Podcasts about Coding

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When Dave Mabe backtested his strategy, it outperformed his own discretionary trading — and changed how he approached everything. In this episode, we discuss gapping breakouts, expectancy, systematic trading, drawdowns, and the reality gap between backtests and live execution. A practical conversation for traders serious about building durable edge. In this episode, we explore: ·        How Dave got introduced to markets: From early exposure to investing through his family to actively seeking more control over his capital and moving from swing trading into day trading. ·        Why rules matter: The transition from discretionary decisions to systematic frameworks — and why trading without a process is a fast path to inconsistency. ·        Backtesting as a “superpower”: What backtesting really does for strategy development and confidence in your edge. ·        Reconciling backtests with real life: Practical realities of execution, slippage, and market structure — and how to build a feedback loop so your live results get closer to your imulations. ·        Drawdowns and mindset: How to handle periods where a strategy doesn't behave as expected, and why many traders quit in drawdowns rather than at all-time highs. ·        Scaling a trading business: The difference between scaling size versus scaling breadth — and why uncorrelated strategies matter. ·        Practical first step for systematic traders: How to start adding structure to your trading with backtesting, even if you're not a programmer.   About the guest:   Dave has been a professional trader and technologist for over two decades. As a former CTO of Trade-Ideas, he has unique experience at the intersection of algorithm design, real-time market data, and automated execution. Outside trading, he writes a popular daily newsletter on backtesting and systematic strategy development, and hosts the Line Your Own Pockets podcast focused on systematic approaches to markets. Links + Resources: · Link to Better Backtesting —Dave's free multi-day email course on building strategies and improving them over time. · Trade-Ideas, Amibroker, RealTest — examples of backtesting and strategy development platforms discussed in context.   Sponsor of Chat With Traders Podcast:  Trade The Pool:  http://www.tradethepool.com Time Stamps: Please note: Exact times will vary depending on current ads. 00:00 Intro and Background 08:29 Stock Selection and Systematic Trading Rules 11:32   Position Sizing, Expectancy and Risk Management 16:50   Discovering Backtesting and First Backtests 18:40   Backtesting Principles, Sample Size and Common Pitfalls 20:34   Gradual Automation and Live Trading Implementation 22:17   Trading Journal and Reconciling Backtest vs Live 27:27   Scaling through Automation: More Trades, Better Results 29:26   Drawdowns, Psychology and Handling Setbacks 34:14   Tools, AI and Software for Backtesting and Coding 39:56   Common Trading Myths Debunked (Partials, Stops) 48:01   Getting Started: Practical Steps, Resources and Closing   Trading Disclaimer:   Trading in the financial markets involves a risk of loss. Podcast episodes and other content produced by Chat With Traders are for informational or educational purposes only and do not constitute trading or investment recommendations or advice. Learn more about your ad choices. Visit megaphone.fm/adchoices

Ever feel like your practice is doing everything right, but the numbers don't add up? From coding errors to missed billing opportunities, revenue leakage can quietly undermine a practice's financial health. In this episode, we explore the hidden cracks in OMS practice revenue – where money slips through unnoticed – and discuss practical strategies to plug those leaks and strengthen reimbursement processes.  Disclaimer 

Explore how AI could reshape crypto and finance, redefining traditional systems and introducing new threats. As AI-powered agents promise efficiency, Haseeb, Tom, Tarun, and guest Illia Polosukhin critique Citrini's controversial predictions on a global financial crisis and consider whether AI might just save or further complicate crypto's role in the economy. Welcome to The Chopping Block — where crypto insiders Haseeb Qureshi, Tom Schmidt, Tarun Chitra, and Robert Leshner chop it up about the latest in crypto. Joining us is Illia Polosukhin, co-founder of NEAR Protocol and contributing author to the original transformers paper that's revolutionized AI. Buckle up as we delve into AI's burgeoning role in the crypto world, dissect the sensational claims from Citrini's article predicting an AI-triggered financial crisis, and explore the potential of agentic coding in reshaping traditional systems. Let's get into it! Listen to the episode on Apple Podcasts, Spotify, Pods, Fountain, Podcast Addict, Pocket Casts, Amazon Music, or on your favorite podcast platform. Hosts ⭐️Haseeb Qureshi, Managing Partner at Dragonfly ⭐️Tarun Chitra, Managing Partner at Robot Ventures ⭐️Tom Schmidt, General Partner at Dragonfly  Guest⭐️ Illia Polosukhin, Co-founder of NEAR Protocol Disclosures THE 2028 GLOBAL INTELLIGENCE CRISIS by Citrini and Alap Shah https://www.citriniresearch.com/p/2028gic Timestamps 00:00 Intro 01:06 AI Agents Meet Crypto 08:06 Dark Forest Threat Model 15:31 How Close Are We 18:41 AI Coding Risks in Crypto 27:27 Citrini 2028 Crisis Explained 35:01 Demand Shock Missing Money 37:55 Automation Limits and Human Value 44:13 AI Zero Days and Botnets 51:40 Escrow Courts and Enforcement 56:05 Illia on Vibe Coding Future Learn more about your ad choices. Visit megaphone.fm/adchoices

In this episode of Excess Returns, we sit down with Matt Russell of Business Breakdowns to explore how AI is actually being used in investing today. We go beyond the hype and break down practical use cases for AI in portfolio management, stock research, due diligence, monitoring, and idea generation. From deep research models and agentic AI to prompt engineering and workflow design, this conversation walks through how professional investors can use AI tools to increase productivity, improve decision-making, and reduce blind spots without losing their edge. If you are an asset manager, analyst, allocator, or DIY investor wondering how AI will impact investing and stock picking, this episode offers a clear, practical roadmap.Main topics covered:The evolution from early large language models to deep research and agentic AI for investorsLLMs vs agent-based AI and why the distinction matters for investment researchHow AI fits into an investor's workflow, from due diligence to portfolio monitoringUsing AI to monitor KPIs, earnings calls, and cross-industry signals in real timeHow AI can help kill bad ideas faster and surface deal breakers earlyPrompt engineering for investors, including mindset framing, audience targeting, and output designBuilding mental models into AI systems to reflect your investment philosophyAI tech stacks for investors, including writing tools, deep research models, and browser-based AIIteration, experimentation, and standardized testing of prompts across model upgradesThe impact of AI on alpha generation, active management, and generalist vs specialist investorsOrganizational adoption strategies for investment firms considering AICustomization, agentic workflows, and what AI in investing could look like five years from nowTimestamps:00:00 How AI tools increase investor productivity01:16 Why early ChatGPT was a head fake for investors03:07 The inflection point with deep research and agentic AI05:00 LLMs vs agents explained in plain English07:01 Where AI fits inside an investment workflow09:28 Replacing manual earnings transcript work11:40 Real-time monitoring and AI alerts19:24 Using AI to kill bad investment ideas faster22:01 Trust but verify, hallucinations and safeguards25:29 Matt's AI tech stack for investing30:00 Prompt engineering breakthroughs33:00 Standardized experimentation across new AI models36:07 Building idea generation prompts step by step40:15 Using AI as an editor and critical reviewer43:50 Does AI compress investor skill differences46:10 How funds should adopt AI internally50:40 Fear of falling behind in asset management53:05 Generalists vs specialists in an AI world55:18 AI and the pursuit of alpha57:00 Customization, agents and the future of investing01:01:10 Coding agents and building tools with AI

Guido Appenzeller half Larry Page und Sergej Brin bei ihrem Business-Plan für Google, gründete selbst und ist heute Partner beim wohl weltweit größten Wagniskapitalgeber Andreessen Horowitz. Im OMR Podcast erklärt der Deutsche, warum wir uns im größten Zyklus seit dem Dotcom-Boom befinden und wieso „Coding is dead“ für ihn kein Scherz ist. Während Europa noch reguliert, investiert er Milliarden in die Zukunft. Doch eine Sache lässt selbst den Experten zweifeln: Erleben wir gerade den Aufstieg der wertvollsten Firmen aller Zeiten – oder wird die massive Kapitalverbrennung in einem Knall enden, der die frühen Jahre des Internets erinnert?

