Podcasts about fractional

.entry-img img{ display:none !important; } .single .hentry .entry-img{ display:none !important; } Understanding why some companies run short of the one resource they simply cannot operate without, cash in the bank, even when they are hitting revenue and profit targets has become an essential leadership skill. Cash flow problems rarely appear in the headline numbers, yet they can quietly derail growth plans, strain supplier relationships, and, in the worst cases, threaten the survival of an otherwise profitable business. For founders, CEOs, and finance leaders, success depends on looking beyond the profit and loss statement to understand the timing, predictability, and movement of cash. Organisations that master cash flow are better equipped to scale with confidence, navigate uncertainty, and seize opportunities while competitors struggle to meet their obligations. In this episode of The GrowCFO Show, host Kevin Appleby is joined by Scotty Palmer, Fractional CFO and Founder of Palmers Advisors, to explore one of the most common challenges facing growing businesses: why profitable companies still run out of cash. Scotty explains how tight margins, hidden costs, and rapid growth without effective cash flow planning can quickly create a liquidity crisis, even when the profit and loss statement looks healthy. Drawing on his experience advising small and mid-sized businesses in the food and beverage sector, he shares practical examples of how cash constraints can emerge despite strong financial performance. The conversation also explores the tools and disciplines that help businesses strengthen cash flow and improve decision-making. Scotty discusses the role of financial modelling, KPI tracking, and AI-powered forecasting in creating greater visibility over future cash needs. He explains how a better understanding of unit economics, more accurate cost allocation, and challenging assumptions about seemingly profitable product lines can uncover hidden value and improve financial resilience. Throughout the discussion, he demonstrates how a fractional CFO can act as a strategic partner, helping founders balance ambitious growth with the financial discipline needed to build a sustainable business. Key topics covered: How a fractional CFO helps profitable businesses avoid cash crunches by improving visibility into true costs and cash conversion Why food and beverage businesses are especially vulnerable to cash-flow problems due to thin margins and complex cost structures A client case where disciplined financial modeling and KPI tracking helped increase business performance 10x Practical strategies to balance passion for product with commercial viability, including pricing, cost allocation, and product mix decisions How Scotty uses AI tools and spreadsheets to build agile financial models and improve decision-making speed for clients Scotty's longer-term vision of building a specialist team of food and beverage advisors to support more founders at scale Links Scotty Palmer on LinkedIn Kevin Appleby on LinkedIn GrowCFO Mentoring Timestamps:  0:00:01 – Scotty's background and journey from corporate accounting at Honey Baked Hams to becoming a fractional CFO for food and beverage businesses 0:02:57 – The personal and financial challenges of leaving a stable corporate role to build a fractional CFO practice, and the central importance of predictable cash flow 0:07:14 – Why the food and beverage sector is high-risk for cash shortages despite apparent profitability, and how thin margins amplify operational missteps 0:08:39 – Case study: managing a large retailer opportunity, understanding true costs, and avoiding overextending cash to chase volume 0:22:37 – Using cost analysis, pricing strategy, and product-level profitability to turn around a struggling taproom restaurant 0:29:21 – Leveraging AI (Claude, Gemini, Google Sheets) to power financial modeling and scenario analysis without heavy financial systems 0:40:05 – Advice for corporate finance professionals considering a move into fractional CFO work, including risk, reward, and impact Find out more about GrowCFO If you enjoyed this podcast, you can subscribe to the GrowCFO Show with your favorite podcast app. The GrowCFO show is listed in the Apple podcast directory, Spotify and many others. Why not subscribe there today? That way, you never miss an episode. GrowCFO is a great place to extend your professional network. Join GrowCFO as a free member today and participate in our regular networking events and webinars. Premium members can also access our extensive training center and CFO Digital Toolkit. You can enroll in our flagship Future CFO or Finance Leader programs here. You can find out more and join today at growcfo.net

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

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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

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.

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

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.

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.

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.

