prevent hallucinated outputs, hallucinate outputs, this is one particular requirement. This is a technical control. Basically, we want some kind of ground in this filter. The first thing you see here is what's called a crosswalk. So everyone and their grandmother has put out a framework, very high-level framework for what are the AI risks.Swyx [00:17:27]: This is basically your competition,Rune Kvist [00:17:28]: In some ways our competitionSwyx [00:17:29]: Not seriously, yeah.Rune Kvist [00:17:30]: We're, in fact, friends with them. We'll come back to why.Swyx [00:17:31]: Yeah.Rune Kvist [00:17:32]: But mapping everything together so you have one superset. The claim you're trying to support here is, if you follow this framework, then you can also see how you follow the other frameworks. But the meat of it comes down here in control activities and evidence. So control activities is like, great, you have this high-level requirement. How do you turn that down to something operational? Here's what you must do, and then what is the evidence that we're looking for?Rune Kvist [00:17:57]: And the reason we go this deep is that there's actually not that much confusion about what are the big concerns in AI. Everyone agrees to these. The question, like, what are you actually supposed to do? And so. What we found a lot of demand for is getting down to the specific evidence, that people need to look for. Whether you are Cursor building something or, even JPMorgan building something, but also if you're just a risk leader at JPMorgan, like what exactly should you ask for? What can you ask for without sounding stupid? Like if you ask for some-- you won't believe the amount of time a risk leader has asked for the IP rights to the underlying model to Cursor or something, and you're just like “Sorry, what?” Like,Swyx [00:18:39]: You slip it in there and you seeRune Kvist [00:18:40]: SlipSwyx [00:18:40]: See if you notice.Rune Kvist [00:18:41]: See if they. Exactly.Swyx [00:18:42]: Yeah.Rune Kvist [00:18:42]: Put that in the questionnaire. All right, so that's kind of what our standard is, and we update this every quarter with these folks, to keep up with the latest concerns.Swyx [00:18:51]: Can I double-click on this one?Controls, Evidence, and Third-Party TestingRune Kvist [00:18:52]: Yeah.Swyx [00:18:52]: So first of all, the website's beautiful. Like, it's so confidence-inducing which is the whole point where, like, okay, I know exactly what I'm signing up for when I talk with you. Like, I don't even have to talk to you. I can just see your whole, certification, which is great. But, like, okay, so from here, like D001.1 configure a groundedness filter, how does that get applied? Like, you have a person thatRune Kvist [00:19:16]: Yeah,Swyx [00:19:16]: Goes through it?Rune Kvist [00:19:17]: If you, go backVibhu [00:19:19]: I did see somewhere there's like, you know, fifty-one requirements, a hundred thirty controls. There's like a wholeSwyx [00:19:25]: Right. I just want to. Like, to me, this doesn't translateVibhu [00:19:27]: Yeah.Swyx [00:19:27]: Into a test or an eval.Rune Kvist [00:19:28]: Yes. So if you go into, on the left-hand side. So actually, if - before we go in there are three types of requirements. The first is technical controls, like you must implement some guardrails.Rune Kvist [00:19:42]: Two, there are test controls. So you must have an independent third party go and run some tests against you. I'll show you one of those in a second. And then three, there are policy controls. For example, you must have a person whose name is on the line when you guys f**k up, and you must have a plan for how you tell your customers and how you engage with them. They're kind of more traditional, standard type stuff. So in this particular instance, we just check whether they in fact have a ground in filter. So we will partner with an auditor. So we partner with auditors like KPMG or like Schellman who go in and do the thing auditors do, which is to check the evidence. In this case, that might be a screenshot, it might be part of the code that they need to review to see that it actually. Just that it exists.Swyx [00:20:21]: Oh, okay.Rune Kvist [00:20:22]: And then the second thingSwyx [00:20:22]: So you're not testing the effectiveness of it.Rune Kvist [00:20:24]: That's the second thing. So if you go downSwyx [00:20:25]: Yeah.Rune Kvist [00:20:25]: To the third-party testing for hallucinations out on the left, that's basically the next requirement. This is where we test how well does it actually work.Swyx [00:20:32]: Okay, and is it you testing or the auditor?Rune Kvist [00:20:34]: We test them.Rune Kvist [00:20:35]: We test them.Swyx [00:20:36]: That's a lot of work.Vibhu [00:20:37]: How long does testing take? So if I want to get certified, justCertification Timelines, Remediation, and Quarterly UpdatesRune Kvist [00:20:40]: Yeah.Vibhu [00:20:40]: How long does the end roughly take?Rune Kvist [00:20:42]: Yeah, the end, almost always is dependent on, like, our customers needVibhu [00:20:47]: Yeah.Rune Kvist [00:20:47]: To look something for us. It takes somewhere between, like, 3 to 10 weeksSwyx [00:20:52]: Yeah.Rune Kvist [00:20:52]: Depending on how up to snuff they already are. So some people show up to us with, like, extremely rigorous security programs. When we test them, it