In this episode of the Ardan Labs Podcast, Ale Kennedy talks with Jens Neuse, CEO and co-founder of WunderGraph, about his unconventional path into technology and entrepreneurship. After a life-altering accident ended his carpentry career, Jens taught himself to code during recovery and eventually built WunderGraph to solve modern API challenges.Jens shares the evolution of WunderGraph from an early-stage startup to a successful open-source platform, including pivotal moments like securing eBay as a customer. The conversation highlights the importance of resilience, community-driven development, and balancing startup life with family, offering insight into what it takes to build meaningful technology through adversity and persistence.00:00 Introduction and Current Life07:19 Dropping Out and Carpentry Career10:52 Life-Altering Accident and Recovery18:01 Learning to Walk and Finding Direction27:46 Discovering Coding and Technology31:17 Starting the Startup Journey33:07 Discovering the Power of APIs40:50 Building a Team and Leadership Growth48:17 Founding WunderGraph59:07 Pivoting to Open Source01:05:32 eBay Breakthrough and Validation01:10:08 Balancing Family and Startup LifeConnect with Jens: LinkedIn: https://www.linkedin.com/in/jens-neuseMentioned in this Episode:Wundergraph: https://wundergraph.comWant more from Ardan Labs? You can learn Go, Kubernetes, Docker & more through our video training, live events, or through our blog!Online Courses : https://ardanlabs.com/education/ Live Events : https://www.ardanlabs.com/live-training-events/ Blog : https://www.ardanlabs.com/blog Github : https://github.com/ardanlabs

Apparently you can vibe code your way into hacking 7000 robot vacuums. A man used Claude to try and vibe code a way to control his own vacuum to be able to be controlled with an Xbox controller, and accidentally took control of SEVEN THOUSAND of them. And they have cameras. Thankfully, he didn't abuse that power. But in the wrong hands, this could be very very bad.Watch the podcast episodes on YouTube and all major podcast hosts including Spotify.CLOWNFISH TV is an independent, opinionated news and commentary podcast that covers Entertainment and Tech from a consumer's point of view. We talk about Gaming, Comics, Anime, TV, Movies, Animation and more. Hosted by Kneon and Geeky Sparkles.Get more news, views and reviews on Clownfish TV News - https://more.clownfishtv.com/On YouTube - https://www.youtube.com/c/ClownfishTVOn Spotify - https://open.spotify.com/show/4Tu83D1NcCmh7K1zHIedvgOn Apple Podcasts - https://podcasts.apple.com/us/podcast/clownfish-tv-audio-edition/id1726838629

Trump denies reports that his top military adviser has warned against an attack on Iran, the U.K. imposes nearly 300 new sanctions on Russia to mark the fourth anniversary of the Ukraine war, Australia launches a public antisemitism inquiry following the Bondi Beach attack, U.K. MPs approve a motion to release documents related to former Prince Andrew's trade envoy appointment, Colombia's ELN guerilla group declares a ceasefire ahead of legislative elections, a U.S. judge declines to dismiss the prosecutors in the Charlie Kirk murder case, declassified CIA documents on a Cold War-era interrogation research program resurface online, ICE is accused of cutting its training hours and dropping a course on constitutional law, British family doctors are given £3,000 incentives to prescribe weight loss drug medications, and IBM plunges 13% as Anthropic announces a new AI coding tool. Sources: Verity.News

Rob Walling is a godfather of the bootstrapped SaaS movement — he's started 6 companies (5 bootstrapped), built and sold Drip for 8 figures, and created the infrastructure behind MicroConf, TinySeed (which has raised nearly $60 million and invested in over 210 SaaS companies), and Startups for the Rest of Us (820+ episodes over 15 years). But here's what surprised me: Rob told me he's more of a creator these days than a software founder. The guy who built and sold an email marketing platform now gets his dopamine from podcasting, writing books, and making YouTube videos. And his experience on both sides gives him a perspective on the vibe coding trend that I think every creator needs to hear. In this episode, we get into the actual mechanics of how Rob runs his business — the team of 11 people, the $100,000-$120,000 monthly payroll, the four brands he wishes were two. We talk about how he eliminated stress from his life through therapy, hiring owner-level thinkers, and handing the project management to someone else entirely. And we have a real conversation about why vibe coding a SaaS product is probably not the opportunity you think it is — even if you have a big audience. This is part 1 of a 2-part episode; part 2 lives on Rob's podcast, Startups for the Rest of Us. → Rob Walling on Twitter/X → Rob Walling's YouTube Channel → Startups for the Rest of Us (Podcast) → MicroConf → TinySeed → Drip (Rob's 8-figure exit) → SavvyCal (co-founded by Derek Reimer) Full transcript and show notes *** TIMESTAMPS (00:24) Introduction — why Rob Walling is a unicorn in the bootstrapped SaaS world (02:40) Mapping the full Rob Walling business ecosystem: podcast, MicroConf, TinySeed, books, YouTube (05:15) How Producer Ron keeps the trains running on time across four brands (06:44) Inside the team of 11: roles, full-time commitment, and why Rob stopped hiring part-time (07:53) The psychology of making your first full-time hire (and Rob's 8-year wait for MicroConf) (09:33) Moving from task-level to project-level to owner-level thinkers (10:27) Four brands, two LLCs — the insurance story behind the split and why Rob wants to consolidate (12:18) Why Rob doesn't want his name on everything (and the legacy question) (14:41) Identity shifts: from SaaS founder to serial entrepreneur to content creator (16:31) The vibe coding reality check: why building SaaS is 10x harder than creating content (19:09) Why SaaS churn makes recurring revenue harder than it looks for creators (21:04) The construction analogy: tool sheds vs. skyscrapers and where vibe coding breaks down (24:53) Data from 234 investments: only 10-15% of successful SaaS companies lack a technical founder (27:00) The bigger opportunity for creators: equity partnerships instead of vibe coding (29:00) 'Build your network, not your audience' — why audiences plateau for SaaS growth (31:53) A week in Rob's life: deep work Mondays, advising Wednesdays, and the 329 TinySeed founders (34:00) How Rob eliminated stress: therapy, delegation, and giving up project management (38:46) Hiring for high-functioning: screening for 'Producer Ron'-level operators (41:21) The positive tension of deadline stress and why containers make you ship (43:09) Post-exit motivation: 6 months of comic books, guitar, and getting bored into purpose *** RECOMMENDED NEXT EPISODE → ⁠#291: 48 Hours With Clawdbot: How I'm Using It and Initial Reactions *** ASK CREATOR SCIENCE → Submit your question here *** WHEN YOU'RE READY

SummaryThis episode features a lively discussion on Olympic hockey, healthcare compliance, and the pitfalls of diagnostic coding. The hosts share insights on how practices often manipulate diagnoses for payment, the importance of proper documentation, and the impact of payer policies on clinical decisions.TopicsDiagnostic coding manipulationHealthcare audits and complianceImpact of payer policies on clinical decisions