Gudrun talks with Debajyoti Choudhuri. He is staying at KIT as a short term guest. He is Associate Professor in the School of Basic Sciences at IIT Bhubaneswar, India. He did his M.Sc. and Ph.D. in Mathematics at the University of Hyderabad. His research interest lies in the analysis of elliptic PDEs using Functional Analytic and topological methods. In this he touches and has a slight overlap with the research of Gudrun. The conversation starts with the discussion about a small paper which Debajyoti put on the archiv. It is about understanding how to work with the Fractional Laplacian. This means extending the classical Laplace operator Δ to non-integer powers. This operator is the main part in PDEs which model, e.g, anomalous diffusion, probability theory, image processing, finance, and nonlocal mechanics. (-Δ)s, where 0 < s < 1. What makes It different to the ordinary Laplacian? While the traditional Laplace operator is local, i.e. it depends only on values of u and its derivatives near x, the fractional Laplacian is nonlocal, it depends on values of u everywhere in space. Thus, for the analytical and numerical treatment one needs very different methods. There are several possible definitions. Some of them can be found in the Wikipedia article which is cited below. On ℝn, the cleanest definition is the Fourier definition which follows the idea: Take the Fourier transform. Multiply by |ξ|2s. Transform back. In the short paper which is discussed the singular integral definition is used: For 0 < s < 1: (-Δ)^s u(x) = C(n,s) PV ∫ [u(x) - u(y)] / |x - y|^(n + 2s) dy This makes the nonlocality explicit: every point y contributes to the value at x. The method central in studying Laplace problems is variational. It considers an (infinite) family of generalised problems and works on the existence of so-called weak solutions. These problems are formulated with the help of Sobolev spaces. The weak solution for the Laplace problem is an element of the space H1=W1,2. This means the solution and its (generalised) gradient are bounded in L2 in the domain in which the problem is solved. This has physical meaning and due to known properties (embedding) of Sobolev spaces the pointwise (strong) solutions often can be constructed when enough regularitiy of the weak solutions is proved. Fractional Laplacians naturally live in fractional Sobolev spaces. These are not that easy to connect to physical properties and a few of the equivalent definitions in the context of classical Sobolev spaces are not equivalent any more everywhere. Common approaches for numerics for PDEs including the fractional Laplacian are: Fourier spectral methods (periodic domains) Finite element methods for fractional PDEs Matrix-function methods (As) Caffarelli–Silvestre extension methods Quadrature approximations of singular integrals The Extension trick introduced by Caffarelli and Silvestre in 2007 (their original paper is cited below) is also discussed as part of the short note. p-laplacian augurs well in the sense because the unicity of the definitions of the s-laplacian is still lacking. The conversation then turns to how Debajyoti found his way into mathematics and the topic of PDEs and how life and work feel like in his university. More information: Webpage of Debajyoti Choudhuri Debajyoti Choudhuri: A quick sneak-peek at the s-fractional Laplacian operator (2022) Wikipedia on the Fractional Laplace operator Mateusz Kwaśnicki: Ten equivalent definitions of the fractional Laplace operator (2015) E. Di Nezza, G. Palatucci, E. Valdinoci, Hitchhiker's guide to the fractional Sobolev spaces, Bull. Sci. Math., 136(5), 521–573 (2012) L. Caffarelli, L. Silvestre, An extension problem related to the fractional Laplacian, Communications in Partial Differential Equations, 32, 1245–1260 (2007)

