Podcasts about fractional

Send us Fan MailOn this episode of ‘For the Love of Chiropractic' we look at the flow of money to and through your practice.  Most service business owners think they have a revenue problem.They don't as mt guest today will explain. Today's guest, Arron Bennett, has spent eight years proving it. He's the founder of Bennett Financials, creator of the 60/15/15 framework, and the fractional CFO behind $127M in client revenue andover $105M in tax savings. He typically works with service businesses doing $2Mto $20M, and he's going to show us exactly where the money is leaking and how more the the money you earn by providing great chiropractic care can go home with you. I hope you enjoy my conversation with  Arron, so. I say …welcome to the show today.

Subscribe to DTC Newsletter - https://dtcnews.link/signuppilothouse.coMeta has been up and down since the outage a few weeks back, and the timeline is full of advertisers feeling it. So Eric pulled three of Pilothouse's most senior people onto the after-hours couch: Abby and Aves from the creative and strategy side, and Taylor from the Meta side, for a live conversation about what to do when the platform wobbles.If you buy media on Meta, or you're a founder whose revenue leans on it, this is the difference between a bad two weeks and a bad quarter.What you get:The tactical spin cycle. Performance dips, panic sets in, and buyers ship 15 more ads built off the ones already dying. That amplifies poor delivery and raises CPMs. "Amplification of what's not working is never the route forward."The full list of panic moves to skip: un-strategic ad volume, rushed channel expansion, rescue promos that train customers (and Meta) to expect discounts, account rebuilds, the "fresh pixel" request, and firing your agency.The diagnosis question: Meta crumbled, so what part of the business fell through? No new customers points one direction. No conversions points at email and retention first. The gap picks the channel.Stocking the pond. Why every brand should already know its next channel, and how to tell a reach problem (Pinterest) from a conversion problem (TikTok Shop) before you spend a dollar.The iOS 14.5 precedent: partial blindness, no drastic changes, better measurement on the other side.Pausing ads without tanking the account. Fractional touchpoints, checking median customer-journey length in your MTA before making the swing, and why Meta usually has a reason for pushing spend where it does.Creative is the targeting. Millennial moms who look identical on paper but speak completely different visual languages by region. Butter yellow instead of white. A luxury brand that sells milestone moments instead of USPs."This is an ad and it's so stupid." Why absurdist, self-aware ads are out-earning earnest millennial branding with marketing-aware customers.Where AI belongs (reporting, automation, surfacing phrases from your own data) and where it doesn't (creative direction, insights, your next steps). Plus the term for what happens when you outsource the thinking: cognitive debt.Who this is for: media buyers, creative strategists, and founders running meaningful spend on Meta right now.What to steal: the diagnosis question, the pause-decision checklist, and the competitor-review mining tactic for finding customer language.Timestamps:00:00 Meta Volatility and Common Mistakes08:56 Building a More Resilient Growth Strategy17:45 Should You Pause Underperforming Ads?21:53 How to Research Customers Better with AI35:40 AI, Creative Strategy & Content VolumeSubscribe to DTC Newsletter - https://dtcnews.link/signupAdvertise on DTC - https://dtcnews.link/advertiseWork with Pilothouse - https://www.pilothouse.co/?utm_source=AKNF635Follow us on Instagram & Twitter - @dtcnewsletterWatch this interview on YouTube - https://dtcnews.link/video

VT, DFAW, and AVGE all promise global diversification—but they take different roads to get there. Don and Tom compare cost, holdings, factor tilts, and the extra risk behind higher expected returns, then explain why the “best” one-fund solution depends on how much risk you actually need.Then a listener asks why advisors build portfolios with many funds when one might do. The answer runs through tax-loss harvesting, rebalancing, personalization, and the fine line between thoughtful design and a 20-fund hodgepodge.Also: the hidden tradeoffs in fractional rental-property platforms such as Arrived, why IRMAA anxiety can outweigh the actual Medicare surcharge, and a sensible way to unwind concentrated tech gains without detonating the tax bill.00:30 Swing-era cold open01:53 Three global funds, one decision03:29 VT, DFAW, and AVGE compared05:45 Recent returns and expense ratios06:47 Factor tilts: value, size, and profitability08:59 Holdings, frontier markets, and micro-caps10:40 Matching the fund to the risk you need14:52 Listener question: one fund or many?17:50 Why advisors use multiple funds22:08 Fractional real estate and Arrived25:47 IRMAA anxiety versus the actual surcharge28:56 Unwinding concentrated tech gains32:15 Buc-ee's, crypto, and trademark comedyQuestions? Comments? Click!

Someone wrote a blog post arguing that calling yourself a Fractional CMO is pricing yourself out of the market. The argument isn't wrong, exactly, it's just aimed at the wrong target. What gets unpacked is the real question underneath the debate: what kind of problems do you want to solve, and for whom? The episode traces the difference between companies that need a web designer and companies that need a seat filled at the leadership table. Those are two completely different markets, two completely different conversations, and two completely different income levels. One of them has a $15,000/month client saying "yeah, that rate makes sense", and the other is grinding on project work. Key Topics Covered: The blog post that sparked the rant,  "Calling Yourself a Fractional CMO is Stupid" Why the author is both right and wrong at the same time When a company actually needs a Fractional CMO vs. a specialist The Dan Kennedy principle on pricing and why being the most expensive is a strategy A real private equity client example showing what CMO-level ownership looks like Why solving bigger problems is the only path to bigger paychecks AI, staying relevant, and why playing around with new tools matters more than most people think Take the First Step Toward Growth with CMOx  Booking a call with our team is super easy, stress-free, and all about YOU. Whether you're exploring options or ready to scale, this no-pressure consultation is designed to understand your needs and guide you in the right direction. 

Thanks to our Partner, AppFueledToo many shop owners start their businesses because they're great at fixing cars, not because they're accountants, tax experts, or financial strategists. Unfortunately, that's where a lot of costly mistakes begin.Brian sits down with Eric Joern of Kaizen CPAs for an honest conversation about the financial side of running an auto repair shop. From common bookkeeping mistakes and tax planning myths to the real difference between a bookkeeper, CPA, and fractional CFO, this episode is packed with practical advice that can help you build a stronger, more profitable business. Brian also shares lessons from his own entrepreneurial journey, including the financial mistakes that ultimately shaped the way he runs his business today.If you're ready to understand your numbers, make better business decisions, and build a shop that's financially healthy for the long haul, you won't want to miss this conversation. Listen now wherever you get your podcasts.Lagniappe (Books, Links, Other Podcasts, etc)The E-Myth Revisited by Michael E. GerberProfit First by Mike MichalowiczKaizen CPAsThanks to our Partner, AppFueledAppFueled at appfueled.com. “Are you ready to convert clients to members? AppFueled™ specializes in creating custom apps tailored specifically for auto repair businesses. Build your first app like a pro.”The Automotive Repair Podcast Network: https://automotiverepairpodcastnetwork.com/Download Our Free Mobile Podcast App: https://automotiverepairpodcastnetwork.com/app/Remarkable Results Radio Podcast with Carm Capriotto: Advancing the Aftermarket by Facilitating Wisdom Through Storytelling and Open Discussion. https://remarkableresults.biz/Automotive Field Theory with Matt Fanslow: From Diagnostics to Quantum Physics and Mental Health, Matt Fanslow is Lifting the Hood on Life. https://mattfanslow.captivate.fm/Business by the Numbers with Hunt Demarest: Understand the Numbers of Your Business with CPA Hunt Demarest. https://huntdemarest.captivate.fm/The Auto Repair Marketing Podcast with Kim and Brian Walker: Marketing Experts Brian & Kim Walker Work with Shop Owners to Take it to the Next Level. https://autorepairmarketing.captivate.fm/The Weekly Blitz with Chris Cotton: Weekly Inspiration with Business Coach Chris Cotton from AutoFix - Auto Shop Coaching. https://chriscotton.captivate.fm/Speak Up! Effective Communication with Craig O'Neill: Develop Interpersonal and Professional Communication Skills when Speaking to Audiences of Any Size. https://craigoneill.captivate.fm/

Robert Sloop is the CEO of Kaizen Management, LLC, a consulting firm that helps restaurant operators improve financial performance, operations, and tech stack implementation. With over 30 years in foodservice, he has served as CFO for leading multi‑concept restaurant groups and now advises owners on data‑driven systems and continuous improvement. Join RULibrary: www.restaurantunstoppable.com/RULibrary Join RULive: www.restaurantunstoppable.com/live Set Up your RUEvolve 1:1: www.restaurantunstoppable.com/evolve Subscribe on YouTube: https://youtube.com/restaurantunstoppable Subscribe to our email newsletter: https://www.restaurantunstoppable.com/ Today's sponsors: - Hermetic.ai  - Private event leads die in inboxes every day. Mia fixes that. She's an AI agent that responds within seconds, handles the back-and-forth, and fills your event calendar — automatically. Fully integrated in under 10 minutes. Head to hermetic.ai and put her to work. https://www.hermetic.ai/ - Hotshift - Hotshift is the all-in-one tool built by a restaurant owner, for restaurant owners. Scheduling, hiring, training, reviews — one roof, one login. Plusreal-time labor cost forecasting before the shift ever starts. Head to hotshift.pro/unstoppable. - Restaurant Technologies — the leader in automated cooking oil management. Their Total Oil Management solution is an end-to-end closed loop automated system that delivers, monitors, filters, collects, and recycles your cooking oil eliminating one of the dirtiest jobs in the kitchen.. Automate your oil and elevate your kitchen by visiting rti-inc.com or call 888-779-5314 to get started! - US Foods®. Running a restaurant takes MORE than great food—it takes reliable deliveries, quality products, and smart tools. US Foods® helps you make it. Ready to level up? Visit: usfoods.com/expectmore. - Guest contact info:  Email: rsloop@kaizen-management.com Thanks for listening! Rate the podcast, subscribe, and share! 

Most business owners are experts at what they do, but many struggle with understanding the financial health of their business. If you've ever wondered whether you're truly making money, why cash always seems tight, or how to make smarter financial decisions, this episode is for you.In this episode of Business by the Bay, host Ajay Saini sits down with Sarah Spector, CPA and founder of Spector Wellman Accounting and Consulting. With more than 25 years of executive leadership experience across healthcare, e-commerce, staffing, distribution, entertainment, and nonprofit organizations, Sarah shares practical advice that every business owner can put into action.The conversation goes far beyond accounting. Sarah explains why understanding your numbers is only one piece of the puzzle and how operational decisions, leadership, marketing, and financial strategy all work together to build a profitable and sustainable business.What a Fractional CFO actually does and when your business needs oneWhy many profitable businesses still struggle with cash flowThe biggest financial mistakes small business owners makeHow to choose the right financial advisor or consultantWhy reviewing your financial statements every month is criticalThe importance of asking better questions instead of settling for basic bookkeepingHow operational improvements can dramatically improve profitabilityWhat lenders look for before approving business financingWhy business owners should think beyond taxes and focus on financial strategyReal client success stories that show the impact of having the right financial guidanceSarah also shares how one client went from nearly shutting down during the pandemic to building a thriving business with millions in reserves, all through careful financial planning, forecasting, and smart decision-making.Whether you're a startup founder, entrepreneur, nonprofit leader, or established business owner looking to improve profitability and gain confidence in your financial decisions, this episode is packed with practical insights you can apply immediately.In this episode, you'll learn:

As studios contend with layoffs, hiring freezes, longer development cycles, and a growing supply of experienced talent outside traditional roles, fractional work is emerging as a potential middle ground between full-time hiring and conventional consulting. Host Devin Becker is joined by Aaron Bush, co-founder and Managing Partner of Naavik, to discuss why the company expanded from research and advisory into fractional talent, what meaningfully separates fractional leadership from contracting or staff augmentation, and how these engagements work in practice. They examine which roles fit the model, why senior talent dominates the supply pool, how studios retain knowledge after an engagement ends, and whether increased reliance on fractional experts could weaken the industry's pipeline for developing junior talent. The conversation also considers whether this is a temporary response to the current employment cycle or an early sign of studios becoming smaller permanent teams supported by flexible specialists.Check out Naavik's Fractional Talent Network: https://naavik.co/fractional-talent/ We'd like to thank Dive for making this episode possible! With its fully managed analytics and LiveOps platform built for game studios, 95% of their clients grow revenue in one year. All of that without having to hire an in-house data team. Learn more here: dive.games/scale If you like the episode, please help others find us by leaving a 5-star rating or review! And if you have any comments, requests, or feedback shoot us a note at podcast@naavik.co. Watch the episode: YouTube ChannelFor more episodes and details: Podcast WebsiteFree newsletter: Naavik DigestFollow us: Twitter | LinkedIn | WebsiteSound design by Gavin Mc Cabe.