Artificial Intelligence in Medical Education: Opportunities, Risks, and GuardrailsIn this episode of The Pediatric Lounge, the hosts welcome back Dr. Rani Gareige, director of medical education and designated institutional official at Nicklaus Children's Hospital and a clinical professor at Florida International University, to discuss artificial intelligence in medical education now and in the future. They preview Nicklaus Children's Hospital's 61st annual postgraduate pediatrics CME conference in Fort Lauderdale (Hilton Marina Resort, March 20–22), highlighting sessions on IBD, short stature, dermatology, psychological screening, AI in practice management, social media communication, genetic testing/personalized medicine, and Florida's new requirement for EKG screening to clear athletes starting ninth grade. The conversation covers common AI tools learners use (ChatGPT, Claude, OpenEvidence) and institutional concerns about HIPAA/PHI, including blocking public tools and using a secure in-house system (“Ask Nick”) and closed or constrained approaches (e.g., tools that search only approved sources or documents provided, such as Google Notebook). They explore concerns about de-skilling and when to introduce AI in training, faculty development needs, and a precepting framework (DEFT-AI: Diagnosis, Evidence, Feedback, Teaching, and Recommendations for AI use) to assess clinical reasoning. The episode also discusses AI for simulated patient interactions (bad news delivery, motivational interviewing), ambient AI scribing pilots, clinician responsibility to review notes, and AI-driven coding that may reduce undercoding and administrative burden. The discussion concludes that AI will not replace physicians, but clinicians who use AI wisely may replace those who do not, stressing the importance of policies, ethics, transparency, and maintaining empathy and the art of medicine.00:00 Podcast Intro and Guest02:25 CME Conference Details03:13 Hot Topics and New Laws04:44 EKG Screening Program07:42 AI Tools in Training11:42 IRB and Data Privacy14:39 Meeting Minutes Automation16:48 Closed Models for Clinicians19:13 AI Hallucinations and References24:16 Deskilling and Timing AI30:11 Teaching Frameworks for AI32:46 Back to Evidence Basics33:40 Questioning the Evidence34:48 AI and Human Empathy37:45 AI as Clinical Assistant41:01 Recertification in the AI Era46:32 Ethics and Prompting50:40 AI Scribing and Guardrails54:35 Coding and Care Gaps57:15 Future of Medical Education01:01:13 Virtual Trials and Wrap-Up01:0Support the show

Part II of the groundbreaking Talk Ten Tuesday series concludes as Tami McMasters Gomez returns to share the real-world impact of UC Davis Health's neurodiversity coding internship.In this powerful follow-up, Tami moves beyond the vision and into outcomes — workforce transformation, productivity gains, retention success, and the measurable value of inclusive hiring in HIM.This isn't a theory. It's operational innovation in action.If you care about strengthening the coding workforce, expanding talent pipelines, and redefining what excellence looks like in the healthcare revenue cycle, you won't want to miss this conversation.Broadcast segments will also include these instantly recognizable panelists, who will report more news during their segments:• POV: Penny Jefferson, Director of CDI Services at the University of California, Davis Medical Center, will share her point of view during the broadcast.• SDoH Report: Marie Stinebuck, CEO for Phoenix Medical Management, will report on the latest news concerning the social determinants of health (SDoH).• CDI Report: Cheryl Ericson will provide an update on clinical documentation integrity (CDI).• The Coding Report: Christine Geiger will report on the latest coding news.• News Desk: Timothy Powell, ICD10monitor national correspondent, will anchor the Talk Ten Tuesdays News Desk.

In today's episode, we'll explore how vibe coding can be used in the K–12 classroom to help students develop critical thinking skills. Visit AVID Open Access to learn more.

Ian and Aaron discuss Ian shipping (!) the HelpSpot website and cooking on Outro, Aaron's complete overhaul of Solo, why it's nice to have a wife, and so much more.Register today for the Mostly Technical Pre-Party at Laracon EU.Sponsored by SavvyCal Appointments, Bento, Laravel Private Cloud, IttyBit, Ray by Spatie, and Redberry.Interested in sponsoring Mostly Technical?  Head to https://mostlytechnical.com/sponsor to learn more.(00:00) - Hell Week 3 (12:01) - The Haters Were Right (22:09) - Ian Shipped! (27:09) - Does Aaron Know Who Snoop Is? (32:29) - Cooking On Outro (46:17) - Counselors Update (01:05:28) - The Learning Skill (01:16:47) - It's Nice To Have A Wife (01:19:23) - Gamification of Software Links:OpenCodeXterm.jsWill KingJason BeggsHelpSpotOutroSoloCounselors

"The people who figure that out first won't just ship faster - they'll build things that others can't even spec, because the spec emerges last from a process that only exists in the doing of it." ~ Jesse Posner When your AI knows your goals better than you remember them in the moment, something fundamental has shifted. This episode explores Jesse Posner's "The Secret to Vibe Coding" and the emerging art of human-AI partnership through the lens of real agentic workflows, the philosophy of naming what's happening while it's happening, and what it looks like when the spec emerges last from a process that only exists in the doing. Check out the original article: The secret to vibe coding by Jesse Posner (Link: https://x.com/jesseposner/status/2025680970784137238) References from the episode The Secret to Vibe Coding by Jesse Posner: for those without an X account, the article has been reposted on Nostr by average_bitcoiner. (Link: https://primal.net/average/the-secret-to-vibe-coding-and-the-only-skill-that-matters-in-the-age-of-ai) The Code Liberation by Max Hillebrand: the piece I mentioned as a possible follow-up Read episode - more philosophical take on the same ideas, keep an eye out for that one. (Link: https://primal.net/maxhillebrand/930187aa8c1e9a92) Host Links ⁠Guy on Nostr ⁠(Link: http://tinyurl.com/2xc96ney) ⁠Guy on X ⁠(Link: https://twitter.com/theguyswann) Guy on Instagram (Link: https://www.instagram.com/theguyswann) Guy on TikTok (Link: https://www.tiktok.com/@theguyswann) Guy on YouTube (Link: https://www.youtube.com/@theguyswann) ⁠Bitcoin Audible on X⁠ (Link: https://twitter.com/BitcoinAudible) The Guy Swann Network Broadcast Room on Keet (Link: https://tinyurl.com/3na6v839) Check out our awesome sponsors! HRF: The Human Rights Foundation is a nonpartisan, nonprofit organization that promotes and protects human rights globally, with a focus on closed societies. Subscribe to HRF's Financial Freedom Newsletter today. (Link: https://mailchi.mp/hrf.org/financial-freedom-newsletter) OFF: The Oslo Freedom Forum is a global human rights event by the Human Rights Foundation (HRF), uniting voices from activism, journalism, tech, and beyond. Through powerful stories and collaboration, OFF advances freedom and human potential worldwide. Join us next June. (Link: https://oslofreedomforum.com/)

Prior authorizations among Medicare Advantage plans have drawn criticism and concern from patients, providers, lawmakers, and regulators. But hospitals and doctors are uniquely positioned to advocate for their patients' access to and coverage for care. What's necessary is the need to understand the rules of the process. And Medicare Advantage plans have many of them.During the next live edition of the venerable Monitor Monday, the Internet broadcast, Richelle Marting, a healthcare attorney, and certified coder, will help you understand when and how Medicare Advantage plans can use prior authorizations for the critical protections you need to know to advocate for patient care.Broadcast segments will also include these instantly recognizable features:• Monday Rounds: Ronald Hirsch, MD, vice president of R1 RCM, will be making his Monday Rounds.• The RAC Report: Healthcare attorney Knicole Emanuel, partner at the law firm of Nelson Mullins, will report the latest news about auditors.• Risky Business: Healthcare attorney David Glaser, shareholder in the law offices of Fredrikson & Byron, will join the broadcast with his trademark segment.• Legislative Update: Matthew Albright of Zelis, will report on current healthcare legislation.

