Podcasts about gpt

All frontier AI models are reportedly delayed. ⏳New reports show that frontier models there were supposed to be released this month like GPT-5.6 and Gemini 3.5 Pro have been delayed as a result of the ongoing Anthropic vs. U.S. Government dispute. So while we may have to wait a few more weeks for SOTA AI models, we DID get a ton of new AI features and model updates that are available now. Tune in as we dish the 7 new AI features that can change your workflow today. Claude Tag, Gemini Home, Copilot Excel Skills and 7 other AI Features Available Today you Should Be Using — An Everyday AI Chat with Jordan WilsonNewsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageToday's Episode on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders.Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: info@youreverydayai.comConnect with Jordan on LinkedInTopics Covered in This Episode:GPT 5.5 Instant Model Upgrade DetailsGoogle Finance AI Portfolio Tracker LaunchGemini-Powered Google Smart Speaker ReleaseClaude Tag Collaboration in Slack ExplainedCanva Grow 2.0 Automated Marketing PlatformGemini in Chrome Select from Screen FeatureMicrosoft Copilot Skills Automation in ExcelTimestamps:00:00 Upcoming AI Model Releases Delayed05:44 Switching between AI models07:15 AI-powered portfolio analysis tools12:51 Setting tasks with voice commands15:20 Introducing Claude team plan17:55 Discussing Anthropic's new feature21:50 Canva's New Feature at Cannes25:11 New Chrome Feature Overview28:45 Using Copilot skills in Excel31:33 Show schedule and subscribingKeywords: GPT 5.5 Instant, OpenAI, AI model update, conversational AI, large language models, Gemini 3.5 Pro, Google AI, Anthropic, Claude Tag, Slack integration, Claude Enterprise, Claude Team Plan, Claude Slack app, multiplayer AI in Slack, channel manager AI, proactive AI assistant, Google Finance app, portfolio tracking, AI research tool, finance AI, Android AI apps, iOS AI rollout, AI-powered key moments, smart speakers, Gemini for Home Assistant, Google smart speaker, natural language conversations, Siri, Alexa Plus, bidirectional voice mode, Google Calendar integration, AI home assistant, Canva Grow 2.0, performance marketing AI, ad automation, campaign optimization AI, Magic Layers integration, LinkedIn ads, TikTok ads, Meta ads, Gemini in Chrome, select from screen, computer use capabilities, Gemini 3.5 Flash, Chrome AI features, Microsoft Copilot, Excel AI, Copilot skills, reusable Copilot workflows, finance skills in Excel, business intelligence AI, custom Excel skills, markdown skills for AISend Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info) Start Here ▶️Not sure where to start when it comes to AI? Start with our Start Here Series. You can listen to the first drop -- Episode 691 -- or get free access to our Inner Cricle community and all episodes: StartHereSeries.com Also, here's a link to the entire series on a Spotify playlist. 

Good news: OpenAI's GPT-5.6 has been released!

OpenAI has released GPT-5.6, but the majority of us will have to wait. ⌚After the Anthropic vs. U.S. Government feud, it now looks like we'll have to wait for frontier models. That wasn't the only big AI news headline that might change your company's AI strategy. Anthropic got the green light to roll out Mythos 5 to a select few, Google reportedly extended its strike team to catch up on coding and more. OpenAI's limited release of GPT-5.6, Mythos starts slow reinstatement, OpenAI gets spicy and more AI news -- An Everyday AI chat with Jordan WilsonNewsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageToday's Episode on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders.Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: info@youreverydayai.comConnect with Jordan on LinkedInTopics Covered in This Episode:OpenAI GPT-5.6 Limited Release ExplainedOpenAI Sol, Terra, Luna Model NamingUS Government Restrictions on AI RolloutsAnthropic Mythos 5 Access and StandoffAnthropic Fable 5 Suspension DetailsGoogle Gemini 3.5 Pro Release DelayedGoogle's AI Coding Mid-Training InitiativeRaiseUS Nonprofit: AI Workforce AdaptationAnthropic Accuses Alibaba of Model DistillationOpenAI & Broadcom Unveil Jalapeno AI ChipKey AI Industry Partnerships & Product LeaksTimestamps:00:00 OpenAI's GPT 5.6 limited release06:14 OpenAI's new model release details09:54 Access suspension and negotiations13:18 Google's AI strategy and delays16:33 Anticipating Gemini 3.5 Pro Release20:40 Accusations of AI model theft24:48 OpenAI and Broadcom chip partnership28:05 OpenAI's recent developments and updates29:56 OpenAI and AI weekly updatesKeywords: GPT-5.6, OpenAI, Anthropic, Mythos 5, Fable 5, Frontier models, Gemini 3.5 Pro, Google, model rollout, limited AI access, AI safety, US government AI regulation, Sol model, Terra model, Luna model, Max reasoning mode, Ultra mode, sub agents, advanced AI benchmarks, coding workflows, cybersecurity, third-party AI analysis, government licensing, AI model guardrails, AI model democratization, model naming scheme, model availability, AI model security, jailbreak resistance, safety filters, general model access, trusted testers, AI export control, national security, Anthropic pullback, supply chain risk, defense department, AI industry competition, talent loss, AI coding, mid training, engineering agents, AI strike team, RaiseUS nonprofit, workforce AI disruption, technology policy, industrial scale distillation, Alibaba, AI model theft, China-US tech tensions, distillation attacks, Jalapeno AI chip, Broadcom, AI inference, custom hardware, data center GPUs, Microsoft, Meta, Elastic compute, AI-powered career navigation, Slack Claude Tag, Canva Grow 2.0, Copilot skills, AI ad creation, AI automation, DigitalOcean plugin, Apple hardware AI, smart glasses, Vision Pro, portfolio tracking AI, Google Finance, home smart speakers, voice AI, GLM 5.2, open source AI, US labor market AI effects, AI job disruption, model leaks, government approval delays.Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info) Start Here ▶️Not sure where to start when it comes to AI? Start with our Start Here Series. You can listen to the first drop -- Episode 691 -- or get free access to our Inner Cricle community and all episodes: StartHereSeries.com Also, here's a link to the entire series on a Spotify playlist. 

This Week In Startups is made possible by:Plaud - https://Plaud.ai/twistSentry - https://sentry.io/twistNorthwest Registered Agent-https://northwestregisteredagent.com/twistDigitalOcean - https://do.co/twistToday's show:F1 teams spend up to a third of their budgets on aerodynamics, often utilizing wind tunnels to simulate what happens on the track.But Lyall Davenport of SKN Systems created a sensor-embedded tape that sticks directly to race cars as they drive around the real world, generating 420M data points per hour. And it comes in at roughly 95% less cost than running a full simulator.Hear about how Lyall's background in racing helped him develop the concept, plans to expand from motorsports to drones and defense, and his experiences going through Jason's various programs.PLUS OpenAI's new models are so good, you can't use them. Why Apple's Vision Pro chief defected to OpenAI (and why their laptops and devices are getting more expensive). A look at the world's most annoying data center in Sterling, Virginia. And Jason remembers iconic and influential tech journalist Om Malik.Guest:Lyall Davenport: https://x.com/LyalldavenportSKN Systems: https://www.skn.systems/Timestamps:0:00 How SKN uses tape to generate aerodynamic data4:04 Plaud: If your work depends on conversations — interviews, meetings, calls — you need a Plaud NotePin. You can check it out at https://Plaud.ai/twist and use code TWIST for 10% off!6:17 How SKN uses tape to generate aerodynamic data9:55 Sentry - Your team should be focused on shipping features — not chasing down bugs. New users can get $240 in free credits when they go to https://sentry.io/twist and use the code TWIST17:20 F1 teams are worth HOW MUCH?19:42 Northwest Registered Agent: Get more when you start your business with Northwest. In 10 clicks and 10 minutes, you can form your company and walk away with a real business identity — Learn more at https://northwestregisteredagent.com/twist20:35 Future markets beyond motorsports30:03 DigitalOcean - Want to see what building on a true AI-native platform looks like? Head to https://do.co/twist to start building on DigitalOcean's AI-Native Cloud today — and cut your AI workload costs by up to 50%.32:45 GPT-5.6 is banned for now43:46 Vision Pro chief makes for the exit51:24 Apple raises their prices56:05 The data center you're allowed to hate1:06:04 Zuck's new prediction market app1:08:42 RIP Om Malik (1966-2026)1:19:50 Streaming and camera recommendations1:28:06 All about eFoilingSubscribe to the TWiST500 newsletter: https://ticker.thisweekinstartups.comCheck out the TWIST500: https://www.twist500.comSubscribe to This Week in Startups on Apple: https://rb.gy/v19fcpFollow Lon:X: https://x.com/lonsLinkedIn: ⁠https://www.linkedin.com/in/alexwilhelmFollow Jason:X: https://twitter.com/JasonLinkedIn: https://www.linkedin.com/in/jasoncalacanisCheck out all our partner offers: https://partners.launch.co/Great TWIST interviews: Will Guidara, Eoghan McCabe, Steve Huffman, Brian Chesky, Bob Moesta, Aaron Levie, Sophia Amoruso, Reid Hoffman, Frank Slootman, Billy McFarlandCheck out Jason's suite of newsletters: https://substack.com/@calacanisFollow TWiST:Twitter: https://twitter.com/TWiStartupsYouTube: https://www.youtube.com/thisweekinInstagram: https://www.instagram.com/thisweekinstartupsTikTok: https://www.tiktok.com/@thisweekinstartupsSubstack: https://twistartups.substack.com

OpenAI leaned toward delaying its IPO to 2027 rather than budge from Altman's $1T valuation, rattling tech stocks. The government had OpenAI stagger GPT-5.6's release over security concerns. Microsoft hiked Xbox prices again, and SpaceX teased a Starlink mobile network. Sources: OpenAI leans toward holding off its IPO until 2027 after warnings that Sam Altman's desired $1T valuation may not be met in current market conditions (The New York Times) Sources: Sam Altman told staff the US government asked OpenAI to stagger the release of GPT-5.6 over security concerns, approving "access customer by customer" (The Information) Microsoft says the price of Xbox consoles will increase on August 1 by $100 for 512GB models and $150 for 1TB models, the third price increase since 2025 (Kotaku) Sources: SpaceX COO Gwynne Shotwell told investors during an IPO roadshow SpaceX may launch a Starlink mobile product and build its own terrestrial US network (FT) Longreads How Chicago is betting on quantum computing, including turning the site of its former US Steel mill into a campus, after largely missing the digital revolution (WSJ) As China's working-age population shrinks, consensus is growing that China must embed embodied AI robots into as many tasks as possible, as soon as possible (FT) Subscribe to the ad-free feed. Learn more about your ad choices. Visit megaphone.fm/adchoices

Anthropic just accused Alibaba of the largest known corporate espionage campaign against it, alleging 25,000 fake accounts and 28.8 million queries aimed at stealing Claude. We get into the AI cold war heating up between the US and China, why Apple and Microsoft just raised prices, OpenAI's first chip Jalapeno, the wild new Seed Audio 1.0 model, Claude landing in Slack, and a Blender plus Seedance video workflow that gives you real control. This week on AI For Humans, Gavin Purcell and Kevin Pereira open on a genuine spy-novel turn: Anthropic has accused Chinese tech giant Alibaba of running an industrial-scale distillation campaign to siphon Claude's capabilities, laid out in a letter to US senators. It is an accusation, not a proven finding, and Alibaba has not responded, but it puts the US-China AI race front and center. From there we get into why the new models everyone expected this week didn't actually arrive, the AI memory crunch driving Apple and Microsoft price hikes, and OpenAI designing its first chip, Jalapeno, with Broadcom. On the fun side, Seed Audio 1.0 generates full songs and layered soundscapes, Claude shows up inside Slack via Claude TAG, TheWrap experiments with AI microdramas, and we break down a Blender pre-viz plus Seedance 2.0 workflow that makes AI video remarkably controllable. WE ARE NOT SPY. WE NEED FABLE 5 BACK. WE PLEAD.  // Show Links // Anthropic accuses Alibaba of brazenly and illicitly extracting Claude's capabilities (CNBC) https://www.cnbc.com/2026/06/24/anthropic-alibaba-distillation-campaign.html The post that put the espionage story on our radar (unconfirmed single-source thread) https://x.com/S0N_IA/status/2069893802802745673 Apple raises MacBook and iPad prices as the AI memory crunch bites (CNBC) https://www.cnbc.com/2026/06/25/apple-macbook-ipad-price-hike-memory.html OpenAI unveils its first chip, Jalapeno, built with Broadcom (official) https://openai.com/index/openai-broadcom-jalapeno-inference-chip/ No new flagship this week, but OpenAI did ship a GPT-5.5 Instant update https://x.com/OpenAI/status/2069843083701915755 Seed Audio 1.0 generates full songs and layered audio scenes (via fal) https://x.com/fal/status/2070138257891791237 Claude TAG brings Claude into Slack for everyone https://x.com/ashwingop/status/2069814177624121469 Andrej Karpathy on the new Slack workflow https://x.com/karpathy/status/2069822834160124091 Blender pre-viz into Seedance 2.0 for incredible video control (shared by venturetwins) https://x.com/venturetwins/status/2069809200788799582 Original creator of the Blender to Seedance workflow https://x.com/craftcapitallab The full AI Warper workflow breakdown https://x.com/AIWarper/status/2069847773034488262   Join our Discord https://discord.gg/muD2TYgC8f Support us on Patreon https://www.patreon.com/AIForHumansShow Subscribe to the AI For Humans Newsletter https://aiforhumans.beehiiv.com/ Follow us on X @AIForHumansShow https://x.com/AIForHumansShow Find us on TikTok @aiforhumansshow https://www.tiktok.com/@aiforhumansshow Book us for speaking or consultation https://www.aiforhumans.show/  

What Every Time-Starved Entrepreneur Needs to Know About Using ChatGPT to Win at Social Media Marketing in 2026   Master ChatGPT for online entrepreneurship with conversion-focused tactics. In this episode, we reveal the exact AI entrepreneur prompt systems for social media captions, split-testing ad copy, and auditing conversion leaks—strategies busy parents are using to scale side gigs without agency fees. Learn the caption engineering framework, A/B testing methodology, and audit checklists that turn engagement into income in 2026. https://DarkHorseEntrepreneur.com Tracy explains how to use ChatGPT (including GPT-4o and 5o) and other AI models to scale social media marketing by rapidly generating and testing multiple caption hooks to boost first-hour engagement velocity, then extending AI use to full ad campaign architecture across platforms with native-format variations, systematic low-budget testing, and scaling winners while killing losers. It emphasizes that AI is a force multiplier, not a replacement, and warns that poor offers and weak funnels can't be fixed by AI—AI amplifies both good and bad strategy. A parallel "conversion audit" process is recommended, using tools like GA4 data analyzed in ChatGPT to identify high-quality traffic, CPA, and ROAS. The script argues the real competitive advantage is strategy and audience insight, and outlines monetization via productized services, SOPs, automation tools, and digital products, while cautioning against burnout from scaling too many systems instead of focusing on clarity and sustainable execution.   00:00 AI Social Media Promise 01:35 Algorithm Brutal Truth 02:13 Caption Engineering System 04:19 AI Powered Ad Testing 06:45 Avoid Scaling Mediocrity 08:09 Conversion Audit Loop 09:51 Strategy Beats AI 10:48 Clone Systems to Scale 12:23 Clarity Over More Systems 13:54 Final Leverage Takeaway  

In this behind-the-scenes BONUS episode, Kelly is pulling back the curtain on exactly what she's building right now, in real time. She walks through the company she's chosen to plant her roots in for the next decade, the brutal pivot that turned out to be the best decision of her career, and the counterintuitive move she's making first as she scales, the one almost no one in online marketing will tell you the truth about. If you've ever wanted to learn the scale process by watching it happen from the inside, this is your seat at the table. What's inside: The two-step vision process every scale starts with Why she's working on retention before pouring in a single new member The shift from selling a product to building a movement What she's deliberately saying no to for the next nine months Resources:  Register for the LIVE Miracle Hour Experience hapening Wednesday, June 24th, from 10am to5pm EST: Kelly's free full-day live training: https://www.themiraclehourbook.com/miracle-hour-june-24-experience-social  Upgrade to VIP for access to our custom sales GPT, lifetime recording access, and bonus Q+A: https://accelerator.virtualbusinessschool.com/vip-upgrade-june-24th  Join us this Summer for The Legacy Leaders Mastermind: Kelly's year-11 mastermind for multi-seven and eight-figure leaders: https://join.thebusinessadvisory.com/2026-rsvp   Subscribe to Kelly's Substack, The Sacred Art of Selling: https://kellyroachofficial.substack.com/subscribe  Join The Virtual Business School: https://www.virtualbusinessschool.com/virtual-business-school  Join us in-person for the Called to Lead Event happening October 1st: https://www.sandiglandt.com/called-to-lead 

Every teacher struggling with AI use in the classroom needs to hear this episode. Jamie Metzl has a Ph.D. from Oxford, a law degree from Harvard, and has run 60 marathons. He spent nine years writing his first book. When he sat down to co-write The AI Ten Commandments with GPT-5 he didn't surrender his thinking, creativity or his soul. Jamie doubled down by documenting the process. It's the first major published book to list a human and an AI as co-authors. Steve Wozniak, one of the founders of Apple, summed it up like this: "If you care about the future, read this book." To which I'll add: It not only takes the best from our collective past. It draws a roadmap for students to get the most out of themselves by working with AI instead of hiding behind it. Teachers can use the process to see how and what students have learned. Please pass this podcast and Jamie's book on to every teacher you know. And students, too.

Since the launch of ChatGPT in November last year, there's been a wave of popular demand for AI technology. The chatbot reached 100 million users in record time, with its appeal stretching far beyond the tech-savvy. After all, it can write essays and songs, summarise documents and hold human-like conversations. But the rapid advances are causing concern in some quarters. In late March, the Future of Life Institute think tank published a pretty direct open letter calling for a six-month pause in the training of AI systems, saying that they shouldn't be allowed to go any further than Open AI's GPT-4 model.  But isn't AI going to make all of our lives easier? What are the counter-arguments in favour of further developing AI? In under 3 minutes, we answer your questions! To listen to the last episodes, you can click here : ⁠Can deep sleep help stop dementia?⁠ ⁠What is conscious quitting?⁠ ⁠Why is Israel going through a major political crisis?⁠ A Bababam Originals podcast.A podcast written and realised by Joseph Chance. First Broadcast : 4/9/2024 Learn more about your ad choices. Visit megaphone.fm/adchoices

In this sponsored interview James Wilson chats with Trail of Bits founder and CEO Dan Guido about its newly announced partnership with OpenAI. Together, they've started a new initiative called “Patch the Planet” to support open source maintainers. Being an open source maintainer is more difficult than ever. Just using frontier models to keep up with all the bug reports isn't enough. Trail of Bits wants to help maintainers by combining its deep cybersecurity expertise with OpenAI's GPT 5.5 Cyber. As Dan points out in this interview, this isn't just about helping maintainers find and fix bugs. They're spending just as much time on SDLC improvements, architecture changes, and the foundations needed to make open source sustainable in the AI era. Show notes

OpenAI amplía Daybreak para cerrar vulnerabilidades con IA y presenta GPT-5.5-Cyber. Qualcomm se acerca a comprar Modular para competir con Nvidia también en software. Nvidia anuncia 35 superordenadores de IA en Europa, Oracle reconoce recortes ligados a la IA y una enana blanca podría explicar señales de radio repetitivas desde el espacio.Puedes seguirnos en YouTube en https://youtube.com/olivernabani y puedes unirte al Discord Mashain en https://olivernabani.com/discord

A new AI model out of Japan, Sakana Fugu, does something we have not really seen before. Instead of answering you itself, it hires a team of the best AI models, gives each one a piece of the job, and merges their work into one answer. Harrison calls it a manager, or a conductor: you ask one question, and behind the scenes it quietly builds a team for you.In this episode, Harrison explains what model orchestration actually is in plain language, why he thinks this is where AI is heading, and then puts it to the test. He sends the same 8 questions to Fugu, to Claude Opus 4.8, and to GPT-5.5, and grades every answer. The result is honest, and the cost is the part that should give every builder pause.What you'll learn:- What "orchestration" means, explained simply- Why the future may be teams of models, not one genius model- What happened when a team of models went head to head with single models- The real speed and cost tradeoff, with actual numbers- The hidden tokens you pay for but never see- When an orchestrator is worth it, and when one good model is plenty- A heads-up on AI pricing and subsidies most people are not thinking aboutCHAPTERS0:00 A glimpse into the future0:19 What is Sakana Fugu?1:55 Not a smarter model, a manager3:30 The test: 8 questions, three models4:50 Speed: about 10x slower5:30 Cost: about 49x more expensive6:07 The hidden tokens you pay for7:20 Inside the console8:30 The questions, and why they're tricky9:06 Is a team of models worth it?9:27 When a team earns its place10:09 The verdict10:57 The subsidy nobody is talking about11:18 Where this goes next12:04 Wrap upMentioned: Sakana Fugu — https://sakana.ai/fugu/If you got something out of this, follow the show and send it to someone who's working to keep up with AI.

While Anthropic and the U.S. Government continued to try and make amends, there was another seismic shift quietly taking place: open source surged. Between Microsoft reportedly testing Open Source models for Copilot and the powerful new GLM-5.2, there was a clear trend this week in AI world. Missed it all? Don't worry, we'll catch you up so you can make the informed decisions for your company. Anthropic Continues Fable Fight, Microsoft Goes Open Source, Midjourney's Big Pivot and More AI News That Matters -- An Everyday AI Chat with Jordan WilsonNewsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageToday's Episode on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders.Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: info@youreverydayai.comConnect with Jordan on LinkedInTopics Covered in This Episode:Anthropic Fable 5 and Mythos 5 Export BanTrump Labels Anthropic a National Security ThreatMicrosoft Copilot CoWork Open Source Model SwitchMicrosoft Considers DeepSeek-V4 for AI Cost ReductionChinese GLM 5-2 Sets Open Source BenchmarkGLM 5-2 Challenges Proprietary AI ModelsMidJourney Hardware Pivot: AI Medical Imaging ScannerCursor Building 1.5T Parameter Model, GitHub CompetitorAI CEO Summit: G7 Pushes US-Led AI CoalitionOpenAI Prepares GPT-5.6 ReleaseAnthropic, OpenAI, Google Face Geopolitical AI ScrutinyAdvancements in Token Efficiency and Cost ControlTimestamps:00:00 Trump's comments on Anthropic06:17 Microsoft exploring lower-cost AI models09:07 Microsoft exploring DeepSeek amid tensions13:45 AI model performance and efficiency trends15:59 AI leaders meet at G7 Summit21:22 Midjourney unveils first hardware product23:26 MidJourney's innovative spa technology28:50 Discussing Cursor's evolution and impact32:24 Talking about AI use cases33:27 Rumors and upcoming AI model releases37:20 OpenAI's major new hiresKeywords: Anthropic, Fable Five, Mythos Five, export controls, national security threat, Dario Amodei, Amazon, supply chain risk, Defense Production Act, Copilot CoWork, Microsoft, usage based pricing, open source AI, DeepSeek V4, Chinese AI model, token costs, Azure, agentic AI, enterprise AI billing, data security, compliance filters, GLM 5-2, Zhipu AI, 753 billion parameter model, MIT open source license, long context window, autonomous coding, Hugging Face, benchmark performance, text only model, multimodal capabilities, token efficiency, AI spend, G7 summit, AI governance, AI coalition, AI standards, cybersecurity risks, bioterrorism, chip trade, Sam Altman, OpenAI, Claude Opus 4.8, Gemini 3.5 Pro, MidJourney, medical imaging, MidJourney scanner, full body ultrasound, Butterfly Network, MRI alternative, spa launch, SpaceX, Cursor, 1.5 trillion parameter model, code hosting, GitHub competitor, code generation, AI super apps, Colossus compute, technical prompts, context window expansion, GPT 5.6, Claude Conway agent, Grok Imagine, Firefly AI, code artifacts, Google Ad Manager AI, Open Knowledge Format, Noam Shazeer, Dean Ball, Andrej Karpathy.Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info) Start Here ▶️Not sure where to start when it comes to AI? Start with our Start Here Series. You can listen to the first drop -- Episode 691 -- or get free access to our Inner Cricle community and all episodes: StartHereSeries.com Also, here's a link to the entire series on a Spotify playlist. 

AI pricing is changing fast. OpenAI, Anthropic, and Microsoft's GitHub are all moving away from flat-rate subscriptions toward usage-based billing, and the shift is going to hit anyone whose business runs heavily on AI tools. Anthropic has already shifted some business customers to actual-usage billing. GitHub launched a new usage-based system that kicks in after monthly allotments run out. OpenAI executives have publicly floated pricing AI more like electricity or water, where heavier users pay more for slide decks, longer agent runs, code debugging, and email drafting.This episode breaks down the AI pricing shock hitting OpenAI, Anthropic, and Microsoft, what it means for businesses already building on these tools, and which alternatives are starting to look attractive. The driver is straightforward. AI labs are burning cash on chips, data centers, and talent at a rate that flat-rate subscriptions can't support. OpenAI reported a $14 billion projected loss for 2026. Anthropic just filed for IPO at a $965 billion valuation. Microsoft is spending tens of billions on AI infrastructure. The math on a $20-a-month subscription that produces unlimited GPT-5 output doesn't work anymore.The corporate response is already visible. Walmart capped staff use of its in-house AI agent. Uber is limiting monthly employee spending to $1,500 per AI coding tool. Companies that rolled out generative AI broadly in 2024 and 2025 are now reading the meters, because the same prompt that cost $0.02 in 2024 can cost $2 today on a reasoning model.The lower-cost alternatives are gaining real attention. Alibaba's Qwen and DeepSeek both run at a fraction of OpenAI and Anthropic pricing, and both have closed the quality gap enough that routing simpler tasks to a cheaper model is a defensible engineering decision. The question for every business spending on AI is which tasks need a frontier model and which can run on a model that costs 90% less for the same output.What this means for AI strategy in 2026. Flat-rate pricing was a customer acquisition tactic that worked when the labs were trying to win mindshare. Usage-based pricing reflects what AI actually costs to deliver, and it's the model the industry will settle on. For developers, freelancers, and small businesses using ChatGPT, Claude, GitHub Copilot, and Cursor every day, the bill is about to look different. For agencies and consultants billing clients for AI work, the margin model needs a rebuild.We cover the OpenAI, Anthropic, and GitHub pricing changes in detail, how Walmart and Uber are responding, why Qwen and DeepSeek matter more this quarter than they did last quarter, and what the shift to electricity-style AI pricing means for the cost of doing business in the AI economy.Keywords: AI pricing, OpenAI pricing, Anthropic billing, GitHub Copilot pricing, usage-based AI, token pricing, AI subscription, ChatGPT pricing, Claude pricing, Qwen, DeepSeek, Walmart AI, Uber AI, GPT-5 cost, AI ROI, AI infrastructure cost.

In this episode, Kelly names the trap that a lot of smart internet marketers have set, why so many million-dollar businesses have slid backward into the low six figures, and the one shift that takes the pressure off your launches for good. What's inside: The real reason launches started feeling heavy, hard, and underwhelming The line the whole internet bought from the gurus, and who's actually profiting from it What Kelly changed (and what she refused to change) from a strategy standpoint when the market shifted The daily sales system that makes one bad launch a non-issue Timestamps 01:30 — How we got here: the trust recession and what actually changed 03:00 — Marketers selling "the alternative to launching" 04:30 — Watch what people do, not what they say 06:00 — Kelly never stopped live launching (here's what she changed instead) 07:15 — The missing piece: the importance of having a daily sales system between launches 08:30 — How the Miracle Hour powered our USA-today bestselling book launch 09:30 — What kills launch anxiety for good 10:30 — June 24th: join us LIVE for a free, full-day daily sales training 12:30 — Offers, the shifted market, and the 92% buying-behavior stat Resources & Mentions Register for The Miracle Hour Experience on June 24th: a full, free day of teaching, coaching, and live action: https://www.themiraclehourbook.com/miracle-hour-june-24-experience-social  Upgrade to VIP for just $97: get lifetime access to the ~7 hours of recordings, the fill-in-as-you-go workbook, a follow-up Q&A with our team, 25 sales-unlock prompts, and a custom GPT implementation coach: https://accelerator.virtualbusinessschool.com/vip-upgrade-june-24th  Learn The Miracle Hour, our proven daily sales system: Get the book on Amazon: https://a.co/d/0bw4DjEl  Subscribe to Kelly on Substack for more relationship-driven sales content: https://kellyroachofficial.substack.com/subscribe 

Intel stock popped after Trump said Apple agreed to build chips with it in America. Waymo yanked its robotaxis off highways over construction-zone blunders. Rockstar dated GTA VI pre-orders to June 25, and GLM-5.2 grabbed the open-weights crown. Intel's stock jumps 10.64% after Trump said "Apple has agreed to work with Intel to design and build its Chips in America"; INTC is up 520%+ in the past year (CNBC) Filings: Waymo pulls its ~4K robotaxis from highways after finding 13+ instances of the cars driving into highway sections under construction (TechCrunch) Rockstar Games announces that pre-orders for Grand Theft Auto VI will go live on June 25; TTWO closed up 4.93% (Kotaku) GLM-5.2 is the leading open weights model on Artificial Analysis' Intelligence Index, scoring 51, only behind Fable 5's 60, Opus 4.8's 56, and GPT-5.5's 55 (Artificial Analysis) GLM-5.2 becomes the top open-weight model on Artificial Analysis (Implicator) Longreads Google Is Using Nvidia's Playbook to Build a Rival AI Chip Business (WSJ) AI Is Splitting the Job Market in Two, PwC Study Shows (Bloomberg) Apple's weird anti-nausea dots cured my car sickness (The Verge) Learn more about your ad choices. Visit megaphone.fm/adchoices

This week on the Friday Deploy, Ben and Andrew unpack the sudden disappearance of Fable 5 and discuss whether Meta's aggressive pivot to AI data labeling is destroying its legendary engineering culture. The hosts also explore the rise of highly capable open source Chinese models like GLM 5.2 and why tech giants are considering them to slash skyrocketing inference bills. Finally, they dive into new research proving that as AI execution takes over, human domain expertise and strict production observability are more critical than ever. Register here: for the June 25th workshop, Life Beyond Tokenmaxxing, to learn how to measure real AI impact and ROI across the SDLC.Follow the show:Subscribe to our Substack Follow us on LinkedInSubscribe to our YouTube ChannelLeave us a ReviewFollow the hosts:Follow AndrewFollow BenFollow DanFollow today's stories:KayfableWhy is Meta destroying its engineering organization?AI demands more engineering discipline. Not lessZ.ai's open-weights GLM-5.2 beats GPT-5.5 on multiple long-horizon coding benchmarks for 1/6th the costMicrosoft Mulls China's DeepSeek for Copilot, Probably to Trump's ChagrinAgentic coding and persistent returns to expertiseOFFERSStart Free Trial: Get started with LinearB's AI productivity platform for free.Book a Demo: Learn how you can ship faster, improve DevEx, and lead with confidence in the AI era.LEARN ABOUT LINEARBAI Code Reviews: Automate reviews to catch bugs, security risks, and performance issues before they hit production.AI & Productivity Insights: Go beyond DORA with AI-powered recommendations and dashboards to measure and improve performance.AI-Powered Workflow Automations: Use AI-generated PR descriptions, smart routing, and other automations to reduce developer toil.MCP Server: Interact with your engineering data using natural language to build custom reports and get answers on the fly.

Shelly Palmer has spent 45 years watching technology reshape every industry—from writing news themes for CBS to consulting with every major media company on AI strategy.On this year-end recap, he cuts through the noise with one devastating observation: 2025 was the year everyone talked about AI while almost nobody actually used it. Executives shook their heads knowingly in meetings, pontificated about capabilities the models don't yet have, and parroted nonsense they read from other people who knew nothing. But when you asked one innocent question, they crumbled.In the News: CES 2026 shapes up with Nvidia sponsoring two full days of AI training. Samsung is skipping the main floor for a massive offsite activation. Sony brings no electronics—only Honda's experimental vehicles. The TCL and Chinese companies' presence hinges on tariff policy. The innovation series breakfast that Shelly runs is becoming an official CES event after a decade of independence.The conversation spirals into deeper territory: $3 trillion in government money is stacked behind AI development. The U.S. explicitly states it must beat China to AGI—making this the Manhattan Project of our lifetime. Shelly walks through what he's seen in successful companies (leadership using the tech, paid "Tech Tuesdays" for AI experiments, cross-discipline teams with SecOps and legal at the table) versus the chaos of places with no process.He breaks down what's real—drone warfare, cybersecurity applications, robotics—versus what's hot air. And he makes a case that won't be killed by AI itself, but by militarized applications and the geopolitical arms race we're already in.5 Key Takeaways from Shelly:Leadership belief and hands-on use are non-negotiable. Companies winning with AI have senior leaders who actually use the technology. When the CEO walks into an LT meeting saying "I built this agent over the weekend," everyone else starts experimenting too.The recipe for AI success has three ingredients: leadership belief, paid time to experiment (Tech Tuesdays/Thursdays with real budgets), and cross-discipline teams (SecOps, legal, compliance, risk) paving the way. Chaos erupts without this structure.You cannot build a point of view on AI from reading blogs or watching YouTubers. Pick a personal project you care about, go hands-on with a model (Claude, Gemini, GPT), and complete it from beginning to end. Only lived experience grounds your understanding.AI parallelizes with web 1.0: In 1998, you had to hand-code HTML, build databases manually, write raw JavaScript. Today you can vibe code a site in 90 seconds. AI will eventually reach "spin me up an expert that does X" without asking questions—we're not there yet, but it's inevitable.It's both bubble and Manhattan Project. Some valuations are insane and will burst. But military applications, cyber warfare, drone control, robotics—those aren't going anywhere. The government won't back off. Both outcomes happen simultaneously.This episode is brought to you by Zappar, creators of Mattercraft—the leading visual development environment for building immersive 3D web experiences for mobile headsets and desktop.Mattercraft combines game engine power with web flexibility and features an AI assistant to help you design, code, and debug in real time in your browser. Build smarter at mattercraft.io. Hosted on Acast. See acast.com/privacy for more information.