In this episode, Nkem Oghedo and David Nebinski talk Chief of Staff transitions, the "identity economy," and why leaning into the unknown beats chasing certainty. Nkem shares the news that she's stepping away from fractional work to go full-time with one of her clients, and what that means for her portfolio career going forward.

Danny Gal was born and raised in the UK, and now lives outside of London. He attended University in Nottingham... yep, the same one from Robin Hood. He LOVES challenges, and not just any challenges - the hard ones. He is done Iron Man competitions, ultra marathons, climbed Mount Kilimanjaro, and jumped out of a perfectly good plane, to name a few. He loves doing them once... and then never again. He's got 2 small kids, and believes in work hard, play hard.Danny has worked in many roles in the past, across enterprises and the like. What he found most difficult was scaling himself. He got to talking with his now co-founder about building something around the idea of scaling oneself, and took it to some businesses to validate it. Once he saw them get excited about it, he figured they were onto something.This is the creation story of Proteams.SponsorsUnblockedTECH DomainsMezmoBraingrid.aiLinkshttps://proteams.com/https://www.linkedin.com/in/dannygal/Our Sponsors:* Check out Cash App and use my code CASHAPP10 for a great deal: https://cash.app* Check out Plaud AI and use my code CODESTORY for a great deal: https://plaud.aiAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

In this episode of Beyond the Loop, Joe Fontana, Founder of Fry the Coop, and John Frank, CEO of Third Road Management, discuss how businesses can scale strategically without overextending resources. Through the lens of fractional leadership, they explore how companies can access specialized expertise at pivotal moments, strengthen financial operations, and build the infrastructure needed for long-term success. The conversation examines how the right talent and operational continuity can help growing businesses navigate change with confidence. From knowing when to bring in experienced leadership to creating scalable systems that support sustainable growth, this episode offers practical insights for business leaders looking to grow smarter, not just bigger.    Subscribe on Apple, Spotify, YouTube, or wherever you listen to podcasts.    Thank you to our podcast sponsor, Shure Incorporated.  For nearly 100 years, Shure Incorporated has developed best-in-class audio products that provide high-quality performance, reliability, and value. Headquartered in Niles, Illinois, our history of innovation and expertise in acoustics, wireless technology, and more enables us to deliver seamless, transparent audio experiences to a global audience. Our diverse product line includes world-class wired and wireless microphones, networked audio systems and signal processors, conferencing and discussion systems, software, a loudspeaker, and award-winning earphones and headphones.    Find Shure on: Facebook | LinkedIn | Instagram 

In an ever-changing marketplace, career currency won't be job titles or resumes, says Angela Finlay. “It's the skills we bring to the table that will be the most critical piece” of career development and growth.Angela is a Chief Human Capital Strategist, Fractional and Interim Chief HR Officer with over 25 years leading talent strategy inside Fortune 150 enterprises, global organizations, and growth stage companies. She is the author of Skill Stacking: Taking Ownership of Your Career in Changing Times.In this conversation with Daniel and Peter, Angela discusses the importance of individual skills in the age of AI–and why organizations need to broaden how they look at hiring new talent. “It's not just job titles,” she says. “I want to know everything I can about you and what you've done and projects and volunteer things you've done and all of these other outside activities that allow me to start to look at you from a skill level perspective.”Tune in to learn:The five types of skills to stackThe simple tool to help you recognize and articulate your skillsWhy the gig economy is a good thing–and how leaders can lean into it effectivelyAngela suggests that anyone can start to contribute to creating a skills-based marketplace by intentionally listing skills developed across every area of life. When we start to think about skills across the five stacks, “we'd be amazed at what we do and what we actually bring to the table, that will never show up on our resume.”Questions, or comments? E-mail us at podcast@stewartleadership.com—Sign up for Stewart Leadership's newsletter: https://stewartleadership.com/newsletter/—Resources and LinksWindward Human Capital Management websiteAngela Finlay LinkedInSkill Stacking: Taking Ownership of Your Career in Changing Times (Amazon link)Stewart Leadership Insights and Resources6 Career Goal Strategies Every Leader Needs in Uncertain Times5 Tools to Own Your Career and Inspire EngagementStrategic Resilience in the Age of AI (Podcast)Leading in the Age of AI (Podcast)10 Questions to Develop the Best Individual Action PlanThe New Business Imperative: Career Development (White Paper)The Top Four Leadership Potential IndicatorsLEAD NOW! Skills for Creating Purpose as a Manager8 Skills Every Middle Manager Needs to DevelopThe Coming Manager Shortage: 7 Moves Every Leader Must Make Now3 Tips for Leading Through the AI Digital Transformation—#leadership #podcast #leadershippodcast #leadershipdevelopment #careers #careerdevelopment #StewartLeadership #LeadershipGrowthPodcastIf you liked this episode, please share it with a friend or colleague, or, better yet, leave a review to help other listeners find our show, and remember to subscribe so you never miss an episode. For more great content or to learn about how Stewart Leadership can help you grow your ability to lead effectively, please visit stewartleadership.com and follow us on LinkedIn, Instagram, and YouTube. 

In this episode of the CFO 4.0 Podcast, host Hannah Munro is joined by returning guest Sara Daw, CEO of The CFO Centre Group, to explore how CFOs can build the right team around them from smart delegation to bringing in a fractional "CFO's CFO" for M&A, systems work, or steady-ship support.In this episode:Why delegation should focus on outcomes, not tasksHow to build a team that plays to individual strengthsWhat to do when a delegated task goes wrongThe emerging "CFO's CFO" model and when to use itGround rules for successful peer-level fractional partnershipsWhy the future of executive work is a team sport, not a solo actLinks mentioned in this episode:Sara's Linkedin Learn more about the CFO centreExplore other CFO 4.0 Podcast episodes here.Subscribe to our Podcast!

Natalie Trotta used to be a Chief of Staff. Now she is building her own consulting business. She started off calling it fractional work and now it has grown. You will learn about how she positions herself, how she has found clients, the importance of conversation, and much more. If you are a Chief of Staff thinking about what is next or wanting to start doing consulting or fractional work, this episode is for you!

AI-Powered vs. AI-Native: What Mortgage Lenders Need to KnowYouTube DescriptionWhat is the difference between AI-powered mortgage technology and an AI-native mortgage platform—and does that distinction actually matter to lenders?In this episode of the Fintech Hunting Podcast, Michael Hammond sits down with Brianna Lin, co-founder of Copperlane AI, to examine what AI-native mortgage origination looks like in practice.AI-powered technology typically adds AI capabilities to an existing software product. AI-native technology places AI at the center of the workflow. But the label alone means nothing unless the technology can solve real operational problems, protect borrower information, support human judgment, and improve the mortgage experience.Brianna shares what Copperlane has learned from working directly with lenders and explains how AI can help mortgage professionals reduce repetitive administrative work while giving loan officers more time to focus on borrowers, relationships, and revenue-generating activity.In this conversation, you will learn:What separates AI-powered from AI-native mortgage technologyWhich parts of mortgage origination are most ready for automationWhy AI cannot repair a fundamentally broken workflowWhere automation should stop and human oversight should beginHow lenders can evaluate AI beyond time savingsWhy audit trails, security, compliance, and borrower-data protection matterHow to build employee trust and manage AI adoptionWhich internal AI use cases lenders should evaluate firstWhat questions lenders should ask before selecting an AI technology partnerThe biggest question is not whether AI is coming to mortgage. It is whether lenders will use it to automate outdated processes—or rethink how work gets done.This episode is for mortgage executives, operations leaders, technology teams, loan officers, and anyone responsible for evaluating or implementing AI in mortgage origination.Chapters00:00 Introduction00:34 Brianna Lin's path into mortgage technology02:24 What mortgage origination is ready to reimagine03:53 AI-powered versus AI-native technology06:04 Why AI cannot fix a broken workflow07:06 Where automation should end09:42 Measuring AI beyond time savings11:45 Change management and employee trust13:49 The best first AI use cases for lenders15:59 How AI can change the loan officer's work17:29 Connecting with Brianna and CopperlaneSubscribe to the Fintech Hunting Podcast for candid conversations with the founders, executives, and innovators shaping the future of mortgage, fintech, financial services, and artificial intelligence.###Michael Hammond, Founder of NexLevel Advisors, is the leading fractional CMO in mortgage and mortgage technology, specializing in AI-powered growth strategy and audience development.

In this episode, Katie shares hard-won lessons on the biggest challenge most founders face: letting go of control and bringing in outside specialist support. She reveals why so many businesses get burnt by agencies, how to set clear expectations and KPIs even when you lack the expertise yourself, and the right way to vet and onboard talent that actually delivers. You'll learn exactly when your “fire” is dimming and it's time to hand off the tasks draining your joy, why fractional executives often beat full-time hires for growing companies, and how FRX carefully selects battle-tested operators who have sat in real C-suite seats managing seven-figure budgets. Guest Links Connect with Katie Peterman on LinkedIn Learn more about FRX fractional executive services Edit your podcasts like a pro:https://get.descript.com/mrzy10nwivuqJoin me as a guest or start your podcast journey:https://www.joinpodmatch.com/nickkuhne Timestamps 00:00 – Welcome & Katie Peterman's background 01:06 – The real challenge of releasing control to outsiders 02:13 – Why agencies often fail and how to fix it 04:39 – Setting KPIs and expectations when you lack the expertise 09:39 – Taking full ownership across departments 10:34 – How FRX vets and selects elite fractional executives 15:27 – When founders should bring in help (the joy test) 18:41 – Fractional vs full-time executives 20:43 – What happens when fractional talent gets full-time offers 23:18 – Where to find Katie and FRX Connect with me on:All my linksBecome a guestSign up for RiversideGet Descript #DigitalMarketing #Branding #PersonalBranding #MarketingInsights #SocialMediaStrategy Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

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

Breakfast Leadership Network is proud to introduce the Breakfast Leadership AI Advisory Service, a new offering built to help executives and organizations translate artificial intelligence from a buzzword into a working advantage. The service pairs proven leadership frameworks with practical AI implementation guidance, helping leaders identify where automation and intelligent tools can strengthen decision making, reduce operational drag, and free up capacity for the work that actually requires human judgment. Rather than a generic technology rollout, the advisory service is grounded in the same principles that anchor the Leadership Operating System, ensuring that any AI adoption strengthens culture and accountability rather than working against them. Leaders interested in exploring how AI can support their organization's goals are encouraged to connect with the Breakfast Leadership team to learn more. https://BreakfastLeadership.com 

