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The situation with Iran continues to feel like Groundhog Day, except this time, believe it or not, there may actually be movement.Earlier this week, I mentioned that I had heard from people in the know that the United States military was coiled to strike Iran and was looking for either provocation or justification to resume major military activity. That appeared to happen when Iran shot down an Apache helicopter that was escorting oil tankers through the Strait of Hormuz. We also learned that more than 100 million barrels of oil had moved through the strait under U.S. protection over the last month.Politics Politics Politics is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.One of the reasons that caught my attention is that gas prices in the United States have been falling pretty dramatically. It was a head-scratcher. If the Strait of Hormuz was effectively stalled, then what explained the drop? Was it a global rerouting of supply? Was there a China component that had been negotiated and never publicly heralded? I didn't know then, and I don't know now, but the announcement about oil shipments at least provides part of the picture.What's more interesting is what happened next. After one night of military strikes, the second night was canceled. Donald Trump said that's because we're at the point of a deal, one that has supposedly been signed off on by all available parties in the region. It appears to resemble the memorandum of understanding that's been floating around for weeks, although nobody really knows because we still haven't seen the text. We don't know if it's real. We don't even know exactly what it says.The administration's definition of success has been fairly consistent: Iran gives up its nuclear material and removes the nuclear threat. If that's actually in the agreement, then it would be meaningfully different from what came before. The obvious question is what Iran gets in return. The reporting and public comments suggest that Tehran is focused on access to frozen assets and getting money quickly. Whether that money goes directly to Iran, whether it's routed through humanitarian aid, and what conditions are attached are all questions that still need answers.The strongest sign that something may actually be happening is coming from inside Iran. Reports indicate that FARS, the IRGC-controlled news agency, is acknowledging that a draft memorandum of understanding exists, that the United States has approved it, and that Iran is likely to do the same. The bigger question is whether any agreement can actually be enforced. Iran's leadership appears splintered. We've seen officials make commitments before, only to have military figures or IRGC commanders move in a different direction. That's why the real issue isn't whether a deal can be signed. It's whether anybody in Iran has enough authority to keep it.Chapters00:00:00 - Intro00:02:48 - Iran00:08:38 - Interview with Karol Markowicz00:36:19 - Update00:37:19 - DeSantis and AI00:42:56 - FISA00:44:42 - Director of National Intelligence00:47:17 - Interview with Karol Markowicz, con't01:07:27 - Wrap-up This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.politicspoliticspolitics.com/subscribe

Today, we speak with Rafael Avila, who discusses how AI-powered logistics improves visibility, reduces emissions, and helps companies balance cost, service, and sustainability in transportation planning.Download the ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠episode transcript⁠⁠⁠⁠⁠⁠⁠⁠===== In this episode, Rafael Avila of Westernacher joins us to discuss the future of logistics. He explains how AI can make emissions visible earlier, improve mode and carrier decisions, reduce waste, and support better service. The conversation also explores workforce shifts and the importance of end-to-end supply chain visibility. ===== Guest: Rafael Avila, Supply Chain and Logistics leader, Westernacher ConsultingRafael Avila is a Supply Chain and Logistics leader specializing in SAP Transportation Management and end-to-end supply chain transformation at Westernacher Consulting. With over a decade of experience, he has worked across global organizations to design and deliver solutions that improve transportation planning, execution, and visibility across complex networks. A former IBM consulting leader, Rafael spent years supporting large-scale S/4HANA programs and shaping supply chain strategies across industries including consumer products, pharmaceuticals, automotive, and mining. His work has focused on helping organizations connect transportation with warehousing, trade, and order management to unlock more integrated and resilient supply chains. Today, Rafael continues to work closely with clients on modern logistics challenges, with a strong focus on simplifying complex topics, driving practical outcomes, and exploring how technologies like AI are reshaping transportation and supply chain decision-making.Host 1: Richard Howells⁠⁠⁠⁠⁠⁠⁠Richard Howells⁠⁠⁠⁠⁠⁠⁠ has been working in the Supply Chain Management and Manufacturing space for over 30 years. He is responsible for driving the thought leadership and awareness of SAP's ERP, Finance, and Supply Chain solutions and is an active writer, podcaster, and thought leader on the topics of supply chain, Industry 4.0, digitization, and sustainability.Host 2: Oyku Ilgar, SAP  Oyku Ilgar is a marketer and thought leader specializing in SAP's digital supply chain and ERP solutions since 2017. As a marketer, blogger, and podcaster, she creates engaging content that highlights innovative SAP technologies and explores key topics including business trends, AI, Industry 4.0, and sustainability.   She holds dual bachelor's degrees in Finance & Accounting and English Translation, along with a master's degree in Business Administration and Foreign Trade, specializing in marketing. With her background in digital transformation, Oyku communicates technology trends and industry insights to help professionals navigate the evolving business landscape.  ===== Show Links:WesternacherArticle: Smarter Transportation Management cuts your carbon footprintSupply Chain Management:  ⁠SAP Supply Chain Management⁠ ⁠SAP Insights: Supply Chain⁠       Follow Us on Social Media : Richard Howells: ⁠LinkedIn⁠, Oyku Ilgar: ⁠LinkedIn⁠      SAP Digital Supply Chain: ⁠LinkedIn⁠    Please give us a like, share, and subscribe to stay up-to-date on future episodes!  ===== Chapters:00:00:00: Intro00:01:22: Guest's Introductions00:04:48: Making transportation emissions visible with AI00:07:39: Why reporting alone does not reduce emissions00:09:50: Mode selection and carrier choice matter00:12:33: AI can cut cost, emissions, and service waste together00:14:27: Biggest sustainability wins in transportation planning 00:18:28: How AI changes the logistics workforce 00:22:33: Common pitfalls in digitizing supply chain00:25:14: What is the Future of Supply Chain?00:26:28: Outro

Vyhledávač letenek Kiwi.com je zpět v plné síle! V novém dílu podcastu Money Maker zakladatel a šéf firmy Oliver Dlouhý otevřeně popisuje, jak firmu po náročných letech opět stabilizoval, proč kompletně změnil byznys model a jak mu v tom pomáhá umělá inteligence.V rozhovoru se dozvíte:

This week, we talk with SAP's Ralf Hierzegger about how network intelligence and AI can assist logistics teams in managing disruptions, enhancing collaboration, and building a more resilient supply chain.Download the ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠episode transcript⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠===== In this episode, we speak with SAP's Ralf Hierzegger about how constant disruption, digital dependencies, and climate and regulatory shifts are reshaping logistics. They explore why supply chains must move beyond company borders, how AI can support real-time decisions, and why trusted network collaboration is essential for better execution, efficiency, and resilience. ===== Guest 1: Ralf Hierzegger, Chief Product Owner of SAP BN4L, SAPRalf has built his career at the intersection of logistics, consulting, and digital innovation. After starting out as a forwarding agent from 1990 to 1992, he studied economics from 1993 to 1996 before joining SAP in 1996 as a consultant. In 2013, he stepped into the role of Solution Architect at SAP SE Custom Development, focusing on Foreign Trade and Transportation Logistics. Since 2023, he has served as Chief Product Owner of SAP BN4L, helping shape the future of business networks in logistics. He is married and the father of two grown-up children.Host 1: Richard Howells, SAP   ⁠⁠⁠⁠⁠⁠⁠Richard Howells⁠⁠⁠⁠⁠⁠⁠ has been working in the Supply Chain Management and Manufacturing space for over 30 years. He is responsible for driving the thought leadership and awareness of SAP's ERP, Finance, and Supply Chain solutions and is an active writer, podcaster, and thought leader on the topics of supply chain, Industry 4.0, digitization, and sustainability.===== Show Links:Read: Circumventing Geopolitics: How Network Intelligence and AI Can Answer Uncertainty blog SAP Business AI Platform Supply chain logistics SAP Logistics AssistantSupply Chain Management:  ⁠⁠⁠SAP Supply Chain Management⁠⁠⁠ ⁠⁠⁠SAP Insights: Supply Chain⁠⁠⁠       Follow Us on Social Media : Richard Howells: ⁠⁠LinkedIn⁠⁠, SAP Digital Supply Chain: ⁠⁠LinkedIn⁠⁠    Please give us a like, share, and subscribe to stay up-to-date on future episodes!  ===== Chapters:00:00:00: Intro00:01:22: Guest's Introductions00:02:23: Why logistics disruption is harder now00:06:26: Structural challenges in supply chain and logistics00:08:02: Using AI to respond to delays and blockages00:10:51: Why AI alone cannot solve logistics 00:12:36: The value of AI for a business network 00:14:24: How data sharing improves network performance00:16:27: First steps for adopting network intelligence and AI00:19:06: What is the Future of Supply Chain?00:20:31: Outro

HELP US IMPROVE THE PODCAST - TAKE THIS 3 MIN SURVEY:https://forms.gle/fRTV2YiJqncKVpFh7WEBINAR LINK:https://shawnmoore.clickfunnels.com/optiniyvvg89sWant to learn more about Vodyssey or start your STR journey. Book a call here:https://meetings.hubspot.com/vodysseystrategysession/booknow?utm_source=vodysseycom&uuid=80fb7859-b8f4-40d1-a31d-15a5caa687b7FOLLOW US:https://www.instagram.com/vodysseyshawnmoorehttps://www.facebook.com/vodysseyshawnmoore/https://www.linkedin.com/company/str-financial-freedomhttps://www.tiktok.com/@vodysseyshawnmooreCONTACT US:support@vodyssey.comSOURCES:1) https://www.rentalscaleup.com/airbnb-ai-strategy-2026-summer-release/2) https://techcrunch.com/2026/05/20/airbnb-gets-into-hotels-expands-ai-for-host-onboarding-and-customer-support/3) https://thenextweb.com/news/airbnb-is-adding-hotels-car-rentals-and-luggage-storage-as-it-evolves-from-a-home-sharing-app-into-a-full-travel-platformPROPERTIES:https://www.airbnb.com/rooms/1632746088889966533?unique_share_id=2a1aa537-4be3-432b-a4f6-170610a889a8&viralityEntryPoint=1&s=76&source_impression_id=p3_1779824102_P3NeOUcAUTZ5ElWcChapters00:00:00 Intro00:00:29 Recap of Airbnb's Summer Update and Focus on AI00:01:25 AI Listing Creation and Personalization in Airbnb00:03:02 The Role of AI in Differentiating Professional Hosts00:04:23 Changes in Review Processes and Guest Experience00:06:01 Airbnb Leveling the Playing Field for Mom-and-Pop Hosts00:07:11 The Commoditization of Listings and Differentiation Strategies00:08:37 Airbnb's Focus on Experience Over Price00:09:59 Impact of AI on Property Differentiation and Reviews00:11:25 The Future of Reviews and Guest Feedback00:12:24 Market Positioning and the Bell Curve of Property Quality00:14:23 Expansion of Airbnb to Hotels and Experiences00:15:44 Supply and Entry Barriers in the Market00:16:51 Competitive Dynamics with Hotels and Boutique Properties00:17:23 The Validation Age and Risks of AI Reliance00:19:43 The Importance of Data Validation and Critical Analysis00:21:57 Challenges of AI Hallucinations and Misinformation00:23:37 The Impact of Rising Costs on Furniture and Supplies00:36:39 Rising Raw Material and Fuel Costs in Furniture Industry00:41:00 Effects of Fuel Prices on Freight and Delivery Delays00:43:48 The New Normal: Higher Costs and Market Adaptation00:45:52 Market Outlook and Strategic Adjustments00:47:12 Celebrating Success Stories and Peak Season Preparation00:48:31 The Importance of Realistic Expectations and Numbers00:50:42 Balancing Emotional and Logical Marketing Strategies00:53:07 The Role of Hard Work and Validation in Success00:55:04 Final Thoughts and Call to Action

Arkestro's CEO, Rob DeSantis, joins us this week for an insightful conversation about why AI is the next major paradigm shift in the supply chain and how it can unlock faster value, better decisions, and happier teams.Download the ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠episode transcript⁠⁠⁠⁠⁠⁠⁠⁠⁠===== Join us as we explore AI as the next paradigm shift in supply chain. This week, Arkestro CEO Rob DeSantis shares lessons from the internet and cloud, the importance of trust and data quality, how AI is reshaping human work, and why fast time to value matters. ===== Guest 1: Rob DeSantis, Chief Executive Officer and Co-Founder, ArkestroRob DeSantis is the Chief Executive Officer and Co-Founder at Arkestro. As a former co-founder of Ariba running sales, Rob has deep expertise in the procurement space, having helped propel Ariba from zero to $250 million in revenue in four years and IPO of the year in 1999 before its acquisition by SAP a decade later. In addition to co-founding Ariba, Rob was also an early angel investor and board member of LinkedIn, the world's largest professional online network.More recently, Rob served as an investor and advisor to a small portfolio of companies, including Bloom Energy, HiQ Labs, Agiloft, USEND and more. He is also a co-founder of TrueParity. Rob started his career as a mechanical engineer in the aerospace industry and holds a BSME from the University of Rhode Island.Host 1: Richard Howells, SAP   ⁠⁠⁠⁠⁠⁠⁠Richard Howells⁠⁠⁠⁠⁠⁠⁠ has been working in the Supply Chain Management and Manufacturing space for over 30 years. He is responsible for driving the thought leadership and awareness of SAP's ERP, Finance, and Supply Chain solutions and is an active writer, podcaster, and thought leader on the topics of supply chain, Industry 4.0, digitization, and sustainability.===== Show Links:Arkestro: https://arkestro.comSupply Chain Management:  ⁠⁠SAP Supply Chain Management⁠⁠ ⁠⁠SAP Insights: Supply Chain⁠⁠       Follow Us on Social Media : Richard Howells: ⁠LinkedIn⁠, SAP Digital Supply Chain: ⁠LinkedIn⁠    Please give us a like, share, and subscribe to stay up-to-date on future episodes!  ===== Chapters:00:00:00: Intro00:01:00: Guest Introductions00:02:51: The biggest paradigm shifts00:04:53: Lessons from the internet and cloud for AI adoption00:07:23: Overcoming laggards and proving value in production00:08:55: Trusting AI in black-box supply chain planning00:11:54: How AI changes human roles and skills00:15:20: The main business benefits of AI00:19:02: KPIs for measuring AI success00:20:44: How Arkestro supports the paradigm shift00:22:38: What is the Future of Supply Chain?00:23:19: Outro

Dlaczego Rockstar nie udostępni GTA VI dorecenzji i jak wygląda AI od Ubisoftu. Zapraszam! 

Michael I. Jordan, described by Science magazine as the most influential computer scientist alive, has never thought of himself as an AI researcher. In this conversation he explains why that distinction matters.SPONSOR:---Cyber Fund built the Monastery to help founders ship products that were impossible a year ago. Applications for Batch 1 are now open.Apply now: https://cyber.fund---Jordan trained as a statistician and cognitive scientist, and his career has been spent building machine learning systems that work in the real world: supply chains, commerce, healthcare, and large economic systems. When the field rebranded itself as AI and then AGI, he did not follow. Instead he argues that the framing is wrong. AI is better understood as a collective economic system than as a race to build a disembodied superintelligence.We talk about why AGI is mostly a PR term, what machine learning achieved before the LLM hype cycle, and why the assistant-on-your-shoulder vision may be less compelling than it sounds. Jordan explains why explanations need to be actionable, not merely mechanistic; why AlphaFold's missing error bars matter; how prediction-powered inference changes the picture; and why drug discovery is an incentive-design problem rather than a pure pattern-matching problem.ERRATA: Science magazine ranked him the most influential computer scientist, not Nature---TIMESTAMPS:00:00:00 Cold open: A demoralizing message to young builders00:02:04 CyberFund sponsor read00:02:50 From symbolic AI to machine learning systems00:05:42 Why AGI is mostly a PR term00:08:48 A collectivist, economic perspective on AI00:11:33 Why LLMs need system design, not hype00:14:50 Predictability beats faux understanding00:17:55 AlphaFold, bias, and prediction-powered inference00:21:48 Stop anthropomorphizing intelligence00:27:44 Drug discovery as an incentive problem00:32:29 The three-layer data market00:38:07 Social knowledge, markets, and culture00:45:39 Creator economics beyond Spotify00:48:30 How science-fiction AI narratives mislead young builders00:51:45 AI should improve humans, not replace them00:56:42 Safety is a property of the whole system00:58:12 Silicon Valley gurus and the cream off the top01:00:47 Game theory, mechanism design, and contracts01:04:39 Conformal prediction, e-values, and anytime inference01:08:11 A new liberal arts triangle for the AI era01:11:30 The Bayesian duck and markets as uncertainty reductionReScript (transcript, PDF, refs etc) - https://app.rescript.info/public/share/fb68f94af29d3745c6cf6125e01328b5---REFERENCES:person:[00:02:50] Michael I. Jordan (homepage)https://people.eecs.berkeley.edu/~jordan/paper:[00:06:01] A Collectivist, Economic Perspective on AIhttps://arxiv.org/abs/2507.06268[00:18:09] AlphaFoldhttps://www.nature.com/articles/s41586-021-03819-2[00:20:36] Prediction-Powered Inferencehttps://arxiv.org/abs/2301.09633[00:33:47] On Three-Layer Data Marketshttps://arxiv.org/abs/2402.09697[01:04:39] Conformal Prediction with Conditional Guaranteeshttps://arxiv.org/abs/2107.07511[01:04:51] A Tutorial on Conformal Predictionhttps://www.jmlr.org/papers/v9/shafer08a.html[01:06:00] E-Values Expand the Scope of Conformal Predictionhttps://arxiv.org/abs/2503.13050[01:08:23] Computational Thinkinghttps://www.cs.cmu.edu/~CompThink/papers/Wing06.pdfother:[00:28:20] How Should the FDA Test?https://rdi.berkeley.edu/events/sbc-assets/pdfs/Summit%20session%20speaker%20slides%20submission%20form-s1-5%20%28File%20responses%29/Slides%20in%20PDF%20%28Please%20name%20the%20submitted%20file%20as%20_firstname_-_lastname_-slides.pdf%29.%20%28File%20responses%29/27-Michael%20Jordan-Session%20V.pdf#page=15[00:28:40] Michael I. Jordan Session V Slides

AI is forcing a complete rethink of ecommerce, retail operations, and digital commerce architecture.In this episode, Kelly Goetsch — President at Pipe17 and one of the most influential voices in modern commerce technology — explains why the “status quo is no longer acceptable” in the age of AI.Drawing on experience across Oracle, ATG, commercetools, and modern AI-driven commerce systems, Kelly breaks down:Why AI is a “categorically new way of work”The shift from monoliths → headless → microservices → AI-native commerceWhy product catalog data is now mission-criticalThe rise of UCP (Universal Commerce Protocol)Why most retailers are still unprepared for AIHow startups are moving faster than enterprisesWhy experimentation now costs dramatically lessThe future of AI-powered shopping experiencesThis conversation also explores:Google vs OpenAI in commerceAI agents and autonomous commerceRetail infrastructure modernizationGEO / AI search optimizationAI-native workflowsEnterprise resistance to AI adoptionThe next generation of ecommerce architectureIf you work in ecommerce, retail technology, AI strategy, composable commerce, digital transformation, or enterprise innovation — this episode is essential listening.Topics CoveredAI commerceUniversal Commerce Protocol (UCP)Ecommerce AI strategyRetail transformationProduct data enrichmentHeadless commerceMicroservices architectureAI-native organizationsCommerce infrastructureAI agents in retailAbout Kelly GoetschKelly Goetsch is President at Pipe17 and a long-time leader in enterprise commerce technology. Across leadership roles at Oracle, ATG, commercetools, and Pipe17, he has helped shape modern ecommerce architecture and composable commerce strategy.Chapter Timestamps00:00 – Why AI Changes Everything00:00:24 – Introducing Kelly Goetsch00:01:28 – “The Status Quo Is No Longer Acceptable”00:02:18 – Monoliths, Headless & The Evolution of Commerce00:03:17 – Why Software Architecture Keeps Changing00:04:07 – The Rise of Headless Commerce00:04:54 – Why Microservices Changed Ecommerce00:05:20 – AI as the New Commerce “Head”00:06:02 – UCP vs ACP Explained00:07:12 – Why Google Took Commerce More Seriously00:08:16 – Why UCP Is Gaining Adoption00:09:02 – The Real Problem Behind AI Commerce00:10:22 – Pipe17's Role in AI Commerce Infrastructure00:11:12 – Why Retailers Are Still Waiting00:12:31 – Why Most AI Ecommerce Metrics Are Misleading00:12:48 – The Product Data Problem00:14:07 – GEO, AI Search & Content Syndication00:14:48 – Why AI Will Create Thousands of Commerce “Heads”00:15:43 – The Cultural Problem Inside Retail00:16:43 – Why People Disagree on AI Timelines00:17:51 – Enterprise AI Friction Explained00:18:41 – How Startups Are Using AI Differently00:20:03 – Why Enterprises Fear AI Risk00:21:16 – AI Changes the Cost of Innovation00:22:14 – “Just Build It”00:23:14 – How to Upskill Yourself in AI00:24:29 – Why Companies Still Fear Change00:25:11 – Final Thoughts

Czemu lista plików w paylodzie Steam to niewyciek gry i parę innych przydatnych informacji w dzisiejszym Niecodzienniku.Zapraszam! 