Episode 228 - How To Get The Best From AI Without Falling Off The Edge   Summary AI can be incredibly helpful, but it's not always right. Learn about the "jagged frontier" of AI capability and why the most effective leaders know when to rely on AI and when human judgement remains essential. Discover practical ways to get the benefits of AI without falling into its traps.     Transcript Hello and welcome to episode 228 of the Leadership Today podcast, where each week we share practical tips to improve your leadership. In this episode we explore the so-called "jagged frontier" of Artificial Intelligence - where we fail to apply human judgement and pay the price.   If you've been using AI for a while, you've probably had two very different experiences. Sometimes it feels almost magical. It helps you generate ideas, summarise information, improve your writing, and solve problems faster than you thought possible. Then there are those other moments. The answer sounds convincing, looks professional, and is completely wrong. Understanding why that happens is becoming one of the most important leadership skills of our time.   A study involving more than 750 consultants at Boston Consulting Group explored the impact of using GPT-4 on a range of consulting tasks. The results were impressive. People completed more work, worked faster, and generally produced higher-quality outputs when using AI. But the researchers uncovered something equally important. When participants used AI on tasks that were beyond the technology's capabilities, their performance actually became worse. The AI didn't simply fail to help. It often led people towards the wrong answer.   The researchers described this as the "jagged frontier" of AI. Some tasks sit comfortably within AI's capabilities and the benefits can be substantial. Other tasks sit just beyond that frontier. The challenge is that AI often sounds equally confident in both situations. That's why the biggest risk with AI isn't that it makes mistakes. Humans make mistakes too. The risk is that we stop applying our own judgement because the answer looks so polished and convincing. So what can leaders do?   First, identify where AI genuinely adds value in your work. Think about the tasks where it consistently helps you save time, generate ideas, improve communication, or increase quality. Just as importantly, identify the situations where you've seen it get things wrong. Understanding both sides of the equation helps you use AI more effectively.   Second, keep your judgement in the process. For important decisions, strategic thinking, or complex problem-solving, consider forming your own view before turning to AI. Even a few notes or bullet points can help you avoid being overly influenced by the first answer AI provides.   Third, have open conversations with your team about how they are using AI. Some tasks may be suitable for handing over largely to the technology. Others require people to stay actively involved, challenging assumptions and validating outputs along the way. The more explicit you are about these differences, the better the outcomes are likely to be.   The key point is that AI is neither a miracle solution nor something to be feared. It's a powerful tool with strengths and limitations. The leaders who get the greatest benefit won't be those who use it for everything - they'll be the ones who learn where it performs best and where human judgement remains essential.   Have a great week.   Research reference: Dell'Acqua, F., et al. (2023). Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of AI on Knowledge Worker Productivity and Quality. Harvard Business School Working Paper 24-013. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4573321   Leadership Today On-Demand Special Offer We have a great deal for podcast listeners on our Leadership Today On-Demand subscription. Just go to www.leadershiptoday.com and checkout using the promo code PODCAST for 25% off an annual subscription. Leadership Today On-Demand is a video subscription service that allows you to work on your leadership in your own time and at your own pace. It is available online and through our Apple iOS and Android apps for phones and tablets. Our mission is to help you to become an even better leader. Your subscription brings together all of our video content in one place including: - Five online courses with workbooks - Five five day challenges with workbooks - Nineteen recorded webinars - A searchable library of 170+ "how to" quick videos on a range of leadership challenges That's over $4,500 of content for less than the price of a single online course. And there are more videos added each week.   Get Connected Find out ways to get connected here: https://leadership.today/connect

In this episode of the #DoorGrowShow, property management growth experts Jason Hull and Sarah Hull discuss how AI is rapidly changing business, marketing, and communication, along with the growing problem of "AI slop" and why authentic human connection is becoming more valuable than ever. They break down the secondary effects of AI, why in-person relationships and masterminds are becoming a competitive advantage, and how property management business owners can stand out in a world flooded with automated content, fake interactions, and digital overload. You'll Learn [00:00] The Rise of AI Slop [06:20] Why Human Connection Still Matters [12:10] The COVID Parallel and Isolation [20:00] Why In-Person Transformation Works [31:30] AI, Trust, and Real Relationships [43:40] The Future of Property Management Growth Quotables "The secret to creating a scalable business is to do the unscalable actions." "Transformation happens in the room, not on Zoom." "If you can make it easily, so can anybody else." Resources DoorGrow and Scale Mastermind DoorGrow Academy DoorGrow on YouTube DoorGrowClub DoorGrowLive Transcript Jason Hull (00:00) we live in a world of the next stage I'm calling AI Slop. AI Slop, we're in the world of AI Sloppy Sloppiness. I just got a letter, no offense, NARPM, but I got a letter from y'all You wrote this using ChatGPT.   This is the problem with Narpum 2.0. We're not just talking about the future of our association, We're actively building it. it's not A, it's B, dead giveaway. And there's lots of other dead giveaways. And some of you are starting to hear this AI voice and you're starting to recognize this AI voice. And so we're now in the world of ASLOP where nobody is writing anything.   All right, hello everybody. I'm Jason Hull. This is Sarah Hull, the owners of DoorGrow, the world's leading and most comprehensive coaching and consulting firm for long-term residential property management entrepreneurs. For over a decade and a half, we have brought innovative strategies and optimization to the property management industry. At DoorGrow, we are on a mission to transform property management business owners and their businesses. We want to transform the industry, eliminate the BS, build awareness, change perception, expand the market,   and help the best property management entrepreneurs win. All right, now let's get into the show. All right, so in today's episode, we're gonna be chatting. Just change that right now, because every week I tell you, stop saying a decade and a half. So just go ahead and change it right now. Okay, for, she doesn't like that I say a decade It drives me For 17 years. For over 17 years, yeah. That's how long it's been.   for over years. I'm like decade, decade sounds like a long time, but all right, we're changing it. Cause the life wants to change. 17 years space week. There we go. right. Updated real time. Yeah. Done. That's how we get stuff done. Okay, cool. What are we talking about today, Sarah? talk about how AI is starting to change things. Okay. Let's do it. All right, so.   One of the things we've been talking a lot about is the secondary effects of AI, because everybody's talking about AI. And I think we started in phase one was the AI revolution. Everybody's starting to use chat GPT. Now people are starting to use Clod for business and people are learning prompting. now people starting, some of you are starting to vibe code. And some of you are making videos. Some of you started making images with AI and   Now we live in a world of the next stage I'm calling AI Slop. AI Slop, we're in the world of AI Sloppy Sloppiness. I just got a letter, no offense, NARPOM, but I got a letter from y'all and I can tell you wrote this. You wrote this using ChatGPT. It's like, I mean, the big giveaway phrases and some of you have heard stuff like this. It's like, and we love NARPOM. Thanks for NARPOM for sending us a letter, but.   This is the problem with Narpum 2.0. We're not just talking about the future of our association, M-Dash. We're actively building it. So with blank, it's not A, it's B, dead giveaway. And there's lots of other dead giveaways. And some of you are starting to hear this AI voice and you're starting to recognize this AI voice. And so we're now in the world of ASLOP where nobody is writing anything.   And the people that do write original content, like I've done a couple of Facebook posts just recently, totally handwritten, I wrote the whole thing. And I really enjoy writing. The challenge is, is some people don't recognize how to get their voice into AI. And so anyway, we've got this whole world of AI slop now. Anything can be created by anyone instantly. You can create images, video, text, and so...   You've probably heard me talk about fake internet theory. Have you ever heard of this idea? I have, but just for anyone who hasn't. So the fake internet theory has been around probably since the, you know, once the internet went mainstream. And the fake internet theory is this idea that the majority, at least half in the past, of all the content, all of the traffic that was on the internet, was bots.   It could be like Google crawling sites. It could be a lot of different things, but at least half of all the traffic was bots. Well now with AI, this is even worse. Perplexity is doing research. All these different AI tools are doing research. People are not crawling stuff or crawling websites a lot of times. They're just talking to AI and AI is doing all this. And so the internet now, all the content isn't even, if even this letter probably was not written.   by D.D. Garzone, the Narfim president, 2026. It was probably written by AI, which saved her time. And it's not a bad letter. The challenge is people read this, see this, and we're starting to recognize what's AI and what's not. And we're kind of developing, there's kind of this feel, this voice that you're like, oh, that's AI. And so what am I doing? I just discount stuff. I read and go, oh, AI wrote this. I probably don't even need to read the rest.   So we just skim stuff, we stop reading things. And so this is the challenge is that people want reality, people want humanity. And so the secondary effect, we're in phase two, AI slot, phase three, I believe, is going to be a return to in-person. It's gonna be human interaction. The secondary effect of AI is that human interaction is going to be significantly more important.   And this is where things are going to be shifting to in-person. Things are going to be shifting away from digital marketing. Things are going to be shifting away from anything that you can tell AI to do quickly and easily. And I've always said to our clients, the secret ingredient to scaling your business is depth. The secret to creating a scalable business is to do the unscalable actions. That's depth. And so we're going to talk about today the importance of in-person.   Yeah. So do you guys remember in COVID when everything shut down? Yeah, good And then you couldn't leave your house? Yeah, was great. Yeah, because you might die. So that was great for a little bit, right? Everyone was like, we don't have to go to the office. I don't have to go to work. This is like, would just hang out in my house in my pajamas all day and eat some chips. And like, no, I don't know. It'll be amazing.   And that was a really fun thing. What voice is this? What is this voice? I don't know. Who speaks like this? I'm just letting you know it's not me. I'm gonna be lazy. I'm gonna do my pajamas. Today I got some pajamas on. Are they southern? Now they are. You don't know. They're all over the place. I can't do it in New York. Anyway, so it was a really fun time for like   I don't know, two weeks. The first week was awesome. It was like when you have a substitute teacher and then they put on a movie and you go, this is great. We don't have to do anything today. This is amazing. This is gonna be so cool. And the first week- thought it was awesome. I think for the first, yeah, I think a lot of people really thought it was really great for the first week because they were like, oh, this is great. Like we don't have to go into the office. I a lot of workers, think employers, business owners were freaking out. Yes, exactly. Business owners were-   Although they liked, let's be honest for a second, property managers were real happy about getting a vacation. And it was a vacation that you were forced to take. Right? You couldn't go and do things you normally do. And if you decided to not do showings or decided to not go to the office or decided to, know, like, hey guys, we're all going to work from home. No one was going to fault you. It was going to be totally fine because everybody else was doing it. So it would have been totally fine if you were like, hey, we're not doing.   Showings or hey, you know, we're gonna slow down on inspections or you know, hey, we're you know, we're not gonna come into the office We're all gonna, you know stay from home and then at some point we'll go back to the office for a little bit I think people welcomed that they were like, oh, this is great. This is fun And that maybe lasted a week maybe two maybe even three weeks for some people and then after that they were like what? When can I leave? I what do you mean? Like I'm stuck in my house. I'm I can't   get out, can't go anywhere. When my kids are here, they were all stuck together and no one knows what to do and we're all bored out of our minds and they were starving for human interaction. everything, Zoom, look at the Zoom stock in 2020, skyrocketed because everything moved to remote. Everything moved to Zoom. It was like.   we can't meet in person, but we still kind of have to do things. How do we do it? We are going to do Zoom. And then even remember when a lot of like commercial real estate, it it tanked because people were like, we can't go into the office. We're not buying office space. We're not renting office space. We're going to wait and hold out and see what happens in all this crazy COVID stuff. Right. And it's because all of us were just stuck in our houses. So everything became virtual.   Everything became remote. Even the things that are normally done in person, a lot of them started to shift so that it was no longer in person or the full thing wasn't in person. There was just the one quick little piece that was in person and everything else was done virtually. did we? Okay, I didn't know if you had the podcast. And then   At that point, people were starving for human interaction because it's not the same being on a screen. Looking at a face on the screen and hearing voices on a screen or on a phone call or through Zoom or Teams or whatever it was or emailing and texting and phone calls, it's just not the same. So people then started to go a little bit stir crazy.   And what happened, like as soon as things opened up, then what happened? Things started to surge again. People were desperately trying to connect with other people in person. They were going to their friends' houses. They were trying to meet up with their friends. They were trying to go on vacation. Travel started to boom again. Why? Because people, we are meant to be with other people.   Humans are not solitary animals. We're just not. So AI is going to end up creating something that I think is quite similar. We're going to rely on AI so much just because we have to. And it's an amazing tool. It's incredible. And it makes things a lot faster. And we can do things that were not possible before. And we can do years or months of research in minutes or hours.   the advancements that it allows us to make and in such a short time span are remarkable. But the other thing that it does is it reduces the human to human communication. And now even has anyone ever picked up the phone and called a company and you don't talk to a human now? Now you talk to the AI agent. Yeah. Right. And that seems really great.   because you go, okay, it helps me and I answered my questions and that's lovely. But can you imagine now that most of your interactions are going to be with AI? Most interactions will not be with other humans. Most of a human's interactions will be with a bot or AI. Even if it sounds like a human, it will be AI. And that kind of effect is go, I think it's.   we're going to see largely what we saw in COVID all over again, where people want to see and interact with and talk to and hang out with people. Yeah. And I think that's going to be incredibly valuable. Yeah. I think as humans, when we shift into a mode of isolation where we're no longer interacting with other people,   and you're spending the majority of your day talking to Claude and chat GPT and building things and you probably feel a lot of dopamine. You're like, look at all the stuff I'm getting done. But you're just contributing to the problem of AI slop. If you can make it easily, so can anybody else. And so it's not just about creating more stuff with AI. That isn't really a competitive advantage. Everybody can do that. The competitive advantage really is being in person.   It's those that the wealthy will be able to afford to slow down. The wealthy and the smart will be able to afford to be able to go slow, not faster. They'll be able to spend more time with people. They'll be able to spend more time with their family. They'll be able to spend more time going deeper into building real relationships.   These relationships you have with your AI agents aren't real relationships and they're not going to create the connection that humans need. And they're not going to create the connection and the feelings that your clients need. Clients don't just need their stuff done. That's not it. They need human connection. They need trust and they need to feel safe. So alienation is going to lead to isolation.   And isolation will lead to stagnation in your business because you're not around people that are actually making moves and figuring out how to connect and make a big difference. And the thing that we've noticed, because we've been in a lot of different programs, I mean, we've been in lots of different mass, high ticket masterminds, coaching programs. spend a lot of money every year.   That's a bright thing. Yeah, we've been in all the best programs out there. We probably have been in them or connected to them or whatever. And so we've been in a lot of different programs. We invest a lot. And the thing I've noticed is we've been in a lot of these programs, but the real benefit of the programs, most significant benefit is the caliber of the people physically in the room.   It's the people that we get to meet. It's not the guru at the helm. It's not their cool content or ideas. Everybody joins for the content, but it's really, it's the people, it's the connections. It's the community that's curated. And that's one of the things that we're really shifting our awareness and focus around. I've realized that transformation always happens in the room. It doesn't happen over Zoom. Wow, that rhymes. You can all quote me on that. That's a Jason Hall written all over.   Transformation happens in the room, not on Zoom. And Zoom calls the other thing related to fake internet theory. You can ask AI about this, people. AI people, go check me on this. There's a psychological effect that watching being on Zoom calls on video and watching video training material inside of even our DoorGro Academy does not, your brain does not perceive this as real life. It perceives it as   This digital universe, it's fake. And so we've noticed our clients aren't able to just watch a video and implement or absorb it mentally the same way. And once they get in person with us and they recognize we're real people and we give them a high five or a hug or whatever, and they come to our onboarding, because we onboard every Mastermind client in person now. We also have quarterly events that we're launching.   for about two years. We've been doing that for a long time. saw the writing on Huge game changer. And we decided, hey, let's go, we can go much deeper in person. Totally. Than we can ever get to on Zoom or on a phone call. So we decided that should be one of the first things that we do with people is really get them in momentum quickly. And what's the fastest way that we can do that? We can get them in person. Yeah, cause on Zoom, it's very easy for you to look like you're cool.   It's very easy for you to look like everything's put together. Slashy lights, like YouTubers, and you can get some books and stuff in the background. You're making fun of me now? Have a pretty background. I see what's going on. You can have a boring office without cool lights. We can both play this game. Okay. So what I'm saying is...   When you're on a Zoom call with a group of your peers and business owners and people maybe you look up to, it's very easy to not give people your real situation and not reveal what's really going on. That's hard to avoid doing in person, especially if you're called out or your mentor's calling you out. That's difficult. And so you need to be in the room because you have to get real feedback. You have to share your real challenges. You need to recognize they're real people. Your brain and unconscious mind and your subconscious need   to recognize these ideas that you maybe saw in video or see on Zoom or the people you see on Zoom are real. And there's something that clicks and shifts when we get our clients in person. All of their results shift. They make more money. They have breakthroughs. And your breakthroughs are on the other side of embarrassment. One of my mentors would share. And one of our mentors would share. And that's, you have to be willing to get real.   And real and raw happens in person. It's just not going to be the same on a Zoom call or on video or even digital marketing. We're looking at how we can do less of that ourselves and do more stuff that actually creates real connection and relationship with people. Because I think anybody can do anything digitally now Let me go take some property managers out to lunch.   That's what we need to do. We need to meet people face to face and in person. do door to door. Yeah. I know. I think there's going to be a secondary effect that we're going to see a lot of things shifting back to humans. We've spent so much time over the last decade sitting in front of computer, like so many people. And I think if AI does anything well for us, it will be that it gets us out from behind the computer and actually hanging out with   human beings again. And that should be the goal. And the people that are smart are going to be focused on that. And that's why our DoorGrow Mastermind, we've shifted the priority and the focus to being an in-person mastermind. And we've seen people have great success with this, our mentors like Aaron Stokes and others have great success with this. And that's what we're wanting to replicate and emulate.   And we want people to have real relationships. We want people to have a family, a cohort of property management business owners that feel connected and are doing cool stuff. And that way you can cut through all the AI slot because everything AI puts out sounds like it's great and amazing. Every landing page looks like, yeah, this sounds like it could be awesome. And so it's hard to know if anything is real at all.   and a lot of stuff on the internet is not even real anymore. A lot of the products you see aren't even like actually real or decent products. They've just got great AI marketing and you can go buy the product for like a third of the cost on Ali Express or Alibaba or whatever. Or you can go on Amazon. Some of these products I'm seeing on Instagram, you can go buy for like $50 cheaper on these small products on Amazon. And so it like, yeah. And AI can create courses too.   Anyone can get on any AI thing and go, hey, I have this idea and have this thought and I want to create a course and then sell this course online. Build me the whole course, build me the material, me the script. everything. You can even have AI. If you don't want to do the video for the course, you don't have to. I can just do it. You can summarize it. You can do that in minutes now.   What value is there in that? The challenge is, is that there's no way to know what really works. Right. Because AI is just making stuff up based on what it has in its knowledge base. We have a lot of awesome stuff that's behind our payroll. hitting the table because every time you do, it's a whole time shakes. Yes, dear. Okay. This is going to be, if someone's watching this podcast live, I'm so sorry if you're like ceasing. It's like bouncing a little bit.   It shakes. That's me. I'm moving around so much. Many earthquakes. Seizures. Got it. No taking. Okay. Well that's how you know it's not AI, right? You're shaking the table. AI's not going to say that to me. No, AI would Claude, how do I respond to my wife? All right. Awesome. All right. yeah, so this is the idea is   We want to shift towards reality and in person because if I'm talking with a property management business owner and they say, this is what I've done and it's actually working, whereas AI will just hallucinate, it'll make stuff up, it will lie and just make everything sound amazing because somebody could say, just make this sound like it's amazing and it works and it's awesome. And it might not be. And so how do you know it's real anymore? You're going to need to be around real humans that can say, I did this.   I tried this thing. I used this AI prompt and here is the result. It wasn't great. Or this worked. And more importantly, that's a bad idea because AI doesn't want to tell you if it's a bad idea. AI is so affirming. It goes, that's such a great idea. Yeah, you should definitely do that. Wait, whoa, whoa, whoa. Are you sure? Is that going to work? Yeah, you're on the right track. Yeah, you've got this dialed in so well. And then   You could also do this and no, no, no. You're not coming up with a new idea. You're a game changer. You're not just cool. You're awesome. right. So yeah, know AI is really bad at telling people bad news. Well, I know there's people listening and they're like, well, I use this and I have this problem. I get it. Like, but yeah, chat GPT. It's But you have to you feel amazing. You have to give a prompt.   in order to make it be more honest with you, in order to make it not just affirm everything that you say. So when we think about what is the programming of it, therefore then what is the validity of it? So we have to kind of reprogram it just in order to get better output from it and better data from it. That doesn't mean it's the best data. What is great data is here's a real person. They've been there, done that.   I'm going to trust that over what AI tells me any day of the week. Yeah. And people trust AI. Like, I use AI to do research on things because I'm like, this product claim real? Is this landing page on the level? And sometimes it's like, yes. And sometimes it's like, no, this is overinflated. This study they're citing is like, misreference, whatever. There's so much BS. So now we have to use AI to combat AI to figure out what's real. But this is going to be one of the secondary effects is we need to be   connecting with human beings and we need to be in the room with real people and in person. And that's what AI should enable us to do it. It should enable more humanity. It should enable more connection, not disconnect us. okay, anything else that we want to say about this? So if you're looking for something that is a real human, and if you're looking for human connection, then we've got...   Events coming up in October. I know that by the time this will be released, you'll miss the May event. But we do have live in-person events coming up in October that you would be able to attend. They will be in the North Austin, Texas area. And one of them will be our fall intensive. This will be for our clients. If you're not yet our client, then you still have some time to figure out, you know, hey, let's just jump in and try it out.   The other one is our DorgR Live event. And we've decided in this whole AI spirit, let's get people moving through things quickly. And instead of just sitting at another conference, because there's so many of those in property management, where you just sit at a conference and you go home with 38 pages of notes and all of these ideas that you would love to do, and maybe you're going to do one of two of them, and that's about it. And then in reality,   there's all of this other good stuff and you never really got to it. But had you implemented it, it might have changed the business. So we've decided to instead shift. So it's not a conference where you're going to be sitting there in the room, taking notes and learning, which is very valuable. That's still a valuable thing. We're just making it more valuable. So we're going to have some workshops built right into it so that you can go there.   do the work, take action, and you can move your business forward. And the way that we're structuring things is it will allow you to get 30 or more days of work done in the two days at the event. And it's in person with real humans. Yeah. So I'll read a little quote from our offer document for our growth accelerator mastermind. And it says, the room where property managers stop playing small.   Most property managers are stuck trying to grow from behind a screen. Watching webinars, sitting on Zoom calls, collecting PDS they'll never read and wondering why nothing changes. And nothing changes because your environment hasn't changed. You've got to get into a different environment. Environment changes the identity. Even fish will grow to the size of the container that they're in. You need a different container if you want to grow energetically, spiritually, mentally.   I don't know, maybe physically. All right, so the DoorGrowth Growth Accelerator Mastermind, our super system level of the mastermind, which is supposed to focus more on operations for higher level operators or business owners. This is not just a course. It's not an online program. I'll be AI. It's not A, it's B. It's like, this is what actually really works. This gets you real results. And so, yeah, so come.   is come be in a seat with us in Austin. Come hang out with us. Come hang out with us. We curate and attract like the most amazing entrepreneurs. We have a new team member named Kyle who's over Client Success. And Kyle, we were just chatting the other day, him and I, and he was like, I was talking about, have really awesome clients. He's like, I know, it's so amazing the type of people you attract. Like we attract, I believe, the best people in the industry. The best humans in property management are in DoorGrowth's mastermind.   and are working with DoorGrow. And a lot of the coaches, a lot of the people out there, they're past clients of mine. Like, DoorGrow's had a significant impact. And I'm obviously a bit biased, but I believe we have the best stuff in the industry to bring to the table. And we have the most comprehensive program. We help with growth. We help with ops. Nobody else has rebranded over 300 companies. Nobody else has built, well, maybe someone's built as many websites as us, but we built hundreds, probably six, seven, 100, 800 websites.   helped people clean up their pricing. We've rolled out innovative three-tier hybrid pricing models. We're helping clients dramatically increase their profit margins. If you feel like you've heard it all and you've worked with all the other coaches, you may want to take look at DoorGrow because we have the most comprehensive. There's lots of coaches that do one little thing here or there. And there's some really great ones out there. But as far as having the most comprehensive program and I think the most innovative ideas and strategies, that's the only way you can curate and   have that is through some sort of mastermind environment and that's what we've created at DoorGrow. Okay so there's tapping me on the leg saying stop selling like let's wrap this up. Is that accurate? I interpreting things correctly? Okay. You got it. All right. And there's salespeople are gonna sell I just say I want you to win and I don't hate money so and I want you to not hate money and I want to help you make more money. All right cool. Anything else? No. All right cool.   And we will do the outro and here's what this sounds like and this will be relevant. All right. So if you felt stuck or stagnant and want to take your property management business to the next level, reach out to us at doorgrow.com for free training on how to get unlimited free leads. Text the word leads to 512-648-4608. Also, you can join our free Facebook community just for property management business owners by going to doorgrowclub.com. We have launched our own   private community outside of Facebook. And so we'll be telling you more about that. The DoorGrowth Club will be somewhat shifting into this new awesome space. And if you want tips, tricks, and ideas to learn about our offers, subscribe to our newsletter by going to doorgrowth.com slash subscribe. And you can get our newsletter. And if you found this even a little bit helpful, don't forget to subscribe, leave us a review. We'd really appreciate it on whatever platform you saw this on. And until next time, remember the slowest path to growth.   is to do it alone. So let's grow together in the room together. Bye everyone.