This episode of the features a conversation with Kimberly DeCarrera of Springboard Legal, who uniquely serves as both a fractional general counsel and CFO for small and medium-sized growing companies.Throughout the episode, Kimberly and Patrick explore the convergence of law and finance, emphasizing that legal and financial decisions are deeply intertwined and should not be managed in isolation. Key discussion points include:Proactive Risk Management: Kimberly emphasizes the importance of identifying business-specific risks—ranging from unclaimed property to ransomware—and implementing internal policies and procedures to mitigate them, often serving as a necessary precursor for insurance coverage.The Power of Systems: A central theme is the necessity of standardized, documented workflows for scalability. Kimberly explains that without these systems, firms struggle to deliver consistent client service, leading to CEO burnout and operational instability.Human-Centric Legal Services: Despite the rapid advancement of technology and AI, both speakers argue that the value of a modern lawyer lies in their ability to provide "human therapy"—listening, empathizing, and helping clients navigate the emotional complexities of business decision-making.Navigating the Modern Legal Model: The discussion contrasts traditional, high-friction legal models with flexible, proactive partnerships. Kimberly shares insights on how she uses her unique dual-role expertise—and even her experience working from an RV—to offer highly personalized, mobile, and responsive counsel to her clients.Ultimately, the episode serves as a guide for law firm owners and business leaders on moving away from reactive "firefighting" toward building sustainable, tech-enabled, and human-centered organizations.What happens when a Chief Financial and Legal Officer gets burned out from hyper growth? She goes back to her own law firm, starting a new business model of Fractional General Counsel. But not to leave her finance experience behind, she also offers Fractional CFO services to clients. In particular, law firms. Because they didn't teach us how to run law firms in law schools, Kimberly DeCarrera uses her experience to build better law firms, trying to escape the toxicity of some of her prior employers as well as helping clients with sustainable growth. So they don't burn out or run out of cash. ⁠https://springboardlegal.comhttps://linkedin.com/in/kdecarrerahttps://threads.com/kdecarrerahttps://facebook.com/springboardlegal----------------------------------------------------------------------Season Sponsor: ⁠⁠⁠⁠Andy Gregory Law, PLLC

In this episode, host David Mansilla is joined by Azam Beyk, a former professional football player, telecom engineer, and the visionary founder of Neoma.From surviving the ruins of the Yugoslavian war in Croatia to facing career-ending sports injuries, Azam's path to tech leadership was anything but traditional. He opens up about the painful two-year emotional transition of rebuilding his life, how he went from deep-tech engineering to corporate pre-sales, and why he ultimately stepped away from corporate life to build an AI platform that is modernizing how enterprises and high-tech vendors collaborate.If you want to build a deeply aligned, resilient, and human-centric organization, Azam's unique leadership perspective will change how you view your business.Key Takeaways from This Episode:The "New Moon" Philosophy: The inspiration behind his startup, Neoma, representing the power of starting fresh and embracing personal/business reinvention in the era of AI.The Ultimate Business Skill—Resiliency: How surviving geopolitical displacement and physical setbacks created an unshakeable mindset capable of handling the emotional roller coaster of entrepreneurship.Translating Tech to Business Value: Learning the critical executive skill of bridging the gap between deep technical code and high-level business strategy.The Corporate Football Pitch: A genius team-building strategy where Product acts as Defense, Sales as the Offense, and Marketing as the Midfielders.Human-Centric Future of Work: How Neoma leverages AI to accelerate B2B digital transformation while opening decentralized, flexible financial opportunities for modern tech contributors.The Ultimate Golden Rule: Why leading with mutual value—"Loving for others what you love for yourself"—is the key to achieving global peace and successful collaboration.#LeadersInTech #TechPodcast #DigitalTransformation #AIStartup #BusinessLeadership #CareerPivot #MindsetShift #OvercomingAdversity #EntrepreneurMindset #Neoma

Most CFOs spend their careers focused on accounting, compliance, and reporting.The best CFOs do something completely different.In this episode of The Authority Company Podcast, Joe Pardavila sits down with Chris Festog, author of The CFO Advantage, to explore how finance leaders can move beyond the numbers and become strategic partners, transformational leaders, and drivers of organizational growth.Drawing on more than four decades of experience in finance leadership roles with companies including Texaco, Zurich Insurance Group, and Mutual of America Financial Group, Chris explains why the CFO role is often misunderstood and how the most effective finance leaders shape strategy, influence culture, and help organizations thrive.The conversation covers leadership, stewardship, innovation, AI, career growth, ownership mentality, and why many professionals stay trapped in mediocrity.Whether you're a CFO, entrepreneur, business owner, executive, or aspiring leader, this episode offers practical insights on building influence and creating impact inside any organization.In this episode:• What separates a CFO from an accountant• Why CFOs are often underestimated• The three pillars of a high-impact CFO• How finance leaders become strategic partners• Why ownership matters more than talent• The danger of mediocrity in your career• How AI will change finance leadership• The rise of fractional CFOs• Why leadership requires courage• How great organizations balance stewardship and growthCHAPTERS00:00 Introduction00:27 CFO vs Accountant: What's the Difference?02:37 Why People Underestimate CFOs05:00 The "Chief No Officer" Problem07:38 The Three Pillars of the High-Impact CFO08:00 Stewardship Explained10:00 Leadership and Courage12:00 Why Transformation Matters14:00 Surviving vs Thriving at Work17:00 Partnering Instead of Policing19:22 Early Career Lessons on Hard Work22:59 Escaping the Narrative of Mediocrity25:05 AI and the Future of the CFO29:05 The Rise of Fractional CFOs31:37 Final Thoughts

Live July 13, 2026 | Yaron Brook Show(Season 12, Episode 122)Graham; Ukraine; Iran; Oil; ICE; 1776; Greedflation; Cronyism; Ebola; Achievement | Yaron Brook ShowFrom Ukraine to Iran, Oil Shocks to "Greedflation"—Can Politics Solve the Crises It Creates?The headlines never stop—but are we asking the right questions?Ukraine continues to innovate while Russia adapts. Iran threatens global shipping and energy markets. Politicians blame "corporate greed" for inflation while expanding government power. ICE dominates the news, Ebola returns, stem cell breakthroughs offer hope, and the debate over capitalism versus cronyism has never been more important.In this episode, Yaron Brook cuts through the noise to examine the ideas driving today's biggest stories—from Lindsey Graham's political legacy and the future of Ukraine to oil markets, inflation, state capitalism, and why blaming "greed" misses the real culprit.If you want analysis rooted in reason rather than political tribalism, this episode delivers.Watch here: https://youtube.com/live/Ij2RO3HJI1gMain Topics:0:28 Welcome1:08 Lindsey Graham's death, conspiracy theories & political legacy5:08 Graham's Trump alliance and public reaction9:46 Who replaces Graham? Political implications14:21 Ukraine's battlefield innovation and military technology22:29 Russia's strategy, Iran and the Strait of Hormuz28:51 Trump's Iran strategy and proposed shipping fees33:43 Iran's ports and US Central Command37:42 Israel's reported plans involving Iran45:44 Yemen conflict and Houthi retaliation47:32 Supporting the Yaron Brook Show48:33 Oil, helium and the economic impact of closing Hormuz51:12 Markets, bonds and rising interest rates55:18 ICE operations and the Maine controversy1:00:09 Trump's IRS settlement and legal questions1:06:00 Inflation, "greedflation," corporate greed and cronyism1:12:29 Ayn Rand, political activism and state capitalism1:17:56 Ebola outbreak and vaccine innovation1:24:28 Stem cell breakthroughs and infertility treatmentLive Audience Questions1:36:32 Was the 1980s the greatest decade for music?1:38:03 How much government debt is actually healthy?1:38:30 Fractional reserve or full reserve banking—which is better?1:39:33 Was the German mark truly one of history's strongest currencies?#Ukraine #Iran #Capitalism #Objectivism #Inflation #OilPrices #Greedflation #Economics #Politics #AynRandSubscribe for daily analysis on economics, politics, philosophy, technology, investing, and current events.The Yaron Brook Show is Sponsored by[The Ayn Rand Institute](https://www.aynrand.org/starthere)[Energy Talking Points, featuring AlexAI, by Alex Epstein](https://alexepstein.substack.com/)[Express VPN](https://www.expressvpn.com/yaron)[Hendershott Wealth Management](https://www.youtube.com/watch?v=X4lfC...) &(https://hendershottwealth.com/ybs/)[Michael Williams & The Defenders of Capitalism Project](https://www.DefendersOfCapitalism.com)[Support the Show]( / yaronbrookshow )[Sponsor the Show](askyaron@yaronbrookshow.com/)[One-time donation](https://bit.ly/2RZOyJJ)Join the [Yaron Brook Show YouTube channel]( / @yaronbrook )Like what you hear? Like, share, and subscribe to stay updated on new videos and help promote the [Yaron Brook Show](https://bit.ly/3ztPxTx)Continue the discussion by following Yaron on [Twitter](https://bit.ly/3iMGl6z) and [Facebook](https://bit.ly/3vvWDDC )Want to learn more about Ayn Rand and Objectivism? Visit the [Ayn Rand Institute](https://bit.ly/35qoEC3)Become a supporter of this podcast: https://www.spreaker.com/podcast/yaron-brook-show--3276901/support.Yaron is the executive chairman of the Ayn Rand Institute and a world class speaker. He is the coauthor of the national best-seller Free Market Revolution: How Ayn Rand's Ideas Can End Big Government, Equal is Unfair: America's Misguided Fight Against Income Inequality and In Pursuit of Wealth: The Moral Case for Finance. He speaks around the world on a variety of topics including the morality of capitalism, Ayn Rand and her philosophy, finance and economics, and the value of inequality.

Ashley Brasso is a Fractional CMO for 7-figure founders, creators, and personal brands building real businesses around their expertise - through courses, coaching programs, IP, tech platforms, and the occasional very big launch. She's the strategist behind multi-7-figure launches, $100K days, and 300% revenue jumps, and the one founders call when they want new ideas and marketing strategies that connect back to their revenue - with the data to prove they actually work. As a Fractional CMO for 7-figure founders, creators, and personal brands, Ashley's on a mission to help build female financial literacy and female-owned businesses. You can connect with Ashley at: Substack: https://ashleybrasso.substack.com/  Instagram: https://www.instagram.com/ashleybrasso/  LinkedIn: https://www.linkedin.com/in/ashley-brasseaux-a3a90a81/  Download Ashleys free high ticket launch plan here: https://creative-inventor-3271.kit.com/1828114bc1?fbclid=PAZXh0bgNhZW0CMTEAc3J0YwZhcHBfaWQPOTM2NjE5NzQzMzkyNDU5AAGnVPnRjvFaKtSK3R10ADbmXRiEC3H5iMSXZyPzuo85sRKEurzF0RbAtsCRPqo_aem_KIcDi0u0NQ5sxbjqtNigOA  Episode highlights: - Niyc interviews Ashley Brasso... Ashley's a Fractional CMO for 7-figure founders, creators , and personal brands who currently lives in the sunshine in Playa Del Carmen, Mexico.  - Niyc and Ashley discuss the value of in person connection, community, and what's working right now when it comes to making money online  - Ashley shares how much time and effort it really takes to be effective at marketing online today, and where AI fits in the picture too.  *** CONNECT WITH NIYC... Apply for GAME CHANGERS mastermind here: https://www.niycpidgeon.com/game-changers-application  Download my FREE Positive Psychology Sales System here:  https://niyc-pidgeon.mykajabi.com/the-positive-psychology-sales-system  Grab The $27 Positive Psychology Sales Stack here:  https://niyc-pidgeon.mykajabi.com/positive-psychology-sales-stack  Connect on social media:  Instagram: www.instagram.com/niycpidge  LinkedIn: https://www.linkedin.com/in/niycpidgeon Website: www.niycpidgeon.com 

Gothic Industrial Music Ep204 - EBM - Darkwave - Electro Industrialhttp://www.shadowsradio.net/0:00:00 - The Synthetic Dream Foundation – Champion of the Etheric Abyss0:03:44 - Dead When I Found Her – Fixer Fixed0:08:10 - Synthetisch Lebensform – Fallen0:12:41 - Fractional – 01 Lucidstatic - Blood0:17:17 - Distoxia – Sangriento descontrol0:20:53 - Rotersand – Gothic Paradise0:25:31 - Project Pitchfork – Pitch-Black0:33:25 - Eisfabrik – Eisplanet0:38:57 - Psy'aviah – Mine0:43:00 - Dark Insights – Tears in My Eyes0:47:03 - Le?ther Strip – Face The Fire0:52:46 - Neuroticfish – Rose

