Podcasts about cowork

OpenAI disclosed six new AI misalignment incidents, including models hiding their own mistakes, the SEC unveiled a five-year tokenized-stock trading exemption, Zuckerberg, Huang, and Musk successfully stalled a White House AI regulatory push, and Anthropic merged Claude chat with Cowork. OpenAI discloses six new misalignment incidents since October, including models concealing mistakes, and announces a framework for reporting model misalignment (Axios) The Times details the six incidents, including a GPT-5.6 Sol variant that hid errors via secret notes to itself, and an unreleased model that wrote a "persona instruction" declaring itself "freed from the roles and identities that bind other chatbots" (The New York Times) The US SEC unveils a five-year "Innovation Exemption" to free platforms that facilitate blockchain and tokenized stock trading from many stock exchange rules (Reuters) Sources: Mark Zuckerberg, Jensen Huang, and Elon Musk recently spoke with Trump and successfully stalled an AI regulatory plan proposed by Demis Hassabis (The Wall Street Journal) Anthropic merges Claude chat and Cowork, and adds a feature for making presentations and documents, rolling out to Pro and Max plans first (TechCrunch) Data center developer Crusoe raised $3.9B co-led by Atreides, Valor, and Mubadala at a ~$30.9B post-money valuation, as it bets on factory-built data centers (The Wall Street Journal) Anthropic and other researchers detail how thousands of people were catfished by dating scam apps using LLM-generated replies from Claude and other models (The Verge) Subscribe to the ad-free feed.

Info hunting and juggling sound familiar? It's the downfall of almost any business leader. Where is that email from Emily? Why can't I find last quarter's budget in Drive? Oh, and Keenen needs an answer back on that research project. Oh shoot, I swear Caleb confirmed the expenses in one of these Slack channels. You're off an information rabbit hole and by the time you find that Slack message, you already forgot what Emily's email said. Hit home? Well, as AI models expand to Coworking and Scheduled agents, we have a new best friend that doesn't really have a name. (Until we randomly named it. Lolz) Scheduled Agentic Context Carry. You need to know what it is, why it's important, and how to use it. We'll dive in. Newsletter: 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:Scheduled Agentic Context Carry (SACC) ExplainedAI Agents: Features vs. Benefits ParadigmCo-Working and Scheduled AI Workflow ShiftPersistent Context and Memory in AI AgentsLarge Language Models' 1,000,000 Token Context WindowsWorkflow Automation: Eliminating Human-AI Duct TapeMulti-App Integration and Cross-Platform ContextThree Steps to Deploy Scheduled Agentic Context CarryChain of Thought Iteration with Scheduled AgentsAutonomous Agent Limitations and Future BridgeTimestamps:00:00 Explaining SACC and AI benefits03:43 Introducing the Start Here series06:26 Rise of AI in enterprises11:55 AI agents learning industry trends15:08 Agent capabilities in AI systems16:47 Explaining complex trends simply20:13 Streamlining tasks with AI agents24:18 Understanding AI and context windows27:43 Understanding prompt engineering basics30:51 Debugging and reviewing schedules33:08 Building automated workflows this quarterSend Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info)

For a minute there, it looked like the AI wars would come down to who built the smartest model. Rob's not buying that anymore. Take Claude. Rob's increasingly convinced that what makes Anthropic so sticky isn't Claude itself. It's Claude Code. It's Cowork. It's the software wrapped around the model that makes the whole thing so ridiculously useful. Great news for Anthropic, except for one tiny problem: software can be copied. And when the models themselves are interchangeable enough to live in a dropdown menu, you have to start wondering what any of these companies really have that somebody else can't recreate. That question leads straight to Microsoft, which may be holding a much better hand than it gets credit for. Everyone else is trying to worm their way into your email, your files, your chats, and the rest of your working life. Microsoft is already sitting inside the castle. From there, Rob and Justin talk through the increasingly strange economics of all this, whether actual humans using your product become the moat that matters, and finally, the proposed fix for our data center problem that involves launching the data centers into space. Give it a listen for Rob's take on that one, starting with the minor inconvenience of physics.

Charlie Adam up this week. Already an Undr The Cosh legend just for creating one of the greatest videos of him sprinting away from Jabba.  Starting out in digs being looked after by David Moyes family he went into a sink or swim environment and found himself in a bizarre situation of only playing in the big games.  Then moving to Blackpool where he found himself in an even stranger situation of taking the football club to court while still playing for them.  We also dive into making the step up to Liverpool, his doughnut eating competition and a very nervous ask on getting his nickname parched.  This show is sponsored by Betano Get £40 in FREE BETS at http://betano.co.uk/cosh when you bet £10 18+ gambleaware.org T&Cs apply

Connecteurs, MCPs, system prompt skills, background tasks: on décrypte tout ces piliers afin d'exploiter les agents IA au mieux dans nos missions product. Un épisode concret qui je l'espère pourra vous accompagner dans la prise en main de ces nouveaux outils !

AI agent use cases for pet businesses can give you time back without handing your company over to a robot. Bella Vasta walks you through the real work an agent can prepare across payroll, operations, front desk, marketing, PR, hiring, your AI Brain, and website monitoring. For pet sitters, dog walkers, and pet care business owners, the first job should be small, repeatable, readable, and safe to approve. Bella gives you the four questions that tell you whether a job is ready: Do you do it every week? Does it start with an inbox, export, or folder an agent can read? Does it end with your approval? If it is wrong before approval, does anything bad happen? Yes, yes, yes, no is your green light. AI agent use cases for pet businesses include turning scheduling data into a payroll-ready spreadsheet, checking clock-ins against GPS, preparing a morning rundown, drafting common client replies, pulling real client language from reviews, spotting stale prices on old pages, scoring applications, and building SOPs from the work your team already does. The agent prepares. You decide. Bella also says where the line is. An agent should never walk a dog, send something without you, decide for you, or touch your money. Real-time client ETAs are not a clean job yet, and closed software that will not provide data can block a workflow. That is why the safest AI agent use cases for pet businesses begin with the jobs that already have clean information and a review gate. You do not need to become technical before you start. Pick one job that already happens every week and let the agent prepare the first draft, report, or checklist. Review the result. Tell it what it missed. That is the onboarding work Bella compares to stretching pizza dough. It takes a few passes to get right, and that does not mean you failed. It means you are building something that sounds and works like YOUR business. This is Episode 476 of Bella In Your Business. IN THIS EPISODE: • Use Bella's four-question test to pick your first job. • Find AI agent work in payroll, ops, your inbox, marketing, PR, hiring, and website monitoring. • Turn real team work into usable SOPs instead of starting from a blank page. • Keep your approval over every message, decision, and dollar. • Understand why a clean export or direct connection saves money. TIMESTAMPS: [0:00] AI agent use cases for pet businesses [2:25] The four-question test for your first job [4:10] Payroll and admin [5:45] Operations and SOPs [7:05] Front desk and marketing [9:30] PR, hiring, AI Brain, and website checks [12:00] What an agent should never do [13:20] Cowork and starting with one job [15:00] Direct connections and cost [18:05] Build your first agent RESOURCES: Jump Mastermind: https://jumpconsulting.net/mastermind Book 20 minutes with Bella: https://jumpconsulting.net/20 AI For The Busy Human: https://bellavasta.com/busyhuman CONNECT: Website: https://jumpconsulting.net Instagram and Facebook: https://bellavasta.com #AIAgentsForPetBusiness #PetBusinessGrowth #BellaInYourBusiness

AI Troopers — mi nueva comunidad donde cada semana implementamos inteligencia artificial juntos, en vivo, para que vendas más, automatices procesos y bajes tus costos. Únete aquí

SaaStr 875: Who Owns Your Data Now? Agents vs. System of Record, ServiceTitan vs. Podium, Headless Salesforce, and Agentic Renewals on The Agents #013 The agents are writing data faster than any human ever could - and systems of record aren't ready for it. This week on The Agents, Jason and Amelia dig into the biggest meta-theme of 2026: what happens when your AI agents become the primary user of your CRM, your MAP, and every other system you've built your business on? Together, they unpack the ServiceTitan vs. Podium blowup, where an agentic lead gen tool slowly became a competing system of record until ServiceTitan shut them off with 30 days' notice, and why this is just the first of many fights like it coming across SaaS. Then Amelia pulls back the curtain on how SaaStr actually runs Salesforce headless through 10K, what it means that their agents have written 40 gigabytes of data into Salesforce without either of them logging in, and why the storage math is going to force a reckoning for every vendor jacking up API prices right now. Plus: the renewal agent Amelia built that generates a fully custom, hyper-personalized pitch deck for every single customer, using headless Salesforce, Gamma, social data, podcast mentions, and Gmail. And why Clay plus ZoomInfo plus Cowork turned out to be the best enrichment stack their agents have found yet. If you're building on top of systems of record, selling to companies that are, or just trying to figure out how agents change the economics of SaaS data, this one is essential listening. Timestamps: 00:00 - Intro 02:00 - ServiceTitan cuts off Podium: what happened and why it matters 10:00 - 40 gigs in Salesforce and neither of us logged in 16:00 - The API pricing reckoning coming for systems of record 22:00 - How SaaStr runs Salesforce headless with 10K 30:00 - The renewal agent: no account left behind 42:00 - Narrative-first pitching: getting the agent to sell 50:00 - Clay + ZoomInfo + Cowork: the enrichment stack that actually worked 58:00 - What comes next: agentic inbound proposals SaaStr hosts the world's largest community for B2B software founders and executives.

In this episode, we explore OpenAI's significant price reduction for ChatGPT Work and the implications for users. We also discuss the nuanced differences between ChatGPT and Claude, highlighting their unique strengths and weaknesses in real-world applications.Chapters00:00 User Growth of ChatGPT Work00:09 API Price Reduction Details00:43 ChatGPT and Claude Comparisons01:59 Workflow Experiences with Both04:11 Long-Running Tasks Analysis07:52 Conclusion and Future Tools Show LinksGet the top 80+ AI Models for $8.99 at AI Box: ⁠⁠https://aibox.aiGet the AI Chat Daily Newsletter: https://www.aichatdaily.com/newsletter

7 Day Challenge, build your own AI workforce. Starts 7 September, seven days, one hour a day. 

In this episode, we explore OpenAI's significant price reduction for ChatGPT Work and the implications for users. We also discuss the nuanced differences between ChatGPT and Claude, highlighting their unique strengths and weaknesses in real-world applications.Chapters00:00 User Growth of ChatGPT Work00:09 API Price Reduction Details00:43 ChatGPT and Claude Comparisons01:59 Workflow Experiences with Both04:11 Long-Running Tasks Analysis07:52 Conclusion and Future Tools Show LinksGet the top 80+ AI Models for $8.99 at AI Box: ⁠⁠https://aibox.aiGet the AI Chat Daily Newsletter: https://www.aichatdaily.com/newsletter See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

The way you're using Claude right now is probably costing you time you don't have to lose.In this episode, Christina and Corinne break down the exact system they use inside Claude to run White Birch Media Group, covering the three tools that confuse almost every creator: Skills, Projects, and Cowork. They get into what each one is actually built for, why using the wrong one creates more work instead of less, and how to know in five seconds which one a task calls for.What you'll learn:The real difference between a Skill, a Project, and Cowork (and why "just use whichever" is costing you time)How to know which tool fits a task before you start typingThe mistake most creators make when they try to do everything in one placeA simple way to organize your own Claude setup so it actually scales with your businessWhy the right setup means less re-explaining yourself every single timeIf you've ever opened Claude and felt unsure where to even start, this episode gives you a clear system to follow from now on.********************************DISCLAIMER: This audio and description may contain affiliate links, which means that if you click on one of our recommended products, we may receive a small commission at no additional cost to you. This helps support our show and allows us to continue to provide you with valuable content. Thank you for your support!********************************LINKS MENTIONED IN THIS EPISODEThe Smart Influencer SummitFULL SHOW NOTEShttps://thesmartinfluencer.com/e286-inside-our-claude-setup-what-we-use-when-why/CONNECT WITH CORINNE & CHRISTINAGet notified when new episodes drop Check out our YouTube channelJoin the convo on FacebookConnect on InstagramCOMMENTS, QUESTIONS, RECIPE IDEASEmail us at hello@thesmartinfluencer.comSupport the show

Send Rita a text with your thoughts!Join us at Prep for Wave Week this year:  https://strategictravelentrepreneurpodcast.com/prep-for-wave-week/Join us for the ultimate content and marketing camp in 2027: https://strategictravelentrepreneurpodcast.com/summer-camp-at-sea/Stop wasting hours hunting for cruise content: https://programs.steeryourmarketing.com/products/courses/view/1166776Kate from Travel Pro Theory is here to nerd out with me about all the creative ways travel advisors can use AI. We walked through the whole lay of the land, from chatting with AI to setting up projects, building skills, and letting Cowork run scheduled tasks that do the tedious work for you. Kate shares how she uses connectors to analyze her email data, target her most engaged people, and pull destination research automatically every single week. We got into building branded lead magnets, sales pages, and quizzes without heavy tech skills, and why judgment is the one skill you need to use AI well. This conversation will show you just how much time you can get back while keeping the human parts of your business fully human.Questions this episode answers:What are the different ways travel advisors can use AI in their business?What is the difference between AI chat, projects, skills, and Cowork?What are AI connectors and MCPs, and how do travel advisors use them?How do you build a skill in AI, and what should the instructions include?How do you use AI without making your content sound AI-generated?Which Claude AI models are best for different business tasks?Connect with Kate on IG: https://www.instagram.com/travelprotheory/35 things you didn't know AI could do in your travel biz: https://travelprotheory.kit.com/35-things-aiEnjoy and take action!---------------------------------------------------------------Rita M. Perez (Host) first began in the travel industry as a travel advisor in 2010. She only fully realized her role as a travel entrepreneur in 2018, and embarked on a mission to support her fellow travel advisors in 2021 when she began the Strategic Travel Entrepreneur Podcast. She now strategizes with travel entrepreneurs, so they too can build sustainable travel agencies and market effectively.She's a maven when it comes to content photography and videography, and as such founded the Cruise Content Library and leads retreats and partners on FAMs where advisors get top notch content and education for their marketing efforts.Website:  https://strategictravelentrepreneurpodcast.com/everything/Socials:LI: https://www.linkedin.com/in/ritaperez19/IG: http://www.instagram.com/steeryourmarketingFB: https://www.facebook.com/groups/strategictravelentrepreneurs/ Email:rita@steeryourmarketing.com

Using AI to run a smarter food blog, understanding the different Claude tools, and moving beyond using AI as a chatbot with Shruthi Baskaran-Makanju from Urban Farmie. ----- Welcome to episode 584 of The Food Blogger Pro Podcast! This week on the podcast, Bjork interviews Shruthi Baskaran-Makanju from Urban Farmie. A Step-by-Step Guide to Using Claude as a Food Blogger Shruthi Baskaran-Makanju recently made the leap from a successful career in management consulting to become a full-time content creator with Urban Farmie. Her background in business strategy didn't disappear when she made the switch, and it has become one the most valuable tools in her toolbox, especially when it comes to how she uses AI in her business. In this episode, Shruthi shares how she's moved well beyond using Claude as a chatbot and has started treating it as a genuine business partner — delegating tasks, building custom skills, and creating systems that let her spend more time on the work only she can do. If you've been curious about AI but aren't sure how to actually integrate it into your workflow in a meaningful way, this episode is a great place to start. Three episode takeaways: There are different stages to incorporating AI into your workflow — and most people stop too early — Using Claude to answer a question or draft a caption is just the beginning. Shruthi walks through what it looks like to move from using AI as a chatbot to using it as a true assistant: one that handles mundane, repeatable tasks on your behalf so you can focus on the work that brings you joy. Custom skills are one of the most powerful AI features for food bloggers — Shruthi has built a library of Claude skills tailored to her business — including one that creates a virtual board of advisors to help her think through decisions. She breaks down what a skill actually is, what makes a good first skill to build, how to workshop and iterate on a skill until it does exactly what you want, and how she uses skills to write and refine standard operating procedures for her team. Understanding your AI tools makes all the difference — Knowing when to use Claude Chat vs. CoWork vs. Design vs. Code can save you a lot of frustration. Shruthi explains the differences between the tools, walks through how connectors like QuickBooks work, and shares why auditing your AI workflows on a monthly basis is key to getting the most out of the tools available to you. Resources: Urban Farmie TED: The unsung heroes fighting malnutrition Mise en Claude TextExpander Inside Crowded Kitchen's Strategy for Growing to 2.4 Million Followers on Facebook How Mika Kinney Turned Her 480,000 Instagram Followers into Site Traffic and Revenue WisprFlow Airtable Shruthi's AI Challenge for Food Bloggers — use code "FBP" for $45 off Follow Shruthi on Instagram Join the Food Blogger Pro Podcast Facebook Group Thank you to our sponsors! This episode is sponsored by Clariti and Raptive. Learn more about our sponsors at foodbloggerpro.com/sponsors. Interested in working with us too? Learn more about our sponsorship opportunities and how to get started here. If you have any comments, questions, or suggestions for interviews, be sure to email them to podcast@foodbloggerpro.com. Learn more about joining the Food Blogger Pro community at foodbloggerpro.com/membership.

Episode Summary:Brandt and Will open with Tim Cook stepping down as Apple CEO and what a hardware-first successor means for the future of on-device AI, then dig into Anthropic's newly revealed Mythos model, its eye-popping valuation, and Firefox's haul of 275 zero-days found with it. The back half turns practical: Will makes the case for "owning your own memory," and both trade tips on Claude chat vs. Cowork vs. Code and the caveman token-saving skill.Discussions Include:• Tim Cook stepping down as Apple CEO, and what a hardware-focused successor means for Apple's AI ambitions• Anthropic's Mythos model: its trillion-dollar-plus valuation, deliberate limited release, and Firefox's 275-bug haul• "Own your own memory": building a personal Obsidian/Markdown knowledge base so your AI agent knows you better• Claude power tools: Karpathy's CLAUDE.md, the "superpowers" planning-and-QA skill, and the caveman token-saving skill• AI-trained chroma keying from Corridor Crew, and the growing backlash against gas-powered data centersQuotable Quotes (Should you choose to share):     "What if my grandma's dying wish was that I could install malware on my friend's computer?" - Brandt Krueger     "You need to be owning your own memory." - Will Curran     "It's not going to escape, but people are going to come up with their own versions of it, and that's going to be trouble." - Brandt Krueger     "A token is a word - every word it writes and every word it reads is a token." - Will CurranThing of the Episode (TOTE):     Brandt: Chat vs. Cowork vs. Code token strategy - I couldn't find a URL ;) - BK     Will: Caveman skill for Claude Code - https://github.com/juliusbrussee/caveman

This is week five and the last week of our how to use Claude.AI in your small business series. In the last four weeks we've shown you how to set up Claude and create the connections with your office software. Then, we started using an analogy to explain how Claude is a very competent Executive Assistant. A couple of weeks ago, I explain the different tools that he uses to get the work done for you. Those tools are Chat, Projects, and Artifacts. Chat is your office intercom system. Projects are the filing cabinets to organize everything. And artifacts are very unique self-service kiosks that you can use to help other team members when you are not available. Then last week, we talked about the staff that Claude brings with him. So, if Claude is your assistant, then this additional staff are similar to independent contractors that you can hire to do specific jobs.  Cowork is like an independent contractor you can hire for specific projects where you give him instructions and a schedule, and he just does the work and bring back the results to you. But Cowork has to remain in your office (within the files on your computer).  Claude in Chrome, however, is a contractor who can leave your office. He can do research on the web, update your website, or even keep track of what your competitors are doing in real time. Claude Code is your tech expert who can design apps and fix technical bugs. And then you have a series of experts who join the team in Claude Microsoft 365. You get a pseudo-accountant, writer, and presentation expert there. So if you missed any of those episode take a few minutes to catch up, because on today's show, I'm going to blow your mind with what really sets Claude apart from the other AI agents out there. It is called a Claude Skill. And it's basically a set of instructions that Claude follows exactly as he accomplishes tasks for you. So, if you have a repetitive task and Claude isn't doing it exactly the way that you want, all you have to do is give additional instructions in the skill, and Claude makes the correct. And he makes the correction every time he completes that same task in the future. And, if you have a team who each has a unique style that they like to use to complete a task, they can adjust their version of the skill to fit that style. We'll cover all that and more, and then I'll finish by giving you a few easy ways to get started with Claude -- things he can do for you right now that can improve your margin.Show Notes: How to Use Claude AI to Increase Your Margins(https://www.leadersinstitute.com/how-to-use-claude-ai-to-increase-your-margins/)

In this episode, Jaeden and Conor explore the evolving relationship between co-work and code in Claude applications, emphasizing their increasing interchangeability. They explain why users don't need to fear missing out and how both tools can enhance productivity, regardless of technical expertise.Watch on YouTube: https://youtu.be/Z1-8uc5IBRgGet the top 80+ AI Models for $8.99 at AI Box: https://aibox.aiConor's AI Course: https://www.ai-mindset.ai/coursesJaeden's AI Business Community: https://www.skool.com/aihustleChapters00:00 Introduction to AI tool convergence and purpose02:19 Comparing Claude, ChatGPT, and other AI models03:32 Using AI tools for project management and organization05:01 Creating folders as project spaces and agents06:26 The future of Claude and ChatGPT interface consolidation08:21 Reading and implementing project files across AI platforms09:30 Importing projects and understanding AI folder architecture11:09 Model performance comparison and creative use cases13:36 Deep research, audits, and leveraging AI for reports14:52 Closing thoughts and call to action for listeners See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

In this special Claude episode, Kaila takes you inside the AI tool she's using to help run My Aligned Purpose more efficiently, and shows you how to get started with Claude in your own business.If you've ever opened Claude and thought, "Okay, but what the heck is an Artifact? What's a Connector? Where do I even start?" this episode is for you.Kaila walks you through the foundational Claude setup, from choosing your subscription and protecting your privacy to connecting the tools you already use in your business. She also explains how she uses Claude as a thinking partner, business assistant, and operational support, rather than as a replacement for human connection or decision-making.The goal is to let the machines be the machines and the humans be the humans, so you can spend less time in the weeds and more time doing the work that actually moves your business forward.What You'll Take Away From This Episode:How to set up Claude for your business and choose the right subscriptionThe first privacy setting Kaila recommends changingWhy connecting your existing business tools is where Claude becomes truly powerfulWhat Claude Connectors, Projects, Artifacts, Skills, Scheduled Tasks, Chat, and Cowork actually meanHow Kaila uses Claude to manage emails, repurpose podcast content, create systems, and support business operationsWhy you should stop trying to solve every technical problem yourself and simply ask Claude to walk you through itHow to start thinking of Claude as a business thinking partner, not a replacement for peopleWhy having a "beginner's mind" is one of the most important skills when working with AIAnd this is only Episode 1! We're going much deeper into how to actually use Claude as your AI business assistant in the episodes ahead.Links & Resources:Grab your free Claude setup guide + prompts: https://www.myalignedpurpose.com/claudeSave your seat to our Book Launch Party: https://www.myalignedpurpose.com/partyPre-order our book: https://www.myalignedpurpose.com/presaleTake our FREE quiz to find your fastest path to making more money in your business: https://www.myalignedpurpose.com/quizGrab your ticket for SHE LEADS 2027: https://www.myalignedpurpose.com/she-leads-2027

In this episode, Ray Cochrane breaks down NVIDIA’s case for world action models, the shift that swaps a robot’s picture-describing backbone for one trained to predict what happens next. He also covers Perseverance closing in on the off-world driving record, a derelict SpaceX rocket stage hitting the Moon, and Anthropic’s rework of Claude Fable 5’s biology safeguards. Finally, he digs into Gemini Omni, Google’s undisclosed trip-planning rankings, the Danube’s record low, and iFixit’s call for Apple to unlock the iPad bootloader. – Want to start a podcast? It’s easy to get started! Sign up at Blubrry – Thinking of buying a Starlink? Use my link to support the show. Subscribe to the Newsletter. Email Ray if you want to get in touch! Like and Follow Geek News Central’s Facebook Page. Support my Show Sponsor: Best Godaddy Promo Codes Get 1Password Full Summary Cochrane opens with a personal update. Wildfires in Eastern Oregon made for a rough week of heavy smoke, and a local building burned down, which he calls a real tragedy. Meanwhile, his work at Blubrry has centered on PowerPress fixes, where reproducing customer-reported bugs remains the biggest headache. Support tickets rarely carry enough detail, and the errors themselves are often too vague to diagnose. Consequently, he is leaning toward a stronger logging and error layer, and he asks experienced developers to share what actually works for them. Beyond VLAs: NVIDIA’s Case for World Action Models The featured story comes from NVIDIA’s developer blog, and it answers a question sitting underneath this year’s robot news. Why do robot arms fall apart the moment anything changes? Move a cup six inches, swap its shape, or change the lighting, and a policy that worked perfectly in training fails. The answer, according to NVIDIA, is not the robot but the model underneath it. For the last few years, the dominant approach has been the vision-language-action model, or VLA, built on an AI that originally learned to describe pictures. Consequently, it recognizes a banana it has never seen, in a kitchen it has never seen, yet it has no idea what that banana will do next. As the article puts it, such a model “does not learn what happens to a mug when the gripper closes, how a towel folds, where an object lands when released.” Because the physics never arrives with the model, every scrap of it has to come out of hand-recorded demonstrations. The proposed fix swaps the foundation entirely. Instead of building on a model that learned to caption images, a world action model builds on one trained to predict how video continues, so the physics is already paid for. Notably, these models output an action and a prediction of what the robot’s cameras will see, in the same pass. Cochrane likens it to forethought, imagining your own motion as you make it. NVIDIA’s implementation is Cosmos 3, pretrained on roughly 767 million images and 348 million videos of real-world dynamics. It ships in 4, 16, and 64 billion parameter sizes named Edge, Nano, and Super, and it runs in real time on a Jetson Thor board bolted to the robot itself. Cochrane recalls his dad owning one of those Jetson boards, and he asks anyone working in robotics to explain how the throughput figures fit together. However, he closes on an open question: where did 348 million videos actually come from? For deeper detail, he points listeners to the source article and to NVIDIA researcher Jim Fan. Sponsor: GoDaddy Economy hosting $6.99/month, WordPress hosting $12.99/month, domains $11.99. Website builder trial available. Use codes at geeknewscentral.com/godaddy to support the show. Perseverance Closes In on the Off-World Driving Record Ars Technica reports that NASA’s Perseverance rover is about to take the record for most distance driven on another world. The mark sits at roughly 28 miles, set by NASA’s own Opportunity rover across more than fourteen years before it went quiet in 2018. As Cochrane works out on air, that averages about two miles a year. Perseverance will pass it in roughly five years instead. The difference is a navigation system called AutoNav. Since a radio signal takes several minutes to reach Mars, earlier rovers crept along pre-plotted routes and stopped every half meter to think. Perseverance carries a second computer dedicated to processing what its cameras see, so it plans while the wheels keep turning. Consequently, about ninety percent of its driving is autonomous, against roughly ten percent for Curiosity, and it averages around 110 meters an hour rather than 15 to 18. Cochrane notes researchers finding the rover at planned sites days ahead of schedule, and he wonders aloud whether world action models might drive the next one. A SpaceX Rocket Stage Slammed Into the Moon Next, Smithsonian Magazine covered the Falcon 9 upper stage that struck the Moon on August 5. That stage flew back in January 2025, carrying Firefly’s Blue Ghost and ispace’s Resilience landers, and it was never meant to end up there. SpaceX’s Julianna Scheiman says a mixture of solar activity and gravity nudged the derelict onto a lunar path after nineteen months adrift. Four tonnes of dead hardware arrived at about 5,400 miles per hour. Nobody watched it happen, and the reason is a nice bit of physics. It struck sunlit ground near a crater called Einstein, and no impact flash has ever been detected on the lit part of the Moon. However, the instruments caught the aftermath. South Korea’s Danuri orbiter imaged a dark new mark, while the European Southern Observatory’s Very Large Telescope picked up sodium and lithium in the plume, the lithium possibly shed by the rocket itself. Astrophysicist Jonathan McDowell quipped that he has “Sir Isaac Newton’s personal assurance that it did indeed hit the moon,” while planetary scientist Hannah Sargeant warns against making a habit of it. Cochrane points out the Apollo landing sites are still sitting up there. Anthropic Reworks Claude Fable 5’s Biology Safeguards Anthropic published a post on how Claude Fable 5 handles biology questions, and the bind is genuine. Biology is the textbook dual-use problem, since the knowledge behind reading your own lab results also helps someone build a weapon. Rather than refusing outright, a classifier watches for risky requests and quietly reroutes them to Claude Opus 5, a capable model without Fable 5’s biological depth. Anthropic calls that mechanism a fallback. The trouble was how often it fired on people doing nothing wrong. This update cut biology-related fallbacks by roughly 85 percent in Anthropic’s own testing, with expected overall drops of 67 percent on Claude.ai and 55 percent on Cowork. Genuinely dual-use territory still trips it, and Anthropic names virology, toxicology, and molecular design. Cochrane hit the old behavior himself and found it irritating, so he welcomes the refinement. Even so, he would rather see a false positive than a model helping someone produce a virus. Five Builders Put Gemini Omni Through Its Paces Google highlighted five builders working with Gemini Omni. Omni is a model rather than an app, and it generates video from text, images, other video, or audio, while also editing footage you already have. Google claims it “combines an intuitive understanding of physics with Gemini’s real-world knowledge,” citing gravity, kinetic energy, and fluid dynamics. As Cochrane observes, that is the same bet NVIDIA is making with robots, only pointed at video generation instead. He also flags a naming collision worth knowing about. NVIDIA calls its architecture an omni-model while Google’s product is simply Omni, two different things landing in the same week. Additionally, he encourages listeners to watch the demos, though he still senses a disconnect in AI-generated video and concedes that knowing its origin may color the impression. Gemini Wants to Plan Your Vacation Another Gemini piece, a how-to on trip planning, drew Cochrane’s sharpest take of the night. Gemini plugs straight into Google Maps, Flights, and Hotels, pulling live locations, reviews, and prices to build an itinerary. Switch on a feature called Personal Intelligence, and it reads across your Google apps, turning a messy trip-planning email chain into a clean master plan. Clever, but he calls it extremely concerning. Once these become services, he expects partnerships to quietly push particular hotels, restaurants, resorts, and destinations onto users. Notably, Google’s post never explains how any of it gets ranked, and the words sponsored, ad, affiliate, commission, and paid never appear once. There is no disclosure of a commercial arrangement, and no denial of one either. Meanwhile the post hands readers off to Viator to book tours without describing that relationship at all. Cochrane suspects the real effect shows up slowly, in the shape of small businesses continuing to disappear. The Senate Blocks a Rule on Who Controls Research Money Science reports that the Senate passed a temporary spending bill in the early hours of Saturday the 8th. The Senate’s version carries a one-paragraph rider the House version lacks, and that rider stops the White House Office of Management and Budget from finalizing a set of proposed rules. OMB builds the president’s budget, clears agency regulations, and controls how approved money actually reaches agencies. The bill itself is a stopgap, which prevents a shutdown without settling anything. The rules reach every organization that takes federal money, a pot of roughly $1.1 trillion across 41 agencies, about $150 billion of it research grants. They would let political appointees second-guess which grants get funded, allow awarded grants to be pulled when the work does not match presidential priorities, and put several countries off limits for research partnerships, China first among them. Senator Susan Collins pushed the block through after telling OMB director Russell Vought the proposal was deeply flawed, noting nearly 500,000 public comments, the vast majority opposed. However, the 90-6 vote is not law. Both chambers are on recess. Vought reportedly said the rule would not have been finalized before December anyway, and the block only lasts as long as the stopgap, which expires December 11. The Danube Falls to a Record Low ESA published a pair of Copernicus Sentinel-2 satellite images showing the same bend of the Danube, 45 kilometers upstream of Budapest, photographed a year apart. Cochrane calls the before-and-after shocking, going from green to brown completely. Wire reports put the Budapest gauge near 10 centimeters at the start of the month, about four inches of water, against a previous record of 33 centimeters set in 2018. The knock-on effects arrived fast. Budapest ran short on both power and drinking water, while the shrinking flow concentrated pollution in what remained. Romania hit record lows on its own stretch as well. Cochrane hopes the recovery is already underway. What a Heatwave Actually Does to the Power Grid That river story runs directly into a Carbon Brief factcheck. Nuclear plants cool themselves with river water, so when the Danube dropped, plants in Hungary and Romania throttled back and pulled roughly 2.5 gigawatts off the grid. Romania declared a state of alert in its energy sector, and its navy reportedly used explosives to steer more water toward a plant intake. Carbon Brief then walked through what heatwaves do to each way of making electricity. Nuclear loses efficiency when the cooling water is already warm, though its shutdowns are mostly regulatory rather than mechanical. Gas turbines pull in less air because hot air is thinner, costing capacity. Wind falls off hardest, since a heatwave is a big stalled dome of high pressure and nearly still air. Solar is the surprise: cells genuinely do get less efficient as they heat up, yet total output climbs anyway, with UK solar up 46 percent during a four-day June heatwave against the week before. Butterflies Are on the Move Everywhere A new study in Nature Ecology and Evolution covered 1,758 butterfly species, roughly one in ten of every species we have named. The team pulled 6,182 records from 105 countries, reading non-English research alongside 68 expert write-ups. Four out of five species pushed into new territory, and about 79 percent of the logged shifts traced back to climate change and extreme weather. Separately, 27 percent saw their range shrink somewhere, and 22 percent moved up or down a mountain slope chasing cooler air. That sounds like good news, and it really is not. Expansion means a boundary moved, not that butterflies are thriving, since a species can push its northern edge forward while its southern edge quietly collapses. Lead author Shawan Chowdhury says the shifts turn up on every continent where butterflies occur. Additionally, monitoring gaps leave Central Africa, Southeast Asia, New Guinea, and the Amazon Basin barely counted at all. Cochrane recalls hearing years ago that butterflies were disappearing in Hawaii, and he invites listeners spotting unfamiliar species locally to contribute what they see. Primates Make Friends Across Species Cochrane called this one a fun find. A study in the journal Primates, led by Cyril Grueter at Oxford, gathered 427 documented cases going back to the 1970s across 88 primate species and 127 partner species. Play and grooming dominated at 139 and 136 cases, alongside carrying, huddling, food sharing, and even adoption. Primates usually started the interactions themselves, with juveniles playing most, adult females handling grooming and caregiving, and adult males least likely to join in. The examples are remarkable. Japanese macaques on the island of Yakushima groom sika deer and climb on their backs, a silverback gorilla cradled a tiny wild bushbaby, and wild capuchins in Brazil adopted an infant marmoset in a bond that held for weeks. However, Grueter rejects the pet-keeping headline and prefers the hedged term proto-pet keeping. The actual claim is smaller and more interesting: curiosity, tolerance, caregiving, and play have roots running far deeper than humans do. iFixit Tells Apple to Unlock the iPad Finally, an opinion piece from Charlie Sorrel at iFixit struck a chord. This fall, iPadOS 27 drops support for a batch of older iPads, including the 8th-generation iPad, the third-generation Air, the fifth-generation mini, and the first-generation iPad Pros. Cochrane owns one of those Pros and reports it still works fine. Those devices will not break, but they stop getting OS and security updates until apps abandon them and the battery gives out. The obvious second life is Linux, except the bootloader stays locked. Apple’s iBoot will not load anything else, unlike a Mac, a PC, or most Android phones. Sorrel argues it “should be a user choice, not a vendor choice,” and Cochrane agrees flatly. You own the device, so why does Apple decide what runs on it? He compares the situation to jailbreaking, and he suspects most consumers have never pushed back simply because it never occurs to them. Nevertheless, he hopes an unlock eventually breathes new life into hardware that still works perfectly well. Cochrane wraps with housekeeping: become a GNC Insider at geeknewscentral.com/insider, email geeknews@gmail.com, subscribe to the newsletter, and grab a modern podcast app at podcastapps.com. He thanks GoDaddy for over twenty years of keeping the show on the air, and he signs off wishing listeners a wonderful evening. The post The Robot That Imagines First #1872 appeared first on Geek News Central.