Jaeden & Jamie discuss the acquisition of the AI agent Open Claw, created by Peter Steinberger, by OpenAI for a near billion-dollar valuation. They examine the unique aspects of Open Claw that led to its success and eventual acquisition, and explore the concept of "vibe coding" as a powerful tool for developing new applications and automating tasks, even for non-developers, highlighting both its potential and security trade-offs. Our Skool Community: https://www.skool.com/aihustleGet the top 40+ AI Models for $20 at AI Box: ⁠⁠https://aibox.aiWatch on YouTube: https://youtu.be/ICo9BT6ZvhYChapters00:00 The Acquisition of OpenClaw: A New Era for Solo Startups01:59 The Rise of OpenClaw: Features and Viral Success04:58 The Unique Approach: Open Source and User Empowerment08:58 Vibe Coding: The Future of Software Development10:47 Conclusion and Call to Action

Think you're just “not a people person”? Most tech leaders quietly believe this about themselves, and it's exactly what's holding them back.In this episode, Martijn Versteeg, founder of peer leadership community Group Effort and former CPTO with a background in organizational psychology, makes the case that it's not: human behavior follows predictable patterns you can understand and work with, just like any system. The conversation covers a six-variable model for understanding what drives behavior and disengagement on your team, why popular personality tools like MBTI and DiSC often do more harm than good, and a clear structure for delivering bad news without the usual stress buildup. We also get into what it really takes to let go of hands-on coding when you move into leadership, why developing a product mindset matters even if product isn't in your title, and the psychological risks of heavy AI use that most teams still aren't thinking about.Key topics discussed:The 6 human needs that predict human behaviorWhy MBTI and DiSC often do more harm than goodHow to stop avoiding difficult conversationsDeliver bad news clearly using a 10-second ruleWhy becoming a bottleneck is a slow career killerBuilding a product mindset when you're in techThe mental health risks of heavy AI useWhat peer groups give you that books can'tTimestamps:(00:00:00) Trailer & Intro(00:03:06) Why Small Steps Matter More Than Career Turning Points(00:05:11) About Martijn Versteeg(00:07:01) How Can I Learn People Skills Systematically?(00:13:19) Six Human Needs That Predict Behavior(00:17:28) How Does It Compare to Maslow's Hierarchy of Needs?(00:19:49) Why Are Personality Tests Like MBTI Unreliable?(00:23:20) How Do I Use Pain and Pleasure to Drive Growth?(00:28:30) How Do I Handle Conflict and Difficult Conversations?(00:32:47) A Model for Delivering Bad News in 10 Seconds(00:36:12) How Do I Transition from Tech Lead to Engineering Leader?(00:41:12) How Do I Let Go of Coding as a Leader?(00:42:49) The Vanilla Orchid Story: Why Leaders Must Let Go(00:46:55) How Can Engineers Develop a Product Mindset?(00:53:17) What Are the Hidden Risks of AI for Mental Health?(01:02:19) What Is the Value of Learning Through Podcast Conversations?(01:07:19) Why Consuming Knowledge Is Not the Same as Producing(01:09:06) 3 Tech Lead Wisdom_____Martijn Versteeg's BioMartijn Versteeg is the founder of Group Effort, a Netherlands-based collective that empowers tech and product leaders across Europe through peer groups, offsites, and specialized training. As a key figure in the global product community, he is also an organizer of the Product Mastery Conference, where he helps curate insights for the next generation of product leaders.Before founding Group Effort, Martijn built and successfully sold an EdTech IT platform and spent over five years as an Agile coach and Scrum Master. His unique perspective on leadership is rooted in high-performance athletics; at just 22 years old, he served as the National Rowing Coach for Singapore.Today, Martijn is a vocal advocate for community-led learning. He frequently challenges leaders to move past the search for “golden nuggets” of wisdom and instead focus on the consistent, incremental iterations that solve the “hard people stuff” in scaling organizations.Follow Martijn:LinkedIn – linkedin.com/in/versteegGroup Effort – groupeffort.nlNewsletter – groupeffort.nl/newsletterFree training on Massive Action-Taking for Product Leaders – groupeffort.nl/actionLike this episode?Show notes & transcript: techleadjournal.dev/episodes/248.Follow @techleadjournal on LinkedIn, Twitter, and Instagram.Buy me a coffee or become a patron.

Jaeden & Jamie discuss the acquisition of the AI agent Open Claw, created by Peter Steinberger, by OpenAI for a near billion-dollar valuation. They examine the unique aspects of Open Claw that led to its success and eventual acquisition, and explore the concept of "vibe coding" as a powerful tool for developing new applications and automating tasks, even for non-developers, highlighting both its potential and security trade-offs. Our Skool Community: https://www.skool.com/aihustleGet the top 40+ AI Models for $20 at AI Box: ⁠⁠https://aibox.aiWatch on YouTube: https://youtu.be/ICo9BT6ZvhYChapters00:00 The Acquisition of OpenClaw: A New Era for Solo Startups01:59 The Rise of OpenClaw: Features and Viral Success04:58 The Unique Approach: Open Source and User Empowerment08:58 Vibe Coding: The Future of Software Development10:47 Conclusion and Call to Action See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Todays conversation is hosted in person in NYC. Isaiah Washington and I discussed how he built AI tools built with Claude Code as a largely non technical person for his organization. We also discussed his experience at Facebook and Insight and his current role at Artemis. I greatly enjoyed the conversation and hope to have many more like it in the future.

Alexander Embiricos is the Head of Codex at OpenAI, leading the development of the company's flagship AI coding systems that power automated software generation, debugging and developer workflows. Under his leadership, Codex has become one of the most widely adopted AI developer platforms.  AGENDA: 05:13 Will Coding Be Automated? Why AI Could Create More Engineers, Not Fewer 07:17 Do We Need PMs? The "Undefined" Product Role and When It Matters 08:06 The Real AGI Bottleneck: Human Prompting, Validation, and "Too Much Effort" 13:04 Three Phases of Agents: Coding → Computer Use → Productized Workflows 13:52 Enterprise Reality Check: Security, Permissions, and Safe Agentic Browsing 17:57 Is Inference the New Sales and Marketing?  18:49 What % of Codex Was Written by AI? 21:33 Do OpenAI Use AI for Code Review? 23:31 Is there any stickiness to AI coding tools? 28:22 What Does "Winning" Mean at OpenAI? Mission, Competition, and Moats 32:04 The Future UI: Chat or Voice 34:10 Agent-to-Agent Workflows: Designing for Approvals, Compliance, and Automation 35:39 Do Coding Models Have a Data Moat? 36:50 How does Codex View Data: Will They Build Their Own Mercor and Turing? 37:27 How Does Codex View Consumer: Will They Compete with Lovable? 41:56 Benchmarks vs "Vibes": How People Actually Judge Models 42:43 Cursor's Edge and the Case for Building Your Own Models 47:37 Is SaaS Dead? What Still Defends Value (Humans + Systems of Record) 51:28 Talent Wars and Career Advice for New Engineers in the AI Era 01:01:03 Guardrails, the Fully AI-Managed Stack, and a 10-Year Vision for Everyone      

Jack and Jackson discuss what's ailing software stocks, and list Wall Street picks. And a tractor executive has the latest on farm incomes and planting tech.  Learn more about your ad choices. Visit megaphone.fm/adchoices

Michael Truell, CEO of Cursor, sits down with Patrick Collison, CEO of Stripe and an investor in Anysphere, to talk about Collison's history with Smalltalk and Lisp, the MongoDB and Ruby decisions Stripe still lives with 15 years later, why he'd spend even more time on API design if he could do it over, and whether AI is actually showing up in economic productivity data. This episode originally aired on Cursor's podcast.   Resources:  Follow Patrick Collison on X:   https://twitter.com/patrickc Follow Michael Truell on X: https://twitter.com/mntruell Follow Cursor: https://www.youtube.com/@cursor_ai Stay Updated:Find a16z on YouTube: YouTubeFind a16z on XFind a16z on LinkedInListen to the a16z Show on SpotifyListen to the a16z Show on Apple PodcastsFollow our host: https://twitter.com/eriktorenberg Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