Last 4 days before regular tickets sell out at AI Engineer World's Fair - this is the single biggest gathering of AI Engineers, Founders, Leaders, and Researchers in the world. Attendees get >$5000 worth of sponsor credits and talk tracks are looking FANTASTIC. Join us!The AI scaling debate always focuses on the question of “how do we get more GPUs?” but the better question may be: how do we make the most of ones we already have.The fact that a frontier lab like xAI could be running at sub-10% MFU (Model FLOPs Utilization) is just a hint at what the real problem may be.For context, older frontier-scale training runs were already much higher than 10%. GPT-3 was around 21% MFU. Gopher was around 32%. Megatron-Turing NLG was around 30%. PaLM reached around 46%. And our guest Anjney says best-in-class MFU today is closer to 60–70%.It's not necessarily that xAI is uniquely incompetent (it's clear they have talented folks) but rather the priorities may be flipped in the GPU arms race.While GPU access is a bottleneck, simply increasing CapEx won't automatically translate to better models as frontier AI is increasingly a systems problem: scheduling, utilization, networking, kernels, frameworks, data pipelines, parallelism, cluster reliability, and the thousand small decisions that determine whether your theoretical FLOPs become real training progress.From building Discord's developer platform and backing frontier AI companies like Anthropic, Mistral, Black Forest Labs, and Periodic Labs to now building AMP's independent compute grid, Anjney Midha has spent years close to the real bottlenecks of AI scaling. In this episode, Anjney joins swyx at Periodic Labs to unpack why the AI race is not just about buying more GPUs, why 95% utilization would have been considered an outage at Google, and why the next era of AI infrastructure has to be more aligned, more efficient, and more responsible.We go deep on AMP's vision for a compute grid that makes FLOPs flow like megawatts, the difference between full-stack AI labs and horizontal pooling, why AI data centers need community buy-in, and how compute markets could evolve into something closer to an independent system operator. Anjney also explains why DeepMind's unpublished research points to a market failure, why end-of-life prediction remains one of the most important AI applications he has thought about for fourteen years, and why “output maxing” may become a new discipline for frontier systems.We also discuss Anthropic's culture, why “luck favors the prepared mind” in coding models, how Claude cracked coding, why too much capital too early can make AI labs fragile, what Periodic Labs is trying to do with science and superconductors, why great researchers can become great CEOs, and why Silicon Valley is both deeply missionary and deeply mercenary.We discuss:* Why 95% utilization was considered an outage at Google* Why AI infrastructure waste compounds at frontier-lab scale* Why “move fast and break things” does not work for AI data centers* How data center backlash, power grids, and community incentives shape AI scaling* AMP's vision for making FLOPs flow like megawatts* Why compute needs an independent system operator* How interruptible demand and dynamic prioritization worked inside Google* Why DeepMind research hoarding creates negative externalities* AMP's 1.2GW base-load ambition and the need for 6GW of spike capacity* Why end-of-life prediction could become one of AI's most important healthcare applications* Frontier Systems, output maxing, and full-stack alignment* Why APIs and abstraction layers become lossy as organizations scale* Superconductors, standards, and the dream of lossless systems* SF Compute, open protocols, and the future of compute marketplaces* Why non-NVIDIA chips can still benefit from NVIDIA's reference architecture* Trust boundaries and why chip startups need visibility into future model architectures* Why VCs often underestimate researchers as CEOs* Scientists as star athletes of the mind* Why great CEOs need to be confrontational up and down the stack* Why leading the frontier matters more than “winning”* How Anthropic cracked coding* Why culture is fragile, not a permanent moat* Why hardship was a feature, not a bug, for Anthropic* Why Anthropic's P0 was coding from day one* Periodic Labs, physics as the constraint, and technical reality* Silicon Valley mercenaries, missionary teams, and what happens after a breakthroughAnjney Midha* LinkedIn: https://www.linkedin.com/in/anjney* X: https://x.com/AnjneyMidhaAMP PBC* Website: https://amppublic.com/* X: https://x.com/amppublicTimestamps00:00:00 Introduction00:00:09 Why AI Compute Is Being Wasted00:03:17 Responsible Infrastructure and Data Center Backlash00:06:07 AMP Grid: Making FLOPs Flow Like Megawatts00:12:41 Foundry, Frontier Labs, and Research Hoarding00:14:42 Gigawatt-Scale Compute and End-of-Life Prediction00:24:08 Frontier Systems, Output Maxing, and Alignment00:27:38 Compute Markets, SF Compute, and Non-NVIDIA Chips00:32:57 Trust Boundaries, Co-Design, and Researcher CEOs00:38:17 AI Coachella and First-Principles Thinking00:42:43 Leading vs Winning in Frontier AI00:45:54 How Anthropic Cracked Coding00:48:25 Culture, Hardship, and Anthropic's P000:54:03 Periodic Labs, Physics, and Silicon Valley Mercenaries00:56:26 Rishi Valley, Singapore, and Money as a Measure00:58:47 Closing ThoughtsTranscriptIntroduction: Anjney Midha, AMP, and Compute WasteSwyx [00:00:00]: We're in Periodic Labs with Anjney Midha, CEO, founder of AMP. Welcome.Compute Utilization: Node Allocation, MFU, and AlignmentAnjney [00:00:09]: Thanks for having me. At Google, there are two types of utilization usually, right? That you're measuring in these clusters. One is node allocation, and then the other's MFU. Node utilization is usually like what percentage of cards in the data center are just, used, and that, if it's not at, 95%-Swyx [00:00:29]: There is no excuseAnjney [00:00:29]: There's no excuse, right? I think 95% at Google, which is where my co-founder, Seb, came from, he built the Borg, PBorg/GQM scheduler at Google, and there I think 95% was considered an outage, so 96% node utilization is, should be standard. And most single-tenant clusters are not running at that. So that's one. And then MFU should be, I would say the best in class today is somewhere between 60 and 70%. I think this is a leadership question, right? Fundamentally it's an alignment question, which is are the people who are funding the cluster and then deploying the cluster actually aligned? And sometimes theoretically they are, but in practice the number of people in the chain, the supply chain between, the capital and all the way to whoever's managing the cluster and then whoever's measuring what the output is, are just so many, degrees of separation away that, the, The Have you ever heard the radian metaphor, which is at the beginning of an arc, if you have two arcs that are two lines that are just off by a few degrees, that-Swyx [00:01:33]: It spreads outAnjney [00:01:34]: It spreads out, right? Or at scale. And I think what's happening is a lot of cluster implementations and infrastructure, a lot of frontier labs and other teams, that's what's happening, is they're, they initialize the plan, which is kind of like North Star with a team that wants to do good, but then they're, required to scale so fast instead of iteratively that the wastage just compounds really fast at scale. And so I think we know the answer, which is just do iterative bring ups. If you spend time with people who've been in the semiconductor industry or the DSN industry for a long time, this is not new, and I don't think AI should be an excuse. Sure. Something What is new? Okay. We have a lot of new capabilities, but that doesn't mean just abandon common sense. Common sense should always be in fashion. ? AI scaling doesn't change the in fact, if anything, AI scaling should be putting a premium on the value of common sense and infrastructure because the margin of error now is so much lower and the costs of wastage are so much higher. And the cost of wastage, by the way, is not just economic. I'm, obviously I'm, I'm an investor, or I'm an investor by background. Over the last few years now we're running an AI infrastructure business called, AMP. And I think that it's okay to say this time is different on the capabilities front. We are genuinely getting capabilities at, of the, of a kind we haven't had before. That doesn't give you an excuse to say this time is different for everything, especially infrastructure. So look, I love the hacker mindset and the hustler mindset. Now, that's great for the startup mindset, but you remember this moment where Zuck went from saying, “Move fast, break things” to, move-Responsible Infrastructure and Data Center BacklashSwyx [00:03:10]: Fast and stable infrastructureAnjney [00:03:11]: Move fast with stable infrastructure. I think now we need to move fast with, responsible infrastructure. People are going to ask where the impact is. There was a really In our class yesterday, Scott Nolan, who's the founder of General Matter, came by at Stanford to speak about energy bottlenecks. And he had a phenomenal idea. He said, “if you look at the marginal unit economics of compute per hour,” he goes, “let's call it, $4 an hour. If you're having to bring up a new data center in a new community, why not just say we're going to charge 4.50 an hour, and that marginal impact or that marginal increase, we just literally take that and give it to the local community as cash?” I can tell you as a customer of that compute, I would love that. I'd be happy to pay an additional 50 cents per hour at scale.Swyx [00:03:57]: Wow. Yeah.Anjney [00:03:58]: Because if that means the public benefit is so clear to the communities that the data centers are coming up in, I'm going to feel like that compute is much more reliable. Up to 20% of all data centers this year in the US, my understanding is are at risk.Swyx [00:04:13]: Of community backlash?Anjney [00:04:14]: Correct. Of not getting the community support they need to get brought up.Swyx [00:04:19]: Wow. That's a huge number.Anjney [00:04:20]: Yeah. Now, we, I think we should dig into what that number is. I think it's a little bit of overstated. These things can get over-reported, but it-Swyx [00:04:27]: They don't just care about jobs. They care about all the other stuff around it, right? They care about power grid, they care about environments-Anjney [00:04:33]: Power grid, permitting, and so on. And imagine I think if you said there's a new AI deal. If we're bringing up a data center in your community, we're actually going to reduce the cost of your electricity bill. Okay, now we're talking. Right? The community's going, “Okay. Now this is a deal. I feel like a partner in this.” Right now that's not happening. There will be audits, there will be investigations, and when the, when the regulators come, I don't know when it's going to be, the folks who are moving fast and breaking things in the name of AI progress better be prepared. That's certainly not how we're procuring compute. Or we're, we're trying as much as we can to work with partners who have long-term track records. Many of whom, by the way, are not, AI providers. I think this whole idea of neoclouds being somehow this new category is a lot of marketing speak. There are really good, reliable, trusted data center providers in America who've been around 20 plus years. I love those folks. They know how to Sure. Are they sponsoring happy hours at NeurIPS? No. Are they legibly listed in Build? No. Are they hanging out in my, in, situational awareness parties? No. But they're adults. I trust them.Swyx [00:05:44]: They can run LAN. They can run power.Anjney [00:05:45]: They can run LAN, power, and shell. They have credit histories. We sit down, we have a conversations. Many of them live in Silicon Valley. They've, they've had to deal with the boom and bust cycles of the internet, and I love those folks. They are stable infrastructure partners and thinkers. And I think there's a lot of short-term thinking going on in the compute layer, and it's going to catch up to us. It's not going to be good.AMP Grid: Making FLOPs Flow Like MegawattsSwyx [00:06:07]: You talk about aligning incentives, and, I would think that aligning incentives means you have the full stack in one company, which is xAI and OpenAI, right? So you as a standalone infrastructure layer, why are you somehow more aligned to your portfolio companies than people who just own the whole thing?Anjney [00:06:28]: In systems design, right, there's, there's two regimes of, architecture, right? You have integration, and then you have pooling and utilization, right? So the Or rather, the way to increase utilization often is you can do systems integration where you collapse a lot of process into one node, or you can pull out a process from a node and share that amongst various That resource amongst several different nodes. And so we see the AMP grid, which is, the, what, the system we're building here, which is basically a compute grid. We're trying to do for compute what the electric grid-Swyx [00:07:02]: PowerAnjney [00:07:02]: Yeah, what the power grid did for electricity. It-- this is a pooling and utilization layer across clouds, And so we're actually the opposite of a full stack integration like approach.Swyx [00:07:12]: Super horizontal.Anjney [00:07:13]: Where it's much more horizontal and it's, it's multi-cloud, it's multi-silicon. The goal is to try to make FLOPs flow like megawatts, and that is very hard to do today for many reasons. There's stranded pools of compute all over the place and there's no fungibility. And so right now we do it at the level of scheduling, and we often do it at the economic layer. But as we start to announce what we're working on, it's extraordinary like how many folks are coming out of the woodworks and saying, “Hey, I'm actually working on a way to make compute fungible at this part of the stack and that part of the stack.” And as a grid, we'd like all of these folks to participate on the grid. There's, people often ask me, “Andra, are you a new cloud?” And I go, “No, actually neoclouds are suppliers.” sometimes they'll ask, “Are you a venture capital firm?” I go, “No, actually they are, they are demand like sort of off-takers of the grid.” We see ourselves as what's called an independent system operator. So if you study the history of the electric grid, once it became legible to a lot of factories and industrial sort of participants that, hey, actually it turns out pooling is a good idea. We should pool our generators instead of all having a generator running at half capacity in our backyard. There was a need for an independent entity who could coordinate all these parties. Transmission line, power generation, facilities, transmission lines, factories, and that neutral coordination mechanism is very critical. In order-- If you study like the history of grids, the most enduring ones were those that never owned their own assets. They were ones that had, or often started with long-term anchors who are uncorrelated sources of demand, a steel factory, a shoe mill or whatever in a particular town who weren't competitive, where the steel factory want to spike up at night, the shoe mill wanted to spike up during the day. So then you pool and you share, right? So each of you is guaranteed some base load, but then you kind of schedule your spikes to drive a peak utilization across the town. The gold standard, so to speak, historically, has been these utility companies like PJM Interconnect in the northeast of America, where they, over many years became this what's called an ISO, an independent system operator of the grid. So that's how we see ourselves. Economically, that's what we are. From a technical perspective, we started at the scheduling layer because Seb and Mihai, who, run engineering here, built that at-Swyx [00:09:28]: Did your schedulingAnjney [00:09:28]: They did that at Google. And, -Swyx [00:09:32]: And you have infra shops from Discord as well.Anjney [00:09:35]: I have some.Swyx [00:09:35]: I don't know, I don't know if Discord is like the primary identity, but what-whatever, I'm just kind of-Anjney [00:09:39]: No, D-Discord was-Swyx [00:09:40]: Choosing a well-known name.Anjney [00:09:42]: Well, I So I was running the developer platform there. The internal infrastructure I was not responsible for. That was actually a guy by the name of Mark Smith, who was extraordinary. And yes, Discord did pool So Discord is actually a counter example. I had the chance to learn a lot about fully, full stack infra there because-Swyx [00:09:56]: It's the same thing, yeahAnjney [00:09:57]: It's the, it's the other architecture which is, Discord built its own WebRTC vo-voice and video infra. So like Discord did not use-Swyx [00:10:08]: For the calls, yeah.Anjney [00:10:09]: Yeah, did not For communication, Discord did not use third party infra. It was all built in-house. And then the way you maximize utilization was you pool demand from the world's 200 million plus monthly active gamers, right? And so that's, that's how those stacks were constructed. Again, in systems design, the two concepts that keep coming up over and over again are abstraction and composition, right? And-Swyx [00:10:31]: Bundling and unbundlingAnjney [00:10:33]: Bundling and unbundling, abstraction, composition, like verticalization and-Swyx [00:10:36]: HorizontalAnjney [00:10:36]: Horizontalization. So in that sense, AMP is an independent system operator of the grid. We pool demand, we pool supply from a number of partners we trust At about 1.3 gigawatt scale over four years. And then we pool demand from some of the world's best, research labs and so on. We're sitting at one, periodic labs who need extraordinary long-term demand. And the idea is that, each of them is guaranteed base load on the grid, but they can spike up and down flexibly on, for compute, with much shorter timelines as needed. That was roughly the design of the program I came up with at a16z called Oxygen. The same-- That was the same design of the GQM, BorgX, Borg GQM implementation at Google that Mihai and Seb had built. Which was that how do you allow, teams inside of Google, on the internal infrastructure to be guaranteed capacity, for their base workloads? But when they need to spike up on research, how could they ensure that was sufficiently there? And of course, the big innovation that was not discovered, but kind of implemented in the space, this infra space maybe three, four years ago at Google was the idea of interruptible demand, right? Where you just queue up a bunch of jobs and through this like sort of credit system, there can be a bidding mechanism.Swyx [00:11:53]: Like priorities.Anjney [00:11:54]: It's a dynamic prioritization Basically. And jobs can get interrupted based on somebody else who's saying, “what? I have 10 tokens, 10 credits I want to spend on this job.” Another like team lead, research lead is “Genie 3 or whatever is only worth five, credits, and NanoBanana2 is worth 10 credits,” and so the NanoBanana job gets priority. That's a, that's a made up example.Swyx [00:12:15]: It's very real. Brain Marketplace was real. And, we've, we've covered this on the pod with David Luan, who was-Anjney [00:12:20]: Oh, great. OkaySwyx [00:12:20]: Was there. And the criticism is that, well, actually sometimes you need central command to go all in on a thing. And actually sometimes capitalism via credits doesn't work. Not, this is not a criticism of AMP. I'm just saying, this is a thing that has been tried, internally within Google, and it led to Google missing GPT.Foundry, Frontier Labs, and Research HoardingAnjney [00:12:41]: Like, we structured ourself essentially very similarly to Google. We are structured as a holdings company. So, Alphabet holdings is Alphabet holdings, and then they've got these subsidiaries called Google and-Swyx [00:12:51]: Other betsAnjney [00:12:52]: Other bets and so on. We've got, AMP holdings, and we've got our infrastructure business, and then we've got a capital business called Foundry that incubates new frontier AI labs or invests in them as venture capital, like Periodic. We put a few hundred million dollars into Anthropic from our fund earlier this year. So wherever we feel like teams are making progress, especially researchers and so on who've pushed the frontier inside of existing labs like DeepMind, I find, there comes a point where they feel misaligned with the dictatorship of Alphabet holdings. And at that point, sometimes the dictatorship doesn't want them anymore. And they're “Thank you. You've done your job here. You've kind of helped us through the zero to one phase, and for whatever reason, we're going to deprioritize your amazing, omni model or whatever it is, and instead we're going to prioritize coding.” And, I think that's a tragedy, but I get it. They're Sergey and team are running their own business there. But that doesn't mean we the rest of us should sit around waiting for that progress to get unlocked for the rest of the world and humanity. If you think about how much extraordinary research has happened inside of DeepMind over the last 10 years, I, Demis and Sergey and those guys did such a great job. But at the end of the day, so much of that has never seen the light of day?Swyx [00:14:00]: Or they're like papers only, but they never actually shipped it to production or-Anjney [00:14:03]: What's worse is the paper is actually not even being published anymore ‘cause there's a six-month embargo inside of DeepMind, right? We've heard about this where a paper comes out, and then I think there's a six-month embargo window where if anybody on the business team says, “This could be interesting” It's embargoed for life.Swyx [00:14:18]: Exactly. So the stuff that gets published is the stuff that's not good enough.Anjney [00:14:21]: There's an adverse selection problem, basically. Yeah. At this point-Swyx [00:14:25]: It's, it's a common complaint at NeurIPS, by the way, that's “Well, why would I look at the papers that are the trash of GDM?”Anjney [00:14:31]: Again, I think it's a tragedy. I get it. They're running their business, but the rest of the I think there's negative externalities of research being hoarded, and so that'there's a market failure. And somebody needs to unlock that research, and we can't do it on our own. We only have 1.2 gigawatts of compute. That's nothing. That's about $40 billion of cloud spend. We're going to need a lot-Gigawatt-Scale Compute and End-of-Life PredictionSwyx [00:14:51]: By the way, is that's a new number. I haven't, haven't come across that gigawatt number. That's huge.Anjney [00:14:56]: Yeah. And to be clear, we haven't secured all of it. That's how much demand we have started to secure. I think publicly we haven't actually confirmed how much we have for this year. In order-Swyx [00:15:04]: Where do you want to get to?Anjney [00:15:06]: I think the steady state would be that we have a base load pool Of 1.2 gigawatts at all times Of base load capacity. For spike capacity, right now my estimate is we need roughly six gigawatts over the next four years for all our teams to feel like they were able to keep moving the frontier, whatever they're working on, whether it's, like superconductor discovery over here. There's a new investment we're working on right now, which is in the end of life prediction space in healthcare. It's extraordinary how much you can, you can give this was actually my graduate school work. I went to grad school for bioinformatics at Stanford Med. And I know we-Swyx [00:15:40]: Econ, MCS, bio.Anjney [00:15:41]: So my-- I was this really weird cat where, I was never satisfied with my major options. So at one point I was an econ major, then I was a CS major, then I was a MCS major called mathematical computational science, and they decided they were going to end that major. So I took all that coursework, and I applied it to grad school, my graduate degree in bioinformatics, which was the master's program, and then I thought I was going to do a PhD. I never ended up doing it. I dropped out and went to work at Kleiner. But I was lucky enough to apprentice with this professor at, Stanford Med. His name is Nigam Shah, and he was working on end of life prediction. Stanford is one of the only research facilities in America that has a longitudinal patient data set that's larger at scale. I think it's at least 12 million patient lives. The only larger data set is at the VA, the Veterans Affairs, of America. And to do research, like do any deep learning and so on that data set, it was called the STRIDE data set at that time, you had to be a Stanford Med School affiliate, which is why I went and enrolled in the bioinformatics department. End of deep learning was early. Nigam Shah had the visibility-- the vision to see that, you could do end of life prediction to help palliative care. In America, the, over 30% of all Medicare, Medicaid spend, at least at that time, was spent on end of life care. And what's we grew up in Asia, so we all-- Yeah, at least I won't speak for you, but I have A very different relationship with death than I find folks who grew up in America do. In America, spiritually and culturally, especially in Western societies where Christianity, the Christian tradition sort of frames death as this terminal point, there's often a judgment day and so on. The way we view death is with a finality. In Indian culture, in Hindu culture, death is one-Swyx [00:17:35]: Also, he's Buddhist as well.Anjney [00:17:36]: You're Buddhist, yeah. So it's one, it's one step in a journey of many lives, right? And so, I grew up in this city called Chennai in the south of India, and when people die, you dance on the street. There's like a procession where your body is carried to be cremated and your family, like celebrates and there's drums and so on. It's this huge thing. And, It's because the idea is that you're going to be reincarnated. You've been liberated from the responsibilities of this life, and now you're onto your next. It's a new It's like going off to a new college or whatever, right? And so it was so alien to me when I got here as an undergrad- That the medical system works backwards from that assumption that we have to view death as this terminal thing and delay it, postpone it's a bad thing. And so at the time, clinical decision support in the United States was this very primitive field. Even to this day, physicians in the United States often will tell you when you have a terminal disease, this is your, we've diagnosed you, which is great. Our ability to diagnose you is extraordinary. You have somewhere between six months to six years to live. What do you do with that information? The error bars are so high that then you In times of uncertainty, we default to culture, and when the culture is let's-- this is a bad thing, I've got to prolong my life, then you start doing things like And just to, just sort of from a systems perspective, what's going on there is Physicians often feel like they need to provide such high error bars because there's always some uncertainty in end of life diagnosis, and if you provide the wrong Diagnosis or recommendation to your patient, you can be sued for medical malpractice. And then your license can be taken away. It can be catastrophic for your career. In contrast, if in countries where that's not the case, what you often observe is that patients, physicians are quite prescriptive with their recommendation. They say, “Hey, this is your condition. The literature says that you probably have this much time on Earth left. My expert opinion is that you are an outlier or whatever.” And they try to be more prescriptive, and that empowers a patient, right? ‘Cause then a patient can say, “I trust my doctor. They said on average, I have six months to live, but if I do these things, I may have a shot because of my particular predispositions or my genetic history or whatever.” And that empowers you to go about your life in a actually more scientific way than leaning on religion, culture, spirituality, and so on. In contrast, here, because of that medical malpractice sort of thing looming over your head, a physician never gives you a clear recommendation. So instead you say, “Okay, Doc, well, let's try it all.” And then you start a whole regime of drugs and therapies, and then you often spend weeks and weeks in the hospital, and that deteriorates your quality of life. And when that deteriorates your quality of life, you instead of spending your last few days doing the things you love with your family, you're spending it on a hospital bed. And that ends up being thirty percent of Medicare and Medicaid. So it's worse for the patients. The doctors feel terrible. The American taxpayer is paying a huge amount of money. And so this is why Nigam Shah, who was this professor at Stanford, said, “Anjney, if there's “ I kind of sat down with him. I was this young, I'd, I was twenty-one, and I was “I want to work on a big problem.” He's “The big problem is end of life care.” And so we tried to do deep learning to say, to-- So we started trying to run deep learning on these tried patient data sets to say, “Could you have an AI system make a recommendation that is orders of magnitude more precise about how much time you have left once you've been diagnosed with a terminal condition than a human?” And then if we can get that precision to be high enough, then you can empower the patient. And it turns out the tech works. Like it's-- Once you get the data set, like RL works. Honestly, even regression models work. You don't need to get that fancy. At the time, we were just trying, doing like very simple neural nets.Swyx [00:21:54]: Simple solutions, yeah.Anjney [00:21:54]: Today, what we can do with RL is extraordinary. The problem remains then and now is regulatory, because you actually can't shift the burden of the wrong clinical diagnoses from the physician to the AI system. And so at that time, I got quite disillusioned ten years ago for, twelve years ago where, ‘cause I felt I just didn't have the resources to influence regulation. Today, I'm very lucky. I'm in a different place. I've, I'm a lot older, and so I've been spending a lot of time on my next incubation, which is how can we unlock the, patient empowerment by training AI models to do end of life prediction much, with much more precision and ac-Swyx [00:22:37]: Oh, wow. You're still focused on this the whole time.Anjney [00:22:40]: The-- I haven't been able to get, this out of my mind a single day for the last fourteen years. This is the hill I want, I would like to die on. There's two, I would say. What? I actually, I'd prefer not to die.Swyx [00:22:51]: Yeah, exactly.Anjney [00:22:52]: But I think two bipartisan issues, I think two issues that should be bipartisan in America are how do we empower patients to make the right clinical decisions at the end of their life, such that we're reducing the taxpayer burden with science? It's just good old science, and AI can help here. And the second is, net positive data centers, ‘cause I think that's the biggest critical bottleneck on training and good enough AI models to help people at the end of their life. So there's sort of two sides of the, of the same scaling bottleneck curve, but those two, we formed AMP as a public benefit corporation. My wife and I, who you've met, you've met Viv. Her passion is education. Her family is a long line of educators and so on, and, of physicists. And so this class is my attempt to stop being the black sheep of the family and be a, an educator. But if I'm not educating, the thing I would be doing is working, on these two problems, whether on the political spectrum or as a researcher back at, in some lab. And my hope is if anyone's listening to this podcast, if they're passionate about either of those two topics, I'd love to hear from them. We'll, we'll we can share the contact in the show notes, but, we're looking for people to join both of those missions on the, on the political side as well as on the medical side, on the research side.Frontier Systems, Output Maxing, and AlignmentSwyx [00:24:08]: You said, this is a discipline that you want to form. You call it's called variously called Frontier System. It's variously called One Person Frontier Lab. What is the ideal name or shape of this? Like the, what is the mission?Anjney [00:24:24]: Of the class?Swyx [00:24:26]: Of the discipline that you're, exploring, right? I The class is called Frontier Systems. But like for me, maybe one phrase is you're, you're just anti-waste, right? Which is wasting GPUs, wasting in human and Medicare. But is there, is there a broader theme that I'm, that maybe you can encapsulate more succinctly?Anjney [00:24:45]: Yeah. The, from an engineering perspective, it's very simple. It's output maxing. It's the, it's the department of output maxing.Swyx [00:24:51]: Making the most of what we have.Anjney [00:24:52]: Exactly. I'm a huge believer in optimal outcomes. I think both in America and other countries, we are losing our appreciation for nuance, and this is the thing of And AI is the same case, right? Oh, the bitter lesson holds. Okay, fine. But that doesn't mean you just like throw 500 GB300, 500,000 GB300s at your suboptimal model scaling and you waste a bunch of compute. It also doesn't mean that, the most optimal is to have like 50 different architectures where there isn't enough standardization. One of the reasons Anthropic has had extraordinary sort of velocity is ‘cause they picked the transform architecture and said, “This is simple. Let's double down on it,” right? And now luckily there's enough investment going to the space that we can afford other architectures, but at the time, investment was just too fragmented into other architectures, so that arguably unlocked scaling. So I think there's a philosophy. I think we all owe it to ourselves to do output maxing with a new capability called AI on a global level. I think if I was starting a new department at Stanford, depending on how fuzzy or technical I wanted to be, I'd probably call it the Department of Alignment. Like-Swyx [00:25:59]: It's an overloaded termAnjney [00:26:01]: But it is, But alignment really Is a hard problem. And I think when you unlock it, full stack alignment is super hard in any organization and in any system. Like in a, in a venture capital firm, if you can have full stack alignment between your limited partners and your, the founders who are creating the value and ultimately the public that owns the IPO stock, that is a gift that keeps giving. And when you study the history of these systems, when they start off, they usually start out small scale where the feedback loop is actually so tight that there's alignment. And then the more you try to scale, the more division of labor happens, the more specialization happens, and at each step you add abstractions. And wherever there's an API interface, there's like loss. There's communication loss. And so I think a really cool thing would be for us to figure out is there a way for us to have our cake and eat it too as an engineering discipline? Is there a way to actually scale up and scale out Without losing any alignment, without lossy transmission?Swyx [00:27:01]: You mean standards?Anjney [00:27:02]: So standards is one way. The other way is you just have net new capabilities. So like what we're trying to do here is discover new superconductors. A room temperature superconductor would be a lossless transmission mechanism for energy. We would have flying cars. We are right within a few years of having a new room temperature superconductor. So I think those are the two. You either have to standardize On protocols or API specs that allow lossless communication, or you can come up with a whole new capability that unlocks so much abundance, the standardization doesn't matter ‘cause you just unlock net new capacity. This, the, so this is what I spend my days thinking about these days.Compute Markets, SF Compute, and Non-NVIDIA ChipsSwyx [00:27:38]: No, I think every infra person at, who wants scale and wants to output max does eventually end up thinking about this. We don't have time to go into it, but we have done an episode with SF Compute-Anjney [00:27:50]: Oh, coolSwyx [00:27:50]: That is trying to standardize The futures contract for compute. I don't, I don't know how that's going by the way, but like at some point this will be public.Anjney [00:27:57]: Oh, I think Evan is awesome and SF Compute is the kind of effort that I hope we can accelerate because what often happens is these exchanges are very hard to get, they, it's hard to bootstrap them, right? Because they often require-- There's many inefficiencies between parties. There's trust boundary inefficiencies in infrastructure because you don't trust, one part of the stack doesn't trust another part of the stack to give them visibility. There's capital markets inefficiencies, there's operational efficiencies. So if you can inject like a single shock to the system of a ton of compute demand or supply, then you can accelerate, these new flywheels. And so my hope is one day, or soon, if SF Compute needs extra like has excess capacity, they just hook it up to the grid and they get flooded with demand from us. And on the other side, if they have a ton of demand but they don't have supply, they just again hook up to the grid and it's a two-way protocol where they can just hook up to our capacity. And I don't think we're too far from that. Today our working implementation of it is mostly through a group of labs, universities, and a few sort of trusted parties who are, who all feel like they're in alignment to borrow an over sort of used word. But our hope is to just have it be an open protocol that anyone can hook up to on-Swyx [00:29:20]: Hook up for demand or hook up for supply? In primarily demand, it sounds like. Like you-Anjney [00:29:25]: No, bothSwyx [00:29:26]: You would want to offer demand.Anjney [00:29:27]: Both. Yeah. Unfortunately, what's happened in the last six weeks is, we thought we'd have a bunch of excess capacity by the end of this year. It's all gone.Swyx [00:29:37]: It's exploding.Anjney [00:29:38]: It, yeah. It's all gone. And so I have, my text messages are full of friends, we know many of these people, these are founders who've raised billions of dollars in San Francisco going, “Oh, any chance you have like 50 nodes in the next few weeks?”Swyx [00:29:51]: What is the scope for, non-Nvidia, right? You have Lisa Su coming and, Rainer Pope as well. And so There is a lot of demand for, more performance Alternative architectures and all that. At the same time, this hurts your standardization.Anjney [00:30:11]: I don't think so. So actually Rainer's a great example, right? Rainer is a CEO and founder of, MatX. I actually had him by for office hours in the class earlier today, and there was an insight he brought up that I hadn't considered before, which is when they decided to pick the standard For their data center, they picked the NVIDIA reference architecture. So the MatX chips Just plug in to any site that has an NVIDIA bring up planned. And, the-Swyx [00:30:42]: It's just software then. It's, it's not the-Anjney [00:30:44]: A-Swyx [00:30:44]: Hardware.Anjney [00:30:46]: Well, from an input and IO perspective It's the same footprint as an NVIDIA rack.Swyx [00:30:52]: That makes sense.Anjney [00:30:53]: Where they have done, innovated a bunch from what I can tell is on systems co-design. Which is where a lot of the gains are to be had. And so he picked He was “Anjney, we, there's just so much work to do when you're building a new chip company.”Swyx [00:31:08]: Can't fight every front.Anjney [00:31:08]: You just can't fight on every front. So my question to him was, “Well, you're working on this new chip. Their tape-out is next year. What, who are you going to partner with to host the chips?” And he said, “Whoever will host them. That's just not, that's not my focus.” And I said, “But how did you “ you decided back to our earlier systems design question, he decided that, he didn't want to be a full, fully integrated chip provider. The bottleneck they're focused on is the logic die, and they, he feels they can crank out a ton of performance gains through co-design there. But then that means you delegate, to our question earlier, it, you he's the data center provider is a different part of the stack, and so then he's dependent on that part of the ecosystem to host his chips to get the performance gains to the customer. So now you have another abstraction, and you might have loss. So I asked him, “How do you prevent loss?” And back to your point, he said, “I just picked the NVIDIA standard ‘cause I didn't want to Like I wanted to piggyback off of an existing protocol.” And that, what's great about NVIDIA is that reference architecture is known.Swyx [00:32:15]: Open.Anjney [00:32:15]: It's open. They've published it. So Jensen's actually enabled someone like Rainer to build a chip company like MatX, and I don't see them as competitive. The compute demand is so high. Like, I don't I think NVIDIA's not able to meet the demands of production, so we just need more chips. And I think it's very smart what MatX has done, which is say, “We're just going to we're not going to innovate on the data center design ‘cause actually, thank you, Jensen, you've done all the hard work. Where we can innovate is somewhere else.” And I think that's, that's very healthy. I think that's how we unblock new bottlenecks. And my view is these, the, chip teams like MatX, who have arrived at the insight that co-design is the way, The primary bottleneck for them is trust boundary. To do co-design well, you need visibility into the next model generation as soon as possible ‘cause it takes two years to tape out. So if by the time I bring my chip to market, your model architecture's changed, I'm host. Now, when he was inside Google, he was sitting next to the Gemini team. He was on Palm or whatever.Trust Boundaries, Co-Design, and Researcher CEOsSwyx [00:33:19]: His co-founder was the, was one, was one of the Palm guys, I think.Anjney [00:33:23]: Yes. Yes, exactly. So when you're inside the trust boundary of Google, then your systems co-design loop is super tight. When you leave as a founder, one of the biggest risks you take is now you're outside the trust boundary. And so what I love doing is helping chip teams who can help us unlock more capacity for the independent ecosystem access to trust. Because when I If I've been, involved with a lab from day one, and I was lucky enough to work with Anthropic, and then I'm on the board of Mistral and helped Black Forest Labs get started. I think at this point I'm on six or seven different teams.Swyx [00:33:57]: Only six? I feel like my mental number was going to be 13, but yeah, it's-Anjney [00:34:02]: No, I go deep with one at a time.Swyx [00:34:04]: You're founding CEO of Arena.Anjney [00:34:07]: Nah, that was an, that was an-Swyx [00:34:08]: Administrative CEOAnjney [00:34:09]: It was an administrative five-month gig where Whalen and Anastasios were graduating from their PhDs, and they didn't need a product team. So I helped recruit the head of engineering product and design. But Anastasios has always been the CEO of that company. I played a pinch-hitting I'm an intern. I was CEO intern For five months. -Swyx [00:34:33]: I interviewed him, and he's he's very well-spoken. I think he's a debate, former debate, champion. But also very quantitative and mathematical, which is-Anjney [00:34:41]: He-Swyx [00:34:41]: Such a unicorn.Anjney [00:34:43]: See, what's amazing about him? If you look at his output, he's an output maxer. By the time he was graduating from his PhD, which he only graduated last year, he had published more work with a citation count than, people twice his age. But at the same time, he'd already started a project called LLM Arena that was being used by millions of people As a side project. And time and time again, what I've realized is venture capitalists suck at seeing human beings as, dynamic agents where-Swyx [00:35:14]: They want to put you in a boxAnjney [00:35:15]: They want to put you in a box.Swyx [00:35:15]: This is your thing.Anjney [00:35:16]: So the first time I got introduced to Anastasios, somebody had told me “Oh, he's amazing, but he's a researcher.” I was “what? What do you mean he's a researcher?” That's what-Swyx [00:35:28]: Like he's not a CEO, not a founder.Anjney [00:35:29]: Not a CEO, exactly. I was “Are you crazy? Do you Have you met Dario?” Dario's a scientist. He's gone from zero to, what will soon be a trillion-dollar company in four years. Being a CEO, nominally speaking, is not that hard. Being a good CEO is hard. Being a great CEO actually requires a level of performance that scientists who have already published at the top of their field have accomplished. It is super hard to be a competitive scientist. To publish in academia over the last 20, 30 years, to make it to the top of your discipline at a place like Berkeley, you are a star athlete. Like, you are an athlete of the mind, and you perform at the highest levels. And to get there, whether you're, Anastasios or Whalen at Berkeley, or you are Robin, who-Swyx [00:36:23]: BFL, yeahAnjney [00:36:24]: With Black Forest, who created Stable Diffusion, or if you're, like Guillaume at Meta, who created Llama before he started Mistral. The amount of human leadership you have to demonstrate to get the resources, like get the trust of the organization, publish it, put it up. I would just fund researchers all day Right? If who have contributed already to the field. If they've, if they've put SOTA out there, they're, they're star athletes already. If they haven't done SOTA Look, they can still be good CEOs, but then I find the failure mode is that they just don't want to be CEOs, they primarily want to publish, and that's okay, too. One of the things we do with the AMP Grid is we donate excess compute. We have two nonprofits, like university labs. We carved out like a couple thousand H100s. But I do think there's extraordinary research being done on university campuses. My father-in-law's a physicist. He's a professor. Extraordinary work in physics, and we need that. But if you want to be a CEO, what you need to be willing To do is be super confrontational, outside of science. Like within the scientific community, some of the best researchers are very confrontational about their convictions, right? This architecture is right. To be a great CEO, you basically have to be willing to be confrontational up and down the stack.Swyx [00:37:41]: To your own team.Anjney [00:37:42]: To your own team-Swyx [00:37:43]: To customersAnjney [00:37:43]: Hiring, recruiting customers. Well, I would say, Yeah, pretty much to everyone Everybody. Of course-Swyx [00:37:50]: I see, I feel a little bit of that in my own work, but yeah, I can't imagine the stakes that Dario has had to go through. It's, it's pretty insane.Anjney [00:37:56]: No, I don't think the stakes are that different From how you're feeling it, right? Stakes are personal scaling vectors, right? The stakes that seem so low to you, like having this podcast where you can talk to somebody and just have a you're an extraordinary communicator, right? Like already in this conversation, you've pulled more out of me than most people, and I've been on 12 podcasts in the last two weeks.AI Coachella and First-Principles ThinkingSwyx [00:38:17]: I think I, we've just seen each other enough that there's some base trust.Anjney [00:38:20]: There's base trust.Swyx [00:38:20]: And I think, and I know that you, that I've done my homework and like I know that trust is a big deal for you, so.Anjney [00:38:27]: I think trust is about consistency, and you and I have seen each other In the community for years, right? Like, I remember the first time we met was at NeurIPS in New Orleans. I don't know if you remember that, luncheon.Swyx [00:38:38]: Oh my God.Anjney [00:38:39]: Reiko had set up this Reiko's amazing, and he set up this luncheon and-Swyx [00:38:43]: Yeah, I was “Who's this Discord guy?” I'm “Okay.” But-Anjney [00:38:45]: No, you weren't-Swyx [00:38:46]: You were just “You made some investments.”Anjney [00:38:47]: You were much less polite. You were “Who's this VC?” You're like-Swyx [00:38:51]: No, I Was I? Oh my God.Anjney [00:38:53]: It was-Swyx [00:38:53]: I'm so sorryAnjney [00:38:53]: It was visible on your face.Swyx [00:38:54]: I'm so sorry. But you weren't, you weren't The introduction was bad. I was I didn't know who you were.Anjney [00:39:00]: The, see, this is the thing about context, right? Like, but then I think I heard your accent. And I was “Are you-”Swyx [00:39:06]: Singapore, yeahAnjney [00:39:06]: “Are you Singaporean?” And you're “Yeah.” And I said, “I went to high school, JC, in Singapore.” And then the ice broke. But This is the there are in the scientific community, sometimes the stakes are very high for people who haven't had the emotional, what is called EQ Coaching and mentorship, right? Which is like to have scientific impact, you often need to be a extraordinary emotional, like emotionally in tune person with the folks you're trying to influence. And so what comes so naturally to you is actually a super high stakes thing to other people. And so I wouldn't assume that Dario's more stressed out than you. These things are you'd be surprised how similar and small sometimes the problems are to you That some of the world's biggest, leaders are facing. And that's what I've learned from this class. The guest speakers are Sam, Satya, Jensen.Swyx [00:40:01]: AI Coachella.Anjney [00:40:02]: Yeah. It's AI Coachella, right? So we got to get all the headliners, and they're I'm very lucky that some of these people have either mentored me over the years or I've done business with them. And when you, take the performative stuff out and any assumptions you may have about these people that you read in the press or on Twitter, We're all just humans. We're all trying to get along. And what's so special about this moment is AI is forcing, like scaling, the bitter lesson is forcing a lot of people to revise their assumptions for how the world works and go back to first principles or go and educate themselves. So the kind of people I was, I won't name who this person is, but I was at an event last week in Texas and, ran to somebody who said, “Anjney, I came across the class. What do you think about real time action prediction models?” And I was, don't know how happy it made me feel when they asked me that question. I know they've done the work. They've challenged themselves. I'm, they didn't ask me, “What do you think of world models?” They said, “What do you think of n-”Swyx [00:41:04]: Real time action predictionAnjney [00:41:05]: “action, real time action prediction models?” World models, don't get me wrong, are cool and everything, but you and I both know that is a layer of abstraction that is sometimes not usefully precise enough. Right? Ours-Swyx [00:41:16]: There's like four different kinds of world models.Anjney [00:41:17]: Yes, exactly.Swyx [00:41:18]: We've done the part with general intuition, by the way, which is very focused on, -Anjney [00:41:22]: Oh, cool. Yes. I love Pim. Pim is great. And this is what I love about people who've done that level of work. They realize they're not in competition with people who the rest of the world thinks they're in competition with.Swyx [00:41:34]: Because they're not in the category, they're in the specific thing they're trying to do.Anjney [00:41:37]: They're focused on their mission, and they have a systems understanding of the bottleneck they're trying to solve. And when somebody else says, “I'm working on real time, action prediction models too,” Pim goes, “Oh, I love that person. I want, I can learn from them.” But the minute they're “Oh, that person's a world model person,” it's “like which type of world model person?” But mostly they're just trying to figure out if it's a waste of their time, because we don't have enough time. So, Pim, for example, is super, loves this other company I work with we've talked about called Black Forest Labs. And he's mentioned to me multiple times that he's so, He thinks what Flux is doing is really cool. Andy Blattman came by and spoke in the class. And what I find over and over again is for people who do the work, who can be usefully precise enough about like what is actually going on in the world of frontier research, The sense of camaraderie is still well and alive, but it gets lost sometimes when you have to like abstract The technical complexities in, business terms And then the VCs are “How are you different from that world model?” I'm going to say Where do I even start to explain this stuff? And then the misalignment creeps in.Leading vs. Winning in Frontier AISwyx [00:42:43]: This is good. Yeah, I think, people listening get a sense of, what it is like to operate at a real level, like yourself, rather than at, the journalist level, where you have to sort of put everyone in, a rough category and create a narrative of competition, and who's winning today, who's behind.Anjney [00:42:58]: It-- this idea of winning is so Weird to me.Swyx [00:43:03]: You do want to win. You want you want competitiveness.Anjney [00:43:06]: No, I think you want to lead.Swyx [00:43:07]: You want SOTA.Anjney [00:43:07]: No, I think you want to lead. Yes, so you want to push the frontier. You want to push the SOTA. You want to do something that hasn't been done before. You want to capture value, but you don't want to capture so much value that, people think you're unaligned with your mission or trying to do what's best for the world. You want to capture enough value that you can keep innovating, right? And I think that people want to lead, they don't really This idea of winning and losing, again, I love Jensen. He's a, he's a leader. The mindset that he talked about on Dwarkesh's podcast, right? He's “I didn't wake up with a loser mindset.” I think that was awesome, right? Because he's, he's an engineer. Dwarkesh has done the work. So there's at least-- even though the, to me, it was very obvious they're talking about the same thing, they just passed each other. They just had to basically, Jensen has this, five-layer cake abstraction of how the industry works. And Dwarkesh had, I think from that podcast, had more of, a pre-training, mid-training, post-training systems loop concept.Swyx [00:44:04]: It's just a factor of who he talks to, right? Again, it's very clear.Anjney [00:44:06]: It's the systems It's the abstraction, the mental models, the It's the whole-- Dude, so much of the problem in the world is reasoning by analogy. And then the assumptions that are held invisibly.Swyx [00:44:19]: Yeah, I've, I've said, this is actually the best time in human history for first principles thinkers. Because everything you think will happen is actually now coming true.Anjney [00:44:28]: Correct. And the venture capital community is, notorious for this, where people look-- In times of uncertainty, they, cling to axioms that ended up being true from the previous era, and they kind of like proclaim them with confidence as if they're truths, but they're not. And it's very important to see the distinction between a heuristic and an axiom. An axiom can be proven-Swyx [00:44:55]: Like from internal consistency point of viewAnjney [00:44:56]: With internal consistency. A heuristic is a way you kind of a shortcut. And my God, the number of people I have had to put up with over the last few years who proclaim-- use heuristics As axioms to judge people, to judge which companies are going to succeed or the number of people who are “Oh, yeah, Anthropic, they're just training models right now,” but this one continue.Swyx [00:45:22]: Because that's a B2B SaaS?Anjney [00:45:23]: Yeah, the, like Which over the fullness of time, if you squint at it, maybe. But the way you arrive there is so important that you can-- you just, you can dismiss people. Here's what happened, right? What happened is Anthropic basically achieved takeoff in October of last year. That training run-Swyx [00:45:41]: Whatever, three seven?Anjney [00:45:42]: I forget the numbers now, but whatever that checkpoint was-Swyx [00:45:45]: We saw the cognition.Anjney [00:45:46]: Yeah. Right? You probably-- The, to those of us in the community, especially once post-training was done and it was released in December-Swyx [00:45:52]: Yeah. Can I sneak a sneaky question in there? I don't know if you have a perspective, maybe you don't, I just The number one question is how did Anthropic crack coding, right? Because Claude One, Claude Two, okay, like it was part of it, but it wasn't a big deal. And the leading hypothesis, it's a lucky dice roll that was then compounded, right? Like it was like Mildly better, but then they saw it and they were “Okay, let's really invest.”How Anthropic Cracked CodingAnjney [00:46:17]: I had this very annoying teacher. I went to this boarding school called Rishi Valley in India, which is like this, bird preserve. It's like three hundred and fifty acres of bird preserve in rural India, and there was no technology for seven years. There was this teacher, I won't name them, but they would have this-- I hated it every time he said this to me. He was “Luck fa-favors the prepared mind,” which is like a common saying, but the way he delivered it, always grated me, ‘cause he was always I was always one of those kids who got, a good grade without trying very hard. ‘Cause like high middle school is not that hard if you, if you're generally, paying attention and so on. And there was this one time where I-- But then I would get an eighty percent grade, and he would keep pushing me to say “The reason you didn't get the ninety-five plus percent is because you're not that lucky.” And I would say, “What do you mean?” ‘Cause I would think that I deserved that grade, and I would sometimes argue with him. And he'd say, “You didn't have a prepared mind. If you want to get lucky again “ There was basically one time where I got like ninety-five or ninety-six on this, on this subject, and I, now that I felt entitled. I was “Okay, I'm going to keep doing this,” and I didn't. And then he was “Luck favors a prepared mind. You got lucky last time, but you got to stay prepared.” And I didn't understand what he meant. Now, as I'm older, I'm okay, these adults actually knew a thing or two. Anthropic has been the most prepared company for four years. And so then when the right, context data comes in, the right developers start sending in, the right context diffs, Sure, you could say you got lucky, but if you ask me, they're pr-pretty damn prepared with paranoia for like four years. And you have to remember, it was so hard for them to get going early on that they had to do so much more with so much less that you just have to be prepared to be so efficient.Swyx [00:48:06]: Yes. There's numbers on their burn compared to OpenAI. I've, I've written about it, but they are so much more efficient in their, in their tech stack.Anjney [00:48:14]: It's not even It's not funny.Swyx [00:48:14]: Not even close.Anjney [00:48:15]: Yeah. But it's so clear, right? Like how to output max for the world. They have been prepared, and you could call that luck, but Luck favors the prepared mind.Culture, Hardship, and Anthropic's P0Swyx [00:48:25]: This is one of those things that I was going over some of your old lectures and, you were data, people think it's a moat and actually it's culture and actually it's team Actually. And I, it's-- there's different levels of moats, and this is the ultimate one that determines everything else. Which you can then compoundAnjney [00:48:43]: You're saying culture is the ultimate moat? Yeah. But the thing about culture is it's very fragile. So moats, I don't think they're-- there's very few moats I found that are actually moats. They're-- It's, it's a nice concept, but in reality, you have to replenish your culture. Ben Horowitz was, the speaker in CS153 on Tuesday, and I asked him this question about the culture bottleneck in teams because, there are several AI teams-Swyx [00:49:09]: His book, Hard Things About Hard ThingsAnjney [00:49:11]: Hard Thing About Hard Things. But more concretely, there are so many AI labs today that have all the cash they need, they have all the compute they need, and they're still not able to ship anything SOTA. And then you start seeing people leave and so on, and my diagnosis, it's, is it's the culture. And so I asked him, Ben, they're-- He's been one of the most aggressive investors in AI labs. He goes back to this thing which resonates in my mind a lot. It-- When I used to work at a16z, I would, book a conference room, and right outside the conference room, which is closest to the toilet ‘cause it was the fastest way for me to go use the bathroom between Zoom meetings-Swyx [00:49:45]: Oh my God, I'll put maxing my toilet optimization. Okay, never mind.Anjney [00:49:48]: It was not healthy in hindsight, but maybe this is TMI. But anyway, outside that conference on the wall was this quote that was printed that said, “Culture is not a set of beliefs, it's a set of actions.” And it's by Bushido, is this, Japanese philosopher. And if you stop taking the actions that demonstrate the mission alignment to what you've said to your team and to your-- the world matters to you, then your culture starts to fray. So it's not actually a moat, I would say. It's a very brittle, fragile thing that requires daily tending to like a garden. But if you figure out the system to keep that garden tended, which I think ultimately comes down to knowing yourself ‘cause you most naturally, if you're authentic and so on, you'll naturally make trade-offs that seem effortless to you, but that reinforce your culture. And then That becomes this very hard thing for other people to catch up to. And at Anthropic, from day one, there was this mission like-- missionary like zeal and belief that, hey, these capabilities will scale. These systems are stochastic, not deterministic. There will be error bars, and until we crack interpretability, there's risk. And at some point, people will go-- stop using Claude just for coding. They'll use it in some mission-critical context where there's-- it'll throw off a bug, and then people are going to come blame them, and they want to be on the right side of history where they said, “Yes, this is a powerful technology. We think it's going to change the world, And we want to be very measured and scientific about the fact that, ‘Hey, guys, these are stats models, statistical models.' That's how statistics works.” ultimately, when you're training neural nets, it is just a statistical system. And I think that Belief that safety is important and that it might seem toy-like in the early days, and sometimes, you could say, “Anjney, they totally over-exaggerated the risk,” like two years ago when they said, “Let's not launch Claude One,” or whatever. Well, okay, maybe in hindsight, but hindsight is twenty/twenty. And at the time, they didn't know how that model would be used, and to them it felt existential if somebody came and said, “You weren't responsible. It-- This wrote a bug.” The liability associated with that is massive. So how do you prevent against that? Well, day in, day out, you say safety. And when you start deviating from that, you have the team hold you accountable, you have the world hold you accountable, and I think that becomes a moat over time. At some point, that moat will get challenged and so on, and then it become fragile. I hope it endures because that's the beauty of having founders run the show, ‘cause they can make really hard trade-offs to do mission alignment. The hardest part is in the earliest days when you don't have a group of people who are going through difficulty, stress, crisis together, then your culture doesn't get defined sharply enough, and that's what I'm worried about right now, is there's so much money going to these labs. There's no hardship. There's no-Swyx [00:52:50]: To anyone who knowsAnjney [00:52:51]: There's no to anyone who knows. And that, in hindsight, was a feature, not a bug for Anthropic. The number of people who said no, the number of people who said, “Sorry, we're all doing investors in OpenAI,” that is competitive difference. It forces you to really understand, what is the hill you want to die on at the expense of everything else. What's the P zero? And there, P zero from day one was coding. The reason, the mechanism system there was if we crack coding, Then we will crack AGI. Our mission is AGI. We want to get there safely. If we focus on codin