works extremely well. We can get that done very quick. Some people come to us, and they're not that far along. We give them kind of the spec that they need to build towards, and then their security teams and engineers get to work and build to meet the standard. The testing itself typically takes a couple of weeks, including the time for them to remediate. Often, we'll find something that we cannot pass, where this is actually just not up to the standard. - you won't pass the standard. And then they will need to go and implement additional safeguards or additional remediation that makes them more robust so that they can actually kind of hand on heart look at their customers in the eyes and say, like, “Hey, we've done truly our very best.”Vibhu [00:21:35]: And they're certified for a year and have quarterly updates?Rune Kvist [00:21:38]: Correct, yeah.Vibhu [00:21:39]: And, yeah, it's pretty interesting. I think, you know, what's changed since. So this is certifying agents in production, right? Your customers, like you've had Lovable, ElevenLabs, Intercom, and they've all gone through this certification.Rune Kvist [00:21:50]: Yes.Vibhu [00:21:51]: What has changed? So I see you post, like, you know, Q2 added MCP agent,How Agent Risks Are Changing: Coding, MCP, and Agent-to-Agent InteractionsRune Kvist [00:21:56]: Yeah.Vibhu [00:21:56]: agent communication. Any other things that you want to kind of highlight since the first iteration? What comes in quarterly?Rune Kvist [00:22:03]: Yeah. So some of the changes have just been agents are not just one thing. So, like, if you take agents like Cursor and compare them to Sierra, they're really quite different. And compare them to Harvey again, compare them to you out of againSwyx [00:22:16]: ElevenLabs, yeah.Rune Kvist [00:22:17]: ElevenLabs, they're all quite different. And so we wanted to design a standard that works for all of the types of agents. And we started with one that was, like, pretty text-based, like, honestly, pretty customer support-focused. That's where there's a lot of existing demand. And then over time, we've picked, some of the frontier companies in each of these other domains that we could work with and build out the standard, so, such that we know that the same standard works for code, it works for customer support, works for automation, et cetera. So that's been one big thing. Yeah, then some of the things that have been top of mind recently, Mythos is bringing up a lot of concerns for security leaders. We're starting to get more and more questions around agent interactions. It's very nascent, at the moment, but it's starting to emerge. There've been a lot of, questions related to OpenClaw and MCP. Again, like agents starting to interact with each other, is really top of mind. Then as coding agents have really taken off, that's also where banks and hospitals, et cetera, are getting more and more precise on what it is they need. So really dialing in as that start to be, like, where most of the tokens flow through in the world, getting much sharper on that.Vibhu [00:23:26]: Can you share for people that are listening that don't really think about this? Like you mentioned, there's the obvious stuff, you know, hallucination, citations. What are best practices that people should do when building agents? Like, if they come to you pretty ready with certification like, you know, they'll probably pass certification. What are the things people don't think about that they should have?Best Practices for Agent Builders: Stress Tests and GuardrailsRune Kvist [00:23:46]: The most important thing is that a lot of companies have not done a serious stress test. They spend most of the time, perhaps rightly so, optimizing for how does it work in the good case, the average case, how high-quality is the output for the customer. And a lot of these companies are pretty new, so they haven't spent a lot of time stress testing the what is there as an adversary on the other side? What are some of the complicated corner cases that you've not really considered? So I think that's, like, a frame of mind. And you'll also see this in startups. It often takes a while until they hire their first security person. They- And that's a whole different kind of risk surface than just building a good product. So a lot of that applies. Most companies actually also have the right kind of architecture. Most of them will have some kind of guardrails in place, either some that come out of the box from their model provider or they'll have built their own filters that sit in between. They just don't work very well. The difference between putting a classifier in place that, like, maybe goes and checks whether you're giving medical advice when you shouldn't and says, “Hey, if this looks like medical advice, filter it out.” Lots of companies have that in place. The question is whether it works. And it's actually pretty fiddly to sit down and think about all the ways in which you could ask for medical advice, read the academic literature on what are the kinds ofRune Kvist [00:25:03]: Framings or tricks you might play to get an AI to give you medical advice when you really shouldn't. And so there's, like, an area of expertise that's just missing. So what we find is that most people have the right building blocks in place. They don'- It doesn'- It's not