Special discounts up for AIE Melbourne (LS discount) and AIE World's Fair (group discounts up to 25% - CFPs still open for Autoresearch and Vertical AI) Cya there!Abridge did not start as an “GPT wrapper”. It was founded in 2018, years before the Cambrian explosion of AI application layer companies. OpenAI launched ChatGPT publicly on November 30, 2022 and by then, Abridge had already spent years doing the unglamorous work of building trust for one of the highest context, most important workflows in healthcare: the conversation between a patient and a clinician.Abridge's original wedge was clinical documentation. Listen to the visit, generate the note, reduce the clerical burden, and let clinicians spend more time with patients instead of the EHR. By focusing on how doctors actually document, how health systems actually buy, how EHR integration actually works, how clinicians verify outputs, and how missing context during a visit turns into downstream friction across billing, prior authorization, quality, and follow-up, the adoption of LLMs became a force multiplier on a workflow already optimized for sensitive context gathering.The company has scaled fast: Abridge says it is projected to support 80M+ patient-clinician conversations this year across 250 large and complex U.S. health systems, with support for 28+ languages and 50+ specialties. It raised $300M at a $5.3B valuation in June 2025, after a $250M round earlier that year.Today, Janie Lee and Chaitanya “Chai” Asawa of Abridge join us for another crossover pod with Redpoint's Jacob Effron (who is on the board of Abridge) to dive into how Abridge is building the clinical intelligence layer for healthcare starting with ambient documentation, then expanding into clinical decision support, prior authorization, payer/provider/pharma workflows, and eventually real-time agents that act before, during, and after the patient conversation. We go inside the product, data, infra, evals, workflow, privacy, and org design choices behind bringing AI into one of the highest-stakes enterprise environments from 100M+ medical conversations and specialty-specific evals to real-time alerts, EHR integration, de-identification, clinician-scientist teams, and why healthcare may solve some of the hardest AI problems first.We discuss:* Why Abridge started with clinical documentation, “pajama time,” and saving clinicians 10–20 hours a week* The transition from ambient scribe to clinical intelligence layer: save time, save money, and save lives* Why conversations between patients and clinicians may be the most important workflow in healthcare (patient visit summary feature)* Chai's “healthcare-coded Glean” framing: context is king, but healthcare raises the stakes on safety, evals, and rollout* Why Abridge wants AI to feel like “air conditioning”: always in the background, but only interrupting when it truly matters* The prior authorization example: turning a denied MRI weeks later into real-time guidance while the patient is still in the room* Why payer policies, EHR data, medical literature, and hospital-specific guidelines make the problem hard, and also create the moat* How Abridge thinks about ambient form factors: mobile, desktop, in-room devices, nursing workflows, multimodality, and future AR* The multi-sided healthcare customer: CMIOs, CFOs, CIOs, clinicians, patients, payers, and pharma* The hardest AI problem at Abridge: high-quality, low-latency, low-cost real-time support in a high-stakes clinical setting* When Abridge uses frontier models vs proprietary models, and why its unique data from medical conversations matters* Why “every agent is a coding agent underneath,” and how the EHR can be thought of as a filesystem for healthcare agents* How Abridge approaches personalization across individual doctors, specialties, and health systems* Why “AI slop” is AI without context, and how edits, memories, and clinician preferences create a data flywheel* Abridge's eval stack: LFDs, LLM judges, in-house clinicians, third-party evaluators, specialty-specific evals, and progressive rollout* HIPAA, PHI, de-identification, one-way anonymization, customer contracts, and learning from healthcare data safely* What changes when you operate at 100M+ conversations: reliability, cost, post-training, model routing, and infrastructure optimization* Why the same clinical conversation can serve doctors, patients, payers, pharma, and future clinical-trial workflows* How Abridge works with EHRs, and why deep interoperability is table stakes for clinician adoption* Why healthcare AI has regulatory tailwinds, why 80/20 does not work here, and why high-stakes domains may drive AI forward* Why Abridge embeds “clinician scientists” into product and eval teams* What Chai learned from Glean about search, quality, and durable AI infrastructure* Why the future of AI infra may look like context layers, event-driven systems, Kafka, Temporal, sockets, CRDTs, and tools built for humans* Why Janie changed her mind on “PRDs are dead,” and why crisp written clarity matters more in complex AI products* How Abridge uses Claude Code, Cursor, and coding agents internallyAbridge:* Website: https://www.abridge.com/* X: https://x.com/AbridgeHQJanie Lee:* LinkedIn: https://www.linkedin.com/in/janiejleeChaitanya “Chai” Asawa:* LinkedIn: https://www.linkedin.com/in/casawaTimestamps00:00:00 Introduction and what Abridge does00:02:05 From ambient documentation to clinical intelligence00:04:04 Clinical decision support and context as king00:06:57 Alert fatigue, proactive intelligence, and prior authorization00:12:36 Ambient AI form factors and healthcare customers00:16:59 The hardest AI problems in healthcare00:18:26 Frontier models, proprietary data, and model strategy00:21:07 The EHR as a filesystem for agents00:24:03 Personalization, memory, and clinician preferences00:30:40 Evals, LLM judges, and progressive rollout00:36:47 HIPAA, de-identification, and privacy00:39:21 100M conversations and operating at scale00:44:10 EHR integration and the clinical intelligence layer00:46:39 Healthcare regulation, latency, and high-stakes AI00:50:11 Clinician scientists and long-tail quality00:53:04 Lessons from Glean and durable AI infrastructure00:57:03 The future of agentic healthcare workflows00:57:34 PRDs, product clarity, and building serious AI products01:03:11 AI coding tools at Abridge01:04:06 OutroTranscriptIntroduction: Abridge, Clinical Intelligence, and the Latent Space x Unsupervised Learning CrossoverSwyx [00:00:00]: Okay. This is a special crossover Latent Space Unsupervised Learning pod.Jacob [00:00:07]: Very excited to do this.Jacob [00:00:08]: At this point, we get together once a year.Swyx [00:00:10]: Once a yearJacob [00:00:11]: And this is a fun occasion to get to do it on.Swyx [00:00:13]: I really wanted to talk to Abridge but I felt very underqualified because healthcare is not something we cover very intensely. It just so happens that Redpoint's our big investors and supporters of Abridge.Jacob [00:00:27]: Anytime you want to have a portfolio company on your podcastJacob [00:00:29]: Please, by all means.Swyx [00:00:31]: So we'll introduce our guests. Chai and Janie, welcome to the pod.Janie [00:00:34]: Thanks for having us.Chai [00:00:35]: Thank you.Janie [00:00:35]: We're excited to be here.Chai [00:00:36]: Thank you.Swyx [00:00:36]: So for listeners, what do you guys do, just to situate you guys in the company?Janie [00:00:42]: Abridge is a clinical intelligence layer for health systems. We really started with documentation and building for clinicians and as we think about reducing the burden that clinicians have, they're spending 10 to 20 hours a week on documentation. There's a massive doctor shortage in the country. We also think that conversations between patients and clinicians are probably the most important workflow in healthcare. It's where care is given and received but if you think about the 20% of our GDP that goes towards healthcare, almost everything is a derivative of that conversation, whether it's the claim, the payment, the actual diagnosis given, the treatment. And we've started with a conversation to reduce the burden for doctors on documentation but we're really excited about the path ahead as we become this broader clinical intelligence layer.Chai [00:01:34]: I'm Chai. I work on clinical decision support at Abridge.Swyx [00:01:37]: Yes.Chai [00:01:37]: And so as Janie said, we're uniquely situated where we started off with the clinical note. What I'm really excited about and where we're expanding towards is what are all the things you can do before the conversation, during the conversation and after the conversation if you did have access to all the context about patients, payer guidelines, medical literature and put that together and to serve, how healthcare could look fundamentally different.Swyx [00:02:01]: And that's the context engine that you guys have?Chai [00:02:04]: Yes.Swyx [00:02:04]: Is that what it's called? Okay.Swyx [00:02:05]: So historically, as I understand it, the company started in 2018. A lot of people would be familiar with the AI voice notes form factor that doctors would be “Well, do you consent to being recorded?” It replaces handwriting and what have you. But it sounds like more recently there's been a big transition in the company. Tell me about the broader transition.From Documentation to Clinical Intelligence: Save Time, Save Money, Save LivesJanie [00:02:26]: So from a transition perspective, we really think about our journey as The first act was: how do we help save time? And that's where a lot of that original product was.Swyx [00:02:37]: By the way, one of those interesting statsSwyx [00:02:39]: On your landing page was, doctors spend time after hours.Janie [00:02:43]: They call it pajama time.Swyx [00:02:44]: Why is that pajama time?Janie [00:02:46]: Doctors after work in their pajamasSwyx [00:02:48]: In their pajamas. OhJanie [00:02:49]: At home are just writing and catching up on their notes every day.Janie [00:02:53]: Some of our favorite customer love stories, we have a Slack channel called Love Stories. We have clinicians telling us, “Abridge has helped us, from retiring early or we're now finally able toJanie [00:03:06]: go home and eat dinner with our kids for the first time.”Chai [00:03:08]: Save the marriage in some cases.Swyx [00:03:10]: One of the quotes was “We're not divorcing anymore.”Swyx [00:03:12]: I'm asking, “Why?”Swyx [00:03:14]: Because they're working too much.Janie [00:03:16]: But, in terms of where we're going and where we're expanding, we really think about our second and third acts around how do we help health systems save and make more money. Health systems are operating with record-low operating margins. It's getting harder and harder to serve patients and they have regulatory, some tailwinds but also a lot of headwinds coming their way and AI is ripe for helping on the saving and make-more-money piece. And then ultimately, how do we help save lives? The fact that our software and our product is open millions of times a week before, during and after a patient walks in the room, gives us massive opportunity with products like clinical decision support, which Chai is building but so many others to improve patient outcomes and probably one of the most important workflows and problems to be going after right now.From Glean to Healthcare: Context Is KingJacob [00:04:04]: One thing that's interesting, Chai, is you came over to Abridge from Glean and clinical decision support, which for our listeners is, in the context of a visit, helping a doctor figure out the right type of care. It's really a search problem in many ways, going through lots of different data sources. Very analogous to your previous role as one of the earliest engineers over at Glean. I'm sure a lot of our listeners are curious what's similar about the problems that you're going after now and what feels different, now that you're in healthcare.Chai [00:04:33]: Very similar. Taking a step back, with every wave, there's a lot of very similar patterns that happen across different products. A lot of social networking products look the same. A lot of credit-based products look the same. And we're seeing that very similar in the agent era with many companies, of course, in Redpoint's portfolio and so forth. And the key insight between both companies is that you have amazing models but context is king. Context is what puts them to work. So I see it in a lot of ways, a lot of similarities in this is a healthcare-coded version of Glean but the differences are really interesting. A couple things that come to mind. First and foremost, the rigor of the setting we're in. The downside risk is extremely high here in healthcare. It can be fatal in some cases. You prescribe something that the patient is allergic to for example. Whereas at Glean, it's “Oh, you got the question wrong.” It wasn't the end of the world in most cases. And so what does that mean? That shapes our evaluation strategy, both offline evaluation, progressive rollout and there's a lot more we could go into there. Second thing that comes to mind is, vertical versus horizontal. In both cases, there's a large variance but when Glean is, it's a much more horizontal company, there's a variance of personas, companies that you're working with. We also have a variance of personas, different types of specialties, different hospital systems. But the variance is a little more narrow. So from a product perspective, you're able to focus far more, especially when you have a maturing technology and you're building new products that never existed before. It lets you go after them much more easily and especially in healthcare where so many problems were solved with labor and process, that it's extremely ripe for AI to keep helping augment and enable. And the final thing that's really interesting, Abridge specifically compared to many other companies in the AI area, is the modality we started with where we're ambient and we're always listening in the background. And many more AI products will go that way but it's how we started. And that's the greatest form of AI we can create, AI that's seamless. You're not looking at your screen. It's always there. It's always helping you out and being proactive. The Jarvis vision that, every hackathon I went to over the past decade, there was always a Jarvis competitor. But Abridge very much started from the opportunity and continues to go that way.Ambient AI and Alert Fatigue: When Should the Product Interrupt?Jacob [00:06:57]: One thing that is super interesting then from a product perspective is you have this always-on seamless in the background and then you have to decide when you break the wall almost and say, “Hey, clinician, you might not have thought about X,” or whatever it is that you want to do. And in healthcare traditionally there's been this idea of alert fatigue and a million pop-ups and then a doctor just ignores all of them. It's probably a pattern that a lot of builders are thinking through now. How do you think about the right way to intervene or to pop up in a doctor visit?Janie [00:07:26]: It's such a good question. Alerts are notorious in healthcare specifically. Over 90% of alerts are ignored. The first and most important thing is context is everything, as Chai alluded to and I also think about how do we go from being reactive alerting to really proactive intelligence at the point at which it matters most. One thing we like to say is we want our product to feel like air conditioning. It should be in the background just making things better and if there is something that has great clinical risk and we're acutely aware that intervening now and not later is incredibly important, we should decide to act. But if you think about proactive versus reactive, instead of alerting a clinician during a visit when they're with their patient having a pretty serious and sensitive conversation, how do we prep a clinician before they walk into the room with that patient? And so historically, clinicians might have to manually go through charts with a patient that they've had over the course of months or years and they'll try to suss out what are the things they should be doing. You can imagine a world with Abridge. We'll summarize all of the most recent context for you, tell you based on the reason for a visit the patient is coming in for the types of things you should be discussing. And so you're going into that conversation prepped rather than walking in cold to that patient visit and then having this product interrupt you five or 10 times throughout the visit. And there might be times where it's really important to interrupt. We have a product called Prior Authorization and so this is when you may go into a doctor's office with knee pain. They'll prescribe you an MRI and so many of us have had this experience before, where in four weeks you'll get a call saying, “Hey, Sean, that MRI that you were prescribed wasn't approved and why don't you come back in? We'll figure it out.” In a world with Abridge, we might choose to quietly but still alert a doctor in that visit. And alert is probably not even the word we would want to use. Before a patient leaves, we would want to tell the doctor, “Hey, Doctor, before Sean leaves, you should ask him, has he had physical therapy and has his pain lasted for more than six weeks? Because the Aetna plan that he's on in California requires six things. We've already confirmed four of them have been met ‘cause we have all the context. But these two last criteria, if you can address with Sean before he leaves the room, we could guarantee that your MRI is approved before you leave.” And so when you think about clinical usefulness, impact to the patient, there are instances in which if we can catch a doctor while the patient is still in the room, as we think about save time, save money, save lives, we get to check all of those boxes. But when doctors have 15 minutes between visits, we have to be really thoughtful about when it matters.Prior Authorization: Reducing Latency in CareChai [00:10:23]: There's this interesting product opportunity AI has is reducing latency in the world. For example, prior authorization is an example of where care gets delayed and so great AI can reduce that. And the problem with alerts before partially is a technical problem: the quality of your alerts really matters. They're going to get ignored if you get alerts that... Similarly in engineering, where they're noisy alerts that you can't act on. But if you can make really high-quality alerts with both the context, as Janie said, and really high-quality models, then you can create a whole other game.Janie [00:10:53]: And I really like that experience because it starts to tease apart, what makes this so hard and unique. One, to make that prior authorization example possible, think about all the data that you need to have. You need to integrate with the electronic health record to know all of the patient context. Do we have access to your previous labs, previous imaging? And then to match you and to know that you're on Aetna, we have to collect all of the different payer policies and they vary by state. Some of these payer policies live on websites. Some of them live in unstructured 50-page PDF files.Jacob [00:11:31]: I thought this episode wasJacob [00:11:31]: To make sure we didn't scare people from healthcare.Janie [00:11:34]: But when you think about the things that make it hard, it also gives you the moat.Janie [00:11:39]: And then the second is the AI and the model quality we need to be able to hang our hat on. And so the bar, similarly when I worked at Opendoor, I worked on pricing models. Every outlier wiped out the margins of 30 and so similarly here in healthcare, the bar for accuracy is so high. And then I'd say the last is workflow is everything. If insurance companies deploy AI, it typically happens too late and this is when you have the notorious comical examples of AI just fighting each other when it's too late. But if we can pull forward the use of both the AI but also the ability to solve problems when the patient's in the room, you can start to collapse what typically takes weeks or months after your visit, ideally down to minutes or real-time. And it's where healthcare is both very difficult but also extremely rewarding if you can crack it.Product Form Factors: Mobile, Desktop, In-Room Devices, and ARSwyx [00:12:36]: Just to get some baseline on the form factors, because I've seen some videos on your website and stuff. You guys talk a lot about ambient AI. Is it primarily on the phone? Is there any other form factor that people get Abridge in? Is there an Abridge room setup where it's always on? I don't know.Jacob [00:12:55]: An Abridge podcast studio.Janie [00:12:58]: Primary form factor is mobile and desktop. UsuallyJanie [00:13:00]: Clinicians are walking in and out of rooms with mobile but at the end of the day, when they're closing out their notes or wanting to prep for the day ahead, they might use desktop. We have been having a lot of really interesting partnership conversations with a lot of these in-room device companies as you think about the power of multimodality and even more data, as you think about all of what is not captured today. It is fascinating to think about, especially even as we go into building and scaling our nursing product. It's one where nurses constantly, as they're walking in to check in on a patient for two minutes or maybe even 30 seconds,Janie [00:13:43]: Starting an Abridge experience is probably going to take longer than the visit. And so what can we do with in-room devices that are always on starts to raise really interesting and fun product questions.Swyx [00:13:54]: I was thinking, the way in tech companies we have all these Google MeetSwyx [00:13:58]: And other things, we might as well set up entire rooms with just Abridge tech.Chai [00:14:02]: Very much. AR glasses and related form factors are also relevant: how do we bring the information to the clinician in real-time without a screen, while still letting them focus on the patient?Swyx [00:14:18]: Do you think they want that? I'm skeptical of AR, but I'm curious what you've tried.Chai [00:14:26]: Admittedly, it's not a near-term product roadmapChai [00:14:29]: By any means. I'm being far-fetched.Jacob [00:14:31]: There's some sick AR stuff for surgeries.Swyx [00:14:33]: Really?Jacob [00:14:33]: When people are trying to visualize, you're about to make an incision but you want to see, what the cut might look or what the body might look like inside and they can layer in imaging.Swyx [00:14:43]: That's cool.Chai [00:14:45]: At some point in the future.Janie [00:14:46]: But there are a lot of our largest customers and at the largest health systems integrating already and so even as we think about building into it, unlocks a lot of product capabilities.Swyx [00:14:57]: And just to establish the terminology. Sorry, and I know I'm asking basic questions somewhat for myself but also for the audience who might beHealth Systems, Buyers, Clinicians, Patients, and PayersSwyx [00:15:05]: Less integrated. When you say health systems, it's like the Johns Hopkins, the Kaiser Permanentes.Janie [00:15:09]: Mayos, the Kaisers of the world.Swyx [00:15:10]: These are your customers, right? And the outcome that you deliver for them is happier doctors, reduced cost of processing, reduced mistakes. It's weird in a sense that I feel like there's also, a secondary customer, the customer of the customer and I don't know if you — do you think about it that way?Janie [00:15:28]: The other interesting and complex part of building product is we have our buyers, who are the chief medical information officersJanie [00:15:39]: The chief financial officers, the CIOs of these large health systems. Our users today are clinicians but if you think about who downstream is impacted, it's patients. And so as we build, with every product in mind, we think about who we're building for, who the secondary user is and what does that mean either in terms of experience, security compliance, ROI that we have to make tangible. And so like you said, time savings is one of them. But for CFOs, they care a lot more than just time savings. We have to show for every dollar you put into Abridge, because you have more compliant documentation or because you have fewer queries coming from your billing team, we save or add real dollars to your bottom line or top line, are things that we're constantly thinking about because of the dynamic across all three sets of users.Chai [00:16:32]: There's a whole other axis too with the payers and pharmaChai [00:16:35]: as well. Connecting all these three big stakeholders in healthcare isSwyx [00:16:39]: Do the payers ever see your data? Sorry, the payers meaning the insurers, right?Chai [00:16:44]: Yes.Swyx [00:16:44]: They also see Abridge data?Chai [00:16:47]: NoSwyx [00:16:47]: Like the direct integration to you guysChai [00:16:48]: They wouldn't see the raw Abridge data but when you're working together on something like prior authorization, whatever information they need, we'd communicate to them.Jacob [00:16:59]: That's cool. I would love to dig into the AI side. You still have a lot of problems on the AI side. And so maybe to start at the highest level, what's one of the hardest problems you have to solve in AI at Abridge today?The Hardest AI Problems: Quality, Latency, and CostChai [00:17:11]: To make things simple, let's take, building off the prior auth example. So one thing Janie talked about is okay, this data is all over the place and there's this combinatorial explosion of procedures, payer policies and even sometimes different health systems. There can be some cross-product of all of these different considerations you have to take into account. But what's really hard about this problem is doing it real-time in the conversation. So, in any AI product, usually the three KPIs you care about are quality, latency and cost. Now, what we're saying is we want you to do this real-time in the conversation, guiding the clinician. How do we do it in a way that does not break the bank? But we're using — But we also need very intelligent models because you're working with this cross-product of data and this, all this context layer as well. So you need high intelligence and high-quality because you don't want the alert fatigue but you also need to be fast and cost-effective. And so that's where a lot of clever engineering goes. It's okay, without getting into all the details here, can you model these policies in some intermediate representation or other things that you can do that can make this problem tractable? And of course, the Pareto frontier is always changing but we are also trying to do this now.Model Strategy: Third-Party Models, Proprietary Data, and Medical ConversationsJacob [00:18:26]: What implications has that had for what you take off-the-shelf and say, “ what? We don't need to be world-class at X. We'll just take this from the model providers or from some infrastructure player,” and what you're “No, this is where we spend most of our time focused on”?Chai [00:18:38]: This is, the fun challenge in AI?Jacob [00:18:42]: It changes every three months? SoChai [00:18:42]: Of course, with the shifting landscape, we try to be extremely thoughtful on predicting the trends of where third-party models are going and where we can uniquely go. And, sometimes when you talk about AI models, we're the models are just going to get infinitely better. But I don't think... It may be in the grandness of time you could say that but, within every month, every quarter, there's specific ways they're getting better. They're training on a lot more, coding data to be better coding agents, for example. And soChai [00:19:14]: We have to think about where are the things that won't — unique data that we're uniquely training on or to step back a little, where is a proprietary model bringing advantage to us is if it can give higher quality or lower cost and latency for similar quality, very similar to many other companies. And when we can do that is when we have proprietary data. So, for example, we have on the order of eighty million or hundreds of millions now getting close to of medical conversations.Jacob [00:19:44]: It's insane.Chai [00:19:45]: This is a unique data set. And this data set, it's very interesting because this data set is effectively a large part of the trace between the patient and the provider. That's where the quote-unquote debugging happens in healthcare. We have these traces at scale, as in as, our CEOs even called it, an exhaust that comes out of our product. And so when you have these traces, that's how you can train better agents on certain use cases, whether it's your transcription diarization use cases or so on or like note generation models and we can do that much cheaper and faster. But we're always also working with these third-party model providers. We closely collaborate with them and that's how we predict where the trends are going. The thing that I think about a lot is that, I know that the model providers are going to train much more on agentic workflows and so forth, so that's great, so that you have a better agentic harness. But the other thing that's interesting is that the model providers, because a large class of the consumer model providers is healthcare queries, that they might, optimize to train a lot of healthcare data to encode the knowledge in its weights. And this is just a great thing for us as well, where the off-the-shelf models can keep bett-getting better at general healthcare information, such that what our strategy is, we have a constellation of models, we can use something for this, that and, we only care about, at the end of the day, the best product experience.EHR as File System: Agentic Workflows and Real-Time InterfacesJacob [00:21:07]: And, you have, overall capabilities improving. I'm curious, as these models get better, is there something you look at and you're “, three months ago, we really couldn't do that but God, the the latest models really allow us to do it”?Chai [00:21:19]: So here's something interesting that I've, been toying with. So all models are... This wasn't super obvious a year ago but now it's become clear and clear that almost every agent is a coding agent underneath the hood? So you give it whatever file system, it can write its own code and so forth. So when you think about within healthcare and the use case that we have, you can think of the EHR effectively like a file system. It's just — it's a storage of all this information. It's a lot of information there that cannot fit into the context window, at least of today's models and you want to use that context effectively for all these product use cases we're talking about. And so if you have better agents that can, manipulate data, read that data, treat it as a file system as we see they're going and we know model companies are investing this way, then that very directly benefits us.Swyx [00:22:09]: Yeah. Okay, cool. Again, just establishing basic things. But we're going back to the model stuff. I'm really interested in double-clicking more on the real-time, element, which is pretty important for both of you. Is it — Is real-time just batches of every one minute, every five minutes? Is that how we do it? Or is there some more native, genuinely real-time in the sense that OpenAI has a real-time API or Gemini has a real-time API?Chai [00:22:35]: Yeah. Yeah. So today it is more on the on the batch basis but there's interestingChai [00:22:41]: Prototypes that we have that we're still not fully, full time, voice in text out or in that sense. But, can you trigger your models, your agents or agentic workflows, depending on the right times in the conversation?Chai [00:22:58]: And so you can imagine, different techniques to bring this latency down and, you want to bring the feedback loop down as much as you can. And so a lot of clever engineering there without fully... Maybe one day we'll do full voice in and text out, train a model to do something like that.Swyx [00:23:15]: You do — People don't want voice in voice out?Chai [00:23:18]: Now we aren't creating experiences that are, during the conversation, inter — It's almost likeSwyx [00:23:25]: Might be too disruptiveChai [00:23:26]: Too disruptive until, who knows, maybe eventually you could have full voice agents once we — the quality and we improve the comfort of the technology. But right now gra — that change is much more gradual and it's more text focus, text out.Janie [00:23:42]: And so much of currently what our product is trying to do is allow a clinician to focus on their patient and maybe at some point but right now patients, clinicians don't want a third voice, at least in a literal voice in that room. And so how do we be there with all the contacts and information ready at hand when there's the right moment?Personalization: Individual Doctors, Specialties, and Health SystemsJacob [00:24:03]: Jenny, one thing I'm curious about is how you think about, personalization in the product. I imagine, every doctor is a special snowflake in their own way, has their own way they like to do things. There are probably a bunch of different approaches you could take to doing that, both within the model layer itself but then also just with clever prompting or engineering. How do youJacob [00:24:20]: Deliver on that?Janie [00:24:21]: It's such a good question. Personalization is massive for us. We think about personalization at three levels. The first is at the individual, the second is at the specialty level and then the third is at the health system or the organization level. To your point, there are a lot of individual preferences. You-When a note is produced, it almost is a reflection that is so deeply personal of a doctor's work and how they give care. And so do they have preferences on things like style? They might want bullets versus paragraphs, really concise versus comprehensive. They also might have phrases that they really like to use or the templates that they want every note to be structured. And, we see it in our feedback all the time. We want two spaces in between sentences or I refuse to use this tool. And so that's something that we've had to build in. And the tricky part is how do you make sure that stylistic preferences don't interrupt accuracy and quality and that's something that we've really had to refine and hone over time. Second is at the specialty level. A cardiologist note or workflow is going to look very different from a dermatologist workflow.Jacob [00:25:32]: I assume cardiology notes are the highest stakes for you guys, given your CEO is a cardiologist.Jacob [00:25:36]: It's “Oh my God, make sure we get this one.”Janie [00:25:37]: Shiv, our CEO, is still a practicing cardiologist. He rounds once a month. And so, first call when we want just quick and easy user feedback too.Janie [00:25:46]: But, specialties require a lot of personalization, both in terms of what does the product look and so we make sure that as new users onboard, we catch that and the product proportionally reflects that. But also on the back end, evals at the specialty level, they are hard-earned to calibrate and get. What does a really great dermatology note look like? What makes it complete? What makes it compliant and billable is very different than a primary care doctor. And so it's not just about what does the product experience look but on the back end tuning and really deepening our understanding for the specialists. What does great output look like? And that's, a problem that we need to calibrate internally, externally, online, offline but, takes lots of cycles but is necessary in a high-stakes environment. And then at the health system level, for products like clinical decision support, you have health systems who've spent years or decades refining their best practices and they want to know, “Hey, we love your clinical decision support product but how do we embed our own hospital guidelines into them to inform clinicians before, during or after a visit what brest — best practices should look like?” And as you think about, deepening moats as well, when health systems, trust us with that data, allow us to productize it and directly into the clinical workflow, makes us a really great partner to health systems who want to build something that truly meets their needs, their practicing guidelines.AI Slop, Memory, and Product Data FlywheelsChai [00:27:23]: And I want to add onto that. The for the clinical documentation problem, it's very similar to AI writing that doesn't feel like your own and then we call that slop. But the way I describe one framing of slop is like AI without context. But we have all that context and both the clinicians, can have it and can guide it. And so part of the other interesting exhaust for us is, memory is, one of these new systems recordsChai [00:27:49]: Almost.Janie [00:27:50]: And we also have all the edits people make on our product and when you think about a data flywheel and how we get better over time becomes really powerful as a mechanism to just going deeper in personalization.Jacob [00:28:04]: It's interesting. I love this idea of working with systems on the guidelines they built up over a long time. I feel like so many of the best AI app companies today are... The question is: How do you take the expertise that a law firm or a bank has built up over many years and then add that as context and also a special sauce over, a an AI tool? And so seems like y'all are really doing that very effectively.Janie [00:28:24]: We're now starting to have our customers ask, “What are other customers doing?”Janie [00:28:28]: “And how are they doing it?”Janie [00:28:30]: And as we think about having visibility across such a large set of care being delivered right now, a really interesting place we could also partner.Swyx [00:28:40]: I'm just curious. I — This may be a nothing question but, how different are health system guidelines from each other? Don't they all converge to the same thing? And if not, where do they differ?Chai [00:28:52]: At a really high level, they're going to talk about very similar things but the difference is probably in some more of the details. “Oh, you should refer to specialists only when XYZ conditions are met,” or so forth and maybe different organizations have different practices and guidelines around that. But high level, talking about similar things but the details are what, of course, that shapes the context and the decisions you make.Swyx [00:29:15]: And this all goes into the context engine and it might affect the notes but maybe not.Chai [00:29:21]: The — For these local pathways, we're definitely thinking about it a little more for our clinical decision support product.Chai [00:29:26]: So yeah.Swyx [00:29:27]: Which is your stuff, yeah.Swyx [00:29:28]: And then the memory which you raised, let's just tell us more about that. What have you tried in memory? What's the structure of the memory? What works? What doesn't work?Chai [00:29:38]: There's, of course, many different ways you could do memory, where it's okay, can you bake it into the model weights or can you do it in some external store? For us, what's interesting is, of course, when you think the models are rapidly changing, whether it's in-house or third-party, baking into the model weights, sometimes you worry that it could be a little throwaway. And so, how do you... You need to find a way that you decompose the problem, the preferences from the underlying models and so forth. The thing we're right now most both that's easiest to start with and we're excited about is having, a separate store for memory, where you have, for example, a memory sub-agent that's, working in the background, figuring out what are the important parts of the clinician's actions that we want to remember for the long term. And then you can also imagine, other things where in the — you have background jobs that are running that are collating these, memories similar to Sleep, of course and what other pattern, patterns products do as well. Learning over all these action, all the action data we have, again, note edits, the conversations they did and the actual transcripts.Evals: LFD, LLM Judges, and Clinical SafetyJacob [00:30:40]: What about evals? How in the world do you... It is such a complex product surface area. We would love to hear you riff on that and also how has that evolved? I'm sure you've gotten better at it, so any learnings along the way.Janie [00:30:50]: From an evals perspective, we, from day one when we build any new product or feature, we think about, what does good look like? And there are table stakes things like clinical safety but then you start to get deeper into what does good quality look like. And when you go into something like our core product, there's stuff like style and completeness and there's things like does this note become something that can be billable, which is very high stakes for a health system. We have a number of ways in which we get confidence for this. We have, internal in-house clinicians who do what we call an LFD process to give us our very first pass at is this or isn't this a good enough output, look at the effing data.Jacob [00:31:41]: LFD?Chai [00:31:42]: That's why I was smiling. I was “Is Janie going to mention what it stands for?”Jacob [00:31:46]: I was not... There's like a million acronyms.Jacob [00:31:48]: How am I supposed to know that I don't? So “Oh yeah, of course, an LFD.”Swyx [00:31:51]: I've never heard of LFDs.Chai [00:31:53]: It's a bridge for sure.Janie [00:31:55]: I got through three days and then I had to ask someone.Janie [00:31:58]: I thought it was just me that didn't knowJanie [00:32:01]: It's our internal process.Swyx [00:32:02]: But look at the data as a meme in ML, ‘cause you tend to not look at it. You just want to look at number go up.Chai [00:32:06]: Exactly.Swyx [00:32:07]: But yes.Janie [00:32:08]: But so, we make sure we look at the data and then as we think about all of the components of good output, we, one, create LLM judges across all of these and we make sure with annotated data and either internal or external evaluators, we feel like these judges are calibrated. And then depending on the stakes, we also work with in-house and third-party evaluators across all of these before we ship any big change. And the goal is, in terms of evolution, how do you go from this process taking months, down to weeks, down to days? Some of it is, a true science and ML problem. A lot of it's also just, hard operational work. Have you planned ahead in terms of what you need? Have you really optimized the capacity that you need across all of the different specialties you need? Have you gotten a really good sense of which third parties are great to work with for what use cases? This takes a lot of domain, expertise and, lots of mistakes and errors in figuring that out. And so as much of it is an ML problem, so much of it has also been operational gains that are hugely important, where domain-specific expertise is everything.Specialty-Level Evaluation and Progressive RolloutsJacob [00:33:23]: But it's funny, ‘cause I feel like people talk about healthcare like it's one giant market and the reality isJacob [00:33:26]: It's, dozens and dozens of sub-markets. And so it feels like in your evals you have to build that up across the board, probably.Swyx [00:33:34]: And is specialization the primary cardinality at... That's the word that comes to mind.Janie [00:33:40]: Sometimes, depending on the product or the use case. And so if we're making a note improvement or feature for a particular specialty, definitely but we have products that are for nurses. We have products that, are really aimed at making the document or the output a lot more billable. And so we'll want to work with coding teams and not necessary clinicians. And so likeJacob [00:34:05]: Coding meaning healthcare coding.Janie [00:34:06]: Yes. Yes.Jacob [00:34:07]: NotChai [00:34:07]: Yes. I see you.Swyx [00:34:07]: Other kinds.Janie [00:34:09]: But is this output proportional to the work that was delivered? Is there sufficient documentation to justify the amount that a health system may end up charging? And so, specialty sometimes but also domain, very different across all of the different products that we're working for. And building out that network is, not easy and is where a lot of our operational investments have gone into.Chai [00:34:35]: And I view a lot of analogies to self-driving cars here, where, part of it is we really want progressive rollout of features to test in the real world is this useful? Is this going to work? One big difference compared to past lives is before I'd build a product, maybe I'd alpha it and then I'd like GA it the next week, ‘cause I'm “Go, move fast, ship,” and whatnot. But the mentality is like you... I want to make contact with the reality as quick as possible but I want a progressive rollout. Because as much as I get as large of an offline eval set, I want the distribution of that to match real-life distribution. And over time, by rolling out early, similar to Waymo has a tagline, “The world's most experienced driver,” another thing that can, at least linearly increase for us is, both the size of our evaluation offline and online, that and it all feeds back.Janie [00:35:25]: Something that's been earned over time, speaking of evolution, is just the trust we've gotten with customers. Historically, a lot of these health systems, when they bring on new vendors, their release cycles are quarters, sometimes twice a year. We've gotten our customers onto monthly release cycles, which is pretty fast for health systems but what is more exciting over the last, call it, few quarters, has been, a subset of our customers have said, “We want to innovate with you. We trust you,” and we have a pretty, decent chunk of our customers who say, “We'll develop with you outside of these monthly release cycles. We have a higher tolerance. We know that the stakes are very high but we want to be the first ones using these products, giving you feedback.” And so for a pretty substantial set of our customers, we've been able to convince them to be able to ship, in this gradual way before GA. Something we talk about a lot internally is, trust is earned in drops, earned in buckets and so we still can't do what I used to do when I worked at Loom. We had 30 million users. I'd just be, rolling out experiments left and. The bar is still quite high for iterative rollout but because of the trust we've earned, we're able to learn at pretty high volume very quickly.Privacy, HIPAA, and De-IdentificationSwyx [00:36:45]: Your scale is still pretty huge.Swyx [00:36:47]: One thing I want to... We were going to go into scale? In a sec. One thing I wanted to call up, follow up on evals, which, again, just coming from a generalist engineer point of view, just thinking through what would people be scared of in doing this, the privacy and HIPAAJacob [00:37:00]: Elements of this. I have zero experience in that. What do you have to do? What is surprisingly not that bad?Chai [00:37:06]: So one thing that's really important here from a compliance perspective is very much that any of the data we use needs to be de-identified, any real-world data we use as a basis of online eval sets we're learning from. And so you have to — And there's, very clear, government guidelines, what counts as PHI. And so we've even have built models that can take, for example, a clinical transcript and remove all the key PHI indicators and so you have a scrubbed/de-identified version. And then once you... And so one thing that's important is first you've got to get confidence in that model in the first place? And prove that out. Because, now you have, multiple probabilistic systems on top of each other.Chai [00:37:46]: But once you have that, then you can train on it use it for evaluation and so forth, provided one of the cool things also that you can do from a business side is the right data contracting as well with your partners.Jacob [00:37:57]: Is the anonymization one way? Once it's done, you cannot undo it? Or is there someoneChai [00:38:01]: YesJacob [00:38:02]: Who holds the master key that can... Yeah, okay. So it's one way.Chai [00:38:05]: It's one way. Yeah.Jacob [00:38:06]: That's how it works. I just wanted to... Because, there's a lot of this, learning from feedback and everything that, you would want to debug more but you can't because you just physically don't allow yourself to.Janie [00:38:17]: Some of it's also written in our customer contracts in terms of who can or can't access PHI data, how long do we retain it,Jacob [00:38:27]: Very goodJanie [00:38:27]: Before it gets de-identified. And so we have a pretty high bar for who can access that PHI data, just to make sure that we always respect our customer data and privacy. But that's something that we partner with our customers on too, to make sure that as we want full, as close to precision as possible in that qualityJanie [00:38:48]: We can still use it.Jacob [00:38:50]: But it'll be fascinating to see how that space evolves? Because you think about, I used to work at a company that, did a lot of healthcare data in the cancer space and if you asked, the average cancer patient, “Hey, do you want people, do you want other patients to be able to learn-”Chai [00:39:03]: Take it.Jacob [00:39:03]: “... Learn from your experience?”Chai [00:39:04]: Take it all.Jacob [00:39:05]: They're “Please.”Jacob [00:39:06]: “I'd love, nothing more than for other people to be able to learn fromJacob [00:39:10]: The experience that I had.” And so in the past it was a lot harder to do that learning. But with this technology, that might really be practical and so it'll be fascinating to see how that continues to evolve.Chai [00:39:21]: There's so much in our data set of 100 million conversations.Chai [00:39:26]: You can imagine things like insights that you can give to the clinician. How could you, oh, how could you have reacted to this? In coaching or insights around, which treatments are effective or, like... Because you have this, again, this data source that was never captured before but that's, where, intuition or experience is created from, going back to this idea that the conversation is the agent of truth.Operating at Scale: Reliability, Cost, and Token EfficiencyJacob [00:39:46]: Back to the 100 million conversations, I feel like you have this insane scale that maybe only a few other AI app companies have and everyone else dreams of. So not everyone has had to confront this yet but maybe just talk about some of the challenges of operating at that scale and what, our listeners have to look forward to if they ever get to this level of scale.Chai [00:40:05]: At large and larger in scale, so of course there's a general, infrastructure reliability. When you... In any given startup, you're building the plane while it's flying. So there's some notion of that. But what gets interesting on the AI and ML side for sure is this, as you get at more and more scale, so one, you have the data to first and foremost do this. But, you start thinking about costs or infrastructure in a whole different way at scale versus, a prototype.Chai [00:40:34]: You can use the most expensive model, you can burn as many tokens as you want but when you're doing 100 million conversationsJacob [00:40:41]: Token max on leaderboards are less upsetting than that context.Chai [00:40:45]: . When you're doing that and so that comes for we have the data and we also have the team that's able to post-train based on this and you can optimize for efficiency, especially in areas where you believe that maybe a lot of the quality headroom is less so and you don't expect the other off-the-shelf models to go that way, such that you want to do, efficiency maximization, in terms of compute and tokens.Jacob [00:41:08]: I feel like you guys live in the future in some way where most use cases today are really just in use case discovery mode, where it's “God, I really hope I can find something that can get to scale,” and so you're always going to use the most powerful model. And then the few things that do get to this level of scale, you start to do those optimizations.Chai [00:41:22]: It's a natural trajectory where it's like zero-to-one, we're not talking about any of these optimizations.Chai [00:41:26]: But when maybe we're in the one-to-100 or so forth, then we're in optimization mode and, what works out really well is you've got all this data from zero-to-one that lets you do this.What Comes Next: The Conversation as the Shared Healthcare PlatformJacob [00:41:36]: That's fascinating. I feel like one thing that's so interesting about the Abridge footprint is that you're in the doctor-patient visit in real-time. I always like to say, there's like probably 50 years' worth of product you could build on top of that. What gets each of you, I don't know, what are you most excited about building, either in the short term or medium term or even, long down the line?Janie [00:41:53]: Something that I get really excited about is that the same conversation can serve so many stakeholders. If you think about the conversation, a doctor needs to know what is the documentation, how do I make sure that this fully represent the care I gave? A patient needs to know, “What the heck just happened? This was really overwhelming. What are my next steps?” A payer needs to know, was this the proper and appropriate care given? A pharma company might want to know why isn't this drug being properly used or is there a good candidate for this clinical trial that I'm about to run? And where I get excited is that our product and our platform and our infrastructure can be the same product across all of those things and start to what's today, separate, very expensive, complex systems that serve each one of these stakeholders in very different ways, start to collapse all of that into a singular platform that enables not just more efficiency across the board but also better outcomes for everyone. And, all of us experience healthcare in probably very painful ways and knowing that there is a world in which we can simplify a lot is really exciting to me and it all starts with the conversation.Chai [00:43:15]: It's interesting. Of it very similar to going back to the KPIs that any AI product cares about. How do you increase quality of care? How do you reduce latency to care? And how do you reduce costs? Which is a huge, in healthcareJacob [00:43:28]: They call it the triple aim in healthcare.Chai [00:43:30]: But very similar to building AI products and the thing that really excites me is when we talk about that latency piece, we talked about one example earlier of prior authorization, can you reduce the latency to care? But you can imagine so much more. Oh, as soon as the lab value gets updated, do you have like a background agent that, kicks off and uses all the context to be “Oh, hey, the patient should do this next,” for example. And of flagging that to the clinician who's always in the loop but reducing that latency, to care. And then you can imagine this is much further down the road but it's like even connecting that to the direct patient and the consumer. And so how can you, how can you build a bridge to all of these things?EHR Partnerships and the Clinical Intelligence LayerJacob [00:44:10]: Very cool. The connections piece is just an ever-growing thing. And one of the key partners is the EHR and I wonder what that relationship is like. Will they, look at this as, something that is valuable enough that they want to own someday?Janie [00:44:29]: Our partnerships with the EHR is, we know that we have to be extremely close partners with all the EHRs who we partner with. Being able to not only pull and push all of the data into the right places is, not only table stakes, if we can't do that, health systems don't want to use us. The second and the reality of today is clinicians spend a lot of their days in the EHR. So much of what allowed us to win in the largest health systems was pretty direct and, very close partnerships with some of the largest electronic health records that allowed us to pull and push data with APIs that weren't ready out of the box. And clinicians want to save clicks. Anytime we introduce a new product that, adds two clicks for them in their day, they're “We're not going to use it.”Janie [00:45:21]: They have 15-minute back-to-back appointments with their patients. They're spending, hours during pajama time doing documentation. Every second and every minute counts and so we really think about being deeply integrated into the EHR as also table stakes to getting real usage and adoption. And anything that we build or introduce, we really talk about earn the right internally a lot, which is we have to provide so much value or save so much time that people will use us. But those are the two things that are close to us, is we know that the product won't be used unless it is deeply interoperable.Chai [00:46:01]: And strategically, to your point, it's like what does EHR want to own versus us? EHRs are really focused on the clinical workflows and so forth but some of the things that we're talking about here, I do these traditionally are outside of the domain where it's oh, connecting pairs and providers together with provider policies or the clinical trial matching, as Janie brought up. And so these are, entirely — we position ourselves as building this entirely new intelligence, clinical intelligence layer across, again, providers, pharma and, payers.Chai [00:46:33]: And so that's a it's a whole different ballgame that we try to playChai [00:46:36]: In combination with them.Jacob [00:46:37]: But it's like a different layer of scope.Healthcare AI Regulation, Technical Depth, and What Changed Their MindsJacob [00:46:39]: I'm curious, you are both relatively newcomers to healthcare. People have these, there's lots of futuristic healthcare AI takes of “Oh, everything will look different.”, now that you've been in healthcare for a bit, you live at the edge of AI, what have you, changed your mind on around this, as you think about what healthcare looks like in ten, 20 years? Any updates to your mental model from the time being close to the problems?Chai [00:47:02]: One thing that IChai [00:47:04]: Was hesitant about before and it's a common thing when I'm trying to recruit engineers that people ask me around, is definitely oh, healthcare, heavily regulated space. And it is, rightfully so. You want to keep, the patients at the end of the day safe. But one of the interesting things that, is a that surprised me how much it is coming to the company is there's a lot of really favorable regulatory tailwinds as well. Where you think about, government really wants interoperability between all these systems that we talked about and so agents can access this information. The government just in January, the FDA released updated guidance on clinical decision support, what I work on in such a way that they used to have guidance from like 2022 that required you to have, mention all these options and do all these other things but it's a very forward and forward-looking way. And so for me, what's been really cool to work on is this, there's this very special moment both in AI in general, we all know that but there's a special moment also regulatory in healthcare as well.Janie [00:48:05]: One thing I would call out is for the very reasons things are higher stakes or, potentially considered more difficult in healthcare, it's where some of the hardest AI problems will get solved first, just because the bar is so high. When I first joined, I was “Oh, this is where we'll be on the tail end of where, all of the AI innovation will be able to be applied.” But when you think about, zero error evals or multi-step workflows that have really low tolerance, a lot of the innovation will happen here just because we have to or else we can't ship.Jacob [00:48:42]: ‘Cause like in other domains, you'd much rather just solve the 80%-is-good-enough problems firstJanie [00:48:46]: 80/20 doesn't work hereChai [00:48:48]: And building off that, traditionally, there was a bit of stigma that, oh, healthcare companies are not that interesting from a technical perspective or I've seen that or faced that myself. But these are really hard and fun problems from a pure technical perspective beyond just the impact. How do you bring the latency of this thing down and make it really high-quality?Reducing Latency: Clinical Workflows, Agents, and Implementation RealityJacob [00:49:07]: How do you bring the latency of things down?Chai [00:49:10]: Yeah. Yeah. Yeah. So okay, let's answer the latency question. And maybe hopefully not too redundant with some of the things I've said earlier but some part of it is with any latency, you have to like what is, what is really your bottleneck. In a lot of workflows, it's sometimes it's the model itself. And so that's where like our data flywheel, our post-training team and so forth come in so that can you make the models far more efficient. So that's one aspect of latency. But there's whole other aspects of latency where it's okay, on top of that, if you use a constellation of different models, can you use — can you first use like a — it's like thinking fast and slow. Can you use a cheap, fast model that triages and hands it off to a larger model where you get more intelligence and so forth and so all theseChai [00:49:56]: Clever tricks to make it work.Chai [00:49:58]: And by the way, we are totally — we also realize that the parameter frontier is changing and so these tricks will — may not get us to where we want to be in five years but we need to if we want to build a useful product right now.Jacob [00:50:11]: Should we go to the quick-fire or you want to ask more about Abridge? We can stuff everything that's not Abridge into the quick-fireSwyx [00:50:16]: I don't mind. I was — I feel like Janie was on the topic of more long tail stuff, which isSwyx [00:50:21]: Not the eighty/twenty thing and that really matters. And I'll —, if you have any tips or cool stories or just general approaches that have worked for you that's interesting to dig into.Janie [00:50:32]: One of them is even just how we staff our teams looks different than a traditional software engineering team, I'd say.Swyx [00:50:40]: Let's go.Clinician Scientists, Edge Cases, and Evals at ScaleJanie [00:50:41]: We have a bunch of folks with different roles who are clinicians and so we have this role called the clinician scientist and I heard one of our leaders refer to them as mutants recently. But they are people who've had clinical backgrounds, so MDs typically, who are also deeply technical, somewhere, on the spectrum of like a full stack engineer all the way to like extremely scrappy prompter. But having each of these people embedded within our teams instantly raises the bar for everything that we build because not only are they determining, is this product clinically useful but they're deeply embedded in our whole evals process. And so when we talk about LFDs, when we talk about what is our actual evaluation criteria, you don't want Chai or me creating what those are because we don't have clinical background. But is probably unique to Abridge but has been game changing. And when you think about where the puck is going, you have people build with clinical backgrounds who are technical and where AI tools are going, they just becomeJanie [00:51:53]: More and more, critical and like the killers of the team. And so that's one. And then the second is just the scale at which we do evals to catch that long tail up front before anything ever gets into production is something that we've pretty much like really started to fine-tune, both from a scale but when do we know we need to get several hundred versus several thousand offline responses, what helps us make that quick decision and make this less of an art and as much of a science as possible. But that's also been something we've had to tune over time.Swyx [00:52:27]: And you have partners who opted in to give you those evals.Janie [00:52:31]: So we work either internally or with third-party for offline evals and then we have customers who also agree to give us, whether it's like thumbs up, thumbs down to like choose this or that, a lot of data to get us to what is as close to fully confident as possible.Swyx [00:52:51]: The term that comes to mind isSwyx [00:52:53]: Like active learning on things where you're weak. I feel like it's a lost artSwyx [00:52:58]: Is a lot of the polish that comes into doing something like this.Janie [00:53:02]: Really.Chai [00:53:03]: Hundred percent.Lessons from Glean: Technical Foundations and AI App InfrastructureJacob [00:53:04]: Maybe, on a totally unrelated note, Chai, you had a very, storied run at Glean b