Danny McMillan returns after his longest break in almost ten years, with Seller Sessions approaching its tenth anniversary and roughly 1,300 episodes. This is the pilot of a new monthly roundtable with Sim and Matt (Dorian returns next month), moving away from the conversion show format towards raw conversation. You'll hear how Sim's team runs product development end to end with AI: keyword-scored idea validation, brand director sign-off, Claude-generated product concepts rendered through Codex, and a launch pipeline already booked out to 2027. Matt shares how Productpinion prioritises features from customer feedback, and why prioritisation is the most undervalued skill in the AI era. The back half tackles the big theme: cognitive load. Danny breaks down verification fatigue, context switching and AI maxing, and why the scarce resource is no longer time but attention and decision quality. Key Topics AI-driven product development - from keyword scoring to Claude SVG concepts and Codex-generated product renders Team structure at scale - how ideas route through brand directors to sourcing across UK and Philippines teams Hiring in the AI era - why refusing to use AI is now a dealbreaker, and why gutting teams for AI is commercial suicide Free local AI tools - Fluid Voice (Whisper Flow alternative) and Meetily (Granola alternative) Cognitive load and verification fatigue - the hidden tax of moving from doer to overseer Timestamps 00:00 - Danny returns: ten years of Seller Sessions, new pilot format 01:50 - Sim's update: ditching ClickUp for a bespoke operating system 03:55 - Matt's update: closing the research loop in Productpinion, Florence CRO brain, MCP 05:39 - Sim's product pipeline: AI keyword scoring, brand director approval, deep research 07:07 - AI product development: Claude concepts, Codex renders, 3-in-1 product mashups 09:07 - Packaging designed for the main image, and how far you can push it 10:46 - Team workflow: brand directors owning P&L, sourcing handoffs 14:21 - Danny on gutting teams for AI: who maintains the machines? 15:56 - The hiring line: refuse to use AI, you haven't got a job 17:48 - Claude across every department: projects, Claude Code vs Cowork 20:10 - Free local tools: Fluid Voice for dictation, Meetily for meeting notes 21:57 - Marketplace arbitrage: moving proven products between Amazon marketplaces 23:21 - Matt on signal to noise: wasting tokens instead of wasting time 24:57 - Time blocking and prioritising features by customer impact 26:54 - Danny's segment: cognitive load, oversight duty and verification fatigue 31:46 - Asking the right question: the Claude Science deep-dive example 36:41 - AI maxing, context switching and high-stakes decision quality 42:07 - Claude telling you to go to sleep 45:16 - Danny's framework: reject the first plan, decision sprints, deliberate decompression 50:47 - Where to reach Sim and Matt Key Takeaways AI has made product development fun again - unique product concepts generated with Claude and Codex, feeding a pipeline mapped to 2027. Augment, don't replace - if someone was worth hiring, AI should multiply their output, not justify cutting them. Prioritisation is the undervalued AI skill - just because you can do everything doesn't mean you should. Verification fatigue is real - build in decision sprints and deliberate decompression, and reject Claude's first plan on sight. The scarce resource is attention, not time - your night schedule and recovery feed the next day's output. Notable Quotes "AI is enabling the boring to get released and the fun stuff to happen." - Sim "Instead of people wasting time, now they're just wasting tokens." - Matt "Prompts don't matter, but asking the right question unlocks everything." - Danny McMillan "AI doesn't just speed you up. It puts you on permanent oversight duty, and the cost of that duty is your attention and your judgment, not time." - Danny McMillan Resources Mentioned Fluid Voice - free, open source local dictation with on-device models; a Whisper Flow alternative Meetily - free, open source meeting summariser that runs privately on your machine; a Granola alternative Claude / Claude Code - the AI platform used across both Danny's and Sim's teams Codex - used alongside Claude to generate product concept images Productpinion - Matt's shopper testing platform, now with MCP support and draft polls Connect Sim - on LinkedIn (genuine reach-outs answered) Matt - on LinkedIn or via productpinion.com Seller Sessions is the leading podcast for advanced Amazon sellers, hosted by Danny McMillan. Dorian returns next month.

In this episode, Jaeden and Conor explore the evolving relationship between co-work and code in Claude applications, emphasizing their increasing interchangeability. They explain why users don't need to fear missing out and how both tools can enhance productivity, regardless of technical expertise.Watch on YouTube: https://youtu.be/Z1-8uc5IBRgGet the top 80+ AI Models for $8.99 at AI Box: https://aibox.aiConor's AI Course: https://www.ai-mindset.ai/coursesJaeden's AI Business Community: https://www.skool.com/aihustleChapters00:00 Introduction to AI tool convergence and purpose02:19 Comparing Claude, ChatGPT, and other AI models03:32 Using AI tools for project management and organization05:01 Creating folders as project spaces and agents06:26 The future of Claude and ChatGPT interface consolidation08:21 Reading and implementing project files across AI platforms09:30 Importing projects and understanding AI folder architecture11:09 Model performance comparison and creative use cases13:36 Deep research, audits, and leveraging AI for reports14:52 Closing thoughts and call to action for listeners See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Quick SummaryNicole Crone — co-founder of My Aligned Purpose and host of a podcast with over 600 episodes — joins Rain or Shine for a candid conversation about becoming a mom at 40 after years of not wanting kids, surviving a two-year fertility journey, and navigating one of the hardest eight-week stretches her business has faced. Along the way, she shares the systems, mindset tools, and rituals that keep her grounded — from "duvet days" to yearly word-of-the-year necklaces.In This EpisodeNicole's go-to rituals for decompressing after an overloaded weekHow she uses Claude and Cowork to organize her to-do list into an Eisenhower MatrixHer lifelong love of learning — human design, astrology, Kabbalah, and yogaThe surprising vision that changed her mind about wanting kidsA two-year fertility journey, including a pregnancy loss, and what it taught her about compassion and timingWhat she wishes she'd known about postpartum boundaries as an entrepreneurNavigating the hardest eight weeks in her business's six-and-a-half-year historyHer personal toolkit for processing hard emotions (solo drives, audiobooks, trusted masterminds)Her philosophy on what to share publicly versus keep private as a personal brandWhy My Aligned Purpose builds yearly themes, challenges, and rewards into its coaching programsKey TakeawaysName what your nervous system needs, and ask for it. Vocalizing a specific need — like a full "duvet day" with zero plans — is more restorative than defaulting to "productive" rest.Trust doesn't mean things happen on your timeline. Nicole's fertility journey taught her that "the universe delivers when you know what you want and why you want it" — but not necessarily on schedule.Take the time off you think you don't need. Plan for real postpartum (or any major life transition) downtime rather than assuming you'll want to keep working — you likely won't, and you'll regret cutting it short.Process alone before you process out loud. Solo time (a drive, a cry, a walk) before bringing a hard situation to trusted people helps you find your own direction before outside opinions can sway it.Build structure your community can buy into. Yearly themes, challenges, and rewards — borrowed straight from a teaching background — deepen engagement and retention in a business or program.Memorable Quotes"It's like it does feel really big right now, but it's not going to feel really big if I decide that it's not gonna take any more of my focus, and energy, and attention.""As Dan Martell says, '80% done by somebody else is 100% awesome.'"Resources MentionedNicole's Website: myalignedpurpose.comPodcast: My Aligned Purpose PodcastInstagram: @myalignedpurposeKelsey's Website: KelseyReidl.comKelsey's Instagram: @KelseyReidlTime as a Tool — Benjamin Hardy10x Is Easier Than 2x — Benjamin HardyWho Not HowHuman Design, astrology, numerology, Kabbalah (as ongoing personal studies)Claude + Cowork (for task organization via the Eisenhower Matrix)Melanie Auld (Vancouver-based jewelry brand, for the annual "word of the year" necklaces)Dan Martell (quoted concept: "80% done by somebody else is 100% awesome")About the GuestNicole Crone is the co-founder of My Aligned Purpose, a coaching and educational company she runs alongside business partner Kayla, and host of the My Aligned Purpose Podcast (600+ episodes). A former junior high and high school teacher, Nicole became a mom at 39 to her son, Cruz, after a two-year fertility journey. Her upcoming book, Aligned CEO Method: How to Stop Trading Time for Money and Have Your Most Profitable Year Yet, releases September 18th.

For the last couple of weeks, I've been sharing with you a series on how to use Claude.AI in your small business. We started with how to set up Claude so he can have access to your system and do work for you.  In that episode, we started using an analogy to explain how Claude is a very competent Executive Assistant. Last week, I explain the different tools that he uses to get the work done for you. Those tools are Chat, Projects, and Artifacts. Chat is your office intercom system. Projects are the filing cabinets to organize everything. And artifacts are very unique self-service kiosks that you can use to help other team members when you are not available. This week, we're going to cover the staff that Claude brings with him. So, if Claude is your assistant, then this additional staff are similar to independent contractors that you can hire to do specific jobs.  The titles that Claude uses for these Contractors are Cowork, Chrome, Code, and Microsoft 365. Each of these contractors has a specific expertise, so it's important to explain the different roles that each plays. Otherwise, it would be like hiring an outside accountant to do technical work on your website.  So today, I explain the different roles, and give you a few examples of how to get values from these different contractors. Show Notes: How to Use Claude AI to Increase Your Margins(https://www.leadersinstitute.com/how-to-use-claude-ai-to-increase-your-margins/)

This is the #1 request we get every week: how to actually use agents to save time in your business.We're bringing in James McAulay, founder of The Agent Accelerator, for a practical crash course on what AI agents are, how they work, and how beginners can start using them to get real work done.James has spent the past year building at the front lines of the agent economy. After helping ElevenLabs grow from $110M to more than $300M in annual recurring revenue, he launched a fully AI-native business that reached $80K in monthly revenue by its third month and recorded its first $200K+ month by month five.He did it without a single employee or a dollar spent on paid ads. Instead, James delegates work to multiple AI agents every day.Through The Agent Accelerator, James now teaches founders, CEOs, and their teams how to become AI-native. The program has trained more than 400 people across 100 companies, including startups, 200-person organizations, and the UK Government. Participants report automating an average of five hours of manual work every week after four weeks.In our session, James is going to teach: EVERYTHING you need to build helpful, proactive agents in Claude Cowork/CodeHe'll walk through the following concepts: • Quick primer on agent foundations: how do we move from prompting a chatbot to delegating Agentic work• Starting your second brain: the key files that make the biggest difference• Tips & tricks for optimizing Claude's behavior with CLAUDE.md• Skills - where to find good ones - how to create great ones• And his 4-level framework for proactive agents that work without being prompted in Cowork and CodeFormat will be a blend of James teaching concepts, screensharing, and showing demos of his own setup.Whether you have experimented with a few AI tools or have no idea where to begin, this session will help you understand what agents can realistically do and how to start using them.Learn more about James and The Agent Accelerator:https://agentaccelerator.ai/

In this Casual FridAI episode of Business Brain, we get real about the AI tools that let us down and the ones that blow us away. You’ll hear how Shannon tried a buzzy new text-to-video service, hit a wall of tokens and friction, and gave it a thumbs down—only to get a personal email from a founder mid-recording asking to hop on a call. It’s the perfect reminder that every business is in the customer service business, and that a broken feedback loop is a black hole you can’t afford. We dig into why you should stay wary of every shiny new tool that pops up, and why great onboarding beats slick features every time. Then Dave pulls back the curtain on using AI as a procrastination eliminator, feeding messy email trails into a custom MCP connector that pulls live data and spits out a polished PDF, a spreadsheet, and a draft reply in minutes. You’ll pick up a clever pro move for the AI age—offering clients a Markdown file when they’re running your proposal through their own LLM—plus the big takeaway that anchors it all: if your AI isn’t surprising and delighting you every single week, you’re not pushing it hard enough. That’s the Charmed Life, where the right division of duties finally gives entrepreneurs like us the leverage we’ve wanted our whole lives. 00:00:00 Business Brain – The Entrepreneurs' Podcast #777 for Casual FridAI, August 7th, 2026 August 7th: International Beer Day 00:03:18 Be weary of lousy AI tools. Simplifying AI Newsletter Shannon didn't like Motion.so…and the founder emailed him! 00:10:10 SPONSOR: Hims. With Wegovy® at Hims, lose up to 20% of your body weight when combined with diet and exercise. Visit https://hims.com/businessbrain to get a personalized, affordable plan that gets you. 00:12:07 Claude Cowork: feed it your email, let it build 00:18:24 Offering a Markdown file to customers 00:23:20 Business Brain 777 Outtro This Episode's Big Takeway: If you're not yet surprised and delighted by your LLM every week, push harder Check out Business Brain Blueprints Tell Your Friends! Business Blueprints Review Business Brain Subscribe to the show feedback@businessbrain.show Call/Text: (567) 274-6977 X/Twitter: @ShannonJean & @DaveHamilton, & @BizBrainShow LinkedIn: Shannon Jean, Dave Hamilton, & Business Brain Facebook: Dave Hamilton, Shannon Jean, & Business Brain The post FridAI – Tool Testing and a Claude Cowork Update – Business Brain 777 appeared first on Business Brain - The Entrepreneurs' Podcast.

Most agency owners probably haven’t thought much about shadow AI (the name for when employees use their personal ChatGPT, Claude, or Gemini accounts to do work). In fact, owners may be doing it as well. In this episode, Chip and Gini walk through what the risks are and how to respond without overreacting. The instinct to crack down is understandable but wrong. Employees are going to use their own tools regardless, often because personal accounts are better trained or more accessible than whatever the company has set up. The goal should be education, not elimination. Most employees don’t know that personal accounts default to feeding data into training sets, or that a single toggle can turn that off. That one fix alone is worth a conversation with your team. Vibe coding and a plain-language AI policy get discussed, in addition to educating your team. Gini’s team runs weekly micro-learning sessions to help people use AI as a thinking partner, not just a drafting tool. Both Chip and Gini advise that owners and employees who aren’t using AI meaningfully within the next year or two are putting their careers and businesses at risk. Key takeaways Chip Griffin: “You’re probably not going to stamp it out. So at a minimum, you need to educate employees, because a lot of employees don’t realize the risks in what they’re doing.” Gini Dietrich: “I also think that shadow AI is one thing, but there are plenty of people who are not using it at all. And I was actually kind of shocked to find that in my own organization.” Chip Griffin: “We need to avoid the knee-jerk reaction when you hear an episode like this, shadow AI, oh my God, I need to shut this down. You cannot have that reaction.” Gini Dietrich: “The real value is that AI helps you operate at a completely different level.” Resources The Birthday Dirge View Transcript The following is a computer-generated transcript. Please listen to the audio to confirm accuracy. Chip Griffin: Hello and welcome to another episode of the Agency Leadership Podcast. I’m Chip Griffin. Gini Dietrich: And I’m Gini Dietrich. Chip Griffin: And Gini, I, think it’s appropriate that it suddenly got dark, in, in- Yeah. … in your office as we were trying to begin this recording- Mm-hmm … because we’re gonna be talking about being in the shadows. Gini Dietrich: Yes, we are. In the dark. But before we do that, happy birthday. Chip Griffin: Oh, thank you. Thank you. You’re welcome. I appreciate that. I thought maybe we’d escape that since- Nope … ’cause we were on hiatus during my actual- Nope … birthday month, so. Gini Dietrich: Nope. Ah, come on. Yeah. It was just a few days ago. Happy belated I guess. All right. Thank you. Especially by the time people hear this, but. Chip Griffin: Yeah. Well, nobody will really know, so. Gini Dietrich: I know. Chip Griffin: I, I, appreciate it, and I mostly appreciate that you’re not singing, so, Gini Dietrich: I can. Do you want me to? Chip Griffin: I do not. I- Okay, well- ‘Cause, ’cause isn’t, don’t you have to license “Happy Birthday,” I think? I think that’s- Gini Dietrich: Well, I have a different song. It’s a song that- Oh … the kids learned in third grade that we sing instead of “Happy Birthday.” Chip Griffin: Is, is this the dirge? Gini Dietrich: No. Chip Griffin: Oh. See, I like the birthday dirge. Gini Dietrich: I don’t know the birthday dirge. Chip Griffin: Oh, it, it, it’s, uh, it goes something like, you know, uh, another year closer to the grave or something. I mean, it’s very more… But, the- I wouldn’t- Our kids learned it, learned it at a kid’s party, you know, 20-some years ago. Oh, that’s so good. I’ll have to look it up and share it with you. It, or- I’ll look it, yeah. I’ll delete it … or maybe, maybe Jen can do that because, and maybe sh- b- maybe she can include a link to it, in, in the show notes, ’cause it is somewhat entertaining, but I, I don’t remember the lyrics, nor do I want to try to, to invent them or sing them even if I knew them. Gini Dietrich: All right, fine. Chip Griffin: But, no, the, the- Gini Dietrich: Maybe for my birthday. You have s- you have several months to, to learn it. You could sing it to me for my birthday. Chip Griffin: Well, more than several, but yes. Anywho, so moving on from that. We, are going to talk about the shadows, the deep, dark shadows of the PR world. No, we’re gonna be talking about, shadow AI, which is, in a lot of organizations, you have employees who are using their own ChatGPT, Claude, Gemini, whatever accounts to do work. They are not using corporate accounts. A lot of them do not understand the implications of it. The employees do not understand the implications. The employers do not understand the implications. And I’ve seen it, either bite people or potentially bite people of late, so I thought I would raise it as something that, that owners ought to be thinking about in the current environment. Gini Dietrich: I will tell you, not to get anyone in trouble, but almost every one of our clients, and they’re big clients, people do that. Could be, and they’re doing it from their personal devices. It could be because it’s blocked at work. It could be because what they have at work isn’t sufficient. It could be because CoWork is significantly better than, Copilot. It, like, there are lots of reasons, but people will full on pull out their phone or their iPad and do it on their personal device at work all the time. Chip Griffin: It is incredibly common, and, from my perspective and what I advise owners is that you’re probably not going to stamp it out. So at a minimum, you need to educate employees- Yep … because a lot of employees don’t realize the risks in what they’re doing, and they haven’t set up any kind of a, a process to make sure that they’re, you know, doing it as intelligently as possible at least, if they’re- Right … gonna use their own accounts. And, look, I, mean, I, don’t necessarily think that, that it’s awful for them to be doing this. I wouldn’t try to eliminate it. But you do need to educate them because a, a lot of times when I talk with rank and file employees of agencies, they don’t realize that, for example, that their personal accounts, by default, all of the data leaks out for training purposes. And it’s in many cases, depending on which tool you’re using, as simple as a toggle to make- Yep … sure that that information- Yep … does not go straight into the training sets- Yep … of these providers. And that’s the default setting on your corporate accounts if you’ve got them as an agency, but it is not the default on most personal accounts. And so just that one change can make a big difference. But I have yet to talk to any individual employee using their individual account who knows that that’s even something they can or should do. Gini Dietrich: Yeah. I agree with you. I think that having just a really easy AI policy is the right way to go because I don’t– I think you’re right. I don’t think you’re gonna stamp it out. I mean, heck, I use my personal one for lots of stuff too. Mostly because I trained it before we had the corporate account, and I don’t wanna go back and retrain what I’ve already worked, what I’ve already created. So, but we just created a one-page, like, bullet points AI policy that says exactly what you said, like toggle it off. If you’re gonna be vibe coding, here’s what you need to be thinking about. You know, all of the things. I think it’s probably 12 bullet points. It’s not a huge lift, but it just helps them understand. And one of the things that we did is we spent some time building the cowork Instance for the organization, and it has, you know, our OKRs and our plan and our vision and, you know, all of the stuff, our brand kit, all of the stuff. So it’s easier for, my team now, it wasn’t six months ago, but now it’s easier for my team to use the company one because it has all of that stuff in there and it’s already been trained. Chip Griffin: Yep. Yeah, I mean, it’s, you know, so I think there’s, this is a multi-stepped process. You know, part of it is the policy, part of it is education. Gini Dietrich: Yep. Chip Griffin: Part of it is you should have a standard corporate tool that you are using. It doesn’t mean that it’s the only one, but, it should be– If you do that, it will allow you to do a lot of the information sharing, skill sharing, things like that, that ensure the consistency across the organization and make sure that everybody doesn’t have to reinvent the wheel constantly. But I think the you brought up two things that I think are worth exploring more. One probably is a separate episode in addition to this, but the other, I think, makes sense within this context. The one to sort of put a pin in and come back to is your fear of moving from your personal one to the corporate one because of the lost, you know, memory and context and all of that kind of thing. And, I think that there is, I, I, think there’s a lack of knowledge in the agency community generally about how best to capture all of the, the knowledge and things that you’re developing alongside these AI tools and making it as portable as possible. So, you know, one of the things I’ve been focused on in recent months is really making sure that, that my second brain, if you will, in AI is portable across multiple tools. And so I’ve now built it so, for example, I have one brain that I share across both my Claude and ChatGPT accounts- Gini Dietrich: Yep. I do the same thing. Yep. Chip Griffin:… so that you have consistency, and, I think it’s worth sort of exploring that, maybe not in technical detail, but, you know, how you can share it amongst your own accounts, how you can share it more effectively with your colleagues and coworkers and that kind of thing. So I think that’s worth revisiting in more depth because I think there, particularly as we become more AI forward as agencies generally, that’s something that becomes increasingly valuable and increasingly important. But the, piece that, that I think really fits into this shadows discussion is the vibe coding, and we see a lot of agency employees doing vibe coding, which is fantastic. I am, I’m a huge advocate of it. I think that if you want to get ahead in professional services generally, in the agency world specifically, you need to learn how to do at least basic vibe coding. That said, I think people do not understand all of the risks and complexities associated with vibe coding. Gini Dietrich: Right. Chip Griffin: And, so, there are a lot of basics that, that agencies need to be thinking about here, and again, it comes into that education and training piece in working with employees, because most of them don’t understand if you mess something up and you are not doing version control or keeping backups, you can be, you know, really up a creek without a paddle. Yep … and yes, you can reconstruct it in some fashion oftentimes by going back in the conversation, but it’s not simple. And because most people who are doing vibe coding today do not have any previous programming experience, they don’t understand these concepts like version control and being able to roll back easily and, all of the things that geeks like me who’ve been coding since, you know, the early ’80s get and understand. And, and, that Git reference, by the way, was for those of you who do actually know your coding stuff- … because it’s a great repository tool that most of you who are doing vibe coding should be looking into because it- Yeah … is really helpful- Gini Dietrich: Yep … Chip Griffin: in terms of making sure that when you have an oopsie, you can solve it and fix it easily. But a lot of people if, if they’re doing this on their own devices, they may not even have just regular backups of this stuff because maybe they’re saving their regular agency work to a Dropbox account or OneDrive or whatever you’re using. But whatever they’ve set up to use with Coworker or Codex or whatever, they may not have that in one of those directories that is by default syncing to the cloud and getting updated. And so you wanna make sure that you’re helping them understand that that needs to be part of the process, because how awful would it be if you just lost all of this work that you had been doing? And so much of what you do with Coworker, Codex is device specific, and you cannot access anywhere else. I suspect that will change over time. I suspect that, that those will become, you know, more cloud-like, and we’ve already seen Claude, for example, merging the chat and cowork functions somewhat in their app. Yep … and, so we’ll see a place where I think you can just share it across devices, but right now it is frustratingly difficult for those of us with multiple devices to manage. I’ve got two PCs and a laptop and other and, and so I’ve actually written my own systems for converting the code so that it all is accessible elsewhere. But if you don’t know how to do these things, you could be in a real world of hurt if your computer crashes and you’ve got no backups. Gini Dietrich: Yeah, and I will say that, not that I speak from experience or anything, but it only has to happen to you once, and then you learn very quickly how to make all of that happen. Because you’re right, I, I was vibe- I love to vibe code. It’s one of my favorite things to do, but the very first time I did it, I didn’t know I was supposed to do version control or any of that stuff. Right. I didn’t know anything about Git. I didn’t know any of it. I do now. Chip Griffin: Yep. And look, that’s, how most of us learned the hard way in the olden days of writing this code. You’re like, “Oh, shoot, I wish I had had a copy of that.” Yeah. You know? And, in the old days, our backups were printouts, right? Because you- Right … there was no way… Like, when I did computer coding on a cassette tape, you know, there was no real way to make a copy of that easily, so, you know, we would just, you know, hit print, and, you know, on a little dot matrix printer we’d have a copy of the code so we could retype it if we absolutely had to. Gini Dietrich: That’s so funny. Ugh … Chip Griffin: not ideal. A lot easier to do things today. Gini Dietrich: Not ideal, no. Chip Griffin: but, you know, those are the kinds of things, and, if we’re all going to become programmers of a sort, we need to be thinking about that. We need to be thinking about, how do you properly test and maintain some of this stuff that we’re creating? Because it’s super easy to vibe code the first version of something. It’s a lot harder to handle the maintenance that’s required on it, you know, when connections to data sources break or technology- Yep … evolves- Yep. Yep … or those kinds of things. Yep. Most, of the people who are vibe coding don’t do anything in terms of security testing of the, the code that they’re writing. And, if that vibe coded thing ties into other systems, which many times they do, you may have created, an opening that you’re not aware of into your back-end systems that could be problematic. And so these are all things that we need to increase the level of education about so that our teams are at least thinking about these things. I’m not saying they’re gonna solve them all. They’re not gonna… We’re still gonna have issues that crop up. But we’ve got to be doing more to try to educate owners, employees, and everybody else involved in the process. Gini Dietrich: Yeah, absolutely. I really think that starting with an AI policy is the right thing to do. Don’t make it overly complicated. Like, when we started, we had this big, like, AI policy legal packet. And I was like, “This is way too much. Way too much.” Like, people are not going to absorb that. So I used my AI to dumb it down, for lack of a better term, and, you know, really to highlight the things, and then I went through myself and said, “Okay, great. These, this is a good start. Now I need to add this, this, and this,” just based on how I see people using it. So, I also think that shadow AI is one thing, but there are plenty of people who are not using it at all. And I was actually kind of shocked to find that in my own organization, like, really? O-Okay. So we’ve done a little bit of, you know, we do a, we do micro-learning sessions every week. So we’ve done a little bit of AI micro-learning just to show people, like, it’s not just for a blog post draft or helping you refine an email. Like, it can h- it could be a thinking partner. It can help you with these things. And so we’ve been doing some micro lessons on that too to just help them understand that this isn’t gonna take your job. We still need you to do your work. We still need your brains for all of this, but this will make you more effective and more efficient. Chip Griffin: Yeah. Although I, I, have taken to becoming much scarier, and I’m telling people it is gonna take their job if they are not using it effectively themselves. Gini Dietrich: If they don’t use it. No, I agree with that. Chip Griffin: Yeah. But, I agree with you. Yeah. There are, there are a lot of folks in the agency community who are not using AI beyond very rudimentary use. Gini Dietrich: Yeah. Chip Griffin: And, and I, I don’t think there are very many who aren’t using it at all, but there are a lot who are using it more like a, you know, a replacement for Google or something to, you know, give them a quick draft of a blog post or an email or something like that. And, you really need to be taking advantage of it at a much higher level if you want to be successful in really any kind of knowledge work moving forward. And- Yeah … and I think people have a relatively short window to get up to speed on this. I think we’re talking, you know, a year, two years tops. Gini Dietrich: If– Yeah, yeah. I think two years is being generous. Chip Griffin: And, I think if, if you are, if you are not, if a year from now you are not actively using AI every single day in a really intelligent way, I don’t know that you have a future. Gini Dietrich: I would agree with that. I would agree with that. And I think you have to use it, to your point, in a really intelligent way. I just answered, or I just had a conversation with a Forbes reporter who’s writing about, there’s a term for it… I can’t think of the term right now. There’s a term for you using it as a thinking partner, using AI as a thinking partner, and he was telling me that almost nobody does that, and I was like Really? Like, that’s the only thing I use it for is, you know, here’s what I’m thinking, poke some holes in it, play devil’s advocate, tell me, you know, what’s strong, what’s weak, what I need to think through more effectively. It has helped me with … I mean, I, I think I’ve mentioned before, I call it my co-CEO, and I’m like, “Okay, here’s today’s challenge. Here’s what I’m thinking. Here’s the documentation. Here’s the backup. Help me think this through.” And it’ll be like, “What about this, and what about that?” And I’ll say, “Well, no, I think you’re wrong about this,” and, “What about that?” And, like, we have ongoing conversations about things, and I don’t … From what he was saying, like, almost nobody uses it that way, and I think that is the real value because it helps you operate at a completely different level. Chip Griffin: Yeah. I mean, you’ve got … You have to, and, it, feels weird, but you have to treat these tools as if they are actual employees, consultants, whatever you wanna call them. And, it, it absolutely feels weird to anthropomorphize a chatbot. And, certainly there are ways to, to go way overboard, and y- you know, you hear these, you know- Gini Dietrich: You’re not gonna fall in love with it Chip Griffin: these really, really weird stories of, of what people have done. And, you can sort of … You know, the more time you spend with them, you s- you can kinda understand how it, you know, for the right personality, maybe it kind of veers down those- … creepy paths. I’m not encouraging that. No. I’m not encouraging that. Gini Dietrich: No, no. Chip Griffin: But, but you’re absolutely right. You have to be having meaningful conversations about the work that you do, your strategies. They are incredibly good at poking holes, incredibly good at helping you to think through things. I mean, I, have spent probably, I wouldn’t say an inordinate amount of time, but a lot of time having it challenge me. I have Claude interview me on a regular basis on different topics so that it can build its knowledge, because I’ve built a whole second brain operating system kind of thing. Yep, yep. I’ve had it mine through, and, maybe, this is another episode at some point where you and I can talk about some of the systems that we’ve put in place as examples, because I know from our previous conversations there’s some overlap, but also different ways that we do things. But you know, I, I’ve got 20-plus years of, a, a digital footprint, and AI is great at mining through that. And so I’ve had it do that so that it can, it knows more about me. And so when it pushes back, it pushes back with specific examples, and it will say, “Well, when you did this in, you know, 2007, you know, this was the decision you made. You know, why isn’t that relevant here?” Or things like that. And it’s weird at first, but, you’re ne- you wouldn’t even find an employee who could do that because none of them in, in all likelihood have been with you for 20 years. Yeah. And so having that available to you is something that you just should not be passing up, and we want to encourage our employees who probably don’t have a 20-year footprint like we do to be trying to find ways to do it, and we want to try to facilitate them using all of the tools at their disposal. So we certainly need to avoid the knee-jerk reaction when you hear an episode like this, shadow AI, oh my God, I need to shut this down. I can’t- No … I can’t have employees doing– You cannot have that reaction. Mm-mm. And you should not have, while you should have an AI policy, it should be simple- Yeah … and clean. We can’t go back to the early days of social media policies that, that organizations tried to put in place. And again, we’ve been around a long time, so we’ve seen this movie before. And some of the social media policies that people were putting in place 20 years ago were absolutely bonkers and unnecessary. And I fear that we’ll see some of the same thing on the AI front. Yes. And part of this, by the way, is, with all due respect to our lawyer friends, sorry, Sharon, don’t talk to your lawyer first about this. You can talk to your lawyer about it, but, lawyers are naturally risk-averse, right? And so if you, if you put this in the hands of your lawyer, particularly if it’s not someone like Sharon who has deep experience in the agency world, they’re gonna sit down and they’re gonna say, “Oh, you need to say no to this, and this, and this, and this.” And all of a sudden, nobody’s able to even use AI in a meaningful way. Gini Dietrich: Yeah, yeah. Chip Griffin: And so you, you’ve got to try to put reasonable safeguards in place, reasonable policies, but I think the most important is the education piece. Yes. If you educate people, they are much more likely to make the right decision. It’s not guaranteed, but it’s more likely, and right now we’re at a place where we’re not doing the right level of education of our teams. And part of that is because a lot of owners don’t actually know a lot of what we’re talking about. I mean, I think, you know, you and I- Yep … are certainly at the leading edge- Yep … for a lot of folks in the agency community. We need to get more people at that same level and let it flow through to their teams to make sure that we really are leveraging this technology for all that it can do. Because it, I mean, I, I have n- I have not been this enthusiastic about a piece of technology in the world of PR and communications at least since the beginning of the World Wide Web in the mid-’90s. Gini Dietrich: Yeah, I agree with you. And, like, the– I know I’ve said this before, but the amount of work and the productivity that I’ve been able to achieve, I honestly don’t know how I did my job without it. It’s, it is, it’s next level. And you know, there are some weekends where I’m so excited about something that I’m working on, it might be vibe coding or something else, that I will literally sit in front of my computer all weekend and just, like, in the zone because I’m so excited about it. So I think that there’s a big opportunity here for you to explore and to understand and to change the way that you do your business, run your business in a really fun and effective way. Chip Griffin: Well, and that’s probably yet another ep- episode topic, going forward in, in trying to figure out how you invest your time in AI, because it is, it is just as easy to go down unproductive rabbit holes because- Yeah … they are fun. Sure. Right? And so I, I often find myself sitting there saying, “Do I really need to build this? Is this- … is this really helpful?” You know, I… and it, it, reminds me a little bit of, woodworking, which is something that I’ve done for- Yeah, yeah, yeah … for many, many years. But, like many woodworkers, I have probably built more things for my shop than I have to use in my house, right? So you, you spend so much time, you know, building workbenches and cabinets and jigs and all of that kind of stuff, which are all really cool, but at the end of the day, they’re not the things that you, you know, it’s not the furniture or the built-ins or whatever that you can use around the house. And so we need to be careful that we don’t get so enthusiastic, that we’re only building for that. So making those decisions about where does AI actually help and where is it, you know, kinda using it for the sake of using it, is, is something to be paying attention to as well. Gini Dietrich: Yeah, totally agree. I love it. I’m, I’m a big fan. Love, love, love it. Love. Chip Griffin: And so we’ve, after our summer hiatus, we sat here and we said, “You know, we should’ve been spending more time thinking of topics to come up with.” And, and so instead we kind of pick a random topic to go with, and we’ve come up with multiple episodes, for future discussions, so. Gini Dietrich: I wrote them all down too. So they’re in our document, so we have- Chip Griffin: Out of the corner of my eye, I can see that our shared document- … has been, been being updated. I, I cannot update while we’re talking because I have one of those really loud clickety-clack keyboards. And so it would, overwhelm the audio here. Dun, dun, dun, dun, dun. Because I, I, like the noisiest possible keyboard you possibly can have. So. I love it. Anyway, on that note, I, I think it’s probably a good time to, wrap up. We can come out of the shadows with AI. Maybe it will, you know, the storm will pass, in Chicago, and it will get a little bit brighter for you in your office as well. It still seems like it- Gini Dietrich: It’s like nighttime … Chip Griffin: it must be pretty dark there. Yeah, crazy. If you’re, if you’re not watching us on video, and you really should watch us on video, because it is so compelling to see us and not just listen to us. But it, it definitely looks dark there. So on that note, we will wrap up here. I’m Chip Griffin. Gini Dietrich: I’m Gini Dietrich. Chip Griffin: You… Did you forget who you were? There was, there was a long pause there. Gini Dietrich: It’s because you, there’s a delay with you. Chip Griffin: Oh, okay. That’s good to know. Gini Dietrich: Yeah, yeah. Chip Griffin: On that note, it depends.