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

Every day, billions of transactions settle between strangers who have no idea which bank the other uses. That lack of friction is not automatic. Nine-tenths of the money in daily circulation has been created by commercial banks, but it stays trustworthy only because central banks stand behind it, and keep the system in balance.In this week's episode Tim Phillips talks to Stephen Cecchetti (Brandeis University, CEPR) about what happens when new forms of digital money test that architecture. Cecchetti is one of the authors of the eighth Barcelona Report in The Future of Banking series, part of the Banking Initiative at IESE Business School, just published by CEPR as a free download.Will retail central bank digital currencies, tokenised deposits, and stablecoins upset the delicate balance of system that has been running for decades? Stablecoins, for example, do not create money, but they claim the status of money without the institutional guarantee that makes money trustworthy. Three jurisdictions — the US, the EU, and the UK — are each resolving the same underlying contradiction in different ways. None has fully resolved it.The research behind this episode:Niepelt, Dirk, Stephen G. Cecchetti, Hélène Rey, and Xavier Vives. 2026. Digital Money: The Future of Banking 8. London: CEPR Press. Available as a free download from CEPR.To cite this episode:Phillips, Tim, and Stephen G. Cecchetti. 2026. “The digital money supply.” VoxTalks Economics (podcast). Assign this as extra listening. The citation above is formatted and ready for a reading list or VLE.About the guestStephen Cecchetti is the Rosen Family Chair in International Finance at Brandeis University, a Research Fellow of the Centre for Economic Policy Research (CEPR), and a Research Associate at the NBER. He was previously Economic Adviser and Head of the Monetary and Economic Department at the Bank for International Settlements, and Director of Research at the Federal Reserve Bank of New York. His research spanning monetary policy, financial stability, and banking regulation has shaped both academic and policy debate over three decades. He blogs at moneyandbanking.com.Research cited in this episodeWalter Bagehot's lender of last resort doctrine. In Lombard Street: A Description of the Money Market (1873), Bagehot argued that a central bank under stress should lend freely against good collateral at a penalty rate. The prescription remains the intellectual foundation for how central banks manage runs and systemic crises. Cecchetti invokes it to make the point that no private substitute for a central bank backstop has ever proved durable, and that the doctrine is now, one hundred and fifty years on, being tested by instruments its author could not have imagined.Monetary uniformity, mobility, and elasticity. The three institutional conditions underpinning general acceptance of money, developed in analysis by the Bank for International Settlements and discussed extensively in the report. Uniformity means a pound is a pound regardless of which bank holds it. Mobility means claims move between users and institutions at low cost and settle with finality. Elasticity means the supply of money can expand when it is under stress. Together they explain why we accept a deposit at face value without doing any analysis of the bank that issued it; and together they identify exactly where new forms of digital money create institutional gaps.Silicon Valley Bank failure, March 2023. SVB's collapse illustrates both the lender of last resort functioning and the limits of no-bailout commitments. Cecchetti notes that SVB's liabilities were still trading at par on the Thursday before its Friday failure because the Federal Reserve stood behind them. He also notes that Circle, the issuer of USDC, held $3.3 billion of its reserves at SVB and was effectively bailed out in the resolution. The episode is one of two occasions in the past twenty years where money market fund-like instruments have been backstopped by the Federal Reserve under stress.Genius Act (United States). Principle-based stablecoin regulation expected to come into effect in the US around 2027. Under its provisions, only stablecoins issued by bank-affiliated issuers will have access to the Federal Reserve; only those will therefore have the institutional backing needed to function as money. Stablecoins issued by non-bank entities will not.Markets in Crypto Assets Regulation (MiCA), European Union. The EU framework for crypto assets, which entered into force in 2024. For stablecoins, MiCA requires issuers to hold 30 to 60% of their reserves in bank deposits, with no provision for central bank backing. The stated rationale is to keep deposits within the banking system; Cecchetti notes this creates a different category of vulnerability and leaves the question of what happens under stress unresolved.Bank of England stablecoin proposal (United Kingdom). The Bank of England's approach differs from both US and EU frameworks by explicitly requiring large stablecoin issuers to hold significant reserve deposits at the Bank of England, making them in effect narrow banks with a direct central bank backstop. Cecchetti regards this as the most coherent of the three approaches in terms of institutional logic, though the same fundamental question applies: whether holding to that design under stress would be politically sustainable.Tether and the jurisdictional challenge. Tether, the largest stablecoin issuer, is registered in El Salvador having previously operated out of the British Virgin Islands. Its tokens are held by users in multiple countries, traded on exchanges in multiple jurisdictions, and backed by US Treasury securities. Cecchetti uses this to illustrate why local regulation, however well-designed, is necessary but not sufficient; effective oversight of instruments that are genuinely global requires international standards and coordination.Fractional reserve banking and the goldsmith model. The institutional structure described in the episode has roots in mid-seventeenth century England, when goldsmiths began issuing more paper receipts than they had gold in their vaults. The goldsmiths became bankers; the paper became money; the vulnerability to runs became a structural feature of private money creation that persists today. Cecchetti uses the history to make the point that while technology changes how we store and transmit information, the underlying architecture of trust in private money is as old as Newtonian physics.More VoxTalks Economics episodesMaking banking safe, Stephen Cecchetti and Kermit Schoenholtz. Our financial system is supposed to be more resilient than before the global financial crisis, but that didn't save Silicon Valley Bank, Signature Bank or First Republic. So what went wrong?Related reading on VoxEUNew coins on the block: Digital currencies and the financial system. The authors of the Barcelona Report warn that “Digital money will be reliable only where sound institutions and robust technology come together.”