rocket science, but the finicky thing is, like, getting into the corners and testing whether it works such that you can look your customers in the eye, or maybe a bank or maybe a hospital and be like, “This is going to work for you.”Vibhu [00:25:26]: I see. So we talked a lot about the agent-level certification. Where do you guys go from here? So announcing series A camera, we talked about this a bit. There's the whole security risk of Fable, government stepping in. You guys are kind of announcing that you're also going into model certification?Toward Model Certification: The Government–Lab Trust GapRune Kvist [00:25:46]: When we do a bit of cutting afterwards,Vibhu [00:25:48]: YeahRune Kvist [00:25:48]: We will not yet be announcing this,Vibhu [00:25:49]: NiceRune Kvist [00:25:50]: The question that is top of everyone's minds now is at the model level. And Mythos, then Fable, has really brought this to the fore that in addition to the commercial risk and the kind of economic security risks that are happening at the agent layer, the models are going to present risk in the national security category. The shape of the problem is very similar. You have some people that are on the hook if something goes wrong. In the case of agents, it's often security leaders in the enterprise. In this case, it's the government. They don'- haven't necessarily spent their entire lives thinking about what are the new risks that come here, what is the kind of data you might be looking for, how might you test that? But they do have to make sure that their concerns are addressed. You have some frontier AI companies that are deeply technical. They know a lot about the risks, but they fundamentally have an incentive to not always be truthful. So you have a trust gap between the government and the labs. And in every other industry, you end up with some kind of body sitting between, a neutral third party sitting between those people. There's no other industry where you allow people to audit themselves. So there is going to be a need for a third party that can take the rigor of the labs to run frontier technical evals, but can also speak legible trust in the way that the government trusts PwC to go and run financial audits. And they know that they output audit reports in a way that's consistent, that's easy to read, that's factual, that's, trustworthy. Those two things need to be brought together. And what we've learned from our work with agents is that if you want those-- that communication between those two parties to be smooth, there has to be one common standard that is public, that people can go and inspect. What are the risks that matter? Within each of these risks, what are the kinds of threat models that you're really looking for? You need to specify for each of those risks, what are the guardrails that need to be in place, and what are the tests they need to run to see whether those guardrails are effective? And then you need to go and run audits that are - technical audits that are consistent. So if you're trying to bring trust, it's extremely important that you methodically work your way through the risks. You can't send one researcher in and say, like, “Come back with whatever you find.” You need to be able to explain exactly what you did, exactly what you tried, exactly what you did not try, and therefore the kinds of promises you can and cannot make at the end of it. I think ofNeutral Third Parties, CAISI, and Model Risk AuditsRune Kvist [00:28:13]: Fable as a direct symptom of this problem that the government was told that there's a risk. The government may struggle to assess just how big that risk is. They call Anthropic, and Anthropic is trying to tell them, “Hey, actually, every model can be jailbroken.”Swyx [00:28:28]: That's not what you want to hear, right?Rune Kvist [00:28:32]: As the government, that might be hard to trust.Rune Kvist [00:28:36]: And we think that a broker is the most natural solution. In other markets, you see something like, in financial markets, you see Moody's. Moody's goes in, and they look at a bond, and they output a rating. They say like, “Here's the evidence we found. Here's the rating.” We don't decide whether anyone should buy this bond or not buy this bond. Well, that depends on their risk appetite. But we do provide this common information layer that everyone can rely on. In the case of Moody's, the government, points to them and say, “Hey, pension funds, you should probably really take care. You shouldn't risk your pensioners' money, so you can only invest in triple-A rated bonds.” That means that now the government doesn't have to staff thousands of financial technical experts to rerun forecasts every week to see whether things are correctly rated. They get to point to some neutral third party. So my hypothesis is, my hunch is that you will see a third party that sits between the government and the labs, and it could either be the government builds it themselves. So something like CAISI was set up to do exactly this. And the questionSwyx [00:29:44]: Sorry, I'm not familiar with CAISI.Rune Kvist [00:29:45]: CAISI is the Center for AI Standards and Innovation.Swyx [00:29:49]: Okay.Rune Kvist [00:29:50]: I won't get into the details, but it's a body of NIST that typically sets standards. So it's basically a government body that has AI