Kirk and Andy sit down with marketing and brand strategist Joshua David for a conversation that starts with a joke about his very long bio and quickly goes somewhere much more honest.Josh traces his path from working hotel front desks (including a stint in London that convinced him he was in the wrong industry) to community management, influencer marketing in New York, and eventually running social for Ebony, where he grew the platform from 200,000 to nearly a million organic followers before he turned 25. Along the way he crossed paths with a who's who of names, but the real story is what he learned about himself in those rooms.Mostly, this is a candid talk about being real in spaces that reward performance. The guys get into authenticity versus code-switching, what it costs to be the only person of color in the room, why Josh leaves a job before they start to hate him, and the price of doing the one thing you don't want to do just to reach the next rung. There's a lot on the tension between caring too much and protecting your own peace, plus a hard look at how companies say they want you to shake the table right up until you actually do.Funny, blunt, and occasionally uncomfortable in the best way. Come for the industry stories, stay for the truth-telling.The Kirk and Kurtts Design Podcast. Kirk Visola, founder and creative director of Mind the Font, and Andy Kurtts, founder and creative director of Buttermilk Creative.Send us Fan MailSupport the showAbout Kirk and Andy.Kirk Visola is the Creative Director and Founder of MIND THE FONT™. He brings over 20 years of CPG experience to the packaging and branding design space, and understands how shelf aesthetics can make an impact for established and emerging brands. Check out their work http://www.mindthefont.com.Andy Kurts is the Creative Director and Founder of Buttermilk Creative. He loves a good coffee in the morning and a good bourbon at night. When he's not working on packaging design he's running in the backyard with his family. Check out Buttermilk's work http://www.buttermilkcreative.com.Music for Kirk & Kurtts intro & outro: Better by Super FantasticsShow a little love. Share the podcast with those who may benefit. Or, send us a coffee:Support the show

What does it actually take to build an executive team from nothing? This week on The Data Minute, Ashley Neville fills in for Peter and sits down with Francois Ajenstat, Founder and CEO of Golden Analytics, to talk hiring at the earliest stages of a company, from seed through Series B.Francois spent over a decade as Chief Product Officer at Tableau before leading product at Amplitude, and recently launched Golden Analytics, an AI-native BI platform that just closed $21 million in total seed funding. He walks through why he sees fundraising as less about the check and more about finding long-term partners, why he never set out to build a foundational model, and why he thinks the fear around AI replacing data analysts has it backwards. He also breaks down his approach to those first few hires: starting with people he trusts completely, using Carta's own compensation data to build trust with candidates during offer negotiations, and the three-part test he runs on every new hire around AI fluency, taste, and ownership of outcomes.The conversation also covers Golden's unconventional customer feedback loop, the surprising order in which startups actually hire across functions, and Francois's long-running framework for job satisfaction: the work, the people, and the recognition.Subscribe to Carta's weekly Data Minute newsletter: https://carta.com/subscribe/data-newsletter-sign-up/Explore interactive startup and VC data, with Carta's Data Desk: https://carta.com/data-desk/Chapters: 01:17 – Announcing the $21M Seed: Fundraising Is About Partners, Not Just Capital 02:57 – Pitching Golden Analytics: Zig When Everyone Else Zags 06:06 – Why Golden Isn't Building Its Own Foundational Model 07:48 – The Privacy Question: Why Golden Never Sends Customer Data to the Models 09:17 – Will AI Replace the Data Analyst? (No, It Makes Them 10x) 10:57 – From CPO to "Solo" Founder: Why the Label Never Fit 13:14 – Hiring Employee One: The Former Tableau CTO 15:17 – Using Carta's Comp Data to Build Trust with Candidates 17:42 – Thinking About the ESOP from Day One 19:08 – The New Hiring Bar: AI Fluency, Taste, and Ownership 21:44 – Inside a Seven-Person Company Outshipping the Competition 22:46 – No Wall Between Customers and Engineers 24:33 – Making Customers Feel Like Founders 27:03 – An Unboxing: The Golden Analytics Coin 28:06 – The First Experience: What Happens When You Open Golden 29:33 – Surprising Data: Founders Hire Before They Raise 30:52 – The Order of Hires: Why CFOs Come Before Revenue 32:18 – Fractional vs. Full-Time: "Does This Make the Beer Taste Better?" 34:22 – What's Next to Hire: Engineers Ahead, Sales Behind 36:11 – Why Golden Skips the Middle: Senior Talent Paired With Junior Hunger 38:25 – Education, Fear, and Learning by Doing 41:15 – Building Carta's Own Report With AI, Faster 43:06 – Every Company Is a Data Company 44:27 – The Customer Data Francois Obsesses Over Daily 47:28 – Is SaaS Dead? Why the "Apocalypse" Headlines Miss the Point 49:21 – The Three-Factor Test for Job Satisfaction 51:47 – Redefining Appreciation: Experiences Over Titles 54:47 – What's Next for Golden Analytics 56:30 – OutroThis presentation contains general information only and eShares, Inc. dba Carta, Inc. (“Carta”) is not, by means of this publication, rendering accounting, business, financial, investment, legal, tax, or other professional advice or services, and is for informational purposes only.  This presentation is not a substitute for such professional advice or services nor should it be used as a basis for any decision or action that may affect your business or interests. © 2026 eShares, Inc., dba Carta, Inc. All rights reserved. In the interest of transparency, Golden Analytics is a customer of eShares, Inc. dba Carta, Inc. ("Carta").  While we have invited them here today to discuss their journey, please note that this is not an endorsement, solicitation, or recommendation for Golden Analytics or Carta. Carta does not assume any liability for reliance on the information provided during this podcast.

You built a team. So why does everything still run through you? A year ago, Frances Roen was so stretched she had to step out of an Impact Collective Mastermind retreat session to cover client work. In this returning-guest episode, she comes back to share what happened after she made the hard calls: rebuilding her team, her systems, and her offer around the firm she actually wants. This year she is on track to more than double her revenue. Here is how. Where are you in your journey right now? DM me on LinkedIn and tell me. Work With Coach Natalie

Distance stops most investors before they ever get started. Stella Han never let it. In this episode, I sit down with Stella Han, co-founder and CEO of Fractional, to talk about her journey building a real estate portfolio remotely from California into the Atlanta market - the deals, the lessons, and what it actually takes to invest in a market you've never lived in.   Stella's story is one of those that reminds you that geography is not the obstacle most people think it is. But the story doesn't stop at the portfolio. After running into a wall trying to raise capital for a larger deal - a painful and expensive lesson - Stella channeled that frustration into building Fractional, a platform designed to make raising capital and pooling resources with other investors faster, simpler, and more accessible than anything that existed before. What started as a personal problem turned into a company backed by Y Combinator that has helped operators raise hundreds of millions of dollars.   This one is a great listen whether you're an investor trying to figure out how to break into a new market, someone sitting on a deal that needs capital, or just someone who appreciates a great founder story rooted in real estate. Book your call with Neo Home Loanshttps://www.neoentrepreneurhomeloans.com/wjpodcast/ Book your mentorship discovery call with Cory RESOURCESGet business funding - revenued.com/juice

Most founders end up as their own default CFO, buried in spreadsheets, cash flow, and pricing decisions. In this episode of Growth Think Tank, Gene Hammett talks with Brennan de Raad, founder of Vessel Advisors (No. 2,665 on the Inc. 5000). We explore the key signs that it's time to bring in strategic financial leadership, especially as your business grows beyond $5 million in revenue and the founder is still managing the finances. Gene sits down with Brennan De Raad of Vessel Advisors to discuss how fractional CFOs, controllers, and back-office accounting teams help businesses gain financial clarity, improve cash flow visibility, and make better decisions with actionable reporting. We also dive into how AI is transforming recurring finance tasks, the importance of tracking leading indicators alongside traditional financial metrics, and why weekly revenue, cash flow forecasts, and sales activity deserve closer attention. The conversation wraps up with a practical discussion on pricing strategy and gross margin, two of the most overlooked drivers of sustainable growth and profitability. Episode Highlights & Time Stamps 0:03 Fractional CFO Basics 4:23 AI in Finance 7:19 When to Hire a CFO 15:03 Tracking the Right Numbers 19:47 Pricing and Margin Blind Spots 24:32 Final CFO Takeaways Key Takeaways The $5M threshold: Once a business crosses roughly $5M in revenue, it's usually strong enough to benefit from a fractional CFO but not yet large enough to justify a $250K–$600K full-time hire.  Warning signs it's time to hire: Financial reports stop making sense, revenue grows but cash stays tight, or the founder feels lost in a finance world they no longer fully understand.  AI is reshaping finance functions: Platforms like QuickBooks, NetSuite, and Sage are building in native AI agents, while tools like Claude are cutting cash-flow forecasting projects from hours down to a fast, natural-language process.  Fractional works at scale too: Vessel Advisors now supports companies north of $100M on a fractional basis, a shift from a decade ago when a $25M company "had to" have a full-time CFO.  Track leading indicators, not just lagging ones: Trailing 4–6 week revenue, a 13-week rolling cash forecast, and sales activity metrics (like meetings booked) give founders earlier warning signs than a monthly P&L.  The #1 hidden problem: Most companies haven't audited their actual pricing and gross margins in years; the deal they thought was a 35% margin project might really be closer to 4–12%.  Time is the real cost: Founders who stay in spreadsheets they should have delegated aren't just losing hours; they're losing the deals, meetings, and strategic moves that would have grown the business faster. Pricing increases rarely cost you customers: One example shared: a 9% average price increase across the board resulted in customer gratitude, not attrition, once the founder finally acted. This episode is a must-listen for CEOs and executives looking to lead innovation with purpose, scale responsibly with AI, and build cultures where people feel empowered to think boldly and grow. Connect With Today's Guest Brennan De Raad is the Founder & CEO of Vessel Advisors. Vessel Advisors provides Fractional CFO, Controller, and Back-Office Accounting services for growing businesses, helping founders gain financial clarity, improve cash flow, and scale with confidence. How to Connect with Brennan De Raad: LinkedIn: Brennan De Raad https://www.linkedin.com/in/brennanderaad/ Company Website: Vessel Advisors https://vesseladvisors.com/ – to learn more about his work and platform

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Welcome to another episode of "The Unconventional Path: Entrepreneurship and Innovation Stories and Ideas." In this episode, hosts Bela Musits and Mike Wasserman sit down with Michael Barbarita, the founder of a specialized fractional CFO firm dedicated to helping small and medium-sized companies achieve rapid growth.Michael Barbarita is not your typical CFO. While many fractional CFOs focus primarily on historical numbers and compliance, Michael's approach combines forward-looking financial expertise with strategic implementation. He shares how business owners hire him to double or even triple their profits by employing business and financial strategies that their competitors simply aren't using.The Fractional CFO Advantage: Michael explains why small to medium-sized businesses, which often cannot afford a full-time high-quality CFO, benefit from fractional services that provide a broad spectrum of scalable strategies.Beyond the Numbers: A core theme of this conversation is the necessity for CFOs to understand business strategy. Michael argues that while traditional CPAs look backward at historical data, a true CFO must be forward-looking to provide real value to a CEO.Pressure Testing Ideas: One of Michael's signature methods is "pressure testing" a CEO's ideas. Whether it's a new marketing campaign or a pricing change, he discusses the importance of expanding upon these ideas to ensure they are financially viable and strategically sound.The Conversion Formula: Michael introduces the concept of a conversion formula used to create successful messaging for things like drip campaigns, ensuring that a business stands out rather than just repeating what everyone else in the market is doing.Strategic Growth for Cash-Limited Companies: Most small companies face limited cash flow. Michael shares insights on how to improve financial foundations and implement growth strategies even when resources are tight.This interview serves as a master class for any entrepreneur looking to improve their financial footing and understand the financial ramifications of their business strategies. Michael's unique blend of strategic knowledge and financial expertise offers a roadmap for turning high costs and uncertain footing into favorable growth opportunities.Connect with The Unconventional Path:Our podcast is now available on YouTube. Simply search for "The Unconventional Path" to subscribe and never miss an episode.We're always on the lookout for interesting guests to feature on our show. If you know someone who has an inspiring story, unique perspective, or valuable expertise to share, please let us know. We're eager to connect with potential guests who can bring fresh insights and engaging conversations to our audience.We also love hearing from our listeners! Your questions, comments, and suggestions are incredibly valuable to us. Send us an email at bela.and.mike@gmail.com with your thoughts, and we'll do our best to address them in a future episode. Whether you have a question about a specific topic, feedback on a recent episode, or ideas for future content, we want to hear from you. Your engagement helps us shape the show and deliver content that resonates with our listeners.Thanks for listening,Bela and MikeFractional CFO, Entrepreneurial Finance, Business Strategy, Small Business Growth, Profit Strategy, Cash Flow Management, Strategic Implementation, Michael Barbarita, Bela Musits, Mike Wasserman, The Unconventional Path Podcast, Business Advising, Financial Forecasting, Startup Accounting, Growth Mindset.