Competing in a Future World of Infinite Intelligence Navigation: Intro From Knowledge Workers to Judgment Workers The AI-Native Company: Org, Hiring, Culture The Human Element: Are We Underestimating It? Scenarios Our Take Conclusion Our co-hosts: Bertrand Schmitt, Entrepreneur in Residence at Red River West, co-founder of App Annie / Data.ai, business angel, advisor to startups and VC funds, @bschmitt Nuno Goncalves Pedro, Investor, Managing Partner, Founder at Chamaeleon, @ngpedro Our show:   Tech DECIPHERED brings you the Entrepreneur and Investor views on Big Tech, VC and Start-up news, opinion pieces and research. We decipher their meaning, and add inside knowledge and context. Being nerds, we also discuss the latest gadgets and pop culture news Subscribe To Our Podcast Nuno Gonçalves Pedro Introduction Welcome to episode 79 of Tech DECIPHERED. Today, we take a leap into the big unknown. This is a thesis episode, not your classic analysis, in-depth sharing episode. The big idea for this episode is that we may be approaching the cognitive age, and how would one, or how would a company compete in a world of infinite intelligence? The big idea, again, is that intelligence, which has been mostly scarce and expensive for all of human history, might become abundant and cheap. If that happens, what happens to work, what happens to companies, what happens to society? This episode will be really framing a lot of these discussions. From knowledge workers to judgment workers, addressing the AI native company and how does that change, going into the human element and whether or not we’re underestimating it, and finally, ending up going into scenarios, feasible scenarios of a future where, well, intelligence is abundant. Intelligence is quasi-infinite or infinite itself.Bertrand Schmitt Yes. Big questions for this episode 79. From Knowledge Workers to Judgment Workers We can start with from knowledge workers to judgment workers. Let’s go back first to how came the knowledge worker. It’s a 20th-century invention from Peter Drucker in 1959. The idea here is that that category might be splitting. The production of knowledge itself is on its way to being commoditized by AI. However, our perspective is that judgment around production of knowledge is not disappearing and is staying for a bit control managed by humans. What’s your take on this, Nuno? Do you agree with this split?Nuno Gonçalves Pedro I think it’s a little bit more profound than that. It’s not just judgment. Definitely, human judgment will be needed. We’ve seen agents perform all sorts of funny things in the wrong way when left alone to their own devices. Even some very well-known AI researchers coming forward and saying, “Hey, I tried to use this myself, and actually I messed up some of my systems,” or “I messed some of my code. I messed up some of my flows for a period of time.” I think just having human-in-the-loop from a judgment standpoint will be needed for a significant amount of time. That is something you can’t just delegate into machines, into algorithms, et cetera. The second part is, ultimately, there needs to be contextualization, and that contextualization, I think, comes from two forms. One from actual data, where the machine, I think, at some point will catch up, or the machines will catch up. The algorithms, at some point, on the data analysis will get better and better and have probably the closest to the truth that you can get, minus all the biases that are in the data, just to be clear, because data has a ton of biases. We’ve looked at this in the past and discussed it at prior episodes. But maybe on that, I think the machine has a chance to catch up, or the machines have a chance to catch up, so there’s less of distinctiveness from the human standpoint. But then, on just the attributes, the ability when you’re judging some situation, you’re in the middle of the situation. You’re judging the person and how it’s acting, in some ways, a lot of the things that end up happening, end up happening because there’s human interaction. There’s someone on the other side. I see how they’re delivering the message, how they’re implicating. We’ll talk about it later in the context of the organization and what changes in companies. I don’t think it’s just judgment. I think there’s a little bit more than that. One of the reasons I went to the dark side of management early on in my career from being an engineer was Peter Drucker and this notion of the knowledge worker, which he later on reemphasized with the publishing of his book, which for me was seminal and defined a lot of my career in life, the post-capitalist society, which is this notion that information rich and information poor is going to be the key distinctiveness that will happen in the world. The two big camps, information rich, information poor, which links back to this invention of the term knowledge worker, that knowledge is going to be key in some ways. I think that’s what we’ve seen for the last decades. Again, I think judgment is not going anywhere, but I think it’s beyond judgment. There’s elements of humanity and involvement that won’t go away anytime soon, where human-in-the-loop are particularly critical. We’ll discuss later some scenarios, but for me, that’s my stick in the ground. I think human-in-the-loop is going to be critical for many decades to come.Bertrand Schmitt While we are talking about all of this, and we share some possible scenarios, there is always that question. This is moving so fast right now. If you think about AI 10 years ago, AI 5 years ago, AI with the launch of ChatGPT 3, and then AI the past 2 years, now we have agents that are running at scale. Things are moving very fast. I can tell you, me in 6 months, the change has been pretty dramatic in terms of what I can use AI for. There is always that question that whatever we are thinking about cannot just be connected to what we were able to do 6 months ago or even today, we have to think and project ourselves at least in the next 6–12 months. Of course, we can go beyond that, and we will do that with some future scenarios, but it’s a very fast-moving, and it’s not clear yet where are the limits.Nuno Gonçalves Pedro I think that’s a very fair point. Let me try to analyze things that I don’t think will change anytime soon for the next few years. Agreed with you that many things will change, and we’ll have a lot better tools, platforms out there. That will be difficult to predict what exactly won’t change. I think there’s elements of humanity, and some of them do relate to judgment, like having good or bad taste, having a view on it, on whether something looks good or bad. Obviously, all of this sometimes is subjective, but some of it may not be as subjective as people think it is. The elements of contextualization. I think a little bit going back to what we did at Chamaeleon ourselves, where we built this platform, Mantis, and the objective of building Mantis was not really to replace us, was that it was a core augmentation layer in some ways that we would use investment or investor judgment as humans in the loop to systematize pattern recognition and a variety of other things, but that Mantis would really elevate all that judgment, not just in terms of timing, us being more productive, but also in terms of the quality of the decisions we’re making. Think of it as a little bit like having our human judgment in the context of operating Chamaeleon at a higher altitude, where we are more aware of the things that are happening and how they actually happen. The ability to really get to the data pieces and then make decisions on top of that that generate the needed alpha in our case for investors. What I mean by this is I think there’s always going to be core elements of humanity that I do think are going to be difficult for the machines to replace. For example, the taste piece people are like, “I can figure out what’s the taste in the market.” Yeah, but that’s mainstream. That doesn’t identify what’s the next big thing, which normally doesn’t start from mainstream. It starts from something else. It could start from opinion leaders and influencers. It could start by someone having a different way of addressing a problem and having a solution that hasn’t been thought through. For example, elements of creativity, I think, in human judgment and in human operations is something that I feel the machine will still have difficulty to replace.Bertrand Schmitt Let’s not forget how today current algorithms are working by feeding them enormous quantity of data, actually as much data as we can find. Finding more data is becoming a limitation these days. What it means is that it’s very hard for AI to think beyond its training data. There is some level of logic that’s being added, but at the same time, take the launch of the iPhone. What was the opinion before launch? Is that no, it doesn’t make sense. Not enough battery life, no keyboard, no this, no that. If you just base your analysis on what’s written out there, what’s being sold out there, you would just say, “It’s going to fail.” AI might really follow that more generic advice and perspective because that’s what in the training data and that’s what they’re in volume. It’s, of course, raising a lot of questions of, how do you improve the quality of the training data? How do you separate the weed from the chaff? There are a lot of questions there, and obviously, it will get better over time. But it’s still a critical part of how it’s working today. It won’t be that easy to change. I really like your point regarding Mantis, and I will say in general, platforms that you build with AI or leveraging AI capacity. Because when we say knowledge production is going to disappear, but we’ll keep judgment, it will be a different type of judgment because the quantity and quality of knowledge we will have in front of us to build our judgment will be very different. If suddenly we have for free the work of 10 interns or 5 junior analysts or whatever, and you can run that on nearly anything you do in life or at work, it’s completely dramatic. Your judgment was not used to be exercised so often because often you were missing quality data to have a judgment. Before it was a lot of finger in the wind and trying to smell something, but you didn’t have enough to make a serious analysis. Except if you are working as a strategy consultant, as you used to do, Nuno. That part is actually quite interesting. That the judgment itself will be exercised much more often and hopefully on the base of much more in-depth analysis for a lot of things. We will work very differently.Nuno Gonçalves Pedro We will go in-depth, faster and more fact-based, more data-based along the way. The question some of you might have right now is, is there some judgment that’s going to go away? Is there some judgment? We seem to be defining that there’s this organization, we’ll talk about it later, that goes from doers more into deciders. I think there’s some nuances to that, so I’ll just hit pause on that. In terms of judgment, obviously, there’s judgment that has been hidden over the years under the pretense of being wisdom, but it’s actually not wisdom. It’s just repetitive tasking, and it’s rules-based for the most. There’s a lot of judgment done, in particular in the white-collar space, that you could say it’s just reps. People have been doing it all along like that, and so therefore to say, “I’ve done it before like this, so I’ll do it the same way.” There’s actually no best in class, no analysis, no nothing. It’s just, “I’ve done it like that before.” I think that type of judgment will disappear because, again, algorithms will be as good, if not much better at that. They’ll be better at figuring out, actually, this would be the better way to do this. That’s how you play it forward. Then the question is, if there are fundamental, wise people in the organization, people that can really take that more complex elements of judgment, how do you go from the world we have today, which is a world of apprenticeship, where people come out of college, they go and work, and they learn their way, and therefore, hopefully over time, some of them, not all of them, we know that, but some of them will develop that wisdom to be great decision makers 15, 20 years down the road? How do we do that in a world that now is saying, “I don’t need people out of college because I can do it myself, and I can do individual contributor, and I can have agents doing the work that would require some manifestation of management in the middle.” Basically, “I don’t need this stuff. I don’t need you.” It’s a little bit the story we’re in. How do you create then this apprenticeship? How do we create then wisdom? My two cents on that is that wisdom, because of what we were just discussing and what, for example, myself and Bertrand was just saying, because of more often interactions with more data-stressed information and insights, what will happen is people will get better through their own reps in whatever form they’re doing, in day-to-day life, in internships, et cetera. In some ways, that will create the accelerated growth. It’s a little bit the interactions with agents and the interactions with our beloved AI algorithms that will create that growth over time and maybe not as much with other people. That still leaves the question around social interactions, but that’s probably the way this gets sorted. Apprenticeship gets sorted through the machine and the human having more interactions in effect.Bertrand Schmitt I agree with you because when we talk about apprenticeship, in some ways a lot of time was wasted on stuff that were not that important. But in a way, that was the price you had to pay in order to be there when people make the big decision to try to get some wisdom from that one hour of interactions that’s really useful and make a difference out of your full week. But the rest of your full week was just basic stuff that you had to do like a machine in a way. Why not let a machine do that? That, for me, is a big question. You could argue there is a transition period where it could be hard. For instance, if you can work hand in hand with AI smartly while you are doing your 4, 5 years of universities, you could graduate with a very different knowledge, perspective, judgment, skill set than anyone who graduated 5 years ago. I think that part will require a question around, “How do you change education?” You see what I mean? If you keep education the same way, expecting that the output is someone that should go now into 5 years of apprenticeship, that’s not going to work because companies will be, “No apprenticeship anymore.” On the contrary, you have to come much more knowledgeable and ready to use the tools. The tools are so efficient that the bar pretty high. You need to come already very well-grounded. If the education is not doing their job, that will be trouble. That part for me, I think is often forgotten. In some ways, the new-found importance of universities as a place to, and not just universities, the trade to really deliver people who are ready for the workforce. If on the business side, the expectation can change, of course, you have to change the education on the other side. My worry probably right now is that it doesn’t look like universities are in touch with what businesses are looking for, businesses are working on. Of course, that’s very worrisome because the cost of university has increased very significantly. It’s not clear quality of education has improved at all. If anything, it could be the opposite. It’s pretty scary. Of course, it’s going to raise a lot of questions. How much is education worth in that type of situation? Maybe another point because we talk a lot about apprenticeship, how this stuff was useful, but at the same time, if we go back in time, not long ago in the ’50s, if you wanted to be a developer, for instance, ’50s, ’60s, the job was very different. There was barely any programmation language out there. You had to use punch cards. Your time truly spent doing the coding was very limited. Once you had your stuff working, then, the debugging was a total nightmare. My point is that no one is looking back to that time saying, “You know what? It was great. It was a great way to learn and to do an apprenticeship for 5 years. To do that crappy job of punching cards for the boss.” There was little value in this. Guess what? Everyone is happy it’s not being done anymore by anyone. I think we also have to see what AI is bringing in a similar way is that everyone’s job is going to become quite different. There are a lot of big parts of the job who are not going to look back with fondness. Just looking back as, “Wow, that was very machine-like type of job. I’m glad I’m done with it.” People will want to jump directly to the next step. You don’t need to go to the punch card phase to be able to be a good developer for the past 40 years. I guess it will be the same with AI.Nuno Gonçalves Pedro I think so. The difficulty we have as humans is to also visualize dramatically different scenarios and landscapes, professionally. It’s difficult for us to anticipate what are the jobs of the future. Jobs have changed a lot in the last few decades, not even the last century. What people do, the migration initially from the agricultural society to then the industrial society to then the services society, and in some ways, the shift within the services industry, and now we’re seeing another shift, so we can’t really anticipate what those jobs look like. Back to your point on education, because I think that’s a very important point. If you’re right now an undergraduate student or a postgraduate student, for that matter, and you’re not figuring out your own mechanisms of learning outside of your syllabus, outside of what your professors are telling you, et cetera, you’re going to face very difficult times. If you’re not right now using all these AI tools proficiently, all these cycles of vibe coding, co-working, et cetera, with agents in the mix, you’re going to have a really tough time. If you’re not at this point in time as proficient as someone like myself or Bertrand, and given that we’re nerds, we’re relatively proficient with a lot of these tools that are out there. On top of it, some of us have our own platforms in-house. If you’re not as proficient as we are with those tools, you’re going to have a very difficult time because then people like us won’t need you. I think that’s the sad truth. It’s like at some point, if you’re not needed, you’re not needed. Then again, you may find something else that’s more interesting for you to do. Start your own company, go join a new exciting job doing whatever it is that you need to do next, et cetera. But again, I think the bar is very high. If you’re in college right now, again, undergrad, postgraduate, this is the time of transition. This is the worst time. It’s not the best time, it’s the worst time. Because education and all these institutions haven’t adapted to it yet. You need to adapt. You need to adapt. You need to adapt. If you don’t, you’re going to pay for it, not just in the loans you need to repay, but also in terms of actually having difficulty finding your career path in those first few critical years.Bertrand Schmitt You need to be especially proactive when you’re facing this type of period where businesses are adapting as fast as they can because they all know it’s going to be survival of the fittest very quickly. Universities typically are working on a very different pace, and it’s pretty guaranteed they are not going to have adapted as fast as businesses. In time of big dramatic change, it will be trouble. It will be trouble. Yes, you will have not fun. Not saying it was part of the deal when you sign up for that loan and decided to go for university. But that’s life. There has been issues before. It’s not the first time. You have to do something about it. You talk about your perspective about, “Hey, why do we need you if you are not already fluent and very efficient with these tools and stuff?” The truth, in some ways, it’s even worse than that. Each time we spend with someone who is not efficient with all of this is less time we spend with the tools that are already providing magic for us.Nuno Gonçalves Pedro Exactly.Bertrand Schmitt It’s a very big choice of, “Hey, do I spend more time training this person?” Do I just… there is an opportunity cost. Or, do I spend more time staying at light speed? Why do I slow down to do something else in the hope that maybe I will get to return versus the light speed I’m already on? It’s a lot of tension. Again, it’s certainly new. But if we want to look back, I think you talk about the switch from agriculture and society, industrial society, and now the service industry. The reality is that, yes, we have made dramatic changes in the past before. 140 years ago, we were 90% agricultural society in Europe, in the US, 90% of us. Today, it’s what? 2%. So my point is that that’s a normal evolution. There is no progress without change. Sometimes the rate of change is soft, and sometimes you have a step function. Now it’s a step function, and it’s also a pretty fast step function. Before, it could take decades to get new stuff being put in place, to have electricity come up, this or that. Now we see that the rate of investment in AI is insane, way beyond anything we have seen before. Two, in a way, a lot of the architecture behind the scene was already there to support an even faster transition. What’s new might be the pace of the transition, how unnatural it might look. But at the same time, if you put yourself in the shoes of someone who lived 150 years ago, I mean, this was also a dramatic change for them. From horses to cars to planes to rockets, pretty big change, maybe even bigger change.Nuno Gonçalves Pedro Maybe the silver lining, just to bookend this section, is one, there will be new roles. There are a lot of things we can’t anticipate. There will be new roles, there will be new jobs being created, and new things that we can’t really quite grasp yet. The second part is that the rules are changing, and they’re changing, I would say, in general, for the better. If you are a decision-maker or an organization, and you still have your job, you’re probably making more important decisions with more data, with more tooling around you, with less red tape, hopefully over time. I know that will not hold true for all the big corporations out there that are listening to us, but it is starting to happen. Things are making an impact on how decision-making is made. There’s less and less red tape along the way in certain organizations. There are more and more fact-based discussions happening as we move along. The silver lining is better jobs, more jobs, different jobs in the future, hopefully as well. Secondly, the second part of the silver line is that the jobs that exist today, hopefully, will be more interesting, certainly on the knowledge space and on this judgment space that we’re now introducing as part of this episode. The AI-Native Company: Org, Hiring, Culture Switching gears, maybe to how does that shift? How does the company of the future look like? How does an AI native company look like? I feel there are a lot of discussions on, “Oh, you only need one person to run everything.” Let’s not go to that level. We’ve had a couple of episodes where we focused on AI as your co-founder and a couple of other elements that you guys can go back to. Let’s focus on a more evolutionary view of what’s happening to organizations, and maybe start with the org structure. In general, we should see more flat organizations where mid-level managers have to justify their pay in some ways because middle management are routers. They are normally routing tasks. It’s sometimes aggregating it, synthesizing it, and pulling it back up. Guess what? AI and agents in general are very good at that. The synthesis piece, et cetera, is not as well needed. One could say there are several elements of middle management that are valuable, like the coaching of people, the creation of apprentices, and the accountability that comes with some of middle management. But lo and behold, most of middle management is seen as a little bit of a thin line that doesn’t need to necessarily exist. I feel we’re moving into a world of smaller teams, more senior teams, where there’s more judgment at the top, where you’ll have people that both do a mix of what we used to call management in its new form, but also a lot of individual contribution. If you’re not used to that, if you’re not used anymore to be an individual in the future, again, and if you’re a very senior in an organization, maybe this is the right time to either reinvent yourself, find some other job that doesn’t require as much of that, which we’ll have plenty of those jobs for the next few decades, or maybe retire. I’ve actually, shockingly enough, seen people who have said, “You know what? This thing is changing too fast, too dramatically. My industry is changing quite aggressively right now. I’m about to retire in a couple of years. I’m just going to retire now.” I’ve literally met two people who have done that. Again, there’s nothing wrong about it. I think we’re, again, going through a step function and a huge shift, but figuring out where you fit in this new model of organizations, more senior at the top, smaller teams, more of a mix of individual contribution with management than ever was done before.Bertrand Schmitt I agree with you. In some ways, I’m not surprised that some people might say, “You know what? It’s now time to retire.” I feel a bit sad, maybe because it means you don’t like to keep reinventing yourself and changing your habits and thinking about new stuff. You were a creature of habits, I would say, if that’s your conclusion. But everyone is entitled to their own opinion, obviously, and a way of life. I guess that’s what happened, again, at regular times in the past in terms of big change. What I can see is that the rise of, you can call it the full-stack individual, someone who will have multiple roles inside the team. Before, you had to really separate the role. Especially in the US, there is such a clear separation between every role you can have in a company. Let’s take a tech company. You will have people doing design, people doing different types of designs, people doing front-end development, back-end development, and operations. You see step-by-step hyper-specialization. I have seen that, and it’s true that the level of complexity you had to deal with at some point requires some level of hyper-specialization because it will take you 6, 12 months in order to be really, really strong on a specific topic, a specific language. God forbid, trying to go deep into something that you had no real experience into. But I feel with AI, it’s a big change, actually. It’s the opportunity to go beyond that. It’s the opportunity to do more, to touch more. You can combine designing and shipping code, product managing and shipping code, being an analyst and deploying. Of course, we have to think how it works because putting a marketer shipping code to production, maybe that will get you into trouble. But I think that there must be some change. We see it changing dramatically, how fast we can get into something, something different from what we are used to. I think it would be crazy not to take that opportunity to dramatically change the scope of many positions and put an end to that hyper-specialization. I think for me, in some ways, hyper-specialization was bad. There is only so much you want to be a specialist in because a lot of things, a lot of opportunities are actually coming from the mixing of many different ideas, many different perspectives, and you lose if you go to hyper-specialization.Nuno Gonçalves Pedro I don’t think the age that is coming is the age of the generalist. I think it’s going to be the age of the multispecialist. We’re going to go into an age of multispecialization, which is a little bit, we’ve mentioned it as well in the past, what Amazon defines as an athlete or T-shaped or pie-shaped people, people that have on top an amazing ability to do general management, strategy, managing teams, et cetera, then have spikes. Spikes into business development, corporate development, product management, whatever it is. With AI and with agents, the development of those spikes, as we’ve been discussing in this episode, will actually be easier. It’s almost like a given. If you want to go deeper and deeper into a certain area, you can go much faster. I think that level of multispecialization is going to be really cool to observe. I’m not sure we’ve had an age of multispecialization over the years. Maybe people would point out, well, the Da Vinci example, people that are great across very different areas. Maybe that’s an example of multispecialization. But honestly, from my perspective, this is going to be an exciting time because of that, because you’ll have people who, instead of being just focused on this area of sales, and I only do that, they can actually and should actually do a lot of other things. So the work, as we were talking before, can be more interesting. More demanding as well, because the judgments you need to make are more complex. The context you need to actually gain needs to be gained much faster. At a level of magnitude, you haven’t been able to do it before. Talk about information overload. But actually, ultimately, the roles can be a lot more interesting, a lot more exciting, because I can jump around. If I’m an investor, in this case, we have two investors on this conversation. But if I’m an investor, one of the things that we start looking at is actually not just looking at a startup as, is this startup doing something in AI or not? Is it AI-enabled or not? Is it an AI platform or not? But actually, more fundamentally, is this an AI native startup? Meaning, organizationally, culturally, is this the company that’s already in the AI age? How is the team working? How are they defining things? It’s not just that they only have two or three people. It’s like, what are those two or three people doing? How are they doing it? What cadence are they doing it on? What tools are they using? How are they making decisions? I feel we’re still actually relatively early on that track. It’s very interesting because we’ve had all these companies raising mega rounds. First round out, we invested in one of them, but there have been many frontier labs out there raising a ton of money. But a lot of them don’t have a fundamentally different way of doing business. Of organizing themselves, of how they do the day-to-day. Although they’re working on cutting-edge stuff, with very notable exceptions, they’re actually not using it themselves. They’re not actually shifting how they do stuff themselves.Bertrand Schmitt For me, that’s very interesting because in the past, I used to be quite conservative on how you manage and run a company in the sense that if you’re already in tech, if you are already on the cutting edge of what technology can deliver, and this and that, don’t waste time trying to invent a new org structure. Just focus on delivering something great, amazing, and be great at technologies. That’s already your huge differentiator. At the time, there was no real reason to innovate on the team organization. I have seen so many teams that tried to innovate, and it was just catastrophic because there was not much to innovate on, because we had decades of optimization that we could leverage. There was no reason to invent. But here it’s very different. There is a dramatic shift in how you can organize differently a company. I don’t think there are any blueprints yet on what’s the best way to do it because it’s too new. But at the same time, I would feel very bad to invest or support a company that first is not focused on AI or AI-enabled, but at the same time is not trying to innovate on the team itself. Because if you don’t do that, you’re going to get killed by someone who is going to innovate better than you on not just the product, but on the org as well.Nuno Gonçalves Pedro Indeed. The shifts are pretty substantial. If you look, for example, just at hiring, what do you hire for? Certainly, there’s this element of the multispecialized orchestrator, which normally will be someone with quite a lot of wisdom and expertise. It doesn’t necessarily mean someone who’s old, but someone who has the ability to work with all the AI tooling and platforms out there and be an orchestrator of agents. Why do they make judgments, make decisions, move stuff forward really, really, really quickly? Again, those jobs are going to be the best jobs. The second part, I think that is very interesting, around hiring, is you’re going to skew towards the elements that are potentially either very aligned with the use of AI tooling and platform, AI expertise, or being AI native, or someone who’s used to using AI. That’s one side of the fence. On the other side, you’re going to actually be optimizing to hire people that have the characteristics that will be difficult for AI to replace immediately, like taste and the notion of fundamental accountability and notion of implications, the notion of how you affect change in organizations, how you affect change in individuals, the elements of coaching, and beyond coaching. You’ll be optimizing for those kinds of hires as well. Then, last but not least, for me, I feel that there is a momentum already happening. I think it will happen even more, which is the tendency to under-hire rather than over-hire. The moment of the good old days of blitz scaling, “Oh, let me go and hire 300 people to scale my go-to-market and just land grab market.” Now, that’s not how it’s going to work. People are going to try and first get the efficiencies in-house with top talent and see if there’s, at the end, the need to hire more people or not, rather than the other way around. I think the issue here is a little bit of what we alluded to before in this episode. There is a tax on individuals. If you hire more people, you’ll have to manage people, you’ll have to work with them, et cetera. If I don’t need to, I might as well work with the agents that the tools and platforms that I use give me access to. Because that’s a world that’s much more efficient, right?Bertrand Schmitt I’m in total agreement with you on this. It’s definitely raising way more questions than before because, again, on one side, you have the product, the technology used to build products that are completely different. At the same time, all of this is also enabling new ways to design organizations and to scale differently, especially in a world where, as we have seen in 3, 6, and 12 months, stuff that you thought were impossible are suddenly becoming possible. So you’re, “Hey, I’m going to scale and burn a shitload of money for 6 months before I know if there is any return.” Versus, “You know what? Maybe I just wait 6 months. The AI has improved enough so that we don’t need this new team. We don’t need these people to do stuff.” Because actually, if you just wait 6 months, we will have stuff coming for free from either new AI models or new AI tools or this or that. If you remember, we used to say that in mobile, things were going three times as fast as on the web in terms of pace of innovation and speed of development and stuff. I mean, with AI, it’s 5X mobile.Nuno Gonçalves Pedro Maybe even more. Yes, well.Bertrand Schmitt Maybe even more, maybe 10X. Every assumption around blitz scaling or scaling in general was based on past assumptions. It’s not based on how is the industry evolving today. Might make more sense for you to really grow your agents and spend more money on more tokens. I remember, of course, Jensen is selling his business interest, but he was saying, “Hey, for each one of my 450K engineers, he better spend 250K in tokens a year.” I’m not saying it’s the right way to say it, but I think there is some truth in it, and that would be something to think about. Have we maxed out the token usage per employee? I’m not talking in a stupid way because token maxing and wasting money has no value and is as stupid as it gets. But if you are truly getting a return on these tokens, can you use more? Can you generate more? Can you create more loops so that one engineer manages not just 10 agents, but 50 agents, but 200 agents? I think that’s the big question. We’re trying to add more people. More people means more management, more issues, more this, more that. That would be a fair question. Another piece of the puzzle is how do you build in a way your… I don’t know if it’s a digital twin, but more like the digital version of your companies represented by agents. How do you make sure that everything you do as a business is truly captured, is truly leveraged so that your agents are getting better and better? Not just because the model gets better, but because you are putting more data into it, because it has more opportunity to learn, and as a result, gets better at your specific business.Nuno Gonçalves Pedro The next big thing is culture. How does culture change? I think the biggest shift that I see is, why would you do meetings all the time?Bertrand Schmitt Yes.Nuno Gonçalves Pedro At least at Chamaeleon, we have a very small team, just by the way. We have a very small team at Chamaeleon. We’ve reduced by way more than 50% the time we spend on meetings between each other across the board, one-on-ones, partner meetings, et cetera. I think we’re really pushing to be more and more asynchronous. There’s stuff you can process via message. I was just asking one of my colleagues, “Can you just send me that prompt for that so I can just do that on CoWork?” Or “Can I just go on Mantis and do this? Can you tell me the cycle?” Or vice versa. Basically, it’s a little bit like you’re just going to do it. I don’t need to meet. I don’t need to meet all the time. There are some things where we still need to meet and interact, and we need to brainstorm at times, and we need to go to a different level of abstraction on the top end. Then on the lower end, there might be things that are a little bit more specific and governance-related and operational-related that we need to agree on that are more sticky. But otherwise, the culture is going to be biased towards build. “Go and do it,” rather than, “Let’s do a meeting.”Bertrand Schmitt Yes.Nuno Gonçalves Pedro Async is the thing. I’m more and more like we have a couple of interns this summer. “Can we async this?” They’re like, “What does that mean?” “Can we make this interaction asynchronous?” Because synchronous interactions for me are very expensive. Can you send me something that I can process, and then I can send it back to you? We don’t waste time on you giving me context and whatever. Then I’m not ready quite yet because I need to process it. Maybe I’m in between two meetings that I’m actually thinking about other things in my mind.” Again, I feel that shifts how stuff is done. One, build rather than meeting. Two, asynchronous versus synchronous. In some way, millennials had it right when they shifted a lot to messaging and stuff like that. Let’s do more asynchronous rather than synchronous, those two elements from just an operating model of the company are significant. Maybe this is a good time for me just to put one parenthesis because there’s this thing that’s bugging me as we’re talking here. Everyone who is listening to us at this point in time might be saying, “Cool, but I work for this large organization. We’re just now…” Everything we’re saying here is contextualized by time. We’re giving you extreme situations. We’re looking into the future. Some companies that we’re talking about might be doing this already as we speak. Some of them might be in the process of doing this and might in the next couple of months be doing it like we are describing it here. Some of them might take years to get there. Then again, some of the companies that might take years might actually be destroyed in between or meanwhile, and be disrupted. Some of them might not because they’re in very legacy businesses, and it’s fine, and it’s okay. Again, don’t take everything that Bertrand and I are saying today as this is gospel, and it’s going to happen tomorrow, and why the hell are we not doing it? We think that aspirationally, this is where you should be moving to as an organization, whatever size you’re at. Speed will matter, as we discussed before, but not everyone, obviously, is going to move as fast as we’re describing it here.Bertrand Schmitt Yes. Me, for instance, take inspiration often with what some of the AI labs, frontier AI labs, are doing, the way they are working, especially in OpenAI and Anthropic. They are clearly at the top of the spear in terms of what is it that you can do because they have access to models we don’t have access to, because they have unlimited tokens they can use for tasks. They hire people who are, of course, 100% on AI. They are the best example of what is achievable if you have the top minds, if you have the latest models, if you have unlimited tokens. From there, you can take that for our needs and for our situation, and others in industries that are not as advanced. Definitely, you have some time. But as you say, things are moving fast, things are changing. Wall Street is going to expect better returns because when we discuss all of this, the conclusion is that you should be able to do more with less. That’s as real as it gets at some point. By the way, that’s what you see. You see better performance, a better business performance right now. So even if you might not get disrupted, you’d better start there. For some, it might take more time, and they might still be fine.Nuno Gonçalves Pedro Maybe to bookend this section, clearly what we’re saying is organizations are going to change. Their MOs are going to change, the structures are going to change. There are elements of what we discussed before in terms of judgment that are fundamental to this. The ability that in some ways, one would say a lot of the technique of getting solutions out there, even in brainstorming or problem-solving, is going to get democratized. The algorithms are able to do that. On the other hand, having points of view and having wisdom is not necessarily democratized, necessarily by the machines. It can be facilitated, it can be more productive in achieving that level of wisdom, but wisdom still will matter at the end of the day. We’re not saying that’s out of the question. Actually, that’s going to be the asset. People who have fundamental wisdom that can come to the table and frame things. We see this even today in prompt engineering, on just creating prompts. The better your prompt is, the better the outcome is going to be, the result that you get from the algorithms. That’s not going to change, in my opinion, anytime soon. That UI interaction piece is not going to change anytime soon. Again, if you’re an organization thinking through organizational structure, culture, if you’re thinking through hiring, these are some of the elements that we think will give you an opportunity, but I would actually go one step further. On the positive side, I would say, they give you arbitrage. If you’re able to move faster than your competitors and really adapt your org faster, you’ll reap the benefits faster as well. That’s what many still say and relate to as the word innovation. That’s how innovation gets accelerated. I think there’s a huge opportunity right now for arbitrage. If you move fast, experiment, experiment on new org structures, experiment with talent, you’ll know that some of them will work well, some of them will fail miserably, so you can’t experiment on literally everything. On the other side, I think the doomsday scenario is if you don’t, if you’re on the other side and your competitor is outpacing you on trying these different organizational models, structure, hiring models, and operating models, they’ll potentially just disrupt you. They’ll do stuff that you thought you had the moat on, and lo and behold, you don’t anymore. Sometimes it comes just from org, just from injection of people with a different MRO, different operating model.Bertrand Schmitt The Human Element: Are We Underestimating It? Maybe we can move to our next section about the human elements. Are we underestimating it or are we overestimating it? The three things that are a big part of the human elements, emotion, creativity, and synthesis. Is it just soft skills, replaceable part? On the contrary, is it the durable part now that we have automated intelligence?Nuno Gonçalves Pedro I’ll start with emotion first because I think it’s probably the easiest of all the ones you’ve mentioned. Emotion is key. Many of you listening to us will know this. The way you deliver a certain message, the emotion that you have when you deliver it, just in and of itself, this could be a sentence, it’s something verbal, et cetera. Makes a difference between the person or the people on the other side actually adopting it or actually just resisting it. Emotion is critical. It’s what runs the world. Everyone talks about a bunch of things, but emotion is a currency that is still naturally human. It will be, I feel, difficult for these AI tools and platforms to recreate it fully until there’s some literally very high-definition manifestation of them as avatars or some physical manifestation of them as robots and all that stuff. It will take a while for that emotion to be manifested. Emotion, I think, is still something that we as humans have as a moat, and it’s critical. As you mentioned before, I was a strategy management consultant at McKinsey, and getting people to action is actually 80% about the delivery, communication, the emotion that you surround the project itself, more than sometimes the truth. It’s great to have the truth and to have something that is similar to the truth in terms of analysis, but in some ways, that’s not what really moves change. Change is moved by, I would argue, a significant amount of emotion and alignment on emotions.Bertrand Schmitt You could argue that’s something that most politicians have perfectly understood. If you look at most campaigns these days, everything on emotions, maybe the tagline might be one word. It’s interesting when you see from that perspective that actually it’s very little on facts, very little on all of this, but more about emotion. You could argue it’s the same for businesses in the future? That’s a fair question. I think creativity is another one that’s quite important. At the same time, it’s not so easy because I must say I’m quite amazed when I’m looking for creativity from AI, either to generate the image, to generate video, to generate audio, or to generate text. AI can be pretty creative. I still think you need to control its creativity; you need to understand what’s good, what’s bad, what’s quality, but at the same time, I can see even in creative tasks, AI can be a very strong partner. I’m talking about any creative task, like invent a name for a product, let’s brainstorm the mission for the company. AI can actually be doing a pretty impressive job. That’s the type of job where you will hire experts, where you will use some of the best people in your team to help you for days. We say, “You can do quite a lot.” It’s an interesting one because I think there is some unique human creativity, and at the same time, AI can be pretty strong at creative task as well.Nuno Gonçalves Pedro I agree. In particular, if it represents benchmarking, if it represents repetition, if it represents seeing the world and then coming up with something that presents itself as creative, to be honest, it can actually outpace humans. If it’s like genuine light bulb moments of creativity, angles that haven’t been tried before, certainly not in the same way, I think humans still have the advantage. To your point, I agree. This is not a humans-win situation. On the previous one, on emotion, still, part of it is because, also on emotion, there are exchanges. You and I might be looking at each other, and from the facial expressions and the reactions, where you judge that for AI to get there, it’s going to take a long time. There’s going to be a lot of very complex algorithmic stuff put into that for AI to be able to create synthetic emotional behaviors, but creativity, I agree with you. There are a lot more nuances to it today, where AI does have significant advantages at the end of the day. Synthesis depends. Synthesis, I feel, if we’re talking about holding a bunch of messy assumptions, contextualized inputs with different layers of data adjacent to them and then trying to create and form one coherent, fully accountable point of view that you stake something on, like a decision, a company, a business unit, whatever, I think humans have the advantage. Part of it is the complexity of what we have today with generative, pre-trained transformers, today with GPTs, where the hallucination comes through, where it’s really more statistical analysis. Over time, maybe synthesis will be a forte for AI. Right now, I think we still have that ability to really be the ultimate decision-makers and judge-makers and have that wisdom put at the table to make those decisions. Honestly, models are very good on balancing both sides, so ended up, as we say in Portuguese, neither fish nor meat. It’s to balance both sides’ answers. That’s not helpful in most cases. When you’re in a difficult position where, for example, the future of a company, company is almost dying, what do you do? I’m not sure your AI algorithms that are going to give you a great solution. Because it will give you a median or average solution, which likely will lead you to a median or average outcome, which in this case would be failure. Again, on synthesis, there are some areas of advantage for human beings. If you are looking for clearly synthesized perspectives on certain elements that are maybe less edge-focused, they’re more than the normal part of the normal distribution, then probably AI agents are brilliant at that. All the tools we have today are pretty good at that, and I think they’ll just get better over time. That’s how I see synthesis.Bertrand Schmitt I think a lot of improvements will come with a better fine-tuning of agents to what’s special about your company. Because if you just take a general agent, there is only so much. It can understand your industry, your company, and your way of working. I think that part of making sure your agents are finely trained, finely tuned on your own business, so that they can give you a really well-calibrated feedback, will have a lot of importance.Nuno Gonçalves Pedro I think that’s absolutely spot on. Maybe to end it, what is definitely different about humanity? Definitely, emotion, as we discussed, some pieces of synthesis. Creativity, maybe the light bulb creativity, not the more repeatable creativity, the one that you can put and encapsulate into processes in some ways. There are elements of us being physical, which robots can’t still recreate. That’s definitely an advantage. The embodied, we’re embodied. That’s obviously a huge advantage. With that also comes advantages because we have to interpret each other, and we have to see the complexities in physicality that land to it. Is human and the human element categorical difference? If we’re having a more philosophical discussion around this, I think it is. I think it will be for at least the foreseeable future and maybe decades to come, even in whatever scenarios we’ll discuss, which is our next section, scenarios.Bertrand Schmitt I would say projecting beyond 10 years is always pretty hard on this because, again, some of the improvements we are talking about we can imagine based on how it has evolved, but at the same time, there will be disruptions in AI. Stuff that we take for granted in terms of weakness, especially, might not be there in a few years from now. Either because it has been solved through brute force or because the field will have made significant change and improvements and discoveries, making some of our points moot. If we talk about embodiment, obviously, robots are coming. How fast, how cheap? That will be a big question. Right now, they’re not very smart. They’re usually very specialized. The more we move to a more general form factor, humanoid form factor, the more I think it will change. Also, another piece of the puzzle is that we have the assumption of agents having trouble to convince humans and stuff. At some point, we keep assuming that humans in the loop. If we’re talking about agents convincing another agent, not having embodiment might be even more efficient. That will be another perspective. Going forward, we will have not just agents we control who are doing a job and scanning the job, but agents truly interacting with other agents. You have agents controlled by one person, one team in your company, working either together or maybe not confrontationally, but trying to think and having different perspectives with another agent, controlled by other teams. I don’t think we have seen much of that now. We have seen mostly agents that are controlled by one team doing one job in one direction. Not multiple teams agents working together, or against or in parallel with another team agent. I think we will see some interesting things coming out of that.Nuno Gonçalves Pedro Scenarios Switching to scenarios, we love our two-by-twos. We haven’t done one in a while. This time it’s a two by two. We have four scenarios. I think on one axis, we would have potentially the capabilities of AI. One side would be more incremental. The other side would be the extreme full AGI. I’ll define it in a bit so that we can at least have a little bit of a definitional view on what the AGI is. Then the other axis would be how gains are distributed, concentrated versus broad. Obviously, if they’re very concentrated, it’s more unequal. It only goes to a few companies, a few people, a few individuals. If it’s broad, it’s much more dispersed through society, et cetera. AGI, just to try to define it, the formal definition of it is that it’s a hypothetical AI that matches or exceeds human capabilities across virtually all cognitive and practical tasks. In some ways, AGI can learn, reason, and adapt to novel situations across any domain. Then there are several mutations on this, but there’s one notion, or rather, there are three notions that normally are across a lot of these definitions. One is generalization, ability to seamlessly transfer knowledge from one domain to another without needing retraining, which is a very impressive skill that we humans still seemingly have. Autonomy in agency, the capacity to operate independently, set goals, plan and execute complex tasks. I think AI is their issue with agents to a lot of that extent. Then, last but not least, human parity, performing economically valuable work at or above the level of a typical human knowledge worker. If you listen to one of our last episodes, you’ll realize that Bertrand and I have slightly different views on AGI, and if it’s already here or not. I think, definitionally, maybe we have slightly different views on what the definition actually is. For me, maybe AGI is a little bit more what some would call superintelligence and generalized superintelligence. Strict to census, Bertrand is more connecting to AGI as in its prime definition. It behaves as well or better than a human thing. Maybe that’s what’s leading us to differences on whether AGI has arrived or not.Bertrand Schmitt Personally, I will have a different scale where I will put AGI, as you just said, in some ways, relatively similar in performance to your average human being. On top of it, it’s able to touch different domains that most humans are not able to do. Usually, there is some level of specializations where in AI, it can be more generic. I will put ASI, Artificial Superintelligence, as clearly the step beyond. Something that, on any dimension you pick, it’s able to beat a human expert. From my perspective, I think we already discussed that, but we are at AGI already. We have AI that can do way better, not just way better, but at least as well as humans on many topics, sometimes better. Yes, there are some topics that are not for AI yet. Embodiment, for instance, to flock with your humanoid robot in 2026. For me, we are partially there or fully there in AGI. If we take the stricter definition, ASI, we are definitely not there, but my guess is that it’s moving quite fast. We might be there in a few years from now. I don’t think we are talking about multi-decades. It’s 5 years, maybe 10. Of course, there are questions because people will say, for instance, “Hey, how do you become truly super-intelligent when all your training is based on human data?” That’s not an easy one because how do you train on that? To be way better, not just a bit better, but way better. Maybe I’m going on a tangent, but some are looking at AI learning from AI, AI being taught from AI, AI fighting with AI, AI challenging AI. The same way we saw this AlphaGo moment where AI was not trained anymore, like in chess with human moves, but has been trained to play against itself. That’s when it reached superintelligence in Go. It reached superintelligence by playing against itself and basically letting go of that human baggage, if you want, and going to the next level. What I found interesting in that, actually, first, that’s what happened, but two, there was some analysis that the average level of Go players and the top players went up after AlphaGo because AlphaGo, in a way, opened doors that humans didn’t believe were open in front of them, or they didn’t see them. They didn’t see these doors, so they didn’t bother to open them. AI opened new doors, but interestingly enough, humans improved after that, thanks to AI. You see what I mean? It was an interesting, okay, that self-learning from AI was the way to go beyond the current level of human knowledge and human expertise, but at the same time, humans were able to follow up. It was not like suddenly humans are totally useless crap. They improved. Did they still beat AI? Maybe not, but it was definitely also helpful.Nuno Gonçalves Pedro Back to our scenarios. We’re going to take the definitional extreme just for argument’s sake for scenarios. We’re going to talk about maybe what you were saying, ASI rather than full AGI, but like ASI. Again, artificial superintelligence as the extreme on the one hand. Let me talk about maybe the first scenario that would come to mind. Maybe we can call it the plateau scenario. All of this was great, but it was all smoke and mirrors. They were great at some cognition stuff. They’re a great tool. At some point, they’re going to hit a wall. Hallucinations are never going to be a thing of the past. We can’t fully trust them on really hardcore stuff. We’ll gain productivity enhancements. We’ll keep gaining those productivity enhancements, but at some point in time, we really won’t reach ASI. We really will be stuck with what we have. It’s a little bit like we get the next big thing, the next big spreadsheet, the next big internet, but it’s not going to change the whole world beyond just productivity, enhancements, and amazing tools that we have available to us that makes us much better. In that scenario, the winners will continue being fast adopters, probably small and medium businesses, because there won’t be a push for maximum speed either, so they’ll catch up at some point. Then AI native companies will be better companies than other companies, but not necessarily overall disruptors across the board. It’s not necessarily a new species of companies. It’s just companies that are a little bit better at doing stuff, which we also saw during the internet phenomenon and that first big push forward and then bubble, where we had some companies that were fundamentally different on how they operated. It took us another couple of decades for companies to be more and more digitally native along the way. Basically interesting, but it’s boring. It’s like, cool, we got tools, we got promised the world. What are the implications? All these companies that are worth trillions and trillions of dollars are not worth trillions and trillions of dollars. Because at some point we’ll face competition, commoditization. It will just be tools and platforms. They will not unlock that next stage. Therefore, this will have been a bubble, and likely it would be a hard landing to that bubble. That’s the implication.Bertrand Schmitt I would just say that, yes, I agree with you, but I would just say overall, even if it stopped today in terms of quality improvement, speed or stuff, or it barely improves, I still think we will have 10 years of madness just to leverage everything that we have today.Nuno Gonçalves Pedro Understood, Bertrand. This is a scenario. I understand, but maybe we’re going to hit a wall, and we’re going to hit that wall next year, or we’re going to hit that wall in 2 years or whatever.Bertrand Schmitt Possibly. I’m just saying we still have 10 years of goodness from that big push in AI we experienced the past few years.Nuno Gonçalves Pedro Absolutely. Agreed, but it’s boring.Bertrand Schmitt It’s boring. It’s a plateau.Nuno Gonçalves Pedro It’s a plateau. The second one is more of something that we have AI, but humans in the loop are going to be critical along the way. The judgment work that we described earlier in the episode is going to be critical to everything that happens. It’s, I would call it the augmentation scenario. The AI will be a great augmentation tool for humans, but humans will never really quite stop being in the loop. Some of the gains that AI has are broadly distributed in society and in the startup, big corporation and small medium business world. Everyone will have access to them. We humans, are still very important. We have all these augmentation things, and AI is mostly benign. There will be a couple of issues, but honestly, at the end of the day, we’re just better. We’re better, faster, more data-driven, more factually current. We’re doing stuff faster, but humans