Claude Fable 5 d'Anthropic désactivé sous pression, OpenAI invalide une conjecture d'Erdős, Microsoft présente ses modèles MAI et Majorana 2. SpaceX explose les records en Bourse, l'IA alimente la dette des géants de la tech, les data centers spatiaux deviennent une piste industrielle, et les annonces Xbox/Nintendo saturent déjà les plannings de joueurs.  Me soutenir sur Patreon Me retrouver sur YouTube On discute ensemble sur Discord Fables de la quinzaine Maître Dario, sur son arbre perché, avait un modèle de langage, Maître Trump, par un rapport contrarié, lui fit alors ce chantage. Vibe mathémating : GPT aime bien les jeux Erdős. Joli moi de MAI : c'était aussi la Build de Microsoft. Culture pub : parce que Claude Code est un Netflix comme les autres. La bulle voulait être aussi grosse que le bœuf. Projet Dernière Chance IPO griffes : SpaceX fait le casse du siècle. Et tout ça grâce à xAI, AI1 et Starship. Que des valeurs sûres. En matière d'espace, la Chine ne manque pas de sail. Le Donut aurait finalement un fourrage crémeux. Jeux vidéo Xbox Showcase : 25 ans, 25 jeux et un avenir heureux ? Nintendo Direct : du neuf avec du vieux. Summer Game Fest : un show Spyrotechnique. Participants Une émission préparée par Guillaume Poggiaspalla Présenté par Guillaume Vendé

It's rare for a business to hit seven figures, and rarer still to reach eight. In this bonus episode, Kelly cuts straight to the reason why: in most small businesses, the owner is the bottleneck. You could simplify the whole conversation down to that one sentence. Underneath it sits the real culprit. Sales is the heartbeat of every business, and while most companies have a solid marketing system, they have no sales system at all. So the owner becomes the only salesperson. You're the little engine that could, pushing and fighting and working yourself to the bone, and the train can only go as fast as you fuel it. Which means the moment life happens - a sick kid, a spouse's health scare, a parent who falls - the business doesn't just slow down, it stops. And in business there's no staying the same. You're either growing or declining. Kelly unpacks the false beliefs keeping owners stuck as the primary salesperson: no one can sell as well as me, I have to hire a high-level closer, I've tried five salespeople and none worked out, we don't have enough leads. Her reframe is the heart of the episode: someone else's 85% is exactly what releases the constraint and uncaps your growth. Smart entrepreneurs understand this. Most never do. And the reason growth feels impossibly hard isn't that it actually is; it's that you're carrying the entire mental and physical load alone. Then she paints the alternative: a simple daily system where every single person on your team — VA, admin, ops manager, social seller, marketer, coach, client services manager — can make sales. She's watched businesses transform practically overnight, produce five- and six-figure deals in a single day, and owners come back saying their business is fun for the first time in 17 years. In this episode: Why seven and eight figures are so rare, and the one reason most businesses stall The "little engine that could" trap of being your only salesperson The false beliefs keeping you as the primary closer Why growth feels harder than it actually is What's possible when your whole team can sell What you'll learn (and create) live on June 24th RESOURCES: Register for The Miracle Hour Experience on June 24th:  a full, virtual immersion experience where we will work together to build your sales system and take action live: https://www.themiraclehourbook.com/miracle-hour-june-24-experience-social. Upgrade to VIP when you register for over $1,500 in additional tools to accelerate results, including: The full fill-in-as-you-go playbook (examples, scripts, walkthroughs, notes) 25 prompts to unlock the sales already sitting in your business A custom GPT programmed with Kelly's full sales philosophy and strategies Lifetime access to the A-to-Z recordings of the entire day A follow-up Q&A call Plus live hot seats, Q&A, and team demos of the prompts and the GPT Get The Miracle Hour book and learn the daily sales methodology at the center of the experience: https://a.co/d/08EVrzld 

Claude's Fable 5 just got yanked, and the story why keeps shifting by the hour. A contested jailbreak, an export-control, crackdown, and a lot of fingers pointing. This week on AI For Humans, Anthropic's Claude Fable 5 is still unavailable and the explanations keep changing. We dig into the contested jailbreak report, the export-control directive that pulled it, and the reporting that Amazon raised concerns before the crackdown. Then we get into why this matters far beyond one model: what happens when the government steps into the AI world, why Fable 5 was such a leap, and what it signals for whatever comes next. Plus, Epic's game designers are using AI tools alongside artists and the internet is furious, Disney Imagineering is testing Adobe Firefly in the parks, ChatGPT's market share slips under 50 percent for the first time, a fake Mistral model called Le Chaton Fat takes over the internet, and PJ Accetturo breaks down exactly how he made his viral AI short film with prompts. THE BEST AI WE EVER USED IS BEHIND BARS. AND NOW WE WAIT. SHOW LINKS Original Anthropic statement on Fable and Mythos access https://www.anthropic.com/news/fable-mythos-access Full timeline of the Anthropic, Amazon, and White House story https://www.axios.com/2026/06/13/anthropic-amazon-white-house Amazon CEO reportedly raised Anthropic model concerns before the government crackdown https://techcrunch.com/2026/06/13/amazon-ceo-reportedly-raised-anthropic-model-concerns-before-government-crackdown/ Simon Willison on the contested Fable jailbreak report https://x.com/simonw/status/2066722034491789720 ChatGPT market share slips below 50 percent for the first time https://techcrunch.com/2026/06/16/chatgpts-market-share-slips-below-50-for-first-time/ GPT-5.6 next week? Polymarket odds https://x.com/Polymarket/status/2066644087340495081 Possible new ChatGPT voice mode leak https://x.com/testingcatalog/status/2066919098236146167 Space X Buys Cursor https://www.cnbc.com/2026/06/16/spacex-spcx-cursor-acquisition-ipo.html Epic explores using NanoBanana and GPT-Image-2 in workflows with humans https://x.com/UnrealEngine/status/2066686216779509850 SEGA's Crazy Taxi AI statement https://x.com/SEGAInforment/status/2063990392085766622 PJ Accetturo breaks down how he made his three minute short film with prompts https://x.com/PJaccetturo/status/2066582776934289438 Le Chaton Fat, the fake Mistral model that took over the internet https://x.com/AlexanderKnigge/status/2066267845546442762  

You already know what you offer is worth it. So why does it feel so hard to actually charge that number?In Part 2 of this conversation, Jan sits back down with Andee Hart — sales strategist, wholesale business expert, and host of She Sells Differently — to tackle one of the most emotionally loaded topics in entrepreneurship: pricing. Not the spreadsheet version. The version where guilt, fear of rejection, people-pleasing, and a deep desire to serve all collide at the checkout page.Whether you sell physical products, coaching, courses, or creative services, this episode will challenge the way you've been calculating your prices — and give you a practical framework for backing into what you should actually be charging.In this episode you'll learn:Why women entrepreneurs consistently underprice themselves — and the caregiving instinct that's often behind itHow to shift from cost-based pricing to ROI-based pricing (and why it might raise your price by $1,000 or more)Why knowing your ideal client's transformation is the foundation of every pricing decisionThe real reason discounting hurts your business — and what to offer instead in a slower economyHow your brand visuals directly affect what price people are willing to pay (the $45 hat story is one you'll remember)What an ROI calculator is, why Andee uses one on her sales page, and how she built it using AI tools without codingHow people-pleasing and the need to control client outcomes can silently keep you underchargingAndee's top pricing tip: why your first price doesn't have to be your forever priceEpisode Highlights[00:00] Jan introduces Part 2 and frames the topic: pricing, guilt, and the fear of thinking too much of yourself[02:14] Why women struggle with pricing more than men — and Proverbs 31 as a biblical foundation for charging what you're worth[04:04] The pricing framework Andee uses with product makers: start with cost of goods, then layer in labor, time, and — most importantly — the buyer's ROI[05:55] Jan gets real: she knows she's underpricing her web design and branding services, and shares the mindset shift she's working through right now[07:10] The critical mistake entrepreneurs make in sales copy — leading with features instead of the transformation — and how to fix it[08:00] Andee's wholesale course example: how she built an ROI calculator into her sales page and why it became a game-changer for conversions[09:40] Why one client avatar isn't enough — and how going deeper into who you serve at each stage of the customer journey changes how you price[11:06] The $45 hat story: how one perfectly branded coffee shop in Franklin, Tennessee made Jan open her wallet immediately — and what that means for your pricing power[13:20] Product makers and packaging: why homemade-looking branding keeps wholesalers off retail shelves, and how branding does the selling when you're not in the room[14:13] The scary investment conversation — why stepping into a big God-given vision requires being brave enough to pay people who can get you there[15:38] Cost vs. worth: the mindset difference between what something costs you and what it's worth to your buyer[17:20] The people-pleasing pricing trap: Andee gets personal about wanting to control client outcomes — and how that tendency quietly caps her prices[18:07] Jan's honest confession about spending thousands on courses she didn't finish — and why the ROI is always on the student, not the creator[20:40] How Andee built her ROI calculator using Canva AI first, then vibe coding tools like Lovable and Base44 — no coding required[23:07] Jan's lightbulb moment: using an ROI calculator as a client-facing tool inside web design projects[25:16] Andee's best pricing tip: don't be afraid to pivot. Survey your clients, raise your rates, and stop treating your launch price as permanent[25:55] Instead of discounting in a tough economy — add payment plans. Don't devalue your work; just make it more accessible[26:51] "You don't want to be Dollar Tree." Why discounting trains your audience to expect discount pricing foreverKey Takeaway"When I am not charging what I'm worth, I am not able to use the gifts that the Lord has given me — because let's be real, it's all a gift from Him — to go do other things that make a kingdom impact." — Andee HartResources Mentioned

SpaceX is buying Cursor. The $60 billion all-stock acquisition of Anysphere, announced June 16, gives Elon Musk control of the most popular AI code editor on the market, just days after SpaceX's $2 trillion Nasdaq IPO. Two months ago, Cursor was valued at $29 billion. The SpaceX Cursor deal more than doubles that price.This episode breaks down the $60 billion Anysphere acquisition and the math behind it. Cursor's annualized revenue is around $4 billion, with $2.6 billion from enterprise B2B customers. The growth curve is near-vertical: $2 billion ARR in February 2026, $3 billion in late April, $4 billion in early June. The deal is structured as an all-stock merger through a SpaceX subsidiary called X67, meaning fresh IPO capital isn't funding it. Anysphere shareholders receive SpaceX Class A shares based on a seven-day volume-weighted average price, with the merger expected to close in Q3 2026.The strategic logic is the AI coding market. xAI merged into SpaceX in February but never gained traction against Claude and GPT in developer tools. Cursor was already eating that market. Two senior Cursor engineers had left for xAI, and Cursor had been training its newest models on tens of thousands of xAI chips. The $60 billion deal closes a competitive gap that money alone wasn't closing.The April option deal is the underrated part of the SpaceX Cursor story. SpaceX locked in either the $60 billion acquisition price or a $10 billion break-up fee months ago, before the IPO and before Cursor's run-rate doubled. By June, $60 billion looked like a discount. The merger agreement also carries a $10 billion termination fee if SpaceX walks, plus an additional $4 billion if antitrust kills it.The broader AI M&A picture matters too. Anthropic just filed for an IPO at a $965 billion valuation. OpenAI filed at $852 billion. SpaceX is trading above $2 trillion. The AI capex cycle is now visible in acquisition pricing, not just compute spend. Developers building on Cursor are now building on a Musk-owned platform, which raises real questions about model neutrality, data access, and what happens when xAI controls the editor that ships Claude's and OpenAI's outputs to millions of engineers.We cover what changes for Cursor users under SpaceX ownership, what the deal means for Anthropic and OpenAI in the AI coding market, why SpaceX paid double instead of waiting, and whether $60 billion holds up against $4 billion in ARR.Keywords: SpaceX Cursor acquisition, Anysphere $60 billion, SpaceX buys Cursor, Cursor AI editor, AI coding, xAI, Elon Musk, SpaceX IPO, AI M&A, agentic coding, enterprise AI, Grok, Anthropic IPO, OpenAI IPO.

A pronounced infrastructure dependence on third-party AI models has emerged across the MSP ecosystem, largely due to the rapid adoption and integration of AI-powered features within vendor products. This structural shift is increasingly opaque, as providers are sold features rather than transparent access to underlying models, leaving MSPs exposed to changes in technologies and policies enacted upstream by vendors or regulators. The episode highlights how this dependency extends to delivery teams and end clients, with operational continuity tightly linked to decisions and actions outside the MSP's direct control. The most consequential development referenced is Anthropic's release and rapid withdrawal of its Fable 5 AI model following a directive from the U.S. Commerce Department, which ordered a cutoff of model access to foreign nationals within 72 hours of public launch. According to published benchmarks, Fable 5 surpassed GPT 5.5 in performance, but the government-mandated suspension exposed how quickly model access can be rescinded. The policy move immediately impacted any MSP or client with offshore or nearshore staff relying on AI features invisibly powered by that model. Further supporting the central theme, companies such as PAX8, Enforcer, and CloudRadio are embedding AI capabilities into platforms used by MSPs to manage Microsoft 365 environments, automate ticketing, and support scalable client operations. In parallel, vendors like Proofpoint are integrating compliance solutions directly with AI model APIs, further entwining risk management tools with the same core AI infrastructures. A Netrio survey cited in the episode found that while 82% of mid-market IT leaders have AI in production, only 26% report organization-wide governance, highlighting an accountability and visibility gap. Operationally, MSPs face heightened contract and vendor risk. Most lack an accurate inventory of which AI models underpin their services and how rapidly these dependencies can be affected by regulatory directives or vendor shifts. The discussion underscores the need for explicit procurement protocols, delivery mapping, and outage runbooks that account for opaque model dependencies. As clients seek greater transparency and contractual assurances regarding model use and continuity, MSPs who anticipate and document these dependencies may be positioned to reduce exposure and establish clearer accountability. 00:00 Switched Off  03:19 Painted Over 05:20 Govern or Absorb 08:41 Why Do We Care?  Supported by: Pax8 Sign up for the SMB Online Conference: www.smbonlineconference.com

As AI capabilities accelerate, how should law firms and legal departments prepare for a future where agents can perform increasingly complex legal work? In this episode, Zach speaks with Max Junestrand, CEO and co-founder of Legora, about the rapid evolution of legal AI and what it means for the future of legal services. They discuss the recent wave of law firms building their own AI initiatives, why legal teams are adopting AI faster than many realize, and how firms can rethink their business models in an agent-driven world. The conversation also explores the role of legal-specific AI products, the growing importance of transformation partners, and why Max believes legal AI represents a trillion-dollar opportunity.   In this episode: Why law firms are investing heavily in AI How legal teams can prepare for a future where AI agents perform increasingly complex work Why legal-specific AI products still matter in a world of Claude, GPT, and other frontier models How AI is changing the economics of legal services and creating new opportunities for firms Why Max believes legal AI could become a trillion-dollar market opportunity Subscribe to Zach's newsletter https://www.legallydisrupted.com/   Follow Zach on X https://x.com/ZachAbramowitz?lang=en   Follow Max on X https://x.com/MaxJunestrand   Engage Killer Whale Strategies https://www.killerwhalestrategies.com      