CoreStory is building code intelligence platforms that address the fundamental limitation of today's coding agents: their inability to navigate complex enterprise codebases. While foundation models excel at greenfield development, they fail at real-world engineering tasks in systems spanning millions of lines of code. CoreStory's context layer delivers a 44% improvement on SWE-bench, the industry's standard benchmark for measuring coding agent effectiveness on actual GitHub issues. In this episode of BUILDERS, I sat down with Anand Kulkarni, CEO of CoreStory, to explore how his team is enabling the shift to AI-native engineering and seeding the category of spec-driven development across Microsoft, GitHub, and Amazon. Topics Discussed: Building with GPT-3 API 18 months before ChatGPT went public Why even GPT-5 and Opus 4.5 struggle with enterprise codebases on SWE-bench The narrative shift required when selling AI pre- and post-ChatGPT CoreStory's 44% improvement in coding agent performance through context intelligence How "spec-driven development" got adopted by Microsoft, GitHub, and Amazon without formal analyst relations The parallel between JIRA monetizing Agile and CoreStory enabling AI-native engineering Three-channel distribution: direct enterprise, coding agent partnerships via MCP, and hyperscaler/GSI routes Why specs become the source of truth while code becomes disposable in the AI era GTM Lessons For B2B Founders: Match your narrative precision to technical depth: CoreStory deploys three distinct positioning strategies based on audience sophistication. For AI practitioners tracking benchmarks, they lead with "44% SWE-bench improvement"—a metric that immediately signals meaningful progress on the hardest problem in the space. For engineering leaders aware of AI tooling but not deep in the research, they focus on velocity gains and ROI metrics. For executives, they describe reverse-engineering codebases into machine-readable specs. The key insight: technical audiences dismiss vague value props, while non-technical audiences get lost in benchmark details. Map your positioning to how your audience measures success in their world. Seed category language through earned adoption, not manufactured consensus: Anand initially called their approach "requirements-driven development" before simplifying to "spec-driven development." Rather than pitching analysts, they used the term consistently in customer conversations, gave talks at GitHub Universe, and shipped demos showing the workflow. When customers naturally adopted the language and community leaders began using similar terminology independently, Microsoft and GitHub followed with their own implementations (like GitHub's SpecKit). The lesson: category language sticks when practitioners choose to use it because it clarifies their work, not because a vendor pushed it. Focus on customer adoption as proof of concept before seeking broader market validation. Position against emergent practices, not just incumbent products: CoreStory doesn't position against legacy code analysis tools—they position as the enabler of AI-native engineering, the discipline that will displace Agile. Anand's insight from watching JIRA's success: "People don't love JIRA. What they love is Agile as a way to move away from waterfall." CoreStory is betting that 10x velocity gains from AI-native practices will drive the same categorical shift. When you're early in a technology wave, attach to the practice change (how teams will work differently) rather than feature comparisons with existing tools. Movements create markets. Design channel strategy around customer problem awareness: CoreStory's three channels map to different stages of buyer sophistication. Direct enterprise comes from teams already deep in AI engineering who've hit the context limitation wall. Coding agent partnerships (via MCP integration with tools like Cognition and Factory) serve builders wanting better AI tooling who haven't diagnosed the context problem yet. Hyperscalers and GSIs distribute into modernization and maintenance projects where AI enablement is emerging as a requirement. Each channel serves a distinct buyer journey stage. Don't force one go-to-market motion—design multiple paths based on where different customer segments are in understanding the problem you solve. Navigate pre-legitimacy markets by hiding the breakthrough: Before ChatGPT, selling anything AI-driven faced immediate skepticism about whether it was "real" or just smoke and mirrors. Anand couldn't lead with AI without triggering disbelief. CoreStory focused on delivered outcomes—"here's what you'll be able to do"—with AI as the mechanism, not the message. Post-ChatGPT, the challenge flipped: everyone expects AI, but now the differentiation question becomes harder. If you're building on emerging technology before market consensus forms, deemphasize the technology until buyers have context to evaluate it. Once the market validates the technology category, shift to demonstrating your specific technical advantage within it. // Sponsors: Front Lines — We help B2B tech companies launch, manage, and grow podcasts that drive demand, awareness, and thought leadership. www.FrontLines.io The Global Talent Co. — We help tech startups find, vet, hire, pay, and retain amazing marketing talent that costs 50-70% less than the US & Europe. www.GlobalTalent.co // Don't Miss: New Podcast Series — How I Hire Senior GTM leaders share the tactical hiring frameworks they use to build winning revenue teams. Hosted by Andy Mowat, who scaled 4 unicorns from $10M to $100M+ ARR and launched Whispered to help executives find their next role. Subscribe here: https://open.spotify.com/show/53yCHlPfLSMFimtv0riPyM

He Went From $0 to $8,100 a Month In 90 Days Using Only AIIn episode 146 of The Side Hustle Experiment Podcast  John (https://www.instagram.com/sidehustleexperiment/ ) and Drew catch up on their recent business activities, discussing their successes and challenges. They delve into the impact of AI on marketing strategies, pricing adjustments, and the importance of data analysis for optimizing ad performance. The conversation also touches on influencer marketing, personal experiences with AI tools, and the role of technology in health and fitness. They conclude with reflections on the future of AI in business and the necessity of maintaining a unique perspective in content creation.Don't forget to Like, Subscribe, and hit the bell so you don't miss future episodes with top entrepreneurs and creators.Chapters00:00 Profitable Days and Revenue Growth03:04 Pricing Strategies and Upselling Techniques05:46 Ad Performance and Conversion Rates08:53 Data-Driven Decision Making11:27 AI Tools and Their Applications14:37 Building Custom Software Solutions17:21 Efficiency in Marketing and Sales20:07 Leveraging AI for Content Creation23:08 Coding and Automation in Business25:53 Innovative Ideas and Future Plans31:58 Optimizing Conversations with AI32:58 Leveraging YouTube for Product Development34:29 Creating Engaging Products from Existing Content35:57 The Future of Work and AI Integration37:21 AI in Customer Service and White Collar Jobs39:07 AI's Role in Health and Wellness41:25 Trusting AI for Medical Insights43:58 The Limitations of AI in Creativity46:32 Using AI as a Tool, Not a Crutch49:15 The Importance of Unique Perspectives51:32 AI's Role in Personal Growth and Reflection54:50 Finding Balance in Lifestyle Choices#makemoneyonline #sidehustleexperimentpodcast #sidehustles Follow us on Instagram: https://www.instagram.com/sidehustleexperimentpodcast/ Listen on your favorite podcast platformYoutube: https://bit.ly/3HHklFOSpotify: https://spoti.fi/48RRKcPApple: https://apple.co/4bmaFOk Check out Drew's StuffInstagram: https://www.instagram.com/realdrewdTwitter: https://twitter.com/DrewFBACheck out John's StuffInstagram: https://www.instagram.com/sidehustleexperiment/Twitter: https://twitter.com/SideHustleExp FREE ResourcesFREE Guide: How to Make Money Reviewing Products https://bit.ly/3HIGFSP