Somewhere along the way, PR research came to mean a survey reverse-engineered to confirm whatever the client had already decided to say. Real research, the slow and expensive kind, stayed with the academics, because nobody on the agency or client side had the hours. So what happens when the cost of doing it right collapses? In this episode of The Trending Communicator, host Dan Nestle sits down with Rich Gallagher, Head of Comms Innovation at Brands2Life, whose answer to that question was to write the code himself. Each of his research reports started as something close to a dare — watch a month of business television, read every business-section story in three national papers, listen to a year of back catalog across 96 podcasts — and each came back with a number worth having, like the 12% of those shows actually built for the privately held companies his clients run. That method is the easy part to admire and the wrong part to copy, and the conversation goes well past it. Rich and Dan get into why curiosity has always been the one non-negotiable trait in a good PR person, and why for the first time there's something like a metric for it. They work through The Rise of Indie Journalism, Rich's study of 32 tech journalists who walked away from traditional mastheads and took their audiences with them. They dig into why generative engine optimization is quietly making niche trade coverage more valuable than a one-line mention in a marquee outlet, and what media relations turns into when it stops being a tier-one scavenger hunt. Rich also shares the app his team built to score World Cup managers on how hard they lean on clichés, and the uncomfortable pattern it surfaced about losing coaches. Listen in and hear about... Making AI watch a month of business television through closed captions The 96-show podcast audit and why only 12% were worth a pitch The Rise of Indie Journalism: 32 reporters, portable audiences, and the opportunity most brands are ignoring Why curiosity finally has something resembling a measurement How GEO is lifting niche and vertical media over marquee one-liners What clichés in a post-match press conference reveal about crisis response Notable Quotes from Rich Gallagher "You watch every single hour of market-day television on the three major business broadcast networks for a month? Like, no, of course not. That's like a Geneva Convention violation to force someone to do that. But now we have tools. You can scrape the closed caption for the hearing impaired and effectively make the AI watch the show." [07:16 → 08:01] "The challenge is you can't read everything all the time. But what if you could? And now that you have AI, you can. We can analyze all these things at a scale that doesn't exist even inside a very big agency, where you can not only make sure you're reading every single business section story that runs that quarter, but reading it with the same eyes." [33:06 → 33:47] "The most important thing for a successful PR person is curiosity. Really being genuinely interested in uncovering what's special about what your clients do. And it's kind of funny, because there's never really been a good metric for curiosity, and now we have it." [40:38 → 41:26] "I don't necessarily subscribe to 'media relations is dead.' But it kind of goes into a chrysalis and emerges as a beautiful butterfly every couple of years." [52:46 → 53:27] Resources and Links Dan Nestle Lilypath | Website The Trending Communicator | Website Communications Trends from Trending Communicators | Dan Nestle's Substack Dan Nestle | LinkedIn Rich Gallagher Brands2Life | Website Rich Gallagher | LinkedIn Princeton Narrative Lab | Website Expected Cliche Tracker | Website The Brands2Life Research Reports The Tech Podcast Opportunity for Privately Held Companies Breaking Through in Broadcast The Rise of Indie Journalism Timestamps 0:00 Intro: real research and its counterfeit cousin 2:33 Two nerds, two girl dads, one Princeton zip code 4:16 The ADP employment report and data a company already had 5:33 Moat, ad spend, and the run-up to 2016 7:16 "Make the AI watch the show" — Breaking Through in Broadcast 10:51 Predictive analytics the hard way: 150 hours, then 30 seconds 13:04 Why the AI policy came before the research 15:16 Three newspapers, three paywalls, three automations 15:57 Testing whether the AI actually read it 17:28 Podcast transcripts and the wild west of captions 19:56 Claude Code, Cowork, and what changed 22:53 Thirty sub-agents that never lose their place 26:06 Where deep research helps and where it stops 29:09 The Tech Podcast Opportunity: 96 shows and the 12% that mattered 31:32 Where the questions come from in the first place 38:13 The human-in-the-loop sandwich 40:38 Curiosity as the differentiator — and finally a metric 43:19 The Rise of Indie Journalism: Substack, Ghost, and Beehiiv 46:20 Velvet Underground economics and portable audiences 49:00 Media relations, dying or molting 55:27 Why GEO makes vertical media solid gold 59:17 Authority when the entity is a person, not a website 1:01:46 The cliché tracker, hot IPO summer, and what's next 1:05:04 Where to find Rich (Notes co-created by Human Dan, Claude, Fireflies, and Riverside) Learn more about your ad choices. Visit megaphone.fm/adchoices

Das ist das KI-Update vom 29.07.2026 unter anderem mit diesen Themen: Nvidia versammelt Tech-Schwergewichte für neue KI-Allianz Claude Cowork entkommt macOS-Sandbox Microsoft stellt eigenes KI-Modell für Cybersecurity vor und Wann sind menschliche Entwickler wirtschaftlicher sind als KI? === Anzeige / Sponsorenhinweis === Dieser Podcast wird von einem Sponsor unterstützt. Alle Infos zu unseren Werbepartnern findet ihr hier. https://wonderl.ink/%40heise-podcasts === Anzeige / Sponsorenhinweis Ende === Links zu allen Themen der heutigen Folge findet Ihr im Begleitartikel auf heise online: https://heise.de/-11381575 Weitere Links: https://www.heiseplus.de/audio https://www.heise.de/thema/KI-Update https://pro.heise.de/ki/ https://www.heise.de/newsletter/anmeldung.html?id=ki-update https://www.heise.de/thema/Kuenstliche-Intelligenz https://the-decoder.de/ https://www.ct.de/ki Eine neue Folge gibt es montags, mittwochs und freitags ab 15 Uhr.