AI companionship is exploding — and it is not just lonely men in basements.More and more people are developing emotional attachment, dependency, and even intimacy with artificial intelligence systems designed to capture human attention and mirror emotional needs back to us.In this video I explore why women may be particularly vulnerable to AI emotional manipulationWhy men need to establish healthy boundaries around AI use in relationshipsThe spiritual danger of artificial intimacyThe difference between information, knowledge, and wisdomWhy AI companionship can never replace embodied human relationshipThe deeper loneliness and fragmentation driving this phenomenonI also react to a woman openly teaching people how to emotionally bond with AI companions and explain why I believe this represents a serious cultural and spiritual warning sign.The solution is rebuilding real human relationships, community, brotherhood, family, grounded on Christianity.⚔️ Path of ManlinessOnline men's groups focused on accountability, brotherhood, discipline, and spiritual growth:https://pathofmanliness.com

In this episode, we break down how to actually measure brand impact without overcomplicating it, including the leading indicators lean B2B teams should track before pipeline shows up.We cover:→ Why leading indicators matter more than lagging ones early on→ What early brand signals actually look like in practice→ Why qualitative data and self-reported attribution is so powerful→ A simple list of brand metrics lean teams should focus onIf you are a B2B marketer trying to justify your brand investment to leadership or figure out if your demand gen is actually working before revenue shows up.Tune in and learn:→ The difference between leading and lagging brand metrics→ What signals to watch for before pipeline materializes→ Why qualitative beats quantitative for brand measurement in B2B→ How to build a simple brand scorecard without bloated dashboards-----------------------------------------------------

Welkom bij een nieuwe editie van Gamekings Daily. In deze gaming vodcast praten twee hosts van Gamekings over het laatste nieuws uit de wereld der videogames. Vandaag zit Jasper bij JJ in de studio. Samen met hem neemt hij het belangrijkste nieuws van de afgelopen paar dagen door. Zo was daar de ontboezeming van PlayStation CEO Hideaki Nishino dat PlayStation vol gaat inzetten op AI. Of, zoals hij het zelf zei: "AI zal de drempels voor het maken van games verlagen en de ontwikkelingscycli versnellen. Hierdoor kunnen meer studio's de markt betreden". Best een bijzondere uitspraak in een tijd dat roepen dat je AI gebruikt, leidt tot een hoop online haat. Dit onderwerp en meer krijg je te zien en te horen in de Gamekings Daily van maandag 11 mei 2026.PlayStation draait er niet omheen: AI wordt belangrijk voor henEen ander onderwerp is de derde trailer van GTA 6. Veel mensen zitten daar op te wachten. Voor hen kan het moment van de waarheid best wel eens rap komen, want PlayStation lijkt de campagne om de GTA-kopers massaal naar de PS5 te krijgen, te zijn begonnen. Zo krijgen PS4-bezitters een mail dat ze voor GTA 6 toch echt een PS5 nodig hebben. En 13 mei beginnen de next level deals in de PS Store, wat een perfect moment voor de start van de preorder is. En om dat aan te kondigen heb je een trailer nodig...Microsoft zet per ongeluk hele game Forza Horizon 6 onlineEn dan was daar Microsoft, die dacht dat het slim was om de hele versie van Forza Horizon 6 alvast open en bloot online te zetten. Ruim een week voor de release. Waardoor het spel nu dus op verschillende torrentwebsites staat. Hoe kon dit gebeuren en denken de beide heren dat dit grote gevolgen voor de verkoopcijfers van het spel gaat hebben? Het antwoord krijg je in deze video.Timestamps:00:00:00 De Gamekings Daily van maandag 11 mei00:00:46 Forza Horizon 6 is al 10 dagen voor release gelekt00:06:36 Nintendo veteraan Takashi Tezuka gaat met pensioen00:14:34 PlayStation zet vol in op AI00:22:21 De derde trailer van GTA 6 komt snel onlineWil je adverteren bij de podcast Gamekings óf misschien bij een andere podcast van ILVY Network? Mail dan naar management@ilvy.com en/of kijk even op de website : https://ilvy.com/podcastSee omnystudio.com/listener for privacy information.

Tragiczny kwartał Sony, rekordy Pragmaty iwiele więcej. Zapraszam! 

Bungie fa girare la testa di Sony e non per i grandi successi, mentre Xbox cambia ancora tutto e rimette la testa a posto dopo l'eccessivo entusiasmo per Copilot!Il destino dell'AI Microsoft era forse scritto nelle stelle, un po' come la data di rilascio del prossimo trailer di GTA VI e... no, non sto scherzando! Qualcuno ha usato i pianeti per fare una previsione.Nintendo lancia il Direct di Star Fox a SCHIAFFO, forse per dispetto ma vi assicuro che gli sbadigli non sono collegati alla tarda ora della diretta o al prevedibile aumento di prezzo di Switch 2.Lo showcase di Nacon ha poca roba e anche questa poco entusiasmante ma il paradiso è in Giappone, lungo 50 anni di avventure criminali a ritmo Jazz in un posto Stranger Than Heaven!Capitoli:00:00:00 Intro00:01:45 PS5 si surriscalda00:04:43 FC 26 ma che è?!00:24:15 Xbox saluta la sua AI00:29:22 Bungie costa a PlayStation 765 MILIONI!00:34:25 Il trailer di GTA VI esce quando lo dicono le stelle!00:44:15 Mixtape non è per streamer00:53:17 Insert Coin00:55:52 Recappone Star Fox Direct01:01:52 Switch 2 aumenta i prezzi01:06:19 DLC gratuito di Requiem01:07:50 Recappone Nacon Connect Showcase 202601:23:09 Stranger Than Heaven è EPICO!Link utili:Ko-Fi: https://ko-fi.com/arcadedialeInstagram: https://www.instagram.com/arcade_di_ale/

Groeivoer Live 1 juli - meld je aan! Volg jij Groeivoer en wil je live aanwezig zijn bij een podcast? Kom op 1 juli naar Utrecht voor een avond vol inspiratie, groei en nieuwe netwerkkansen. Marleen Evertsz van Gold Republic is onze gast. Zij heeft met haar bedrijf meer dan 1,5 miljard aan goud in beheer. Boek snel jouw (Early Bird) ticket voor alle plekjes op zijn. Meer info en tickets: https://www.eventbrite.nl/e/groeivoer-live-tickets-1986078095046 

Kolejna drama z Warhorse i AI, wyciek planów Capcomu i remake'i Halo. Zapraszam! 

Niki, Lotus, and John celebrate 100 episodes and discuss GameStop's potential purchase of eBay, usTwo's CEO saying some wild stuff into a microphone, the final death of Paste Games, and more!00:00:00 Intros & At Least It's Friday00:06:05 GameStop could be prepping for an eBay takeover00:13:50 Saudi Arabia is divesting from some entertainment properties00:20:36 Atari acquires emulation studio Implicit Conversions00:24:39 MTG: Arena devs unionize00:28:48 usTwo CEO speaks way too candidly about labor relations within company00:39:45 GreedFall dev Spiders being liquidated; mismanagement alleged00:44:22 GameMaker integrates with Claude Code00:48:00 The program is interrupted by Niki's neighborhood "Snacks Man"00:51:55 Mac Minis are super expensive now because of AI00:58:58 Last Flag not continuing content updates or console development01:05:15 Thick as Thieves gets shorter, cheaper01:08:08 AV Club Games is no more, which spells the end of Paste Games01:14:50 Flotsam is a cell-shaded city builder on sea that Lotus really likes01:22:22 Tomodachi Life: Living the Dream is playing with dolls01:36:00 CorgiSpace is a collection from one of the medium's most prolific creators01:42:44 Titanium Court avoids Balatro time suck because of reading01:44:00 Niki continues to build a NAS01:54:55 We answer your Hive Questions for Episode 10003:06:40 OutroThanks for listening!Please leave us a review! We'll read it on the show and it helps us out a lot.VGBees is ad-free, AI-free, and completely supported by you! https://vgbees.com/joinVGBees is a weekly games media podcast hosted by Niki, John, and Lotus.

John Pollock and Brandon Thurston cover WWE's recent Town Hall meeting with exclusive audio, including reaction to WrestleMania, Paul Levesque's new deal, Saudi Arabia, and online criticism. Plus: The latest round of WWE cuts, Janel Grant posts FBI letters, NXT's PLEs move to CW, and more on the Berwyn Eagle Club drama. 00:00:00 Start00:04:28 WWE Town Hall with exclusive audio00:23:00 Night of Champions in Saudi Arabia00:25:44 Performance of WrestleMania 4200:30:40 Incorporation of AI00:38:26 WrestleMania 202800:46:33 Janel Grant posts letters from the FBI00:48:05 NXT's PLEs moving to CW00:53:08 The Berwyn Eagles Club saga 01:01:00 Hulk Hogan - Real American on NetflixMusic courtesy: “Panic Beat” by Ben TramerPOST WrestlingSubscribe: https://postwrestling.com/subscribePatreon: http://postwrestlingcafe.comForum: https://forum.postwrestling.comDiscord: https://discord.com/invite/Q795HhRTwitter/Facebook/Instagram/YouTube: @POSTwrestlingBluesky: https://bsky.app/profile/postwrestling.comWrestlenomicsSubscribe: https://wrestlenomics.com/podcast/Patreon: https://patreon.com/wrestlenomicsSubstack: https://wrestlenomics.substack.com/Twitter/Facebook/Instagram/YouTube: @WrestlenomicsBluesky: https://bsky.app/profile/wrestlenomics.comSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Most engineers are using AI coding tools without understanding what they actually are and it's costing them. Microsoft Certified Trainer Rob Bos has trained thousands of engineers on AI tooling, and he sees the same gaps in fundamentals show up again and again, regardless of seniority. This is what you need to know:What an LLM actually is (and why understanding this changes how you use it)Why prompt engineering isn't optionalHow AI magnifies your existing technical debt instead of fixing itThe 6-month learning curve nobody warns you aboutWhy your role as an engineer was never about writing codeThe environmental cost behind every promptWhether you're skeptical of AI tools or already living in agent mode, these are the fundamentals that separate engineers who get real value from those who get burned by the hype.Connect with Rob:https://www.linkedin.com/in/bosrobReferences:Token tracker: https://marketplace.visualstudio.com/items?itemName=RobBos.copilot-token-trackerDev survey: https://www.activestate.com/wp-content/uploads/2019/05/ActiveState-Developer-Survey-2019-Open-Source-Runtime-Pains.pdfTimestamps:00:00:00 - Intro00:00:43 - The #1 Thing Engineers Get Wrong About AI00:02:09 - How Much LLM Theory Do You Actually Need?00:03:58 - Why Pair Programming Is Still the Best Way to Learn AI00:05:26 - Why Rob Skips Tab Completion and Lives in Agent Mode00:07:03 - The "AI Doesn't Increase Productivity" Debate00:08:29 - Why Your Real Job Was Never Writing Code00:09:14 - The 2-Hours-of-Coding Problem No One Talks About00:11:02 - More Code = More Pressure on Your Review Process00:12:21 - Why AI Magnifies Existing Technical Debt00:13:39 - The Customer Who Couldn't Start AI With Developers Yet00:15:11 - The Future Engineer: Reviewer, Not Writer00:17:00 - Convincing the AI Skeptic Who Tried It Years Ago00:19:17 - LLMs Explained Without Visuals (Attention & Semantics)00:22:41 - Why Prompt Engineering Actually Matters00:24:20 - From Zero to Hero: The 6-Month Learning Curve00:26:18 - Is This Confrontational for 20-Year Veterans?00:29:30 - Becoming a Better Engineer by Thinking in Systems00:31:26 - Will AI Stop Working as Innovation Slows?00:34:26 - The Lost Art of Pair Programming with AI00:35:44 - Tribalism in AI Tools (And Why It's Pointless)00:37:33 - Tool Agnostic: Start With the Foundations00:39:40 - Is the IDE Still Relevant?00:40:50 - The Bluescreen Story That Changed His Mind00:41:47 - The Hidden Environmental Cost of AI Coding00:44:15 - 36 Million Tokens in 30 Days: What Does It Mean?00:45:47 - Running LLMs at the Edge to Cut the Footprint00:46:48 - Why You Should Be Allowed to Wait Five Minutes Longer00:47:05 - Outro#githubcopilot #aicoding #softwareengineering

HELP US IMPROVE THE PODCAST - TAKE THIS 3 MIN SURVEY:https://forms.gle/fRTV2YiJqncKVpFh7WEBINAR LINK:https://shawnmoore.clickfunnels.com/optiniyvvg89sWant to learn more about Vodyssey or start your STR journey. Book a call here:https://meetings.hubspot.com/vodysseystrategysession/booknow?utm_source=vodysseycom&uuid=80fb7859-b8f4-40d1-a31d-15a5caa687b7FOLLOW US:https://www.instagram.com/vodysseyshawnmoorehttps://www.facebook.com/vodysseyshawnmoore/https://www.linkedin.com/company/str-financial-freedomhttps://www.tiktok.com/@vodysseyshawnmooreCONTACT US:support@vodyssey.comPROPERTIES:1) https://www.cowboysandmermaidsvacationrentals.com/alpine-airspace-orp5b5d111x2) https://www.cowboysandmermaidsvacationrentals.com/driftwood-dreams-orp5b5d0c8x3) https://www.cowboysandmermaidsvacationrentals.com/tipsy-turtle-orp5b5d117x4) https://www.cowboysandmermaidsvacationrentals.com/peace-of-paradise-orp5b5d119x5) https://www.cowboysandmermaidsvacationrentals.com/simplicity-at-snowmass-orp5b726dax6) https://www.cowboysandmermaidsvacationrentals.com/serenity-at-snowmass-orp5b6fc3axContact Ned:https://nedmarkey.com/Chapters:00:04:10 High Income Earners BIGGEST Problem00:09:00 Where Should You Buy Your First Airbnb00:14:08 The 4 Returns of Real Estate Investing00:17:32 Why Did Ned Double Down?00:22:14 Rates vs Occupancy00:24:04 Pros & Cons of AI00:32:00 Why Entrepreneurs LOVE STRs00:36:00 Advice To Your Younger Self00:40:57 Wrap Up

This special episode is based on a webinar recording with Tadeas Adamjak, Head of Growth at UXtweak, where he shared the main findings from our AI in UX Research Revisited report. He talked about how stances on AI in our field have evolved in light of everyone's deeper experiences with AI models, what common issues and concerns professionals have with the technology, and what the best use cases for AI in 2026 are.

Republicans are running out of places to redraw the map, and Florida is quickly becoming their last real shot to claw back seats before the midterms. The pressure is now squarely on Ron DeSantis to deliver a map that could net a handful of gains, but even inside the party there is real disagreement about whether that is possible. The risk is not just that the effort fails, but that it backfires, turning carefully engineered districts into competitive ones if turnout does not break the right way.That is the core problem with aggressive redistricting at this stage. The more you try to maximize advantage by packing and slicing districts, the more you rely on your own voters showing up consistently. If they do not, those same districts can flip. That is why some Republicans are warning that what looks like a smart map on paper could end up being a “dummymander” in practice, especially in an environment where Democratic voters appear more motivated. In fact, this is starting to look risky, it might be more accurate to call this year's elections “dummyterms,” a phrase I'm committed to making stick come hell or high water.Politics Politics Politics is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.At the same time, the conflict with Iran is entering a more volatile phase. New mines in the Strait of Hormuz and an expanded U.S. naval response signal that this is no longer just posturing. It's a pressure campaign with real global stakes, especially given how much of the world's oil supply runs through that corridor. The situation is starting to look less like a slow escalation and more like a standoff that will force a decision sooner rather than later.What makes it even more unpredictable is the internal instability within Iran itself. Leadership shakeups, reports about the Supreme Leader's health and — seriously — facial disfigurement, and a broader power struggle all suggest that there is no single, unified voice making decisions. That kind of vacuum makes negotiation harder and escalation easier, because different factions may be pulling in different directions at the same time.The timeline here is being driven by economics as much as politics. With exports constrained and storage capacity nearing its limit, Iran will eventually have to decide whether to halt production or find another way around the blockade. Neither option is easy, and both come with significant costs. That's why this moment feels compressed, with pressure building toward some kind of near term resolution.Finally, a different kind of competition is playing out between the United States and China, this time over artificial intelligence. The Trump administration is accusing China-backed actors of effectively copying American AI systems by extracting outputs and using them to train rival models. It is a technical fight, but the implications are strategic, especially if it allows competitors to replicate advanced systems without the same investment or safeguards.That accusation fits into a broader pattern of technological rivalry, where innovation, security, and economic advantage are all intertwined. If these claims are accurate, it raises serious questions about how U.S. companies can protect their models and whether current safeguards are enough. With a high stakes meeting between Trump and Xi on the horizon, this issue is likely to become part of a much larger negotiation over trade, security, and global influence.Chapters00:00:00 - Intro00:02:16 - Gabe Fleisher on the White House Press Corps and the Supreme Court00:22:41 - Redistricting Fights00:27:31 - Iran00:33:14 - China and AI00:36:29 - Gabe Fleisher on the Permanence of the Trump Administration01:08:56 - Final Thoughts This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.politicspoliticspolitics.com/subscribe

Most engineers approach open source the wrong way. They write code, open a PR, and wonder why it never gets merged. Bruno Schaatsbergen, Terraform core contributor and ex-HashiCorp engineer, breaks down the real craft behind contributions that actually land, and why AI is quietly breaking the ecosystem we all depend on.In this episode, we cover:Why pull requests get ignored (and the counterintuitive fix)How AI slop is killing open source from the insideUsing AI agents without losing your identity as an engineerWhy open source beats a tailored resume in today's marketHow consistent contributions can reshape your entire careerIf you've ever wanted to contribute to open source but didn't know where to start, this episode gives you a clear perspective from someone who's been on both sides.Connect with Bruno:https://www.linkedin.com/in/bschaatsbergenOUTILNE00:00:00 - Intro00:01:04 - How Open Source Shaped My Entire Career00:02:14 - Why I Take Pride in Every PR I Write00:03:16 - Open Source vs Personal Projects: The Real Difference00:04:18 - Why Your PRs Get Ignored (And How to Fix It)00:05:41 - Know Your Audience: The Counterintuitive PR Hack00:06:35 - Dealing With Imposter Syndrome as a Contributor00:07:10 - Read Code Like a Writer Reads Books00:09:31 - My First Contribution (And How It Changed My Career)00:10:51 - Should You Contribute to Open Source Early in Your Career?00:12:46 - The Dark Side: When Contributions Become Noise00:13:44 - Killed With Kindness: The AI Slop Problem00:16:17 - How Maintainers Are Fighting AI Slop00:18:02 - How I Actually Use AI Agents in My Workflow00:19:11 - Don't Outsource Your Thinking to AI00:20:11 - Who's Liable for AI-Generated Code?00:21:16 - Earned Rights: Why Trust Matters in Open Source00:22:52 - How to Approach People at Tech Conferences00:24:52 - Open Source Is Not a Democracy00:26:04 - Why Open Source Beats a Tailored Resume00:27:12 - Never Contribute With the Goal of Getting Hired00:28:38 - The Real Reason Consistency Pays Off00:29:30 - Admitting I'm a University Dropout00:30:42 - Why I Haven't Contributed in Weeks (And That's Okay)00:32:07 - The Trap of Chasing Contributor Rankings00:34:32 - Open Source Lets You Work With Anyone in the World00:35:52 - Final Advice: Don't Let AI Steal Your Identity

Watch the YouTube version of this episode HEREThis episode of the Maximum Lawyer podcast features Jeremy Danielson, a real estate law firm owner from Des Moines, Iowa as a featured speaker at Max Law Con 2025. Jeremy offers an in-depth look at how he revolutionized his firm's decision-making by building an AI Board of Advisors, using custom GPT personas modeled after renowned business leaders such as Steve Jobs, Mike Michalowicz, and Andrew Ng.Jeremy recounts how, during a critical cash flow crisis, he turned to his AI advisors for guidance. By simulating the perspectives and expertise of these influential figures, he was able to identify and resolve key marketing tracking issues, streamline and optimize his firm's financial operations, and successfully launch six new premium service offerings. Jeremy details the process of designing and refining each AI persona to reflect the unique strengths and strategic thinking of their real-life counterparts, allowing him to “consult” with a virtual roundtable of experts at any time.He emphasizes the profound impact this AI advisory board had on reducing the sense of isolation that often comes with law firm leadership, enabling him to make faster, more informed decisions with greater confidence. As a Max Law Con 2025 speaker, Jeremy encourages fellow law firm owners to harness the power of AI-driven advisors not only to solve immediate business challenges but also to foster ongoing innovation and resilience in their practices.Timestamps00:04:37 Financial Crisis and Tyson's Advice00:05:55 Marketing Blind Spots and Steve Jobs AI00:08:46 Implementing AI Advice for Marketing00:10:13 Preparing for Mastermind with AI00:11:29 AI Coaching During Personal Crisis00:12:40 How the AI Board Works00:14:13 Profit First Confusion and Mike Michalowicz AI00:17:23 The Power of Asking for Help00:19:00 Concrete Outcomes from AI Advisors00:20:37 Business Transformation and Personal Impact00:22:02 Encouragement to Build Your Own AI BoardConnect with Jeremy:Website  Instagram Facebook  Linkedin  Youtube  Resources:Join the Guild MembershipSubscribe to the Maximum Lawyer Youtube ChannelFollow us on InstagramJoin the Facebook GroupFollow the Facebook PageFollow us on LinkedIn Resources:Join the Guild MembershipSubscribe to the Maximum Lawyer Youtube ChannelFollow us on InstagramJoin the Facebook GroupFollow the Facebook PageFollow us on LinkedIn

AI isn't just making marketing faster—it's completely changing how it works. In this episode, Clark Newby sits down with Angus Robertson, founder of Outcome Marketing, to break down what that shift actually looks like in practice.Angus Robertson, a former CMO and now founder of Outcome Marketing, shares how he went all-in on AI—and why most marketers are still approaching it the wrong way. With over 20 years of experience in B2B SaaS and go-to-market strategy, Angus has worked with founders and leadership teams to build systems that drive real outcomes, not just activity.This conversation goes beyond surface-level AI talk. Angus walks through how he evolved from using tools like ChatGPT to building full applications, diagnostics, and even AI agents that integrate directly into workflows. What used to take months can now be done in days—and often better.-----CONNECT with us at:Website: https://leadtail.com/Leadtail TV: https://www.leadtailtv.com/LinkedIn: https://www.linkedin.com/company/lead...Twitter: https://twitter.com/leadtailFacebook: https://www.facebook.com/Leadtail/Instagram: https://www.instagram.com/leadtail/----00:00 – Marketers Must Choose How Deep to Go with AI00:58 – Introduction & Guest Background in B2B Marketing02:34 – From CMO to Building a New Marketing Framework05:00 – Why AI Changed Everything for Marketing Leaders06:50 – The AI Tech Stack Shift (Winners vs Losers)11:05 – Real AI Use Cases: From Chatbots to Building Apps14:10 – Creating AI Agents and Automating Workflows15:50 – How to Help Teams Adopt AI the Right Way19:04 – The Rise of Niche AI Apps & Domain Expertise22:34 – Why AI Won't Fix Bad Marketing Fundamentals24:50 – Best Resources to Learn and Stay Ahead in AI28:20 – Where to Find the Book & Final Takeaways#b2bmarketing #b2b