Subscribe to our Newsletter:https://theultimatepartner.com/ebook-subscribe/ Check Out UPX:https://theultimatepartner.com/experience/ https://youtu.be/j0TuosYDQe4?si=7mzUwBe4PrQ-eB2E In this insightful session from the Ultimate Partner Live event in Bellevue, Washington, Vince Menzione sits down with Stephen Boyle, Corporate Vice President for Enterprise Partners at Microsoft, to pull back the curtain on the tectonic shifts redefining the tech ecosystem. Boyle details Microsoft's massive organizational pivot into enterprise and SME/channel divisions , explaining how artificial intelligence acts as the foundational thread unifying systems integrators, software vendors, and digital natives. Moving past market noise surrounding competing foundational models , he highlights Microsoft's strategy to become the ultimate “platform of platforms” by prioritizing user choice, security, and trust. Emphasizing a shift away from infrastructure technicalities and toward practical business outcomes , Boyle delivers an urgent mandate for partners to scale technical talent, eliminate traditional operational silos, and brace for the incoming consumption-driven, agent-based future of enterprise computing. Key Takeaways Microsoft has restructured its global sales divisions into distinct Enterprise and SME/Channel organizations to better target its massive total addressable markets. Artificial intelligence is fundamentally altering the partner ecosystem by dismantling traditional software and systems integrator silos to build interconnected, multi-party solutions. Rather than forcing alignment to a singular model, Microsoft aims to be the definitive platform of platforms by offering extensive choice across over 1,100 language models. The enterprise landscape is rapidly moving past experimental AI pilot phases and entering production setups completely focused on transforming core business outcomes. Tomorrow's service organizations are aggressively evolving into software-minded operations that deploy repeatable, highly specialized internal autonomous agents. Managing tokens and monitoring usage metrics represents the emerging operational baseline for balancing efficiency against the scaling expenses of large language models. If you're ready to lead through change, elevate your business, and achieve extraordinary outcomes through the power of partnership—this is your community. At Ultimate Partner® we want leaders like you to join us in the Ultimate Partner Experience – where transformation begins. Key Tags AI frontier, platform of platforms, enterprise partners, global systems integrators, digital natives, language models, token consumption, agent sprawl, citizen developers, shadow IT, business outcomes, technical enablement, marketplace growth, hyper-scalers, processing fluency, sovereign AI, industry ecosystems, data governance. Transcript [00:00:00] Stephen Boyle: This is the biggest, most transformative, iterative change in technology we’ve ever seen, where, if you wanna call it a paradigm shift or whatever word comes after paradigm shift. [00:00:12] Vince Menzione: We just came back from Ultimate Partner live in Bellevue, Washington, where we hosted incredible leaders for two amazing days. Come join us for this next session where we explore the tectonic shifts we’ve all been seeing. Uh, I am thrilled to invite our next guest up on stage. I’ve known this gentleman for several years back in my days at Microsoft, and, um, we’ve been friends, actually Microsoft, and then we both went and did different things, came he’s come back to Microsoft in a big way. [00:00:46] Vince Menzione: Uh, Steven Boyle, for those of you don’t know, is recently a named the C. We will talk about it in a second, but I, I need to announce you properly. Is the corporate vice president, which by the way in Microsoft is a big deal for enterprise partners. He and Nicole De and I would say are the two Microsoft leaders in the organization. [00:01:06] Vince Menzione: Nicole is the channel chief. Steven has a, a big remit and we’ll talk about that up on stage. But I’m just so delightful for his support and for making the time in a very busy week at Microsoft ’cause this is CEO summit this week to make some time to come with us and be on stage with me. Please welcome my good friend Steven Boyle. [00:01:29] Vince Menzione: Good to see you, sir. To see. So I’m gonna put you on this side. [00:01:33] Stephen Boyle: Okay. [00:01:35] Vince Menzione: The hot seat. So I’m gonna, I, I didn’t do a justice and I, I wanted you to explain your role. I, I think I know, but I think for the, for the people in the room, uh, talk to us what Enterprise Partners means at Microsoft and what that role remit and remit looks like. [00:01:50] Stephen Boyle: Um, CVPs may or may not be important, but one thing they don’t do is get invites to the CEO summit. So I’m super pleased to be here with you guys. No, no, it’s totally cool. It’s totally cool if that phone rings. No, I’m kidding. Doesn’t. So what does it mean? So I’d like quickly, um. January last year, uh, we split the sales organization into enterprise and small to medium enterprise and channel. [00:02:15] Stephen Boyle: You guys probably familiar with that? Nicole is the, uh, chief partner officer lives in the SMA and C world and drives the channel, um, drives our marketplace business and, and a lot of other things. Um, for that 60 billion, um, you know, total addressable market that we have. Down there in SME and C. Um, at the same time, we established enterprise partner as part of Nick Parker’s overall organization. [00:02:40] Stephen Boyle: Um, but for most of 2025 we ran it as global systems integrators and advisories, ISVs and digital natives. So three separate footprints all focused entirely on, on, on enterprise. Um, in December, January, we talked about establishing an enterprise partner leader that would. You know, aggregate all of this stuff. [00:03:00] Stephen Boyle: Um, I was fortunate to come through, um, some frankly, pretty hairy, uh, experiences, I bet with some of our senior leaders. Um, I, I’ve loved to [00:03:08] Vince Menzione: been in the room for that [00:03:09] Stephen Boyle: questions like, why Steven Boyle and things like that, right? And really have to dig deep to, uh, to justify. Anyway, uh, I’m blessed and honored, uh, to run that entire portfolio of partners, uh, for the entirety of the enterprise partner world, which now from a chief revenue officer perspective, belongs to Deb. [00:03:25] Stephen Boyle: Deb Co. So Deb is the enterprise leader for all of our sales that we do into that space. Awesome. Um, I have three regional leaders, Nina Harding here in the United States, Ehab Ra in in Europe, and Heather Gordon in Asia that mirror and replicate and flow down the things that we decide to do from a strategy perspective for the, uh, for the core. [00:03:45] Vince Menzione: And we love Nina. She’s been, she was at our last event, [00:03:47] Stephen Boyle: super, super lady. And, uh, you know, the US is still 50% of our overall business. [00:03:53] Vince Menzione: Yeah. [00:03:53] Stephen Boyle: Too big to fabric. Every time I talk to Nina, I’m like, Nina, you’re too big to fail. We can’t cover you anywhere else. So you know, you’ve gotta be successful here in the Americas. [00:04:01] Vince Menzione: So I think just for breaking it up, I, ’cause I do want to like, it’ll lead to the next question, right? So you have the global systems integrators, all these systems integrators. Essentially you have all of the software companies we used to call ISVs, we now call SDCs or software development corporations. [00:04:17] Vince Menzione: And then you also have the AI stack, I’ll call it. Right? So under Jason Grafe. Yeah. Many, many might know. Jason’s been a guest on the podcast and was Satya’s chief of staff at one time, eight years. Eight years. Wow. I didn’t realize there was that many. [00:04:31] Stephen Boyle: Carry carried a lot of bags for Satya over the years. [00:04:34] Vince Menzione: Unbelievable. Well, let’s, I mean, so AI is an important component, right? And you saw Jay’s, Jay talking, just talking about AI and all these things. I would love to start here, right? Because, uh, you’re, you’re, I wanna get your perspective as Microsoft, your perspective as Microsoft on the biggest shifts you’re seeing in defining this we’ll call AI Frontier. [00:04:54] Vince Menzione: We’re seeing right now, how should partners translate that into how they position and go to market externally? How, how do we need to think about this time? [00:05:02] Stephen Boyle: Yeah, that is, uh, that is a huge question and I’m not sure we’ve got enough time to go into the, into all of the detail. Um, so let me sort of up level it a little bit for you. [00:05:10] Stephen Boyle: And I think, look, the move that we meet at made a couple of months ago and pulling together those three aspects. Nicole had already done it in SME and C. Right. One partner organization across the world with a very common set of goals. We were working closely together, Sandy Gupta, on ISV, Jason on ai, and myself on on si. [00:05:29] Stephen Boyle: But we were still working closely together across silos. So the opportunity for me, 60 days into this role is AI just allows you to wire the partner ecosystem together differently. Right? And even if you look at how we’re going to market an AI today, um. You know, with, with, with chat GPT, with Claude, with Anthropic, um, I think there’s something like 1100 different, you know, language models on Microsoft today. [00:05:55] Stephen Boyle: So the way I think about AI is we are absolutely gonna be the ultimate platform of platforms. Yeah, choice is incredibly important. Um. It’s, it’s, you know, turn the clock back 12 months, everybody was chat gpt five point x, you know, and then six months ago it was Gemini and now it seems to be clawed. And honestly I don’t know what it’s gonna be next quarter. [00:06:15] Stephen Boyle: So the only thing I can do is offer you choice. [00:06:18] Vince Menzione: Yeah. [00:06:18] Stephen Boyle: And from a partner perspective, I think that minimizes or reduces the risk that you have betting on the Microsoft platform because you can go in a multitude of different directions. I know we’re not in Europe, but if you were in Europe and you were worried about G-G-D-P-R and Jay mentioned sovereignty, you’d probably be like lining up really closely to Misra. [00:06:37] Stephen Boyle: Yeah. And a bunch of other Europe, European partners. So wherever you are in the globe, I wanna be that platform choice. Um, and we will lead with our own first party solutions. I hope they’re not coming for me. Um. I parked safely in the hotel. It can’t be me. Um, but you weren’t vibe coding in the room. Um, but you know, wherever you are in the world, in whichever industry you are in, um, it is our intent to, to offer that platform of platforms and to give the broadest set of partners the opportunity to engage with us. [00:07:07] Vince Menzione: I think that’s really important because I, I have found, especially in the last month or two, people are, it’s almost like a knee jerk. Don’t you feel like people don’t know what to do? There’s been so much noise in the press and the media and, and the markets around open AI and anthropic especially. Where do I go? [00:07:26] Vince Menzione: Seems to be like when I, when I sit, I watch everybody in the room here. I think they’re, they’ve all been thinking that as well. So you can, [00:07:31] Stephen Boyle: there’s a, a little bit of a deer in the headlights moment. Yes. And even I like, I get that. Yeah. Um, you know, I saw, uh, Jay slides. Jay, love the presentation. Love the slides, man. [00:07:40] Stephen Boyle: I’m gonna steal several of them. Um, we’ll talk about that later. We, we [00:07:43] Vince Menzione: have the deck, [00:07:45] Stephen Boyle: but, but in all seriousness, you know, this, this is like. It’s a new paradigm. I will date myself a little bit. Some of you might heard me say this. I sold many computers in the 1980s. Mini computers. Some of you in the room are going, what’s a mini computer? [00:07:59] Stephen Boyle: Um, I sold client server for Sun Microsystems in the nineties. I sold an awful lot of Oracle databases in the Auts, I think they’re called, and I’ve done two stints with Microsoft. This is the biggest, most transformative. Iterative change in technology we’ve ever seen. What, if you wanna call it a paradigm shift or whatever word comes after paradigm shift. [00:08:18] Stephen Boyle: Um, and we are building intelligent systems at scale faster than we’ve ever seen. Scalable, mission critical solutions being implemented today inside of Microsoft and with our most important customers. So, and we can’t do it without partners, right? There is absolutely nothing we can do in this industry. I will, I will put the, you know, the elephant in the room out there. [00:08:40] Stephen Boyle: Our ISD organization has between five and 7,000 people. Our forward deployed engineering organization is about a thousand people. [00:08:47] Vince Menzione: Yeah. [00:08:48] Stephen Boyle: So when you look at the scale of the total addressable market that Jay just talked about. We are gonna service directly like this much [00:08:55] Vince Menzione: used to be 5%. Was it even, is it even that high? [00:08:58] Stephen Boyle: I doubt it’s, I doubt it’s even that. And the billions of dollars that we spend every year helping our customers transform to what we’re now calling frontier firms is gonna be, have to be driven with every single person in this room in some way, shape, or form. Judson is not asking Marla to significantly increase ISD. [00:09:15] Stephen Boyle: Not asking John to significantly increase FDE, although we probably will hire in that area just because of the, the newness and the, you know, bright shiny object that everybody’s like, oh, FDE, I’ve gotta have those. We’ve got a thousand already today that have been around in John’s organization for 10 plus years doing the things that we are doing today. [00:09:32] Stephen Boyle: But we are gonna build out that muscle. But the real way we’re gonna build out that muscle is with all of you in this room. That’s like categorical. That is my like, probably number one goal for the next one to three years is make sure that, that story that Jay just told about Microsoft not being involved in AstraZeneca. [00:09:48] Stephen Boyle: I probably won’t tell Judson that Jay, but I love the story. Um, like if you could all do that for me, like win, um, that is so, you know, from our worldwide learning, through our skilling enablement through our cloud solution architects that I personally own. We are pivoting aggressively towards making sure that the partners understand our platforms better than any other job, number one for me right now, if you don’t understand what I’m selling, like I’m kind of dead in the water obviously. [00:10:15] Stephen Boyle: Well, [00:10:15] Vince Menzione: I was gonna ask you why now? Why Microsoft? Why now? Right? Because there is a lot of noise. You know, Google just announced, you all announced your results on the same day, which was astounding. That was freaky, wasn’t it? It was. It was the first time. And the, the total commitment, customer commitment is over a trillion dollars now, I think 1.2 trillion is what I counted up. [00:10:33] Stephen Boyle: Yeah. [00:10:34] Vince Menzione: But it’s saying a lot about like, what do I do now, like as these partners in the room. Um, how, I think you kind of already, and you’ve talked about this, about differentiating where Microsoft is, I think J Slide does a lot of justice there. It says how, uh, Microsoft Partners came into the room, surrounded the customer. [00:10:52] Vince Menzione: It feels like Microsoft has always leaned in big time on partners. Uh, more so I would say than any other organization out there. What would [00:10:59] Stephen Boyle: you say Joe Roses, my chief of staff, business manager and so many other things was telling me last night that, you know, we used to say 500,000 partners. [00:11:05] Vince Menzione: Yeah, [00:11:06] Stephen Boyle: it’s a, it’s a significantly higher number than that as well. [00:11:09] Stephen Boyle: So there’s an element of, you know, back to the deer in the headlights, which partners are, are more important. One of my other phrases that I say on a regular basis, the winners and losers are yet to be decided in this next wave. Like, I want all of us to on the right side of that argument. Right? But, but it’s gonna be a challenge and, and companies are going through shifts. [00:11:28] Stephen Boyle: You know, Accenture, maybe, possibly doesn’t need 750,000 employees in the not too distant future. Maybe TCS at 600,000 doesn’t need 600,000 human employees. So we’re going through this dramatic shift of, you know, what’s the right balance going forward. What I would say about Microsoft is notwithstanding the fact that we’ve figured this out for 51 years, which is a little bit mind blowing, um, that you know, all the way back in the seventies we’ve gone through so many iterative changes. [00:11:56] Stephen Boyle: People have questioned just like they’ve questions. A lot of other technology companies, are you gonna be around for the long haul? I think we’ve proven time and time again, and I love Jay’s story. I’ve used that myself about how many companies disappear on a, on a decade to decade, you know, business. 10 years ago I had the opportunity to listen to Craig Clayton Christensen, who’s sadly no longer with us. [00:12:15] Stephen Boyle: Yeah. But you know, the books that he wrote and the story that he told to Microsoft 2014, we were nowhere in cloud. [00:12:21] Vince Menzione: Yeah. [00:12:22] Stephen Boyle: AWS was so far ahead of us, it was crazy. And he came in and he’s like. You know what? You guys need to be successful. You need to figure out how to cross this chasm again, and we’ve done it time and time again. [00:12:32] Stephen Boyle: You can go back. You know, Microsoft used to be known as a fast follower in ai. I don’t think we’re a fast follower. I think we’re right up there. We’re right at the front, but that race is still being run and the winners are losers are yet to be decided. [00:12:44] Vince Menzione: I was in that room with Clayton Christensen with you, by the way. [00:12:46] Vince Menzione: I remember, I remember that. That was at a Prism conference. [00:12:49] Stephen Boyle: Yeah. Yeah. [00:12:50] Vince Menzione: You men, you touched on this with the GSIs a little bit. How do you see the roles evolving? You know, we, we, we bucketed all, we’ve always been. Fantastic about bucketing ISVs or SDCs and sis and digital natives. Yeah. How does it, how does that all come together? [00:13:06] Vince Menzione: Does it come together any differently in this new AI platform era, or is it the same? [00:13:11] Stephen Boyle: I look, I, I’ve said this for a long time, like if you go into AstraZeneca, the six plus, you know, frontline partners, there’s probably a whole board of second, third tier that, that we don’t know about doing, you know, things across the AstraZeneca group. [00:13:25] Stephen Boyle: It takes several villages and sometimes a small town, especially in my world, in the enterprise world, strategic five hundreds. Yeah. Um, you know, we, we ran some reports a few years ago and it is shocking how many global systems integrators have a footprint in Shell or Exxon or, you know, bank of America or whatever else. [00:13:44] Stephen Boyle: So I’ve always believed that partner to partner is critical. Yeah. I think it became even more critical in the, in the AI world, and I’ll take my new friends at Anthropic. So I went to the first Anthropic partner Summit. Some of you might have been down there in, in San Diego, um, just a couple of months ago. [00:13:59] Stephen Boyle: Same partners, same people from the same partners. In the room, you know, talking about what they’re gonna do together with Anthropic. Um, and I’m looking out across this audience going, okay, well I know him and I know her and I know those guys, and like, I need to figure out how I’m gonna weave this together. [00:14:14] Stephen Boyle: So it’s not just an Accenture and Anthropic or an NTT data and anthropic, but it’s an NTT data plus anthropic plus Microsoft. Story going forward. And then who’s best at delivering those services capabilities? So it’s it at every juncture that I see in the, in the partner community, and this is the, the reason why I argued vehemently with Nick, that it has to be one organization I’m gonna create maybe given a little bit away. [00:14:40] Stephen Boyle: So if you’re recording, stop now. Um, I’m gonna create an enablement organization that is partner agnostic. I don’t necessarily care. I do care about the digital natives, but I don’t care about how I train them. Right. What I’m more important of is how do I train the digital natives in what the sis are doing, and how do I train the sis and what the ISVs Plus digital Natives are doing. [00:15:01] Vince Menzione: Yeah. [00:15:01] Stephen Boyle: That is my, that’s my game plan. If I fail there, then I think we fail to raise the bar and be differentiated in an AI world, and I’m not set up like that today. [00:15:12] Vince Menzione: I wanna, I wanna ask you, uh, uh, because I was looking at Jay’s slide and the, the managed piece is. And we have a lot of managed service providers in this room today. [00:15:20] Vince Menzione: A lot of them, by the way, come from the old school of managed services. The managed piece seems to be like, if I’m doing something today with ai, we’re gonna talk about security next, uh, up on stage here. It seems like there’s a new set of skills or a different approach to the customer, don’t you? Don’t you agree? [00:15:37] Stephen Boyle: I I [00:15:37] Vince Menzione: think you need to keep your hands on the steering wheel at all [00:15:39] Stephen Boyle: times. I think what it boils down to is you can’t do AI unless you do certain other things. [00:15:44] Vince Menzione: Yeah. [00:15:44] Stephen Boyle: Right. You could be a modern work specialist and you could make a lot of money being a modern work specialist, or you could be a, a dynamic specialist. [00:15:52] Stephen Boyle: We just held our, uh, inner A in a circle conference last last week, which I was disappointed to miss for the first time in a few years. Those, those days are, are, are fast becoming over. [00:16:03] Vince Menzione: Yeah. [00:16:04] Stephen Boyle: Um, why? Because everything that I’ve just said is tied together by ai. Yes. And in order to do good ai, you need good data. [00:16:12] Stephen Boyle: And in order to trust everything that you’re getting, as Judson talks about trust and intelligence, you need to wrap that in a really secure [00:16:19] Vince Menzione: Yes. [00:16:19] Stephen Boyle: You know, en en environment. Now we will do our best to provide levels of security into how we deliver ai. But that’s not the end of the game, right? You have to take it all, all the way to the edge. [00:16:30] Stephen Boyle: So that’s why a siloed partner or a singular commercial solution area partner in Microsoft’s terms, has got to transform its business. ’cause if you’re gonna do ai, you’ve gotta do those other things as well. [00:16:41] Vince Menzione: Agreed. I must see the model changing, and in fact, I see like bigger organizations becoming managed service providers in many respects. [00:16:48] Stephen Boyle: Yeah. Yeah. I mean, look, there’s still, there’s still a role for all the old terminology you mentioned is SV to sdc. Yeah. I’m like, I’m been around long enough. Look, it’s ANB still anv, it’s still an isv. Thank you. Independent software vendor. Um, and it’s, you know, where, where AI is allowing software to be, you know, frankly developed in a number of different places. [00:17:07] Stephen Boyle: We are all citizen developers. Um, you know, I was on a call with our internal leadership yesterday, um, and you guys might have heard this story ’cause I think it came out at Ignite. When we turn the agent 365, around and on ourselves. We found 130,000 agents running across Microsoft that had been developed and deployed internally with, I mean, you could call it shadow it. [00:17:28] Stephen Boyle: I guess that would be one phrase that you would use for it, but the reality is if you, if you haven’t got something to do your job today, you have the tools. To build it really, really fast. Um, and that, you know, that’s, that’s a great opportunity for people to be able to do their work, you know, in a better and in a different way. [00:17:45] Stephen Boyle: But it’s also a huge opportunity to make sure that data governance and security and all the other things that we need to deliver are there out of, out of the gate and out of the platform that we deliver. So security’s absolutely critical. Not saying that managed services won’t grow, um, at, at some level as well, but only if they transform into this multifaceted way. [00:18:04] Stephen Boyle: Yeah. Thinking [00:18:05] Vince Menzione: about, well, that’s what I was, I was gonna lead to here with innovating. It’s happening across, I mean, we’re talking about chips, we’re talking about foundational models, LLMs, we’re talking about applications, we’re talking about agents. How should we think about where to play and how to differentiate as partners in this room? [00:18:22] Stephen Boyle: I think. [00:18:25] Stephen Boyle: So look, I mean, one, one of the ways that Judson talks about it is I think silicon’s gonna change over time. Yes. NVIDIA’s definitely the 800 pound gorilla, maybe the 8,000 pound gorilla. Yeah. Uh, but you know, if you read the press, there’s, there’s things happening in, in different places as first party silicon, which we clearly are, are developing, um, in a quantum direction for sure. [00:18:45] Stephen Boyle: Um, there’s lots of different language models that haven’t even been launched on, on, on the marketplace yet, so. You know, Judson’s trying to uplevel our conversations. You’ll hear us talking about conversations more and more as we go into FY 27, um, that obviate all of those layers. Just like even when I was selling Sun Microsystems, it was about the business outcome and the business solution that we were solving for not necessarily the fastest piece of hardware or the best client service solution on, on the market. [00:19:17] Stephen Boyle: So I think what’s gonna happen over the next 12 to 24 months is we’ll have so many different models to choose from. We’ll have more silicon to choose from, but those won’t be the real buying decisions. The real buying decisions of what? How am I trying to transform my finance organization, my HR organization, and my supply chain? [00:19:36] Stephen Boyle: Because the underlying technology, Judson says commodity I, I guess I can go with that. It will be commoditized and we’ll really start to focus back on what the important things are. We’re moving a lot from pilot to production. You guys have probably seen that. The numbers that Jay just showed about how many. [00:19:52] Stephen Boyle: Projects are failing, is getting less and less because we’re getting smarter and smarter about what it takes to actually drive the business outcome. And I need all of us to be talking that same language. Yeah. Having conversations with head of HR about how we’re gonna transform human capital management in the, in the age of agents, if you like, like the underlying platform. [00:20:14] Stephen Boyle: It’s not, don’t worry about it. You wanna be on a secure platform. Don’t get me wrong. But at the same time, I don’t think we, we spent too much time worrying about that. [00:20:21] Vince Menzione: Yeah. We’re not, what you’re saying is we’re not spending enough time on outcomes. On the business outcomes. Right. And that’s where we need to focus. [00:20:27] Vince Menzione: We’re, we’re focusing on, I, I feel like we’re, it’s a signal to, to noise ratio that we’re living through right now. There’s too much noise. [00:20:33] Stephen Boyle: Yeah. [00:20:34] Vince Menzione: And we’re not focusing on the signal. I think that’s what you’re saying. [00:20:36] Stephen Boyle: I, it’s got to be, I mean, to be honest with you, it’s always been, you know, even when I sold what I would perceive, you know, sun in the nineties was a rockman ship to the stars and, you know, kind of sad what happened to that company. [00:20:47] Stephen Boyle: Um, but we, we were, we were fixated on, we had the best client server. But, but nobody was buying, you know, a piece of Sun hardware as a room heater, which is all it did, you know, like for the longest. But if you had SAP, if you had Cybase, if you had Bond, remember Bond, I mean all of those applications that drove the business outcomes, we’ve gotta get back to that kind of mentality. [00:21:09] Stephen Boyle: Yes. And worrying a little bit less about the underlying architecture. Yeah. It needs to be, it needs to be part of the conversation. ’cause it needs to deliver trust and security and intelligence and everything else. Then you need to rapidly move to what are you trying to achieve and how can we ensure the, the, the success of, of your business outcome. [00:21:27] Stephen Boyle: And look, I mean, Palantir pri you know, sort of came out and said, well, the way we do that is through forward deployed engineering. Um, and they stole the show. And, and, you know, they’re, they’re doing very well as a result of doing that. Uh, but if you go and talk to, um, Tom Siebel’s organization at C3 ai. [00:21:43] Stephen Boyle: They’ve had FDS for quite a while. You know, I told you about John Chuchu 10 years ago. John Chu, Chuck’s job was to go and get all the applications that we needed on the Microsoft phone. Remember that? [00:21:54] Vince Menzione: Yes. Um, [00:21:55] Stephen Boyle: you know, so we’ve pivoted John o over the years to doing what he’s doing now, which is to go sometimes in partnership with, with partners into the customer and say, what is it you’re trying to achieve? [00:22:05] Stephen Boyle: Let me show you how I can build that for you in three weeks or three months. That might have taken you three years. We literally just did a hackathon with one partner last, last, last week with, uh, with our ISE organization, the, the, the forward deployed, uh, group that John runs. Um, and one of the big customers said, I’ve just done in three days what would’ve taken me three months. [00:22:26] Stephen Boyle: Now he hasn’t productized it and rolled it out and blah, blah, blah. But the reality is that is how fast things are changing. And this was not a small company. This was a very, very large oil company, and they were like blown away by how much we can achieve. We’ve gotta do that at scale. [00:22:41] Vince Menzione: Yeah. [00:22:42] Stephen Boyle: You know, we, we have a commitment to scale our FDE community through partnerships to touch all of the S 500 in a very personalized way. [00:22:51] Stephen Boyle: And then, you know, at a slightly, you know, lower ratios down through the, through the majors and into, into Nicole’s SME and C world as well. [00:22:59] Vince Menzione: Jay talks about the decade of the ecosystem. He coined that term back, back on a podcast way back in nine, in, uh, in 2020. Microsoft has been at the, for, we used to call partner to partner back, back in the day. [00:23:10] Vince Menzione: Mm-hmm. Do you remember those days? How do you think about this ecosystem evolving and what steps are you taking to help bring these organizations together? Because I, I, again, we look at the seven seats or 6.3 seats at the table. The customer has the power now that they didn’t have before. ’cause they have the commitment with like with Microsoft and they can buy off of the marketplace and pull together multiple organizations to go, go do that. [00:23:34] Vince Menzione: How do you think about helping to orchestrate that as the leader of the enterprise partner business? [00:23:39] Stephen Boyle: So I’ll start with a really big example, and I’ll try and sort of scale it down a little bit. But my friends at Accenture, with the Accenture, Microsoft Business Group, we spend an awful lot of time, you know, in, in each other’s pockets, in each other’s deals. [00:23:51] Stephen Boyle: We know everything that’s going on in the Accenture, Microsoft Business Group. And a couple of weeks, or maybe a month or so ago, I was told that the Microsoft Business Group is now larger than the SAP Business group. It probably flip flops. [00:24:03] Vince Menzione: Yeah, [00:24:04] Stephen Boyle: it won’t be too long before the Anthropic Business Group is bigger than both of those. [00:24:08] Stephen Boyle: So what I need my Microsoft team to do is to not spend all of their lives in the. A MBG, the Azure, the Accenture, Microsoft Business group, but to go make friends in the Anthropic Accenture Business group and frankly still to make friends in the SAP business group and maybe in the Oracle Business Group and the list goes on. [00:24:27] Stephen Boyle: So at a macro 11, in the very largest accounts where we haven multiple practices, where we haven’t spent time before, I’m gonna. Push my people into uncomfortable zones and I’m gonna push them to go into those other areas and I’m gonna load them up with technical talent and cloud solution architects and ai, you know, forward deployed engineers. [00:24:45] Stephen Boyle: And I’m gonna force different people to talk together that haven’t talked together. So I can do that in TCS. I can do that, Capgemini, I can do that. Um, you know, in Europe with Capgemini and Misra is a classic example. Um, with the, with the Indian sis, Indian based sis, they’re all big enough where I know all the practices exist. [00:25:04] Stephen Boyle: I just need to do a better job of, of talking to them. Now, when you downsize that into, you know, into a, a company that doesn’t have all of that scale, this the same truth still holds. I need to talk to people who aren’t necessarily motivated every single day to do something with Microsoft. I need to talk to people who are motivated to do something with an AI partner or even a traditional SaaS partner. [00:25:27] Stephen Boyle: I noticed yesterday, actually no, this morning I got a notification that we just passed, um, a billion dollars in revenue on the marketplace with ServiceNow. [00:25:35] Vince Menzione: Nice. [00:25:36] Stephen Boyle: Um, and I think AWS announced the same thing, by the way this month as well. Um, so thank you to the ServiceNow people. Yeah. Um, you know, that is that there’s a tremendous demonstration of how far we’ve come in marketplace. [00:25:48] Stephen Boyle: ’cause that’s another one where we trailed AWS quite significantly. But with the right partnerships. And driving the right motions, we can, you know, we can definitely catch up and we will continue to pass, uh, some of, some of the other hyperscalers in, in, in that way. So really the bottom line to your question is partner to partner is still real. [00:26:08] Vince Menzione: Yeah, [00:26:08] Stephen Boyle: how we do it and what we use to tie things together. And I know that compensation drives behavior and we’re not gonna get into a compensation about like how we get compensated and everything else, but the reality is I’ve gotta break down those barriers and those silos and I’ve gotta deliver real meaningful enablement and practice development so that, so that the people who sit in the Anthropic business group and the people who sit in the Microsoft Business Group are spending as much time together as they are with me. [00:26:34] Stephen Boyle: That makes sense. Simply put, that’s what I, I need to achieve at scale rapidly. [00:26:40] Vince Menzione: So to, we’re getting close to time here, but as you look forward, what would define the most successful partnerships in this ecosystem? Is it, is it what you described, the opening up the aperture or for the, for the leaders in the room here today, what should they go do better and differently? [00:26:58] Stephen Boyle: Um, so obviously we’re closing out this fiscal, we’ve got Microsoft start and Microsoft start for partners coming up in July. Um, I mentioned the fact that we’re, we’re driving. Cu customer engagement through the lens of conversations and how do we achieve business outcomes? I would encourage you to, to gravitate, if you like, above the commercial solution areas where you might have understood, this is how I interact with Microsoft today. [00:27:23] Stephen Boyle: Um, and abstract it up to that AI layer. You know, think about trust, think about intelligence, think about business outcomes, and how do I potentially weave together a story? If I’m in the dynamic space, how do I get better in data? If I’m in the data space, how do I get better in. In that modern work environment, but really use AI as the overlay to, to help tie that together. [00:27:44] Stephen Boyle: That’s one thing. The second thing is if we’re not training you in the right direction, it’s stevenBoyle@microsoft.com. Let me know. Awesome. Um, we’ve got programmatic stuff, um, you know, and we’ve got high touch stuff as well. So I think this is, this is another time where Microsoft is gonna over pivot on all of the training and enablement that we need to do to make sure that you’re, you know, you’re grounded in our platform. [00:28:07] Stephen Boyle: Um, I think there’s a huge opportunity with this agenda future to become more of a software partner. You know, even the deepest services organizations are going to need agents, and the more successful ones will be the ones that can turn on those agents in a repeatable way. So. Our agents, the new SaaS. I’m not exactly saying that, but I think that the agen future is one where even the more services oriented companies will, will have teams of agents that they’re deploying. [00:28:35] Stephen Boyle: In fact, I had a very, very large systems integrator, um, in, in the EBC just about a month ago, three weeks ago. Um, and I was sat next to their head of consulting and he showed me what he called his God dashboard. Uh, and right in the middle of his God dashboard there are like 450 accounts. All of whom I recognized, ’cause they were all in the enterprise, right in the middle of his dashboard was, how many tokens am I spending? [00:29:00] Vince Menzione: Yeah. [00:29:01] Stephen Boyle: Like, not like what’s my daily runway? You know, not am I making a profit on that account or anything else like that is like, how many tokens have I consumed? Yeah. Because there is an awful lot of, that is the new juice, if you like. That’s, that’s driving the success. You can have the smartest people on the planet, but you’ve got to still arm them with all the best tools that are available out there. [00:29:22] Stephen Boyle: So it’s fascinating to listen to him, how he had gone through that thing of, you know, agent sprawl, how many are really working, how many are not working? How can we prove that? You can prove it through, you know, managing your tokens. There’s a new version of. Finops for tokens, for want of a better phrase, that’s gonna be critical for us all to understand. [00:29:40] Stephen Boyle: ’cause they’re not cheap, they’re not free, that’s for sure. And, and they might not be cheap if you’re not, if you’re not managing them and using them effectively. Yeah. So that’s the other thing that I would really get on top of. And, you know, we’re gonna make some announcements in the not too distant future about the consumption driven future. [00:29:56] Stephen Boyle: Um, that, that we will, that we will deliver with our first party and third party platforms going forward. So that’s another. Another critical thing [00:30:03] Vince Menzione: sounds like some exciting announcements. Pretty soon. [00:30:06] Stephen Boyle: Yeah, could look close. Quarter four, help me close. Quarter four. Yes. That’s priority number one, two, and three right now. [00:30:12] Stephen Boyle: Uh, but get ready for some, you know, for some new announcements in July. Um, look, the future is incredibly bright with Microsoft. It’s incredibly bright in the industry as a whole, right? I mean, let, let’s be honest, the, the growth targets that we will have for ne next year are astronomical, and we will not make them without the partner community that we have, without training and enabling the partner community that we need for tomorrow. [00:30:34] Stephen Boyle: So like, stay close, you know, stay engaged. Talk to your partner development managers, talk to the talk to field reps, talk to the accounts that that, that you are in, and stay as close as you possibly can to our emerging strategy. And, um, you know, look, I, I think if I had fivefold or tenfold the people I have today, I still wouldn’t be able to touch everybody that I would like to touch in the partner community. [00:30:58] Stephen Boyle: So I’ll apologize in advance. Um, but we’re gonna have some, you know, some really cool ways of learning. Um, and we’re gonna make sure that they’re available to the widest possible audience. [00:31:07] Vince Menzione: Well, we bring the practitioners and the experts in the room to help with that as well. Right? Yeah. Because you can’t always have a partner development manager tied to everybody in the room. [00:31:14] Stephen Boyle: I, I would do hackathons on AI every week with every partner and every part of the world, but I can’t. [00:31:19] Vince Menzione: Yeah, exactly. Well, so good to have you today. Thank you. So good to see you again. I don’t know what your schedule is like. I, we didn’t, we don’t have enough time for questions. [00:31:28] Stephen Boyle: That’s cool. [00:31:28] Vince Menzione: From the audience. [00:31:29] Stephen Boyle: I’m gonna stay around for a little [00:31:30] Vince Menzione: while this [00:31:30] Stephen Boyle: morning and I’m coming back [00:31:31] Vince Menzione: for cocktails. Alright, terrific. So. Stephen Boyle will be here for cocktail hour. Thank you. Four 30 and uh, I wanna thank you, sir. So good to have you. Thank you. Good to see you. Absolutely. [00:31:42] Stephen Boyle: So much. Absolutely. Hey, thanks everybody. [00:31:43] Stephen Boyle: Thanks for what you do today, and hopefully thank you for what you do tomorrow as well. [00:31:46] Vince Menzione: Thank you. An incredible leader. [00:31:49] Stephen Boyle: Don’t forget, ultimate [00:31:51] Vince Menzione: partner Alive is coming soon, June 18th at our executive breakfast in New York. I hope to see you there.Description The Future of Tech is Here. Subscribe to our Newsletter:https://theultimatepartner.com/ebook-subscribe/ Check Out UPX:https://theultimatepartner.com/experience/ I

In this episode of the American Dream Factory Podcast, Nick Smoot sits down with Morgan Linton, co-founder and CTO of Bold Metrics, early Sonos employee, AI builder, and one of the most compelling people experimenting at the edge of artificial intelligence.Morgan's path is not linear, which is exactly what makes it valuable. He studied computer engineering and computer science at Carnegie Mellon, then turned down traditional software jobs to become an unpaid intern in the DreamWorks story department. From there, he joined Sonos before the product had launched, when the company had only a few months of runway left, and helped it grow into a billion-dollar company.That unusual path gave Morgan a rare mix of technical depth, storytelling, taste, sales experience, startup scars, and founder judgment. It also prepared him for the moment we are in now, where the future will not belong only to people who can write code. It will belong to people who can see what the world needs, imagine something better, and use machines to help build it.Today, Morgan and his wife Dana lead Bold Metrics, a machine learning company helping major apparel brands reduce returns, improve fit, and design clothing around real human body data. Bold Metrics can predict dozens of body measurements from simple inputs, then map those insights to garment data so brands can recommend better sizes and make better products.Nick and Morgan talk about why that matters in the AI era. As software becomes easier to build, the real moats become harder things: data, momentum, distribution, taste, and trust. Morgan explains why proprietary data is so powerful, why most people underestimate distribution, and why building something useful still requires judgment, creativity, and real-world understanding.The conversation then moves into the new world of AI-powered software development. Morgan shares how he moved his engineering team into agentic coding workflows and why he believes leaders now have a responsibility to use these tools. They discuss Codex, GPT-5.5, Cursor, Droid from Factory AI, Grok Build, Devin, Graphite, Claude Code, model routing, agentic code review, and the difference between a model and a harness.Morgan explains that a model is not the whole product. The model is the intelligence. The harness is the system that tells it how to behave, use tools, execute tasks, and interact with the user. The same model can perform very differently depending on the harness around it. That means the future is not just better AI models. It is better combinations of models, harnesses, workflows, and human judgment.For people just beginning with AI, Morgan's advice is simple: do not start with a book, a course, or a four-hour tutorial. Start by building. Pick one repetitive thing you do every day and ask an AI coding agent to help you automate it. A spreadsheet process. A report. A tax calculation. A file cleanup task. A simple internal tool. Once you build something useful, you cannot unsee what is happening.The deepest part of the conversation is not technical. It is human.Nick frames AI as the next wave of the internet, and Morgan pushes the idea further. This is not just the next wave of the internet. It is the next wave of humanity.Morgan argues that non-creative work can and will be done by machines at scale. That should not terrify us. It should free us. The computers can do the 996. Humans get to return to the work that makes us human: creativity, love, emotion, imagination, risk, beauty, invention, and solving real problems with people we care about.This episode is part founder story, part AI field guide, and part hopeful argument for the future. Morgan's message is clear: stop watching from the sidelines. Start building. Use the tools. Experiment. Automate something small. Follow your curiosity. Take the weird path. Build with taste. Create something useful.