Fractional CEO Services For Small Businesses In this episode, Tim Staton and Kristen McAlister explore fractional CEO services for small businesses, clarifying fractional CEO meaning and what does fractional CEO mean for today's leaders and owners. They discuss fractional CEO jobs, typical fractional CEO salary expectations, and answer the question: What can a fractional CEO make? Kristen McAlister explains how fractional CEO services work for small businesses by providing on-demand executive expertise that delivers immediate impact without the overhead of a full-time C-suite hire. Listeners will learn How to become a fractional chief executive and gain insight into the growing world of fractional leadership roles. Key Topics Covered: The surge in awareness around fractional CEO meaning and the benefits of fractional leadership roles for small businesses Kristen McAlister's journey into fractional and interim leadership, including her experience as a military spouse Real-world examples of how fractional CEO services transform business performance, from financial oversight to strategic growth The critical importance of agility in leadership and what is leadership agility in today's uncertain environment How leadership and agility enable companies to adapt quickly while developing internal teams Addressing common challenges like team cohesion, delegation, and resistance to external fractional leaders Market trends showing rapid adoption of fractional CEO services across the U.S. and globally Practical advice for business owners on when and how to engage fractional leadership Guidance for military veterans interested in fractional CEO jobs and transitioning into fractional chief executive roles, including programs like SkillBridge The future of flexible leadership models emphasizing specialized expertise and work-life balance Tim Staton and Kristen McAlister also touch on Kristen's upcoming podcast “She Owns It,” which amplifies stories of women entrepreneurs. This episode is essential listening for anyone exploring what fractional CEO services can do for their business, seeking greater leadership and agility, or considering a career in fractional leadership roles. Whether you want to understand fractional CEO salary potential, how to become a fractional chief executive, or simply need proven strategies for sustainable growth, you'll walk away with actionable insights.   Connect with Kristen McAlister Fractional CEO | Interim Leadership | Leadership Agility Expert LI: https://www.linkedin.com/in/kristenmcalister/ IG: https://www.instagram.com/kristenmcalister/ She Owns It! podcast: https://www.youtube.com/@SheOwnsItPodcast Book: https://www.amazon.com/dp/B0CV4G9CJ1   Connect With Tim Website: timstatingtheobvious.com Facebook: https://www.facebook.com/timstatingtheobvious YouTube: https://www.youtube.com/channel/UCHfDcITKUdniO8R3RP0lvdw Instagram: @TimStating TikTok: @timstatingtheobvious LinkedIn: https://www.linkedin.com/in/tim-staton-04b41a271/ SKOOL Community: https://www.skool.com/timstatingtheobvious-9537/about?ref=de9c7e65d8ba4eeabc1a8eea413c125b