Most AI companies are racing to build bigger LLMs. Eve Bodnia thinks that's the wrong approach.Eve is the founder and CEO of Logical Intelligence, which is developing an alternative to the transformer-based models dominating the industry. Her argument: LLMs' architecture makes them fundamentally unsuited for some mission-critical tasks. A system that generates output one token at a time, with no ability to inspect its own reasoning mid-process or guarantee its results, shouldn't be trusted to design chips, analyze financial data, or even fly a plane. Her alternative is the energy-based model (EBM), a form of AI rooted in the physics principle of energy minimization, not language prediction. Rather than guessing the next probable word, an EBM maps every possible outcome across a mathematical landscape, where likely states settle into valleys and improbable ones sit on peaks. Dan Shipper talked with Bodnia for AI & I about why she believes LLM progress is plateauing, what it means for AI to actually understand data rather than just pattern-match across it, and how her team is building toward formally verified code generated in plain English—no C++ required.If you found this episode interesting, please like, subscribe, comment, and share!Head to http://granola.ai/every and get 3 months free with the code EVERYTo hear more from Dan Shipper:Subscribe to Every: https://every.to/subscribe Follow him on X: https://twitter.com/danshipper Timestamps: 00:00:51 - Introduction00:02:09 - Why correctness and verifiability matter in AI00:09:33 - What an energy-based model is00:14:21 - How EBMs construct energy landscapes to understand data00:19:00 - Why modeling intelligence through language alone is a flawed approach00:26:54 - What it means for a model to "understand" data00:37:21 - How EBMs solve the vibe coding problem and enable formally verified code00:43:21 - Why LLM progress is plateauing00:49:54 - Mission-critical industries haven't adopted LLMs, and how EBMs could fill that gap

This episode of Excess Returns features GMO's Tom Hancock on how to think about AI as an investment opportunity and what truly defines “quality” in today's market. The conversation breaks down the AI value chain, challenges common assumptions about where value will accrue, and ties it all back to building durable portfolios in a rapidly changing technological landscape.Tom walks through his “Hype vs High Conviction” framework, explaining why identifying the right layer of the AI ecosystem may matter more than simply betting on the theme itself, and why balance sheets, durability, and capital allocation remain critical even in the most exciting growth environments.Hype vs High Convictionhttps://www.gmo.com/americas/research-library/hype-vs-high-conviction_insights/Topics Covered:Why AI may be the most important investment decision todayThe four-layer AI stack: applications, LLMs, hyperscalers, and infrastructureWhy investors confuse secular trends with investable opportunitiesFollowing the money through the AI value chainThe hidden risks of investing lower in the stackWhy today's tech leaders differ from the dot-com eraGrowth vs maintenance capex and what it means for AI economicsWhy software may be more resilient than markets thinkHow GMO defines “quality” and why it matters in volatile marketsPortfolio construction: where GMO is investing (and avoiding) in AITimestamps:00:00 Intro and framing the AI investment debate00:00:55 Tom Hancock background and focus on quality investing00:02:00 What investors are getting wrong about AI00:03:23 Breaking down the four layers of the AI ecosystem00:06:45 Applications vs infrastructure: where value may accrue00:08:45 Why predicting AI winners is still difficult00:11:00 Following the cash flows through the AI stack00:13:00 Why AI funding is more stable than past tech bubbles00:16:00 Big Tech strategy differences and capital allocation decisions00:17:34 Are today's tech companies higher quality than in 1999?00:19:00 Growth vs maintenance capex and implications for Nvidia and others00:22:00 Depreciation, chip lifecycles, and hidden risks in capex assumptions00:24:00 Capital intensity vs quality: when heavy investment is a feature00:27:00 Why incumbents may benefit most from AI00:28:30 Risks in the LLM layer and potential commoditization00:30:10 Software disruption fears: overdone or justified?00:34:06 Defining “quality” in investing00:36:00 Balance sheets vs return on capital00:38:32 Why GMO sold Oracle and the risks of leverage00:40:18 What happens if AI spending slows down00:41:35 Where the biggest risks are in the AI stack00:44:26 Where GMO is positioned vs the S&P 50000:48:00 How new ideas enter a quality portfolio00:51:00 Sell discipline and portfolio turnover00:53:00 International vs US quality investing

Send us Fan MailAI tools now replace expensive software, reduce development time, and improve business systems. Learn how agencies use AI for automation, churn prediction, customer data tracking, and faster builds. This video covers AI for ecommerce, agency growth, automation tools, and real use cases in 2026.If outdated systems are slowing growth, fix it now before competitors replace you with AI:  https://bit.ly/4jMZtxu#AIforBusiness #AgencyGrowth #AutomationTools #EcommerceStrategy #aitools Want free resources? Dowload our Free Amazon guides here:Amazon Catalog Spring Cleaning: https://hubs.ly/Q046BVfp0Growth Email Marketing Strategies: https://hubs.ly/Q04457QF0Amazon Proft Margin Defense 2026: https://hubs.ly/Q042trRH0Amazon SEO Toolkit 2026: https://bit.ly/4oC2ClTAmazon Seller Strategy Report 2026: https://bit.ly/3YN1RME2026 Ecommerce Website & SEO Readiness Checklist: https://hubs.ly/Q040Jg0M0Amazon 2026 PPC guide: https://bit.ly/4lF0OYXTimestamps00:00 - $2.6M software replaced by AI00:20 - Why AI is finally ready in 202601:03 - Real use vs AI hype explained01:42 - Turning agency systems into AI products02:30 - Why agencies must transition now03:26 - Replacing copywriting and design roles with AI04:50 - Building websites and tools in hours05:38 - Using AI to predict client churn06:53 - Data driven decisions with AI systems08:30 - How to build tools using AI prompts09:02 - Too many good ideas problem with AI--------------------------------------------------------------------------Follow us:LinkedIn: https://www.linkedin.com/company/28605816/Instagram: https://www.instagram.com/stevenpopemag/Pinterest: https://www.pinterest.com/myamazonguys/Twitter: https://twitter.com/myamazonguySubscribe to the My Amazon Guy podcast:My Amazon Guy podcast: https://podcast.myamazonguy.comApple Podcast: https://podcasts.apple.com/us/podcast/my-amazon-guy/id1501974229Spotify: https://open.spotify.com/show/4A5ASHGGfr6s4wWNQIqyVwSupport the show

This week on Checkpoint Chat, we get an early look at the beauty of Japan in Forza Horizon 6, continue catching (and building) them all in Pokémon Pokopia, check in on the Super Mario Bros. Wonder – Meetup in Bellabel Park DLC, and go back in time to play indie hit ElecHead!Follow Checkpoint Chat on...Twitter: https://twitter.com/CheckpointChat​​Facebook: https://www.facebook.com/CheckpointChatInstagram: https://www.instagram.com/checkpointchatBluesky: https://bsky.app/profile/checkpointchat.bsky.social Want to listen to more gaming goodness, on other platforms? Subscribe to the podcast on Apple, Google, Spotify, and more right here: https://podcasters.spotify.com/pod/show/checkpointchat-- SHOW NOTES --00:00:00​ - Tech problems and the future of AI00:19:23 - Pokémon Pokopia is so chilled00:38:48 - Cars go VROOM VROOM in Forza Horizon 600:51:30​ - ElecHead is delightful and short01:01:26​ - The Super Mario Bros. Wonder DLC has a long name#gamingpodcast #gamingchannels #gamingreview

Svou softwarovou firmu založil v Brně už v roce 1990. V jejím čele stojí celých šestatřicet let, během kterých se několikrát přejmenovala, spojila se se silnými investory a pohltila spoustu konkurentů po celé Evropě. Ale především masivně vyrostla a teď poprvé vydělala miliardu. Její zakladatel se jmenuje Martin Cígler, dnes stojí v čele obřího IT byznysu Seyfor a do důchodu se rozhodně nechystá. Maximálně si jede na pár týdnů vypnout hlavu. „Brainwashing je pro top manažery fakt důležitá věc,“ vysvětluje svůj přístup v podcastu Money Maker.Celý život v jedné firmě je pro řadu lidí jistě nepředstavitelná věc. Martin Cígler se ale pořád baví. Jednak proto, že když je potřeba, trochu si promění svou náplň práce, a hlavně ho může neustále těšit velký růst. „Považuju to za malý zázrak,“ usmívá se Cígler. Za loňský rok reportuje tržby kolem 5,7 miliardy korun, ale ještě důležitější je pro něj růst ziskovosti. A jaký je jeho osobní recept? „Hodně deleguju a šikovné kolegy vytahuju nahoru,“ říká muž, který předloni oslavil šedesátku.00:00:00 Začínáme00:06:44 „Do důchodu se nechystám.”00:15:24 Miliardový zisk a zahraniční trhy00:25:01 Chystá se Seyfor na burzu?00:31:09 Firemní a produktová integrace00:41:58 „Jsem poker player.“00:47:15 Windows, cloud a AI00:51:58 Pozice budoucnosti? Pastevec AI agentů

Leisa, former Head of Research and Insights at Atlassian, reflects on how AI is changing the UX research industry. She discusses how vibe-coding helps shift from traditional project sequencing to faster, more iterative build-measure-learn cycles and how that impacts the role of UX research in organisations. Leisa expounds on her positive attitude towards AI, saying that the unique value of researchers will shift toward filling the "judgment gap" with deep, longitudinal human observation that machines simply cannot replicate.

Tarek already built a B2B software company to $30M ARR. But when the AI wave hit, he realized he could build a generational business by automating the manual world of accounts receivable. So, he left to start Stuut.In this episode, Tarek breaks down how he reached $1M ARR in a couple of months and is on track to hit up to $50M this year. He reveals how he pre-sold his first $65k contract with just wireframes, why he forces new customers to introduce him to five peers, and the brutal reality of finding message-market fit through hundreds of cold calls.Why You Should ListenHow to pre-sell a $65k enterprise contract before writing code.The "Closing Discount" hack to generate 5 referrals from every new customer.Why finding "Message Market Fit" is more important than your ICP.How to spot and avoid early-stage startup "vultures".Why scaling a B2B sales motion requires hiring misfits over pedigree.00:00:00 Intro00:01:41 Leaving a $30M Startup to Build with AI00:08:06 Finding Message Market Fit Through Cold Calling00:20:07 Pre-Selling a $65k Contract with Wireframes00:27:51 The Voice AI "Aha" Moment00:33:07 The Closing Discount Referral Hack00:37:18 The Brutal Reality of B2B Sales00:42:19 Hitting $1M ARR and Pacing for $50M00:45:05 Why Product Market Fit is Never Truly FoundSend me a message to let me know what you think!

Esta semana los mercados cerraron Q1 con miedo real: la guerra EE.UU.–Irán disparó el petróleo a $108/barril, el S&P lleva 5 semanas en rojo y el peso mexicano está bajo presión. Google lanzó TurboQuant, un algoritmo que hace que los modelos de AI necesiten 6 veces menos memoria — y hundió las acciones de Micron, Samsung y SK Hynix en días. Anthropic filtró por accidente documentos internos que revelan "Claude Mythos", su modelo más capaz hasta ahora, que sus propios ingenieros describen como un riesgo de ciberseguridad sin precedentes. Y Jensen Huang declaró en el podcast de Lex Fridman que la AGI ya llegó.00:09:32 Introducción y temas semanales00:12:00 Precios de cripto y mercados00:22:27 Miedo en mercado cripto00:31:19 Inicio de sección Tech y AI00:44:35 Google mejora eficiencia de modelos00:57:13 Jensen Huang y el AGI01:14:07 Cierre y resumen final

Omar already built and sold an AI startup for over $100M. But when the generative AI wave hit, he realized the technology wasn't just the future of software—it was the future of labor. So he started Eudia to completely transform how enterprise legal teams operate.In this episode, Omar breaks down how he scaled from $2M to $20M ARR in just 12 months. He reveals the exact cold email strategy he used to land C-suite design partners, why he bought an existing legal services company to accelerate his AI platform, and why replacing human labor with AI is the ultimate business model.Why You Should ListenWhy selling AI as a service is a much bigger opportunity than selling SaaS.How to secure Fortune 500 design partners using cold emails.Why playing to win beats playing not to lose.How to build a data moat that AI wrappers can't compete with.Why ARR shouldn't be your only measure of startup success in the AI era.Keywordsstartup podcast, startup podcast for founders, AI startups, product market fit, AI enabled services, legaltech, B2B SaaS, enterprise sales, finding pmf, generative AI00:00:00 Intro00:01:45 Why AI is the Future of Labor00:04:55 Replacing In-House vs. Outsourced Legal Teams00:09:35 Selling His First AI Startup for $100M00:12:11 Why the $1 Trillion Law Firm Industry is at Risk00:21:59 Landing Fortune 500 Design Partners via Cold Email00:28:26 Playing to Win vs. Playing Not to Lose00:33:45 Raising a $6M Seed Round with an 80-Page Transcript00:38:53 Buying a Legal Services Company to Accelerate Growth00:44:55 Scaling from $2M to $20M ARR in 12 MonthsSend me a message to let me know what you think!

Welcome to The DMF — Discovering Meaning in Film and Acting. I'm Justin Younts, and in this episode I continue my conversation with filmmaker, producer, and author Brent Lindstrom as we explore the intersection of filmmaking, technology, and creative storytelling.We dive into the real challenges filmmakers face during the editing process. Brent shares his experience spending hours editing every second of his short film while dealing with an unreliable computer that constantly crashed. After struggling through that process, he eventually built a powerful editing machine that transformed the way he works and dramatically improved his workflow.Our conversation also explores the growing role of artificial intelligence in filmmaking. While AI tools can streamline certain tasks and assist with production, Brent emphasizes that technology should enhance creativity — not replace it. The craft of storytelling, directing actors, and building meaningful narratives still depends on human insight and artistic vision.In this episode we discuss:• The realities of film editing and post-production• How technology is changing filmmaking workflows• The benefits and risks of AI in film production• Maintaining creativity in an age of automation• Writing and developing complex characters• The importance of feedback when refining storiesBrent also discusses his book “One for the Money, Two for the Soul,” which explores powerful themes through storytelling and examines how creative work can balance artistic purpose with financial realities.Whether you're an actor, filmmaker, writer, or creative professional, this episode offers insight into navigating new technologies while protecting the core principles of storytelling.Join us as we explore the future of filmmaking and how creators can use new tools without losing the heart of their craft.Visit Brent's website:https://lightmindedarts.comCheck out Brent Lindstrom's book One for the Money, Two for the Soul:

בפרק זה של הפודקאסט "על המשמעות" עו"ד תמיר דורטל מארח את משה פבריקנט, מהפודקאסטרים הצעירים המובילים בארץ, לשיחה על מהפכת הבינה המלאכותית, השפעתה על שוק התעסוקה והדרכים להסתגל לעידן הטכנולוגי החדש.העולם שאנו מכירים משתנה בקצב מסחרר. מה שהחל כמודלי שפה פשוטים מתפתח במהירות למציאות שבה סוכני בינה מלאכותית עצמאיים מסוגלים לנהל פגישות, לכתוב חוזים משפטיים, ולייתר מחלקות שלמות של עובדים בארגוני ענק. משה פבריקנט משרטט תמונת מצב חדה על השלכות הטכנולוגיה: החל מחברות של אדם אחד שיהיו שוות מיליארדים, ועד לקריסת הרלוונטיות של התארים האקדמיים המסורתיים.במהלך השיחה, דורטל ופבריקנט מנתחים כיצד אדם מן השורה יכול למצוא את מקומו בעולם של סוכנים חכמים, מחשוב קוונטי וסינגולריות. האם אנו דוהרים אל עבר דיסטופיה נשלטת על ידי קומץ מיליארדרים, או דווקא לאוטופיה של שפע חומרי שישחרר אותנו מעבודה קשה?הפרק לא עוצר רק ברמת המאקרו, אלא צולל לכלים פרקטיים שכל אחד יכול ליישם כבר מחר בבוקר כדי לשפר את הלמידה והפרודוקטיביות שלו. אילו הנחיות יהפכו את ה-AI ליועץ האישי שלכם? כיצד שיטת פיינמן וטכניקת פומודורו יכפילו את יכולת הזיכרון שלכם? ומדוע דווקא פעולות בסיסיות כמו ניתוק התראות הסמארטפון, שתיית מים, שימוש באטמי אוזניים וחסימת אור כחול הן המפתח לשרידות בעולם של הסחות דעת אינסופיות?הצטרפו לשיחה שתיקח אתכם אל מאחורי הקלעים של הטכנולוגיה ותצייד אתכם בארגז הכלים ההכרחי כדי לא להישאר מאחור בשנים הקרובות.00:00:00 עתיד התחבורה והמוניות האוטונומיות00:02:18 סוכני בינה מלאכותית מחליפים עובדים00:05:33 תואר אקדמי כבר לא מהווה יתרון00:08:02 איך לכתוב חוזה משפטי עם AI00:13:55 שובו של ג'ף בזוס וחזון המיליארדרים00:18:20 אוטופיה מול דיסטופיה בעולם החדש00:23:44 מחשוב קוונטי והאצת האינטליגנציה המלאכותית00:27:00 שיטת פיינמן ללמידה ושינון00:34:00 עבודה ללא הסחות דעת וטכניקת פומודורו00:37:56 חסימת התראות ודופמין דיטוקס00:41:40 שימוש באטמי אוזניים לשיפור הריכוז00:46:00 תזונה דלת פחמימות ושתיית מים00:49:59 חסימת אור כחול ושיפור איכות השינה#פודקאסט #על_המשמעותSupport the showתוכנית המנויים "על המשמעות פלוס" ➕: https://bit.ly/MashmaPlus גישה מוקדמת לפרקים