experts. Yeah, exactly. Exactly.Swyx [00:29:59]: Very key. Very key.Rune Kvist [00:30:00]: Very key.Vibhu [00:30:00]: I think, you know, it's one of those things where when you just sit back and listen-- look at it, like, is there enough technical expertise in the government to measure, test these things right now? Probably not, right? And Fable is a result of, okay, we've had to scale back and pause things,Rune Kvist [00:30:17]: Yeah. And they have excellent people, but they have an extraordinarily small budget compared to the scale of the challenge that's ahead of us. And I think they have a role to play. The question is kind of like, who does what? We have now outlined the jobs to be done, and they're quite extensive. Every model release, there is an astounding-- Given that they take in any input, their risk surface is astounding. And so the question is really: what can only the government do, and what can the market provide here that can keep up with the pace as AI risk changes? Our perspective is that also at the model layer, the risks that people care about today are not the same ones they cared about three months ago. So the pace of legislation is too slow to deal with pinpointing the risks here. And so we think there's a lot that the market can do to surface timely information. Ultimately, there is a bunch of policy decisions here. Is the national security risks of a model too high?Swyx [00:31:12]: Yeah.Rune Kvist [00:31:12]: That's a political answer. But what we want to make sure is that the process that produces this risk information is compatible with very fast innovation. So you don't want to. This is not a question of like, can you slow the things down? Can you keep, the models locked up until-- for months on end until everyone can make a guarantee? But it is this, can you, in the time it. Given that the US is competing with China on releasing models, can you insert risk information that allows the government to, like, make rapid decisions on some of these questions? Balancing that trade-off between failing to adopt AI is going to put us at risk, but also reckless adoption is going to put us at risk. And that's a very kind of fine balance that they're going to need, like, a lot of high-quality intelligence to make.Chinese Models, Data Flows, and National Security ConcernsSwyx [00:31:55]: Just a side mention, because you mentioned Chinese models, any specific concerns that you're hearing from your CISOs about that? ‘cause I guess it's free, but.Rune Kvist [00:32:05]: CISOs have a bunch of concerns around data flows in general that they're really concerned about. So there's a lot of questions like, if these models are Chinese, where does that, where does that data go? I think a lot of this can be addressed, but they come up often.Swyx [00:32:18]: I mean, they understand they're running on American GPUs.Rune Kvist [00:32:21]: Some of them, some of them understand that they're running on American GPUs.Swyx [00:32:23]: They're not, like, phoning home every time you, like, call home.Rune Kvist [00:32:26]: No. A year ago, there was not a lot of understanding of this. I actually think, you're seeing the security leaders becoming kind of AI literate at a blistering pace, and you're actually also seeing my Twitter timeline that's very pilled and my LinkedIn feed that used to not at all be pilled kind of converge. They're both talking about Fable.Swyx [00:32:45]: Right. Yeah, that's true.Rune Kvist [00:32:46]: They are both talking about whether you can prevent models from being jailbroken these days.Swyx [00:32:51]: Yeah.Rune Kvist [00:32:52]: Like national security national security risks are now the conversation that is actually emerging. Other than that, I think you mostly see a kind of general picture: there are no concerns with any particular model or any particular model output, but there is a general nervousness of having critical infrastructure run on models that are not produced in America by Americans where the American government has control.Swyx [00:33:14]: But it doesn't necessarily show up in your framework that directly, or it might, I don't know.Rune Kvist [00:33:18]: There's a bit of stuff in there actually on the, like, the provenance of the models and disclosing that. But I think there's a bunch of use cases where running a Chinese open-source model is just the best solution.Swyx [00:33:27]: Yeah.Rune Kvist [00:33:27]: And a concern is slightly more macro here, which is not best addressed at any particular certification level.Vibhu [00:33:32]: Is there anything interesting that you see at the. You know, if you're trying to fill that middle gap, that mediation gap, any interesting stuff that you guys forecast would be required other than, you know, what the average person might expect?Cyber, Child Safety, Bio Risk, and Expert CoordinationRune Kvist [00:33:47]: There's a bunch of interesting questions about what are the risks that matter here. So right now, the risk of the day is cyber, because it's very real, very tangible. And some of the risks that are also emerging as pretty real and pretty tangible are things like child safety is becoming both extremely important, but also politically important. And then there are some of the risks that are coming down the pipeline that today feel kind of speculative, but people who spend a lot of time with