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

There's a particular kind of clarity you get when you talk to someone who spends their days breaking into things for a living. Not with malice — with purpose. John Steigerwald, known to most in the industry simply as "Stigs," co-founded White Knight Labs in 2016 with a mission that sounds almost disarmingly simple: build the best penetration testing team anyone has ever seen, and actually deliver results. Nearly a decade later, the company has grown to 40 people, gone international, and is busier than ever. The question worth asking is: why?The uncomfortable answer, according to Stigs, is that the fundamental problems haven't changed. At all."Honestly, it's still 2015," he said during our most recent conversation on ITSPmagazine's Brand Story series. Not as a metaphor. As a diagnosis. The same misconfigurations, the same weak identity policies, the same unlocked back doors that red teamers were exploiting a decade ago are still wide open today. The apps built in a COVID-era frenzy — pushed out fast, tested never — are now running critical business infrastructure. And the organizations using them are only finding out when something breaks.What's changed is the surface area. Cloud, AI, Microsoft 365, vibe-coded production apps — each new layer of technology gets adopted at speed, and each one arrives carrying the same original sin: no one turned on the basics. Stigs used Microsoft 365 as a pointed example. Millions of businesses are running on it with DMARC turned off, default configurations untouched, Copilot layered on top, and not a single CIS Benchmark policy applied. "Every client is vulnerable," he said. "Not just 10% of clients. Every client."That's a striking statement. It's also, if you've been paying attention to breach headlines, not a surprising one.The AI angle adds a new and almost darkly comedic wrinkle. Vibe coding — the practice of using AI tools like Cursor or Claude to generate production-ready code at speed — has given entry-level developers intermediate-level output. Which sounds great, until you realize that the AI models many of them leaned on were trained on outdated, sometimes vulnerable data. Stigs described visiting multiple clients with nearly identical security weaknesses, all tracing back to the same ChatGPT-generated setup instructions. "You and your neighbor did the same thing," he told one client. That's not just a funny anecdote. It's a warning about what happens when an entire industry bootstraps its infrastructure from the same flawed source.And yet, Stigs isn't anti-AI. He uses it every day. He just sees it with the clarity of someone who also finds the holes it leaves behind. His prediction for the near future: a massive wave of secure code review requests, as companies start reckoning with the vibe-coded backlog they've been quietly accumulating. AppSec is about to have a very good year.Looking forward, White Knight Labs is watching the growing intersection of private sector expertise and government infrastructure testing with particular interest. Critical infrastructure in America, long overdue for rigorous physical and embedded testing, is starting to receive that attention. Stigs and his team are already in the room.What makes White Knight Labs different isn't just technical skill — it's the ability to communicate what they find in language that actually lands. In an industry full of reports that gather dust, that matters. The best penetration test in the world is useless if no one acts on it.The door is open. It's been open for years. The question is who you call to finally lock it.To learn more about White Knight Labs, visit their website or reach out directly. Listen to the full conversation on ITSPmagazine.GUESTJohn StigerwaltFounder at White Knight Labs | Red Team Operations Leaderhttps://www.linkedin.com/in/john-stigerwalt-90a9b4110/RESOURCESWhite Knight Labs:  https://whiteknightlabs.com_____________________________________________________________Are you interested in telling your story?▶︎ Full Length Brand Story: https://www.studioc60.com/content-creation#full▶︎ Brand Spotlight Story: https://www.studioc60.com/content-creation#spotlight▶︎ Brand Highlight Story: https://www.studioc60.com/content-creation#highlight Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

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

Wes and Scott talk about the state of AI coding in 2026—from editors and models to agents, skills, slash commands, MCPs, and more. They unpack what these things actually do, how they overlap, and how to use them effectively without overcomplicating your setup. Show Notes 00:00 Welcome to Syntax! 01:39 The tools: editors, terminals, GUIs 05:27 Wes' and Scott's current AI setups 13:17 Picking the right model 18:58 How exactly do agents work? 22:32 Subagents and parallel workflows 24:29 Brought to you by Sentry.io 24:54 What goes in agents.md (and what doesn't) 26:47 Skills vs agents Skills Superpowers 34:03 Slash commands as reusable prompts 36:02 Hooks and keeping your code from going off the rails 38:00 Plugins and bundling your setup 39:24 What MCP is and why it's powerful 40:54 Cloud agents and running jobs remotely 43:47 Choosing the right AI tool 47:41 Sick Picks + Shameless Plugs Sick Picks Scott: ULTRALOQ Bolt Fingerprint WiFi Smart Lock Wes: St. Denis Medical Shameless Plugs Syntax YouTube Channel Hit us up on Socials! Syntax: X Instagram Tiktok LinkedIn Threads Wes: X Instagram Tiktok LinkedIn Threads Scott: X Instagram Tiktok LinkedIn Threads Randy: X Instagram YouTube Threads

AI Assisted Coding: How Spending 4x More on Code Quality Doubled Development Speed What happens when you combine nearly 30 years of engineering experience with AI-assisted coding? In this episode, Eduardo Ferro shares his experiments showing that AI doesn't replace good practices—it amplifies them. The result: doubled productivity while spending four times more on code quality. Vibe Coding vs Production-Grade AI Development "Vibe coding is flow-driven, curiosity-based way of building software with AI. It's less about meticulously reviewing each line of code, and more about letting the AI steer the process—perfect for quick experiments, side projects, MVPs, and prototypes."   Edu draws a clear distinction between vibe coding and production AI development. Vibe coding is exploration-focused, where you let AI drive while you learn and discover. Production AI coding is goal-focused, with careful planning, spec definition, and identification of edge cases before implementation. Both use small, safe steps and continuous conversation with the AI, but production code demands architectural thinking, security analysis, and sustainability practices. The key insight is that even vibe coding benefits from engineering discipline—as experiments grow, you need sustainable practices to maintain flexibility. How AI Doubled My Productivity "I was investing four times more in refactoring, cleanup, deleting code, introducing new tests, improving testability, and security analysis than in generating new features. And at the same time, globally, I think I more or less doubled my pace of work."   Edu's two-month experiment with production code revealed a counterintuitive finding: by spending 4x more time on code quality activities—refactoring, cleanup, test improvement, and security analysis—he actually doubled his overall delivery speed. The secret lies in fast feedback loops. With AI, you can implement a feature, run automated code review, analyze security, prioritize improvements, and iterate—all within an hour. What used to be a day's work happens in a single focused session, and the quality improvements compound over time. The Positive Spiral of Code Removal "We removed code, so we removed all the features that were not being used. And whenever I remove this code, the next step is to automatically try to see, okay, can I simplify the architecture."   One of the most powerful practices Edu discovered is using AI to accelerate code removal. By connecting product analytics to identify unused features, then using AI to quickly remove them, you trigger a positive spiral: removing code makes architecture changes easier, easier architecture changes enable faster feature development, which leads to more opportunities for simplification. This creates a self-reinforcing cycle that humans historically have been reluctant to pursue because removal was as expensive as creation. Preparing the System Before Introducing Change "What I want to generate is this new functionality—how should I change my system to make it super easy to introduce this one? It's not about making the change, it's about making the change easy."   Edu describes a practice that was previously too expensive: preparing the system before introducing changes. By analyzing architecture decision records, understanding the existing design, and adapting the codebase first, new features become trivial to implement. AI makes this preparation cheap enough to do routinely. The result is systems that evolve cleanly rather than accumulating technical debt with each new feature. AI as an Amplifier: The Double-Edged Sword "AI is an amplifier. People who already know how to develop software well will continue to develop it well and faster. People who did not know how to develop software well will probably get in trouble much faster than they would otherwise."   Edu's central metaphor is AI as an amplifier—it doesn't replace engineering judgment, it magnifies its presence or absence. Teams with strong practices will see accelerated improvement; teams without them will generate technical debt faster than ever. This has implications beyond individual productivity: the market will be saturated with solutions, making product discovery and distribution channels more important than implementation capability.   In this episode, we refer to Edu's blog post Fast Feedback, Fast Features: My AI Assisted Coding Experiment and Vibe Coding by Gene Kim.   About Eduardo Ferro Edu Ferro is Head of Engineering and Data Platform at ClarityAI, with nearly 30 years' experience. He helps teams deliver value through Lean, XP, and DevOps, blending technical depth with product thinking. Recently he explores AI-assisted product development, sharing insights and experiments on his site eferro.net.   You can connect with Edu Ferro on LinkedIn.