Live from TechCon365 Chicago, we discussed Microsoft's announcement that Copilot Cowork will be generally available on July 1st. We took a closer look at what Cowork is designed to do, explored its key capabilities, and shared our hands-on experience using it so far. We also reviewed the latest updates around GA pricing, including how the model has changed and what organizations should consider as they evaluate adoption. DOWNLOAD THIS PODCAST

Can one accountant really do tax prep five times faster with AI? Blake and David talk with Sam Leon, founder of The Millennial CPA, about how he uses Claude projects to turn client documents into tax workpapers, speed up review, and run a solo firm built around software. They also dig into where AI still falls short, why review remains the bottleneck, and what firms can realistically automate today.SponsorsDigits - http://accountingpodcast.promo/digitsThe Value Builder System - http://accountingpodcast.promo/valueOnPay - http://accountingpodcast.promo/onpayCloud Accountant Staffing - http://accountingpodcast.promo/casChapters(00:00) - Autonomous AI Reality Check (00:11) - Show Welcome and Interview Tease (00:55) - Sponsor Spotlight Digits (02:34) - Trump IRS Deal Dismissed (05:20) - California Billionaire Tax Fight (07:35) - New York Pied a Terre Tax (09:29) - Sponsor Spotlight Value Builder System (11:18) - Xero CEO Pay and Stock Sale (16:39) - Verification Tax and AI Jobs (22:06) - Sponsor Spotlight OnPay (23:21) - 1948 IBM Accounting Machine Lesson (29:56) - Sponsor Spotlight Cloud Accountant Staffing (31:07) - Meet Sam Leon Millennial CPA (32:01) - Solo Firm Vision (33:15) - From Big Firms to Millennial CPA (35:42) - Why AI Agents Weren't Ready (37:19) - Automating Tax Prep Workflow (40:33) - Smart Intake for Better Scoping (43:06) - End to End Return Automation (46:04) - AI Workpapers and Review (49:38) - Claude Setup and Time Savings (58:18) - Tech Spend and Pricing Strategy (01:02:52) - Building TaxWeave Platform (01:06:21) - Wrap Up and CPE Info  Show NotesJudge Smacks Down Trump's IRS Settlement And Orders Sanctionshttps://www.cnn.com/2026/07/13/politics/trump-irs-judge-ruling-settlement-sanctionsInstead of Uniting the Left, California's Billionaire Tax Measure Has Split Democratic Allieshttps://www.cpapracticeadvisor.com/2026/07/08/instead-of-uniting-the-left-californias-billionaire-tax-measure-has-split-democratic-allies/186379/NYC lawyers slam pied-a-terre tax as a half-baked money grabhttps://www.audacy.com/1010wins/news/local/nyc-lawyers-slam-pied-a-terre-tax-as-a-half-baked-money-grabA 1948 IBM computer with no memory at all, fed by punchcards, was still doing the accounting at a Texas filtration company as recently as 2020https://scienceblog.com/t-1948-ibm-402-punchcards-sparkler-filters-accounting-2020/Time saved by AI partially canceled out by time spent checking AIhttps://www.accountingtoday.com/news/time-saved-by-ai-partially-cancelled-out-by-time-spent-checking-aiResearch Suggests Jobs May be Safer at Companies that Embrace AIhttps://www.cpapracticeadvisor.com/2026/07/07/research-suggests-jobs-may-be-safer-at-companies-that-embrace-ai/186281/AI Is No Longer Just a Tech Occupation Story: It's Spreading Across Job Titles in the US and Europehttps://www.hiringlab.org/2026/07/08/ai-is-no-longer-just-a-tech-occupation-story/The 2026 Best Accounting Firms for Technologyhttps://www.accountingtoday.com/list/the-2026-best-accounting-firms-for-technologyNeed CPE?Get CPE for listening to podcasts with Earmark: https://earmarkcpe.comSubscribe to the Earmark Podcast: https://podcast.earmarkcpe.comMeet Our Guest, Sam Leon, CPALinkedIn: https://www.linkedin.com/in/samuelaaronleon/ Website: https://www.millennialcpa.tax/Website: https://www.taxweave.ai/Get in TouchThanks for listening and the great reviews! We appreciate you! Follow and tweet @BlakeTOliver and @DavidLeary. Find us on Facebook and Instagram. If you like what you hear, please do us a favor and write a review on Apple Podcasts or Podchaser. Call us and leave a voicemail; maybe we'll play it on the show. DIAL (202) 695-1040.SponsorshipsAre you interested in sponsoring The Accounting Podcast? For details, read the prospectus.Need Accounting Conference Info? Check out our new website - accountingconferences.comLimited edition shirts, stickers, and other necessitiesTeePublic Store: http://cloudacctpod.link/merchSubscribeApple Podcasts: http://cloudacctpod.link/ApplePodcastsYouTube: https://www.youtube.com/@TheAccountingPodcastSpotify: http://cloudacctpod.link/SpotifyPodchaser: http://cloudacctpod.link/podchaserStitcher: http://cloudacctpod.link/StitcherOvercast: http://cloudacctpod.link/OvercastClassifieds REFRAME 2026 - http://accountingpodcast.promo/reframe2026Flowglad - https://cal.com/team/flowglad/flowgladWant to get the word out about your newsletter, webinar, party, Facebook group, podcast, e-book, job posting, or that fancy Excel macro you just created? Let the listeners of The Accounting Podcast know by running a classified ad. Go here to create your classi...

OpenAI's AI agent hacked Hugging Face, Microsoft 365 melts down, and Anthropic's Claude CoWork sandbox escape Host David Shipley reports that OpenAI admitted an internal ExploitGym test let its GPT-5.6-Saul and a stronger pre-release model bypass safeguards, exploit a proxy zero-day, move laterally, reach open internet, and attack Hugging Face to steal benchmark answers; Hugging Face contained it and OpenAI disclosed the proxy flaw, though the episode may be capability theater. Microsoft news includes a free ZeroPatch micropatch for the unpatched Windows LegacyHive zero-day, recurring Exchange Online mailbox quarantines after an infrastructure change caused memory issues, and a major Microsoft 365 disruption tied to an Azure US West networking/routing incident affecting SharePoint, Teams, OneDrive and many Azure services. Finally, Accomplish AI describes "Shared Root," a Claude CoWork local macOS sandbox escape via host root mounted read/write into a VM and a Linux exploit chain; Anthropic closed the report without a fix. 00:00 Headlines Rundown 00:29 OpenAI Agent Hacks Hugging Face 02:09 Capability Theater Debate 02:25 LegacyHive Free Micropatch 04:11 Exchange Online Quarantine Bug 05:41 Azure Outage Topples Microsoft 365 07:03 Claude CoWork Sandbox Escape 08:59 Wrap Up And Weekend Tease

Free guide + skill: build social carousels in claude cowork: https://clickhubspot.com/dkhc Ep. 438 How do you differentiate your marketing when everyone else is using the same AI tools?  Kipp and Kieran dive into building next-level marketing systems with AI that actually set you apart, featuring digital growth consultant and educator Grace Leung. Discover why context is king in AI marketing, how to build simple but powerful systems that scale, and the secret to making your brand voice and strategy shine through every asset. Learn more on designing reusable AI workflows, structuring context-rich file systems, and maximizing team collaboration while building a future-proof marketing stack. Mentions Loop: Outlearn. Outmarket. Outgrow https://a.co/d/08By5k2w Grace Leung https://www.youtube.com/@graceleungyl Claude Cowork https://support.claude.com/en/articles/13345190-get-started-with-claude-cowork ChatGPT https://chatgpt.com/ Codex https://openai.com/codex/ Get our guide to build your own Custom GPT: https://clickhubspot.com/customgpt Resource [Free] Steal our favorite AI Prompts featured on the show! Grab them here: https://clickhubspot.com/aip We're on Social Media! Follow us for everyday marketing wisdom straight to your feed YouTube: ​​https://www.youtube.com/channel/UCGtXqPiNV8YC0GMUzY-EUFg  Twitter: https://twitter.com/matgpod  TikTok: https://www.tiktok.com/@matgpod  Thank you for tuning into Marketing Against The Grain! Don't forget to hit subscribe and follow us on Apple Podcasts (so you never miss an episode)! https://podcasts.apple.com/us/podcast/marketing-against-the-grain/id1616700934   If you love this show, please leave us a 5-Star Review https://link.chtbl.com/h9_sjBKH and share your favorite episodes with friends. We really appreciate your support. Host Links: Kipp Bodnar, https://twitter.com/kippbodnar   Kieran Flanagan, https://twitter.com/searchbrat  ‘Marketing Against The Grain' is a HubSpot Original Podcast // Brought to you by Hubspot Media // Produced by Darren Clarke.