the models see them coming down is things like, risks that relate to biology.Rune Kvist [00:34:18]: And specifically whether models will help adversaries produce biological weapons and making that extremely cheap, extremely accessible, producing-- making the chance of another COVID or worse pandemic. COVID was not engineered to be bad, as if you were trying to do that. So I think those are some of the risks that are coming down the pipeline. I think one other thing to just note is that agents are kind of deliberately narrow. So, like, when a frontier agent company puts a chatbot that interacts with customers, they've really tried to narrow the topics it's interested in talking about. Such that if you ask it, like, “What do you think of the president?” it will just decline, which means that the kind of risk area is somewhat smaller. For models, it is infinite. And so there's not a single expert out there who can competently evaluate the risks of cyberattacks and fifteen-year-olds having month-long conversations with a chatbot and seeing whether it will in fact recommend suicide or something horrendous like that, and can evaluate the risks that terrorists can use AI to produce bioweapons. The risk surface is just too big. And so the central challenge actually becomes how do you get those subject matter experts to work within a one coherent framework that outputs one coherent report and rating that the world can go and inspect? ‘Cause that global perspective is central, but there's not a single organization today that could produce that.Swyx [00:35:47]: And you would be the presumptive one when you put out your model standards.Rune Kvist [00:35:51]: We think there can be one company that can, with a consortium of experts, build one coherent standard. I think we've shown that across all of the enterprise risks today. We think it could be one company that could, with a consortium, specify the audit rules, basically like the inputs and outputs that all these technical experts need. What access do they need? How should they treat infosec- info security? They can look at whether the eval- evals are well-produced without necessarily being able to say, “Hey, is this a threat or not a threat?” But overall, evaluating whether the evals are good, well-constructed, that set of audit rules that basically becomes the interface for all these experts, we think one clearinghouse could put together. To be clear. When I say one company, I think of it as one company coordinating lots of this in the same way that when we saw our consortium, it's not like we say we have all the answers on agent security. What we say is we are taking on the role of eliciting all of the concerns and being the secretary that puts it together and runs a tight house such that the standard updates lockstep every quarter, and that the audit reports that come out, in this case, 100-page audit reports, uniform and crisp and clear all to the level of detail that is required for executives that need to make a clear go/go decision. So that's kind of the role that we think we might play.OWASP, Frameworks, and the Operational Audit LayerSwyx [00:37:11]: I think in many ways you're performing the role that OWASP used to do there, and you said, like, you know, competition and partners.Rune Kvist [00:37:18]: Yeah.Swyx [00:37:19]: Can you go more into, like, how they partner?Rune Kvist [00:37:20]: Yeah. So first of all, OWASP is basically an open source community of security practitioners that are coming together to build frameworks for addressing the latest security concerns. We think they are phenomenal at creating frameworks. We'- In fact, we'- First of all, we're partners with them, so we have a joint article. Two, we've learned a lot from them. We think they're a tremendous source of intelligence. What OWASP does not do is building the machine that runs third-party audits such that a company like Cursor or a company like JPMorgan could get a third party to go and review them against this and say, “Hey, you've passed the standard, and here is the report that you can use to build trust and preempt your partners' or customers' questions.” So they fundamentally try to do something different. You - They are part of the information gathering and intelligence gathering and creating clarity, but the operational layer of turning this into promises is not the business they try to be in.Swyx [00:38:14]: The standard is emerging and is doing very well. Was it necessary to then also do underwriting? Obviously it's in the name, so please remember you thought about it first. I feel like if you just have enough consensus, you don't actually need the money angle, but it does help.Vibhu [00:38:30]: I did want to also note, you guys are a profit company too, right? It's not profit where there's a whole business side to it as well?Why For-Profit Standards and Insurers MatterRune Kvist [00:38:39]: Yeah. Yeah, so I'm just getting crazySwyx [00:38:41]: I think about the money part.Rune Kvist [00:38:42]: Yeah. Yeah, let's get into the money part. Let's start from actually your question, profit versus profit. In the security space today, cybersecurity, most of the standards are produced by nonprofits. I think that's an issue.Rune Kvist [00:39:00]: The question you have to ask yourself is, how do you create good incentives for these standards