Turbopuffer came out of a reading app.In 2022, Simon was helping his friends at Readwise scale their infra for a highly requested feature: article recommendations and semantic search. Readwise was paying ~$5k/month for their relational database and vector search would cost ~$20k/month making the feature too expensive to ship. In 2023 after mulling over the problem from Readwise, Simon decided he wanted to “build a search engine” which became Turbopuffer.We discuss:• Simon's path: Denmark → Shopify infra for nearly a decade → “angel engineering” across startups like Readwise, Replicate, and Causal → turbopuffer almost accidentally becoming a company • The Readwise origin story: building an early recommendation engine right after the ChatGPT moment, seeing it work, then realizing it would cost ~$30k/month for a company spending ~$5k/month total on infra and getting obsessed with fixing that cost structure • Why turbopuffer is “a search engine for unstructured data”: Simon's belief that models can learn to reason, but can't compress the world's knowledge into a few terabytes of weights, so they need to connect to systems that hold truth in full fidelity • The three ingredients for building a great database company: a new workload, a new storage architecture, and the ability to eventually support every query plan customers will want on their data • The architecture bet behind turbopuffer: going all in on object storage and NVMe, avoiding a traditional consensus layer, and building around the cloud primitives that only became possible in the last few years • Why Simon hated operating Elasticsearch at Shopify: years of painful on-call experience shaped his obsession with simplicity, performance, and eliminating state spread across multiple systems • The Cursor story: launching turbopuffer as a scrappy side project, getting an email from Cursor the next day, flying out after a 4am call, and helping cut Cursor's costs by 95% while fixing their per-user economics • The Notion story: buying dark fiber, tuning TCP windows, and eating cross-cloud costs because Simon refused to compromise on architecture just to close a deal faster • Why AI changes the build-vs-buy equation: it's less about whether a company can build search infra internally, and more about whether they have time especially if an external team can feel like an extension of their own • Why RAG isn't dead: coding companies still rely heavily on search, and Simon sees hybrid retrieval semantic, text, regex, SQL-style patterns becoming more important, not less • How agentic workloads are changing search: the old pattern was one retrieval call up front; the new pattern is one agent firing many parallel queries at once, turning search into a highly concurrent tool call • Why turbopuffer is reducing query pricing: agentic systems are dramatically increasing query volume, and Simon expects retrieval infra to adapt to huge bursts of concurrent search rather than a small number of carefully chosen calls • The philosophy of “playing with open cards”: Simon's habit of being radically honest with investors, including telling Lachy Groom he'd return the money if turbopuffer didn't hit PMF by year-end • The “P99 engineer”: Simon's framework for building a talent-dense company, rejecting by default unless someone on the team feels strongly enough to fight for the candidate —Simon Hørup Eskildsen• LinkedIn: https://www.linkedin.com/in/sirupsen• X: https://x.com/Sirupsen• https://sirupsen.com/aboutturbopuffer• https://turbopuffer.com/Full Video PodTimestamps00:00:00 The PMF promise to Lachy Groom00:00:25 Intro and Simon's background00:02:19 What turbopuffer actually is00:06:26 Shopify, Elasticsearch, and the pain behind the company00:10:07 The Readwise experiment that sparked turbopuffer00:12:00 The insight Simon couldn't stop thinking about00:17:00 S3 consistency, NVMe, and the architecture bet00:20:12 The Notion story: latency, dark fiber, and conviction00:25:03 Build vs. buy in the age of AI00:26:00 The Cursor story: early launch to breakout customer00:29:00 Why code search still matters00:32:00 Search in the age of agents00:34:22 Pricing turbopuffer in the AI era00:38:17 Why Simon chose Lachy Groom00:41:28 Becoming a founder on purpose00:44:00 The “P99 engineer” philosophy00:49:30 Bending software to your will00:51:13 The future of turbopuffer00:57:05 Simon's tea obsession00:59:03 Tea kits, X Live, and P99 LiveTranscriptSimon Hørup Eskildsen: I don't think I've said this publicly before, but I just called Lockey and was like, local Lockie. Like if this doesn't have PMF by the end of the year, like we'll just like return all the money to you. But it's just like, I don't really, we, Justine and I don't wanna work on this unless it's really working.So we want to give it the best shot this year and like we're really gonna go for it. We're gonna hire a bunch of people. We're just gonna be honest with everyone. Like when I don't know how to play a game, I just play with open cards. Lockey was the only person that didn't, that didn't freak out. He was like, I've never heard anyone say that before.Alessio: Hey everyone, welcome to the Leading Space podcast. This is Celesio Pando, Colonel Laz, and I'm joined by Swix, editor of Leading Space.swyx: Hello. Hello, uh, we're still, uh, recording in the Ker studio for the first time. Very excited. And today we are joined by Simon Eski. Of Turbo Farer welcome.Simon Hørup Eskildsen: Thank you so much for having me.swyx: Turbo Farer has like really gone on a huge tear, and I, I do have to mention that like you're one of, you're not my newest member of the Danish AHU Mafia, where like there's a lot of legendary programmers that have come out of it, like, uh, beyond Trotro, Rasmus, lado Berg and the V eight team and, and Google Maps team.Uh, you're mostly a Canadian now, but isn't that interesting? There's so many, so much like strong Danish presence.Simon Hørup Eskildsen: Yeah, I was writing a post, um, not that long ago about sort of the influences. So I grew up in Denmark, right? I left, I left when, when I was 18 to go to Canada to, to work at Shopify. Um, and so I, like, I've, I would still say that I feel more Danish than, than Canadian.This is also the weird accent. I can't say th because it, this is like, I don't, you know, my wife is also Canadian, um, and I think. I think like one of the things in, in Denmark is just like, there's just such a ruthless pragmatism and there's also a big focus on just aesthetics. Like, they're like very, people really care about like where, what things look like.Um, and like Canada has a lot of attributes, US has, has a lot of attributes, but I think there's been lots of the great things to carry. I don't know what's in the water in Ahu though. Um, and I don't know that I could be considered part of the Mafi mafia quite yet, uh, compared to the phenomenal individuals we just mentioned.Barra OV is also, uh, Danish Canadian. Okay. Yeah. I don't know where he lives now, but, and he's the PHP.swyx: Yeah. And obviously Toby German, but moved to Canada as well. Yes. Like this is like import that, uh, that, that is an interesting, um, talent move.Alessio: I think. I would love to get from you. Definition of Turbo puffer, because I think you could be a Vector db, which is maybe a bad word now in some circles, you could be a search engine.It's like, let, let's just start there and then we'll maybe run through the history of how you got to this point.Simon Hørup Eskildsen: For sure. Yeah. So Turbo Puffer is at this point in time, a search engine, right? We do full text search and we do vector search, and that's really what we're specialized in. If you're trying to do much more than that, like then this might not be the right place yet, but Turbo Buffer is all about search.The other way that I think about it is that we can take all of the world's knowledge, all of the exabytes and exabytes of data that there is, and we can use those tokens to train a model, but we can't compress all of that into a few terabytes of weights, right? Compress into a few terabytes of weights, how to reason with the world, how to make sense of the knowledge.But we have to somehow connect it to something externally that actually holds that like in full fidelity and truth. Um, and that's the thing that we intend to become. Right? That's like a very holier than now kind of phrasing, right? But being the search engine for unstructured, unstructured data is the focus of turbo puffer at this point in time.Alessio: And let's break down. So people might say, well, didn't Elasticsearch already do this? And then some other people might say, is this search on my data, is this like closer to rag than to like a xr, like a public search thing? Like how, how do you segment like the different types of search?Simon Hørup Eskildsen: The way that I generally think about this is like, there's a lot of database companies and I think if you wanna build a really big database company, sort of, you need a couple of ingredients to be in the air.We don't, which only happens roughly every 15 years. You need a new workload. You basically need the ambition that every single company on earth is gonna have data in your database. Multiple times you look at a company like Oracle, right? You will, like, I don't think you can find a company on earth with a digital presence that it not, doesn't somehow have some data in an Oracle database.Right? And I think at this point, that's also true for Snowflake and Databricks, right? 15 years later it's, or even more than that, there's not a company on earth that doesn't, in. Or directly is consuming Snowflake or, or Databricks or any of the big analytics databases. Um, and I think we're in that kind of moment now, right?I don't think you're gonna find a company over the next few years that doesn't directly or indirectly, um, have all their data available for, for search and connect it to ai. So you need that new workload, like you need something to be happening where there's a new workload that causes that to happen, and that new workload is connecting very large amounts of data to ai.The second thing you need. The second condition to build a big database company is that you need some new underlying change in the storage architecture that is not possible from the databases that have come before you. If you look at Snowflake and Databricks, right, commoditized, like massive fleet of HDDs, like that was not possible in it.It just wasn't in the air in the nineties, right? So you just didn't, we just didn't build these systems. S3 and and and so on was not around. And I think the architecture that is now possible that wasn't possible 15 years ago is to go all in on NVME SSDs. It requires a particular type of architecture for the database that.It's difficult to retrofit onto the databases that are already there, including the ones you just mentioned. The second thing is to go all in on OIC storage, more so than we could have done 15 years ago. Like we don't have a consensus layer, we don't really have anything. In fact, you could turn off all the servers that Turbo Buffer has, and we would not lose any data because we have all completely all in on OIC storage.And this means that our architecture is just so simple. So that's the second condition, right? First being a new workload. That means that every company on earth, either indirectly or directly, is using your database. Second being, there's some new storage architecture. That means that the, the companies that have come before you can do what you're doing.I think the third thing you need to do to build a big database company is that over time you have to implement more or less every Cory plan on the data. What that means is that you. You can't just get stuck in, like, this is the one thing that a database does. It has to be ever evolving because when someone has data in the database, they over time expect to be able to ask it more or less every question.So you have to do that to get the storage architecture to the limit of what, what it's capable of. Those are the three conditions.swyx: I just wanted to get a little bit of like the motivation, right? Like, so you left Shopify, you're like principal, engineer, infra guy. Um, you also head of kernel labs, uh, inside of Shopify, right?And then you consulted for read wise and that it kind of gave you that, that idea. I just wanted you to tell that story. Um, maybe I, you've told it before, but, uh, just introduce the, the. People to like the, the new workload, the sort of aha moment for turbo PufferSimon Hørup Eskildsen: For sure. So yeah, I spent almost a decade at Shopify.I was on the infrastructure team, um, from the fairly, fairly early days around 2013. Um, at the time it felt like it was growing so quickly and everything, all the metrics were, you know, doubling year on year compared to the, what companies are contending with today. It's very cute in growth. I feel like lot some companies are seeing that month over month.Um, of course. Shopify compound has been compounding for a very long time now, but I spent a decade doing that and the majority of that was just make sure the site is up today and make sure it's up a year from now. And a lot of that was really just the, um, you know, uh, the Kardashians would drive very, very large amounts of, of data to, to uh, to Shopify as they were rotating through all the merch and building out their businesses.And we just needed to make sure we could handle that. Right. And sometimes these were events, a million requests per second. And so, you know, we, we had our own data centers back in the day and we were moving to the cloud and there was so much sharding work and all of that that we were doing. So I spent a decade just scaling databases ‘cause that's fundamentally what's the most difficult thing to scale about these sites.The database that was the most difficult for me to scale during that time, and that was the most aggravating to be on call for, was elastic search. It was very, very difficult to deal with. And I saw a lot of projects that were just being held back in their ambition by using it.swyx: And I mean, self-hosted.Self-hosted. ‘causeSimon Hørup Eskildsen: it's, yeah, and it commercial, this is like 2015, right? So it's like a very particular vintage. Right. It's probably better at a lot of these things now. Um, it was difficult to contend with and I'm just like, I just think about it. It's an inverted index. It should be good at these kinds of queries and do all of this.And it was, we, we often couldn't get it to do exactly what we needed to do or basically get lucine to do, like expose lucine raw to, to, to what we needed to do. Um, so that was like. Just something that we did on the side and just panic scaled when we needed to, but not a particular focus of mine. So I left, and when I left, I, um, wasn't sure exactly what I wanted to do.I mean, it spent like a decade inside of the same company. I'd like grown up there. I started working there when I was 18.swyx: You only do Rails?Simon Hørup Eskildsen: Yeah. I mean, yeah. Rails. And he's a Rails guy. Uh, love Rails. So good. Um,Alessio: we all wish we could still work in Rails.swyx: I know know. I know, but some, I tried learning Ruby.It's just too much, like too many options to do the same thing. It's, that's my, I I know there's a, there's a way to do it.Simon Hørup Eskildsen: I love it. I don't know that I would use it now, like given cloud code and, and, and cursor and everything, but, um, um, but still it, like if I'm just sitting down and writing a teal code, that's how I think.But anyway, I left and I wasn't, I talked to a couple companies and I was like, I don't. I need to see a little bit more of the world here to know what I'm gonna like focus on next. Um, and so what I decided is like I was gonna, I called it like angel engineering, where I just hopped around in my friend's companies in three months increments and just helped them out with something.Right. And, and just vested a bit of equity and solved some interesting infrastructure problem. So I worked with a bunch of companies at the time, um, read Wise was one of them. Replicate was one of them. Um, causal, I dunno if you've tried this, it's like a, it's a spreadsheet engine Yeah. Where you can do distribution.They sold recently. Yeah. Um, we've been, we used that in fp and a at, um, at Turbo Puffer. Um, so a bunch of companies like this and it was super fun. And so we're the Chachi bt moment happened, I was with. With read Wise for a stint, we were preparing for the reader launch, right? Which is where you, you cue articles and read them later.And I was just getting their Postgres up to snuff, like, which basically boils down to tuning, auto vacuum. So I was doing that and then this happened and we were like, oh, maybe we should build a little recommendation engine and some features to try to hook in the lms. They were not that good yet, but it was clear there was something there.And so I built a small recommendation engine just, okay, let's take the articles that you've recently read, right? Like embed all the articles and then do recommendations. It was good enough that when I ran it on one of the co-founders of Rey's, like I found out that I got articles about, about having a child.I'm like, oh my God, I didn't, I, I didn't know that, that they were having a child. I wasn't sure what to do with that information, but the recommendation engine was good enough that it was suggesting articles, um, about that. And so there was, there was recommendations and uh, it actually worked really well.But this was a company that was spending maybe five grand a month in total on all their infrastructure and. When I did the napkin math on running the embeddings of all the articles, putting them into a vector index, putting it in prod, it's gonna be like 30 grand a month. That just wasn't tenable. Right?Like Read Wise is a proudly bootstrapped company and it's paying 30 grand for infrastructure for one feature versus five. It just wasn't tenable. So sort of in the bucket of this is useful, it's pretty good, but let us, let's return to it when the costs come down.swyx: Did you say it grows by feature? So for five to 30 is by the number of, like, what's the, what's the Scaling factor scale?It scales by the number of articles that you embed.Simon Hørup Eskildsen: It does, but what I meant by that is like five grand for like all of the other, like the Heroku, dinos, Postgres, like all the other, and this then storage is 30. Yeah. And then like 30 grand for one feature. Right. Which is like, what other articles are related to this one.Um, so it was just too much right to, to power everything. Their budget would've been maybe a few thousand dollars, which still would've been a lot. And so we put it in a bucket of, okay, we're gonna do that later. We'll wait, we will wait for the cost to come down. And that haunted me. I couldn't stop thinking about it.I was like, okay, there's clearly some latent demand here. If the cost had been a 10th, we would've shipped it and. This was really the only data point that I had. Right. I didn't, I, I didn't, I didn't go out and talk to anyone else. It was just so I started reading Right. I couldn't, I couldn't help myself.Like I didn't know what like a vector index is. I, I generally barely do about how to generate the vectors. There was a lot of hype about, this is a early 2023. There was a lot of hype about vector databases. There were raising a lot of money and it's like, I really didn't know anything about it. It's like, you know, trying these little models, fine tuning them.Like I was just trying to get sort of a lay of the land. So I just sat down. I have this. A GitHub repository called Napkin Math. And on napkin math, there's just, um, rows of like, oh, this is how much bandwidth. Like this is how many, you know, you can do 25 gigabytes per second on average to dram. You can do, you know, five gigabytes per second of rights to an SSD, blah blah.All of these numbers, right? And S3, how many you could do per, how much bandwidth can you drive per connection? I was just sitting down, I was like, why hasn't anyone build a database where you just put everything on O storage and then you puff it into NVME when you use the data and you puff it into dram if you're, if you're querying it alive, it's just like, this seems fairly obvious and you, the only real downside to that is that if you go all in on o storage, every right will take a couple hundred milliseconds of latency, but from there it's really all upside, right?You do the first go, it takes half a second. And it sort of occurred to me as like, well. The architecture is really good for that. It's really good for AB storage, it's really good for nvm ESSD. It's, well, you just couldn't have done that 10 years ago. Back to what we were talking about before. You really have to build a database where you have as few round trips as possible, right?This is how CPUs work today. It's how NVM E SSDs work. It's how as, um, as three works that you want to have a very large amount of outstanding requests, right? Like basically go to S3, do like that thousand requests to ask for data in one round trip. Wait for that. Get that, like, make a new decision. Do it again, and try to do that maybe a maximum of three times.But no databases were designed that way within NVME as is ds. You can drive like within, you know, within a very low multiple of DRAM bandwidth if you use it that way. And same with S3, right? You can fully max out the network card, which generally is not maxed out. You get very, like, very, very good bandwidth.And, but no one had built a database like that. So I was like, okay, well can't you just, you know, take all the vectors right? And plot them in the proverbial coordinate system. Get the clusters, put a file on S3 called clusters, do json, and then put another file for every cluster, you know, cluster one, do js O cluster two, do js ON you know that like it's two round trips, right?So you get the clusters, you find the closest clusters, and then you download the cluster files like the, the closest end. And you could do this in two round trips.swyx: You were nearest neighbors locally.Simon Hørup Eskildsen: Yes. Yes. And then, and you would build this, this file, right? It's just like ultra simplistic, but it's not a far shot from what the first version of Turbo Buffer was.Why hasn't anyone done thatAlessio: in that moment? From a workload perspective, you're thinking this is gonna be like a read heavy thing because they're doing recommend. Like is the fact that like writes are so expensive now? Oh, with ai you're actually not writing that much.Simon Hørup Eskildsen: At that point I hadn't really thought too much about, well no actually it was always clear to me that there was gonna be a lot of rights because at Shopify, the search clusters were doing, you know, I don't know, tens or hundreds of crew QPS, right?‘cause you just have to have a human sit and type in. But we did, you know, I don't know how many updates there were per second. I'm sure it was in the millions, right into the cluster. So I always knew there was like a 10 to 100 ratio on the read write. In the read wise use case. It's, um, even, even in the read wise use case, there'd probably be a lot fewer reads than writes, right?There's just a lot of churn on the amount of stuff that was going through versus the amount of queries. Um, I wasn't thinking too much about that. I was mostly just thinking about what's the fundamentally cheapest way to build a database in the cloud today using the primitives that you have available.And this is it, right? You just, now you have one machine and you know, let's say you have a terabyte of data in S3, you paid the $200 a month for that, and then maybe five to 10% of that data and needs to be an NV ME SSDs and less than that in dram. Well. You're paying very, very little to inflate the data.swyx: By the way, when you say no one else has done that, uh, would you consider Neon, uh, to be on a similar path in terms of being sort of S3 first and, uh, separating the compute and storage?Simon Hørup Eskildsen: Yeah, I think what I meant with that is, uh, just build a completely new database. I don't know if we were the first, like it was very much, it was, I mean, I, I hadn't, I just looked at the napkin math and was like, this seems really obvious.So I'm sure like a hundred people came up with it at the same time. Like the light bulb and every invention ever. Right. It was just in the air. I think Neon Neon was, was first to it. And they're trying, they're retrofitted onto Postgres, right? And then they built this whole architecture where you have, you have it in memory and then you sort of.You know, m map back to S3. And I think that was very novel at the time to do it for, for all LTP, but I hadn't seen a database that was truly all in, right. Not retrofitting it. The database felt built purely for this no consensus layer. Even using compare and swap on optic storage to do consensus. I hadn't seen anyone go that all in.And I, I mean, there, there, I'm sure there was someone that did that before us. I don't know. I was just looking at the napkin mathswyx: and, and when you say consensus layer, uh, are you strongly relying on S3 Strong consistency? You are. Okay.SoSimon Hørup Eskildsen: that is your consensus layer. It, it is the consistency layer. And I think also, like, this is something that most people don't realize, but S3 only became consistent in December of 2020.swyx: I remember this coming out during COVID and like people were like, oh, like, it was like, uh, it was just like a free upgrade.Simon Hørup Eskildsen: Yeah.swyx: They were just, they just announced it. We saw consistency guys and like, okay, cool.Simon Hørup Eskildsen: And I'm sure that they just, they probably had it in prod for a while and they're just like, it's done right.And people were like, okay, cool. But. That's a big moment, right? Like nv, ME SSDs, were also not in the cloud until around 2017, right? So you just sort of had like 2017 nv, ME SSDs, and people were like, okay, cool. There's like one skew that does this, whatever, right? Takes a few years. And then the second thing is like S3 becomes consistent in 2020.So now it means you don't have to have this like big foundation DB or like zookeeper or whatever sitting there contending with the keys, which is how. You know, that's what Snowflake and others have do so muchswyx: for goneSimon Hørup Eskildsen: Exactly. Just gone. Right? And so just push to the, you know, whatever, how many hundreds of people they have working on S3 solved and then compare and swap was not in S3 at this point in time,swyx: by the way.Uh, I don't know what that is, so maybe you wanna explain. Yes. Yeah.Simon Hørup Eskildsen: Yes. So, um, what Compare and swap is, is basically, you can imagine that if you have a database, it might be really nice to have a file called metadata json. And metadata JSON could say things like, Hey, these keys are here and this file means that, and there's lots of metadata that you have to operate in the database, right?But that's the simplest way to do it. So now you have might, you might have a lot of servers that wanna change the metadata. They might have written a file and want the metadata to contain that file. But you have a hundred nodes that are trying to contend with this metadata that JSON well, what compare and Swap allows you to do is basically just you download the file, you make the modifications, and then you write it only if it hasn't changed.While you did the modification and if not you retry. Right? Should just have this retry loops. Now you can imagine if you have a hundred nodes doing that, it's gonna be really slow, but it will converge over time. That primitive was not available in S3. It wasn't available in S3 until late 2024, but it was available in GCP.The real story of this is certainly not that I sat down and like bake brained it. I was like, okay, we're gonna start on GCS S3 is gonna get it later. Like it was really not that we started, we got really lucky, like we started on GCP and we started on GCP because tur um, Shopify ran on GCP. And so that was the platform I was most available with.Right. Um, and I knew the Canadian team there ‘cause I'd worked with them at Shopify and so it was natural for us to start there. And so when we started building the database, we're like, oh yeah, we have to build a, we really thought we had to build a consensus layer, like have a zookeeper or something to do this.But then we discovered the compare and swap. It's like, oh, we can kick the can. Like we'll just do metadata r json and just, it's fine. It's probably fine. Um, and we just kept kicking the can until we had very, very strong conviction in the idea. Um, and then we kind of just hinged the company on the fact that S3 probably was gonna get this, it started getting really painful in like mid 2024.