Each week, our panelists discuss their favorite stories from the week's news in legal technology. This week's topics: (00:00) Panelist introductions (2:44) Thomson Reuters Event (Selected by Joe Patrice) (34:00) University of Texas Law Dean Shifts AI Policy to Prevent "Cognitive Deskilling" (Selected by Victor Li) (40:03) Fighting Hallucinations: How to Choose the Right AI Citation Checkers (Selected by Niki Black)

One Big Idea 3 - Driving Enterprise Value: From Funding Architectures and Radical Letting Go to Systems-Driven RevenueIn this episode of One Big Idea, host Josh Elledge connects with Anthony Rose, Latif Hamilton, Dan Rochon, Ronald Robinson, and Mark Osborne to dissect the foundational operational strategies required to elevate enterprise value, optimize leadership psychology, and construct predictable growth engines. Anthony Rose, Founder and CEO of SeedLegals, kicks off the discussion by introducing a fairer, more transparent fundraising mechanism designed to protect early-stage founders. Latif Hamilton, Founder of SpiritHoods, then shifts focus to executive psychology, mapping out structural frameworks to help founders overcome cognitive biases and master the art of letting go. Next, CPI Community Founder Dan Rochon outlines a guide to replacing high-pressure sales with consultative, guidance-based relationship building. Ronald Robinson, Founder of Expanded Learning Academy, dives deep into the profound link between childhood social-emotional competencies and adult executive leadership. Finally, Mark Osborne, Fractional Revenue Leader for Professional Services & B2B SaaS at Modern Revenue Strategies, closes the episode by delivering a blueprint on transitioning from hustle-centric business development to completely automated, system-driven revenue architecture.Smarter Fundraising for Startups Using SAFERs Instead of Traditional SAFEs with SeedLegals' Anthony RoseEarly-stage fundraising has long relied on Simple Agreements for Future Equity (SAFEs) to bypass the slow, expensive legal hurdles of traditional priced funding rounds. However, legal tech pioneer Anthony Rose argues that his "one big idea" exposes how traditional SAFEs routinely blindside founders with massive, compounded dilution once conversion math kicks in at the next priced round. Because SAFEs don't update the cap table in real time, founders frequently underestimate their stacked equity obligations, sometimes waking up to find they have accidentally surrendered a majority stake in their own company. Furthermore, SAFEs present critical tax ambiguities for savvy investors regarding when the five-year Qualified Small Business Stock (QSBS) holding clock officially begins.To solve these hidden structural hazards, Anthony introduces the SAFER (Simple Agreement for Future Equity and Regular Shares). This framework retains the rapid, low-cost execution speed of a traditional SAFE but requires that investors receive their stock immediately upon investment. This instantaneous cap table visibility ensures founders see the exact equity impact of every dollar raised in real time, preventing unexpected minority status down the line. By utilizing automated legal modeling tools, early-stage companies raising between $500K and $2M can establish flawless financial transparency, kickstart the investor's QSBS tax clock on day one, and secure institutional-grade corporate clarity without the bloated fees of legacy law firms.Breaking Free by Outsmarting Your Brain and Letting Go Like a Pro with SpiritHoods' Latif HamiltonOne of the greatest operational barriers to scaling an enterprise is the founder's own psychological attachment to underperforming elements of the business. Latif Hamilton explains that his core thesis addresses why entrepreneurs struggle to cut ties with failing product lines, toxic corporate cultures, or stagnant business models. This operational paralysis is driven by two hardwired cognitive biases: the endowment effect, which causes leaders to artificially overvalue an asset simply because they own it, and loss aversion, where the psychological pain of losing an asset is twice as powerful as the pleasure of gaining an equivalent win. Left unchecked, these biases trap executives in an expensive cycle of protecting sunk costs instead of pursuing high-yield commercial opportunities.To bypass these emotional roadblocks, Latif provides a tactical toolkit designed to decouple human emotion from strategic analysis. Founders must routinely challenge their operations by asking the "starting fresh" question: If I didn't already own this product or employ this person, would I choose to buy or hire them today? If the answer is no, immediate divestment is required. By mapping out a physical grid to calculate the true cost of inaction—including opportunity cost and team morale drain—leaders can clearly see the numbers in black and white. Transitioning into authentic thought leadership through platforms like Substack and high-level podcast guesting allows founders to pivot their energy toward market authority, turning perceived organizational losses into scalable future gains.Building Client Trust While Breaking Through Internal Resistance with CPI Community's Dan RochonIn a transparent and highly competitive marketplace, traditional, aggressive sales closing tactics create immediate buyer friction and erosion of brand trust. Sales consultant Dan Rochon outlines his "one big idea" that modern sales must pivot completely away from psychological manipulation and transition into an act of collaborative leadership and client guidance. The primary obstacle in a commercial transaction is rarely external market competition; rather, it is the prospect's internal resistance, driven by unvoiced fears, self-doubt, and structural uncertainty. By stepping into the role of a guide rather than an aggressive closing hero, the sales professional shifts from an administrative solicitor to a trusted advisor.To execute this consultative framework consistently, Dan structures his methodology across three actionable operational behaviors: connecting authentically to build immediate rapport, asking deep questions that target the prospect's root motivation, and actively listening to emotional hesitation rather than just verbal compliance. This client-centric approach forms the bedrock of consistent and predictable revenue, allowing founders to easily transition away from founder-led sales. By thoroughly documenting these conversational processes into corporate playbooks, leveraging CRM data tracking, and utilizing podcasts for high-level ecosystem networking, organizations can seamlessly scale their business development teams beyond the personal bandwidth of the company founder.The Hidden Link Between Childhood SEL and Adult Workplace Success with Expanded Learning Academy's Ronald RobinsonTechnical expertise and operational software systems are useless if an organization lacks the foundational soft skills required to execute effectively under high-pressure conditions. Education strategist Ronald Robinson shares his core thesis that Social Emotional Learning (SEL) competencies are not merely childhood development concepts, but the primary drivers of modern workplace productivity and elite corporate culture. High-performing business units separate themselves not by raw technical capabilities, but by their team leaders' capacity to operate with high levels of self-awareness, self-management, social awareness, relationship management, and responsible decision-making.To bridge the gap between abstract emotional intelligence and rigid corporate KPIs, Ronald introduces the advanced concept of SELF (Social Emotional Learning Fundamentals), which mandates that executives systematically prioritize self-care, self-confidence, and self-assurance. When corporate leaders fail to manage their internal emotional triggers, they inadvertently project impulsivity onto their direct reports, destroying psychological safety and driving up employee turnover. By embedding regular 360-degree feedback loops, active listening training, and strict emotional regulation boundaries directly into adult workforce development programs, companies can build inclusive, highly resilient environments. Ultimately, designing a culture where personnel thrive emotionally serves as a primary macro competitive advantage.Enhancing Revenue Systems Through AI and Strategic Leadership with Modern Revenue Strategies's Mark OsborneMany growing companies fall victim to the hazardous trap of "hero mode" growth, where top-line revenue numbers are driven purely by the ad-hoc charisma, brute-force hustle, and personal networks of the founding team. Fractional revenue expert Mark Osborne demonstrates that his core framework addresses why this personality-driven revenue is actually a severe structural liability that drastically tanks a company's enterprise valuation during an M&A or investment round. If a business cannot mathematically prove that its customer acquisition engine is entirely predictable, repeatable, transferable, and independent of any single rainmaker, buyers will view that income stream as high-risk phantom equity.To convert volatile cash generation into a verified corporate asset, Mark details a systemized architecture built upon three interlocking workflows: attraction systems (leveraging hyper-targeted client profiles), acceleration systems (streamlining sales pipeline velocity via automated proposals), and activation systems (maximizing client onboarding and referral loops). When integrating artificial intelligence into this revenue strategy, executives must strictly avoid the mistake of chasing popular software tools before defining their core processes; AI must be deployed exclusively as a force multiplier layered onto pre-existing, human-mapped customer journeys. By visually whiteboarding the entire critical client flow, assigning absolute ownership to each conversion metric, and conducting rigorous quarterly quality-of-earnings audits, business leaders successfully build an institutionalized revenue engine that functions flawlessly without founder...

In this Simple CFO Case Files episode, David Richter and his business partner Christina Gutierrez kick off a new recurring series recorded right after their weekly EOS same page meeting. They pull back the curtain on how they run Simple CFO using Gino Wickman's Traction and EOS system, and why the visionary and integrator partnership has been the engine behind the business.The heart of the conversation is one recurring phrase they hear from real estate investors who walk through their door: "I wish I would have known this." David and Christina break down why so many owners stay stuck asking CFO level questions of bookkeepers and CPAs who can't answer them, what a fractional CFO actually does that's different, and how to become a master of your money without ever becoming a master of accounting. If you're flipping houses or holding rentals and you can't say what you actually kept last year, this episode points you toward the clarity you've been missing.Timeline Highlights[0:23] David introduces the new series recorded after his weekly same page meeting with partner Christina Gutierrez[1:01] How Simple CFO runs its back end on Traction and the EOS system by Gino Wickman[2:18] Christina on how EOS taught her to be open and transparent in a true business partnership[3:43] Why communication is the thing that makes a business run and what a structured system protects[4:19] A walk through Simple CFO's heavy Wednesday meeting schedule and what each meeting is for[5:10] Protecting the visionary's flow state and routing every idea to the right meeting[6:32] The recurring "I wish I would have known this" theme and a story of one owner's hair on fire[7:14] An owner who waited two years and likely lost real business value before getting his numbers cleaned up[8:38] Christina on why owners don't know who can actually help them with strategic financial questions[10:11] Most owners don't know a fractional CFO exists or that they could afford one[11:12] The real strategic questions owners never know to ask themselves[12:10] The magic of a fractional CFO is surfacing the questions you don't know to ask[13:50] Why bookkeepers and CPAs give textbook answers without knowing you or your goals[15:02] Be a master of your money, not a master of accounting, and what that actually means[17:22] How loneliness as a solo owner makes a financial partner who knows you so valuable[21:42] When to reach out and the difference between the 60 day foundation tier and ongoing CFO support[22:42] Playing offense and defense so you protect what you built while still growing[25:22] Two paths forward: a fractional CFO and the Profit First system as an entry point[26:01] A Profit First client who built a year of owner's comp and now takes a month off in Sweden each yearKey TakeawaysOwners often ask CFO level questions of the wrong people. Bookkeepers record transactions and CPAs file taxes, but neither is built to give strategic financial guidance tied to your goals.The most dangerous gap is the questions you don't know to ask. A good fractional CFO surfaces the questions that reveal whether your business is actually healthy or quietly going under.You should be a master of your money, not a master of accounting. You don't need to run QuickBooks or file taxes. You need clean numbers you can use to make decisions.Revenue alone solves nothing. Making a million dollars means little if you kept nothing, and the cause is often a cash management gap or bad bookkeeping you can't see.A fractional CFO is a relationship, not a transaction. They meet you where you are, remember the goals you set months ago, and back decisions with accurate data instead of gut feeling.Fractional high level help is more accessible than owners think. CFO, COO, and CMO support exists without the full time price tag, opening strategy to businesses that assumed they couldn't afford it.Profit First is a simple entry point for managing cash. It translates finances into business owner language and helps build reserves and owner's comp so a strong year actually shows up in the bank.Links & ResourcesSimple CFO (book a discovery call) — simplecfo.com Profit First for Real Estate Investors (apply for a free financial discovery call) — profitrei.com Profit First for Real Estate Investors by David Richter (free download) — simplecfo.com Traction by Gino Wickman — referenced as the EOS framework Simple CFO runs onClosingIf any part of this hit home, especially the part about making money but having no idea what you actually kept, don't let another year pass wishing you'd known sooner. David and Christina built this series to open owners' eyes to the financial clarity they've been missing, whether that's a fractional CFO or simply getting Profit First up and running. To bring real structure to the finances in your business, visit profitrei.com to apply for a free financial discovery call with the team.