to be good and keep up?Rune Kvist [00:39:09]: Nonprofits tend to not have these adverse profit incentives where they, hollow out their standard and create a race to the bottom, but they're also not at all responsive by default to the communities that they serve. There's no process-- They don't have customers that they serve where they go and ask, “What do you want? What do you want? What do you want?” And when you look at the overall satisfaction with the security standards today, people tend to just not like them very much. You do see in other domains, that profit standards can serve the world quite well. So there are examples, like we talked about Moody's before. It's not without flaws, but, it is absolutely critical societal infrastructure that gets run at an astounding scale today. Your credit score, it's FICO. It's also a profit business. And when you go back even further in history, some of the crash testing standards came out of insurance companies.Rune Kvist [00:40:06]: The insurance companies together founded the Insurance Institute for Highway Safety because they were very interested in, like, how can we use standards to drive down mortality and save money? Go back, prior-- Our name actually pays homage to the Underwriters Laboratories, UL, which, was started right around when electricity came out. Houses started burning down. Insurers, again, were paying the bill, and they were maybe also good people, but their profit incentive was, let's prevent houses from burning down. Let's test all the electrical products, the light bulbs. All the light bulbs in here are probably tested, the toasters, et cetera. And they set up, an entity to create those standards. Today, UL has a profit entity and a profit entity. What they've recognized, they spun - They started profit. They spun out a profit because what they recognized was like, hey, actually to serve customers well, you need a profit entity. The lesson here is one of the ways that the market can align incentives so you're both responsive to customersRune Kvist [00:41:07]: And not hollowing out your standard over time is to align it with insurers because they fundamentally have good incentives. And so if you're a profit standard that works closely with insurers, you get the feedback loop in such that you're really tuned into your customers, but also have their interest at heart. So that's the model that we - the kind of inspirational model that we've learned a lot from, and that's also where the name comes from. In some ways, the term underwriting can both be associated with insurance, but it's also a broad term for, like, making decisions.Rune Kvist [00:41:40]: If you underwrite a decision, you're fundamentally kind of taking ownership for the consequences of it.AI Insurance Contracts, Lloyd's of London, and ElevenLabsSwyx [00:41:45]: Yeah, I mean, what does an insurance contract look like for AI?Rune Kvist [00:41:49]: Yeah. Most of the demand comes today for insurance contracts is, sitting between people who've built AI and people who are buying AI.Swyx [00:41:56]: Yes.Rune Kvist [00:41:57]: And what you want—the reason why people want insurers involved, both for the traditional reasons, hey, if something goes wrong, we want to be compensated, but it's in particular because insurers can bring trust to the equation. Because insurers will pay for the damages, if they're willing to write an insurance policy, that is them saying, “Hey, we think there is risk here, but that is manageable.” And that is kind of a. Their incentive aligns with the enterprises adopting it, so that's a really a good signal to the market. In the same way, actually, one of the things that Waymo tried to get their first permit to even operate in San Francisco was to get a lot of insurers to stack up a huge insurance policy. In the case if something went wrong, not because Google can't pay, but because it was very valuable to have a third party go and look at that dataRune Kvist [00:42:47]: That are trusted by governments, trusted by enterprises as conservative people and say, “Hey, we've looked at it. We're actually willing to take some of this on our balance sheet.” So that's, that's kind of the reason why people are interested in it. What it looks like is, in some ways like every other insurance contract. You specify what are the perils you want to cover, how much do you want to cover them, like up to what limits, and what does it cost to cover that. And in the case of, if we take a really concrete example, ElevenLabs, bought a first of its kind AI agent insurance policy. They work with some of the biggest, enterprises that work with governments. They're really interested in going above and beyond and making promises to their customers. So they wrote a policy that covers just some of the core concerns that their customers have been asking about. And, the crucial thing was really to get Lloyd's of London, the world's oldest insurer, one of our partners, to look at this data and be that third party alongside us to say, “Hey, we think there's something here that's worth underwriting.” and that's actually what it looks like. And so they will show that contract to their customers, and they can see how much they're covered for. They can see what exactly it covers, and that will also probably change next year. They will want to write an