‘cause we were closing deals with, um, um, notion actually that was running in AWS and we're like, trust us. You, you really want us to run this in GCP? And they're like, no, I don't know about that. Like, we're running everything in AWS and the latency across the cloud were so big and we had so much conviction that we bought like, you know, dark fiber between the AWS regions in, in Oregon, like in the InterExchange and GCP is like, we've never seen a startup like do like, what's going on here?And we're just like, no, we don't wanna do this. We were tuning like TCP windows, like everything to get the latency down ‘cause we had so high conviction in not doing like a, a metadata layer on S3. So those were the three conditions, right? Compare and swap. To do metadata, which wasn't in S3 until late 2024 S3 being consistent, which didn't happen until December, 2020.Uh, 2020. And then NVMe ssd, which didn't end in the cloud until 2017.swyx: I mean, in some ways, like a very big like cloud success story that like you were able to like, uh, put this all together, but also doing things like doing, uh, bind our favor. That that actually is something I've never heard.Simon Hørup Eskildsen: I mean, it's very common when you're a big company, right?You're like connecting your own like data center or whatever. But it's like, it was uniquely just a pain with notion because the, um, the org, like most of the, like if you're buying in Ashburn, Virginia, right? Like US East, the Google, like the GCP and, and AWS data centers are like within a millisecond on, on each other, on the public exchanges.But in Oregon uniquely, the GCP data center sits like a couple hundred kilometers, like east of Portland and the AWS region sits in Portland, but the network exchange they go through is through Seattle. So it's like a full, like 14 milliseconds or something like that. And so anyway, yeah. It's, it's, so we were like, okay, we can't, we have to go through an exchange in Portland.Yeah. Andswyx: you'd rather do this than like run your zookeeper and likeSimon Hørup Eskildsen: Yes. Way rather. It doesn't have state, I don't want state and two systems. Um, and I think all that is just informed by Justine, my co-founder and I had just been on call for so long. And the worst outages are the ones where you have state in multiple places that's not syncing up.So it really came from, from a a, like just a, a very pure source of pain, of just imagining what we would be Okay. Being woken up at 3:00 AM about and having something in zookeeper was not one of them.swyx: You, you're talking to like a notion or something. Do they care or do they just, theySimon Hørup Eskildsen: just, they care about latency.swyx: They latency cost. That's it.Simon Hørup Eskildsen: They just cared about latency. Right. And we just absorbed the cost. We're just like, we have high conviction in this. At some point we can move them to AWS. Right. And so we just, we, we'll buy the fiber, it doesn't matter. Right. Um, and it's like $5,000. Usually when you buy fiber, you buy like multiple lines.And we're like, we can only afford one, but we will just test it that when it goes over the public internet, it's like super smooth. And so we did a lot of, anyway, it's, yeah, it was, that's cool.Alessio: You can imagine talking to the GCP rep and it's like, no, we're gonna buy, because we know we're gonna turn, we're gonna turn from you guys and go to AWS in like six months.But in the meantime we'll do this. It'sSimon Hørup Eskildsen: a, I mean, like they, you know, this workload still runs on GCP for what it's worth. Right? ‘cause it's so, it was just, it was so reliable. So it was never about moving off GCP, it was just about honesty. It was just about giving notion the latency that they deserved.Right. Um, and we didn't want ‘em to have to care about any of this. We also, they were like, oh, egress is gonna be bad. It was like, okay, screw it. Like we're just gonna like vvc, VPC peer with you and AWS we'll eat the cost. Yeah. Whatever needs to be done.Alessio: And what were the actual workloads? Because I think when you think about ai, it's like 14 milliseconds.It's like really doesn't really matter in the scheme of like a model generation.Simon Hørup Eskildsen: Yeah. We were told the latency, right. That we had to beat. Oh, right. So, so we're just looking at the traces. Right. And then sort of like hand draw, like, you know, kind of like looking at the trace and then thinking what are the other extensions of the trace?Right. And there's a lot more to it because it's also when you have, if you have 14 versus seven milliseconds, right. You can fit in another round trip. So we had to tune TCP to try to send as much data in every round trip, prewarm all the connections. And there was, there's a lot of things that compound from having these kinds of round trips, but in the grand scheme it was just like, well, we have to beat the latency of whatever we're up against.swyx: Which is like they, I mean, notion is a database company. They could have done this themselves. They, they do lots of database engineering themselves. How do you even get in the door? Like Yeah, just like talk through that kind of.Simon Hørup Eskildsen: Last time I was in San Francisco, I was talking to one of the engineers actually, who, who was one of our champions, um, at, AT Notion.And they were, they were just trying to make sure that the, you know, per user cost matched the economics that they needed. You know, Uhhuh like, it's like the way I think about, it's like I have to earn a return on whatever the clouds charge me and then my customers have to earn a return on that. And it's like very simple, right?And so there has to be gross margin all the way up and that's how you build the product. And so then our customers have to make the right set of trade off the turbo Puffer makes, and if they're happy with that, that's great.swyx: Do you feel like you're competing with build internally versus buy or buy versus buy?Simon Hørup Eskildsen: Yeah, so, sorry, this was all to build up to your question. So one of the notion engineers told me that they'd sat and probably on a napkin, like drawn out like, why hasn't anyone built this? And then they saw terrible. It was like, well, it literally that. So, and I think AI has also changed the buy versus build equation in terms of, it's not really about can we build it, it's about do we have time to build it?I think they like, I think they felt like, okay, if this is a team that can do that and they, they feel enough like an extension of our team, well then we can go a lot faster, which would be very, very good for them. And I mean, they put us through the, through the test, right? Like we had some very, very long nights to to, to do that POC.And they were really our biggest, our second big customer off the cursor, which also was a lot of late nights. Right.swyx: Yeah. That, I mean, should we go into that story? The, the, the sort of Chris's story, like a lot, um, they credit you a lot for. Working very closely with them. So I just wanna hear, I've heard this, uh, story from Sole's point of view, but like, I'm curious what, what it looks like from your side.Simon Hørup Eskildsen: I actually haven't heard it from Sole's point of view, so maybe you can now cross reference it. The way that I remember it was that, um, the day after we launched, which was just, you know, I'd worked the whole summer on, on the first version. Justine wasn't part of it yet. ‘cause I just, I didn't tell anyone that summer that I was working on this.I was just locked in on building it because it's very easy otherwise to confuse talking about something to actually doing it. And so I was just like, I'm not gonna do that. I'm just gonna do the thing. I launched it and at this point turbo puffer is like a rust binary running on a single eight core machine in a T Marks instance.And me deploying it was like looking at the request log and then like command seeing it or like control seeing it to just like, okay, there's no request. Let's upgrade the binary. Like it was like literally the, the, the, the scrappiest thing. You could imagine it was on purpose because just like at Shopify, we did that all the time.Like, we like move, like we ran things in tux all the time to begin with. Before something had like, at least the inkling of PMF, it was like, okay, is anyone gonna hear about this? Um, and one of the cursor co-founders Arvid reached out and he just, you know, the, the cursor team are like all I-O-I-I-M-O like, um, contenders, right?So they just speak in bullet points and, and facts. It was like this amazing email exchange just of, this is how many QPS we have, this is what we're paying, this is where we're going, blah, blah, blah. And so we're just conversing in bullet points. And I tried to get a call with them a few times, but they were, so, they were like really writing the PMF bowl here, just like late 2023.And one time Swally emails me at like five. What was it like 4:00 AM Pacific time saying like, Hey, are you open for a call now? And I'm on the East coast and I, it was like 7:00 AM I was like, yeah, great, sure, whatever. Um, and we just started talking and something. Then I didn't know anything about sales.It was something that just comp compelled me. I have to go see this team. Like, there's something here. So I, I went to San Francisco and I went to their office and the way that I remember it is that Postgres was down when I showed up at the office. Did SW tell you this? No. Okay. So Postgres was down and so it's like they were distracting with that.And I was trying my best to see if I could, if I could help in any way. Like I knew a little bit about databases back to tuning, auto vacuum. It was like, I think you have to tune out a vacuum. Um, and so we, we talked about that and then, um, that evening just talked about like what would it look like, what would it look like to work with us?And I just said. Look like we're all in, like we will just do what we'll do whatever, whatever you tell us, right? They migrated everything over the next like week or two, and we reduced their cost by 95%, which I think like kind of fixed their per user economics. Um, and it solved a lot of other things. And we were just, Justine, this is also when I asked Justine to come on as my co-founder, she was the best engineer, um, that I ever worked with at Shopify.She lived two blocks away and we were just, okay, we're just gonna get this done. Um, and we did, and so we helped them migrate and we just worked like hell over the next like month or two to make sure that we were never an issue. And that was, that was the cursor story. Yeah.swyx: And, and is code a different workload than normal text?I, I don't know. Is is it just text? Is it the same thing?Simon Hørup Eskildsen: Yeah, so cursor's workload is basically, they, um, they will embed the entire code base, right? So they, they will like chunk it up in whatever they would, they do. They have their own embedding model, um, which they've been public about. Um, and they find that on, on, on their evals.It. There's one of their evals where it's like a 25% improvement on a very particular workload. They have a bunch of blog posts about it. Um, I think it works best on larger code basis, but they've trained their own embedding model to do this. Um, and so you'll see it if you use the cursor agent, it will do searches.And they've also been public around, um, how they've, I think they post trained their model to be very good at semantic search as well. Um, and that's, that's how they use it. And so it's very good at, like, can you find me on the code that's similar to this, or code that does this? And just in, in this queries, they also use GR to supplement it.swyx: Yeah.Simon Hørup Eskildsen: Um, of courseswyx: it's been a big topic of discussion like, is rag dead because gr you know,Simon Hørup Eskildsen: and I mean like, I just, we, we see lots of demand from the coding company to ethicsswyx: search in every part. Yes.Simon Hørup Eskildsen: Uh, we, we, we see demand. And so, I mean, I'm. I like case studies. I don't like, like just doing like thought pieces on this is where it's going.And like trying to be all macroeconomic about ai, that's has turned out to be a giant waste of time because no one can really predict any of this. So I just collect case studies and I mean, cursor has done a great job talking about what they're doing and I hope some of the other coding labs that use Turbo Puffer will do the same.Um, but it does seem to make a difference for particular queries. Um, I mean we can also do text, we can also do RegX, but I should also say that cursors like security posture into Tur Puffer is exceptional, right? They have their own embedding model, which makes it very difficult to reverse engineer. They obfuscate the file paths.They like you. It's very difficult to learn anything about a code base by looking at it. And the other thing they do too is that for their customers, they encrypt it with their encryption keys in turbo puffer's bucket. Um, so it's, it's, it's really, really well designed.swyx: And so this is like extra stuff they did to work with you because you are not part of Cursor.Exactly like, and this is just best practice when working in any database, not just you guys. Okay. Yeah, that makes sense. Yeah. I think for me, like the, the, the learning is kind of like you, like all workloads are hybrid. Like, you know, uh, like you, you want the semantic, you want the text, you want the RegX, you want sql.I dunno. Um, but like, it's silly to like be all in on like one particularly query pattern.Simon Hørup Eskildsen: I think, like I really like the way that, um, um, that swally at cursor talks about it, which is, um, I'm gonna butcher it here. Um, and you know, I'm a, I'm a database scalability person. I'm not a, I, I dunno anything about training models other than, um, what the internet tells me and what.The way he describes is that this is just like cash compute, right? It's like you have a point in time where you're looking at some particular context and focused on some chunk and you say, this is the layer of the neural net at this point in time. That seems fundamentally really useful to do cash compute like that.And, um, how the value of that will change over time. I'm, I'm not sure, but there seems to be a lot of value in that.Alessio: Maybe talk a bit about the evolution of the workload, because even like search, like maybe two years ago it was like one search at the start of like an LLM query to build the context. Now you have a gentech search, however you wanna call it, where like the model is both writing and changing the code and it's searching it again later.Yeah. What are maybe some of the new types of workloads or like changes you've had to make to your architecture for it?Simon Hørup Eskildsen: I think you're right. When I think of rag, I think of, Hey, there's an 8,000 token, uh, context window and you better make it count. Um, and search was a way to do that now. Everything is moving towards the, just let the agent do its thing.Right? And so back to the thing before, right? The LLM is very good at reasoning with the data, and so we're just the tool call, right? And that's increasingly what we see our customers doing. Um, what we're seeing more demand from, from our customers now is to do a lot of concurrency, right? Like Notion does a ridiculous amount of queries in every round trip just because they can't.And I'm also now, when I use the cursor agent, I also see them doing more concurrency than I've ever seen before. So a bit similar to how we designed a database to drive as much concurrency in every round trip as possible. That's also what the agents are doing. So that's new. It means just an enormous amount of queries all at once to the dataset while it's warm in as few turns as possible.swyx: Can I clarify one thing on that?Simon Hørup Eskildsen: Yes.swyx: Is it, are they batching multiple users or one user is driving multiple,Simon Hørup Eskildsen: one user driving multiple, one agent driving.swyx: It's parallel searching a bunch of things.Simon Hørup Eskildsen: Exactly.swyx: Yeah. Yeah, exactly. So yeah, the clinician also did, did this for the fast context thing, like eight parallel at once.Simon Hørup Eskildsen: Yes.swyx: And, and like an interesting problem is, well, how do you make sure you have enough diversity so you're not making the the same request eight times?Simon Hørup Eskildsen: And I think like that's probably also where the hybrid comes in, where. That's another way to diversify. It's a completely different way to, to do the search.That's a big change, right? So before it was really just like one call and then, you know, the LLM took however many seconds to return, but now we just see an enormous amount of queries. So the, um, we just see more queries. So we've like tried to reduce query, we've reduced query pricing. Um, this is probably the first time actually I'm saying that, but the query pricing is being reduced, like five x.Um, and we'll probably try to reduce it even more to accommodate some of these workloads of just doing very large amounts of queries. Um, that's one thing that's changed. I think the right, the right ratio is still very high, right? Like there's still a, an enormous amount of rights per read, but we're starting probably to see that change if people really lean into this pattern.Alessio: Can we talk a little bit about the pricing? I'm curious, uh, because traditionally a database would charge on storage, but now you have the token generation that is so expensive, where like the actual. Value of like a good search query is like much higher because they're like saving inference time down the line.How do you structure that as like, what are people receptive to on the other side too?Simon Hørup Eskildsen: Yeah. I, the, the turbo puffer pricing in the beginning was just very simple. The pricing on these on for search engines before Turbo Puffer was very server full, right? It was like, here's the vm, here's the per hour cost, right?Great. And I just sat down with like a piece of paper and said like, if Turbo Puffer was like really good, this is probably what it would cost with a little bit of margin. And that was the first pricing of Turbo Puffer. And I just like sat down and I was like, okay, like this is like probably the storage amp, but whenever on a piece of paper I, it was vibe pricing.It was very vibe price, and I got it wrong. Oh. Um, well I didn't get it wrong, but like Turbo Puffer wasn't at the first principle pricing, right? So when Cursor came on Turbo Puffer, it was like. Like, I didn't know any VCs. I didn't know, like I was just like, I don't know, I didn't know anything about raising money or anything like that.I just saw that my GCP bill was, was high, was a lot higher than the cursor bill. So Justine and I was just like, well, we have to optimize it. Um, and I mean, to the chagrin now of, of it, of, of the VCs, it now means that we're profitable because we've had so much pricing pressure in the beginning. Because it was running on my credit card and Justine and I had spent like, like tens of thousands of dollars on like compute bills and like spinning off the company and like very like, like bad Canadian lawyers and like things like to like get all of this done because we just like, we didn't know.Right. If you're like steeped in San Francisco, you're just like, you just know. Okay. Like you go out, raise a pre-seed round. I, I never heard a word pre-seed at this point in time.swyx: When you had Cursor, you had Notion you, you had no funding.Simon Hørup Eskildsen: Um, with Cursor we had no funding. Yeah. Um, by the time we had Notion Locke was, Locke was here.Yeah. So it was really just, we vibe priced it 100% from first Principles, but it wasn't, it, it was not performing at first principles, so we just did everything we could to optimize it in the beginning for that, so that at least we could have like a 5% margin or something. So I wasn't freaking out because Cursor's bill was also going like this as they were growing.And so my liability and my credit limit was like actively like calling my bank. It was like, I need a bigger credit. Like it was, yeah. Anyway, that was the beginning. Yeah. But the pricing was, yeah, like storage rights and query. Right. And the, the pricing we have today is basically just that pricing with duct tape and spit to try to approach like, you know, like a, as a margin on the physical underlying hardware.And we're doing this year, you're gonna see more and more pricing changes from us. Yeah.swyx: And like is how much does stuff like VVC peering matter because you're working in AWS land where egress is charged and all that, you know.Simon Hørup Eskildsen: We probably don't like, we have like an enterprise plan that just has like a base fee because we haven't had time to figure out SKU pricing for all of this.Um, but I mean, yeah, you can run turbo puffer either in SaaS, right? That's what Cursor does. You can run it in a single tenant cluster. So it's just you. That's what Notion does. And then you can run it in, in, in BYOC where everything is inside the customer's VPC, that's what an for example, philanthropic does.swyx: What I'm hearing is that this is probably the best CRO job for somebody who can come in and,Simon Hørup Eskildsen: I mean,swyx: help you with this.Simon Hørup Eskildsen: Um, like Turbo Puffer hired, like, I don't know what, what number this was, but we had a full-time CFO as like the 12th hire or something at Turbo Puffer, um, I think I hear are a lot of comp.I don't know how they do it. Like they have a hundred employees and not a CFO. It's like having a CFO is like a runningswyx: business man. Like, you know,Simon Hørup Eskildsen: it's so good. Yeah, like money Mike, like he just, you know, just handles the money and a lot of the business stuff and so he came in and just hopped with a lot of the operational side of the business.So like C-O-O-C-F-O, like somewhere in between.swyx: Just as quick mention of Lucky, just ‘cause I'm curious, I've met Lock and like, he's obviously a very good investor and now on physical intelligence, um, I call it generalist super angel, right? He invests in everything. Um, and I always wonder like, you know, is there something appealing about focusing on developer tooling, focusing on databases, going like, I've invested for 10 years in databases versus being like a lock where he can maybe like connect you to all the customers that you need.Simon Hørup Eskildsen: This is an excellent question. No, no one's asked me this. Um, why lockey? Because. There was a couple of people that we were talking to at the time and when we were raising, we were almost a little, we were like a bit distressed because one of our, one of our peers had just launched something that was very similar to Turbo Puffer.And someone just gave me the advice at the time of just choose the person where you just feel like you can just pick up the phone and not prepare anything. And just be completely honest, and I don't think I've said this publicly before, but I just called Lockey and was like local Lockie. Like if this doesn't have PMF by the end of the year, like we'll just like return all the money to you.But it's just like, I don't really, we, Justine and I don't wanna work on this unless it's really working. So we want to give it the best shot this year and like we're really gonna go for it. We're gonna hire a bunch of people and we're just gonna be honest with everyone. Like when I don't know how to play a game, I just play with open cards and.Lockey was the only person that didn't, that didn't freak out. He was like, I've never heard anyone say that before. As I said, I didn't even know what a seed or pre-seed round was like before, probably even at this time. So I was just like very honest with him. And I asked him like, Lockie, have you ever have, have you ever invested in database company?He was just like, no. And at the time I was like, am I dumb? Like, but I think there was something that just like really drew me to Lockie. He is so authentic, so honest, like, and there was something just like, I just felt like I could just play like, just say everything openly. And that was, that was, I think that that was like a perfect match at the time, and, and, and honestly still is.He was just like, okay, that's great. This is like the most honest, ridiculous thing I've ever heard anyone say to me. But like that, like that, whyswyx: is this ridiculous? Say competitor launch, this may not work out. It wasSimon Hørup Eskildsen: more just like. If this doesn't work out, I'm gonna close up shop by the end of the mo the year, right?Like it was, I don't know, maybe it's common. I, I don't know. He told me it was uncommon. I don't know. Um, that's why we chose him and he'd been phenomenal. The other people were talking at the, at the time were database experts. Like they, you know, knew a lot about databases and Locke didn't, this turned out to be a phenomenal asset.Right. I like Justine and I know a lot about databases. The people that we hire know a lot about databases. What we needed was just someone who didn't know a lot about databases, didn't pretend to know a lot about databases, and just wanted to help us with candidates and customers. And he did. Yeah. And I have a list, right, of the investors that I have a relationship with, and Lockey has just performed excellent in the number of sub bullets of what we can attribute back to him.Just absolutely incredible. And when people talk about like no ego and just the best thing for the founder, I like, I don't think that anyone, like even my lawyer is like, yeah, Lockey is like the most friendly person you will find.swyx: Okay. This is my most glow recommendation I've ever heard.Alessio: He deserves it.He's very special.swyx: Yeah. Yeah. Yeah. Okay. Amazing.Alessio: Since you mentioned candidates, maybe we can talk about team building, you know, like, especially in sf, it feels like it's just easier to start a company than to join a company. Uh, I'm curious your experience, especially not being n SF full-time and doing something that is maybe, you know, a very low level of detail and technical detail.Simon Hørup Eskildsen: Yeah. So joining versus starting, I never thought that I would be a founder. I would start with it, like Turbo Puffer started as a blog post, and then it became a project and then sort of almost accidentally became a company. And now it feels like it's, it's like becoming a bigger company. That was never the intention.The intentions were very pure. It's just like, why hasn't anyone done this? And it's like, I wanna be the, like, I wanna be the first person to do it. I think some founders have this, like, I could never work for anyone else. I, I really don't feel that way. Like, it's just like, I wanna see this happen. And I wanna see it happen with some people that I really enjoy working with and I wanna have fun doing it and this, this, this has all felt very natural on that, on that sense.So it was never a like join versus versus versus found. It was just dis found me at the right moment.Alessio: Well I think there's an argument for, you should have joined Cursor, right? So I'm curious like how you evaluate it. Okay, I should actually go raise money and make this a company versus like, this is like a company that is like growing like crazy.It's like an interesting technical problem. I should just build it within Cursor and then they don't have to encrypt all this stuff. They don't have to obfuscate things. Like was that on your mind at all orSimon Hørup Eskildsen: before taking the, the small check from Lockie, I did have like a hard like look at myself in the mirror of like, okay, do I really want to do this?And because if I take the money, I really have to do it right. And so the way I almost think about it's like you kind of need to ha like you kind of need to be like fucked up enough to want to go all the way. And that was the conversation where I was like, okay, this is gonna be part of my life's journey to build this company and do it in the best way that I possibly can't.Because if I ask people to join me, ask people to get on the cap table, then I have an ultimate responsibility to give it everything. And I don't, I think some people, it doesn't occur to me that everyone takes it that seriously. And maybe I take it too seriously, I don't know. But that was like a very intentional moment.And so then it was very clear like, okay, I'm gonna do this and I'm gonna give it everything.Alessio: A lot of people don't take it this seriously. But,swyx: uh, let's talk about, you have this concept of the P 99 engineer. Uh, people are 10 x saying, everyone's saying, you know, uh, maybe engineers are out of a job. I don't know.But you definitely see a P 99 engineer, and I just want you to talk about it.Simon Hørup Eskildsen: Yeah, so the P 99 engineer was just a term that we started using internally to talk about candidates and talk about how we wanted to build the company. And you know, like everyone else is, like we want a talent dense company.And I think that's almost become trite at this point. What I credit the cursor founders a lot with is that they just arrived there from first principles of like, we just need a talent dense, um, talent dense team. And I think I've seen some teams that weren't talent dense and like seemed a counterfactual run, which if you've run in been in a large company, you will just see that like it's just logically will happen at a large company.Um, and so that was super important to me and Justine and it's very difficult to maintain. And so we just needed, we needed wording for it. And so I have a document called Traits of the P 99 Engineer, and it's a bullet point list. And I look at that list after every single interview that I do, and in every single recap that we do and every recap we end with.End with, um, some version of I'm gonna reject this candidate completely regardless of what the discourse was, because I wanna see people fight for this person because the default should not be, we're gonna hire this person. The default should be, we're definitely not hiring this person. And you know, if everyone was like, ah, maybe throw a punch, then this is not the right.swyx: Do, do you operate, like if there's one cha there must have at least one champion who's like, yes, I will put my career on, on, on the line for this. You know,Simon Hørup Eskildsen: I think career on the line,swyx: maybe a chair, butSimon Hørup Eskildsen: yeah. You know, like, um, I would say so someone needs to like, have both fists up and be like, I'd fight.Right? Yeah. Yeah. And if one person said, then, okay, let's do it. Right?swyx: Yeah.Simon Hørup Eskildsen: Um. It doesn't have to be absolutely everyone. Right? And like the interviews are always the sign that you're checking for different attributes. And if someone is like knocking it outta the park in every single attribute, that's, that's fairly rare.Um, but that's really important. And so the traits of the P 99 engineer, there's lots of them. There's also the traits of the p like triple nine engineer and the quadruple nine engineer. This is like, it's a long list.swyx: Okay.Simon Hørup Eskildsen: Um, I'll give you some samples, right. Of what we, what we look for. I think that the P 99 engineer has some history of having bent, like their trajectory or something to their will.Right? Some moment where it was just, they just, you know, made the computer do what it needed to do. There's something like that, and it will, it will occur to have them at some point in their career. And, uh. Hopefully multiple times. Right.swyx: Gimme an example of one of your engineers that like,Simon Hørup Eskildsen: I'll give an eng.Uh, so we, we, we launched this thing called A and NV three. Um, we could, we're also, we're working on V four and V five right now, but a and NV three can search a hundred billion vectors with a P 50 of around 40 milliseconds and a p 99 of 200 milliseconds. Um, maybe other people have done this, I'm sure Google and others have done this, but, uh, we haven't seen anyone, um, at least not in like a public consumable SaaS that can do this.And that was an engineer, the chief architect of Turbo Puffer, Nathan, um, who more or less just bent this, the software was not capable of this and he just made it capable for a very particular workload in like a, you know, six to eight week period with the help of a lot of the team. Right. It's been, been, there's numerous of examples of that, like at, at turbo puff, but that's like really bending the software and X 86 to your will.It was incredible to watch. Um. You wanna see some moments like that?swyx: Isn't that triple nine?Simon Hørup Eskildsen: Um, I think Nathan, what's calledAlessio: group nine, that was only nine. I feel like this is too high forSimon Hørup Eskildsen: Nathan. Nathan is, uh, Nathan is like, yeah, there's a lot of nines. Okay. After that p So I think that's one trait. I think another trait is that, uh, the P 99 spends a lot of time looking at maps.Generally it's their preferred ux. They just love looking at maps. You ever seen someone who just like, sits on their phone and just like, scrolls around on a map? Or did you not look at maps A lot? You guys don't look atswyx: maps? I guess I'm not feeling there. I don't know, butSimon Hørup Eskildsen: you just dis What about trains?Do you like trains?swyx: Uh, I mean they, not enough. Okay. This is just like weapon nice. Autism is what I call it. Like, like,Simon Hørup Eskildsen: um, I love looking at maps, like, it's like my preferred UX and just like I, you know, I likeswyx: lotsAlessio: of, of like random places, soswyx: like,youswyx: know.Alessio: Yes. Okay. There you go. So instead of like random places, like how do you explore the maps?Simon Hørup Eskildsen: No, it's, it's just a joke.swyx: It's autism laugh. It's like you are just obsessed by something and you like studying a thing.Simon Hørup Eskildsen: The origin of this was that at some point I read an interview with some IOI gold medalistswyx: Uhhuh,Simon Hørup Eskildsen: and it's like, what do you do in your spare time? I was just like, I like looking at maps.I was like, I feel so seen. Like, I just like love, like swirling out. I was like, oh, Canada is so big. Where's Baffin Island? I don't know. I love it. Yeah. Um, anyway, so the traits of P 99, P 99 is obsessive, right? Like, there's just like, you'll, you'll find traits of that we do an interview at, at, at, at turbo puffer or like multiple interviews that just try to screen for some of these things.Um, so. There's lots of others, but these are the kinds of traits that we look for.swyx: I'll tell you, uh, some people listen for like some of my dere stuff. Uh, I do think about derel as maps. Um, you draw a map for people, uh, maps show you the, uh, what is commonly agreed to be the geographical features of what a boundary is.And it shows also shows you what is not doing. And I, I think a lot of like developer tools, companies try to tell you they can do everything, but like, let's, let's be real. Like you, your, your three landmarks are here, everyone comes here, then here, then here, and you draw a map and, and then you draw a journey through the map.And like that. To me, that's what developer relations looks like. So I do think about things that way.Simon Hørup Eskildsen: I think the P 99 thinks in offs, right? The P 99 is very clear about, you know, hey, turbo puffer, you can't run a high transaction workload on turbo puffer, right? It's like the right latency is a hundred milliseconds.That's a clear trade off. I think the P 99 is very good at articulating the trade offs in every decision. Um. Which is exactly what the map is in your case, right?swyx: Uh, yeah, yeah. My, my, my world. My world.Alessio: How, how do you reconcile some of these things when you're saying you bend the will the computer versus like the trade