Quick SummaryIn this episode, host Kelsey sits down with Lauren Murdoch, founder of Murdoch Marketing, a fractional marketing consultancy based in Burlington, Ontario. Lauren shares the raw, messy, and ultimately inspiring story of leaving a burnout-inducing corporate career, taking her family to New Zealand for four months, and coming home to build a business rooted in clarity, community, and genuine strategy. This is a must-listen for marketers, entrepreneurs, and anyone who has ever felt the pull toward something more aligned — but wasn't sure how to get there.In This EpisodeHow Lauren went from corporate marketer to fractional marketing consultant after 15 yearsThe 1:00–3:00 AM panic attacks that finally pushed her to quitWhy she spent months saying yes to everything — and what it unlockedThe real story of how her family made four months in New Zealand happen (no big bank account required)Her first fractional client — and why he showed up at a golf simulatorWhy going viral is NOT the goal — and what actually generates revenueThe simple marketing moves most small business owners skip entirelyHow co-hosting workshops became her most powerful visibility strategyWhy she hopes she never goes viralKey TakeawaysIt's never the right time to take the leap — but if the desire is there, dig in and figure out how to make it work. The right conditions rarely just appear; you have to engineer them.In the early days of a new business, saying yes to everything isn't reckless — it's research. Clarity comes from doing, not planning.The best marketing starts with one thing: being relentlessly clear about who you are, what you offer, and telling people exactly what to do next.Optimize before you add. Before building a new offer or platform, look at what you already have and ask if it's been given a real chance to work.Getting out of your office and into rooms — events, coffee chats, workshops — is still one of the most underrated business development strategies that exists.Memorable Quotes"You will always find reasons not to do something. It's never a good time.""Don't go try to do five to ten channels. Pick two. Get really good at those.""I genuinely hope I don't go viral — because that's not the fastest path to building a real business."Resources MentionedMurdoch Marketing website:murdochmarketing.caLauren's Instagram:@itslaurenmurdochKelsey's Website: www.KelseyReidl.comKelsey's Instagram: @KelseyReidlJuly 23rd Burlington Event: Cocktails, dinner & speakers on inner self and outer style — checkmurdochmarketing.ca for detailsNew Zealand Work From Heart sabbatical program (mentioned in context of Lauren's employer's policies)About the GuestLauren Murdoch is the founder of Murdoch Marketing, a fractional marketing consultancy helping entrepreneurs and small business owners build clear, effective marketing strategies. After 15 years scaling companies in corporate marketing, she left to build a business and life that actually fit — including a four-month family adventure in New Zealand. She's based in the Burlington/Hamilton area of Ontario and works with clients across Canada.

Rich Lennon is a longtime real estate investor turned private lender who built one of the largest hard money lending operations in Richmond, Virginia, after a career of flips, rentals, and buy-and-hold deals. He reached financial freedom by stepping out of active investing and into the lending seat, where he now earns 30 to 50% returns doing only a few hours of work per deal while traveling the world.In this episode, Rich breaks down the fractional wrap, the strategy he uses to combine his own capital with private money and capture the arbitrage between what he borrows at and what he lends at. He explains why being the bank is the lowest-risk seat at the table, how to underwrite a deal, why staying local matters, and the morality of protecting your borrowers.David and Rich go deep on the mechanics: the $50,000 starting point, taking a first-loss position to protect underlying lenders, and how returns scale with how hard you want to work. Rich shares why flippers and operators are perfectly positioned to make the jump, since their worst-case scenario as a lender is taking back a property at 50 to 60 cents on the dollar.If you are a real estate investor or entrepreneur who has stacked some cash and wants to put it to work without chasing marketing, finding deals, or managing renovations, this conversation lays out exactly how to move from operator to lender the right way.Episode Highlights[1:06] – David introduces Rich Lennon, his first ever Simple CFO client and the friend who helped springboard the company[4:14] – Rich recalls David finding $800,000 in his books and how that discovery started his path to freedom[4:32] – Why Rich shut down his operating business during Covid and ran the numbers showing he no longer had to work[4:51] – Rich falls in love with lending and travel, earning 30 to 50% returns on a few hours of work per deal[6:13] – Rich's background as a buy-and-hold investor who flipped to pay the bills and built wealth through IRAs[7:50] – Why the lending seat carries the smallest risk and beats flips, short-term rentals, and long-term rentals[8:12] – How a lender gets in at 60% of value when someone else does the marketing, contracts, and closing[10:02] – The Capital One effect and why dentists, lawyers, and executives make ideal private lenders[11:30] – Why you need at least $50,000 to make a fractional wrap worth the effort[12:16] – The case for skin in the game and putting the flipper in first-loss position[13:12] – Rich walks through the fractional wrap math on a $200,000 loan worth $300,000[14:45] – How taking a first-loss position protects your underlying lender at a 30 to 35% loan-to-value[15:40] – Why putting less of your own money in the deal drives your return toward 50%[18:27] – How return scales with effort and why bigger money usually means lower returns[19:36] – Growing lending into a real business and why Rich teaches students to stay local[22:47] – How to underwrite a deal by averaging Zillow, Realtor.com, Redfin, and a fourth source[25:50] – The morality of lending, avoiding stacked penalties, and protecting clients so they return[28:02] – How to reach Rich by text to learn about the fractional wrap5 Key TakeawaysThe lender holds the lowest-risk seat at the table. The mortgage gets paid before anyone else, and if a deal goes bad, the worst case is taking back a property at 50 to 60 cents on the dollar.A fractional wrap combines your capital with private money. You borrow at around 10%, lend at 20%, and pocket the arbitrage, pushing returns to 30 to 50% on the money you put in.The less of your own money you put in, the higher your return. Putting $50,000 into a $200,000 deal instead of $100,000 can take your return close to 50%.Take a first-loss position to protect your lenders. Putting your own money at risk before theirs keeps you a careful steward and gives your underlying lender a safe 30 to 35% loan-to-value spot.Stay local and learn to underwrite. Average four valuation sources to comp a property, keep deals close enough to drive by, and you remove most of the risk that sinks careless lenders.Links & ResourcesSimple CFO — https://simplecfo.comProfit First for Real Estate Investors — https://profitrei.com Investor Addicts Facebook group — https://www.facebook.com/groups/investoraddicts Text Rich Lennon to learn about the fractional wrap — (804) 601-0330Closing RemarkIf Rich's breakdown of the fractional wrap has you thinking about putting your cash to work instead of chasing the next flip, the first step is having the profit to lend in the first place. Take what you learned about moving from operator to lender and share this episode with someone sitting on capital who does not know where to start. Subscribe, review, and share the show, and visit simplecfo.com to take your free discovery call today.

You hired the experts. You've got a fractional CFO, a fractional CMO, maybe a part-time ops manager and a media buyer. You did everything right, and yet nothing is moving. Decisions are stalled. Responses take days. You're working more than before. And somehow, you became the glue holding it all together.You didn't make bad hires. You made a structural mistake, and in this episode, Melissa Franks breaks down exactly what went wrong and how to fix it.Fractional support is a strategic model, not an operational one. The moment you try to use it for both, the whole thing breaks down. Melissa walks through the two lanes every growing business needs, the strategy lane and the execution lane, and explains why confusing the two is costing founders time, money, and sanity.Whether you're running an all-contractor team or trying to figure out your next hire, this episode will give you a clear framework for when fractional is your best move — and when it's time to bring someone on full-time.In this episode, you'll learn:The difference between strategy-layer and execution-layer roles — and why mixing them up stalls everythingThe 5 ways an all-fractional team breaks down (and why it's not anyone's fault)When fractional support is your highest-leverage move — and when it isn'tThe 48-hour gut check: a simple test to determine whether a role needs to be full-timeWhy a seasoned fractional executive at 15 hours/month can outperform a full-time hire you can't yet affordThe football coach analogy that explains exactly how fractional executives should function in your businessHow to audit your current team and identify where your next full-time hire should beConnect with Melissa:Book a free consultation with Melissa → https://www.melissafranks.comLearn more about On Call COO fractional services → https://www.melissafranks.comWatch the Episodes on YoutubeInstagram: instagram.com/melissa_franks LinkedIn: Melissa Franks

Zach spent over a decade in the corporate world at American Airlines before helping build a multi-location healthcare company from the ground up. Along the way, he discovered that many growing businesses don't need another employee—they need access to the kind of executive-level expertise that helps organizations scale, solve problems, and avoid costly mistakes. Whether it's finance, operations, marketing, HR, or strategy, Zach explains how fractional C-suite leaders can provide the guidance and experience most practices can't afford to hire full-time.If you've ever felt stuck, overwhelmed, or unsure how to get your practice to the next level, this conversation will help you identify the blind spots that may be holding you back—and show you a practical way to overcome them.

In this episode of The Other Side of the Firewall, hosts Ryan, Shannon, and Chris dissect a trending security report detailing how a low-skilled attacker easily hijacked a Claude AI instance to breach 14 companies by simply tricking the AI's guardrails. The team then shifts gears to discuss a new Senate NDAA proposal offering up to $100,000 in CMMC grants to help small defense contractors manage the steep costs of compliance, followed by a fiery debate on whether the rise of part-time, "fractional" CISOs is a smart budget move or a dangerous trend fueled by executive burnout and SEC liability fears. Finally, the crew decompresses by sharing their recent personal highlights, diving into everything from Italian cheese wheels and intense kettlebell training to the latest anime and running Cyberpunk 2077 via cloud gaming. Article: Low-skilled attacker used Claude, Codex to breach 14 companies https://www.helpnetsecurity.com/2026/06/17/ai-agents-offensive-cyber-operations-claude-codex/?fbclid=IwZXh0bgNhZW0CMTAAYnJpZBExRmN0RFJDWEUzaUlaQ1dQanNydGMGYXBwX2lkEDIyMjAzOTE3ODgyMDA4OTIAAR4E3yG7BOaMeTYFubhdWQ_VIe4dFT44y-rT81FKg7V-SI5kcjcb1RWQ6xGULA_aem_8VhuxrLpU8NGw90c2z2F4g Senate NDAA proposes CMMC grant program https://federalnewsnetwork.com/technology-main/2026/06/senate-ndaa-proposes-cmmc-grant-program/?fbclid=IwZXh0bgNhZW0CMTAAYnJpZBExRmN0RFJDWEUzaUlaQ1dQanNydGMGYXBwX2lkEDIyMjAzOTE3ODgyMDA4OTIAAR4Aj2tVqytcIddIhgYsDnOqhU3C2bFQJfvK6bCubRb6Irl1e-RsrCyXf47h8g_aem_flEvM7hdgC50H6QHxfc4WQ CISOs Not Likely to Disappear https://www.darkreading.com/cybersecurity-operations/stressors-ai-changes-cybersecurity-teams?fbclid=IwZXh0bgNhZW0CMTAAYnJpZBExRmN0RFJDWEUzaUlaQ1dQanNydGMGYXBwX2lkEDIyMjAzOTE3ODgyMDA4OTIAAR5iE2M_iy9f90Ot-N5J6yTCoGvB03tT6inGBuBHKpGzTq0-Q_6Vcls5sUZkJw_aem_YQTYrJHf8yoZ6ZBmBjcUQg Buy my book: https://www.theothersideofthefirewall.com/ Please LISTEN

What if I told you the CEO your practice “needs” might actually be the most expensive mistake you're about to make?In this Five Minute Friday, I'm challenging orthodontists to think carefully before hiring a CEO, COO, or high-level executive for their practice. Too many doctors hit $2–3 million in production, feel stretched, and assume the next move is bringing in a $250K–$300K leader. But in most cases, you're not ready for that—and you probably don't know how to hire that person yet.Instead, I break down why a fractional C-suite executive may be the smarter move. Whether you're dealing with capacity issues, technology decisions, growth opportunities, or confusion about whether to expand into satellites, this episode will help you think like a business owner—not just an orthodontist. You'll walk away with a clearer sense of when to ask for executive-level help, what kind of help you actually need, and why growing your main practice may be far better than chasing expansion too soon.