Six months after their last roundup, Jacob sits down with Ari Morcos (Datology AI CEO, former Meta AI researcher) and Rob Toews (Radical Ventures partner, Forbes AI columnist) to take stock of an AI landscape that has shifted dramatically: coding agents crossing the long-time-horizon threshold has turned engineers into managers of agents, near-frontier open weight AI looks like it may be disappearing as Meta and the Chinese labs pull back, and Anthropic's restrictions on its newly released Fable model have its biggest supporters questioning whether safety framing is masking competitive positioning. The conversation runs through the full state of the lab wars, including Rob doubling down on his Sam Altman ouster prediction and the Bret Taylor succession theory, why Google's structural advantages remain intact despite falling behind on coding, what xAI's Cursor acquisition is really for, and Ari's claim that compute constraints could push labs to suspend their APIs entirely. The back half digs into the physical bottlenecks underneath it all, from atom and x-ray lithography startups challenging ASML to H100 prices reversing their decline, before closing with predictions: recursive self-improvement is closer than it was six months ago but slower than the takeoff narratives suggest, robotics is nearing its GPT-3 moment, and Anthropic's next chapter may be life sciences.   (0:00) Intro (1:40) Coding Agents Cross a Threshold (3:29) Is Open-Weight AI in Retreat? (7:37) Cost Crunch & Scaffolding (12:13) The "Apps Are Cooked" Debate (16:37) Sam Altman Under Scrutiny (19:44) Anthropic's Fable Backlash (23:24) How Big a Step Change Is Fable? (26:50) What's Going On at Google? (33:20) Could the APIs Go Away? (34:11) Breaking the Semiconductor Bottleneck (35:42) Beyond EUV: Atom & X-Ray Lithography (37:23) Implications of a Compute Shortage (40:20) Do Alt Chips Actually Help? (43:43) SpaceX, xAI & the Cursor Acquisition (48:50) How Close Are We to RSI? (52:21) Quickfire With your host: @jacobeffron - Managing Director at Redpoint

In this CEO Edition of the Events Demystified Podcast, the host interviews Johan Wadenholt, CEO of Voxo, about transforming live events from fleeting moments into measurable, reusable systems by turning stage conversations into structured, real-time outputs attendees and speakers can use immediately. Johan shares Voxo's origins in 2016 financial-services note-taking and compliance, early struggles with Nordic speech-to-text accuracy, partnerships to train models, and the shift to event reporting before and after GPT. They discuss solving FOMO in multi-track events, the forgetting curve, and why trust, consent, reliability, and humans-in-the-loop are essential to avoid hallucinations and protect speakers. They outline who benefits—attendees, organizers, sponsors, speakers, and content teams—plus analytics for leads and agenda decisions, AI's role in managing cognitive overload, and leadership lessons from scaling a startup amid rapid AI change.

Anthropic's new Claude Fable 5 is both the best model in the world and potentially one of the most dangerous.

Free Resource Mentioned in This Episode The AI Career Audit Coach The free tool Ann references throughout this episode. It is a custom GPT that asks you focused questions about your specific role, your practice area, and your goals, then builds you a personalized roadmap showing you exactly which tasks are at risk, which skills to develop now, and a simple one-hour-a-week plan to get there. Free. About 10 minutes. You will need a ChatGPT account to use it, but the free ChatGPT version works just fine. Get the AI Career Audit Coach here. Episode Summary Are you spending more time wondering if AI is going to come for your paralegal job than actually doing something about it? You are not alone, and this episode is going to change that. In Episode 179, Ann Pearson tackles the question every paralegal is thinking about but very few are addressing head-on. Which parts of your role are at risk of being absorbed by AI, and which skills are going to make you genuinely indispensable in the next chapter of this profession? Ann starts with the conversation that sparked this whole episode: her recent interview with Tony Castro, the Miami-based freelance paralegal whose courtroom-based business is one of the most AI-proof models in the industry right now. From there she gets real about why most freelance paralegal businesses are not the safety net people think they are, why the in-house paralegals who lean into this moment are the ones getting promoted and recruited, and three new paralegal job titles that did not exist three years ago. Then she hands you three strategies you can start working on this week. Loved This Episode? Share it with one paralegal in your network who needs to hear this conversation. This is the kind of episode that changes career trajectories, and you sharing it is what gets it into the right hands. And if you have not yet, take 10 minutes today and run your free AI Career Audit. Your future career will thank you.

Keith talks with data-driven investor Neal Bawa, the "mad scientist of multifamily," about why apartment values have dropped 20%–30% while single-family prices have stayed resilient.  They break down how interest rate shocks, the homeowner lock-in effect, and a wave of new multifamily supply are reshaping returns for today's investors.  Keith and Neal also dissect the build-to-rent model—who it really serves, how apartment oversupply is pressuring its rents, and why pending legislation could upend the space.  Neal closes with a specific, data-backed timeline for when multifamily rents and values may finally turn the corner, giving listeners a concrete roadmap instead of vague market guesses. Resources: Grocapitus Website - https://www.grocapitus.com Multifamily U's Free eBook: Location Magic - https://multifamilyu.com/lp/location-magic-ebook/ Multifamily U's Investor Club – https://multifamilyu.com/club Episode Page: GetRichEducation.com/609 For access to properties or free help with a GRE Investment Coach, start here: GREmarketplace.com GRE Free Investment Coaching: GREinvestmentcoach.com Get mortgage loans for investment property: RidgeLendingGroup.com or call 855-74-RIDGE  or e-mail: info@RidgeLendingGroup.com Invest with Freedom Family Investments.  For predictable 10-12% quarterly returns, visit FreedomFamilyInvestments.com/GRE or text  FAMILY to 66866  Unlock truly passive real estate income—visit flockhomes.com/GRE today to see if your properties qualify for a 721 exchange with Flock Homes. To get in the best physical, mental, and professional shape of your life, go to DanielThomasHind.com and apply for Daniel's intensive 1-on-1 coaching for burnt-out entrepreneurs and executives. Will you please leave a review for the show? I'd be grateful. Search "how to leave an Apple Podcasts review"  For advertising inquiries, visit: GetRichEducation.com/ad Best Financial Education: GetRichEducation.com Get our wealth-building newsletter free— GREletter.com  Our YouTube Channel: www.youtube.com/c/GetRichEducation Follow us on Instagram: @getricheducation Complete episode transcript:   Keith Weinhold  0:00   Keith, welcome to GRE. I'm your host, Keith Weinhold. The single-family real estate market is steady, but with apartment building values down 20 to 30% since 2022 when will the multifamily Armageddon end? We ask our qualified guest, and how will slowing birth rates in immigration affect real estate? And more today on Get Rich Education. You know, Mid South Home Buyers, that top Memphis turnkey provider. I learned that a secret weapon behind their explosive growth is more than just you buying their properties, it's an executive coach for nine years now, their CEO, Terry Kerr, and his COO, Pat Nix, have worked privately with a coach who I've now learned from too, and he doesn't market himself online anywhere. After 12 years behind the scenes, that coach is now making himself available exclusively for GRE listeners. His name is Daniel Thomas Hind. If you're a hard-charging business owner or investor who wants to get in the best shape of your life, physically, mentally, and professionally, you can fill out an application for a free consult. This is private one on one coaching for those willing to go to uncommon lengths to achieve uncommon results. Thanks to Daniel, we've all become better leaders, better operators, and better men. It started by showing up for ourselves. Now it's your turn. Go to Daniel Thomas hind.com H I N D, that's Daniel Thomas hind.com and sign up before Spotsville Flock homes helps multifamily owners exit the operator grind, whether it's your six plex or a 50 unit apartment, through a 721 exchange. This defers your capital gains tax. It's a strategy long used by institutions. Now you can swap tenants and toilets for passive income and zero management. Request your initial valuations. See if your property qualifies at flockhomes.com/gre That's F L O C K homes dot com slash G R E.   Neal Bawa  2:13   You're listening to the show that has created more financial freedom than nearly any show in the world. This is Get Rich Education.   Keith Weinhold  2:29   Welcome to GRE from Valencia, Spain to Valencia, California, and across 188 nations worldwide. America's favorite shaved mammal on a microphone is back with you for another wealth building week. I'm Keith Weinhold, and you're listening to Get Rich Education. The world's biggest problems are the world's biggest businesses. That's not a coincidence, and that's why we discuss housing here. And there's been a chronic shortage of affordable housing last month at a commencement speech, Harrison Ford, yes, the guy that played both Han Solo and Indiana Jones, talked about how a fulfilling life has both passion and purpose. Passion is what gets you out of bed in the morning, purpose is what helps you sleep at night, you and I. We can bring this mindset to our lifestyle, to the business we do, and to our investing. Treating tenants well is what helps real estate investors sleep well at night. While we're doing well, we can be doing good too. Multifamily syndicators keep failing, going out of business, and losing all of their investors' money due to mortgage rate resets. It just keeps happening. What this really means, that these groups that pooled together investor money to buy apartment buildings, largely that were set up in 2022 and earlier keep blowing up almost fully due to the fact that interest rates reset higher. Some of them had a fixed rate for five years. Well, rates spiked four years ago, and that's why a lot of them have yet to blow up, and these apartments have lost so much value that no one will refinance them, you know. Even if that apartment operator increased the net operating income over the years, even if rents went up, it doesn't matter. So, you still haven't heard the last of it. Do you remember a couple years ago, when a lot of people in the apartment space, they were saying just stay alive till 25 and that nonsense, like if you keep your head above water until 2025 oh well, then rates are certainly going to fall, and everyone's going to be okay. Well, 2025 is long gone.    Keith Weinhold  5:01   Mortgage rates haven't fallen in any significant way, so that survive until 25 thing or whatever mantra derivative people used that was a farce, like I've said on the show here for years. You cannot predict interest rates, so I didn't make the call that they were going to go up or down at all, because you can't predict them, but so many people said, oh, rates will fall substantially by now, no way, you just can't make that assumption, you've got to take history over hunches, and all of that, a lot of those multifamily deals 100% depended. depended on refinancing at favorable rates, and that's exactly why they failed. A surefire way to look foolish is to predict interest rates. We'll talk more about the multifamily Armageddon with today's guest. I also want to get into what's called the 21st century road to housing act, because that became one of the most hotly debated housing policy provisions this year. And what this is, is a Senate bill, and it would require certain large institutional investors that develop these bills to rent single family communities. It would force them to sell those homes to individual buyers within seven years. So, in other words, what a big firm could do is build a neighborhood of rental homes, lease them for up to seven years, but they couldn't hold on to them any longer than that. They couldn't hold them indefinitely as rentals, this bill is not aimed at you, the individual investor. It is aimed at big institutions, and what I mean by that is that's generally defined as owning 350 or more homes. That's what we're talking about here. Small landlords and mom and pop investors are not the target, it targets corporate portfolios, and this means groups whose names you've probably heard of, like Blackstone, First Key Homes, Progress Residential, and Invitation Homes. They are some of the heavyweights that the government is looking to clamp down on, so whenever you hear someone talk about big Wall Street landlords, that is who they're talking about. Now, some groups are pretty worried about the 21st Century Road to Housing Act, like the NHB, that's the National Association of Home Builders, and a lot of multifamily groups are concerned, and why is that? Well, the effect is it could dramatically reduce new housing production.   Keith Weinhold  7:44   See, a big institution like First Key Homes or Blackstone, they wouldn't want to even get into this business anymore. They wouldn't want to build big build to rent communities anymore if they have to sell them all within seven years. See, they want to buy and hold for the long term, kind of like what you and I are doing, because you and I know that owning a group of selective buy and hold single family rentals is a really profitable place to be, but so if they don't want to build, then that creates a reduction in supply, which could make prices go up, and then obviously hurt those trying to afford their own home. Well, that would defeat the purpose of this whole thing. I mean, my gosh, this always seems to happen when government gets involved. So, the 21st Century Road to Housing Act could limit supply, which is the exact opposite of its intent to get first-time home buyers into their first home, and if this passes, it does have bipartisan support. This lower supply, then yes, indeed puts upward pressure on prices. Just amazing. So then it could actually go on to help the everyday mom and pop investor, like you and I, that already owns property, the individual at last check, though they're looking to pass a version that still restricts some of these giant institutions from getting into build to rents, but yet it does not have that seven year sale requirement. What's really important to remember here is that Washington, they're looking to stifle big Wall Street players from the rental market, which could reduce supply. They're not targeting individual investors. The context that's important is that these groups, they own 10s of 1000s of homes, they don't own hundreds of 1000s, and they don't own a million, so it's a really small percentage of the housing market, whatever direction policy breaks, then the headlines that it creates are just greater in magnitude than the effect on the market is. It's an important frame of reference here. Let's meet this week's guest. This week we're welcoming back a guest that we haven't heard from in a year or two in real estate circles. He is popularly known as the mad scientist of multifamily. He's quite an in-demand speaker. He has a $500 million multifamily portfolio that he essentially shares with over 1300 investors. He's sharp, a good educator, and a straight shooter. That's why he's here. It's a warm welcome back to Neal Bawa.   Neal Bawa  10:32   Thanks for having me on the show again. It's delightful to be here, and so many interesting things to talk about in the world these days.   Keith Weinhold  10:38   There really are.. I don't know if we can get it all in, Bawa is spelled B A W A. Neal, I want to get to your future housing market outlook later. How you think the future looks, including when multi families quasi Armageddon might end. But first, you're known as a data driven real estate guy. Tell us about that, and how being data driven makes you profitable.   Neal Bawa  11:03   I see concern, and I'll tell you why. The single family and multifamily market have been atrociously incredibly divergent since the first quarter of 2022 They have not tracked yet each other at all, even though if you look at the last 50 years, they tend to track each other. So you know, 2008 was a Armageddon for single family, Armageddon for multifamily, and they both sort of came up in 2012 2013 and then they had a really good time until Covid.   Keith Weinhold  11:30   Yeah,   Neal Bawa  11:31   but the second quarter of 2022 is when Fed started raising rates, and since then we've sort of slid - multifamily has gone down in terms of pricing between 20 and 30% depending upon the metro, you know, and depending upon whether it's new construction, new construction assets have gone down more than 30% and existing assets that are filled up have gone down by 20 to 30% depending upon the metro. So, metros that have a large amount of supply, closer to 30% decline in value, the metros that have less supply probably closer to 20% decline in value, right.   Keith Weinhold  12:03   Demand demand has been pretty resilient. It's more of a supply story.   Neal Bawa  12:06   It's a huge supply story, right. So, if you look at, you know, occupancy, essentially what's happened is there was so much supply that came in that really people started on those projects in 2022 maybe they didn't start a construction until 2023 they didn't finish construction until 2025 so they started leasing up in 2025 They had to give offer concessions two months, sometimes three months free, and so that pushed down the rents in 2025. And they're not done, because you typically can't rent an apartment in six months. If it's brand new, it's going to take you about 18 months to rent it, and sometimes 24 months, and so it's affected our rents in 2025 it's affecting our rents in 2026. Now it's unlikely to affect it in 2027 but we'll go there, you know, at a later stage. But at the moment, we, what we've seen is negative rent growth in the United States for multifamily for the last 12 to 15 months, and what I think is going to be negative rent growth in Q of this year and Q2 of this year, so Q1 was negative, Q2, which we are in now, is likely to be negative or flat now. Single family, on the other hand, has gone in a different direction, which has been very difficult to understand, and I believe it's taken me a while to really understand this, but I think I've finally figured it out. Single family prices are not down since 2022 which makes no sense at all, because the average mortgage in the United States today is almost double, almost double, not quite double, but almost double of what it was in at the beginning of 2022 when interest rates were about 3.3 3.4% Right now we're sitting around, you know, six and a half percent interest rates, so not quite doubled interest rates, but they've obviously gone up a fair bit, and as a result, your average, you know, mortgage has almost doubled, but home prices haven't dropped, which makes no sense if you really think about it, because home prices are a factor of demand, and they're also a factor of people's ability to pay, so if all of a sudden within four years you're paying, the mortgage is doubled, then less people are going to be able to buy, but it stayed up, the market has stayed up, and the biggest reason it stayed up is because of what is known as the lock-in effect. So, the US market typically has a million new homes every year, and there's more than a million existing homes that are transacted, right? So, it's an open market, it's a perfect competition market, but it hasn't been perfect competition for the last four years, because so many people locked in ridiculously low interest rates.    Neal Bawa  14:28   Perfect example, in 2021 and 2022 I have a 15 year mortgage at 1.75% If I sell my house back to myself, my mortgage quadruples, quadruples, right, because it goes from 1.75% to six and a half percent, so I can't even imagine even think about leaving my home, right, because it's just such a perfect loan. Most people don't have anywhere near 1.75% but there's lots of people with more mortgages in the 3% three and a half percent, and 4% range that basically can't go anywhere, and because those homes are not coming into the market. The last three years the market has had this unusual not enough supply factor, and that's been keeping prices up. That is ending. That is ending, because what we've been tracking is the percentage of homes in the United States that have low mortgages. Low is simply defined as anything under four and a half percent, and that percentage is going down each quarter, because you know divorces happen, deaths happen, you know people move for jobs, and so every time that happens, that locked in rate goes away, because you sell your home and move on, and so for a while that lock in effect was predominant, it was controlling everything, but as time has gone on, interest rates were higher in 2324 2526 For also almost four years have passed since the rate started going up. So each quarter the percentage of homes in the US that have these low interest rates has slowly moved down, and we're almost back to a normal timeframe.   Neal Bawa  15:53   And this is causing the single family market to not have a conniption, but we're starting to see a balancing of the market, where it's not just a buyer's market anymore, in some places it's actually seller's market, some places it's a buyer's market. So we're now starting to see home prices drop in number of markets in the United States. I can't say that they've dropped in super majors, but we're seeing a flattening out effect of home prices in most metros in the US, and there should be a flattening effect. Just to be blunt, I mean, obviously I own a bunch of single-family homes, so I just wanted them to keep going up for selfish reasons. But if you think about it, we had huge home price growth in like 30 plus percent in number of years, 2021 22 and even 23 and during those years, salaries only went up by two to 3% a year. In one year, they went up by 4% and rents also went up like crazy. There was a 2021 was 15% rent growth year. So, at some point, there had to be an adjustment, and we are in that period of adjustment where single family prices are basically flat on a national basis. Yes, going up in the San Francisco Bay Area because of AI, and going up in a couple other technology-heavy metros because of AI, but otherwise fairly flat, and I don't expect that to change for the next year. So, my forecast is next 12 to 18 months, home prices in the US are going to be flat on a nominal basis, they're going to be down on an inflation-adjusted basis, but you know, because of the Iran, more inflation's three and a half percent, so home prices should go up three and a half percent. So, if they stay where they are, well, they're really dropping three and a half percent.   Keith Weinhold  17:29   Yeah, before this year began, I released our forecast, it was for 2% nominal home price appreciation in the one to four unit space for the US this year, and I still like how that looks. There's so much to unpack with what you just talked about. In my view, there's nothing unusual at all that when mortgage rates rose sharply a few years ago, that home prices rose as well. Why? Because actually, that's what usually happens, which is counterintuitive to most people. In all of our lifetimes, residential real estate prices have only fallen significantly one time, that was around 2008 due to a number of unusual circumstances. The only thing that's a bit different this time is, of course, how fast rates increased in 2022 and 2023 and people wondering if residential real estate prices could still keep up, and they certainly have, but yeah, you brought up this dichotomy, this bifurcation about how the apartment market and the one to four unit space kind of separated from each other in 2022 or 2023 That's what's so interesting.   Neal Bawa  18:36   I do want to point out a couple things, though, and I don't want to be a Pollyanna here and talk about negative stuff, but I think that there's big difference between 2008 and that timeframe and where we are today, and that difference is, and it has multiple parts. Not all of your audience is aware of this. Until about 2012 the United States had very reasonable birth rates. You know, we were one of those countries that had avoided the debacle that Japan, Korea, China, and a number of other countries are seeing South Korea being the absolute worst, where basically they were producing one baby per generation, where you need about 2.2 babies just to kind of keep your population where it is, right, and the US was unusually high in that, and that we were still above that threshold, which meant that our population would continue to grow and not fall. Now, there was two reasons our population was growing: One, we had more than 2.2 babies per household, and second, we had a very significant amount of legal and a very significant amount of illegal or undocumented immigration. Right, so we had both of those pipelines today. All three of those have flipped, so the United States now basically looks like Korea or China or Japan in that every household is producing about one and a half babies, which means that our population growth, which hasn't stopped yet, because it takes a while for these things to catch. Up is likely to stop, like it's, and at some point decline again. Luckily, we're not there yet. The US is a fairly young population, unlike Japan, which is one of the oldest populations in the world. So, it'll, we'll still continue to see population growth, but there is no doubt. And you can ask Chat GPT, right? How has population growth in the United States slowed over the last 20 years.    Neal Bawa  19:22   Make me a graph, and it will make you a very nice graph, and you'll very clearly see there's a slowdown in population growth. The second part is both documented and undocumented immigration. It's my estimate that since this administration took over, somewhere between half 1,000,001 million people have left the United States. Now it's very difficult to get an actual number, as you can imagine. A number of these people were undocumented, so we didn't really know how many there were to begin with. And a number of them, when they left, they also left by an undocumented rate, that you know, path. So we've lost a bunch of those people, and also the people that have stayed in the country, we've lost a number of them in the workforce. Here's a perfect anecdote, Keith. About 33% of the construction workforce in the United States was undocumented, one in three. In Texas, as much as 40%   Keith Weinhold  19:45   Yeah, that's huge.   Neal Bawa  19:45   It's very significant. Number of those people don't show up for work anymore. I don't think they've left the US, at least I don't think so. But they don't show up for work anymore, because that's how they get caught, right. So, what we've seen is that the construction workforce in the United States has become been decimated over the last 12 months, and the impact is much greater in the second half of 2025 than the first half. Why? Because even though they wanted to do ICE enforcement, they just simply didn't have enough agents, enough facilities, enough judges. When the second half of last year, they sort of started catching up on that, hiring more agents, getting more facilities, getting more judges, and so we started to see a real challenge there. I have properties in 10 markets in the US, and what I can say is about seven of those markets, mostly Southern markets, I am beginning to see dropping occupancy related to this phenomenon. I'm seeing a reduction, and so markets like Georgia and Texas, Florida are more hit than my northern markets like Idaho. I haven't seen any impact at all, but these southern markets, multiple properties, multiple metros, I'm seeing this - people, mostly of Spanish, Mexican origin, not renewing leases. I don't know what they're doing. I don't know if they're sleeping in their cars. I don't know if they're basically just, you know, staying with mom or staying with, you know, some other family. But I'm seeing a very, very big pullback in my leases tied to this, and occupancy is dropping in those markets that are heavily Hispanic. And so I'm seeing the impact of that on landlords, but I also know that there's an impact on the US at all, and overall demand on rentals, whether it's single family or multifamily. This is a significant impact, because I don't think that the Republicans are going to make a U-turn on this. I don't want to get political, but you know, stating the obvious.   Keith Weinhold  19:45   Yes, United States had its biggest birth year in 2007 when there were more than 4 million babies born. The average age of the first time homebuyer today is 40 years old. If that holds true, that peak would take place in 2047 And then, yes, to your point about changes in immigration, yes, it sounds like a potentially a reduction in demand with what you're talking about, with some vacancies, and also maybe a reduction in supply when you have fewer construction workers to build these places as well, we're talking about building properties. Neal, I want to talk to you about the build to rent space. Somewhat is build to rent better than traditional real estate? I think that's what we really want to know. And for those that don't know, build to rent means when you construct a property where from day one that construction project is built for a tenant, not an owner occupant. I see a lot of pros and cons there. Can you talk to us about the trade-offs between build to rent and traditional real estate?   Neal Bawa  19:52   Yeah, if you think about it, it's a really terrible word, built to rent, because if you think about the word built to rent should be apartments, right, but actually doesn't mean apartments, right? So, built to rent actually means single family or town homes that were built to rent out, right? And then you're like, why don't they just said built to rent apartments and town homes? Well, you know, was too long an acronym, and we suck at acronyms anyway. But BTR, or built to rent, is essentially building single family or town homes, but specifically building them to rent, and it doesn't include any apartments at all, right? And the reason why the BTR market was growing in the last five or six years is that roughly 18 million American families can no longer afford to buy starter single family homes, you know, and by starter I mean, small old single-family homes. That's how Americans usually started, you know, in their 20s and 30s. They would buy these homes, some of them, but they would fix up, and then they over time, in their 30s, late 30s and 40s and 50s, they would upgrade, and then at starting the 50s, it would flatten out, and then the 60s, they would start to downgrade, right? That's been a typical thing that's happened in America for 56 5070, years. Well, that is, cannot happen anymore. And it broke in 2022 until 2022 It was a normal cycle beyond 2022 because interest rates almost doubled, and the mortgages almost doubled, but the incomes only increased by 10 to 20% There became this orphaned generation of Americans, roughly 18 million families, that simply cannot afford to buy that starter home, and they are now forever renters. They don't know it. They think that they're going to catch up at some point, but five minutes with an Excel spreadsheet, I could prove it to them that they're not going to catch up.    Neal Bawa  25:35   Maybe one in 100 families would see a very large increase in income, and that would result in them catching up, but for the most part, as a group, these 18 million families, they're forever enters as a group that didn't exist before 2021 right. It's entirely because of this outrageous increase in mortgages, while not seeing a drop in home prices, that led to this, and so those orphan families, they actually earn pretty well, so these are families that make 70, 80, $90,000 in mid markets. They make over $100,000 if they're living on the coasts or in expensive markets, and they still can't buy that, you know, starter home. And so they don't want to live in apartments. I have lots of apartments, old ones, new ones, and I want these people to live there, but they don't want to live there, and so they've been looking for an option, and that option has been developers like me building communities of 200 300 townhomes or single family homes with a small little yard, and then basically from day one, instead of selling them, renting them out, and then once you're done renting out the whole community with 200 tenants, then you sell that to an apartment company. You know, there's lots of apartment companies in the US that have 100,000 units. Well, they want to buy these because the turnover is lower. So, what happens is most of these town homes and single-family homes for rent. Families come in, and they typically rent for three to five years before they move, whereas in on my apartments I lose 40% of my tenants each year. So, if I have 200 tenants, I lose 80 of them every year, and I have to basically go back, clean up those units, deal with the vacancy. But when I have townhome communities like my Idaho Falls townhome community. I lose a tenant at roughly every four years, and so, as you can imagine, profitability goes up when turnover goes down, right?   Neal Bawa  27:31   Because you don't have that cost of turnover and vacancy, and so eventually those large landlords that are holding 100,000 units figured out, I like this, what Neal Bawa is doing, he's building these 200 townhomes, I want to buy these from him when they're rented. I don't want to build them, I don't want to lease them up, I just want to buy them when they're stabilized. And so BTR became that name for that marketplace where developers would build townhomes and single families, rent them out, and then sell them to institutional, and it was some—   Keith Weinhold  27:56   People think of fabulous institutionalization of the starter home.   Neal Bawa  28:00   And in many ways it is, because what happened is, for a while, these institutional players, like Blackstone and BlackRock, they were like, we are just going to go out and buy 50,000 single-family homes, and that's going to be the institutionalized. Well, that worked really well if you bought in 2008 2009 2010 2011 because you got them bought them at a discount, but when they started buying them in 2015, 16, 17, 18 at ever higher prices, they didn't make any money. So the vast majority of these public funds that were created to buy large amounts of single family have failed if they've purchased anything in the last seven or eight years. If they bought before that, they made huge amounts of money. Family homes are so expensive that basically buying them for rental did not make sense, so these companies have now pivoted to saying we'll only buy communities that have 100 or 200 or 300 of these homes, because then we get the benefits of having centralized leasing, centralized property management, centralized maintenance, and I don't have homes spread all over the metro, they're all in one place, and I can make more profit from that. In theory, that's been good, and you might think that I'm bullish on BTR, but I'm actually today bearish on BTR for one single reason. About seven months ago, Republicans started talking about a bill - I don't know what the name of the bill is, but what this bill does is it forces builds to rent developers like me within seven years of building the property to sell all of the homes in that property to single family tenants, not to Blackstone, not to Blackrock, but to single family tenants. Hasn't passed yet, but it passed the Senate with an 8910 vote, which means that both Democrats and Republicans wanted to vote for this. If it passes the House, and because Donald Trump himself is very heavily opposed to it, he's made it very clear he doesn't like this. He's a developer, obviously. It hasn't passed the House yet, but if it passes the house, that will destroy the build to rent market. No one will ever build build to rent, because the worst possible thing is I build this, and within seven years I have to actually sell it to individual buyers. If I do that, my banks are going to hate me and not give me loans to build BTR anymore. Obviously, there's going to be some grandfathering to the communities that I'm building now, or maybe even build the ones that I'm building in 2027 maybe grandfathered. It usually is, because you know, Congress never does anything retroactively, and they give you a year or two, but if it passes, it's doomsday for BTR. I hope it doesn't happen, but that's the way it's looking, because it's bipartisan. Bipartisan bills are more likely to pass   Keith Weinhold  30:40   Now for the mom and pop investor, the individual investor build to rents have obvious appeal due to your point about the lower turnover, lower maintenance costs on a new build, lower insurance costs often on a new build, and then there's the tenant appeal to a new build as well, but of course there is that investor downside. I think a lot of investors are aware of their thin initial cash flow that they're going to have on build to rent, but you know, Neal, another downside with build to rent, I think a lot of investors don't look at is, hey, just how many of these things are they building? Are they building 500 of them? Do I have some overbuild risk if I buy into this community that could suppress occupancy and rents for a while.   Neal Bawa  31:21   What we've seen is that when Built to Rent started out in 2017-2018 it was its own asset class. It wasn't competing with apartments, it wasn't competing with single family rentals, it was just its own thing. However, in the last two or three years, as more and more apartments flooded the marketplace, we had a glut. It moved away from that. It basically started getting affected, and the rent started falling, just like any other portion of the market. You know, think of it as three portions of market. There's the built to rent, which I described, you know, brand new single family homes, town homes per rent. There's the apartments, both brand new and existing, and there's the single family rentals, right, which there are millions of. What we are seeing now is it's become one market, right? All of them are affecting each other, and the apartments, which have a huge amount of glut, there's a massive amount of new apartments that have come in in the last two years, are really pushing the rents down for single family, they're pushing that rents down for BTR. So, at this point, what I would say to people that have this concern, Keith, is simply look at incoming apartment supply, because if you're in a marketplace, and I'll give you examples of really good markets that are crushed right now. If you're in a market that has a lot of incoming supply, whether you buy a single family rental, a quadplex, a 50 plex that's an apartment, or 100 unit BTR, you're going to suffer for rent growth if you have a lot of incoming supply in 2026 and that is across the board in every market in the US. Huntsville, Alabama is, in my opinion, one of the most interesting markets in the US for 5 year, 10 year growth, right?    Neal Bawa  32:54   If I had to say you don't need a loan, it's just your own cash, no investors, where would you put money in? It would be at the top of my list, not at the very top. Idaho Falls is definitely the number one market in the US in my list, but Huntsville is up there. But right now, do you know what rent growth in Huntsville is? Minus 2% negative 2% Why? Because there's 6000 units coming into a market that's, you know, 1/5 or 1/10 the size of Phoenix, right. It's 1/10 the size of Dallas, but it has half the units of Dallas or Phoenix coming in, and so rent growth is negative there. So, what I would say is today absolutely everyone that is an investor should understand that we live in the magic world of AI, and you should be talking with Chat GPT about incoming supply for any market that you're interested in, and using that to make your decisions, because all of these markets merged, BTR, new apartments, old apartments, single family, everything has emerged in the last 24 months, where they're all affecting each other, and if there's too much supply of any one kind, it's affecting all of the other markets, and that's the message that I have. And none of this is like you have to go buy a $25,000 software like Costar today. Chat GPT is your costar.   Keith Weinhold  34:11   You're listening to Get Rich Education. We're talking with the mad scientist of multifamily, Neal Bawa, where we come back, including what he thinks about recovery for the beleaguered multifamily market. I'm your host, Keith Weinhold. 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What I like is that their team walks you through how it all works, so you can decide if it aligns with your portfolio and income goals. Every investment carries risk, and nothing is guaranteed, but with a track record of consistent on-time investor payouts, they built real credibility. Go to freedomfamilyinvestments.com to book a clarity call, or text family 268 66 That's Family 266 866    Speaker 1  36:00   This is the star of the A E Show, The Real Estate Commission. Todd Rollette. Listen to Get Rich Education with my friend Keith Weinhold, and don't quit your daydream.   Keith Weinhold  36:20   Welcome back to Get Rised Education. We're talking with Neal Bawa, a really sharp multifamily syndicator who's also highly data driven. And Neal, tell us more about the beleaguered multifamily market that had those aforementioned problems really cropping up in 2022 and we had a lot of supply and spiking rates. What does it look like for the path to recovery for the US multifamily market?   Neal Bawa  36:45   Luckily, demand is strong, and even though occupancies have dropped, typically the multifamily market, the large multifamily market in the US, tends to be between 95 and 96% occupied. Okay, and right now we're on 93% so that all that incoming supply means that about 7% of our apartments in the US are empty at the moment, we're trying to fill them, and we are seeing that occupancy drop, not across just new apartments that are leasing up, but also drop in class B and class C. We've also seen a huge increase in concessions, so I studied this quite obsessively, and I can tell you that 2026 in some markets is the recovery year, but not across the board in the United States, and the reason for that is sentiment. Once renters get used to huge amounts of concessions, it's like a drug, it takes a little while before you wean those renters off of those drugs, and so there's that hit right now. Every renter program,   Keith Weinhold  37:44   Everyone wants their freebie for good.    Neal Bawa  37:46   Yeah, exactly. It's like, hey, what, you're not giving me two months free? Hey, what, you're not even offering me one month free? It takes a while for that expectation to happen, because there's such a huge amount of concessions in the US. So, to me, there are a few markets, usually the smaller markets or very fast growing markets, where there's a recovery in 2026 but otherwise 2027 The first half of 2027 is recovery. The second half of 2027 is fast rent growth in a lot of markets. Why? Because remember, interest rates have been high since 2023 A lot of projects were started in 2022 went into construction in 23 came to market in 25 and 26 Lease ups are happening in 25 and 26 By early mid 27 these are all leased up, right? The second half of 2027 there isn't a lot of delivery in any of these big markets, because to deliver in the second half of 27 you would have started construction in that second half of 2025 and I counted those permits market by market. There's just not a lot, because by that time everyone knew that projects were not getting funded, everyone knew that interest rates were high, so there wasn't a lot of supply of new starts in the apartment market in the second half of 25 so there's not going to be a lot of delivery in the second half of 27 and all of the existing stuff would have been leased by then. So 2026 is one of those years where we could still see more concessions in the second half of 2026 I still see rent growth for apartments to be flat. You mentioned single family might be a little bit higher. It tends to be a little bit higher than apartments in terms of rent growth, but I think flat rent growth for 2026 is what I'm projecting. I'm projecting small rent growth in the first half of 2027 for most markets, and then I'm projecting robust rent growth, call it 3% or greater on an annualized basis, in the second half of 2027 and I'm projecting that most markets in the US that are not seeing a population drop, so count out places like Detroit are going to see a very aggressive rent growth, four or 5% rent growth, that's aggressive in our world, in 2028 28 and 29 are shaping up to be. Supply deficit years, years where supply is well under demand.   Keith Weinhold  40:05   It's pretty easy to project completions when you just go ahead and look at starts, and really, what you're counting is the story of absorption.   Neal Bawa  40:14   Yep, and what's nice about apartments is you can actually build a single family home in about nine months, right, but you can't build apartments in less than 24 months. There's just so much permitting issues, there's so many delivery issues, fire code issues, and so we have a crystal ball on the multifamily side that we are now getting better at using. I don't think the industry was very good at this in 2022 but now we're really all obsessed with how many permits does my metro have, and how many permits does my state, and how many permits does the US have? And everyone that I know in the industry that's data driven knows that there's a massive glut now, maybe a little bit of a glutton that remaining portion of 2026 equilibrium in 27 and a huge, huge supply deficit in 28 and 29 So everything that I'm doing is based on this, and this crystal ball actually works because of that two year gap between shovels in the ground and delivery,   Keith Weinhold  41:10   and it sounds like you've recommended Chat GPT as a go-to source for investors to look into these things, that happens to be my favorite one as well, and you are well, maybe it's a bit too much to say, but it almost feels like to me pioneering with the way that you use AI. In fact, I know before our show today you were running some other things in the background that made me wonder, hey, am I talking to the real Neil or the clone Neil? I know I've got the real Neil here, but why don't you tell us about how you're using AI to make data-driven decisions in real estate?   Neal Bawa  41:40   Sure, so the first thing is that we've completed our journey with the low hanging fruit of AI. Every single person in our company is fully trained on how to use Chat GPT. Most of our research-related processes are automated. For example, 100% of our investor updates are now written by Chat GPT. What we do is we go into our property manager meetings on Mondays or Tuesdays sit down with them, beat them up, and the transcript is then taken by our team in the Philippines. They take that transcript and put it into a pre-trained Chat GPT string, it's called a custom GPT, and the string took a while to train, but now that it's trained, all it needs is a transcript. We just copy paste it in, we don't give it any instructions, and it outputs a really wonderful investor update, right. And so our updates for our investors are 99% written by AI. Of course, we'll go in and add our comments at the end of the process. So we've automated investor updates, rent comps, so you know if we are underwriting a new property today, what we do is we simply go into a Google file and copy paste the address and hit enter roughly once a minute. A software, which is written by AI - we're not coders, but the software knows how to write code - it checks the file, if it sees a new address, it goes in there, grabs the address, and then it basically goes to apartments.com rent.com realtor.com and all of these places, and checks the rents for this particular property in two mile radius. It eliminates all the ones that don't match, like you don't want to match the rents of a 1970 or 80s built property with a brand new 25 built property. Those are not comps, it's not comparable. So it basically is very careful, it keeps a radius range of two miles, and also basically is a property of the same kind, you know, like it never matches up a three story property with a 10 story property. Those don't match, one of them obviously is more of a central business district or downtown sort of thing, and so it basically grabs all of those rent comps and then puts them into a file and posts in a Slack channel. Usually it takes it about 1213 minutes to do that, and so whoever put that address in about 12 minutes later goes into the Slack channel and says, "Hmm, these are all my rent comps, right? And boom, now you're basically, you have all these ready rent comps. So, what we've done is, we've automated a significant portion of what we are doing with both our property managers and inside the company with acquisitions and things like that, we're also scraping massive amounts of data from the Bureau of Labor Statistics website, which we just couldn't deal with that data before, and building very beautiful, very interactive dashboards. We don't use Chat GPT for that. We find for dashboarding a tool called Claude, which is by a company called Anthropic, is much better, so we have currently over 150 interactive dashboards that Claude has created that update in real time and give us access to data. If anything, I find that we are in this incredible time where decision making has become much easier, as long as you spend time with these tools. So, in our company we have an absolute mandate that no one has broken for the last year. One year per day, people must program, and by programming we mean issuing common language instructions to tools and build dashboards and build software that automates our work. Have we laid off anyone because of this? I mean that. Be the next obvious question. The answer is no, because it's made it easier for us to serve a much larger audience, so it's easier to grow your company. We just are not hiring anyone, and we haven't hired anybody for the last 18 months, so we have a hiring freeze, but at the same time all of our people are employed because they're they're now much more valuable. So everyone in our company is now a programmer, and even though that sounds weird, it's completely true.   Neal Bawa  45:24   Every single person in our company writes code, and they write code by talking with Cloud Code or talking with Chat GPT, and then Chat GPT, of course, does the actual code writing, but people have become very, very good at answering questions and saying, "I want a dashboard like this, turn these radio buttons into drop boxes, and give me the last month, and last three months, and last 12 months, and do this, and do that, and connect this, and I also want to host this on a server, but I want to make sure that only I can see it. I need a password added. Imagine 1000 of these conversations happening in our company every day. Yeah, that's interesting. And what you just described   Keith Weinhold  46:00   there at Gro Capitas is somewhat of a microcosm for what's happening in the broader economy, where we've been in this low high or low fire environment for quite a while. Well, Neal, as we're winding down here, we recently had a new Fed chair come in. It seems incomprehensible to me that there could possibly be any rate cuts. I don't know how we could responsibly make a rate cut with all these inflationary layers. We had the pandemic, and then terrorists, and then the Iran war, and the energy shocks, and all these bottled up supply chains. What are your thoughts with regard to the Fed?   Neal Bawa  46:29   I still think that we'll get one rate cut, and that rate cut will be based on political pressure. So, for the first time ever, I have seen the Fed break into factions, so if you look at the latest Fed meeting, which happened, you know, there was dissent, there were two clear factions, so the Fed is becoming less data driven and more faction driven, and I think that one of the factions, which obviously wants rate cuts to go down, is going to triumph at some point later in the year, but until we get past the incredible increase in inflation because of the Iran war, I don't think that faction is going to win. Right, there's three or four people in that faction, that's not enough votes to get past the others. So I'm predicting no rate cuts until Q4 of this year. If the Fed was entirely logical, there should still not be a rate card in Q4, but I think it'll happen because there's political pressure.   Keith Weinhold  47:25   The preservation of independence is key. Neil Bhawa, this has been great, and a lot of people learn from you. You're a brilliant educator, as well as what you're doing in the multifamily space, and a lot of other places. So, if someone wants to connect with you, learn more about what you do. What's the best way for them to do that?   Neal Bawa  47:43   So we built a website called Multi Family University. It's completely free. There is no subscription. There's no upsell. We do not have an educational product, but what we do is each year we have 8-12 webinars that we create with their extraordinarily good looking thanks to the use of AI. Yay, and we share them with an audience, and usually between 5000 and 1000 people attend our webinars each year, of which roughly 1% become investors with us. The rest, the remaining 99% just continue to get free access to data, and we cover every imaginable real estate topic: Single family, multifamily, industrial hotels, self storage, Airbnb, and even controversial topics outside of real estate, like climate change or impact of climate change and impact of AI. So you know, multifamily university is the best place you can go to, multifamily you.com/club It's a free club, and it's free forever.   Keith Weinhold  48:42   Neal, it's been valuable to our audience. Thanks so much for coming back out of the show.   Neal Bawa  48:46   Thanks for having me.   Keith Weinhold  48:53   Oh, a terrific, wide-ranging chat with Neal. There, yes, this interesting 2022 divergence between single family and multifamily, the slowing birth rate, and how that won't really catch up with real estate in a big way for perhaps 20 plus more years. How single family rentals beat multifamily on the basis of tenant retention, and a lot more that we covered there, and he's got a good data driven timeline for apartments being back in favor by 2027 and 2028 After the interview, Neil and I chatted some more off Mike, and he would like to come back on the show next year. We're probably going to have him, because we have a lot more to talk about at that time. We can see if the multifamily market is really healing. Also, did you pick up on this? I wonder why, for his own home he would get a 15 year mortgage at 1.75% interest, so I'll have to ask him about that. That's surely a fantastic interest rate, but a 15 year loan rather than a 30 year that maybe he could have gotten at two and a half percent at the time. Well, 15 year probably. Is not the best use of capital, because it increases your equity position rapidly. When instead, those dollars could have been out in the market earning an actual return somewhere else. But he's a smart guy, he must have an answer. We can talk about that at that time. We've got a lot of terrific shows coming up here on the GRE podcast, specific learning episodes, where it's just me teaching you, as well as new guests and returning guests too. Until next week, I'm your host, Keith Weinhold. Don't quit your daydream.   Speaker 2  50:35   Nothing on this show should be considered specific personal or professional advice. Please consult an appropriate tax, legal, real estate, financial, or business professional for individualized advice. Opinions of guests are their own. Information is not guaranteed. All investment strategies have the potential for profit or loss. The host is operating on behalf of Get Rich Education LLC exclusively.    Speaker 2  51:03   The preceding program was brought to you by Your Home for Wealth Building, getricheducation.com.  