Dave Martin has spent more than two decades in product leadership, with a string of C-suite roles, a couple of exits and a book, The Product Momentum Gap, to his name. He is also dyslexic and ADHD, and has built a career while masking the effort it takes to "think normal". In this episode he makes the case that the advice handed to neurotypical leaders often fails the roughly half of tech workers who are neurodivergent, and lays out a practical playbook for landing your message, leading the room and progressing without pretending to be someone else. Chapters00:00) Welcome, and Dave's background in product(02:03) "I've been masking it": faking thinking normal(02:37) The meeting where your idea is ignored, then credited to someone else(03:28) AI as a "spell check for influence"(04:07) The myth that growth requires pretending to be neurotypical(05:15) Why standard leadership advice fails neurodivergent leaders(06:45) Executive presence, signal presence and signal drift(07:57) Is this universal, or specific to neurodivergence?(09:48) From "dumb kid" to writing C++ at ten(11:27) When a word processor flipped his Fs to As(13:24) The trap: leading with detail(15:42) The boardroom moment that gets you labelled "not strategic"(17:05) Designing for re-tell: what the room repeats when you leave(18:19) Three mistakes that kill your influence(19:36) The CALM framework(21:32) Authority and the signal prep exercise(22:14) Three questions: outcome, one-line recommendation, re-tell(24:44) "Minutes not months": seeding the line that gets repeated(26:56) Learning: vulnerability and psychological safety(28:27) Momentum, well-being and burnout(31:21) Why burnout is a leadership fault(32:01) Mia's story: the head of product who wanted to be CPO(34:20) Recognising the trigger and practising signal prep(37:06) When stakeholders started calling her strategic(38:31) The opposite trap: abandoning detail entirely(39:22) Why some leaders step back into IC roles(41:16) Free training and AI as your spell checker for influence(42:26) Closing thoughtsKey takeaways— Authenticity is not the goal; deliberate communication is. Dave's central provocation is that "be your authentic self" assumes everyone in the room thinks the way you do. For a leader who sees patterns instantly and works in deep, hyperfocused bursts, behaving authentically can mean failing to explain the obvious and struggling to empathise with those who need the journey, not just the destination.— The symptoms are universal, the tax is not. Everybody's message gets lost in meetings. What separates neurodivergent leaders is the cognitive cost of noticing that drift and correcting it. As Randy and Dave agree, the tools discussed here help everyone, but the impact is far larger for those paying the higher tax.— Leading with detail is the career trap. The very trait that makes someone an exceptional individual contributor, the ability to go deep and surface every edge case, can sink them in the boardroom. — Answer a strategic question with edge cases and you are labelled "not executive" with alarming speed, and undoing that label takes months of work.— CALM is the alternative. Clarity, authority, learning and momentum, delivered calmly. Authority comes from being clear on the outcome and the ask, asking for support and guidance rather than permission, and not feeling obliged to justify every edge case.— Signal prep is the practical tool. Three questions: what do I need from this room; what is my one-line recommendation; and what will they repeat when I am not in the room. A bonus question for higher-stakes meetings asks what the room feels now and how you want them to feel when you leave.— Design for re-tell. Dave's example of a leader who reduced a lengthy objective to "minutes not months for our customers", and repeated it, is the clearest illustration. That phrase, not someone else's reframe, is what got repeated in the room afterwards.— Well-being underpins momentum. Dave nearly named the framework around well-being. Without a sustainable pace, leaders cannot lead, and the unprocessed meeting that keeps you awake at 3am is a momentum problem. He frames widespread tech burnout as a leadership failure, because leaders set the expectation.— AI is a spell checker for influence. Just as a word processor turned Dave's Fs into As without changing his brain, AI tooling can help neurodivergent leaders translate their thinking into the right language for the room, supporting the communication without doing the thinking or the judgement for them.Our HostsLily Smith enjoys working as a consultant product manager with early-stage and growing startups and as a mentor to other product managers. She's currently Chief Product Officer at BBC Maestro, and has spent 13 years in the tech industry working with startups in the SaaS and mobile space. She's worked on a diverse range of products – leading the product teams through discovery, prototyping, testing and delivery. Lily also founded ProductTank Bristol and runs ProductCamp in Bristol and Bath.Randy Silver is a Leadership & Product Coach and Consultant. He gets teams unstuck, helping you to supercharge your results. Randy's held interim CPO and Leadership roles at scale-ups and SMEs, advised start-ups, and been Head of Product at HSBC and Sainsbury's. He participated in Silicon Valley Product Group's Coaching the Coaches forum, and speaks frequently at conferences and events. You can join one of communities he runs for CPOs (CPO Circles), Product Managers (Product In the {A}ether) and Product Coaches. He's the author of What Do We Do Now? A Product Manager's Guide to Strategy in the Time of COVID-19. A recovering music journalist and editor, Randy also launched Amazon's music stores in the US & UK.