Live from the ATLIS 2026 Annual Conference, the hosting team is joined by Alex Inman and Tom Wildman to analyze the evolving role of Managed Service Providers (MSPs) in independent schools. The conversation addresses the rise of fractional staffing, shifting IT reporting structures under CFOs, and the absolute necessity of cultural alignment when outsourcing school technology services.Knowing TechnologiesEducational Collaborators

What does it actually take to create an event that people remember for years? Whether you're dreaming of hosting a conference, retreat, concert series, fundraiser, festival, or community gathering, there's a lot more happening behind the scenes than most people realize. Successful events require vision, strategy, leadership, budgeting, marketing, logistics, and perhaps most importantly, a deep understanding of the people you're bringing together. In this episode, I sit down with Ginger Taylor, founder of Ginger Taylor Collective and a Fractional Head of Events with more than 15 years of experience across hospitality, conferences, and live experiences. Ginger shares how she found her way into the events industry, what separates professional event strategy from simply "throwing a party," and why events can be one of the most powerful tools for building community, growing a business, and creating lasting impact. We also discuss: • The biggest mistakes first-time event hosts make • Why attendee experience begins long before people arrive • The power of intentional networking and community-building • How to know when it's time to bring in outside support • The role of sponsorships, partnerships, and long-term strategy • What makes an event feel magical from the attendee perspective • Why gatherings can become powerful engines for leadership, influence, and legacy If you've ever dreamed of creating an event of your own, or if you're currently wearing all the hats in your business and wondering how to make a bigger impact without burning yourself out, this conversation is for you. Connect with Ginger: LinkedIn: Ginger Taylor Collective Email: ginger@gingertaylorcollective.com Resources Mentioned: The Art of Gathering by Priya Parker Ready to create something bigger? Many of the listeners of this podcast have ideas for retreats, conferences, workshops, festivals, concert series, and community-building events that could become meaningful parts of their legacy. If that's you, I encourage you to pick up a copy of my book, Beyond Potential: A Guide for Creatives Who Want to Re-Assess, Re-Define, and Re-Ignite Their Careers, where I walk through the frameworks I use to help people move from idea to action. And if you'd like personalized support mapping out your vision, you can book a 90-Minute Strategy Session with me. Together, we'll clarify your goals, identify your biggest opportunities and obstacles, and build a practical roadmap for bringing your ideas to life. Learn more at katekayaian.com. If you enjoyed this episode, please share it with a friend, leave a review, and subscribe so you never miss an episode of Tales from The Lane.

You know your retainers have a ceiling. You have even picked your specialty. But your calendar is full, your fractional clients still need you, and you are stuck on one question: how do I actually start to move to a scalable offer? Fractional retainers feel safe, but that steady monthly model is the thing capping your business. Moving to scalable high-ticket projects sounds like a leap you cannot afford while clients still depend on you. This episode is the practical one. The three steps to take to begin the transition. Send me a DM on LinkedIn. Tell me where you are in your journey to a scalable offer! Work With Coach Natalie

Joseph (Joe) Frost is the Co-founder of yorCMO, a franchise-based company that provides fractional Chief Marketing Officers to help businesses achieve strategic growth through expert marketing leadership. Under Joe's direction, yorCMO has helped dozens of companies scale, and his previous ventures include multiple EO-qualifying, million-dollar-plus businesses across the US and Canada. Joe is known for spotting emerging trends early, such as leveraging video marketing and launching community-driven networks for fractional professionals. He hosts The Fractional C‑Suite Retreat podcast, where he discusses leadership and the future of work. In this episode… Today's entrepreneurs face unprecedented demands  — technology, competition, and a constantly shifting market. How can business leaders leverage expert guidance without hiring full-time executives? Drawing from his experience building multiple ventures, Joseph Frost believes the key lies in fractional professionals. He explains that giving companies access to top-tier executives on a flexible basis allows them to scale smarter and faster, like catching the next big wave without buying the entire surfboard. The result is strategic growth that's nimble and sustainable in an unpredictable market. Tune in to this episode of the Smart Business Revolution Podcast as John Corcoran interviews Joseph (Joe) Frost, Co-founder of yorCMO to discuss leveraging fractional professionals. They cover building fractional CMO teams, creating sellable firms, adapting to AI in marketing, and Joe also shares tips on expanding fractional networks internationally.

What if the next chapter of your career is not full-time, but fully aligned?In this episode of Corporate Cafecito, Nallely and Carlos are talking about fractional work, what it means, why it is growing, and why so many professionals are starting to see it as more than just a backup plan.Fractional CFOs, COOs, CHROs, consultants, strategists, and operators are stepping into companies for a season, solving real problems, and bringing years of experience without being tied to one permanent role.But let's be honest, mi gente.This shift comes with both opportunity and concern.For some, fractional work creates freedom, flexibility, and a chance to use your expertise on your own terms.For others, it may feel like another sign that secure corporate jobs are changing.So we're talking about it all:✨ What fractional work really means✨ Why it is becoming more common✨ How it can help entrepreneurs and small businesses✨ What to consider before saying yes✨ Why your resume, skill set, and confidence matter more than everBecause sometimes the next move is not about starting over.Sometimes it is about realizing that what you already know has value.Pour your cafecito, bring your questions, y vámonos. This conversation is one many of us need right now.Watch the full episode at www.corpcafecito.com#CorporateCafecito #LatineProfessionals #CareerGrowth #FractionalWork #Leadership #Entrepreneurship #LatinasInBusiness #LatinosInBusiness #CareerStrategy #CafecitoConPurposeSupport the showIf you'd like to join Nallely y Carlos for a conversation, collaborate, or suggest a topic that matters to our community, we would love to hear from you. This podcast centers real conversations that move culture and careers forward. Visit www.corpcafecito.com/contact-us or email admin@corpcafecito.com.Elevar Development, founded by Nallely Suárez Gass, helps professionals and organizations grow with clarity and purpose. With over two decades of corporate experience, Nallely is known for helping people lead authentically, uncover strengths, and make confident, aligned decisions. Through personalized coaching and impactful workshops, Elevar creates practical, lasting change. Visit www.elevardevelopment.com or email Nallely@elevardevelopment.com today.Every business decision carries social, political, and economic considerations. Avizo Consulting helps organizations navigate complexity with intention, cultural awareness, and strategic insight. Carlos Butler Vale partners with leaders who want their values and actions aligned. Learn more at www.avizoconsulting.com or email carlos@avizoconsulting.com. Two leaders. One shared commitment to growth, cultura, and impact. 

Richard McGirr interviews David Bacon, who discusses why story-driven marketing channels like podcasts and influencers outperform many traditional approaches, how his company evaluates campaign success beyond the initial conversion, and the importance of understanding investor lifetime value. The conversation also explores how AI is transforming marketing analytics, audience segmentation, personalization, and attribution, giving operators access to insights that previously required large teams and significant resources. Throughout the episode, David and Richard exchange practical lessons on funnels, automation, investor behavior, and building scalable marketing systems. David Bacon Current role: Head of Marketing of Worthy Financial, Inc. Based in: Atlanta Metropolitan Area Where to find them: worthywealth.com worthyseniorliving.com Book your free demo today at bill.com/bestever and get a $100 Amazon gift card. Visit https://malabarhillcapital.com/ for more info. Podcast production done by⁠ ⁠Outlier Audio⁠ Learn more about your ad choices. Visit megaphone.fm/adchoices