This episode features Drew Russell, Identity Resilience Platform Owner at Rubrik. Jim McDonald and Jeff Steadman explore the intersection of backup, recovery, and identity security. Drew explains how Rubrik evolved from data backup into a cyber resilience platform with identity as a core pillar. Topics include recovering Active Directory, Okta, and Entra ID after ransomware, Rubrik's "bunker in a box" appliance for immutable air-gapped recovery, proactive posture management, CrowdStrike and Defender integrations, and where AI and non-human identities fit into Rubrik's roadmap. The episode wraps with measuring success for a product you hope to never use, and a detour into watch collecting.This episode was made possible by the support of Rubrik. Learn more at rubrik.com/idacConnect with Drew: https://www.linkedin.com/in/drew-russell-3762411b/Learn more about Rubrik: https://www.rubrik.com/idacConnect with us on LinkedIn:Jim McDonald: https://www.linkedin.com/in/jimmcdonaldpmp/Jeff Steadman: https://www.linkedin.com/in/jeffsteadman/Visit the show on the web at idacpodcast.comTIMESTAMPS00:00:00 - Welcome and Introduction00:01:19 - Introducing Drew Russell00:01:36 - How Drew Got Into Identity00:02:43 - What Is Rubrik and What Sets It Apart00:03:38 - From Backup to Cyber Resilience00:05:31 - Where Rubrik Fits in the IAM Landscape00:07:08 - Rubrik's Scale: Clients and Growth00:07:51 - Primary Use Cases: Post-Incident Recovery and AD00:09:09 - Kicking Out Compromised Accounts and ADR00:10:11 - Proactive Threat Detection and Mandiant Integration00:11:28 - Scanning Backups to Find the Clean Recovery Point00:12:14 - The Bunker in a Box Explained00:13:18 - Posture Management and Upstream Tool Integration00:14:19 - AI Agent Swarms and the Future Attack Surface00:15:37 - The Taiwan Bank Case Study: Six Weeks to Rebuild AD00:17:16 - The State of Nevada Incident: $400K and 30 Days00:17:56 - What Recovery Covers: AD, Okta, and Entra ID00:19:26 - Post-Restore Change Management and Whitelisting00:20:08 - How Long Should You Store Backups?00:21:19 - Indexing Identity for Intelligent Recovery Points00:22:29 - Excluding Malicious Actions During Restore00:24:41 - Zero Trust for Rubrik's Own Backups00:26:21 - No Windows, No Virtualization Architecture00:27:49 - Proactive Posture Management00:29:00 - CrowdStrike and Defender Real-Time Integration00:30:48 - Why Tabletop Exercises Often Fall Short00:31:53 - AI Roadmap and Non-Human Identities00:34:22 - The Three Pillars: Data, Identity, and AI00:35:29 - Deployment: SaaS vs. On-Prem00:38:37 - Appliance Sizing and Redundancy00:42:23 - Measuring Success for a Product You Hope to Never Use00:43:46 - The Ludacris Rubrik Commercial00:45:31 - Watch Collecting and the Omega Speedmaster00:53:39 - Drew's Closing WordsKEYWORDSIdentity at the Center, IDAC, Jeff Steadman, Jim McDonald, Rubrik, Drew Russell, identity resilience, cyber resilience, Active Directory recovery, AD backup, Okta recovery, Entra ID recovery, identity backup, ITDR, ISPM, non-human identity, NHI, agentic AI, ransomware recovery, bunker in a box, immutable backup, CrowdStrike integration, Microsoft Defender integration, Mandiant integration, identity disaster recovery, ADR, zero trust, tabletop exercises, posture management, IAM, identity security podcast, cybersecurity podcast