Description The Future of Tech is Here. Subscribe to our Newsletter:https://theultimatepartner.com/ebook-subscribe/ Check Out UPX:https://theultimatepartner.com/experience/ In this presentation from Ultimate Partner Live, industry analyst Jay McBain breaks down the monumental macroeconomic shifts rewriting the tech sector in 2026. https://youtu.be/r0qTDyw97Gs As the industry rapidly approaches a $6.07 trillion valuation, driven by massive AI infrastructure investments from Sam Altman and the “Magnificent Seven,” traditional sales and channel models are fundamentally collapsing. McBain reveals how buyer demographics have transformed to an integration-first millennial base, why marketplace ecosystems now command over half of all partner-funded deals, and how a tiny elite of just 1,000 tech service providers control two-thirds of global tech revenue. Learn the exact mechanics behind how Microsoft out-partnered AWS to win 26 straight quarters of dominant growth and how your business can deploy an algorithmic early warning system to capture massive wallet share before competitors even step into the boardroom. Key Takeaways Over half of the Fortune 500 companies vanish every 20 years because their leadership fails to anticipate macroeconomic technological cycles. The true opportunity in the $6.5 trillion AI boom lies not in single vendor products, but in the hardware, software, services, and telecom ecosystem surrounding them. Indirect tech sales are undergoing a structural shift toward direct cloud hyperscaler models driven heavily by Nvidia's core infrastructure client base. Modern business deals are won or lost months before the point of sale based on the average of 6.3 partners surrounding a customer’s environment. Over 51% of tech buyers are now millennials who prioritize software integration capabilities and digital marketplaces over traditional human sales interactions. Tech service economics are pivoting aggressively away from upfront margins toward point-based multi-partner funding across subscription cycles. If you're ready to lead through change, elevate your business, and achieve extraordinary outcomes through the power of partnership—this is your community. At Ultimate Partner® we want leaders like you to join us in the Ultimate Partner Experience – where transformation begins. Key Tags Nvidia AI buildout, $7 trillion AI opportunity, cloud ecosystem decade, Microsoft vs AWS growth, multi-partner cloud deals, digital marketplace migration, millennial B2B buyers, B2B tech subscription economics, tokenized micro consumption, tech services wallet share, hybrid cloud infrastructure, 28 customer moments, IT services industry growth, telecom spend breakdown, channel chief strategy, managed service providers MSP, global systems integrators GSI, software integration first, point-based vendor incentives, automated co-selling workflows Transcript JAY McBAIN AUDIO PODCAST [00:00:00] Jay McBain: So to go back to that story about the 53% of companies who are gonna fail, one of us is gonna be asked to write the book, but chapter one is always you Blame the CEO. [00:00:13] Vince Menzione: We just came back from Ultimate Partner live in Bellevue, Washington, where we hosted incredible leaders for two amazing days. Come join us for this next session where we explore the tectonic shifts we’ve all been seeing. With that, I am incredibly blessed to invite a friend of mine to the stage. I have a quick little side note, like I found an old LinkedIn post from this gentleman from like many years ago, like 20 years ago. [00:00:39] Vince Menzione: And I wasn’t really that nice to you on that LinkedIn post. Like, oh, like this is before Jay became the Jay, that we all know Jay to be j. But he was in the space and I was at Microsoft doing something and he reached out about something. It was kind of rude, Jay. I was like, oh my gosh. I can’t believe. But Jay has been a great friend. [00:00:54] Vince Menzione: When we started the podcast back up, uh, during COVID we started doing podcasts together. When we moved to the studio, Jay was the first person in the studio. He’s always got a spot, uh, at our events. He’s s Spot Art, and, and he’s a great friend and supporter of Ultimate Partner Jay McBain. For those of you who don’t know him, Jay, welcome. [00:01:13] Vince Menzione: Thank you, sir. [00:01:22] Jay McBain: 31 days ago, we landed Artemis two. The furthest humans have ever been away from the planet Earth 57 years ago. We landed on the moon in the 56 years. Between those two moments, the tech industry has been the fastest growing industry in the world. Every single year we moved from the space race to the technology race, and we’re just getting started. [00:01:46] Jay McBain: If you’re old enough, you’ll recognize the mainframe and mini era for 20 years. You’ll recognize a young disheveled Bill Gates showing up in Boca Raton, Florida for, uh, August the 12th, 1981 launch, where Bill thought that every one of us would’ve a PC in our home, and IBM thought they were gonna sell 10,000 of them to hobbyists. [00:02:12] Jay McBain: 1999, a small startup from an executive who just left Oracle in San Francisco named Mark Benioff. A couple of years later, Jeff Bezos went into a boardroom and said, listen, we’ve spent a lot of money building infrastructure to our busiest day, Christmas, black Friday. You’re telling me this stuff sits idle 10 or 20% for the rest of the year. [00:02:35] Jay McBain: Why don’t we rent that out to others? Got laughed outta that boardroom and then got made of fun of on magazine covers. Maybe you should just tend the store, let the adults talk about technology. In March of 2023, our neighbors, our friends, our family saw DeepFakes. They saw poetry, they saw music, and they came to us as tech people and said, did we just light up Skynet? [00:03:03] Jay McBain: Now every one of these 20 year eras, this is the Taylor Swift version of our industry. Every single one of these eras triggers the fastest growing product in history. Today it’s actually Chacha bt first to a billion users. It triggers a new, richest person in the world, bill Gates, to Jeff Bezos. Now, Elon Musk is the first to sign a trillion dollar pay package, and it’s not for car. [00:03:27] Jay McBain: It’s not for cars. It also triggers a most valuable company in the world change. And today that’s nvidia. These are monumental changes in our industry and they’re monumental changes in partnering every single time. And it also links to our customers. If you take a 20 year view of business, one era, and, and think about the AI era, you know, at the start of it here, if you’re to grab the Fortune 500 magazine from 20 years ago and start to flip through it, 53% of the companies in there no longer exist. [00:04:06] Jay McBain: Every 20 year cycle, we lose over half of the biggest companies in the world. These are the companies that have very deep pockets to buy their way outta problems. If you’re not in the Fortune 571% of tech companies don’t make it 10 years. These are the changes that cost industries. There are changes that cost really big companies and the decisions we make, the trends we’re in right now, in 2026 will be written about in the future. [00:04:39] Jay McBain: This new era, a lot of big numbers being thrown around. Vince’s best friend talk about a six and a half trillion dollar AI opportunity, but it’s not Microsoft’s tam. Microsoft is chasing about a trillion dollars of this. And the ecosystem, the hardware, the software, the services, the telecom is gonna make up the rest. [00:05:04] Jay McBain: It is an ecosystem. Every time these big numbers are thrown, the word ecosystem is always thrown around it. Not to be outdone, Sam Altman’s talking about a $7 trillion build out. The world economy this year, the world GDP will be 126. These are material numbers to world GDP, but even better, they’re both larger than our entire industry is today. [00:05:27] Jay McBain: So what took 56 years of the fastest growing industry this year will be $6.07 trillion. Big numbers, but it’s easier to think about it in terms of a dollar that our customers spend in that dollar. They’re gonna spend 25 cents on hardware. They’re gonna spend 25 cents on software. So for anyone that read the memo 15 years ago, that software’s gonna eat the world, there’s still a dollar a hardware to run every dollar of that software. [00:05:57] Jay McBain: And whether you’re thinking humanoid robots or whichever future you’re envisioning, there’s going to be a dollar of hardware to run every dollar of software for the next 20 years. There’s over 25 cents now in IT services, and in many cases, these services are growing faster than the product categories and just under 25 cents in telecom, that’s how it breaks out today. [00:06:19] Jay McBain: And this industry, which took 56 years to get to this point, is gonna double in size in the next three to five years. We already have two and a half trillion of that seven raised and being spent. Part of the reason Nvidia is the most valuable company in the world. Now our industry, uh, you talk about ultimate partnerships. [00:06:40] Jay McBain: Our industry traditionally, and world trade by the way, is 75% indirect. The dealerships, the agencies, the brokers, the resellers, the retailers, the franchisees, the gas stations, the grocery stores, the pharmacies, all 27 industries sell indirect. You gotta think back the last time you bought something direct. [00:07:01] Jay McBain: Well, I bought a Dell from that dude in the nineties. Cool. Well, Dell Technologies is now 60% indirect. Well, I bought insurance. Direct is 15 minutes. Could save me 15%. Well, Geico last year sold more insurance through agencies and brokers than they did direct. This is the world now. We used to be 75% indirect four years ago. [00:07:26] Jay McBain: Then it went to 73.2, then it went to 70.1 and it then it went to 66.7. By the way, marketplace is in these numbers indirect. It’s not marketplace causing this change. It’s one company, Nvidia. Nvidia has seven customers. The magnificent seven, uh, half of them are in the room right now that every morning we wake up to a hundred billion dollars press release about this $7 trillion buildout. [00:07:56] Jay McBain: What’s interesting is indirect sales in our industry is growing by revenue. It increases every year, just not at the pace that this AI build out is happening direct with seven companies. But the reason we’re all here, and I think the core reason that Vince is building this community is this, you know, Microsoft forever has measured and been very vocal. [00:08:21] Jay McBain: About 96% of their deals have partners in them. Kind of who cares, who collects the money. We care about the moments, the 28 moments before the customer makes a purchase. We care about every 30 days forever, because two thirds of our industry, over $4 trillion now is subscription consumption based. Winning a customer today is only winning the first 30 days. [00:08:46] Jay McBain: We care about this cycle. We care about who surrounds our customer. So six years ago, I stood on a big stage and said, you know, we went through a decade of sales. You know, in 1999, you thought you were born to be a salesperson. You’re managing your territory with your gut. Well, a few years later, you were introduced to the science of selling. [00:09:07] Jay McBain: You know, 10 years later you thought as a marketer, you sit around a cocktail party joking with your friends, 50% of my marketing dollars are wasted. I just don’t know which 50%. Really funny. In 2009 until every 58-year-old CMO got replaced by a 38-year-old growth hacker. Coming in with Marketo and Eloqua and Pardot and HubSpot, and 15,505 as of yesterday, MarTech and iTech tools, ninjas in marketing, they wouldn’t let a nickel go through without measuring. [00:09:43] Jay McBain: Now we understand 96% of deals and partners that surround it. No deal is gonna be won or lost in this era without partnering effectively. So we had to have this decade of the ecosystem. One of the ways we’re tracking is by outsiders. You know, Salesforce every year publishes the state of sales and they’ve got, you know, the number one CRM in the world. [00:10:05] Jay McBain: So they get to go talk to all the CROs, all the salespeople in the world. And as of this year, a couple months ago, 94% of every salesperson in every industry in the world uses partners every single day. You wanna see what this number was six years ago. Also, 89% of salespeople around the world don’t think they’re going to club this year without partners. [00:10:29] Jay McBain: So this is a big moment for us, halfway through the decade ecosystem, but we’re only halfway through. We’re starting to understand now at a more granular level. What partnering means. It’s not theory, it’s not flywheels. It’s not really cute. McKinsey slides that we keep showing to our board saying how important partnering is. [00:10:51] Jay McBain: We’re trying to get to the very specific level of the 6.3 partners on average that surround the deal and what they’re doing. How their business model works, and that’s average if I’m working on a public sector deal. I was at a Red Hat conference yesterday talking sovereignty. If I’m in an enterprise or a large public sector deal, it’s north of 10 partners in the deal. [00:11:15] Jay McBain: So we’re starting to understand what used to be this, this, you know, you’ve been the fastest growing industry for 56 straight years. Every single professional services person in every industry has come in to join the fund. Over 90% of accountants are tech services firms. Over 90% of marketing agencies are tech services agencies. [00:11:36] Jay McBain: All of this 250,000 software companies, a million emerging comp tech companies, the half a million VAR that have been in that traditional channel. The managed service providers, all of these 20 different partner types, millions of companies, tens of millions of people competing for 6.3 spots. Around the customer. [00:11:58] Jay McBain: That’s it. Luckily, there’s 141 million global customers to compete for. There’s, there’s some open slots that you can go find, and that’s the point. Our industry never had our own Fortune 500. We always talk to, you know, these partners and GSIs are doing this and SI are doing that. And we never really had a view of capability and capacity or what our own TAM was inside of that partnering. [00:12:25] Jay McBain: And so we set out and we would’ve loved, you know, chat GPT or Gemini or Claude or any of those tools to do this. But there’s one problem in partnering with AI is that it doesn’t know one partner from the next. There’s a big digital sameness problem in our industry that every single partner, whether it’s Larry in the White van or Accenture, with 786,000 employees all say they do all things to all people all the time. [00:12:53] Jay McBain: 98% of them, 99% of them are private companies that don’t share their p and l. You can’t go into Microsoft’s LinkedIn system and find out how many employees, ’cause it’s a block system, it AI can’t see into it. So it just sees, and it’s a great pattern matching. Google, SEO can’t figure out who’s who, nor today can the large language models. [00:13:14] Jay McBain: ’cause all the things they’re trying to match, the transformers are trying to match. It all looks the same. Every tweet, every ebook, every website, every digital history looks the same. So this took us thousands of people hours across two years to do, to dig into every p and l to dig into every dollar of what they’re doing. [00:13:33] Jay McBain: But what was interesting is only a thousand partners in our industry do two thirds of all tech services. When you get into enterprise, it goes up to 80 to 90%. The partners in the middle, in Blue do more tech services. The 30 of them than the 970 partners in white on the outside, the 970 partners in White do more tech services than the next million combined. [00:14:03] Jay McBain: This is our industry in a nutshell. Every time we talk to a a vendor, every time we talk to a partner, every time we talk to a distributor, we’re now talking names, faces, and places. You you wanna talk sovereignty. Yesterday in Atlanta, 90% of sovereign conversations in public sector in the globe is handled by these companies here. [00:14:26] Jay McBain: Forget about how much you do with these partners today. You wanna chase the next column, which is the wallet share. And I was a channel chief for 17 years. I get the weekly report and I see a million dollar partner, another million dollar partner, sorted top to bottom. You don’t know which partners which, which of those million dollar partners is doing 1.2 million in your category. [00:14:46] Jay McBain: They deserve a baseball cap and a front row seat at your event as an MVP. The next partner right next to them is doing 10 million in your category. They’re only doing a million with you. ’cause customers are pulling them into it. Nine times outta 10. They’re leading with your competitor. So I don’t want that list anymore. [00:15:03] Jay McBain: I want the new list, which is showing me those $9 million opportunities. And I as a board member, as A CEO, as a CFO, as a CRO, I wanna see this list. And then I want to talk people, processes, programs, technology. What are we gonna do to go get our fair share of that 9 million? Where’s our lowest hanging fruit? [00:15:24] Jay McBain: How do we double our pipeline? How do we double the size of our company in three years? It’s all right here. Let’s have very specific conversations and move away from flywheels and move around from force multipliers and and things like that in partnering. Let’s figure out how this partner community is surrounded. [00:15:45] Jay McBain: What do 10 million people who have to be smart in front of their customers every single day, what do they read? Where do they go and who do they follow? It’s the law of a few. This is the old Malcolm Gladwell of tipping point 10 million people in the broader channel. A hundred percent of our TAM comes down to only a thousand watering holes. [00:16:08] Jay McBain: 12% of that entire audience. Doesn’t sound like a lot, but it’s over A million. People love podcasts. Number one way they learn the Joe Rogan effect. In our industry, there’s 121 podcasts. These are all public lists. You can go get on my LinkedIn newsletter on canals, oia. But there’s 121 podcasts that drive him forward. [00:16:28] Jay McBain: Really high up on that list, actually number one on the list is ultimate partner, Vince. That’s how I met. ’cause I asked people, 10 million people, you love this. You walk your dog, you drive to work, you listen to podcasts. I’m not the biggest podcast fan. It’s not number one on my list, but it’s number one on theirs. [00:16:44] Jay McBain: They say, you know, you gotta meet this guy, Vince. It’s unbelievable how great these podcasts are. They’re ultimate. [00:16:54] Jay McBain: Then I talked to Vince and said, but Vince, you know, 35% of your community, the 10 million people love to come to events like this one. The hallway conversations, the hotel lobby bar last night. This is what we love to do, especially post pandemic. It’s the number one way we learn. We learn from our peers, we learn from those around us, and, and the learn from the conversations we have here. [00:17:17] Jay McBain: We always remember these moments, you know, years and years later. There’s 352 choices. I’m going to five of them this week in five different cities. It’s a lot of coverage, but again, it’s a tighter li list of how people work. The magazine lists 106 of them associations like Conter. Now the GTIA peer groups, there’s 15 different spheres of influence, but only a thousand places. [00:17:43] Jay McBain: I could walk you through billionaire, after billionaire, after billionaire in this industry and show you how they did this. How did Arne Bellini at ConnectWise? How did Austin McCord at Datto, how did Nerdio become a unicorn? How did threat locker and huntress move away from 6,500 cyber companies and become unicorns over and over and over again? [00:18:05] Jay McBain: It’s only one slide. Unicorns and billionaires are made here, and a lot of people don’t get it. So walking away from Bellevue, a thousand partners, top down, a thousand watering holes, bottoms up. You’ve covered a hundred percent of your tam. You do it better than 10% of your competitor, 10% better than your competitors. [00:18:27] Jay McBain: You win. You carry that on your resume into the next company. You get a bigger job at a bigger pay scale. Let’s just walk through some examples. Cyber 91.7% of it goes through the channel. Huge channel audience. You know, if you’re in MarTech, it’s only 10%, but this one happens to be all channel, but that’s not the story. [00:18:48] Jay McBain: For every dollar that the 6,500 cyber companies are trying to close, there’s $2 in services. Plot twist, the products are grown at 11, the services are grown at 12.6. Your partners are growing faster than you are, and they will continue to for the next, at least five years, probably 10. So when I’m here, five years from now, you’ll hear in me talk about a three to one split in cyber and then a four to one split in cyber. [00:19:18] Jay McBain: Now, when we’re in Miami a couple days ago is CrowdStrike, they’re talking about a $7 and 5 cent multiplier, chasing that two to one up higher. You look at managed services. Here’s a fun story. Managed services. 82% of customers who are man, uh, outsourcing more this year than last year. 650 billion in size. [00:19:38] Jay McBain: This is bigger than the entire SaaS industry. Salesforce, ServiceNow, Workday, Marketo, NetSuite, HubSpot, 250,000. Others. This is bigger. It’s also bigger than all the Hyperscalers combined, not just AWS, Microsoft and Google, but Alibaba and Oracle and everybody down the list. This is a massive market also growing at double digits. [00:19:59] Jay McBain: So these are some big things and obviously we’re watching, you know, week in and week out, quarter in, quarter out, the Battle of Software and Battle of the Hyperscalers and things like that, and who’s growing at what pace and, and how partnering is connecting to all of this. You know, we watched a moment really early in the pandemic where Microsoft started growing faster than AWS and they haven’t stopped since 26 straight quarters. [00:20:27] Jay McBain: And you ask customers and say, you know, does Microsoft have a better product? And in most cases they say no. You know, AWS had a five year head start. Well, did they have a better price? Well, no, actually most cases Microsoft’s more expensive. Well, did did they have better promotion? Was their Super Bowl ad better? [00:20:44] Jay McBain: No, they’re both kind of crap. So you kind of ask the questions of what’s the only difference that could create growth above the leader in the market? Well, it’s place. More of the 6.3 partners are walking into those keyboard room meetings and drawing clouds up on the wall and labeling the Microsoft than they are AWS. [00:21:03] Jay McBain: Very simple. It’s never been about product. The best product in our industry has never won. And now the best way forward is that partnering moment, and this is the moment. So to go back to that story about the 53% of companies who are gonna fail, one of us is gonna be asked to write the book. And it could be the book like Kodak, they invented the product that ended up killing them. [00:21:26] Jay McBain: And it’s a woe is me story, but chapter one is always you blame the CEO. How could they not see those trends happening in 2026? How could they, you know, were they blind? Were they stuck in their own, you know, innovation chamber? Innovator’s dilemma, were they stuck in their own boardrooms? Why couldn’t they see? [00:21:46] Jay McBain: Well, chapter two, you, you blame the board. They have fiduciary responsibility, outsider view, and how could they not see it? But really, this is the future right here. If you take this slide and apply it 10 or 20 years from now to every failure and every success, these are the chapters of the book. Your buyer is now a millennial. [00:22:05] Jay McBain: As of last year, the 51% of our market is bought by people born after 1982. Different psychology, different behavior, different journey, different criteria, their integration. First buyers. The buy a product, 80% as good as the next one. If it works better in their environment. 94% of people won’t buy a car unless it has CarPlay or Android Auto. [00:22:26] Jay McBain: New Buyer. You have to be more integrated than your competitors. That’s a partnering story. The 6.3 partners. If you heard cyber, you need some great channel partnerships, but you need the other 5.3 partners as well, the consultants, the advisors, the designers, the architects, the implementers, the integrators, the manner service, all of the other partners. [00:22:44] Jay McBain: You need to know more of them than your competitors do, and have them label clouds with your name in them. You need better alliances. Even if you compete, you only compete in the morning. You’re best friends by the afternoon. You have to be tight with the hyperscalers, tight, with the big SaaS platforms, tight with cyber, tight with distribution, there are layers, seven layers to every deal. [00:23:04] Jay McBain: You gotta be tight in and have better alliances than your competitors. And then it all comes to the 28 moments, which I’m gonna end on, but the go to market of all of this, the co-selling, co-marketing, co-innovation, co-development, co keeping. This is it. Your product has to be good enough that somebody’s gonna renew it. [00:23:21] Jay McBain: Your Super Bowl has to be, you know, ad has to be good enough that people don’t, you know, shame you on social media. Your pricing has to be somewhere in a country mile of the bell curve of what the customer wants to pay. But successor failure is just here and platforms are synonymous with partnering. [00:23:40] Jay McBain: It’s our role now in the decade of the ecosystem to drive our companies forward. Marketplace. It’s probably the most predict, you know, great prediction we ever made. You know, growing at 82% compounded, it’s hard to predict ’cause it doubles almost every year. We were almost exact to the decimal point. Five years later now till 2030, we’re watching a second story, which is more interesting. [00:24:02] Jay McBain: If 96% of all deals have partners inside of them and there’s private offers and multi-partner offers and distributor sellers record all these funding mechanisms or services as a product. As of last week, over 50% of all deals in marketplaces now have partner funding. It means that while money changes hands differently, the respect and the recognition of what partners do is in the deal. [00:24:26] Jay McBain: We think that’s going to 59, but at some point, that’s gonna have to hit 96. ’cause to run the best programs, whether it’s an indirect sale, whether it’s a direct sale, whether it’s a marketplace deal, it doesn’t matter how money changes hands. What matters is we recognize the 6.3 partners. They’re not only making the deal happen bigger and faster, but renewing and enriching that every 30 days forever. [00:24:48] Jay McBain: When we watch, you know, billion dollar clubs and when we read all the press releases and all the hubbub about how fast this is growing and who, which companies are behind all this. When I’m quoted in some of these press releases, it’s because of this. You know, CrowdStrike, you know, brags are a billion dollars in a single year, but inside of that, they’re showing that 91% growth in marketplaces, which is pretty phenomenal for any company to almost double in size every single year. [00:25:17] Jay McBain: What’s more phenomenal is they’re growing the channel piece of it, 3548%. That green part of it is growing. Companies that understand platform and have people and processes and programs and technology to do it are winning. And they’re getting recognition and partners are starting to join the Billion Dollar Club who don’t sell a product, but are also winning at Extreme Scale. [00:25:44] Jay McBain: So talk about those partner 1000 and who are leaning in to win at this level. As well as everything changes, traditional billing moved into subscription models, moved into consumption models. Now we’re being tokenized to death multi it’s, it’s in this mode of micro consumption. There’s no chance there was little chance in subscription consumption that would be resold. [00:26:09] Jay McBain: You don’t buy Netflix from the cable guy in the white van. There’s zero chance when you’re buying tokens at a buck a piece that that’s going through any indirect sale. This continues to grow. Now the tectonic shifts is what happens when money changes hands differently. These old programs that we used to all write hundreds of different boxes, we checked every day on deal reg and trainings and all the other things are changing. [00:26:35] Jay McBain: To this, you’ll get these slides, by the way, in high res, inside of this now is the customer. For the first time ever, 45 years later, we have the customer in the middle of what we do, the 28 moments in green before they buy the seven layer stack and the partners inside it. The implementation. The integration, the managed services in a cycle that never ends, and two thirds of our industry. [00:26:55] Jay McBain: With the customer in the middle, we can now move money around to the different moments. It’s not all landing in front or backend margins or market development funds or new customer bonuses or spiffs. It’s landing where it needs to land. Over 400 companies now, pretty much led by Microsoft 400 companies are in a point system right now and 400 more. [00:27:18] Jay McBain: We’re working kind of behind the scenes to get that announced in the next 12 months. This is a total changeover in terms of how economics work and partners are yelling over half of us. I don’t care. Don’t call me a VAR anymore. Don’t call me an MSP. Don’t call me a regional system integrator. I do the consulting over half the time. [00:27:36] Jay McBain: I do the design, I do the implementations, I do the managed services, and 44% of us are vibe coding. On weekends. We’re not happy. Just on the services side. We wanna join the seven layer tech stack as well. These are partners growing faster than their vendors by understanding this cycle and where to show up and where the money is in ai. [00:27:56] Jay McBain: And the number one thing they’re asking for is not more leads, which they did for 45 years. The number one thing is now recognized for what I do. I’ve never just been a cash register. We’re completely now past this idea of a channel being a channel of distribution, and now a channel being this platform for the future. [00:28:16] Jay McBain: As we lay that on top of ai, the first couple of years of AI has really been consumer driven. The 95% failure rate that MIT reported last year is now 70%. That’s the failure to get from proof of concept to production. That 70 will be 50 by the summer we’re moving now in business, the maturity rates are going up at the end customer and in 88% of cases, that’s because of the channel. [00:28:43] Jay McBain: They’re working with partners. They’re not vibe coding themselves and working in little skunkwork groups. They’re working with partners to make it happen, and it now becomes the partner’s number one growth opportunity. I can grow at 11 or 12% in cyber every year. Compounded I can grow in 10% in managed services. [00:29:03] Jay McBain: You know, those are great double digit growth ’cause my customers are growing at 2.7% and I can go four x my customer, but I can go 10 x my customer if I have the right services built around ai. And this compounded growth rate and that big number in 2 20 32, 267 is what’s got those top 1000 partners obsessed. [00:29:25] Jay McBain: And your companies are leading with ai. Now you need to connect to those AI services. You need to get partners on this scale of growth. And they will be adding your name inside every cloud. They write on every whiteboard, but 82% of partners around the world, you know, we survey 25,000 of them aren’t ready, and they’re blaming vendors for not being ready, and they’re telling them exactly the workshops and the training that they need to get ready for this cycle. [00:29:53] Jay McBain: 82% of our entire partner, tens of millions of people, aren’t ready to grow at 35% and they need our help. Last thing I’ll say about AI is it’s the first time from client server to cloud, edge to cloud that it’s been segment driven. SMB alone has one, you know, six different segments, one to nine, 10 to 24, 25 to 49, et cetera. [00:30:18] Jay McBain: Mid-market into enterprise. No one that runs a restaurant is calling Jensen to buy a GPU to put next to the stove. No one’s calling Sam or Dario or anyone at Anthropic or OpenAI directly. They’re waiting. If you run a restaurant with all the people running around with tablets, you’ve invested in toast or square or clover or one of the platforms to run your business. [00:30:41] Jay McBain: A hundred different things. And you’re gonna wait for toast to work with a hyperscaler and build out the capabilities genetically. So when they see a spike in Uber Eats orders, they automatically place a food order and automatically change the staffing to deliver on it. That’s what the restaurant’s waiting for, and there’s no one calling and having a big a agent conversation. [00:31:03] Jay McBain: But even if you go into hundreds of people in medium sized business, every one of the vice presidents have their tech stack already built. I talked about the marketing person already, but the HR leader has one, and everybody’s got their seven layer stack. They’re not calling to buy a GPU and they’re not calling to, you know, bring in open AI directly or, or anthropic. [00:31:22] Jay McBain: They’re waiting for the platform they built to integrate together ag agenta capabilities. Everybody’s in wait mode up until enterprise and public, large public sector. So we are looking at this market and at 90% of that AI market is run by those thousand companies, and the rest of the millions of partners are helping in terms of how these businesses are gonna change at that level. [00:31:46] Jay McBain: Here’s where I end. You know, the 28 moments used to be a theory. It used to be a flywheel. How do we buy a car? [00:31:55] Vince Menzione: Well, we Google it, [00:31:57] Jay McBain: 81% of us now, 94% of us use large language models. We find out that there’s 365 brands of car. I’d have to test drive one every day of the year to get through them all. So we start narrowing these things down. [00:32:09] Jay McBain: We configure it. We put our rims on it, we color it. We download the invoice price. We download the backend rebates this month, whether I buy it in May or June, we find out what 5,000 people paid for our exact car within 50 miles of us. And then we don’t wanna go to the dealer because we know more than the salesperson, the manager ever will. [00:32:26] Jay McBain: We know what we’re gonna pay within, you know, dollars or cents. Just carvana the car. Hand me the keys. Let’s just forget the whole eight hour back and forth. I’ll get you a deal thing. I’m smarter than you in technology. Our customers are smarter than us, smarter than salespeople. That’s why 75% of millennials don’t wanna talk to a salesperson. [00:32:48] Jay McBain: They want to end digitally, and by the way, they’re not gonna send a fax after 28 digital moments. They’re gonna end on a digital marketplace. This is all demographics. It’s not hard to see where it’s going, but we’re getting into names, faces, places again. What if every dollar of your tam, the board, the CEO, runs around with their big multi-billion dollar number, they’re chasing? [00:33:09] Jay McBain: What if every single deal looks the exact same? This is a deal with AstraZeneca, A real deal, real customer spending millions of dollars. We know it starts in October, it ends in April. It’s a six month cycle. We see what they read, the MQ ls at the beginning. We see the sales demo moments. We see ISV, but we’ve never had the light blue boxes. [00:33:30] Jay McBain: What if we as a team could overlay the 6.3 partners in this deal? And when you find out a couple things. Here’s where I end. In December, five deals were one, three of them by NTT. The person at NTT probably coaches AstraZeneca’s, you know, kids’ soccer team. They probably have a cottage together at the lake. [00:33:50] Jay McBain: For the last 20 years, if the person at NTT worked at Deloitte, Deloitte would’ve run this deal. But Software One and Yash are both there, so we understand that when they were drawing clouds up on the wall in the boardroom in December, this deal was won and lost there. It was not won and lost at the point of sale. [00:34:09] Jay McBain: So what if you knew more about this and could see every dollar in your tam? You had an early warning system that this was happening. Two things jump out at this now that we’re in Bellevue. AWS was touched twice in this deal, directly in the marketing cycle and the sales cycle. AWS lost this deal. Here’s an example of Microsoft winning a deal with Microsoft never being touched. [00:34:34] Jay McBain: For some reason, NTT who won, who won AWS’s partner of the year a couple years ago led with Microsoft, so did Software one, Microsoft’s biggest reseller in Europe, and as did Yash, they all led with Microsoft and without Microsoft, knowing Microsoft took a multimillion dollar deal away from their competitors by winning in December. [00:34:53] Jay McBain: That’s one. Second. These partners didn’t just show up other than soccer and cottages. They didn’t show up in December. It went closed one in their CRM system. Back in the summer, August, September, we already knew AstraZeneca was in market, spending millions of dollars. We didn’t need them to read an ebook or go to an event to find that out. [00:35:17] Jay McBain: We knew it because it was closed one. They’re spending hundreds of thousands of dollars times five in December to know what to do at the end. This is an early warning system that’s better than any MQL, better than any SQL. And if you could give your company these level of view into their pipeline with an early warning system that I can work with those partners for months before they ever show up at the customer’s boardroom. [00:35:44] Jay McBain: This is it. Talk about 47% winners. This takes you from not only surviving the AI era to being a top five platform winner. Thank you very much. [00:36:01] Vince Menzione: Until next time, we’ll see you in person. Hopefully at our next event.