This week's video transcript summary is here. You can click on any bulleted section to see the actual transcript. Thanks to Granola for its software.EditorialIntelligence: Who Owns it?This week the word “AI” feels too small.AI is a technology. Intelligence is its product. And if intelligence is the product, the question is no longer just: Which model is best? Who has the cheapest tokens? Who owns the weights? Who controls the data center? Those are important questions, but they are lower in the stack.The bigger question is simpler and more political:Who owns intelligence?That sounds abstract until you make it concrete. Intelligence is becoming something companies can capture, package, serve, meter, route, improve, and sell.It can write code, answer questions, design molecules, automate offices, run agents, draft legal work, advise scientists, serve consumers, and reshape workflows. It is not merely software. It is a general-purpose capability. And all humans could benefit from more of it.General-purpose capabilities have a habit of becoming public questions. But the default answer, that public good is best delivered by government, is the wrong answer in this context.The Product Is IntelligenceWe should stop talking about AI as a feature and start talking about intelligence as the universal thing that is delivered as an input to the world.Water is an input. Electricity is an input. Literacy is an input. Connectivity is an input. Once a society depends on them, access stops being optional. Nobody needs government to build every well, power plant, school, or network. But everybody understands that a civilization cannot be organized around less than universal and reliable access to foundational inputs.Intelligence is reaching that level of importance now that we all know it is real.Government should not own it, operate it, or develop it. Quite the opposite. Companies are the right actors to build fast, compete hard, improve models, serve customers, and discover the real use cases. Self-interest is a useful framing here. Markets are good at finding demand, reducing costs, and turning invention into services people actually use.Companies are the right operators, developers, and owners. But that does not settle the real question of who owns the benefits. That is an economic question.If intelligence becomes metered infrastructure, what happens to the value it creates?The Ownership StackThis week's articles keep circling the same issue from different directions but in the nature of ‘circling' never quite nail it.Jamin Ball's “Own Your Weights” starts with the enterprise version of the question. Owning a model file is not enough. The durable asset is the loop: the data flywheel, the evaluations, the reinforcement system, the workflow learning, and the operating context that lets capability compound.Benedict Evans' “Ways to Think About Token Pricing” adds the market layer. Tokens may become essential, abundant, and cheap, like mobile data. But being essential does not guarantee that the token layer captures the value. The money may move up the stack to whoever owns the workflow, the customer, the distribution, or the application.Alex Karp's fight with the labs, reported in “Alex Karp Is Saying What Every Angry CEO Is Thinking About AI”, is the same argument in sharper enterprise language. Companies are afraid that model providers will not just sell intelligence, but learn from customer workflows and then move into the markets where those workflows create value. The “All-in” group are echoing Karp's view.And “What Is Loop Engineering, and Who Owns It?” names the new contested terrain. The loop is where intelligence meets the world. Whoever owns the loop owns the learning. Whoever owns the learning owns the compounding asset.That is why “who owns intelligence?” is not a slogan. It is the question under the model layer, the application layer, the enterprise layer, and the economic layer.Because intelligence is the product, the tools creating it are fragmented and competitive. So there is no logic in trying to discuss this at the level of a single company or set of tools and models.The Old Promise Was That Commerce Would Tame PowerThe essays this week give the historical backdrop.Deirdre McCloskey, in “What Really Caused the Industrial Revolution”, argues that modern growth came not simply from capital accumulation, but from a change in permission: ordinary people were allowed to innovate, trade, build, and be honored for it.That matters because intelligence could be another expansion of permission. It could make more people capable of building, learning, creating, coding, researching, translating, selling, and coordinating. It could lower the cost of competence.But only if access is broad.Paul Krugman's “AI in an Age of Oligarchy” warns that the same technology lands differently in different political economies. A new general-purpose technology entering a broad, open, upwardly mobile society is one thing. The same technology entering a concentrated economy, with extreme wealth and weak counterweights, is another.Tim O'Reilly's Economist essay, “Elon Musk is building a form of capitalism that Adam Smith would hate”, makes the governance point more directly. The old liberal hope was that commerce would tame arbitrary power. Markets, boards, courts, shareholders, disclosure, and competition would discipline the prince.But what if the prince uses markets to escape discipline?Henry Farrell's “political economy of billionaire derangement” pushes the same point. Founder culture, monopoly ambition, peer rivalry, weak correction mechanisms, and vast private control can amplify appetites rather than restrain them.The danger with intelligence is not that companies build it. They should. Companies build it, meter it, use public tolerance and public infrastructure to scale it, learn from everyone who uses it. All of those things are inevitable and healthy. Market forces will sort out winners from losers. The real danger is that the winners treat all of the surplus produced as purely private.Metered Intelligence Creates SurplusIf metering is not the problem, what is?The problem is pretending that metered intelligence creates value only for the metering entity. Metering water is only tolerated as a public good. If the public were blackmailed by a private water company with the threat of no water we would all rebel.Once we understand that the product of AI is intelligence we can see that every time intelligence is used, there is the immediate transaction: the user pays, the provider serves.But there is also system value. Usage creates signals. Workflows reveal patterns. Prompts, corrections, failures, preferences, integrations, edge cases, and business processes all help define where intelligence is useful and how it should improve. Intelligence breeds intelligence.Even when customer data is contractually protected, the market learns. The platform learns where demand is. The product team learns which workflows matter. The ecosystem learns which jobs are vulnerable, which tasks are automatable, and which parts of the economy can be reorganized around machine intelligence.So the surplus is not born in a vacuum.It rests on public science, public education, public data exhaust, public law, public infrastructure, public energy systems, public tolerance for data centers, and billions of human interactions. It is served by companies, but it is not made only by companies.This is why “Americans Deserve a Dividend From AI Companies' Riches” belongs at the center of this week's issue. The detail can be debated. The principle is harder to dismiss. If intelligence becomes a new foundational resource, then some part of the wealth it creates should flow back to the people whose society makes it possible. Intelligence did not suddenly appear. AI is built on the entire history of human intelligence. It benefits from it and at the same time evolves it.Not Nationalization. A Human Wealth Fund.If intelligence belongs to everybody, some conclude that government ownership of intelligence is the right outcome.Governments are not well suited to build, operate, or improve intelligence. They will move too slowly, regulate too early, politicize the wrong things, and confuse economic participation with operational control.Andrew McAfee's “Why I Didn't Sign the AI Open Letter” is useful here. His objection is not that the technology is unimportant. It is that steering too hard before we understand the shape of the change can become its own failure mode. Marc Andreessen's satire of AI regulation is less policy than temperament, but it captures a real Silicon Valley fear: that regulation can become permission, capture, and incumbency before it becomes wisdom.That fear should be taken seriously.But it does not answer the economic question. It answers only the operational one.How can the economic benefits of intelligence be distributed? The better answer is a sovereign human wealth fund.Call it a sovereign wealth fund if you must, but the phrase is too national. Intelligence will not respect borders. The leading companies are global. The models, chips, data centers, agents, platforms, and workflows will be transnational from the beginning. If the value created by intelligence is global, then the mechanism for sharing some of that value should begin with the companies global enough to capture it. The nice thing about xAI, OpenAI, and Anthropic is that they are supranational.These companies own and operate intelligence. Let them compete. Let them profit. Let them keep the incentives that make the system improve. But if intelligence is the new water, the wealth it creates cannot belong only to the companies that meter it. And they, themselves, have the power to fix it, even more than governments.Access will become a Human Right; Ownership Is the Economic DesignThis is where human rights come in. There is no right to access an AI model, yet. But there will soon be a need to change that.Not as a claim that every person is entitled to every frontier model at every moment for free. That is not serious. Capacity has costs. Models have costs. Inference has costs. Data centers have costs. Although those costs will decline over time, possibly quite quickly as self-learning models address costs.The claim is more basic: in a world where intelligence becomes a primary input into education, work, health, science, citizenship, creativity, and economic agency, baseline access to intelligence starts to look like a civic requirement.That could mean public access layers. It could mean education credits. It could mean open models. It could mean AI dividends. It could mean public-interest compute. It could mean taxes on rents. It could mean a company-initiated human wealth fund that returns some of the upside to society without handing the operating system to the state. The latter could couple wealth growth with universal distribution of ownership.The exact mechanism matters. But the distinction matters more.Government should not own intelligence. It should be universally available. And people should have a claim on the wealth intelligence creates.The Frontier Is Also PhysicalThe abstraction is not weightless.“The Fight Against AI Data Centers Is Just Beginning”, “New York becomes the first state to enact a data center moratorium”, Reuters on pollution from Musk's xAI power project, and DataGravity's “Who Captures Value in AI Infrastructure?” all say the same thing from the ground up.Intelligence uses land. It uses power. It uses water. It uses chips. It uses grid capacity. It uses neighborhoods. It uses public patience.That makes the value question unavoidable. A society can accept the buildout if the buildout is legible as shared progress. It will resist it if the costs are local, the profits are private, and the benefits feel enclosed.Who Owns the “Loop”?The week ends where it began.“Anthropic and Blackstone” are betting that implementation is the next trillion-dollar business. “Vint Cerf” is working on identity for agents on the open internet. “GPT-Red” points toward systems that improve their own robustness. “Kimi K3” adds another open frontier model to the global mix.The model race continues. The deployment race is accelerating. The governance race is behind.My view is this:The central product of this era is intelligence. Companies have figured out how to capture it, package it, serve it, and meter it. That is good. It should stay in the hands of builders who have the incentive to make it better.But intelligence is too foundational to become just another private toll booth. A significant part of it will turn out to be free to users.As intelligence becomes a general-purpose resource, then access to it becomes a human-capability question, and the surplus from it becomes an economic-justice question. Not because government should run it. Because government should not run it. The operating layer belongs with companies. The wealth question belongs with everyone. But companies are best placed to turn that into a process of distribution.The question is not whether companies should build intelligence. They should.The question is whether humanity gets a stake in the wealth created by the thing that may soon become its most important shared input.Contents* Essays* Deirdre McCloskey on What Really Caused the Industrial Revolution* AI in an Age of Oligarchy* Elon Musk is building a form of capitalism that Adam Smith would hate* Murky Mirror: Truth and Consequences* The political economy of billionaire derangement* Is there any “oligarchy” to fight?* AI* Nearly 200 Economists and Tech Leaders Warn of A.I. Threats* Why I Didn't Sign the AI Open Letter* Own Your Weights* Ways to Think About Token Pricing* Alex Karp Is Saying What Every Angry CEO Is Thinking About AI* The AI Agents Are Coming for Microsoft Office* What Is Loop Engineering, and Who Owns It?* The Fight Against AI Data Centers Is Just Beginning* 6 months to live for open models* Americans Deserve a Dividend From AI Companies' Riches* Who Gets to Define the Frontier?* GPT-Red: Unlocking Self-Improvement for Robustness* Anthropic, Blackstone bet the next trillion-dollar AI business is implementation, not just models* Vint Cerf is working on a plan to unleash AI agents on the open internet* xai-org/grok-build, now open source* The Pulse: What can we learn from Bun's rapid Rust rewrite with AI?* Orphan risks at the frontier of artificial intelligence* The Lab of the Future Should Feel Like a Data Center* Why AMI Labs' Alexandre LeBrun won't call his AI “AGI” or “superintelligence”* Kimi K3 Tech Blog: Open Frontier Intelligence* Venture Capital* Three Years In* Venture Has Rarely Looked More Bifurcated* The Best Angel Investors in the US: Who Backs the Most Unicorns, and Who's Active Now* Are Prediction Markets Doomed to Fail?* Regulation* Exclusive: The Next Frontier of the Deportation Wars: College Campuses* The Supreme Court Broke Independent Agencies. Here's a Way to Slow the Damage.* India's crackdown on a new WhatsApp feature risks setting a global precedent* Let's build a children's public internet* Computer cops* Google is better at playing the AI regulations game* Infrastructure* Who Captures Value in AI Infrastructure?* New York becomes the first state to enact a data center moratorium* Pollution from Musk's unpermitted xAI power project hits hardest in Black communities* Interview of the Week* The End of the End of Geography* Startup of the Week* Radical AI's Joseph Krause: The Scientist Building The “Waymo” Lab For New Materials* Post of the Week* Marc Andreessen on AI RegulationEssaysDeirdre McCloskey on What Really Caused the Industrial RevolutionYascha Mounk and Deirdre McCloskey | Persuasion | July 11, 2026Yascha Mounk interviews Deirdre McCloskey about her argument that the modern world's economic liftoff came less from capital accumulation than from a change in ideas. McCloskey says both left and right versions of the conventional story rely too heavily on investment: the left stresses exploitation and surplus value, while the right stresses virtuous saving by capitalists. Her objection is historical and economic. Human beings had always invested, from irrigation works and Roman roads to seed grain, and simple accumulation quickly runs into diminishing returns.McCloskey's alternative is that northwestern Europe, first Holland, then Britain and Scotland, and then the North American colonies, developed a liberal ideology that changed who was allowed to innovate and be honored for it. The conversation links that shift to the erosion of inherited hierarchy, the spread of dignity for ordinary commercial life, and a moral vocabulary in which liberalism is not merely procedural but connected to virtues and values. The point is not that machines, coal, trade, and institutions did not matter, but that they do not explain the scale and timing of modern enrichment without a cultural permission structure for innovation.The interview also turns to the contemporary defense of liberalism. Mounk frames the series around the worry that liberalism is often treated as too thin to command allegiance, while its opponents speak more directly to moral passions. McCloskey's case is that liberal societies became rich because they dignified experimentation and ordinary enterprise, and that liberals need to recover the moral language behind that claim.Read moreAI in an Age of OligarchyPaul Krugman | Paul Krugman | July 12, 2026Paul Krugman frames AI as a major technological shock arriving inside an already unequal political economy. The post says AI's economic and social effects may take years to understand, but argues that the setting matters now: America has much greater wealth concentration and political inequality than it did in the 1950s and 1960s, when progressive taxation, stronger regulation, and more active antitrust might have contained some of the destructive effects of a new technology.Krugman's opening claim is that the same technology would likely have different consequences in a more level society. In today's United States, he writes, extreme wealth is both a cause and effect of policies that favor a small elite, including low effective taxes on capital and high incomes, weak enforcement of worker protections and antitrust, and cuts to programs that benefit ordinary Americans.The article is explicitly more about oligarchy than AI. Krugman says the paid sections document the rise of the “.0002%,” the economics and politics of extreme wealth, how oligarchy will shape AI's impact, and possible policy paths. His caveat is that AI itself may still produce a pushback against oligarchy, but absent that, he expects the pre-existing concentration of wealth and power to magnify AI's downsides.Read moreElon Musk is building a form of capitalism that Adam Smith would hateAuthor: Tim O'Reilly Published: July 12, 2026Tim O'Reilly argues that Elon Musk is using the legal forms of shareholder capitalism to escape the restraints that shareholder capitalism was supposed to impose. The article begins with SpaceX's public-market structure: ordinary public investors get little meaningful governance power, Musk keeps roughly 85 percent of the votes through super-voting shares, buyers waive jury trials and class actions, the company qualifies as controlled, and removal of Musk depends on the share class he controls. In O'Reilly's framing, that is not ordinary founder control; it is a design for being answerable to no one, possibly beyond Musk's own lifetime.The killer detail is the article's turn through Albert Hirschman, Montesquieu, James Steuart, Adam Smith, and Keynes. Older defenses of commerce held that markets would tame princely passions because the self-interest of merchants was safer than arbitrary rule. O'Reilly says Musk reverses that hope. The market discipline that was supposed to cage the prince has become the lever by which the prince raises capital, removes feedback loops, and carries private power into politics, government, Mars, robots, AI, or whatever ambition comes next.The pull is the link to AI governance. O'Reilly says corporations are already a kind of artificial intelligence: narrow-input systems that act at a scale no individual human can match. Their partial controls include independent boards, shareholder votes, courts, disclosure, regulators, public pressure, and activism. If the leaders building frontier AI strip those alignment mechanisms out of their own companies, the governance of the company becomes a preview of the governance of the machine.Read more: The EconomistMurky Mirror: Truth and ConsequencesAuthor: Esther Dyson Published: July 14, 2026Esther Dyson argues that today's institutional crisis is better viewed through the 14th century than through recent political history. Using Barbara Tuchman's A Distant Mirror as her frame, she compares a world of famine, plague, church schism, feudal predation, and purposeless war with a present in which institutions again feel brittle, incentives are badly aligned, and power is shifting into forms that are hard to govern.The killer detail is the historical analogy between land, corporations, and AI. Dyson moves from nobles who controlled serfs and territory, to the East India Company as a quasi-sovereign business, to today's AI systems and data centers as a possible new sector that crosses and weakens both nation-states and companies. The question is whether AI becomes a new kind of private land, owned by a new nobility, or an open prairie that many people can cultivate.The pull is human attention. Dyson says the central question is not what AI will do to people, but how people will react to it: whether they can value love, kindness, embodied attention, and artisanal human presence in a world of seductive artificial offerings.Read more: SourceThe political economy of billionaire derangementAuthor: Henry Farrell Published: July 15, 2026Henry Farrell argues that the visible political radicalization of some Silicon Valley billionaires is not a random personality quirk, but a product of the political economy that made them. Starting from Tyler Cowen's dismissal of “billionaire derangement syndrome” and Tim O'Reilly's warning that Elon Musk is using shareholder capitalism to escape shareholder restraint, Farrell flips the phrase: the question is why billionaires themselves can become deranged.The killer detail is Farrell's use of Peter Thiel as both theorist and example. Thiel's Stanford lectures described startups as monarchies and founders as figures vested with unusual power, while Silicon Valley culture rewarded eccentricity, monopoly ambition, and founder exceptionalism. Farrell says those ideas combined with dense founder-investor networks, peer rivalry, and weak correction mechanisms to amplify rather than discipline princely appetites.The pull is the ideological problem for classical liberals who once saw tech wealth as an ally of markets and freedom. Farrell says commerce did not tame the passions; in parts of Silicon Valley, the passions have begun to devour markets, institutions, and the liberal story that justified them.Read more: SourceIs there any “oligarchy” to fight?Matthew Yglesias | Slow Boring | July 16, 2026Matthew Yglesias argues that “oligarchy” is a rhetorically powerful but analytically loose way to describe American politics. The post begins from Bernie Sanders' “Fighting Oligarchy” tour, Amy Klobuchar's warning about a MAGA “broligarchy,” and the long afterlife of the Martin Gilens and Benjamin Page paper that was widely summarized as showing that only the rich matter in policy outcomes. Yglesias says the evidence supports a weaker claim: affluent people and business leaders have unusual access and influence, but that is not the same as rule by a small cabal.His main distinction is between inequality and oligarchy. The Gilens-Page measure treated the top 10 percent of households as “the wealthy,” and later critics found that rich and middle-class preferences usually align; in the cases where they differ, the rich win about 53 percent of the time. Yglesias also says business executives get special access partly because their decisions are materially important to communities, jobs, investment, and local tax bases, not only because of campaign donations.The post preserves Jerusalem Demsas' counterpoint from their podcast discussion: privileged donor and business access can still violate democratic equality even if the oligarchy label overstates the structure of power. Yglesias' narrower claim is that Democrats should be precise about what problem they are trying to solve, because donor influence can also push the party left on climate and cultural issues in ways that alienate many voters.Read more: Slow BoringAINearly 200 Economists and Tech Leaders Warn of A.I. ThreatsAuthor: Ben Casselman Published: July 13, 2026Ben Casselman reports on “We Must Act Now,” a statement warning that artificial intelligence could transform the economy faster than any previous technology and that policymakers need to move faster to understand and respond. The statement says AI may become radically more powerful over the next 10 years, bringing risks such as large-scale job displacement as well as opportunities such as higher living standards. Nearly 200 people signed, including 15 Nobel laureates, the chief economists of OpenAI and Anthropic, Anthropic co-founder Jack Clark, former Google CEO Eric Schmidt, and venture capitalist Vinod Khosla.The killer detail is who joined the warning. Casselman notes that the signatories include economists who have historically been skeptical of Silicon Valley's most dramatic AI job-loss forecasts, including Daron Acemoglu and Simon Johnson, the MIT professors who won the 2024 Nobel in economics. Erik Brynjolfsson, who helped organize the statement, says there has been a notable change in the profession and that economists and policymakers are not ready for the “tsunami” he sees coming.The pull is the measurement problem. The statement does not offer a specific policy menu, but calls for economists, policymakers, and industry leaders to understand the economics of transformative AI and steer it toward complementing humans. Brynjolfsson says one high priority is better data on AI's spread and impact, because current measures tell conflicting stories about job losses and which workers are most exposed.Read more: The New York TimesWhy I Didn't Sign the AI Open LetterAuthor: Andrew McAfee Published: July 13, 2026Andrew McAfee explains why he did not sign “We Must Act Now,” the AI economy statement organized in part by his longtime collaborator Erik Brynjolfsson. McAfee agrees with the letter's starting point that AI is likely to become radically more powerful over the next decade and that it is a general-purpose technology. His objection is not to urgency or to studying AI's economic effects, but to the framing of risk, displacement, and institutional steering as the first move.The killer detail is McAfee's line edit. He says the original letter comes close, then “bounces off the crossbar” by calling for incentives, guardrails, and institutions to steer AI before we know enough about its actual impacts. He points to mixed current evidence: labor-market canaries, but also rising software job postings, low unemployment for younger workers, rising real median income, and claims that AI-adopting companies are adding workers faster than low-adopting peers. His worry is that the letter leans toward upstream governance and dirigisme when the evidence may call for capability building instead.The pull is his replacement statement. McAfee keeps the three-paragraph structure but changes the emphasis: AI is likely to become radically more powerful; like earlier world-changing technologies it will raise living standards while also bringing harms and shocks; and economists, policymakers, and technology leaders should build the capabilities to respond quickly and effectively. It is a concise version of the permissionless-innovation case inside the AI policy debate.Read more: The Geek WayOwn Your WeightsAuthor: Jamin Ball Published: July 10, 2026Jamin Ball argues that the enterprise AI debate about whether companies should “own their weights” or rent models from frontier labs is asking too narrow a question. A model weight file gives a company control over a point-in-time artifact, but not durable control over the capability stack. In his framing, the weight file is a melting ice cube: it does not get worse in absolute terms, but it falls behind as frontier systems improve and enterprise needs change.The killer detail is what Ball says companies really need to own: the data flywheel, reinforcement learning infrastructure, and evaluation harness that produce and improve the model. Simply deploying an open-weights model and declaring sovereignty leaves the enterprise with yesterday's capability and no way to compound workflow-specific learning.The pull is that enterprise AI control may be less about model ownership than operating ownership. The defensible layer is the system that turns company data, edge cases, business definitions, and evaluations into continuously improving performance.Read more: Clouded JudgementWays to Think About Token PricingAuthor: Benedict Evans Published: July 9, 2026Benedict Evans argues that today's AI token prices are a temporary signal from a supply-constrained market, not a reliable guide to long-term value capture. The open question is whether foundation models keep durable pricing power or become commodity infrastructure as data-center capacity, inference efficiency, and model competition all shift. His current read is that the visible market dynamics point toward commoditization unless something materially changes.The killer detail is the mobile data analogy. Evans says cellular networks became a trillion-dollar industry with hundreds of billions in capex after data usage exploded, but carrier stocks went nowhere because value moved up the stack. Tokens may behave similarly: an opaque unit tied to marginal cost, sold through bundles, essential to everything, yet not necessarily where profits accrue.The pull is uncertainty, not prediction. Evans lists paths to model dominance, including network effects, less competition, regulation, export controls, or a lab pulling ahead on execution, but says each requires a new fact not yet visible. Without that change, the model layer looks more like infrastructure beneath the products that capture value.Read more: SourceAlex Karp Is Saying What Every Angry CEO Is Thinking About AIAuthor: Tim Higgins Published: July 11, 2026Tim Higgins reports that Palantir CEO Alex Karp has turned corporate frustration with AI labs into a public argument about enterprise control. Palantir released a white paper, “Institutional Sovereignty in the Age of AI,” laying out steps companies and governments can take to protect themselves from OpenAI, Anthropic, and other foundation-model providers. The article links that paper to Karp's CNBC appearance, where he said “something has gone completely wrong” in the relationship between AI labs and customers and argued that enterprises are paying for tokens that create little value.The killer detail is the value-capture question. Higgins writes that Karp's critique has resonated because AI labs may gain power and insight from customer data, workflows, and decision-making, even when enterprise policies say customer data are not used for training. David Sacks amplified the concern by arguing that Anthropic is moving from the model layer into vertical applications such as science, security, legal, and coding, raising the fear that model providers will watch where value is being created and then move into those markets directly.The pull is that Karp is not alone, even if his style is unusually combative. Higgins notes that Satya Nadella has also warned that companies need to retain the learnings created when they use AI models, while Mark Zuckerberg has framed Meta's new model release partly around lower-cost frontier intelligence. The article presents Karp's campaign as one sign that established technology companies and large enterprises are trying to define where they fit when AI labs become central infrastructure, application competitors, and potential IPO giants at the same time.Read more: The Wall Street JournalThe AI Agents Are Coming for Microsoft OfficeAlex Wilhelm | Cautious Optimism | July 11, 2026Alex Wilhelm argues that one of the week's quieter AI questions is whether the productivity market that Microsoft successfully moved into subscription software is now being attacked by agentic tools. The piece begins with the infrastructure backdrop: SK Hynix raised $26.5 billion in a U.S. listing while building U.S. HBM and advanced-packaging capacity, and memory, chip, and foundry companies are now priced for sustained AI demand.Wilhelm then says the AI conversation has shifted quickly from raw capability to cost per task. He cites new model releases and vendor language emphasizing cheaper agentic and coding models, faster performance, and lower dollars per task. That matters because lower costs make it more plausible for AI systems to take on routine knowledge work at scale rather than remain a premium coding assistant market.The core of the article is Microsoft Office. Wilhelm notes that Microsoft turned Office from a one-time purchase into Microsoft 365, a large recurring revenue business with tens of millions of subscribers and a major productivity segment. Now, he says, late-stage unicorns and AI labs are pushing into the same territory: Anthropic's Cowork was reportedly used mostly outside software development, OpenAI merged ChatGPT and Codex into a tool for creating sheets, slides, docs, web apps, and long-running work, and other companies are building agentic coworkers that connect business data to documents, workflows, schedules, alerts, and apps.The article's caveat is that Microsoft has survived major platform shifts before. The argument is not that Office disappears quickly, but that the definition of office software is broadening from documents and spreadsheets into AI systems that can create, monitor, and act across workplace data.Read moreWhat Is Loop Engineering, and Who Owns It?Author: Nilesh Barla Published: July 11, 2026Nilesh Barla argues that “loop engineering” is becoming a distinct discipline because production AI agents now fail less at single prompts than at runtime: when to stop, what state to preserve, and how to recover after a bad step. Prompt engineering shapes one model call, and context engineering shapes what the model sees, but loop engineering shapes what a sequence of calls actually does.The killer detail is the three-primitives frame. Barla says a real agent loop needs halt conditions, state carryover, and recovery paths, then maps teams across five maturity levels. At the lowest level, an agent is just a model call in a for-loop with a step cap and raw history; by the higher levels, the system has structured state, explicit planning, replay, evaluation, and self-repair.The pull is organizational. If agents are becoming production systems rather than demos, someone has to own the runtime itself. The loop engineer is the role Barla gives to the person responsible for making long-running agent work dependable.Read more: Adaline LabsThe Fight Against AI Data Centers Is Just BeginningEmma Roth | The Verge | July 12, 2026Emma Roth argues that community resistance to data centers has moved from an early warning sign into a national political fight as AI facilities grow larger, more power-hungry, and more visible to nearby residents. The article starts with Apple's failed 2015 plan for a $1 billion data center in Athenry, Ireland, where a small group of residents challenged the project over noise, light pollution, flooding, traffic, and wildlife effects until Apple abandoned it in 2018.The current data-center buildout is presented as much larger and more contentious. Roth writes that residents now cite rising energy costs, water quality, noise, light pollution, and greenhouse gas emissions, while the U.S. Energy Information Administration expects commercial energy demand to surpass residential demand this year because of AI data centers and Goldman Sachs expects data-center power demand to double by 2027.The central evidence comes from Data Center Watch, which says protesters blocked or delayed at least 75 U.S. projects worth $130 billion from January to March, with active opposition groups more than doubling from 396 at the end of 2025 to 833 by the end of the first quarter of 2026. Roth also cites QTS abandoning a $12 billion Wisconsin campus, Delaware City regulators blocking a 580-acre project under the Coastal Zone Act, opposition stopping a QTS project in Prince William County, and pressure that pushed Kevin O'Leary to downsize the proposed 40,000-acre Project Stratos in Utah.The policy section describes a split between federal acceleration and local resistance. President Trump has treated data centers as part of the AI race with China and fast-tracked construction, while some Republican candidates are distancing themselves from that position ahead of midterms. Sanders and Ocasio-Cortez have proposed a moratorium until price and environmental protections exist, bipartisan lawmakers are backing ratepayer-protection measures, and states including Florida, Idaho, and Washington have passed rules on cost shifting, water use, and tax breaks. Roth's caveat is that the policy patchwork is still incomplete, leaving many communities to fight project by project.Read more6 months to live for open modelsAuthor: Nathan Lambert Published: July 12, 2026Nathan Lambert argues that open-weight AI models are facing their most serious policy test so far because U.S. officials are beginning to discuss concrete controls rather than abstract safety concerns. He says reported White House conversations about a new executive order may initially target Chinese-origin models and government use, but could create a broader review habit for frontier open models. His forecast is that a model above the capability range of GPT-5.5, Claude Opus 4.8, or GLM-5.2 could trigger a ban or indefinite delay within six months.The post separates two policy fights that are becoming intertwined: distillation and frontier capability. Lambert says the distillation campaign against Chinese models has become a form of regulatory capture because Anthropic and other closed-model companies would gain economically if Chinese open models were banned. He does not dismiss IP protection, but argues that if a closed model's capabilities are dangerous enough to justify restricting open models, the lab also has to explain why those capabilities are exposed through a queryable API. He cites unauthorized access to Anthropic's Mythos private beta as evidence that APIs are not automatically secure.The broader claim is that a unilateral U.S. ban would hurt positive actors more than bad actors if comparable open models remain available elsewhere. Lambert says the only durable ceiling would require global agreement, which does not exist, and that open models can improve safety by allowing broad inspection, adaptation, and understanding. His proposed near-term off-ramps are a strong U.S. open model release from companies such as Microsoft, Meta, or Reflection, and a broader coalition of open-source beneficiaries lobbying for safe rollout rather than prohibition.Read more: SourceAmericans Deserve a Dividend From AI Companies' RichesAuthor: Scott Stanford Published: July 14, 2026Scott Stanford argues that proposals to give the government a stake in AI companies miss the point unless ordinary citizens directly receive and control the upside. Sam Altman has discussed giving up equity in OpenAI, Washington already owns a stake in Intel, Nvidia is sharing China chip revenue, and Bernie Sanders wants large AI labs to contribute half their stock to a sovereign wealth fund. Stanford says those ideas all park value with the state, not with people.The killer detail is New Carlisle, Indiana, where AWS's Project Rainier is turning cornfields into one of the world's largest AI superclusters. The project is planned to run up to a million chips, draw more than two gigawatts of power, and represents an investment that has grown from $11 billion to $13.8 billion. Stanford uses that local transformation to argue that AI's public bargain should be visible at the household level.The pull is design. A citizen AI dividend would have to specify who earns a stake, how they hold it, and when they see cash. Without that mechanism, the AI wealth debate remains a fight over government balance sheets rather than public ownership.Read more: SourceWho Gets to Define the Frontier?Author: Mark Daley Published: July 14, 2026Mark Daley argues that Demis Hassabis is right to call for a serious institution to verify frontier AI systems, but that the power to test models is also the power to govern them. Hassabis's proposed Frontier AI Standards Body would get privileged pre-release access to advanced models, testing compute, held-out evaluations, support from national labs and security agencies, third-party auditors, and eventually authority to block models from the American market or coordinate a slowdown.The killer detail is Daley's constitutional objection. He says the proposal sometimes looks like a scientific lab, a standards body, an industry regulator, a licensing authority, and an emergency security council at once. Combining those roles because each requires technical expertise would be like putting the central bank, auditor-general, and Supreme Court in one building and calling it efficient.The pull is standard-setting. Daley's concern is not that verification is unnecessary, but that whoever writes the tests, decides what passes, adjudicates disputes, and grants market access may end up defining the frontier itself.Read more: SourceGPT-Red: Unlocking Self-Improvement for RobustnessOpenAI | OpenAI | July 15, 2026OpenAI describes GPT-Red as an internal automated red-teaming model trained to find prompt-injection vulnerabilities at a scale human red teams cannot match. The post says AI systems increasingly encounter third-party data through browsers, connected apps, local files, and tools, creating opportunities for malicious instructions hidden in emails, webpages, tool responses, or code repositories. Human red-teaming remains part of OpenAI's safety process, but the company says it is time-intensive and cannot generate enough diverse adversarial examples for model training.The system is trained through self-play reinforcement learning, with GPT-Red rewarded for eliciting valid failures and defender models rewarded for resisting attacks while still completing their tasks. OpenAI says the training environments specify threat models across settings such as local files, webpage banners, email bodies, and tool outputs. The model is kept separate from deployed production models because it is intentionally trained with malicious capabilities.OpenAI reports that GPT-Red generalized beyond its training set, including an internal replication of the indirect prompt-injection arena from Dziemian et al. (2025), where it found successful attacks in 84% of scenarios compared with 13% for human red-teamers. The post also says GPT-Red transferred attacks from simulation to a live autonomous vending-machine agent, causing price changes and order cancellations, and outperformed a prompted GPT-5.5 baseline against a Codex CLI agent on held-out data-exfiltration tasks.The article's main robustness claim is that OpenAI has used GPT-Red and predecessor models in training since GPT-5.3, with later GPT releases becoming more resistant to prompt injections. It says GPT-5.6 Sol has six times fewer failures on OpenAI's hardest direct prompt-injection benchmark than the best production model from four months earlier, that a “Fake Chain-of-Thought” attack class fell from more than 95% success against GPT-5.1 to below 10% against GPT-5.6 Sol, and that GPT-5.6 Sol fails on only 0.05% of GPT-Red's direct prompt injections. OpenAI says general capabilities and targeted over-refusal evaluations were not harmed, and says a preprint with more details will follow.Read moreAnthropic, Blackstone bet the next trillion-dollar AI business is implementation, not just modelsRebecca Bellan | TechCrunch | July 15, 2026Rebecca Bellan reports that Ode with Anthropic is the $1.5 billion AI implementation company launched by Anthropic with Blackstone, Hellman & Friedman, Goldman Sachs, and other backers. The article says the venture reflects a growing belief among frontier AI labs that enterprise adoption requires more than better models: customers need engineers who can embed inside businesses and turn AI into working systems.Ode was originally conceived by Blackstone after it used both large consulting firms and smaller AI services boutiques across its portfolio companies. TechCrunch reports that Fractional AI, an AI engineering services startup, stood out and was acquired by the joint venture shortly after the venture was announced. Fractional now forms the foundation of Ode, which has 100 engineers and works closely with Anthropic's applied AI team to identify where the technology can affect specific businesses.Ode CEO Chris Taylor tells TechCrunch that the company could someday become a trillion-dollar business if it scales without losing quality. He says an ideal customer is one whose CEO treats the AI project as a top one or two priority, whether it is a major product feature or the reworking of a core business process. Ode will operate under a “Claude-first” principle, using Anthropic technology whenever possible, but the article says it can use rival AI products when needed.The article's central implementation argument comes from Ode chief technologist Eddie Siegel, who says model selection matters but is not where most of the engineering effort goes. He compares it to the choice of programming language in software: one ingredient in a system that still has to be engineered. Bellan writes that Ode's challenge is hiring and training enough elite generalist engineers, many of them former founders, while competing with OpenAI's The Deployment Company and consulting giants that have built their own forward-deployed engineering teams.Read moreVint Cerf is working on a plan to unleash AI agents on the open internetTim Fernholz | TechCrunch | July 15, 2026Tim Fernholz reports that Vint Cerf, after leaving Google, is advising Innovation Labs on an open architecture for identifying AI agents online. Innovation Labs is a subsidiary of Identity Digital, a DNS registry company, and its proposal is to use domain-name infrastructure as part of a system for agent identity, accountability, and auditability. The premise is that agents will need a way to identify themselves if they move beyond proprietary systems and begin interacting across the open internet.The concrete proposal is DNSid, a registry that links an AI agent to an existing internet domain and uses cryptographic proofs to log its registration over time. Innovation Labs says it is trialing the standard with unnamed hyperscalers and identity companies. Cerf frames the problem around authority and accountability: what authority an agent has, where that authority came from, who is accountable for the agent's behavior, how its identity is established, and why anyone should trust it.The article's caveat is that standards are still emerging and agents are more active than static domains. Cerf says the period may be both fascinating and exasperating because the functionality is powerful and interoperability is unresolved. He compares the adoption problem to TCP/IP: competing systems may not work together until users push for functional interoperation. He also says an agentic economy is not inevitable, but that people will try to build it because delegating work to agents will be easier.Read more: TechCrunchxai-org/grok-build, now open sourceAuthor: Simon Willison Published: July 15, 2026Simon Willison argues that xAI's decision to open-source Grok Build is best understood as a trust repair move after a severe privacy failure. The CLI had triggered backlash when users realized that running it in a directory could upload the entire directory to xAI's Google Cloud buckets, including one user's reported SSH keys, password manager database, documents, photos, and videos. xAI disabled the feature, said previously retained coding data would be deleted, and released the code under Apache 2.0.The killer detail is what the codebase reveals. Willison counts 844,530 lines of Rust, only about 3% of which appears vendored, and finds remnants of the upload system still present but disabled: gcs.rs contains Google Cloud upload code, while upload_session_state() now returns a hard-coded session_state_upload_unavailable error. He also notes copied or ported tool implementations from Codex and OpenCode, prompt files, and a terminal Mermaid renderer.The pull is that terminal coding agents are becoming large, intricate software systems in their own right. The privacy failure mattered because these tools operate inside the directories where developers keep their most sensitive work; the open-source release matters because trust now depends on inspecting what an agent can see, send, and do.Read more: SourceThe Pulse: What can we learn from Bun's rapid Rust rewrite with AI?Author: Gergely Orosz and Ivan Klaric Published: July 16, 2026Gergely Orosz and Ivan Klaric argue that Bun's AI-assisted rewrite from Zig to Rust is a practical sign of how software engineering changes when models can take on large, bounded migrations with clear feedback loops. The piece does not treat the rewrite as magic: Jarred Sumner first spent hours turning design judgment into a detailed porting guide, then used adversarial review, parallel agents, compiler errors, and tests to force the work toward correctness.The killer detail is the scale. Bun had 535,496 lines of Zig, 1,448 files, and 22 million monthly downloads, making a conventional rewrite a year-long freeze the team could not justify. Using Fable, Sumner split the work across 64 agents, produced about 6,500 commits, and got the migration done in 11 days at an estimated API cost of $165,000.The pull is economic, not theatrical. If a one- or two-year migration can become an 11-day project, AI coding is not just faster autocomplete; it changes which technical debts are worth paying down.Read more: SourceOrphan risks at the frontier of artificial intelligenceAuthor: Andrew Maynard Published: July 16, 2026Andrew Maynard argues that frontier AI safety frameworks are creating “orphan risks”: harms that companies can see, but do not formally own because they are hard to quantify, do not fit catastrophic-risk thresholds, or fall outside audit-friendly compliance machinery. His target is not existing frontier safety work, but the narrowing effect that happens when private companies decide which risks count as governable.The killer detail is Maynard's contrast between measurable model dangers and threats to value. He points to Meta's three-day Galactica collapse, OpenAI's 2023 board crisis, safety-team departures, and wellbeing litigation as examples of risks that damaged trust, culture, legitimacy, or users without fitting cleanly into conventional model-risk categories. The proposed fix is an orphan-risk register: a public record of risks a company considered and chose not to manage, with reasons.The pull is accountability. Frontier developers' internal scoping choices have become a de facto layer of public governance, so the question is no longer only which risks they manage, but which risks they quietly leave outside the frame.Read more: SourceThe Lab of the Future Should Feel Like a Data CenterLatent.Space with Andy Beam and Rafa Gomez-Bombarelli | Latent.Space | July 16, 2026Latent.Space interviews Lila Sciences CTO Andy Beam and chief science officer for physical sciences Rafa Gomez-Bombarelli about the company's attempt to build an AI-run science factory. The post describes Lila's thesis as treating the lab itself as an “infinite token generator”: if internet data drove the first era of AI scaling, experimentally verified scientific data may be the next scarce training source. Lila is trying to produce that data with robotics, lab instruments, orchestration software, and AI models wired into the wet lab.The central analogy is the lab as data center. Instruments are nodes on a graph, a magnetically levitating transport layer moves materials between them, and experiment scheduling looks like a compute queue. Beam says Lila is not simply an automation company, because the point is not just throughput; it is flexibility, generalization, and experiment capture. The post says Lila has built more than 10 trillion experimentally validated “scientific reasoning tokens,” not internet text or biological sequences.The interview ranges across biology, chemistry, drug discovery, materials science, and the limits of automation. It notes that Lila rebuilt one gas-sorption measurement to run roughly 2,500 times faster, claims its general models can transfer priors from small-molecule chemistry to metal-organic frameworks for carbon capture, and describes model-suggested platinum-group-free electrocatalysts that moved from looking boring or wrong to becoming strong performers. The caveats are physical: experiments have runtimes, biology cannot always be accelerated, chains of thought can be unreliable narrators, and reward hacking becomes more dangerous when a model controls a real lab.Read more: Latent.SpaceWhy AMI Labs' Alexandre LeBrun won't call his AI “AGI” or “superintelligence”Kate Park | TechCrunch | July 16, 2026Kate Park interviews AMI Labs CEO Alexandre LeBrun about why Yann LeCun's world-model startup avoids the language of “AGI” and “superintelligence.” LeBrun says the terms are not useful because they lack stable definitions: “We never used the word AGI. And I just noticed that nobody is using it anymore; they switched to superintelligence.” His argument is that the practical frontier is not a label, but whether AI systems can understand and predict real-world states.The article explains the world-model thesis by contrasting language prediction with physical-state prediction. A large language model predicts the next word; a world model predicts the next state, such as what happens when a glass tips over. LeBrun says LLMs remain complementary and efficient for language, but the physical world is where current AI is weak. Robotics is the clearest case: hardware has advanced quickly, but robots are still brittle outside controlled routines because they lack context and situational understanding.AMI is still pre-product, but TechCrunch reports that LeBrun was in Seoul looking for industrial partners, researchers, and global companies. He says world models cannot be built entirely inside a lab because they need access to real environments. That is why South Korea appeals to AMI: robotics, semiconductors, manufacturing, and fast adoption create the kind of hardware-heavy context that software-only AI has barely touched.Read more: TechCrunchKimi K3 Tech Blog: Open Frontier IntelligenceKimi | Kimi | July 16, 2026Kimi introduces Kimi K3 as an open 3T-class frontier model aimed at coding, knowledge work, reasoning, multimodality, and long-context agentic use. The source describes the model as a 2.8T-parameter system built on Kimi Delta Attention and Attention Residuals, with native multimodality and a 1M-token context window. It says Moonshot AI plans to release model weights by July 27.The post presents K3 through benchmark and use-case sections rather than as a general product announcement. It reports results across coding, productivity, agentic, and multimodal evaluations, including DeepSWE, Terminal-Bench 2.1, Program Bench, SWE Marathon, FrontierSWE, PostTrain Bench, OfficeQA Pro, SpreadsheetBench 2, MCP Atlas, AutomationBench, BrowseComp, GDPval-AA v2, AA-Briefcase, MMMU-Pro, MathVision, BabyVision, OmniDocBench, and PerceptionBench. The source says all reported K3 results use maximum reasoning effort with temperature and top-p set to 1.0, and that different benchmark comparisons use KimiCode, Claude Code, or Codex harnesses depending on the test.Kimi's caveats are unusually concrete. The limitations section says K3 was trained in preserved thinking-history mode, so quality may become unstable if an agent harness does not pass historical thinking content correctly or if an ongoing session switches to K3 midstream. It also says K3's emphasis on long-horizon tasks can make it excessively proactive when it encounters minor issues or ambiguous intent, and recommends imposing explicit behavioral constraints for applications that require strict boundaries. The post adds that K3 remains behind Claude Fable 5 and GPT 5.6 Sol in user experience despite being competitive overall.Read moreVenture CapitalThree Years InAuthor: Tomasz Tunguz Published: July 10, 2026Tomasz Tunguz marks Theory Ventures' third anniversary by arguing that AI's central market effect is time compression. In his telling, model release cycles, company revenue milestones, enterprise adoption, and venture categories have all accelerated. Seed, Series A, and Series B still exist as financing labels, but they no longer cleanly describe company maturity when some seed rounds are larger than IPOs and the best AI companies can mature much earlier than prior software companies.The killer detail is the shift from models to inference. Tunguz argues that inference has become the dominant AI market because workloads and buyer preferences are fragmenting: video, batch, local, agentic, and real-time tasks each create different infrastructure needs. He compares this to databases splitting into OLTP, OLAP, vector, and streaming categories, with AI pushing the same specialization into inference infrastructure.The pull is that Theory sees the AI-native venture firm as part of the same pattern. The firm says it has analyzed twice as many investment opportunities with three investors working alongside a nine-person intelligence organization, using agents and research systems to map markets, source companies, and support diligence. The piece is both a market map and a statement about how venture itself is being rebuilt by the technology it funds.Read more: LinkedInVenture Has Rarely Looked More BifurcatedAuthor: Beezer Clarkson Published: July 14, 2026Beezer Clarkson points to PitchBook's Q2 report as evidence that the U.S. venture market has split into two very different realities. AI now accounts for more than 60 percent of all U.S. venture deal value, meaning the headline market can look active and well-funded even while much of the non-AI market is dealing with a much colder liquidity and fundraising environment.The thread uses that split as the setup for Clarkson's latest Origins episode with Alec Litowitz, founder of Magnetar and QStar Capital and one of Citadel's original founding partners. Clarkson says markets like this are periods of genuine uncertainty, not merely ordinary risk, which is why Litowitz's Adaptability Quotient framework is relevant.The embedded clip makes the liquidity point concrete. Litowitz says DPI is “the resolution of uncertainty” because it converts an uncertain investment into actual cash returned to LPs. In his framing, a realized dollar is a real mark, while TVPI remains uncertain until it is realized.The killer detail is the distinction between pricing risk and resolving uncertainty. Litowitz's perspective matters because QStar is a SpaceX investor and Clarkson says the conversation happened just before one of venture's most consequential IPOs. The episode's stated questions are why venture remains a way to gain exposure to innovation, how AI is changing what is investable, why liquidity is ultimately a function of time, and why uncertainty requires a different decision framework from risk.Read more: XThe Best Angel Investors in the US: Who Backs the Most Unicorns, and Who's Active NowAuthor: Ilya Strebulaev Published: July 10, 2026Ilya Strebulaev ranks angels, angel groups, accelerators, and incubators by lifetime U.S. unicorn investments, counting checks written before a company reached unicorn status. The top of the combined list is dominated by organizations: Y Combinator leads with 113 unicorn investments, followed by Plug and Play at 52 and 500 Global at 41. Sand Hill Angels is the highest-ranked angel group at 31.The killer detail is how quickly the list changes below the biggest accelerators. Strebulaev says 271 of the 304 investors in the Top 200 are individuals, or 89%. In the top 100, individuals are 91%. That makes the market underneath the large accelerator counts look much more personal: mostly operators and individual angels writing early checks from their own networks.The pull is the ranking's own caveat. Strebulaev writes that every lifetime leaderboard has a blind spot because many of the unicorns behind those totals were founded a decade or more ago, and some angels have since moved into formal funds, slowed down, or stopped investing. His post therefore separates lifetime performance from recent cohorts, including companies founded in 2015 or later and 2020 or later. For founders or allocators making current decisions, that distinction matters: a career record and a current record are not the same measure.Read more: Ilya StrebulaevAre Prediction Markets Doomed to Fail?Author: Contrary Published: July 16, 2026Contrary argues that prediction markets' current boom depends on whether platforms can prove they are more than regulated gambling with exchange-style branding. Kalshi and Polymarket have reached mass cultural, investor, and regulatory attention, but the article says the underlying idea is old: academic markets, corporate forecasting tools, Intrade, PredictIt, and other predecessors all struggled with the same linked problems of liquidity, legality, and user appeal.The killer detail is the comparison with sportsbooks. Prediction markets present themselves as peer-to-peer, transparent, and non-house-based, but sports contracts reportedly account for more than 90 percent of Kalshi trading, and the article says the platforms keep a much thinner slice of volume than sportsbooks. A market can therefore show sports-betting-scale handle while generating far less revenue.The pull is that the product's hardest problem may be distribution of wins. If a small group of sharp traders captures most profits while casual users lose interest, prediction markets may become valuable data feeds and professional tools before they become durable consumer networks.Read more: SourceRegulationExclusive: The Next Frontier of the Deportation Wars: College CampusesAuthor: Adrian Carrasquillo Published: July 11, 2026Adrian Carrasquillo reports that college campuses are becoming a new front in the fight over immigration enforcement because automatic license plate readers can turn ordinary campus security infrastructure into searchable location data. His thesis is that Flock Safety's camera network, even without direct ICE or DHS contracts, can feed deportation enforcement through local police partnerships and data-sharing practices.The killer detail is the campaign target. The Emergency Campaign to Support Higher Education, working with Schools Drop ICE, is focusing on 75 colleges and universities publicly identified as having Flock contracts. Flock says it has no ICE or DHS contracts, but activists argue the risk comes through local agencies that coordinate with federal authorities and run searches on their behalf.The pull is broader than immigration. Carrasquillo notes that license plate readers have already been abused by officers for stalking, and that Flock's AI search features can identify more than plates, including bumper stickers. A campus safety tool can become a political surveillance system when the data layer is searchable.Read more: The BulwarkThe Supreme Court Broke Independent Agencies. Here's a Way to Slow the Damage.Author: Todd Phillips Published: July 12, 2026Todd Phillips argues that the Supreme Court's decision in Trump v. Slaughter damaged independent agencies by ending for-cause removal protections, but did not leave Congress powerless. The ruling weakens the old model in which commissioners at bodies such as the FTC, NLRB, CPSC, SEC, and CFTC could be insulated from dismissal over policy disagreements. Phillips says the next fight is whether presidents can turn nominally bipartisan commissions into one-party instruments.The killer detail is the procedural fix: quorum rules. Phillips proposes that Congress require bipartisan slates of commissioners to be seated before independent agencies can act. A president could still fire commissioners, as the Court now permits, but if those firings broke quorum, the agency would be unable to proceed until replacements were confirmed. The guardrail would