insurance policy that might cover more.Swyx [00:44:04]: When you say Lloyd's, is it reinsurance, or are they sharing somehow at the same level orRune Kvist [00:44:11]: Yeah. So typically, the way, new companies get into insurance is that they partner with insurers such that the insurers take the majority or all of the financial risks. Fundamentally, if insurance is useful, because it brings trust, you have to be able to pay the bill. Lloyd's of London is 400 years old. They've never not paid a claim. They're extremely trusted. What Lloyd's of London struggle to do on their own is to figure out which of the risks are real, what should we be looking for, what are the kinds of technical controls, and running the tests. So they use AIUC-1 as kind of the underwriting framework, and we produce a bunch of eval results that then directly feed in to inform the pricing. So this means that ElevenLabs customers know that payment will be there. They don't have to look to our series A and see, like, do we think they have enough cash on the balance sheet? They will look at Lloyd's.Swyx [00:45:05]: Yeah.Rune Kvist [00:45:05]: Yeah.Swyx [00:45:05]: And Lloyd's, like, famously very creative. I think I remember some headline like, they insured Jennifer Lopez's, butt or something.Rune Kvist [00:45:13]: Correct.Swyx [00:45:13]: Right?Rune Kvist [00:45:13]: And I think, was it, David Beckham's right foot?Swyx [00:45:16]: So, yeah. Right?Rune Kvist [00:45:17]: And stuff like this.Swyx [00:45:18]: So, like, clearly not a large data set.Rune Kvist [00:45:22]: Exactly. It's actually a remarkable institution that's both kind of has some of the truly school virtues of having been around for a long time. They, like, really. They really operate like a trusted entity, and they have appetite to figure out the future. And I think there's a lot of recognition that both there is, like, tremendous amount of risk in AI that is poorly understood today, so getting into this business carries real risks. But also this is where lots of the risk exposure will happen in the future. This is the one market where risk is truly growing. This is the one market that will also take out some of the existing markets. Take, like, auto insurance. When there are no human drivers, how's that market going to look? Well, it's clearly going to change. How are you going to assessSwyx [00:46:08]: You want to insure Waymo?Rune Kvist [00:46:10]: I. All I'll say is the principles for how you insure Waymo are very similar to how you insure other kinds of AI.Swyx [00:46:15]: Right.Rune Kvist [00:46:15]: So again, crash testing, that's what we do for customer share at Lovable. That will also need to happen for Waymo, which is not how you do it for human drivers. So there's this growing awareness that the world is changing very fast, and the only way to learn how to underwrite AI is to write some policies. You may incur some losses and think of that as R&D expense, really. But the question for them is, like, who are the trustedtechnical partners they can get into this business with that can help them navigate and make sure they don't make, kind of foolish mistakes? But also who is willing to hear the wisdom that they have? They've done this before. They've seen it was. They were there when cyber came out. So there are lots of ways in which AI feels completely new, but there's also lots of ways in which risks look the same. And so there's actually a tremendous amount of wisdom sitting in some folks that may have gray hair, but really have, like, a keen sense of, how to quantify risk.Swyx [00:47:08]: Yeah. And the number is. So it's basically like I want fifty million dollars worth of coverage against these perils, and Lloyd's will give you a quote on it, and then you have, like, a small markup or something, and then you turn it around and do that? Is that as simple as it is?Risk Capital, Premiums, and Working with InsurersRune Kvist [00:47:23]: You basically share some of that premium.Swyx [00:47:25]: Yeah.Rune Kvist [00:47:25]: X percent goes to the people who do the pricing of it.Swyx [00:47:28]: You're. It's kind of like a. It's kind of like a merchant bank for insurance type of thing.Rune Kvist [00:47:33]: Exactly. You basically split the fee, and you can think of the insurance supply chain as, like, there's bringing the capital, there is doing the pricing, and there is doing the distribution. And typically, you will pay out some X percent of premium here, Y percent of premium here, and the rest of it will go here.Swyx [00:47:46]: Does all the insurance world work like this, or is there some point at which, like. So if right now you have equity capitalRune Kvist [00:47:51]: Yeah.Swyx [00:47:52]: At some point, maybe you start raising, debt or whatever, and then you have enough of a bank account and enough history, let's say you've been in operation for ten yearsRune Kvist [00:48:00]: Correct.Swyx [00:48:00]: That you don't need Lloyd's anymore?Rune Kvist [00:48:02]: That's totally an option. And I could see some worlds where that makes sense, specifically if there are risks that we feel high confidence that we'd want to insure where the incumbent insurers are too slow to find appetiteSwyx [00:48:13]: Okay.Rune Kvist [00:48:13]: Or simply struggle to evaluate it such that they don't want to do it. But by and large, in general, you