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. Nature Reviews. Cardiology. 2026;23(2):100-115. doi:10.1038/s41569-025-01191-6.

If you've been quietly wondering whether the shifts you're feeling in your business are temporary, or something bigger, this episode names it. All markets mature eventually and push generalists to specialize, but AI is speeding this process up at breakneck speed. In this episode, you'll hear the 5 things that happen in every commoditizing market, why being stuck in the middle is the most dangerous place to be right now, and the 3 moves to AI-proof your consulting business before AI eats the bottom and middle of the market.   Work With Coach Natalie

Kristopher "Kris" Grey is the founder of Creatapult and a seasoned project management consultant with over two decades of experience helping contractors and growing businesses scale without operational chaos. A self-described "construction brat" who grew up inside his family's contracting company, Kris launched his entrepreneurial journey under pressure — just days after the birth of his first child — and turned that crisis into a mission to help business owners build the systems, dashboards, and accountability frameworks they need to protect margins, reduce risk, and lead with clarity through fractional project management leadership.SHOW SUMMARYIn this episode, Jonathan Goldhill is joined by Kristopher Grey of Creatapult about how contractors and other organizations can scale without operational chaos. Kristopher shares his origin story of losing all family income three days after his first child was born, which shifted his view that entrepreneurship and having a “side” income can be less risky than relying on one W2 job. Drawing on his upbringing in a family construction business, he describes common contractor failures such as bad bookkeeping, overreliance on tribal knowledge and heroics, understaffing project management, and the “death spiral” where winning more work leads to schedule slips, quality decline, change-order losses, and margin erosion. They discuss the “Who does what by when” accountability tool, dashboards, backup PMs, and the rise of fractional project management leadership. Kristopher outlines a 90-day execution engine focused on project intake, portfolio stabilization with RAG reporting, and risk tracking, and shares a transit-operator turnaround that enabled growth and COVID resilience.KEY TAKEAWAYSWinning more work can kill a company. Growth without systems creates a "death spiral" — slipping schedules, declining quality, and cash flow collapse, even when revenue is rising.Bad bookkeeping is the #1 contractor mistake. If you don't know your margins, you can't manage your business — you're running a personal ATM, not a company.Project managers lose effectiveness past 2 projects. Overloading PMs is a silent killer of profitability and client relationships."Who Does What By When" is the foundation of execution. Without a clear owner, a clear task, and a hard deadline, everything drifts.Systems are the antidote to turnover. With employees switching jobs every ~4 years, institutional knowledge must be documented — not held in someone's head.Fractional project management lowers the barrier to scaling. Companies don't need a full-time executive to get enterprise-level PM leadership — they just need the right fractional fit.Don't be afraid to ask for help. Pride is the number one source of doom for family construction businesses.Risk tracking is almost always missing. Most contractors react to problems instead of forecasting and mitigating them early.A RAG dashboard (Red/Amber/Green) gives leadership real-time project visibility and frees CEOs from daily firefighting to focus on strategy.QUOTES"It's kind of like a fish drowning in water. You'd think that winning more work would be a good thing… but if they've not been managing those projects well, they're bleeding out." — Chris Grey"If you don't put a deadline on something, your project is always at risk of falling behind by the longest single scheduling item you have.""Pride is probably the number one source of doom for a lot of these companies — the name is often on the building.""Most employees are essentially a statistic or a number for a company — they can be let go at any time.""Always have something on the side. If the thing takes off, run with it.""Growth alone doesn't create successful companies — but execution does." — Jonathan Goldhill (closing)"We were doing more with less — but less stress overall — because the PMs had the tools they needed to be successful."Connect and learn more about Kristopher Grey.https://www.linkedin.com/in/kristophergrey/If you enjoyed today's episode, please subscribe, review, and share with a friend who would benefit from the message. If you're interested in picking up a copy of Jonathan Goldhill's book, Disruptive Successor, go to the website at www.DisruptiveSuccessor.com