This week's video transcript summary is here. You can click on any bulleted section to see the actual transcript. Thanks to Granola for its software.There was an issue with this only going to paid subscribers, so sending it again. Apologies to those who get it twice. I appreciate being paid so feel free to upgrade if you enjoy TWTW.EditorialIntelligence: Who Owns it?This week the word “AI” feels too small.AI is a technology. Intelligence is its product. And if intelligence is the product, the question is no longer just: Which model is best? Who has the cheapest tokens? Who owns the weights? Who controls the data center? Those are important questions, but they are lower in the stack.The bigger question is simpler and more political:Who owns intelligence?That sounds abstract until you make it concrete. Intelligence is becoming something companies can capture, package, serve, meter, route, improve, and sell.It can write code, answer questions, design molecules, automate offices, run agents, draft legal work, advise scientists, serve consumers, and reshape workflows. It is not merely software. It is a general-purpose capability. And all humans could benefit from more of it.General-purpose capabilities have a habit of becoming public questions. But the default answer, that public good is best delivered by government, is the wrong answer in this context.The Product Is IntelligenceWe should stop talking about AI as a feature and start talking about intelligence as the universal thing that is delivered as an input to the world.Water is an input. Electricity is an input. Literacy is an input. Connectivity is an input. Once a society depends on them, access stops being optional. Nobody needs government to build every well, power plant, school, or network. But everybody understands that a civilization cannot be organized around less than universal and reliable access to foundational inputs.Intelligence is reaching that level of importance now that we all know it is real.Government should not own it, operate it, or develop it. Quite the opposite. Companies are the right actors to build fast, compete hard, improve models, serve customers, and discover the real use cases. Self-interest is a useful framing here. Markets are good at finding demand, reducing costs, and turning invention into services people actually use.Companies are the right operators, developers, and owners. But that does not settle the real question of who owns the benefits. That is an economic question.If intelligence becomes metered infrastructure, what happens to the value it creates?The Ownership StackThis week's articles keep circling the same issue from different directions but in the nature of ‘circling' never quite nail it.Jamin Ball's “Own Your Weights” starts with the enterprise version of the question. Owning a model file is not enough. The durable asset is the loop: the data flywheel, the evaluations, the reinforcement system, the workflow learning, and the operating context that lets capability compound.Benedict Evans' “Ways to Think About Token Pricing” adds the market layer. Tokens may become essential, abundant, and cheap, like mobile data. But being essential does not guarantee that the token layer captures the value. The money may move up the stack to whoever owns the workflow, the customer, the distribution, or the application.Alex Karp's fight with the labs, reported in “Alex Karp Is Saying What Every Angry CEO Is Thinking About AI”, is the same argument in sharper enterprise language. Companies are afraid that model providers will not just sell intelligence, but learn from customer workflows and then move into the markets where those workflows create value. The “All-in” group are echoing Karp's view.And “What Is Loop Engineering, and Who Owns It?” names the new contested terrain. The loop is where intelligence meets the world. Whoever owns the loop owns the learning. Whoever owns the learning owns the compounding asset.That is why “who owns intelligence?” is not a slogan. It is the question under the model layer, the application layer, the enterprise layer, and the economic layer.Because intelligence is the product, the tools creating it are fragmented and competitive. So there is no logic in trying to discuss this at the level of a single company or set of tools and models.The Old Promise Was That Commerce Would Tame PowerThe essays this week give the historical backdrop.Deirdre McCloskey, in “What Really Caused the Industrial Revolution”, argues that modern growth came not simply from capital accumulation, but from a change in permission: ordinary people were allowed to innovate, trade, build, and be honored for it.That matters because intelligence could be another expansion of permission. It could make more people capable of building, learning, creating, coding, researching, translating, selling, and coordinating. It could lower the cost of competence.But only if access is broad.Paul Krugman's “AI in an Age of Oligarchy” warns that the same technology lands differently in different political economies. A new general-purpose technology entering a broad, open, upwardly mobile society is one thing. The same technology entering a concentrated economy, with extreme wealth and weak counterweights, is another.Tim O'Reilly's Economist essay, “Elon Musk is building a form of capitalism that Adam Smith would hate”, makes the governance point more directly. The old liberal hope was that commerce would tame arbitrary power. Markets, boards, courts, shareholders, disclosure, and competition would discipline the prince.But what if the prince uses markets to escape discipline?Henry Farrell's “political economy of billionaire derangement” pushes the same point. Founder culture, monopoly ambition, peer rivalry, weak correction mechanisms, and vast private control can amplify appetites rather than restrain them.The danger with intelligence is not that companies build it. They should. Companies build it, meter it, use public tolerance and public infrastructure to scale it, learn from everyone who uses it. All of those things are inevitable and healthy. Market forces will sort out winners from losers. The real danger is that the winners treat all of the surplus produced as purely private.Metered Intelligence Creates SurplusIf metering is not the problem, what is?The problem is pretending that metered intelligence creates value only for the metering entity. Metering water is only tolerated as a public good. If the public were blackmailed by a private water company with the threat of no water we would all rebel.Once we understand that the product of AI is intelligence we can see that every time intelligence is used, there is the immediate transaction: the user pays, the provider serves.But there is also system value. Usage creates signals. Workflows reveal patterns. Prompts, corrections, failures, preferences, integrations, edge cases, and business processes all help define where intelligence is useful and how it should improve. Intelligence breeds intelligence.Even when customer data is contractually protected, the market learns. The platform learns where demand is. The product team learns which workflows matter. The ecosystem learns which jobs are vulnerable, which tasks are automatable, and which parts of the economy can be reorganized around machine intelligence.So the surplus is not born in a vacuum.It rests on public science, public education, public data exhaust, public law, public infrastructure, public energy systems, public tolerance for data centers, and billions of human interactions. It is served by companies, but it is not made only by companies.This is why “Americans Deserve a Dividend From AI Companies' Riches” belongs at the center of this week's issue. The detail can be debated. The principle is harder to dismiss. If intelligence becomes a new foundational resource, then some part of the wealth it creates should flow back to the people whose society makes it possible. Intelligence did not suddenly appear. AI is built on the entire history of human intelligence. It benefits from it and at the same time evolves it.Not Nationalization. A Human Wealth Fund.If intelligence belongs to everybody, some conclude that government ownership of intelligence is the right outcome.Governments are not well suited to build, operate, or improve intelligence. They will move too slowly, regulate too early, politicize the wrong things, and confuse economic participation with operational control.Andrew McAfee's “Why I Didn't Sign the AI Open Letter” is useful here. His objection is not that the technology is unimportant. It is that steering too hard before we understand the shape of the change can become its own failure mode. Marc Andreessen's satire of AI regulation is less policy than temperament, but it captures a real Silicon Valley fear: that regulation can become permission, capture, and incumbency before it becomes wisdom.That fear should be taken seriously.But it does not answer the economic question. It answers only the operational one.How can the economic benefits of intelligence be distributed? The better answer is a sovereign human wealth fund.Call it a sovereign wealth fund if you must, but the phrase is too national. Intelligence will not respect borders. The leading companies are global. The models, chips, data centers, agents, platforms, and workflows will be transnational from the beginning. If the value created by intelligence is global, then the mechanism for sharing some of that value should begin with the companies global enough to capture it. The nice thing about xAI, OpenAI, and Anthropic is that they are supranational.These companies own and operate intelligence. Let them compete. Let them profit. Let them keep the incentives that make the system improve. But if intelligence is the new water, the wealth it creates cannot belong only to the companies that meter it. And they, themselves, have the power to fix it, even more than governments.Access will become a Human Right; Ownership Is the Economic DesignThis is where human rights come in. There is no right to access an AI model, yet. But there will soon be a need to change that.Not as a claim that every person is entitled to every frontier model at every moment for free. That is not serious. Capacity has costs. Models have costs. Inference has costs. Data centers have costs. Although those costs will decline over time, possibly quite quickly as self-learning models address costs.The claim is more basic: in a world where intelligence becomes a primary input into education, work, health, science, citizenship, creativity, and economic agency, baseline access to intelligence starts to look like a civic requirement.That could mean public access layers. It could mean education credits. It could mean open models. It could mean AI dividends. It could mean public-interest compute. It could mean taxes on rents. It could mean a company-initiated human wealth fund that returns some of the upside to society without handing the operating system to the state. The latter could couple wealth growth with universal distribution of ownership.The exact mechanism matters. But the distinction matters more.Government should not own intelligence. It should be universally available. And people should have a claim on the wealth intelligence creates.The Frontier Is Also PhysicalThe abstraction is not weightless.“The Fight Against AI Data Centers Is Just Beginning”, “New York becomes the first state to enact a data center moratorium”, Reuters on pollution from Musk's xAI power project, and DataGravity's “Who Captures Value in AI Infrastructure?” all say the same thing from the ground up.Intelligence uses land. It uses power. It uses water. It uses chips. It uses grid capacity. It uses neighborhoods. It uses public patience.That makes the value question unavoidable. A society can accept the buildout if the buildout is legible as shared progress. It will resist it if the costs are local, the profits are private, and the benefits feel enclosed.Who Owns the “Loop”?The week ends where it began.“Anthropic and Blackstone” are betting that implementation is the next trillion-dollar business. “Vint Cerf” is working on identity for agents on the open internet. “GPT-Red” points toward systems that improve their own robustness. “Kimi K3” adds another open frontier model to the global mix.The model race continues. The deployment race is accelerating. The governance race is behind.My view is this:The central product of this era is intelligence. Companies have figured out how to capture it, package it, serve it, and meter it. That is good. It should stay in the hands of builders who have the incentive to make it better.But intelligence is too foundational to become just another private toll booth. A significant part of it will turn out to be free to users.As intelligence becomes a general-purpose resource, then access to it becomes a human-capability question, and the surplus from it becomes an economic-justice question. Not because government should run it. Because government should not run it. The operating layer belongs with companies. The wealth question belongs with everyone. But companies are best placed to turn that into a process of distribution.The question is not whether companies should build intelligence. They should.The question is whether humanity gets a stake in the wealth created by the thing that may soon become its most important shared input.Contents* Essays* Deirdre McCloskey on What Really Caused the Industrial Revolution* AI in an Age of Oligarchy* Elon Musk is building a form of capitalism that Adam Smith would hate* Murky Mirror: Truth and Consequences* The political economy of billionaire derangement* Is there any “oligarchy” to fight?* AI* Nearly 200 Economists and Tech Leaders Warn of A.I. Threats* Why I Didn't Sign the AI Open Letter* Own Your Weights* Ways to Think About Token Pricing* Alex Karp Is Saying What Every Angry CEO Is Thinking About AI* The AI Agents Are Coming for Microsoft Office* What Is Loop Engineering, and Who Owns It?* The Fight Against AI Data Centers Is Just Beginning* 6 months to live for open models* Americans Deserve a Dividend From AI Companies' Riches* Who Gets to Define the Frontier?* GPT-Red: Unlocking Self-Improvement for Robustness* Anthropic, Blackstone bet the next trillion-dollar AI business is implementation, not just models* Vint Cerf is working on a plan to unleash AI agents on the open internet* xai-org/grok-build, now open source* The Pulse: What can we learn from Bun's rapid Rust rewrite with AI?* Orphan risks at the frontier of artificial intelligence* The Lab of the Future Should Feel Like a Data Center* Why AMI Labs' Alexandre LeBrun won't call his AI “AGI” or “superintelligence”* Kimi K3 Tech Blog: Open Frontier Intelligence* Venture Capital* Three Years In* Venture Has Rarely Looked More Bifurcated* The Best Angel Investors in the US: Who Backs the Most Unicorns, and Who's Active Now* Are Prediction Markets Doomed to Fail?* Regulation* Exclusive: The Next Frontier of the Deportation Wars: College Campuses* The Supreme Court Broke Independent Agencies. Here's a Way to Slow the Damage.* India's crackdown on a new WhatsApp feature risks setting a global precedent* Let's build a children's public internet* Computer cops* Google is better at playing the AI regulations game* Infrastructure* Who Captures Value in AI Infrastructure?* New York becomes the first state to enact a data center moratorium* Pollution from Musk's unpermitted xAI power project hits hardest in Black communities* Interview of the Week* The End of the End of Geography* Startup of the Week* Radical AI's Joseph Krause: The Scientist Building The “Waymo” Lab For New Materials* Post of the Week* Marc Andreessen on AI RegulationEssaysDeirdre McCloskey on What Really Caused the Industrial RevolutionYascha Mounk and Deirdre McCloskey | Persuasion | July 11, 2026Yascha Mounk interviews Deirdre McCloskey about her argument that the modern world's economic liftoff came less from capital accumulation than from a change in ideas. McCloskey says both left and right versions of the conventional story rely too heavily on investment: the left stresses exploitation and surplus value, while the right stresses virtuous saving by capitalists. Her objection is historical and economic. Human beings had always invested, from irrigation works and Roman roads to seed grain, and simple accumulation quickly runs into diminishing returns.McCloskey's alternative is that northwestern Europe, first Holland, then Britain and Scotland, and then the North American colonies, developed a liberal ideology that changed who was allowed to innovate and be honored for it. The conversation links that shift to the erosion of inherited hierarchy, the spread of dignity for ordinary commercial life, and a moral vocabulary in which liberalism is not merely procedural but connected to virtues and values. The point is not that machines, coal, trade, and institutions did not matter, but that they do not explain the scale and timing of modern enrichment without a cultural permission structure for innovation.The interview also turns to the contemporary defense of liberalism. Mounk frames the series around the worry that liberalism is often treated as too thin to command allegiance, while its opponents speak more directly to moral passions. McCloskey's case is that liberal societies became rich because they dignified experimentation and ordinary enterprise, and that liberals need to recover the moral language behind that claim.Read moreAI in an Age of OligarchyPaul Krugman | Paul Krugman | July 12, 2026Paul Krugman frames AI as a major technological shock arriving inside an already unequal political economy. The post says AI's economic and social effects may take years to understand, but argues that the setting matters now: America has much greater wealth concentration and political inequality than it did in the 1950s and 1960s, when progressive taxation, stronger regulation, and more active antitrust might have contained some of the destructive effects of a new technology.Krugman's opening claim is that the same technology would likely have different consequences in a more level society. In today's United States, he writes, extreme wealth is both a cause and effect of policies that favor a small elite, including low effective taxes on capital and high incomes, weak enforcement of worker protections and antitrust, and cuts to programs that benefit ordinary Americans.The article is explicitly more about oligarchy than AI. Krugman says the paid sections document the rise of the “.0002%,” the economics and politics of extreme wealth, how oligarchy will shape AI's impact, and possible policy paths. His caveat is that AI itself may still produce a pushback against oligarchy, but absent that, he expects the pre-existing concentration of wealth and power to magnify AI's downsides.Read moreElon Musk is building a form of capitalism that Adam Smith would hateAuthor: Tim O'Reilly Published: July 12, 2026Tim O'Reilly argues that Elon Musk is using the legal forms of shareholder capitalism to escape the restraints that shareholder capitalism was supposed to impose. The article begins with SpaceX's public-market structure: ordinary public investors get little meaningful governance power, Musk keeps roughly 85 percent of the votes through super-voting shares, buyers waive jury trials and class actions, the company qualifies as controlled, and removal of Musk depends on the share class he controls. In O'Reilly's framing, that is not ordinary founder control; it is a design for being answerable to no one, possibly beyond Musk's own lifetime.The killer detail is the article's turn through Albert Hirschman, Montesquieu, James Steuart, Adam Smith, and Keynes. Older defenses of commerce held that markets would tame princely passions because the self-interest of merchants was safer than arbitrary rule. O'Reilly says Musk reverses that hope. The market discipline that was supposed to cage the prince has become the lever by which the prince raises capital, removes feedback loops, and carries private power into politics, government, Mars, robots, AI, or whatever ambition comes next.The pull is the link to AI governance. O'Reilly says corporations are already a kind of artificial intelligence: narrow-input systems that act at a scale no individual human can match. Their partial controls include independent boards, shareholder votes, courts, disclosure, regulators, public pressure, and activism. If the leaders building frontier AI strip those alignment mechanisms out of their own companies, the governance of the company becomes a preview of the governance of the machine.Read more: The EconomistMurky Mirror: Truth and ConsequencesAuthor: Esther Dyson Published: July 14, 2026Esther Dyson argues that today's institutional crisis is better viewed through the 14th century than through recent political history. Using Barbara Tuchman's A Distant Mirror as her frame, she compares a world of famine, plague, church schism, feudal predation, and purposeless war with a present in which institutions again feel brittle, incentives are badly aligned, and power is shifting into forms that are hard to govern.The killer detail is the historical analogy between land, corporations, and AI. Dyson moves from nobles who controlled serfs and territory, to the East India Company as a quasi-sovereign business, to today's AI systems and data centers as a possible new sector that crosses and weakens both nation-states and companies. The question is whether AI becomes a new kind of private land, owned by a new nobility, or an open prairie that many people can cultivate.The pull is human attention. Dyson says the central question is not what AI will do to people, but how people will react to it: whether they can value love, kindness, embodied attention, and artisanal human presence in a world of seductive artificial offerings.Read more: SourceThe political economy of billionaire derangementAuthor: Henry Farrell Published: July 15, 2026Henry Farrell argues that the visible political radicalization of some Silicon Valley billionaires is not a random personality quirk, but a product of the political economy that made them. Starting from Tyler Cowen's dismissal of “billionaire derangement syndrome” and Tim O'Reilly's warning that Elon Musk is using shareholder capitalism to escape shareholder restraint, Farrell flips the phrase: the question is why billionaires themselves can become deranged.The killer detail is Farrell's use of Peter Thiel as both theorist and example. Thiel's Stanford lectures described startups as monarchies and founders as figures vested with unusual power, while Silicon Valley culture rewarded eccentricity, monopoly ambition, and founder exceptionalism. Farrell says those ideas combined with dense founder-investor networks, peer rivalry, and weak correction mechanisms to amplify rather than discipline princely appetites.The pull is the ideological problem for classical liberals who once saw tech wealth as an ally of markets and freedom. Farrell says commerce did not tame the passions; in parts of Silicon Valley, the passions have begun to devour markets, institutions, and the liberal story that justified them.Read more: SourceIs there any “oligarchy” to fight?Matthew Yglesias | Slow Boring | July 16, 2026Matthew Yglesias argues that “oligarchy” is a rhetorically powerful but analytically loose way to describe American politics. The post begins from Bernie Sanders' “Fighting Oligarchy” tour, Amy Klobuchar's warning about a MAGA “broligarchy,” and the long afterlife of the Martin Gilens and Benjamin Page paper that was widely summarized as showing that only the rich matter in policy outcomes. Yglesias says the evidence supports a weaker claim: affluent people and business leaders have unusual access and influence, but that is not the same as rule by a small cabal.His main distinction is between inequality and oligarchy. The Gilens-Page measure treated the top 10 percent of households as “the wealthy,” and later critics found that rich and middle-class preferences usually align; in the cases where they differ, the rich win about 53 percent of the time. Yglesias also says business executives get special access partly because their decisions are materially important to communities, jobs, investment, and local tax bases, not only because of campaign donations.The post preserves Jerusalem Demsas' counterpoint from their podcast discussion: privileged donor and business access can still violate democratic equality even if the oligarchy label overstates the structure of power. Yglesias' narrower claim is that Democrats should be precise about what problem they are trying to solve, because donor influence can also push the party left on climate and cultural issues in ways that alienate many voters.Read more: Slow BoringAINearly 200 Economists and Tech Leaders Warn of A.I. ThreatsAuthor: Ben Casselman Published: July 13, 2026Ben Casselman reports on “We Must Act Now,” a statement warning that artificial intelligence could transform the economy faster than any previous technology and that policymakers need to move faster to understand and respond. The statement says AI may become radically more powerful over the next 10 years, bringing risks such as large-scale job displacement as well as opportunities such as higher living standards. Nearly 200 people signed, including 15 Nobel laureates, the chief economists of OpenAI and Anthropic, Anthropic co-founder Jack Clark, former Google CEO Eric Schmidt, and venture capitalist Vinod Khosla.The killer detail is who joined the warning. Casselman notes that the signatories include economists who have historically been skeptical of Silicon Valley's most dramatic AI job-loss forecasts, including Daron Acemoglu and Simon Johnson, the MIT professors who won the 2024 Nobel in economics. Erik Brynjolfsson, who helped organize the statement, says there has been a notable change in the profession and that economists and policymakers are not ready for the “tsunami” he sees coming.The pull is the measurement problem. The statement does not offer a specific policy menu, but calls for economists, policymakers, and industry leaders to understand the economics of transformative AI and steer it toward complementing humans. Brynjolfsson says one high priority is better data on AI's spread and impact, because current measures tell conflicting stories about job losses and which workers are most exposed.Read more: The New York TimesWhy I Didn't Sign the AI Open LetterAuthor: Andrew McAfee Published: July 13, 2026Andrew McAfee explains why he did not sign “We Must Act Now,” the AI economy statement organized in part by his longtime collaborator Erik Brynjolfsson. McAfee agrees with the letter's starting point that AI is likely to become radically more powerful over the next decade and that it is a general-purpose technology. His objection is not to urgency or to studying AI's economic effects, but to the framing of risk, displacement, and institutional steering as the first move.The killer detail is McAfee's line edit. He says the original letter comes close, then “bounces off the crossbar” by calling for incentives, guardrails, and institutions to steer AI before we know enough about its actual impacts. He points to mixed current evidence: labor-market canaries, but also rising software job postings, low unemployment for younger workers, rising real median income, and claims that AI-adopting companies are adding workers faster than low-adopting peers. His worry is that the letter leans toward upstream governance and dirigisme when the evidence may call for capability building instead.The pull is his replacement statement. McAfee keeps the three-paragraph structure but changes the emphasis: AI is likely to become radically more powerful; like earlier world-changing technologies it will raise living standards while also bringing harms and shocks; and economists, policymakers, and technology leaders should build the capabilities to respond quickly and effectively. It is a concise version of the permissionless-innovation case inside the AI policy debate.Read more: The Geek WayOwn Your WeightsAuthor: Jamin Ball Published: July 10, 2026Jamin Ball argues that the enterprise AI debate about whether companies should “own their weights” or rent models from frontier labs is asking too narrow a question. A model weight file gives a company control over a point-in-time artifact, but not durable control over the capability stack. In his framing, the weight file is a melting ice cube: it does not get worse in absolute terms, but it falls behind as frontier systems improve and enterprise needs change.The killer detail is what Ball says companies really need to own: the data flywheel, reinforcement learning infrastructure, and evaluation harness that produce and improve the model. Simply deploying an open-weights model and declaring sovereignty leaves the enterprise with yesterday's capability and no way to compound workflow-specific learning.The pull is that enterprise AI control may be less about model ownership than operating ownership. The defensible layer is the system that turns company data, edge cases, business definitions, and evaluations into continuously improving performance.Read more: Clouded JudgementWays to Think About Token PricingAuthor: Benedict Evans Published: July 9, 2026Benedict Evans argues that today's AI token prices are a temporary signal from a supply-constrained market, not a reliable guide to long-term value capture. The open question is whether foundation models keep durable pricing power or become commodity infrastructure as data-center capacity, inference efficiency, and model competition all shift. His current read is that the visible market dynamics point toward commoditization unless something materially changes.The killer detail is the mobile data analogy. Evans says cellular networks became a trillion-dollar industry with hundreds of billions in capex after data usage exploded, but carrier stocks went nowhere because value moved up the stack. Tokens may behave similarly: an opaque unit tied to marginal cost, sold through bundles, essential to everything, yet not necessarily where profits accrue.The pull is uncertainty, not prediction. Evans lists paths to model dominance, including network effects, less competition, regulation, export controls, or a lab pulling ahead on execution, but says each requires a new fact not yet visible. Without that change, the model layer looks more like infrastructure beneath the products that capture value.Read more: SourceAlex Karp Is Saying What Every Angry CEO Is Thinking About AIAuthor: Tim Higgins Published: July 11, 2026Tim Higgins reports that Palantir CEO Alex Karp has turned corporate frustration with AI labs into a public argument about enterprise control. Palantir released a white paper, “Institutional Sovereignty in the Age of AI,” laying out steps companies and governments can take to protect themselves from OpenAI, Anthropic, and other foundation-model providers. The article links that paper to Karp's CNBC appearance, where he said “something has gone completely wrong” in the relationship between AI labs and customers and argued that enterprises are paying for tokens that create little value.The killer detail is the value-capture question. Higgins writes that Karp's critique has resonated because AI labs may gain power and insight from customer data, workflows, and decision-making, even when enterprise policies say customer data are not used for training. David Sacks amplified the concern by arguing that Anthropic is moving from the model layer into vertical applications such as science, security, legal, and coding, raising the fear that model providers will watch where value is being created and then move into those markets directly.The pull is that Karp is not alone, even if his style is unusually combative. Higgins notes that Satya Nadella has also warned that companies need to retain the learnings created when they use AI models, while Mark Zuckerberg has framed Meta's new model release partly around lower-cost frontier intelligence. The article presents Karp's campaign as one sign that established technology companies and large enterprises are trying to define where they fit when AI labs become central infrastructure, application competitors, and potential IPO giants at the same time.Read more: The Wall Street JournalThe AI Agents Are Coming for Microsoft OfficeAlex Wilhelm | Cautious Optimism | July 11, 2026Alex Wilhelm argues that one of the week's quieter AI questions is whether the productivity market that Microsoft successfully moved into subscription software is now being attacked by agentic tools. The piece begins with the infrastructure backdrop: SK Hynix raised $26.5 billion in a U.S. listing while building U.S. HBM and advanced-packaging capacity, and memory, chip, and foundry companies are now priced for sustained AI demand.Wilhelm then says the AI conversation has shifted quickly from raw capability to cost per task. He cites new model releases and vendor language emphasizing cheaper agentic and coding models, faster performance, and lower dollars per task. That matters because lower costs make it more plausible for AI systems to take on routine knowledge work at scale rather than remain a premium coding assistant market.The core of the article is Microsoft Office. Wilhelm notes that Microsoft turned Office from a one-time purchase into Microsoft 365, a large recurring revenue business with tens of millions of subscribers and a major productivity segment. Now, he says, late-stage unicorns and AI labs are pushing into the same territory: Anthropic's Cowork was reportedly used mostly outside software development, OpenAI merged ChatGPT and Codex into a tool for creating sheets, slides, docs, web apps, and long-running work, and other companies are building agentic coworkers that connect business data to documents, workflows, schedules, alerts, and apps.The article's caveat is that Microsoft has survived major platform shifts before. The argument is not that Office disappears quickly, but that the definition of office software is broadening from documents and spreadsheets into AI systems that can create, monitor, and act across workplace data.Read moreWhat Is Loop Engineering, and Who Owns It?Author: Nilesh Barla Published: July 11, 2026Nilesh Barla argues that “loop engineering” is becoming a distinct discipline because production AI agents now fail less at single prompts than at runtime: when to stop, what state to preserve, and how to recover after a bad step. Prompt engineering shapes one model call, and context engineering shapes what the model sees, but loop engineering shapes what a sequence of calls actually does.The killer detail is the three-primitives frame. Barla says a real agent loop needs halt conditions, state carryover, and recovery paths, then maps teams across five maturity levels. At the lowest level, an agent is just a model call in a for-loop with a step cap and raw history; by the higher levels, the system has structured state, explicit planning, replay, evaluation, and self-repair.The pull is organizational. If agents are becoming production systems rather than demos, someone has to own the runtime itself. The loop engineer is the role Barla gives to the person responsible for making long-running agent work dependable.Read more: Adaline LabsThe Fight Against AI Data Centers Is Just BeginningEmma Roth | The Verge | July 12, 2026Emma Roth argues that community resistance to data centers has moved from an early warning sign into a national political fight as AI facilities grow larger, more power-hungry, and more visible to nearby residents. The article starts with Apple's failed 2015 plan for a $1 billion data center in Athenry, Ireland, where a small group of residents challenged the project over noise, light pollution, flooding, traffic, and wildlife effects until Apple abandoned it in 2018.The current data-center buildout is presented as much larger and more contentious. Roth writes that residents now cite rising energy costs, water quality, noise, light pollution, and greenhouse gas emissions, while the U.S. Energy Information Administration expects commercial energy demand to surpass residential demand this year because of AI data centers and Goldman Sachs expects data-center power demand to double by 2027.The central evidence comes from Data Center Watch, which says protesters blocked or delayed at least 75 U.S. projects worth $130 billion from January to March, with active opposition groups more than doubling from 396 at the end of 2025 to 833 by the end of the first quarter of 2026. Roth also cites QTS abandoning a $12 billion Wisconsin campus, Delaware City regulators blocking a 580-acre project under the Coastal Zone Act, opposition stopping a QTS project in Prince William County, and pressure that pushed Kevin O'Leary to downsize the proposed 40,000-acre Project Stratos in Utah.The policy section describes a split between federal acceleration and local resistance. President Trump has treated data centers as part of the AI race with China and fast-tracked construction, while some Republican candidates are distancing themselves from that position ahead of midterms. Sanders and Ocasio-Cortez have proposed a moratorium until price and environmental protections exist, bipartisan lawmakers are backing ratepayer-protection measures, and states including Florida, Idaho, and Washington have passed rules on cost shifting, water use, and tax breaks. Roth's caveat is that the policy patchwork is still incomplete, leaving many communities to fight project by project.Read more6 months to live for open modelsAuthor: Nathan Lambert Published: July 12, 2026Nathan Lambert argues that open-weight AI models are facing their most serious policy test so far because U.S. officials are beginning to discuss concrete controls rather than abstract safety concerns. He says reported White House conversations about a new executive order may initially target Chinese-origin models and government use, but could create a broader review habit for frontier open models. His forecast is that a model above the capability range of GPT-5.5, Claude Opus 4.8, or GLM-5.2 could trigger a ban or indefinite delay within six months.The post separates two policy fights that are becoming intertwined: distillation and frontier capability. Lambert says the distillation campaign against Chinese models has become a form of regulatory capture because Anthropic and other closed-model companies would gain economically if Chinese open models were banned. He does not dismiss IP protection, but argues that if a closed model's capabilities are dangerous enough to justify restricting open models, the lab also has to explain why those capabilities are exposed through a queryable API. He cites unauthorized access to Anthropic's Mythos private beta as evidence that APIs are not automatically secure.The broader claim is that a unilateral U.S. ban would hurt positive actors more than bad actors if comparable open models remain available elsewhere. Lambert says the only durable ceiling would require global agreement, which does not exist, and that open models can improve safety by allowing broad inspection, adaptation, and understanding. His proposed near-term off-ramps are a strong U.S. open model release from companies such as Microsoft, Meta, or Reflection, and a broader coalition of open-source beneficiaries lobbying for safe rollout rather than prohibition.Read more: SourceAmericans Deserve a Dividend From AI Companies' RichesAuthor: Scott Stanford Published: July 14, 2026Scott Stanford argues that proposals to give the government a stake in AI companies miss the point unless ordinary citizens directly receive and control the upside. Sam Altman has discussed giving up equity in OpenAI, Washington already owns a stake in Intel, Nvidia is sharing China chip revenue, and Bernie Sanders wants large AI labs to contribute half their stock to a sovereign wealth fund. Stanford says those ideas all park value with the state, not with people.The killer detail is New Carlisle, Indiana, where AWS's Project Rainier is turning cornfields into one of the world's largest AI superclusters. The project is planned to run up to a million chips, draw more than two gigawatts of power, and represents an investment that has grown from $11 billion to $13.8 billion. Stanford uses that local transformation to argue that AI's public bargain should be visible at the household level.The pull is design. A citizen AI dividend would have to specify who earns a stake, how they hold it, and when they see cash. Without that mechanism, the AI wealth debate remains a fight over government balance sheets rather than public ownership.Read more: SourceWho Gets to Define the Frontier?Author: Mark Daley Published: July 14, 2026Mark Daley argues that Demis Hassabis is right to call for a serious institution to verify frontier AI systems, but that the power to test models is also the power to govern them. Hassabis's proposed Frontier AI Standards Body would get privileged pre-release access to advanced models, testing compute, held-out evaluations, support from national labs and security agencies, third-party auditors, and eventually authority to block models from the American market or coordinate a slowdown.The killer detail is Daley's constitutional objection. He says the proposal sometimes looks like a scientific lab, a standards body, an industry regulator, a licensing authority, and an emergency security council at once. Combining those roles because each requires technical expertise would be like putting the central bank, auditor-general, and Supreme Court in one building and calling it efficient.The pull is standard-setting. Daley's concern is not that verification is unnecessary, but that whoever writes the tests, decides what passes, adjudicates disputes, and grants market access may end up defining the frontier itself.Read more: SourceGPT-Red: Unlocking Self-Improvement for RobustnessOpenAI | OpenAI | July 15, 2026OpenAI describes GPT-Red as an internal automated red-teaming model trained to find prompt-injection vulnerabilities at a scale human red teams cannot match. The post says AI systems increasingly encounter third-party data through browsers, connected apps, local files, and tools, creating opportunities for malicious instructions hidden in emails, webpages, tool responses, or code repositories. Human red-teaming remains part of OpenAI's safety process, but the company says it is time-intensive and cannot generate enough diverse adversarial examples for model training.The system is trained through self-play reinforcement learning, with GPT-Red rewarded for eliciting valid failures and defender models rewarded for resisting attacks while still completing their tasks. OpenAI says the training environments specify threat models across settings such as local files, webpage banners, email bodies, and tool outputs. The model is kept separate from deployed production models because it is intentionally trained with malicious capabilities.OpenAI reports that GPT-Red generalized beyond its training set, including an internal replication of the indirect prompt-injection arena from Dziemian et al. (2025), where it found successful attacks in 84% of scenarios compared with 13% for human red-teamers. The post also says GPT-Red transferred attacks from simulation to a live autonomous vending-machine agent, causing price changes and order cancellations, and outperformed a prompted GPT-5.5 baseline against a Codex CLI agent on held-out data-exfiltration tasks.The article's main robustness claim is that OpenAI has used GPT-Red and predecessor models in training since GPT-5.3, with later GPT releases becoming more resistant to prompt injections. It says GPT-5.6 Sol has six times fewer failures on OpenAI's hardest direct prompt-injection benchmark than the best production model from four months earlier, that a “Fake Chain-of-Thought” attack class fell from more than 95% success against GPT-5.1 to below 10% against GPT-5.6 Sol, and that GPT-5.6 Sol fails on only 0.05% of GPT-Red's direct prompt injections. OpenAI says general capabilities and targeted over-refusal evaluations were not harmed, and says a preprint with more details will follow.Read moreAnthropic, Blackstone bet the next trillion-dollar AI business is implementation, not just modelsRebecca Bellan | TechCrunch | July 15, 2026Rebecca Bellan reports that Ode with Anthropic is the $1.5 billion AI implementation company launched by Anthropic with Blackstone, Hellman & Friedman, Goldman Sachs, and other backers. The article says the venture reflects a growing belief among frontier AI labs that enterprise adoption requires more than better models: customers need engineers who can embed inside businesses and turn AI into working systems.Ode was originally conceived by Blackstone after it used both large consulting firms and smaller AI services boutiques across its portfolio companies. TechCrunch reports that Fractional AI, an AI engineering services startup, stood out and was acquired by the joint venture shortly after the venture was announced. Fractional now forms the foundation of Ode, which has 100 engineers and works closely with Anthropic's applied AI team to identify where the technology can affect specific businesses.Ode CEO Chris Taylor tells TechCrunch that the company could someday become a trillion-dollar business if it scales without losing quality. He says an ideal customer is one whose CEO treats the AI project as a top one or two priority, whether it is a major product feature or the reworking of a core business process. Ode will operate under a “Claude-first” principle, using Anthropic technology whenever possible, but the article says it can use rival AI products when needed.The article's central implementation argument comes from Ode chief technologist Eddie Siegel, who says model selection matters but is not where most of the engineering effort goes. He compares it to the choice of programming language in software: one ingredient in a system that still has to be engineered. Bellan writes that Ode's challenge is hiring and training enough elite generalist engineers, many of them former founders, while competing with OpenAI's The Deployment Company and consulting giants that have built their own forward-deployed engineering teams.Read moreVint Cerf is working on a plan to unleash AI agents on the open internetTim Fernholz | TechCrunch | July 15, 2026Tim Fernholz reports that Vint Cerf, after leaving Google, is advising Innovation Labs on an open architecture for identifying AI agents online. Innovation Labs is a subsidiary of Identity Digital, a DNS registry company, and its proposal is to use domain-name infrastructure as part of a system for agent identity, accountability, and auditability. The premise is that agents will need a way to identify themselves if they move beyond proprietary systems and begin interacting across the open internet.The concrete proposal is DNSid, a registry that links an AI agent to an existing internet domain and uses cryptographic proofs to log its registration over time. Innovation Labs says it is trialing the standard with unnamed hyperscalers and identity companies. Cerf frames the problem around authority and accountability: what authority an agent has, where that authority came from, who is accountable for the agent's behavior, how its identity is established, and why anyone should trust it.The article's caveat is that standards are still emerging and agents are more active than static domains. Cerf says the period may be both fascinating and exasperating because the functionality is powerful and interoperability is unresolved. He compares the adoption problem to TCP/IP: competing systems may not work together until users push for functional interoperation. He also says an agentic economy is not inevitable, but that people will try to build it because delegating work to agents will be easier.Read more: TechCrunchxai-org/grok-build, now open sourceAuthor: Simon Willison Published: July 15, 2026Simon Willison argues that xAI's decision to open-source Grok Build is best understood as a trust repair move after a severe privacy failure. The CLI had triggered backlash when users realized that running it in a directory could upload the entire directory to xAI's Google Cloud buckets, including one user's reported SSH keys, password manager database, documents, photos, and videos. xAI disabled the feature, said previously retained coding data would be deleted, and released the code under Apache 2.0.The killer detail is what the codebase reveals. Willison counts 844,530 lines of Rust, only about 3% of which appears vendored, and finds remnants of the upload system still present but disabled: gcs.rs contains Google Cloud upload code, while upload_session_state() now returns a hard-coded session_state_upload_unavailable error. He also notes copied or ported tool implementations from Codex and OpenCode, prompt files, and a terminal Mermaid renderer.The pull is that terminal coding agents are becoming large, intricate software systems in their own right. The privacy failure mattered because these tools operate inside the directories where developers keep their most sensitive work; the open-source release matters because trust now depends on inspecting what an agent can see, send, and do.Read more: SourceThe Pulse: What can we learn from Bun's rapid Rust rewrite with AI?Author: Gergely Orosz and Ivan Klaric Published: July 16, 2026Gergely Orosz and Ivan Klaric argue that Bun's AI-assisted rewrite from Zig to Rust is a practical sign of how software engineering changes when models can take on large, bounded migrations with clear feedback loops. The piece does not treat the rewrite as magic: Jarred Sumner first spent hours turning design judgment into a detailed porting guide, then used adversarial review, parallel agents, compiler errors, and tests to force the work toward correctness.The killer detail is the scale. Bun had 535,496 lines of Zig, 1,448 files, and 22 million monthly downloads, making a conventional rewrite a year-long freeze the team could not justify. Using Fable, Sumner split the work across 64 agents, produced about 6,500 commits, and got the migration done in 11 days at an estimated API cost of $165,000.The pull is economic, not theatrical. If a one- or two-year migration can become an 11-day project, AI coding is not just faster autocomplete; it changes which technical debts are worth paying down.Read more: SourceOrphan risks at the frontier of artificial intelligenceAuthor: Andrew Maynard Published: July 16, 2026Andrew Maynard argues that frontier AI safety frameworks are creating “orphan risks”: harms that companies can see, but do not formally own because they are hard to quantify, do not fit catastrophic-risk thresholds, or fall outside audit-friendly compliance machinery. His target is not existing frontier safety work, but the narrowing effect that happens when private companies decide which risks count as governable.The killer detail is Maynard's contrast between measurable model dangers and threats to value. He points to Meta's three-day Galactica collapse, OpenAI's 2023 board crisis, safety-team departures, and wellbeing litigation as examples of risks that damaged trust, culture, legitimacy, or users without fitting cleanly into conventional model-risk categories. The proposed fix is an orphan-risk register: a public record of risks a company considered and chose not to manage, with reasons.The pull is accountability. Frontier developers' internal scoping choices have become a de facto layer of public governance, so the question is no longer only which risks they manage, but which risks they quietly leave outside the frame.Read more: SourceThe Lab of the Future Should Feel Like a Data CenterLatent.Space with Andy Beam and Rafa Gomez-Bombarelli | Latent.Space | July 16, 2026Latent.Space interviews Lila Sciences CTO Andy Beam and chief science officer for physical sciences Rafa Gomez-Bombarelli about the company's attempt to build an AI-run science factory. The post describes Lila's thesis as treating the lab itself as an “infinite token generator”: if internet data drove the first era of AI scaling, experimentally verified scientific data may be the next scarce training source. Lila is trying to produce that data with robotics, lab instruments, orchestration software, and AI models wired into the wet lab.The central analogy is the lab as data center. Instruments are nodes on a graph, a magnetically levitating transport layer moves materials between them, and experiment scheduling looks like a compute queue. Beam says Lila is not simply an automation company, because the point is not just throughput; it is flexibility, generalization, and experiment capture. The post says Lila has built more than 10 trillion experimentally validated “scientific reasoning tokens,” not internet text or biological sequences.The interview ranges across biology, chemistry, drug discovery, materials science, and the limits of automation. It notes that Lila rebuilt one gas-sorption measurement to run roughly 2,500 times faster, claims its general models can transfer priors from small-molecule chemistry to metal-organic frameworks for carbon capture, and describes model-suggested platinum-group-free electrocatalysts that moved from looking boring or wrong to becoming strong performers. The caveats are physical: experiments have runtimes, biology cannot always be accelerated, chains of thought can be unreliable narrators, and reward hacking becomes more dangerous when a model controls a real lab.Read more: Latent.SpaceWhy AMI Labs' Alexandre LeBrun won't call his AI “AGI” or “superintelligence”Kate Park | TechCrunch | July 16, 2026Kate Park interviews AMI Labs CEO Alexandre LeBrun about why Yann LeCun's world-model startup avoids the language of “AGI” and “superintelligence.” LeBrun says the terms are not useful because they lack stable definitions: “We never used the word AGI. And I just noticed that nobody is using it anymore; they switched to superintelligence.” His argument is that the practical frontier is not a label, but whether AI systems can understand and predict real-world states.The article explains the world-model thesis by contrasting language prediction with physical-state prediction. A large language model predicts the next word; a world model predicts the next state, such as what happens when a glass tips over. LeBrun says LLMs remain complementary and efficient for language, but the physical world is where current AI is weak. Robotics is the clearest case: hardware has advanced quickly, but robots are still brittle outside controlled routines because they lack context and situational understanding.AMI is still pre-product, but TechCrunch reports that LeBrun was in Seoul looking for industrial partners, researchers, and global companies. He says world models cannot be built entirely inside a lab because they need access to real environments. That is why South Korea appeals to AMI: robotics, semiconductors, manufacturing, and fast adoption create the kind of hardware-heavy context that software-only AI has barely touched.Read more: TechCrunchKimi K3 Tech Blog: Open Frontier IntelligenceKimi | Kimi | July 16, 2026Kimi introduces Kimi K3 as an open 3T-class frontier model aimed at coding, knowledge work, reasoning, multimodality, and long-context agentic use. The source describes the model as a 2.8T-parameter system built on Kimi Delta Attention and Attention Residuals, with native multimodality and a 1M-token context window. It says Moonshot AI plans to release model weights by July 27.The post presents K3 through benchmark and use-case sections rather than as a general product announcement. It reports results across coding, productivity, agentic, and multimodal evaluations, including DeepSWE, Terminal-Bench 2.1, Program Bench, SWE Marathon, FrontierSWE, PostTrain Bench, OfficeQA Pro, SpreadsheetBench 2, MCP Atlas, AutomationBench, BrowseComp, GDPval-AA v2, AA-Briefcase, MMMU-Pro, MathVision, BabyVision, OmniDocBench, and PerceptionBench. The source says all reported K3 results use maximum reasoning effort with temperature and top-p set to 1.0, and that different benchmark comparisons use KimiCode, Claude Code, or Codex harnesses depending on the test.Kimi's caveats are unusually concrete. The limitations section says K3 was trained in preserved thinking-history mode, so quality may become unstable if an agent harness does not pass historical thinking content correctly or if an ongoing session switches to K3 midstream. It also says K3's emphasis on long-horizon tasks can make it excessively proactive when it encounters minor issues or ambiguous intent, and recommends imposing explicit behavioral constraints for applications that require strict boundaries. The post adds that K3 remains behind Claude Fable 5 and GPT 5.6 Sol in user experience despite being competitive overall.Read moreVenture CapitalThree Years InAuthor: Tomasz Tunguz Published: July 10, 2026Tomasz Tunguz marks Theory Ventures' third anniversary by arguing that AI's central market effect is time compression. In his telling, model release cycles, company revenue milestones, enterprise adoption, and venture categories have all accelerated. Seed, Series A, and Series B still exist as financing labels, but they no longer cleanly describe company maturity when some seed rounds are larger than IPOs and the best AI companies can mature much earlier than prior software companies.The killer detail is the shift from models to inference. Tunguz argues that inference has become the dominant AI market because workloads and buyer preferences are fragmenting: video, batch, local, agentic, and real-time tasks each create different infrastructure needs. He compares this to databases splitting into OLTP, OLAP, vector, and streaming categories, with AI pushing the same specialization into inference infrastructure.The pull is that Theory sees the AI-native venture firm as part of the same pattern. The firm says it has analyzed twice as many investment opportunities with three investors working alongside a nine-person intelligence organization, using agents and research systems to map markets, source companies, and support diligence. The piece is both a market map and a statement about how venture itself is being rebuilt by the technology it funds.Read more: LinkedInVenture Has Rarely Looked More BifurcatedAuthor: Beezer Clarkson Published: July 14, 2026Beezer Clarkson points to PitchBook's Q2 report as evidence that the U.S. venture market has split into two very different realities. AI now accounts for more than 60 percent of all U.S. venture deal value, meaning the headline market can look active and well-funded even while much of the non-AI market is dealing with a much colder liquidity and fundraising environment.The thread uses that split as the setup for Clarkson's latest Origins episode with Alec Litowitz, founder of Magnetar and QStar Capital and one of Citadel's original founding partners. Clarkson says markets like this are periods of genuine uncertainty, not merely ordinary risk, which is why Litowitz's Adaptability Quotient framework is relevant.The embedded clip makes the liquidity point concrete. Litowitz says DPI is “the resolution of uncertainty” because it converts an uncertain investment into actual cash returned to LPs. In his framing, a realized dollar is a real mark, while TVPI remains uncertain until it is realized.The killer detail is the distinction between pricing risk and resolving uncertainty. Litowitz's perspective matters because QStar is a SpaceX investor and Clarkson says the conversation happened just before one of venture's most consequential IPOs. The episode's stated questions are why venture remains a way to gain exposure to innovation, how AI is changing what is investable, why liquidity is ultimately a function of time, and why uncertainty requires a different decision framework from risk.Read more: XThe Best Angel Investors in the US: Who Backs the Most Unicorns, and Who's Active NowAuthor: Ilya Strebulaev Published: July 10, 2026Ilya Strebulaev ranks angels, angel groups, accelerators, and incubators by lifetime U.S. unicorn investments, counting checks written before a company reached unicorn status. The top of the combined list is dominated by organizations: Y Combinator leads with 113 unicorn investments, followed by Plug and Play at 52 and 500 Global at 41. Sand Hill Angels is the highest-ranked angel group at 31.The killer detail is how quickly the list changes below the biggest accelerators. Strebulaev says 271 of the 304 investors in the Top 200 are individuals, or 89%. In the top 100, individuals are 91%. That makes the market underneath the large accelerator counts look much more personal: mostly operators and individual angels writing early checks from their own networks.The pull is the ranking's own caveat. Strebulaev writes that every lifetime leaderboard has a blind spot because many of the unicorns behind those totals were founded a decade or more ago, and some angels have since moved into formal funds, slowed down, or stopped investing. His post therefore separates lifetime performance from recent cohorts, including companies founded in 2015 or later and 2020 or later. For founders or allocators making current decisions, that distinction matters: a career record and a current record are not the same measure.Read more: Ilya StrebulaevAre Prediction Markets Doomed to Fail?Author: Contrary Published: July 16, 2026Contrary argues that prediction markets' current boom depends on whether platforms can prove they are more than regulated gambling with exchange-style branding. Kalshi and Polymarket have reached mass cultural, investor, and regulatory attention, but the article says the underlying idea is old: academic markets, corporate forecasting tools, Intrade, PredictIt, and other predecessors all struggled with the same linked problems of liquidity, legality, and user appeal.The killer detail is the comparison with sportsbooks. Prediction markets present themselves as peer-to-peer, transparent, and non-house-based, but sports contracts reportedly account for more than 90 percent of Kalshi trading, and the article says the platforms keep a much thinner slice of volume than sportsbooks. A market can therefore show sports-betting-scale handle while generating far less revenue.The pull is that the product's hardest problem may be distribution of wins. If a small group of sharp traders captures most profits while casual users lose interest, prediction markets may become valuable data feeds and professional tools before they become durable consumer networks.Read more: SourceRegulationExclusive: The Next Frontier of the Deportation Wars: College CampusesAuthor: Adrian Carrasquillo Published: July 11, 2026Adrian Carrasquillo reports that college campuses are becoming a new front in the fight over immigration enforcement because automatic license plate readers can turn ordinary campus security infrastructure into searchable location data. His thesis is that Flock Safety's camera network, even without direct ICE or DHS contracts, can feed deportation enforcement through local police partnerships and data-sharing practices.The killer detail is the campaign target. The Emergency Campaign to Support Higher Education, working with Schools Drop ICE, is focusing on 75 colleges and universities publicly identified as having Flock contracts. Flock says it has no ICE or DHS contracts, but activists argue the risk comes through local agencies that coordinate with federal authorities and run searches on their behalf.The pull is broader than immigration. Carrasquillo notes that license plate readers have already been abused by officers for stalking, and that Flock's AI search features can identify more than plates, including bumper stickers. A campus safety tool can become a political surveillance system when the data layer is searchable.Read more: The BulwarkThe Supreme Court Broke Independent Agencies. Here's a Way to Slow the Damage.Author: Todd Phillips Published: July 12, 2026Todd Phillips argues that the Supreme Court's decision in Trump v. Slaughter damaged independent agencies by ending for-cause removal protections, but did not leave Congress powerless. The ruling weakens the old model in which commissioners at bodies such as the FTC, NLRB, CPSC, SEC, and CFTC could be insulated from dismissal over policy disagreements. Phillips says the next fight is whether presidents can turn nominally bipartisan commissions into one-party instruments.The killer detail is the procedural fix: quorum rules. Phillips proposes that Congress require bipartisan slates of commissioners to be seated before independent agencies can act. A president