do not want to compete with insurers on, bringing risk capital to the game for two reasons. One is that's fundamentally a cost of capital game. They have extremely low cost of capital. Startups have high cost of capital, by and large. And two, you want to hedge your bets, and it's very helpful then to also have a portfolio of home insurance, of car insurance. And we're not about to become a car insurer nor a home insurer.Rune Kvist [00:48:43]: So they have some natural advantages, which makes it much more likely that we'll partner.Swyx [00:48:48]: Yeah.Rune Kvist [00:48:48]: And they bring that, the capital at scale, and we bring the technical expertise.Swyx [00:48:51]: You're, you're going to work with them for a long time.Vibhu [00:48:52]: How are the discussions with the insurers as well? So basically, they're going off of your certification, right? They're trusting the diligence on you that your certification is valid, you tested the right things, and they're backing the money that, you know, you have the right testing in place. So any interesting takeaways from working with insurers?Rune Kvist [00:49:12]: I think the maybe the first thing is they feed into the standard as well. So if there are things that they feel like they need that they're not seeing, we are also taking that as input into the standard, because fundamentally we think a good standard is one that creates a really healthy promise ecosystem, and we think insurers are a critical part of that. And again, they are the most well-incentivized to. They see all the lost data across every. Any particular CISO knows their particular concerns. Insurers see the concerns across the entire portfolio and often have direct access to, like, what exactly happened, who was at fault, et cetera, as they do part of their forensics. So they're actually, like, a great source of intelligence on this. One of the big takeaways from cyber insurance, which is a market that didn't work that well, was that the insurance and the technical expertise was not married up. What our conviction is that standards have to precede insurance. Fundamentally, what everyone first and foremost want, whether you're a CISO at JPMorgan or a CISO at Cursor or an underwriter at Lloyd's of London syndicate, is you want to not have an incidentRune Kvist [00:50:19]: In the first place. You want to know that the risk is well-managed, and only then does insurance start to make sense. So we'll see the standard ecosystem basically run ahead of the insurance. And the reason why we. You asked us kind of why I also do insurance, this is kind of proving what we think a whole promise confidence infrastructure ecosystem needs to look like, and we think it's very compelling to bring that to life, even if we think the standard is kind of the core linchpin that unlocks the rest.Claims, Liability, Air Canada, and Duty of CareSwyx [00:50:44]: There's been no claims yet, right?Rune Kvist [00:50:45]: Nope.Swyx [00:50:46]: This is one of those things where, you know, if people haven't really worked through what it means to cover things.Rune Kvist [00:50:52]: Yeah.Swyx [00:50:52]: So for example, I pay Cursor $20 a month.Rune Kvist [00:50:55]: Yep.Swyx [00:50:56]: And I write a vibe code something that makes, a plane crash, causing $200 million worth of damage.Rune Kvist [00:51:02]: Yes.Swyx [00:51:02]:

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Send me a text – I always love hearing from you! ✨The Bible can feel like a stack of ancient stories, but what if it's actually speaking into your life today? We start with one sentence that changes everything: Hebrews 4:12 says God's Word is “alive and active.” Not stagnant. Not trapped in the past. Capable of cutting through our anxiety, our excuses, and our carefully managed outsides to get honest about what's happening in our hearts and homes.Then we step into the Old Testament with a simple roadmap that makes the big story easier to follow. If you're craving practical Bible teaching, deeper Christian faith, and a clearer way to read the Old Testament without getting lost, press play.Share this with a friend who needs encouragement, and if it helps you, subscribe and leave a review so more women can find the show. What part of your life needs the Word to meet you right now?Support the showFollow on Instagram & Facebook Support this PodcastVisit the Website

For this discussion we brought together experts Dr. Matthias Herzler and Dr. Hennicke Kamp from the German Federal Institute for Risk Assessment and Charles River's Head of Regulatory Affairs Ira Koval to discuss the European Union's Roadmap for phasing out animal testing for chemical safety assessments. What does the roadmap really say, and what will implementation look like? How can we balance the public's call for action with their safety? Listen now to find out! 

Jai Al-Attas started a record label at 16, sold it to Virgin Records at 24, and never went to college. So when he moved to LA and tried to get people from a reentry program and a homeless youth org into music industry internships, the question that kept coming back — "are these people in college?" — landed personally.He didn't have a plan. He had a chip on his shoulder and $1,800 he needed for rent. He spent it on 10 Chromebooks instead.

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