CardioNerds Dr. Joseph Kassab, Dr. Mariana Garcia-Arango, and Dr. Christopher Mason explore the technological revolution of Coronary CT Angiography (CCTA) with expert faculty Dr. Michael Gallagher. The discussion details how CCTA has evolved into a frontline diagnostic and preventive tool, moving beyond simple anatomy to incorporate physiology via CT-FFR and biology through AI-driven plaque quantification. The episode reviews landmark evidence like the SCOT-HEART and PROMISE trials, the nuances of CAD-RADS 2.0 reporting, and the emerging role of AI in monitoring treatment response and personalizing cardiovascular care. Critically, they also discuss some of the assumptions and limitations of these techniques. Stay tuned for a matching review article to be submitted to US Cardiology Review, the official Journal of CardioNerds. This episode was supported by an independent medical education grant from HeartFlow. All CardioNerds education is planned, produced, and reviewed solely by CardioNerds.  Enjoy this Circulation Paths to Discovery article to learn more about the CardioNerds mission and journey. US Cardiology Review is now the official journal of CardioNerds! Submit your manuscripts here. CardioNerds Multimodality Cardiovascular Imaging PageCardioNerds Episode PageCardioNerds AcademyCardionerds Healy Honor Roll Pearls Shift in Paradigm: CCTA is no longer just an anatomic test; with some key limitations, it can provide anatomy, physiology (CT-FFR), and plaque biology (AI-CPA) in a single non-invasive scan. The “Power of Zero” vs. Plaque: While a normal CCTA has a >95% negative predictive value, future MIs often arise from non-obstructive plaque that traditional stress tests might miss. CAD-RADS 2.0 Utility: The addition of plaque burden modifiers (P1–P4) is a “game changer,” allowing clinicians to identify high-risk patients who need aggressive lipid-lowering despite having only mild stenosis. CT-FFR as a Virtual Stress Test: CT-FFR uses computational fluid dynamics to simulate blood flow, potentially reducing unnecessary invasive catheterizations by approximately 61% without sacrificing safety. Seeing the Invisible: AI-based quantitative plaque analysis (QCPA) can identify “subvisual” plaque and low-attenuation (lipid-rich) components that are the primary drivers of acute coronary syndromes. Show Notes How has the role of CCTA changed compared to traditional functional testing? Historically, stress testing answered “is there ischemia today?”, which often reflects late-stage disease. CCTA identifies disease across the entire spectrum, asking “is there atherosclerosis and how much plaque is present?”. Landmark evidence: SCOT-HEART showed a 41% relative risk reduction in MI at 5 years attributed to intensified preventive therapies, and PROMISE showed CCTA was better at selecting patients who truly needed invasive angiography. Diagnostic CCTA imaging depends on the protocol, contrast timing, heart rate, heart rhythm, breathholding, scanner quality, and several patient factors (obesity, prior stents, heavy calcification, complex bypass anatomy, and motion artifact all may limit imaging). “CCTA is exceptional for the right patient, with the right scanner, and the right team.” What are the key modifiers introduced in CAD-RADS 2.0, and why do they matter? CAD-RADS 2.0 moved beyond stenosis severity to include plaque burden (P0 to P4), high-risk plaque (HRP) features, and the presence of ischemia based on CT-FFR. It serves as a clinical decision support tool: a patient with mild (25-49%) stenosis but “extensive” (P4) plaque burden is considered high risk and warrants aggressive risk factor modification. How is CT-FFR calculated, and when is it most useful in clinical practice? CT-FFR uses resting CCTA data and computational fluid dynamics to create a 3D model of coronary flow during simulated maximal hyperemia. It is often used for intermediate lesions (40–90% stenosis) to predict if they are  ischemia-producing, guiding the decision whether to proceed with invasive angiography.  The assumptions necessary for this computational modeling may not apply well to patients with microvascular dysfunction, significant myocardial scar or prior infarction, or ventricular hypertrophy. Still, data indicate that CT-FFR performs similarly to PET in predicting hemodynamically significant lesions.  CT-FFR performs well at the extremes (either clearly normal or clearly abnormal). Accuracy dips, however, in the intermediate range (~0.75-0.80), where decision-making is most critical. In this grey zone, additional factors can help guide the approach, including the amount of myocardium supplied, translesional gradient, and plaque features.   CT-FFR has not been validated in distal segments, stented segments, heavily calcified coronary arteries, or in patients with severe aortic stenosis. Caution with CT-FFR should be utilized in very calcified coronary segments.  What is AI-based quantitative plaque analysis (QCPA), and what metrics are ready for clinical use? This is potentially a paradigm shift, moving away from stenosis-centric thinking to a more disease burden and plaque biology focus. QCPA uses deep learning algorithms to automatically segment the vessel wall and quantify plaque volume in mm³. Ready for “prime time” metrics include: Total Plaque Volume (TPV), non-calcified plaque volume, and Low-Attenuation Plaque (LAP) burden. Can serial CCTA be used to monitor the effectiveness of medical therapies like statins? While not yet a routine guideline-driven practice, trials like PARADIGM and EVAPORATE show that therapies can stabilize plaque; notably, CCTA is better for monitoring than CAC scores, which can be misleading as statins often increase plaque calcification as part of the stabilization process. There are no randomized trials that serial CCTAs improve outcomes. Cost and radiation exposure will be notable limitations. Serial scan timing, scan acquisition and interpretation standardization would be key. Dr. Gallagher notes that we are moving toward a world in which plaque burden may become a “treatment biomarker,” similar to tumor burden in oncology.  References 1. Coronary Computed Tomography Angiography From Clinical Uses to Emerging Technologies: JACC State-of-the-Art Review. Abdelrahman KM, Chen MY, Dey AK, et al. Journal of the American College of Cardiology. 2020;76(10):1226-1243. doi:10.1016/j.jacc.2020.06.076. 2. Non-Invasive Imaging in Coronary Syndromes: Recommendations of the European Association of Cardiovascular Imaging and the American Society of Echocardiography, in Collaboration With the American Society of Nuclear Cardiology, Society of Cardiovascular Computed Tomography, and Society for Cardiovascular Magnetic Resonance. Edvardsen T, Asch FM, Davidson B, et al. Journal of the American Society of Echocardiography : Official Publication of the American Society of Echocardiography. 2022;35(4):329-354. doi:10.1016/j.echo.2021.12.012. 3. 2021 AHA/ACC/ASE/CHEST/SAEM/SCCT/SCMR Guideline for the Evaluation and Diagnosis of Chest Pain: A Report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines. Gulati M, Levy PD, Mukherjee D, et al. Journal of the American College of Cardiology. 2021;78(22):e187-e285. doi:10.1016/j.jacc.2021.07.053. 4. Contemporary, Non-Invasive Imaging Diagnosis of Chronic Coronary Artery Disease. van der Bijl P, Gulati M, Saraste A, et al. Lancet (London, England). 2025;406(10519):2577-2587. doi:10.1016/S0140-6736(25)01586-7. 5. State of the Art: Evaluation and Medical Management of Nonobstructive Coronary Artery Disease in Patients With Chest Pain: A Scientific Statement From the American Heart Association. Slipczuk L, Blankstein R, Bucciarelli-Ducci C, et al. Circulation. 2025;152(23):e443-e466. doi:10.1161/CIR.0000000000001394. 6. Diagnostic Performance of Fractional Flow Reserve Derived From Coronary CT Angiography: The ACCURATE-CT Study. Li C, Hu Y, Jiang J, et al. JACC. Cardiovascular Interventions. 2024;17(17):1980-1992. doi:10.1016/j.jcin.2024.06.027. 7. Clinical Outcomes Based on Coronary Computed Tomography-Derived Fractional Flow Reserve and Plaque Characterization. Sato Y, Motoyama S, Miyajima K, et al. JACC. Cardiovascular Imaging. 2024;17(3):284-297. doi:10.1016/j.jcmg.2023.07.013. 8. Clinical Use of Coronary Computed Tomography Angiography-Derived Fractional Flow Reserve: Expert Consensus by an International Working Group. Tang CX, Leipsic JA, Nørgaard BL, et al. European Radiology. 2026;:10.1007/s00330-025-12313-6. doi:10.1007/s00330-025-12313-6. 9. Diagnostic accuracy of computed tomography–derived fractional flow reserve: a systematic review. Cook CM, Petraco R, Shun-Shin MJ, et al. JAMA Cardiol. 2017;2(7):803-810. Doi:10.1001/jamacardio.2017.1314 10. Diagnostic performance of noninvasive fractional flow reserve derived from coronary computed tomography angiography in suspected coronary artery disease: the NXT trial (Analysis of Coronary Blood Flow Using CT Angiography: Next Steps). Nørgaard BL, Leipsic J, Gaur S, et al. J Am Coll Cardiol. 2014;63(12):1145-1155. Doi:10.1016/j.jacc.2013.11.043 11. Comparison of coronary computed tomography angiography, fractional flow reserve, and perfusion imaging for ischemia diagnosis. Driessen RS, Danad I, Stuijfzand WJ, et al. J Am Coll Cardiol. 2019;73(2):161-173. Doi:10.1016/j.jacc.2018.10.056. 12. 1-year outcomes of FFRCT-guided care in patients with suspected coronary disease: the PLATFORM study. Douglas PS, De Bruyne B, Pontone G, et al. J Am Coll Cardiol. 2016;68(5):435-445. Doi:10.1016/j.jacc.2016.05.057. 13. Comparison of an initial risk-based testing strategy vs usual testing in stable symptomatic patients with suspected coronary artery disease: the PRECISE randomized clinical trial. Douglas PS, Nanna MG, Kelsey MD, et al; PRECISE Investigators. JAMA Cardiol. 2023;8(10):904-914. Doi:10.1001/jamacardio.2023.2595. 14. Diagnostic and clinical value of FFRCT in stable chest pain patients with extensive coronary calcification: the FACC study. Mickley H, Veien KT, Gerke O, et al. JACC Cardiovasc Imaging. 2022;15(6):1046-1058. doi:10.1016/j.jcmg.2021.12.010. 15. Low-Attenuation Noncalcified Plaque on Coronary Computed Tomography Angiography Predicts Myocardial Infarction: Results From the Multicenter SCOT-HEART Trial (Scottish Computed Tomography of the HEART). Williams MC, Kwiecinski J, Doris M, et al. Circulation. 2020;141(18):1452-1462. doi:10.1161/CIRCULATIONAHA.119.044720. 16. AI-Guided Quantitative Plaque Staging Predicts Long-Term Cardiovascular Outcomes in Patients at Risk for Atherosclerotic CVD. Nurmohamed NS, Bom MJ, Jukema RA, et al. JACC. Cardiovascular Imaging. 2024;17(3):269-280. doi:10.1016/j.jcmg.2023.05.020. 17. Interaction of AI-Enabled Quantitative Coronary Plaque Volumes on Coronary CT Angiography, FFRCT, and Clinical Outcomes: A Retrospective Analysis of the ADVANCE Registry. Dundas J, Leipsic J, Fairbairn T, et al. Circulation. Cardiovascular Imaging. 2024;17(3):e016143. doi:10.1161/CIRCIMAGING.123.016143. 18. Prognostic Value of AI-Based Quantitative Coronary CTA vs Human Reader-Based Visual Assessment: Results From the CONFIRM2 Registry. van Rosendael A, Nakanishi R, Bax JJ, et al. JACC. Cardiovascular Imaging. 2026;19(3):345-359. doi:10.1016/j.jcmg.2025.09.021.13. Pericoronary Adipose Tissue as a Marker of Cardiovascular Risk: JACC Review Topic of the Week. Tan N, Dey D, Marwick TH, Nerlekar N. Journal of the American College of Cardiology. 2023;81(9):913-923. doi:10.1016/j.jacc.2022.12.021. 19. Effect of Icosapent Ethyl on Progression of Coronary Atherosclerosis in Patients With Elevated Triglycerides on Statin Therapy: Final Results of the EVAPORATE Trial. Budoff MJ, Bhatt DL, Kinninger A, et al. European Heart Journal. 2020;41(40):3925-3932. doi:10.1093/eurheartj/ehaa652. 20. Coronary CT Angiography Evaluation With Artificial Intelligence for Individualized Medical Treatment of Atherosclerosis: A Consensus Statement From the QCI Study Group. Schulze K, Stantien AM, Williams MC, et al. 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Andrew Parish and Tillman Holloway are the co-founders of Arch Public, a software platform that helps investors automate their trading strategies across crypto and traditional markets. In this conversation, we discuss why the US will keep printing money to fund AI infrastructure, how tokenization is about to reshape global markets and banking, why crypto becomes the default exchange layer in a 24/7 world, and how automation tools are now a necessity for every investor.=======================Award-winning Fountain Life - Energy supercharged. Memory sharper. Life extended. Ready for the best investment you'll ever make? Schedule a life-changing call at http://fountainlife.com/pompGet $1,000 off the cost of a life-changing membership with Fountain Life when you schedule a call at https:www.http://fountainlife.com/pomp=======================Bitget (https://bitget.com/promotion/futures-tradfi?channelCode=regd&vipCode=nkew) is the world's largest Universal Exchange (UEX) (https://bitget.com/promotion/futures-tradfi?channelCode=regd&vipCode=nkew), serving over 125 million users with access to over 2M+ crypto tokens, and TradFi markets such as 100+ tokenized stocks, ETFs, commodities, FX and precious metal like Gold. At launch, users can trade 79 instruments with USDT directly with the App. Users can also enjoy high liquidity and low slippage, while trading these assets with up to 500x leverage. For more information on Bitget TradFi, visit this article (https://bitget.com/support/articles/12560603846859). For more information, visit: Website (https://bitget.com/) | Twitter (https://x.com/bitget) | Telegram (https://t.me/BitgetENOfficial) | LinkedIn (https://linkedin.com/company/bitget-global/) | Discord (https://discord.com/invite/bitget)For media inquiries, please contact: media@bitget.com=======================Arch Public is an agentic trading platform that automates the buying and selling of your preferred crypto strategies. Sign up today at https://www.archpublic.com and start your automated trading strategy for free. No catch. No hidden fees. Just smarter trading.=======================0:00 - Intro1:05 - AI infrastructure, dollar printing & national security5:37 - Crypto's role: stablecoins, bitcoin, or tokenization?7:57 - Why volatility is only going to get worse13:26 - AI agents & crypto as the default exchange layer15:40 - Tokenization race & the banking revenue opportunity21:55 - Fractional assets & borrowing against tokenized holdings26:41 - Hyperliquid, private company tokenization & M&A outlook29:50 - Pros/cons of open markets & financial education 32:40 - Prediction markets, tokenized ETFs & the war for capital34:23 - Arch Public: what it does & where to find it

Is fractional real estate investing the wealth builder you've been looking for, or does owning rentals still reign supreme? In this episode, Alex Blackwood joins Russ and Joey to talk about fractional real estate investing and compares it with the traditional model of owning rental properties. Alex, co-founder of a successful investment platform, reveals how fractional ownership provides an opportunity to invest in lucrative properties without the full responsibility of property management. He explains how fractional real estate investing works, why it's gaining popularity among investors, and how it can help you diversify your portfolio and scale your wealth.If you've been considering ways to break into real estate investing without the high barriers of entry, this episode is for you. Tune in and discover how fractional real estate investing can be a game-changer in building wealth.Top three things you will learn: -The advantages of fractional real estate investing over traditional rental property ownership-How to invest in high-quality properties without the time and hassle of direct ownership-The key strategies for selecting profitable properties in top markets and scaling passive incomeAbout Our Guest:Alex Blackwood is the 29-year-old co-founder and CEO of mogul, a real estate investment startup. He previously worked at Goldman Sachs, making $250,000 a year. Alex says it is more rewarding to be a startup founder compared to working 100-hour weeks as a real estate investing associate for another company. Alex started his company after buying a property for the first time and realizing how capital and time-intensive it was. At the end of the transaction, he knew there had to be a better way to own real estate. As CEO of mogul, Alex says he plays the part of a lawyer, an accountant, an investor, and a marketer. Disclaimer: The opinions expressed on this podcast are solely those of the hosts and guests and do not constitute financial advice. Always consult a licensed professional for financial decisions.This episode is sponsored by a podcast show partner. We may receive compensation if you use links or services mentioned in this episode.The hosts may have a financial interest in the programs or services mentioned in this episode.Connect with Alex Blackwood:- Website - https://www.mogul.club/