We are seeing more generative AI in higher ed, but what does it look like when we use it to actually build course elements? In this visual episode, you will hear how we use “vibe coding” (coding by conversation) to create interactive learning materials in D2L Brightspace while still relying on the basics: alignment, accessibility, and thorough testing. You will also see real examples, including a gamified misinformation activity, a simulation built from faculty-provided content, and a simple HTML announcement you can try right away. Watch the video version on Spotify or the Augusta University YouTube channel for the demos.Visit our show page for free access to more content, including the Continuing the Conversation Activity, plus full episodes and additional resources. 

"Il n'y a qu'une seule chose qui m'intéresse dans ce que je fais, c'est l'utilité." Le D.E.V. de la semaine est Pierre Lemaire, Startup Builder chez Hexa. Cet épisode revient sur la montée du Vibe Coding, ces nouvelles pratiques où l'on crée des MVPs bluffants sans pour autant être un dev &lsquofull stack'. Pierre partage sa vision : valider des idées, apprendre sur le terrain, itérer vite&hellip tout en réaffirmant l'importance de l'expertise métier, même dans un monde boosté par l'IA. Il raconte comment la planification, les choix d'architecture et l'apprentissage restent des points clés du quotidien. Et si l'avenir n'était pas de coder moins, mais de coder bien et plus intelligemment ?Chapitrages00:00:53 : Introduction au Vibe Coding00:01:25 : Découverte de Pierre00:02:36 : Le Vibe Coding expliqué00:05:54 : Débats sur la qualité du code00:07:38 : Utilité vs pérennité00:09:42 : L'avenir des développeurs00:15:06 : Les perceptions du Vibe Coding00:19:52 : L'impact des outils sur la collaboration00:21:49 : Changement de paradigme du développement00:26:06 : Évolution des compétences des développeurs00:28:12 : Recrutement et compétences techniques00:32:38 : Les défis de l'apprentissage en entreprise00:35:48 : Perspectives sur l'avenir du développement00:37:42 : La qualité du code à l'ère de l'IA00:40:17 : Réflexions sur l'intelligence artificielle00:42:46 : Conseils pratiques pour Cloud Code00:46:02 : Stratégies pour un bon usage de Cloud Code00:48:50 : L'importance des standards dans le développement00:51:20 : Recommandations de contenu00:53:13 : Conclusion et réflexions finales Liens évoqués pendant l'émission High AgencySamuel 🎙️ Soutenez le podcast If This Then Dev ! 🎙️ Chaque contribution aide à maintenir et améliorer nos épisodes. Cliquez ici pour nous soutenir sur Tipeee 🙏Archives | Site | Boutique | TikTok | Discord | Twitter | LinkedIn | Instagram | Youtube | Twitch | Job Board |Hébergé par Audiomeans. Visitez audiomeans.fr/politique-de-confidentialite pour plus d'informations.

Many EMRs now embed ICD‑10 and CPT codes directly into the medical record. But is that advisable? The safest approach is still to let the documentation stand on its own. The content of the record should support the coding choices, and coders and auditors should base their work on the medical facts as documented. Codes can—and should—be applied only after the documentation is complete. On today’s CodeCast episode, Terry explains that when providers insert billing codes into the note, the intention may be good, but the risk of contradictions or inaccuracies can outweigh any perceived benefit. Should medical record documentation stand alone, without templated teaching language that was never meant to be included? Should codes appear in the record simply to give the impression of accuracy, rather than allowing the documentation to speak for itself? Subscribe and Listen Find all of Terry’s official links in one place: https://www.terryfletcher.net/links The post Who's Doing the Coding — Providers or Coders? appeared first on Terry Fletcher Consulting, Inc..

AI Assisted Coding: Stop Building Features, Start Building Systems with AI What separates vibe coding from truly effective AI-assisted development? In this episode, Adam Bilišič shares his framework for mastering AI-augmented coding, walking through five distinct levels that take developers from basic prompting to building autonomous multi-agent systems. Vibe Coding vs AI-Augmented Coding: A Critical Distinction "The person who is actually creating the app doesn't have to have in-depth overview or understanding of how the app works in the background. They're essentially a manual tester of their own application, but they don't know how the data structure is, what are the best practices, or the security aspects."   Adam draws a clear line between vibe coding and AI-augmented coding. Vibe coding allows non-developers to create functional applications without understanding the underlying architecture—useful for product owners to create visual prototypes or help clients visualize their ideas.  AI-augmented coding, however, is what professional software engineers need to master: using AI tools while maintaining full understanding of the system's architecture, security implications, and best practices. The key difference is that augmented coding lets you delegate repetitive work while retaining deep knowledge of what's happening under the hood. From Building Features to Building Systems "When you start building systems, instead of thinking 'how can I solve this feature,' you are thinking 'how can I create either a skill, command, sub-agent, or other things which these tools offer, to then do this thing consistently again and again without repetition.'"   The fundamental mindset shift in AI-augmented coding is moving from feature-level thinking to systems-level thinking. Rather than treating each task as a one-off prompt, experienced practitioners capture their thinking process into reusable recipes. This includes documenting how to refactor specific components, creating templates for common patterns, and building skills that encode your decision-making process. The goal is translating your coding practices into something the AI can repeatedly execute for any new feature. Context Management: The Critical Skill For Working With AI "People have this tendency to install everything they see on Reddit. They never check what is then loaded within the context just when they open the coding agent. You can check it, and suddenly you see 40 or 50% of your context is taken just by MCPs, and you didn't do anything yet."   One of the most overlooked aspects of AI-assisted coding is context management. Adam reveals that many developers unknowingly fill their context window with MCP (Model Context Protocol) tools they don't need for the current task. The solution is strategic use of sub-agents: when your orchestrator calls a front-end sub-agent, it gets access to Playwright for browser testing, while your backend agent doesn't need that context overhead. Understanding how to allocate context across specialized agents dramatically improves results. The Five Levels of AI-Augmented Coding "If you didn't catch up or change your opinion in the last 2-3 years, I would say we are getting to the point where it will be kind of last chance to do so, because the technology is evolving so fast."   Adam outlines a progression from beginner to expert:   Level 1 - Master of Prompts: Learning to write effective prompts, but constantly repeating context about architecture and preferences Level 2 - Configuration Expert: Using files like .cursorrules or CLAUDE.md to codify rules the agent should always follow Level 3 - Context Master: Understanding how to manage context efficiently, using MCPs strategically, creating markdown files for reusable information Level 4 - Automation Master: Creating custom commands, skills, and sub-agents to automate repetitive workflows Level 5 - The Orchestrator: Building systems where a main orchestrator delegates to specialized sub-agents, each running in their own context window The Power of Specialized Sub-Agents "The sub-agent runs in his own context window, so it's not polluted by whatever the orchestrator was doing. The orchestrator needs to give him enough information so it can do its work."   At the highest level, developers create virtual teams of specialized agents. The orchestrator understands which sub-agent to call for front-end work, which for backend, and which for testing. Each agent operates in a clean context, focused on its specific domain. When the tester finds issues, it reports back to the orchestrator, which can spin up the appropriate agent to fix problems. This creates a self-correcting development loop that dramatically increases throughput.   In this episode, we refer to the Claude Code subreddit and IndyDevDan's YouTube channel for learning resources.   About Adam Bilišič Adam Bilišič is a former CTO of a Swiss company with over 12 years of professional experience in software development, primarily working with Swiss clients. He is now the CEO of NodeonLabs, where he focuses on building AI-powered solutions and educating companies on how to effectively use AI tools, coding agents, and how to build their own custom agents.   You can connect with Adam Bilišič on LinkedIn and learn more at nodeonlabs.com. Download his free guide on the five levels of AI-augmented coding at nodeonlabs.com/ai-trainings/ai-augmented-coding#free-guide.