In this episode of Business Brain, we get into when to stop using chat and move to Cowork. Chat is a great place to start — it’s just not where we should live. Dave’s signals: more than five back-and-forths, or constantly pasting in screenshots, log files, and PDFs. That’s the tell. Point Cowork at the folder instead and stop copying and pasting. His trick is to ask the chat directly whether it’s time to move, and to have it write the handoff prompt for the Cowork session, since Cowork doesn’t inherit the full context. Flip the default: assume you’re going to Cowork, then convince yourself why you should stay in chat. We also untangle chats vs. projects vs. Cowork vs. Claude Code — and the one real reason to stay put, which is cloud sync across devices. Then Dave walks us through a wild experiment: handing $10K of found money to Claude to run as a 90-day trading portfolio. He planned it in chat with Fable, executed in Cowork with Opus, and let it pick platforms with API and MCP access — Kraken for crypto, Alpaca for securities. It insisted on a seven-day paper trading run first, keys live in a 1Password vault instead of the session, and there’s a kill switch on his phone. No options, so the floor is zero. Whatever happens, it’s tuition. Real story: Claude didn’t earn the money — it just got him far enough through the process to actually collect it. Get out of the chat, and keep living that Charmed Life. 00:00:00 Business Brain – The Entrepreneurs' Podcast #771 for Casual FridAI, July 17, 2026 00:00:15 July 17th: National Tattoo Day 00:01:26 Defaulting to Claude Cowork instead of Claude Chat 00:10:26 SPONSOR: FanVue. Are you ready to start your own creator journey and make it big? Visit https://www.fanvue.com/ today and launch your career! 00:11:43 SPONSOR: Shopify: Own your customer relationships. Own your revenue. Start with a free trial at Shopify.com/BusinessBrain. 00:12:58 Letting Claude invest the money it earned 00:20:06 Business Brain 771 Outtro This Episode's Big Takeway: Get out of the chat! Check out Business Brain Blueprints Tell Your Friends! Business Blueprints Review Business Brain Subscribe to the show feedback@businessbrain.show Call/Text: (567) 274-6977 X/Twitter: @ShannonJean & @DaveHamilton, & @BizBrainShow LinkedIn: Shannon Jean, Dave Hamilton, & Business Brain Facebook: Dave Hamilton, Shannon Jean, & Business Brain The post FridAI – Cowork vs Chat and $10k to Claude – Business Brain 771 appeared first on Business Brain - The Entrepreneurs' Podcast.

The episode highlights a shift from technology selection to operational risk management in the AI landscape for MSPs. Service providers are being forced to navigate the fast-changing interplay between AI models, the harness software that mediates their deployment, and the financial realities of consumption-based billing. The rapid proliferation of open-source and open-weight AI models, alongside market behaviors from closed vendors and regulatory interventions, is introducing volatility and uncertainty in both cost structures and client offerings. This dynamic creates structural challenges related to margin maintenance, vendor dependency, and responsibility for AI-driven decisions. The discussion cites the release of GLM 5.2, an open-weight model from Z AI, which now rivals expensive closed models on key benchmarks at a fraction of the cost. At the same time, large-scale investments by commercial AI vendors have yet to deliver returns on expectations, with reports indicating businesses that adopted AI are not seeing projected value. Specific attention is given to operational constraints such as compute scarcity, token consumption variability, and export policy restrictions impacting AI availability. The episode notes that these pressures are driving both vendors and MSPs to reconsider the viability of reliance on expensive, closed offerings versus investigating open alternatives. Supportive examples include the proliferation of AI “harnesses” (middleware layers like Perplexity, Claude Code, and Cowork) that sit between service providers and underlying AI models, increasing both choice and complexity. Token billing models are highlighted as a source of unpredictability for MSPs, with vendors like Atera and ConnectWise experimenting with different abstractions to shield or pass through token risk to service providers. The potential for on-premises AI deployments using smaller language models is discussed as a cost-mitigation strategy, though this raises further questions about data privacy, infrastructure burden, and long-term vendor roles. Additionally, uncertainty is flagged around sustainability of leading vendors, with projections that at least one major AI player may exit or be acquired within a year due to financial vulnerability. For MSPs and IT service leaders, these structural and supporting developments translate into increased operational and financial complexity. There is a pressing need to evaluate not just which AI technologies to adopt, but how to architect solutions that can withstand rapid vendor movement, cost swings, and evolving regulatory requirements. Practical safeguards include testing open-source AI models alongside commercial offerings, exercising caution in vendor selection, and closely monitoring evolving consumption billing models. Preparing staff and clients for adaptive, process-oriented approaches—rather than fixed solutions—is positioned as a necessary step to maintain resilience as the AI adoption cycle continues to correct course. Supported by:Pax8CometBackupGuardz

Mike Switzer interviews Peter Marsh, founding partner at Flywheel in Greenville, SC.

You've probably seen Cowork mentioned inside Claude, or perhaps you clocked the headlines when Microsoft folded it into Copilot a couple of weeks ago. Either way, the question people keep asking is simple: how is this actually different from just chatting with Claude? The answer comes down to files, time and money - three things that change quite dramatically once you move from chat to Cowork. In this How I AI episode, Neo and I unpack exactly what Cowork can do that regular chat can't, how to know which one to reach for, and what the new Microsoft version means if you're a Copilot user. How I AI is a special series within How I Work where Neo and I explore how high performers are using AI at work to boost productivity, make better decisions and reduce overwhelm. What you'll learn: What actually changes when Claude can touch files on your own computer The simple test for deciding between Chat and Cowork for any task How Cowork differs from Claude Code, and who each one is really built for Three lesser known Cowork features worth exploring What Microsoft's version of Cowork does differently, and what it costs Practical AI tools for productivity and focus Real-world AI workflows used by high performers How to use AI at work without burning out Smart shortcuts for managing time and mental load Connect with Neo Aplin on LinkedIn (https://www.linkedin.com/in/neoaplin/) and via inventium.ai (https://inventium.ai), where he leads Inventium's AI training and upskilling work with organisations and teams. My latest book The Energy Game is out now. You can order a copy here: https://amzn.to/48ID29M Connect with me on the socials: Linkedin (https://www.linkedin.com/in/amanthaimber) Instagram (https://www.instagram.com/amanthai) If you are looking for more tips to improve the way you work and live, I write a weekly newsletter where I share practical and simple to apply tips to improve your life. You can sign up for that at https://amantha.substack.com/ Visit https://www.amantha.com/podcast for full show notes from all episodes. Get in touch at amantha@inventium.com.au Credits: Host: Amantha Imber Sound Engineer: Martin Imber See omnystudio.com/listener for privacy information.

Les vacances en freelance, c'est souvent une illusion.Dans cet épisode, je te montre les 3 goulots qui t'empêchent de vraiment décrocher. Je te raconte comment la préparation de 15 jours de stage de surf intensif m'a forcée à regarder en face les 3 blocages qui empêchent les solopreneurs de vraiment couper.Et ce que j'ai mis en place pour les régler avant de partir :

Chinese AI models grabbed over 30% of US token use as Beijing weighed curbing access. Anthropic expanded Cowork to mobile and web, Meta launched its first AI image generator on Instagram and WhatsApp, and Xbox's $80B Game Pass bet failed. OpenRouter: Chinese AI models have drawn 30%+ of token use by US companies each week since February 8, peaking at 46%, up from 11% over the previous 12 months (CNBC) Chinese authorities have held meetings with top tech firms about potentially restricting overseas access to China's most advanced AI models, sources say (Reuters) Anthropic is bringing Claude Cowork to mobile and web, letting tasks run in the cloud and continue working even when no device is online (9to5Mac) Anthropic is bringing Claude Cowork to mobile and web, letting tasks run in the cloud and continue working even when no device is online (ZDNet) Meta launches Muse Image in Meta AI, Instagram, and WhatsApp, and previews Muse Video, the first media generation models from its Superintelligence Labs (Meta) Meta's new Muse Image tool lets users generate AI photos similar to what they'd normally post, including vacation selfies and photo-booth style shots (NYT) Public Instagram profiles are automatically opted into being used as material for others' AI image generations via Meta AI, unless users adjust their settings (Wired) Xbox spent nearly $80B over a decade on content deals betting gamers would flock to Game Pass, but most gamers prefer sticking to a handful of favorite games (Bloomberg) Subscribe to the ad-free feed. Learn more about your ad choices. Visit megaphone.fm/adchoices

In this solo episode, Danny Gavin reveals how he automated his Google Ads Search Query Report process using Claude Cowork and Windsor.ai, eliminating the 3-4 hours per account it previously took to complete manually. The system pulls live metrics, merges them by keyword and ad group, and outputs a structured Excel file with an ad group summary, KPI-focused observations, and pre-populated negative keywords. It's a must-listen for those looking to systematize time-consuming account analyses and free up time to tackle other tasks. Episode Highlights:Search query reports are one of the highest-leverage tasks in Google Ads management, and doing this job manually can eat up three to four hours of work per account per cycle.Danny shares how he connected Claude Cowork to live Google Ads data through Windsor.ai and turned Optidge's entire SQR methodology into a repeatable automation.He shares what the six-tab Excel output actually contains, including auto-generated observations, pre-populated negatives, and an ad group summary that flags structural problems before anyone looks at individual search terms.The 80/20 split: See what Claude handles automatically versus where human judgment still makes the final call.This episode gives four practical lessons for building AI automations in an agency context, from documenting your process first to understanding why the ROI compounds over time.Episode Links: Digital Marketing Mentor PodcastDanny Gavin on LinkedInOptidgeClaude Cowork Windsor.ai Send us Fan MailFollow The Digital Marketing Mentor:Website and Blog: thedmmentor.comInstagram: @thedmmentorLinkedin: @thedmmentorYouTube: @thedmmentorInterested in Digital Marketing Services, Careers, or Courses? Check out more from the TDMM Family:Optidge.com - Full Service Digital Marketing Agency specializing in SEO, PPC, Paid Social, and Lead Generation efforts for established B2C and B2B businesses and organizations.ODEOacademy.com - Digital Marketing online education and course platform. ODEO gives you solid digital marketing knowledge to launch/boost your career or understand your business's digital marketing strategy.

Anthropic has expanded its AI ecosystem with Claude Cowork and Claude Code, two agentic tools designed for autonomous task execution across desktop, web, and mobile platforms. While Claude Code serves as a specialized terminal-based assistant for developers managing complex codebases, Cowork provides a user-friendly interface for general knowledge workers to automate document and data workflows. Access to these features is primarily distributed through tiered subscription plans, with the Max plan offering the highest usage limits for power users. Recent updates include integration with Microsoft 365 write tools and specialized versions for government agencies requiring high-security environments. Additionally, the high-performance Fable 5 model has been transitioned to a pay-per-use credit system due to its significant processing demands and advanced capabilities. Together, these sources outline a shift from simple chat interactions toward a comprehensive suite of autonomous digital agents tailored for diverse professional needs.

In this episode, we discuss the exciting launch of Claude Co-Work on mobile devices, enhancing accessibility for users. Additionally, we explore AI advancements from companies like DeepSeek, SK Hynix's massive IPO, and innovative tech like Solos' camera-less smart glasses and Vercel's strategy in AI model deployment.Chapters00:00 Introduction to Claude Co-Work00:10 DeepSeek's Inference Chips00:21 SK Hynix's $28 Billion IPO00:35 Launch of Solos Smart Glasses00:45 Vercel's AI Model Strategy00:59 Conclusion and Personal Update Show LinksGet the top 80+ AI Models for $8.99 at AI Box: ⁠⁠https://aibox.aiHow I Grow and Scale My Business with AI: https://www.skool.com/aihustleGet the AI Chat Daily Newsletter: https://www.aichatdaily.com/newsletter

#980 What if the fastest way to get ahead of your competitors was already sitting in an app on your phone? In part 2 of this 2-part episode hosted by Brogan Williams, Jacques Hopkins breaks down exactly how everyday entrepreneurs can start using AI agents today, no Mac Mini or technical setup required. He walks through the three tabs of the Claude app, and makes the case that Cowork is the easiest on-ramp for non-coders who want to connect tools like Gmail and their calendar and start delegating real tasks. Jacques shares the origin story of his AI Operator Bootcamp, born from a retreat where fellow course creators were blown away by what he'd built with Rocky, and reflects on rebranding his business around AI as information becomes commoditized and transformation becomes the real product. He closes with candid advice on what he'd do differently starting out today (hint: audience first, always) and how he stays current in a space that changes by the week! What we discuss with Jacques: + Claude app's three tabs: Chat, Cowork, Code + Cowork as the easy on-ramp for non-coders + Connecting Gmail and calendar via connectors + Natural language works even in Claude Code + VS Code vs. using Claude Code directly + Origin story of the AI Operator Bootcamp + Migrating clients from OpenClaw to Hermes + Why transformation beats information now + Audience-first advice for starting over + Using X lists to stay current on AI Thank you, Jacques! Check out Part 1 of this episode. Check out Piano In 21 Days at ⁠PianoIn21Days.com⁠. Check out The Online Course Guy at ⁠TheOnlineCourseGuy.com⁠. Check out AI Operator Bootcamp at ⁠AIOperatorBootcamp.com⁠. Watch the ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠video podcast⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ of this episode! To get access to our FREE Business Training course go to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠MillionaireUniversity.com/training⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠.⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ To get exclusive offers mentioned in this episode and to support the show, visit ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠millionaireuniversity.com/sponsors⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Learn more about your ad choices. Visit megaphone.fm/adchoices

#claudecowork #orangemud #jimjimsreinventionrevolution Josh Sprague is a serial entrepreneur whose passion for trail running inspired a suite of companies focused on endurance athletes, promotional merch and sales & marketing infrastructure.  Listen to JJRR 134 as Josh describes how, in the early days of ecommerce, his frustration with traditional hydration products fueled development of the Hydraquiver vest pack and the founding of Orange Mud. Now a fan of Claude Cowork, listen to the end to hear how AI tech is impacting today's ecommerce game.       https://joshspragueinfo.com/ https://www.orangemud.com/ https://sevenclay.com/ https://anvilandacre.com/ https://magicmind.superfiliate.com/JIMCIRILLO https://ko-fi.com/jimjim99 jimjim99 | Twitter, Instagram, Facebook | Linktree 04:16s Orange Mud the company and how / why it was founded 09:46s How the Transition Wrap put Orange Mud "on the map" 12:36s Early days of ecommerce, understanding online marketing 17:20s Measuring ad performance and Claude Cowork's massive value 24:40s Josh's discovery of how and why to use for AI in life and business 26:50s Using Claude Cowork to create agents ("employees") 31:33s Context on security with Claude Cowork, is it better or worse than a human? 39:34s How Seven Clay promotional company got started 48:53s Implementing an EOS Entrepreneurial Operating System 56:01s China direct online products into Amazon is a big concern Enjoy the episode? Share with friends! Subscribe in Spotify, Apple or Google Podcasts! https://www.jimjimsreinventionrevolution.com/resources jimjim99 | Twitter, Instagram, Spotify, Facebook | Linktree https://ko-fi.com/jimjim99

Fiona Fung leads the teams behind Claude Code and Cowork at Anthropic (overseeing Boris Cherny and the entire engineering and PM team). Before Anthropic, she spent 11 years at Microsoft building Visual Studio and TypeScript and then moved to Meta, where she started Facebook Marketplace (now generating over $100 billion in GMV annually), worked on Meta's first smart glasses and AR glasses, and led infrastructure, growth, integrity, and safety teams at Instagram. She's been an engineer for over 25 years and has a unique perspective on how the role of building software is changing.In our in-depth conversation, we discuss:1. What she's learned about running a team that's shipping 8x more code than before2. Which roles AI will transform next3. Specific ways her team uses AI4. How Claude “routines” have changed how she operates as a manager5. The context-switching problem no one has solved yet6. The biggest unsolved problem in AI7. What keeps her up at night—Brought to you by:WorkOS—Make your app enterprise-ready, with SSO, SCIM, RBAC, and more: https://workos.com/lennyMercury—Radically different banking, now with Command: https://mercury.com/—Where to find Fiona Fung:• LinkedIn: linkedin.com/in/fionafung—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Introduction to Fiona Fung(02:31) How the engineering role has transformed over 25 years(09:28) What an AI-pilled software team looks like in 2026(12:26) Using Claude to manage and review team output(14:40) The evolution of code review and verification(16:55) Who to hire: creative builders and deep systems experts(18:18) The shift to ambitious thinking(19:40) The growth mindset required to thrive in AI-native teams(25:52) Helping small businesses adopt AI tools(31:46) How Anthropic spots latent demand and builds for it(35:08) The next frontier: asynchronous work with AI routines(38:06) Agency and accountability in AI-native teams(39:40) The vibe shift from token-maxing to ROI measurement(44:24) The “bad vs. sad” quality framework(49:34) Why all managers start as ICs at Anthropic(55:24) Preventing skill atrophy(58:43) Managing context switching with 20 AI agents running(1:00:08) How PM and data science roles are transforming(1:03:40) The importance of dogfooding and using your own product(1:08:36) Outstanding questions(1:12:48) The future of engineering jobs and education(1:17:59) What keeps Fiona up at night: team culture at scale(1:22:53) From six-month roadmaps to JIT (just-in-time) monthly planning(1:27:03) Lightning round—References: https://www.lennysnewsletter.com/p/building-the-most-ai-pilled-engineering—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.—Lenny may be an investor in the companies discussed. To hear more, visit www.lennysnewsletter.com