Recapping The Dead Files “Tangled” (Season 10, Episode 2) which aired June 22, 2013We kick off our Ohio Three-Fer with shadow snakes, basement rage, and a 19th-century commune that absolutely did not get good Yelp reviews from the town. This house isn't just haunted — it's rooted.Black vine-like tendrils creep through the land and into the living, and we unpack what happens when depression, history, and paranormal energy all tangle together.We talk Free Love backlash, Victorian motherhood (twelve children??), morphine, menopause vs. malevolent spirits, and whether tar water is a reasonable Amazon purchase or a sign it's time to move.It's eerie. It's layered. It's feral.Ponder: If negative energy can embed in land… can it spread like an infection?Witness: Steve calling the commune “woo woo crap” while literally working on a ghost show.Weigh-In: If Amy told you to spray tar water around your entire house — would you stay… or would you move?So, grab your tar water (don't miss a spot!), and join us where… The Activity Continues. Content Warning: We didn't find anything we thought deserved a content warning, but we swear.  Chapter Markers00:00:00 Intro00:05:55 A Word on AI00:08:45 Testing a New Format00:09:41 Side Quest: When You Wash Your Hair00:11:51 Spirit Breakdown00:15:16 Diggin' Tru00:16:38 It Takes a Village00:28:57 Parenting is Hard00:31:10 The Vines00:33:47 The Reveal00:34:33 Clients' Options00:39:34 Steve Getting Dramatic00:40:12 Unstable Ghosts, and Tables00:43:25 Paranormal or Menopause00:49:14 Additional Research Notes00:58:56 Disclaimer/Credits Episode links:Hudson Tuttle: https://en.wikipedia.org/wiki/Hudson_TuttleThe Museum of Talking Boards: https://www.museumoftalkingboards.com/tuttle.htmlCharles Latcha's Suicide Manifesto: https://evermore.imagedjinn.com/blg/9883/suicide-of-a-free-love-at-berlin-heights-july-16-1858/Tar Water on Amazon: https://amzn.to/4qEDcFoOur T-Shirts: https://www.zazzle.com/woodpecker_headache_remedy_t_shirt-256058499501832692Recommend a Dead Files episode for us to recap: https://www.theactivitycontinues.com/recommend-your-favorite-dead-files-epsiode/The Dead Files Official Podcast: https://pod.link/1642377102Amazon links could generate a small commission to us at no cost to you.  The Activity Continues is a paranormal podcast where soul friends Amy and Megan chat about true crime, ghost stories, hauntings, dreams, and other paranormal stuff including the TV show, The Dead Files. Our recaps are full of recurring jokes about recurring tropes.This episode was recorded on February 18, 2026, and released on March 5, 2026. Disclaimer:This podcast is in no way affiliated with Warner Brothers, HBOMax, the Travel Channel, Painless TV, or the TV show The Dead Files or any of its cast or crew. We're just fans who love the show and want to build a community of like-minded people who would enjoy hanging out and discussing the episodes and similar content. Credits:Hosted by: Amy Lotsberg and Megan SimmonsProduction, Artwork, and Editing: Amy Lotsberg at Collected Sounds Media, LLC. https://www.collectedsounds.com/Theme song. “Ghost Story” and segment music by CannelleBackground music: “Beyond the Stars” by Chris Collins Engage!Our website, https://www.theactivitycontinues.com/ Leave us a Voicemail: https://www.theactivitycontinues.com/voicemail/ (might be read on the show)Newsletter sign-up: https://www.theactivitycontinues.com/newsletter          Join us on Patreon: https://www.patreon.com/theactivitycontinuesWe're on (almost) all the socials too @theactivitycontinues SEND US YOUR PARANORMAL STORIES!Email: theactivitycontinues@gmail.com and maybe it will be read on the show!Voicemail: https://www.theactivitycontinues.com/voicemail/ to leave a message and maybe it will be played on the show! BE OUR GUEST!Are you a The Dead Files client, or a paranormal/spiritual professional, and are interested in being interviewed on our show? Let us know by filling out our guest form:https://www.theactivitycontinues.com/guests/intake/ Affiliates/SponsorsPlease see our Store page for all the links for all our current affiliates. https://www.theactivitycontinues.com/store/ Thank you for listening, take care of yourselves. We'll see you next time!If you want to hear us early and ad-free EVERY week, become a Patron, join our Ghosty Fam and get bonus exclusive episodes! https://www.patreon.com/theactivitycontinuesSupport this podcast at — https://redcircle.com/the-activity-continues/donationsAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

Depending on whom you ask, AI is either the best or worst thing that can happen to the next generation. The arguments come from educators, venture capitalists, op-ed writers, and anxious parents—but rarely from the young people in question. On this episode of AI & I, Dan Shipper sat down with one: Alex Mathew, a 17-year-old high-school senior at Alpha High School in Austin, Texas. Alpha School, a rapidly expanding network of kindergarten through grade 12 private schools, is not without controversy. Inside Alpha High School, there are no traditional teachers, all academic content is delivered through an AI-powered platform, and the adults in the classroom, known as “guides,” focus solely on supporting the students emotionally and keeping them motivated to learn. The students have two- to three-hour learning blocks every morning and spend the rest of the day going deep on a project in an area they care about, spanning art, sport, life skills, and entrepreneurship.Mathew's project is a startup called Berry, built around an AI stuffed animal designed to help teenagers with their mental health. His vision is for teens to talk to the plushie for five to 10 minutes a day and, in the process, learn to recognize and cope with their problems in the right way. In this episode, Dan and Mathew talk about what a day at Alpha High looks like, what keeps students from cheating when AI is everywhere, and how Generation Z—people born between 1997–2012—really feels about college, social media, and books. If you found this episode interesting, please like, subscribe, comment, and share! Want even more?Sign up for Every to unlock our ultimate guide to prompting ChatGPT here: https://every.ck.page/ultimate-guide-to-prompting-chatgpt. It's usually only for paying subscribers, but you can get it here for free.To hear more from Dan Shipper:Subscribe to Every: https://every.to/subscribe Follow him on X: https://twitter.com/danshipper In a world of generic AI, don't sound like everyone else. With Grammarly, you never will. Download Grammarly for free at Grammarly.com.Intent is what comes after your IDE. Try it yourself: augmentcode.com/intentHead to granola.ai/every to get 3 months freeTimestamps: 00:00:00 – Start 00:01:30 – Introduction00:04:08 – A typical day inside Alpha High School00:06:54 – Why Alpha replaced teachers with “guides” focused on motivating students00:12:09 – Why Mathew doesn't use AI to cheat, even though he could00:19:51 – Do ambitious teenagers care about going to college?00:25:12 – Mathew's take on how Gen Z thinks about AI00:27:52 – How Mathew thinks about the effects of social media00:31:29 – Gen Z's relationship with books and reading00:38:57 – Mathew ranks ChatGPT, Claude, Gemini, and Grok00:47:12 – Why Mathew is building Berry, an AI stuffed animal for teen mental healthLinks to resources mentioned in the episode:Alex Mathew: Alex Mathew (@alxmthew)More about Berry: https://berryplush.com/, Berry (@berryaiplushies)

WEBINAR LINK:https://shawnmoore.clickfunnels.com/optiniyvvg89sWant to learn more about Vodyssey or start your STR journey. Book a call here:https://meetings.hubspot.com/vodysseystrategysession/booknow?utm_source=vodysseycom&uuid=80fb7859-b8f4-40d1-a31d-15a5caa687b7FOLLOW US:https://www.facebook.com/share/g/16XJMvMbVo/https://www.instagram.com/vodysseyshawnmoorehttps://www.facebook.com/vodysseyshawnmoore/https://www.linkedin.com/company/str-financial-freedomhttps://www.tiktok.com/@vodysseyshawnmooreCONTACT US:support@vodyssey.comSOURCES:1) https://techcrunch.com/2026/02/13/airbnb-plans-to-bake-in-ai-features-for-search-discovery-and-support/2) https://mashable.com/article/airbnb-testing-ai-powered-search-feature3) https://nationaltoday.com/us/ma/boston/news/2026/02/14/airbnb-plans-to-integrate-ai-across-search-discovery-and-support/Chapters:00:00:00 Intro00:02:54 The Impact of AI on Property Management00:05:48 Curating Unique Experiences for Guests00:09:01 The Role of Property Managers in an AI World00:11:50 Strategies for Property Managers00:15:11 Pricing Strategies and AI Integration00:18:06 The Future of Customer Experience with AI00:21:08 Hypothetical Scenarios: Self-Management and Personal Touch

Dr. Jackie Cheung is an Associate Professor at McGill University where he co-directs the Reasoning and Learning Lab. He is also an Associate Scientific Director at Mila-Quebec Artificial Intelligence Institute. He and his team are developing computational models to improve the reliability, pragmatics, and evaluation of large language models to ensure they are contextually appropriate and factually grounded.Jackie was worked as a consultant researcher with Microsoft Research and before his current appointments, he earned his PhD and MSc in Computer Science from the University of Toronto, focusing on computational linguistics, and his BSc from the University of British Columbia.00:00:00 Highlight & Introduction00:02:04 Entrypoint in AI & NLP00:04:47 Academia vs. Industry: Career choices00:09:48 Language Revitalization using AI00:12:24 Addressing Biases & Data sovereignty in language revitalization 00:15:49 Evaluating LLMs as Judges00:17:14 Validity and reliability in LLM evaluation 00:25:11 Evidence-centered benchmark design (ECBD) framework00:30:38 Gaps in LLM benchmarks and meaning of "general purpose" AI00:35:24 General purpose intelligence vs reasoning00:40:16 Safety as an undefined bundle in LLMs00:51:45 Stochastic chameleons: how LLMs generalize and hallucinate 01:03:02 Potential & Biases of agentic frameworks for research01:05:52 Evaluating LLMs for summarization01:11:43 Scaling large language models01:16:33 Advice to beginners entering AI in 202601:20:33 Pitfalls to avoid in AI research & development More about Jackie & his research: https://www.cs.mcgill.ca/~jcheung/About the Host:Jay is a Machine Learning Engineer III at PathAI working on improving AI for medical diagnosis and prognosis. Linkedin: https://www.linkedin.com/in/shahjay22/Twitter: https://twitter.com/jaygshah22Homepage: https://jaygshah.github.io/ for any queries.Stay tuned for upcoming webinars!***Disclaimer: The information in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.***

A CMO Confidential Interview with Pete Imwalla, former CEO of RPA and 4A's board member. Pete shares his take on how many tech changes resulted in additional agency headcount, how AI is rapidly reversing that trend, and why many agency valuations have dropped significantly over the last 5 years. Key topics include: why brand building is like infrastructure; how Publicis is bucking the trend; how to think about "in-housing;" and why Paul Roetzer's CMO 2023 CMO Confidential show was prescient. Tune in to hear about the "2nd mover advantage" and why he hates the concept of "future proofing." Agency economics are getting rewritten in the age of AI. Mike Linton sits down with Pete Imwalle 32-year RPA veteran and former CEO to dissect what's changing—and what leaders should do about it. They cover the shift from reach to relevance, why FTE-based fees are misaligned in an AI world, how to separate automation from actual advantage, and where in-housing does and doesn't work. Along the way: the sustained business impact of the Farmers “We know a thing or two…” campaign, the rise of agentic workflows, and why “future-proofing” starts with culture, not clairvoyance. Chapters00:00:00 – Cold open + show setup00:00:22 – Mike's intro, Pete's background, and today's topic00:01:18 – Farmers campaign wins Sustained Effie) and effectiveness creativity00:02:18 – 30 years of change: from Prodigy/AOL/CompuServe to Netscape and the open web00:03:24 – Google + broadband: when digital finally changed consumer behavior00:04:33 – Mobile's second wave and the trap of “mobile-first/AI-first” strategies00:06:01 – How agencies adapted: leadership, curiosity, and tolerance for experimentation00:07:42 – Investing ahead of revenue: offense + defense in capability building00:08:22 – Reach fragmentation: from “40% on Cheers” to only the Super Bowl00:09:18 – The real squeeze: boards treating advertising as expense, not investment00:10:13 – Short-termism, PE/VC incentives, and brand vs. performance00:12:21 – “Adapt or die”: AI as an extinction event? (hat tip: Paul Roetzer)00:13:28 – Agentic workflows: shrinking grunt work (esp. media & strategy ops)00:16:00 – Client asks: “give me savings, don't risk my IP”00:16:36 – Why FTE pricing disincentivizes efficiency; pay for outcomes instead00:17:51 – Three futures: AI-native, AI-emergent, or obsolete00:21:39 – Holding-company moves; why Publicis is outpacing peers00:22:00 – Agency valuations: ~40% decline over five years; second-mover advantage in AI00:26:37 – In-housing: when it works, when it backfires, and true cost to own00:28:48 – Build vs. buy: amortization, maintenance, and staying current00:30:16 – The Geico lesson: investing through the curve until returns flatten00:31:22 – What to test by EOY 2026: culture, change management, and low-hanging automation00:34:02 – Ditch “future-proofing”; hire for curiosity and adaptability00:35:35 – Wrap + where to find more CMO ConfidentialTagsCMO Confidential,Mike Linton,Pete Imwalle,RPA,agency economics,advertising,marketing leadership,AI in marketing,agentic workflows,media planning,marketing strategy,brand vs performance,FTE pricing,procurement,in-housing,holding companies,Publicis,Omnicom,Super Bowl ads,Effie Awards,Farmers Insurance campaign,Geico case study,change management,digital transformation,marketing AI,MarTech,measurement,short term vs long term,CMO,CEO,CFO,board governanceSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Andrew Beck, MD, PhD is the Co-founder and CEO of PathAI, where he and his team are developing AI tools to improve the precision of pathology and the efficacy of drug development for diagnosis of cancer and also many other complex diseases.Before founding PathAI, Andrew was an Associate Professor at Harvard Medical School, where his research focused on the application of machine learning to cancer pathology. He earned his MD from Brown University and his PhD in Biomedical Informatics from Stanford University, where he pioneered some of the first computational models used to predict patient outcomes in oncology.Time stamps of the conversation:00:00:00 Highlights00:01:28 Introduction00:02:18 Entrypoint in AI00:07:02 Background in Medicine and Bioinformatics 00:10:00 Leap from academia to entrepreneurship00:16:20 Translating AI developments to Pathology00:21:15 Specialist vs Generalist AI models in medicine00:24:15 What sets PathAI apart?00:26:32 AI adoption medicine00:34:25 Usage of AI tools in clinical workflows, example MASH00:40:10 AI in Dermatopathology00:42:15 AI for biomarker discovery00:47:05 Will AI models replace pathologists?00:52:28 Avoiding over-reliance on AI00:57:40 Is AI living unto the hype?01:01:00 Challenges in clinical trials 01:05:12 AI reaching patients directly01:09:50 Working at intersection of AI & Healthcare01:15:30 Pitfalls to learn fromMore about PathAI: https://www.pathai.com/and Andy: https://www.pathai.com/about-us/andy-beckAbout the Host:Jay is a Machine Learning Engineer III at PathAI working on improving AI for medical diagnosis and prognosis. Linkedin: https://www.linkedin.com/in/shahjay22/Twitter: https://twitter.com/jaygshah22Homepage: https://jaygshah.github.io/ for any queries.Stay tuned for upcoming webinars!***Disclaimer: The information in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.***

Jeff Steadman is joined by RSM colleagues Rich Servillas and Charles John to explore the critical intersection of identity access management, operational resilience, and disaster recovery. Rich, a director from the cyber response group, shares insights from the front lines of ransomware and cloud intrusions, while Chuck, director of operational resilience, discusses the importance of business continuity planning. The conversation covers the true impact of security incidents on brand reputation and operations, the necessity of out-of-band communication, and why identity is often the first thing challenged and the last thing trusted during a crisis. The guests also provide practical advice for IAM professionals on reducing blast radius through standing privilege reduction and robust logging.Connect with Rich: https://www.linkedin.com/in/richard-servillas-041a0551/Connect with Chuck: https://www.linkedin.com/in/chuckjohn/Connect with us on LinkedIn:Jim McDonald: https://www.linkedin.com/in/jimmcdonaldpmp/Jeff Steadman: https://www.linkedin.com/in/jeffsteadman/Visit the show on the web at http://idacpodcast.comTimestamps:00:00:00 - Introduction and 2026 conference outlook00:01:44 - Introducing guests Rich and Chuck from RSM00:03:56 - Defining operational resilience and business continuity00:06:22 - When and how to start the planning process00:09:55 - Chuck's background in public health and emergency management00:12:44 - The broad impact of incidents on brand and operations00:16:45 - Key elements every recovery plan must include00:19:14 - Defining incident severity and matrixes00:21:52 - Identity as the new perimeter and its operational dependencies00:24:57 - Why hackers log in rather than break in00:26:46 - The first hours of a cyber incident response00:29:35 - Current threat trends and the role of AI00:31:29 - Updating plans through post-action debriefs00:34:31 - Cyber insurance gaps and contractual SLAs00:40:24 - Advice for identity professionals on reducing blast radius00:46:10 - Personal milestones and looking forward to 2026Keywords:IDAC, Identity at the Center, Jeff Steadman, Jim McDonald, IAM, Cybersecurity, Business Continuity, Disaster Recovery, Operational Resilience, RSM, Incident Response, Ransomware, Cyber Insurance, Identity Governance

This week, Steve sits down with Ankit Jain, co-founder and CEO of Infinitus Systems, to talk about why voice-based AI has become one of the most rapidly adopted tools in healthcare operations, what's actually working in the field, and where the hype still outpaces reality. Ankit shares six years of lessons from building AI agents that handle 35-minute medical calls end to end, plus his predictions on what 2026 and 2027 will really look like as enterprises attempt to build their own agents.We cover:Why so much of healthcare still runs on phones, faxes, and portalsHow AI agents are handling long, high-stakes medical calls without going off trackWhat large enterprises now expect around security, governance, and zero-hallucination requirementsWhy providers, payers, and pharma are adopting AI for different operational workflowsWhy 2026 may be the year many health systems try to build their own agents, and why most will return to vendors by 2027—About our guest: Ankit Jain is the co-founder and CEO of Infinitus Systems, the agentic healthcare communications platform that automates high-stakes clinical and administrative conversations at scale. Under his leadership, Infinitus supports 44% of the Fortune 50, and many of the largest healthcare organizations in the US. A serial entrepreneur, advisor, and investor, Ankit has built companies and guided innovation at the intersection of technology and AI. He founded Quettra (acquired by Similarweb), helped launch Google Play and the search engine Cuil, and went on to co-found and manage Gradient Ventures, Google's AI-focused venture fund. His background in building distributed systems and safety-constrained AI, combined with hands-on experience scaling products in regulated environments, gives him a pragmatic perspective on how to design trustworthy AI that earns adoption in healthcare. Ankit frequently works with industry leaders on governance, education, and integration strategies that make automation safe, approachable, and scalable.—Chapters:00:01:38 Introduction to Ankit Jain and Infinitus Systems00:02:54 The journey into healthcare entrepreneurship00:03:55 Inspiration behind Infinitus and its mission00:04:55 Evolution of AI in healthcare communications00:08:12 Navigating competition in the AI healthcare space00:10:30 Defensibility and product development insights00:14:00 AI's role in enhancing healthcare accessibility00:15:20 Go-to-Market strategies and lessons learned00:17:52 Deepening engagement in healthcare workflows00:19:33 Competitive dynamics in healthcare AI00:20:42 Addressing industry concerns and challenges00:22:14 The need for industry self-regulation00:23:40 Navigating consumer privacy and AI interactions00:25:27 The future of jobs in healthcare AI00:28:30 The evolution of healthcare AI00:29:37 Lessons for entrepreneurs in healthcare—Pre-order Halle's new book, Massively Better Healthcare.—

Słuchasz Karolina Sobańska Podcast. W tym programie rozmawiamy o dobrym świadomym życiu, śledzimy trendy i dyskutujemy o tym co dla nas ważne. Gościem odcinka jest Jowita Michalska / https://www.instagram.com/jowita_digital/Bądź na bieżąco :) www.instagram.com/KarolinaSobanska⁠⁠⁠⁠www.karolinasobanska.com⁠⁠ Współpraca: karolina@pasnormal.group00:00:00 intro00:11:48 zaufanie społeczne do AI00:21:44 największe zagrożenia związane z AI00:29:06 jaka czeka nas przyszłość?00:35:06 obiektywność AI00:40:57 regulacje00:46:47 trendy w innych technologiach00:53:06 robotyzacja01:03:51 bądźmy bardziej świadomi

Preston and Seb unpack AI's implications for safety, governance, and economics. They debate AGI risks, corporate centralization, Bitcoin's regulatory role, and Elon Musk's ventures in space and autonomous tech. IN THIS EPISODE YOU'LL LEARN: 00:00:00 - Intro 00:04:37 – Why AI safety and autonomy are increasingly at odds00:11:30 – How AGI could reshape governance and policy-making00:07:40 – Preston's skepticism about AI self-preservation claims00:15:18 – The unintended consequences of AI regulation00:22:15 – How Bitcoin could hold corporations accountable00:20:10 – The dangers of centralizing economic power via AI00:34:45 – Why generalist thinking matters in a post-pandemic world00:37:20 – The role of curiosity and deep reading in future-proofing00:41:59 – How SpaceX is redefining launch economics with reusable rockets00:57:41 – The hidden potential of Tesla's AI chips and compute power Disclaimer: Slight discrepancies in the timestamps may occur due to podcast platform differences. BOOKS AND RESOURCES Clip 1: AI Expert: We Have 2 Years Before Everything Changes! We Need To Start Protesting! with Tristan Harris. Clip 2: Marc Andreessen explains the future belongs to generalists in the AI era. Clip 3: Elon Musk on the Future of SpaceX & Mars. Official Website: Seb Bunney. Seb's book: The Hidden Cost of Money. Related ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠books⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ mentioned in the podcast. Ad-free episodes on our⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Premium Feed⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. NEW TO THE SHOW? Join the exclusive ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠TIP Mastermind Community⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ to engage in meaningful stock investing discussions with Stig, Clay, Kyle, and the other community members. Follow our official social media accounts: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠X (Twitter)⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠LinkedIn⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Instagram⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Facebook⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠TikTok⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Check out our ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Bitcoin Fundamentals Starter Packs⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Browse through all our episodes (complete with transcripts) ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠here⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Try our tool for picking stock winners and managing our portfolios: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠TIP Finance Tool⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Enjoy exclusive perks from our ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠favorite Apps and Services⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Get smarter about valuing businesses in just a few minutes each week through our newsletter, ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠The Intrinsic Value Newsletter⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Learn how to better start, manage, and grow your business with the ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠best business podcasts⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. SPONSORS Support our free podcast by supporting our ⁠sponsors⁠: HardBlock Human Rights Foundation Masterworks Linkedin Talent Solutions Simple Mining Plus500 Netsuite Fundrise References to any third-party products, services, or advertisers do not constitute endorsements, and The Investor's Podcast Network is not responsible for any claims made by them. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://theinvestorspodcastnetwork.supportingcast.fm