Send me a text (I will personally respond)Are you struggling to stand out in the crowded cybersecurity marketplace? Wondering how to build unique marketing or sales assets without a dedicated engineering team? Curious how other leaders are leveraging AI-driven “vibe coding” to create real value, not gimmicks? This episode is packed with proven, creative ways cybersecurity sales and marketing pros are innovating faster than ever.In this conversation we discuss:

Once a relic symbolic of earlier times in medicine, the Inpatient-Only (IPO) List has been added to the junkyard of outdated medical processes and practices. And if you and your team fail to plan and align your system appropriately, you risk major financial, operational, and compliance consequences.The good news: during the next live edition of Monitor Mondays, you'll learn why inpatient status is no longer guaranteed by procedure. You'll also learn how the burden of proof for inpatient care now rests in your documentation, along with what you and your team must do to protect appropriate inpatient admissions. Join us when Dr. Stephanie Van Zandt reveals practical strategies to navigate this new landscape and stay ahead of the curve.Broadcast segments will also include these instantly recognizablepanelists, who will report more news during their segments:·      POV: Penny Jefferson, Manager of Coding & Clinical Documentation Integrity Services for the University of Davis Medical Center, will share her point of view during the broadcast.·      CDI Report: Cheryl Ericson will provide an update on clinical documentation integrity (CDI).·      The Coding Report: Christine Geiger will report on the latest coding news.·      News Desk: Juliet Ugarte Hopkins, MD will anchor the Talk Ten Tuesdays News Desk.

Ian and Aaron discuss Aaron's new AI tool Counselors, something called "Hell Week", what's new with Solo and Outro, and so much more.Register today for the Mostly Technical Pre-Party at Laracon EU.Sponsored by SavvyCal Appointments, Bento, Laravel Private Cloud, IttyBit, Ray by Spatie, and Redberry.Interested in sponsoring Mostly Technical?  Head to https://mostlytechnical.com/sponsor to learn more.(00:00) - Achieving The Impossible (11:26) - Making Things Easy For Humans (19:53) - Ian's Hyperkey Setup (28:59) - Aaron's Hell Week (44:42) - The Future (51:38) - Outro Is Cooking (58:36) - Valentine's Day & Other Updates (01:09:26) - OpenClaw Links:CounselorsHyperkeyRaycastSpace Cadet KeyboardSoloFaster.devOutro.fmAaron's cardboard box saw

Jag Valaiyapathy, who joins to talk about Agentic coding and sf-skills, is a Senior Forward Deployed Engineer, Salesforce CTA. Main Points Links Video Teaser The YouTube Video URL The post 108. Agentic coding and sf-skills | Jag Valaiyapathy appeared first on SalesforceWay.

---------------------- For our listeners, use the code 'EYECODEMEDIA22' for 10% off at check out for our Premiere Billing & Coding bundle or our EyeCode Billing & Coding course. Sharpen your billing and coding skills today and leave no money on the table! questions@eyecode-education.com https://coopervision.com/our-company/news-center/press-release/coopervision-and-aoa-join-forces-launch-myopia-collective Go to MacuHealth.com and use the coupon code PODCAST2024 at checkout for special discounts  Show Sponsors: CooperVision MacuHealth

https://www.linkedin.com/in/brian-ogrady/ - my linkedin https://www.linkedin.com/company/qdrant/ - company linkedin https://qdrant.tech/contact-us - contact us https://github.com/qdrant/qdrant/ - Qdrant GH https://github.com/qdrant/qdrant-edge-demo - Qdrant Edge running on smart glasses Mike on LinkedIn Coder Radio on Discord Mike's Oryx Review Alice Alice Jumpstart Offer Vorpal Mike in USA Today

Hovering Help: When Umbrellas Take Off.  Clear Cases, Cloudy Futures: The Transparent Tech Throwback.  Vibe Coding Viral: Claude Code Turns Typers into Tech Builders.  Mechanical Mochas: When Robots Pour a Pretty Perfect Latte.  Beasts of Battle: Bio-Inspired Bots and the New Face of Warfare.  Chatbot Comfort or Digital Dependence? Gen Z's Lonely Loop.  Bear Biometrics: When AI Gets Nosey with Nature.  Silent Screens, Speaking Walls: The Smart Canvas That Turns Talk into Timeless Art.  Scale, Scan and Heart-Plan: The Pricey Promise of Full-Body Fitness Tech. 

Plus: Pinterest stock slides after projected slower revenue growth. And Ubisoft shares jump on cash forecast. Julie Chang hosts. Learn more about your ad choices. Visit megaphone.fm/adchoices

In this episode from 20VC, Harry Stebbings talks with Anish Acharya, general partner at a16z, about the future of SaaS in an AI world. Anish argues that software is completely oversold and that the general story about vibe coding everything is flat wrong. They discuss why SaaS switching costs are actually going down thanks to coding agents, where startups versus incumbents will win, and whether the apps layer or foundation models will capture more value. They also cover agent overhype, the changing UI paradigm, what defensibility looks like now, and why boring wins versus weird wins in this product cycle. Resources:Follow Anish Acharya on X:  https://twitter.com/illscienceFollow Harry Stebbings on X:  https://twitter.com/HarryStebbings Stay Updated:If you enjoyed this episode, be sure to like, subscribe, and share with your friends!Find a16z on X: https://twitter.com/a16zFind a16z on LinkedIn: https://www.linkedin.com/company/a16zListen to the a16z Podcast on Spotify: https://open.spotify.com/show/5bC65RDvs3oxnLyqqvkUYXListen to the a16z Podcast on Apple Podcasts: https://podcasts.apple.com/us/podcast/a16z-podcast/id842818711Follow our host: https://x.com/eriktorenbergPlease note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see http://a16z.com/disclosures. Stay Updated:Find a16z on XFind a16z on LinkedInListen to the a16z Show on SpotifyListen to the a16z Show on Apple PodcastsFollow our host: https://twitter.com/eriktorenberg Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Anne Cocquyt is a Silicon Valley operator, educator, and bestselling startup author who helps founders turn ideas into real businesses—fast. She's the Founder & CEO of The Guild and The Guild Studio, an award-winning instructor at UC Berkeley, and a trusted advisor to accelerators around the world. In this episode, Anne breaks down how “vibe coding” and AI-powered tools are radically changing entrepreneurship—making it possible for anyone (even non-technical founders) to build MVPs, validate ideas, and reach market in days instead of months. On this episode we talk about: What “vibe coding” is and how non-technical founders can build MVPs in minutes Why traditional startup development (and million-dollar dev budgets) are becoming obsolete How AI is shrinking feedback loops and accelerating product-market fit The skills founders actually need in an AI-first world Practical ways to test ideas, validate demand, and market early-stage products Top 3 Takeaways You no longer need a big dev team or massive funding to launch—AI tools let founders build and iterate MVPs in days, not years. The real differentiator isn't code anymore—it's critical thinking, understanding users, and building something people actually want. Entrepreneurs who succeed will be “umbrella-skilled generalists,” able to build, test, market, and adapt quickly as technology evolves. Notable Quotes “Code is no longer a moat—the little dragon is the vibe coder.” “It's not a question anymore if you can build it. It's a question if you should build it.” “Unless you can wear multiple hats, it's going to be very hard to defend just one skill.” Connect with Anne Cocquyt: LinkedIn: https://www.linkedin.com/in/annecocquyt Twitter/X: https://twitter.com/annecocquyt Other: Website: https://annecocquyt.com The Guild Studio: Letsguild.com/studio  Travis Makes Money is made possible by High Level – the All-In-One Sales & Marketing Platform built for agencies, by an agency. Capture leads, nurture them, and close more deals—all from one powerful platform. Get an extended free trial at gohighlevel.com/travis Learn more about your ad choices. Visit megaphone.fm/adchoices

You can develop a working Mac app in minutes.

Things got kinda catty between OpenAI and Anthropic

Gene Marks discusses high small business confidence, the resilience of plumbing trades, and how new AI agents from Anthropic are rendering traditional software coding obsolete in the tech industry.JANUARY 1941