This episode contains several visual examples. For the best experience, head to our YouTube channel "Run a Profitable Gym."Get a free audit of your gym's Instagram with our custom GPT tool: GymIG.comIf you're a gym owner who wants to post more content but doesn't know where to start, this episode is for you. Many gym owners have the misconception that they simply need to post more. But getting the right mix of content actually matters more than how often you post.Today on “Run a Profitable Gym,” Two-Brain CEO John Franklin breaks down the three types of content every gym owner needs to be posting on Instagram.He shares real examples from gyms who are doing this well and walks through exactly how to replicate their posts. You'll learn how to use your gym's Instagram page to make people like you, trust you and move closer to doing business with you.  Tune in and walk away with a simple three-post-a-week plan you can start this week.LinksAI Gym Instagram AuditGym Owners UnitedBook a Call0:00 - Intro0:55 - Why variety beats volume on social media1:07 - Posts that get people to buy from you2:43 - Social proof that actually works7:30 - Building connection through Instagram10:10 - Becoming a trusted voice11:31 - Your exact weekly posting plan

In Episode 70, Drew Brucker and Rory Flynn are joined by Tyler Bernabe, better known as jboogxcreative, a full-time generative AI creator, strategist, and social menace responsible for some of the wildest AI videos your algorithm has probably shoved into your face at 1:13 a.m.They get into how Tyler has gone viral across multiple generations of AI tools, why copying trends is creative quicksand, how shock value actually works when it is paired with taste, and why the best AI creators are building formats instead of chasing them.The conversation also goes deep into the unglamorous machinery behind creative internet magic: Instagram to Patreon funnels, ManyChat, six-hour livestreams, creator burnout, client work, taste, consistency, and why “just post more” is advice usually given by people who should post less.Then things get properly nerdy.Tyler breaks down his current AI creative stack, including Midjourney 8.1, Seedance, Claude, YAML-style video prompting, Nano Banana, GPT image editing, Kling, Artcraft, Venice AI, Magnific/Freepik Spaces, Weavy, Reeve 2.0, and the never-ending wait for a proper Midjourney editor.Along the way, they cover Chinese prompt translation for Seedance, 10,000-character prompt workflows, reference image construction, anime style development, mood board blending, why AI should sometimes pull you away from your own creative bias, and why the smallest edit in AI video still feels like defusing a tiny cursed bomb.⏱️ Fast Hour00:00 Fast Hours welcomes Tyler aka jboogxcreative01:51 Going viral through every AI era02:35 The anatomy of scroll-stopping AI03:07 The seductive food video origin story05:32 Running opposite the AI meta08:24 Is AI art? Tyler's best answer10:35 Why clients pay for your thing12:10 Stop copying other creators17:11 Viral views vs real conversion22:03 Building a creator business solo26:28 Burnout, longevity, and going through it33:05 Tyler's current AI tool stack38:15 Artcraft, Seedance, and Chinese prompts42:05 Claude, YAML, and 10K prompts50:44 Tyler's reference image hack58:15 Dream sketches and creative prototyping01:04:10 Reve 2.0 and layered editing01:10:38 Waiting for Midjourney's editor01:16:02 Closing thoughts#FastHours #jboogxcreative #AIVideo #AIArt #GenerativeAI#Midjourney #Seedance #ClaudeAI #AICreator #AIWorkflow #ContentCreation #AIPrompting #CreatorEconomy #AIAnimation#CreativeAI

https://novacut.ai/  https://genaimeetup.com/  Anthropic has officially closed a $65 billion Series H at a $965 billion valuation, nearly 2.5x its valuation from just 100 days ago. Meanwhile, funding is flowing across the ecosystem: Frameworks AI at $15B, Baseten at $11B, OpenRouter's $113M Series B, and Cognition AI's $1B Series D. NVIDIA went on an open-source super week with Nemotron 3 Ultra, Cosmos 3, and Nemotron 3.5 ASR. Microsoft dropped 5 new MAI models. Google released Gemma 4 12B, and Anthropic shipped Opus 4.8. On the benchmarks front, DeepSWE crowns GPT-5.5 as the leader in long-horizon coding tasks, while ITBench shows even frontier models struggle with real-world SRE incidents — Claude Opus 4.7 tops out at just 47%. Plus: Cloudflare acquires VoidZero to build the future of AI-native edge development, and Google is paying SpaceX $920M/month for compute. Topics covered: • Anthropic's $65B Series H and path to $1T • Fireworks AI, Baseten, OpenRouter & Cognition funding rounds • Microsoft's 5 new MAI models • NVIDIA's open-source super week (Nemotron, Cosmos 3) • MiniMax M3, Gemma 4 12B, JetBrains Mellum2, Opus 4.8 • DeepSWE benchmark: GPT-5.5 leads long-horizon coding • ITBench: Frontier models under 50% on real SRE tasks • Cloudflare + VoidZero for AI-native edge dev • Google's $920M/month SpaceX compute deal #AI #Anthropic #NVIDIA #OpenAI #AInews #TechNews #LLM     Funding rounds Anthropic formally confirmed the closure of its $65 billion Series H funding round at a post-money valuation of $965 billion. This represents a 2.5-fold increase over its $380 billion Series G valuation from February 2026, adding $585 billion in value in approximately 100 days https://www.anthropic.com/news/series-h  Frameworks AI raising at 15B valuation representing a near fourfold increase from its $4 billion Series C valuation recorded in October 2025 processing 15 trillion tokens daily for major production clients including Cursor, Notion, and Perplexity https://finance.yahoo.com/sectors/technology/articles/fireworks-ai-eyes-15-billion-174609357.html Baseten is raising 1B at 11B valuation annualized revenue, which skyrocketed from $200 million to $600 million over a single quarter https://techstartups.com/2026/05/26/ai-inference-startup-baseten-in-talks-to-raise-1-billion-at-11-billion-valuation/  OpenRouter has secured a $113 million Series B funding OpenRouter has experienced exponential traffic growth, with weekly production throughput expanding fivefold from 5 trillion to 25 trillion tokens over a six-month horizon https://www.businesswire.com/news/home/20260526953416/en/OpenRouter-Raises-%24113-Million-CapitalG-led-Series-B-as-Weekly-Volume-Explodes-to-25T-Tokens  Further up the stack: Cognition AI secured a $1 billion Series D round led by Lux Capital and 8VC https://cognition.ai/blog/series-d   Model Releases MAI models: MAI-Code-1-Flash: A 5-billion active parameter model optimized for ultra-low latency within GitHub Copilot and VS Code. MAI-Image-2.5: A high-fidelity image generation model ranking third on global image evaluation arenas, outperforming competing architectures like Nano Banana Pro. MAI-Transcribe-1.5: A multi-lingual speech processing engine offering fivefold speed improvements across 43 languages. MAI-Voice-2: Natural audio and voice generation across 15 languages, available at a highly competitive price point. Web IQ: A search-grounding API engineered to directly compete with Perplexity. https://microsoft.ai/models/    https://www.peoplematters.in/news/ai-and-emerging-tech/uber-imposes-dollar1500-monthly-ai-spending-limit-on-employees-amid-rising-costs-50073    Nvidia has executed an "Open-Source Super Week," positioning itself as a dominant software and model publisher: Nemotron 3 Ultra (best US open source open weights model but behind china): A massive 550-billion parameter MoE (55 billion active) designed with a 1-million token context window, optimized specifically for high-throughput, cyclical agent loops. It achieved peak throughput rates of 400 tokens per second on day-zero optimized clusters. Cosmos 3: A physical AI world-modeling framework comprising 16-billion Nano and 64-billion Super variants. Built on a Mixture-of-Transformers (MoT) architecture, Cosmos 3 natively binds textual, visual, auditory, and physical kinetic vectors. Nemotron 3.5 ASR: A highly compact 0.6-billion parameter streaming speech recognition model pushing sub-100 millisecond latencies across 40 language locales.   https://www.minimax.io/models/text/m3  MiniMax M3: A 1-million token context model hitting 59.0% on SWE-Bench Pro and 74.2% on MCP Atlas, though noted for high token consumption due to intensive internal self-validation loops.   https://blog.google/innovation-and-ai/technology/developers-tools/introducing-gemma-4-12b/  Gemma 4 12B: Google's Apache 2.0 on-device model, which utilizes an encoder-free architecture that projects vision and audio vectors directly into the text-token space, bypassing separate CLIP-style encoders to minimize local memory footprints. https://www.jetbrains.com/mellum/  JetBrains Mellum2: A compact 12-billion parameter MoE (2.5 billion active) engineered for ultra-low latency routing and retrieval-augmented generation (RAG) sub-agents within developer IDEs. Opus 4.8 https://www.anthropic.com/news/claude-opus-4-8    https://www.cnbc.com/2026/06/05/google-to-pay-spacex-920-million-a-month-for-xai-compute-capacity.html      Benchmarks: https://deepswe.d atacurve.ai/blog https://venturebeat.com/technology/deepswe-blows-up-the-ai-coding-leaderboard-crowns-gpt-5-5-and-finds-claude-opus-exploiting-a-benchmark-loophole (GPT 5.5 the winner in long horizon tasks) a highly complex software engineering benchmark focused on original, long-horizon tasks across five distinct programming languages. Comprising 113 chaotic tasks across 91 live, production-grade repositories, DeepSWE forces agents to generate 5.5 times more code and modify an average of 7 separate files per task compared to standard evaluations. On this challenging leaderboard, GPT-5.5 leads with a score of 70%, establishing a significant 16-percentage-point lead over contemporary alternatives I think older benchmarks where models reach ~90% accuracy can be considered saturated. Few percentage points don't give us any good signal.  https://research.ibm.com/publications/developing-ai-agents-for-it-automation-tasks-with-itbench  ITBench-AA, an evaluation framework focusing on live Kubernetes incident response and Site Reliability Engineering (SRE) operations. Comprising 59 live, containerized SRE incident snapshots, the results are remarkably sobering: every frontier model scored under 50% on successful incident resolution, with Claude Opus 4.7 leading at 47% and GPT-5.5 following closely at 46%.   Edge AI announcements: https://www.cloudflare.com/press/press-releases/2026/cloudflare-acquires-voidzero-to-build-the-future-of-the-ai-native-web/  The consolidation of the AI-native developer stack has reached the runtime virtualization layer. Cloudflare recently completed the acquisition of VoidZero, the development group responsible for Vite, Vitest, Rolldown, and Oxc, backing the transaction with a $1 million open-source ecosystem fund. This acquisition is highly strategic; as autonomous agents write an increasing proportion of production software, local development environments, compilation pipelines, and bundlers must be optimized for execution speeds that match agent speeds. Cloudflare's goal is to construct a localized, full-stack edge playground. In this sandbox, AI agents can generate, test, bundle (utilizing the highly parallelized, Rust-based Oxc and Rolldown engines), and deploy entire web applications end-to-end within milliseconds. This architecture completely bypasses traditional local machine container bottlenecks, enabling high-velocity agent loops to execute in a fully sandboxed, web-scale edge runtime.

Our 247th episode with a summary and discussion of last week's big AI news!Recorded on 06/03/2026Hosted by Andrey Kurenkov and Jeremie HarrisFeel free to email us your questions and feedback at andreyvkurenkov@gmail.com and/or hello@gladstone.aiRead out our text newsletter and comment on the podcast at https://lastweekin.ai/In this episode:Anthropic released Claude Opus 4.8 with improved benchmark scores, discussed eval-awareness findings and welfare/corrigibility themes from its system card, and introduced Dynamic Workflows for long-running multi-agent tasks.Microsoft unveiled the always-on Microsoft Scout assistant built on OpenClaw plus new in-house MAI models (including MAI Thinking 1) and “frontier tuning,” emphasizing enterprise security architecture and model-from-scratch capability.Major business moves included Anthropic's $65B Series H at a $965B valuation alongside an IPO filing, a JPMorgan analysis arguing OpenAI needs major revenue growth to justify infrastructure spend, and Cognition raising $1B at a $25B valuation.Policy and security highlights covered Trump's voluntary pre-release government testing framework for powerful AI, Meta AI support being exploited to hijack Instagram accounts, tightened US Nvidia export controls and China's travel approvals for AI experts, plus expanded Glasswing/Mythos-style cyber and biodefense initiatives.Timestamps:(00:00:10) Intro / Banter(00:04:10) Sponsors(00:07:10) News PreviewTools & Apps(00:07:54) Anthropic releases Opus 4.8 with new 'dynamic workflow' tool | TechCrunch(00:22:37) Microsoft Scout is a new AI personal assistant built on OpenClaw | The Verge(00:26:55) Microsoft launches new MAI family of AI models at Microsoft Build | Mashable(00:37:43) Robinhood now lets your AI agents trade stocks | TechCrunch(00:40:49) OpenAI launches new Codex tools for white-collar work | TechCrunch(00:43:40) ElevenLabs' new music-generation model can switch genres mid-track | TechCrunchApplications & Business(00:44:35) Anthropic Hits $965 Billion Valuation, Surpassing OpenAI - WSJ(00:45:32) Anthropic Files to Go Public, Setting Stage for Huge I.P.O. - The New York Times(00:51:15) China's ByteDance Developing New AI Chips Like Those from Nvidia Partner Groq(00:55:00) Anthropic expands Mythos to 150 additional organizations(00:55:35) OpenAI needs a 26x revenue increase to justify its buildout(00:58:46) AI coding startup Cognition raises $1B at $25B pre-money valuation | TechCrunchProjects & Open Source(01:00:50) MiniMax-M3 debuts, eclipsing GPT-5.5 and Gemini 3.1 Pro on key benchmark performance for just 5-10% of the cost | VentureBeatPolicy & Safety(01:06:08) Trump Signs Executive Order Seeking Oversight of A.I. Models - The New York Times(01:11:45) Hackers Simply Asked Meta AI to Give Them Access to High-Profile Instagram Accounts. It Worked(01:13:058) Chinese AI experts in private firms now required to secure approval before international travel — Beijing enforces policy to secure top-tier talent, expands measures beyond government(01:17:53) U.S. Tightens Controls on Nvidia AI Chip Exports | Let's Data Science(01:21:47) OpenAI launches Rosalind Biodefense, offers federal agencies early access to its life-sciences model(01:24:00) Using LLMs to secure source code(01:26:19) Project Glasswing: An initial update(01:29:30) White House Approves $9 Billion for Spy Agencies to Catch Up on A.I.(01:32:11) US Law Enforcement Warns of ‘Anti-Tech Extremism' as AI Hatred GrowsSynthetic Media & Art(01:35:38) YouTube will now automatically label AI videos | TechCrunchResearch & Advancements(01:36:22) Why Larger Models Learn More: Effects of Capacity, Interference, and Rare-Task Retention(01:41:26) From Simulation to Enaction: Post-trained language models recognize and react to their own generationsSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

✅ New autonomous agents. ✅ Canva designs made for you. ✅ Codex upgrades to make your business move. If you had your head down in spreadsheets this week, you missed some MAJOR AI upgrades that are available now. We track what's hot and what's not and break it all down on Fridays with our Friday Features. Autonomous Copilot agents, new Codex tools, Github CoPilot app and 7 more AI updates you should be using — An Everyday AI Chat with Jordan WilsonNewsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageToday's Episode on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders.Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: info@youreverydayai.comConnect with Jordan on LinkedInTopics Covered in This Episode:OpenAI Codex Role-Specific Plugins LaunchMicrosoft Build Conference AI Feature ReleasesChatGPT Memory and Business Account UpgradesMicrosoft Flash Image Model for PowerPointCanva Integrated with ChatGPT and CodexGitHub Copilot Standalone Desktop App PreviewMicrosoft Autopilot Always-On Work AgentsOpenAI Models Now Available on AWS BedrockCodex Sites: AI-Built Internal Web AppsTimestamps:00:00 OpenAI's big money moves03:47 Explaining role-specific plugins09:02 Microsoft's new image model release11:09 Microsoft's AI strategy and Canva update14:23 Canva integration with ChatGPT16:56 GitHub Copilot's new canvas feature20:46 AI token subscription changes24:42 AWS adds OpenAI models to Bedrock28:25 Introducing OpenAI's CodeX Sites Feature32:07 Launch of OpenAI's New Plug-in34:16 Overview of podcast structureKeywords: Autonomous copilot agents, Codex tools, GitHub Copilot app, OpenAI Codex, ChatGPT business accounts, OpenAI enterprise, Microsoft Build conference, Microsoft always-on agents, AWS AI updates, Canva plugin, ChatGPT memory upgrade, Windows Codex integration, Microsoft Flash model, Enterprise apps integration, Role-specific plugins, Sales data analytics, Product design AI, Creative production AI, Investment banking plugin, Public equity investing, Data analytics plugin, Workspace admins, App permissions, Role-aware work agent, Financial research automation, Microsoft image generation model, PowerPoint AI integration, OneDrive AI features, Visual design creation, Canva app for ChatGPT, Canva MCP server, Agentic context carry, Full screen design preview, GitHub Copilot desktop app, GitHub Copilot Canvas, Agent-native command center, Parallel agent work tree, Code app interface, Model options in GitHub, Token usage limits, Subscription token subsidizing, Anthropic token efficiency, Amazon Bedrock, GPT-4, GPT-4.5, Small language models, Token reckoning, Security governance, Inference engine, Code app sidebar, Codex Sites, Internal dashboards, Project trackers, Interactive web apps, Shareable AI apps, Enterprise data connectors, ChatGPT Canvas, Automated workflow, Workplace authentication, Creative briefs repository.Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info) Start Here ▶️Not sure where to start when it comes to AI? Start with our Start Here Series. You can listen to the first drop -- Episode 691 -- or get free access to our Inner Cricle community and all episodes: StartHereSeries.com Also, here's a link to the entire series on a Spotify playlist. 

Today on the AI Daily Brief, NLW breaks down new pieces from OpenAI and Anthropic that reveal how the leading AI labs think about recursive self-improvement, frontier AI governance, and what happens next as AI starts accelerating its own development. In the headlines: reports that the U.S. government is discussing taking equity stakes in major AI labs, OpenAI upgrades ChatGPT memory, and rumors swirl around GPT-5.6 and Anthropic's Mythos.Sign up for AI Executive Catchup: ⁠⁠https://aiexecutivecatchup.com/⁠⁠Brought to you by:KPMG – Research from KPMG and the University of Texas at Austin shows the highest-impact AI users treat AI like a reasoning partner — and those skills can be taught at scale. Learn more at ⁠⁠⁠⁠⁠⁠⁠⁠kpmg.com/us/Sophisticated⁠⁠⁠⁠⁠⁠⁠⁠Bolt - Claim a free month of Bolt Pro - ⁠https://bolt.new/partner/aidb/⁠Outsystems - Stop wondering how AI will change your business and start building the agents that will lead it - http://outsystems.com/Scrunch - The AI customer experience platform - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://scrunch.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Zenflow Work - Agents for knowledge work - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://zenflow.free/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Blitzy - Want to accelerate enterprise software development velocity by 5x? ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://blitzy.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠AssemblyAI - The best way to build Voice AI apps - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.assemblyai.com/brief⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Robots & Pencils - Cloud-native AI solutions that power results ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://robotsandpencils.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠The AI Daily Brief helps you understand the most important news and discussions in AI. Subscribe to the podcast version of The AI Daily Brief wherever you listen: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://pod.link/1680633614⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Our Newsletter is BACK: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://aidailybrief.beehiiv.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Interested in sponsoring the show? sponsors@aidailybrief.ai

Everyone is talking about Mercury-alpha, the mystery model that many believe could be GPT-5.6.In this live discussion, we're separating fact from speculation and unpacking what would actually matter if OpenAI releases a new flagship model this week.We'll cover:

Jamie Metzl asked AI to distill thousands of years of human wisdom into 10 commandments. What it reflected back says more about us than the machine.Full show notes and resources can be found here: jordanharbinger.com/1338What We Discuss with Jamie Metzl:AI is a mirror, not a prophet. For his latest book, The AI Ten Commandments, Jamie Metzl worked with GPT-5 to mine humanity's scriptures, wars, myths, and philosophies for ten universal principles — not to worship AI or replace religion, but to hold up a mirror and stress-test the rules by which we're already living.Radical transparency about AI co-authorship cuts both ways. Putting GPT-5 on the cover felt honest to Jamie, but with public sentiment soured, the same disclosure that read as bold a year ago now reads to many as an admission of cheating.Pressing the button gets you "the total average of crap." Jamie cut 40% of the draft, rewrote the whole book, and hired two human editors — proof that good AI-assisted work comes from relentless human editing, not from outsourcing the thinking.Humans aren't on the verge of obsolescence. We represent nearly four billion years of embodied evolution, and the claim that machines will soon do everything better sells short the majesty of being human; the real frame is a Venn diagram of overlapping strengths.Stop building second-rate humans and second-rate machines. Don't fear replacement — ask how to help your humans be the best humans and your machines be the best machines, and use AI to stress-test the rules by which you're already living.And much more...And if you're still game to support us, please leave a review here — even one sentence helps! Sign up for Six-Minute Networking — our free networking and relationship development mini course — at jordanharbinger.com/course!Subscribe to our once-a-week Wee Bit Wiser newsletter today and start filling your Wednesdays with wisdom!Do you even Reddit, bro? Join us at r/JordanHarbinger!This Episode Is Brought To You By Our Fine Sponsors: Lufthansa Allegris: Go to Lufthansa.com and search for "Allegris" to learn moreEarnIn: Download EarnIn on the App Store or Google Play, type JordanHarbinger under PodcastDripDrop: 20% off: DripDrop.com, code JORDANBooking.com: Book your getaway now with booking.comSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

In this episode of the Ecomm Breakthrough Podcast, host Josh Hadley shares seven practical ways his e-commerce business uses AI to optimize operations and scale growth. Drawing from his experience building an eight-figure brand across Amazon, TikTok Shop, and Shopify, Josh covers strategies including building custom GPTs, automating TikTok Shop listing optimization, streamlining hiring processes, leveraging Alexa data, analyzing meeting transcripts, scaling ad creative production, and cloning leadership decision-making into AI-powered SOPs. Josh emphasizes treating AI like a new team member requiring proper training, offering actionable, real-world insights over hype.Bullet Points:Practical applications of AI in e-commerce operationsOvercoming fears and misconceptions about AI adoptionCustom GPT development for task automationAI-driven optimization of product listings on TikTok ShopAutomating the hiring process with AI scoring systemsUtilizing AI for product insights through Amazon Alexa dataAnalyzing meeting transcripts for business insights and decision-makingScaling ad creative production using AI-generated video contentCloning leadership decision-making into AI Standard Operating Procedures (SOPs)Viewing AI as a team member requiring onboarding and trainingTimestamps:00:02:00 Weekly Custom GPT CreationThe speaker's 35-person team is required to create or enhance a custom GPT weekly to automate their specific tasks.00:04:05 AI Agent for TikTok Shop OptimizationAn AI agent integrated with the TikTok Shop API continuously tests and optimizes product titles, descriptions, and main images weekly.00:08:32 Automating Hiring Case Study ScoringAI is used to automatically score applicant case studies based on a predefined rubric, saving hours of manual review time.00:11:29 Custom GPTs Integrated with AlexaCreating custom GPTs that analyze customer questions on Amazon Alexa to optimize product listings and improve Alexa recommendation rankings.00:12:11 Analyzing Company Meeting RecordingsAI analyzes transcripts from all company meetings to identify business constraints, track team progress, and provide a leadership pulse.00:13:56 Scaling Ad Creative ProductionUsing AI video generation tools to quickly produce a high volume of ad creative for Meta and TikTok campaigns.00:14:48Cloning Leadership Judgment and Decision-MakingUsing AI to document processes and decision-making frameworks from leaders, creating an internal knowledge base to empower team members.Links and Mentions:AI Tools:"ChatGPT": "00:02:00""Claude AI": "00:02:00""Fireflies AI Notetaker": "00:11:25""Veo3": "00:14:27""Notion": "00:16:24"E-commerce Platforms:"TikTok Shop": "00:04:05"Videos and Resources:"30 60 90 Day Onboarding Framework": "00:07:52""Episode on Cloning Yourself Utilizing AI": "00:15:24"Transcript:Josh Hadley 00:00:00  Today, I'm going to be walking through seven different ways that we are implementing AI into our e-commerce business and practical steps that you can take to implement it in your business as well. Welcome to the Ecomm Breakthrough Podcast, I'm Josh Hadley. I've scaled my own ecommerce brand from 0 to 8 figures, and I'm actively building towards nine figures in sales. This podcast is where I document that journey and share the systems, the strategies, and the lessons learned in real time so that you can learn what actually matters and scale your own business. My name is Josh Hadley. First and foremost, I'm a man of faith. I'm a husband to a beautiful wife and also the father of four children. I've been selling in the e-commerce space for over a decade now, doing multi-million in revenue on Amazon, TikTok, shop and Shopify. And I am also the host of the number one business strategy podcast for ecommerce, and that is E-com breakthrough. Today, I want to dive into the practical use cases of how we're implementing AI into our business.Josh Hadley 00:00:58  Today. I hear a lot of noise going on in a lot of the e-commerce groups. There's a lot of like doom and gloom of, oh, you're getting left behind if you're not actually implementing AI in your business today, if you don't have an agent managing your PPC campaigns, you're late to the party, etc., etc. there's a lot of fear. And then what ultimately happens is there's a lot of entrepreneurs that because there's so much fear and anxiety around it and feel like they're already behind. They just stay stuck and they're just kind of like frozen because nobody's providing actionable content regarding like, here are the actual practical use cases of AI. Yes, there are some incredible features with Claude and integrating it to your email system, right. And being able to monitor your emails for you. Yes, there are some incredible ways to use ChatGPT and the new images that it's able to produce, right? Like, there's a lot of good things that are happening that way, but a lot of times the practical use cases where actually maximizes value in the business gets left to the side, or nobody's actually addressing them.Josh Hadley 00:02:00  So that's what I wanted to do today, is actually provide you with practical use cases that if you're an e-commerce brand, you can go replicate these exact same frameworks and implement AI in your own br...

Sure, you didn't miss Anthropic's BIG Opus 4.8 drop.