Harmonizing the Brand Symphony: Unified Messaging Architecture with Joshua AltmanIn a recent episode of The Thoughtful Entrepreneur Podcast, host Josh Elledge sat down with Joshua Altman, the Managing Director of Beltway Media, to dissect the communication breakdowns that quietly dilute the market authority of growing businesses. Operating near the strategic hub of Washington, D.C., Joshua brings an elite corporate perspective to executive storytelling, utilizing frameworks refined through his work with organizations like the Department of Justice and Dow Jones. This conversation provides an essential strategic overview for small-to-mid-sized business owners and startup founders who struggle with siloed corporate messaging—where PR, outbound sales, internal culture, and digital marketing pull the brand narrative in completely different directions.The Architecture of Consistency: Eliminating Communication Silos through Fractional OversightThe primary point of friction holding back a company's market positioning is rarely the quality of the product itself, but rather a fragmented brand narrative where different departments are singing completely different songs. Joshua Altman explains that when small-to-mid-sized businesses scale rapidly, marketing pipelines, product documentation, and client-facing communication channels organically decouple from the founder's original vision. This lack of messaging unity introduces friction into the sales funnel, confuses key stakeholders, and erodes consumer trust at critical touchpoints. By treating brand communication as an interconnected corporate ecosystem, companies can deploy fractional oversight to synthesize every piece of collateral—from investor pitch decks to automated social content—into a unified, harmonious voice that commands premium industry credibility.To systematically align an organization's public footprint, executives must look beyond basic content calendars and embrace advanced narrative auditing tools. Beltway Media champions the "Four Languages Model," a comprehensive audit framework that forces an enterprise to map and evaluate how its core message is consumed across four distinct dimensions: what audiences read, see, hear, and experience. When an organization meticulously reviews its visual identity, written copy, audio media, and physical customer service touchpoints simultaneously, it can instantly isolate the messaging gaps that cause prospect attrition. This data-driven alignment moves corporate communications away from reactive, ad-hoc task management and into a highly optimized, proactive corporate asset that builds predictable long-term value.Furthermore, building an authoritative presence in a crowded digital marketplace requires executive leadership to actively step onto media platforms, particularly through strategic podcast guesting. Many founders and technical executives initially resist media appearances out of perfection paralysis or a lack of formal broadcasting experience; however, modern audiences aggressively favor unscripted, human transparency over clinical corporate polish. Leveraging podcast appearances allows a leader to deliver an authentic narrative that remains discoverable online for years, generating a continuous pipeline of warm, incoming referrals. When advanced technological infrastructure and strategic media exposure are paired with a unified communications framework, an enterprise can effectively bridge the gap between complex internal data and compelling external impact.About Joshua AltmanJoshua Altman is the Managing Director of Beltway Media and a premier corporate communications strategist with a career spanning both high-level public sectors and corporate private markets. Drawing from deep analytical experience with the Department of Commerce and various enterprise networks, Joshua specializes in translating complex corporate missions into concise, authoritative brand narratives. Outside of his advisory work, he is a dedicated community volunteer, managing dog adoption coordination initiatives throughout the greater Washington, D.C. area.About Beltway MediaBeltway Media is an elite strategic advisory firm that provides specialized fractional Chief Communications Officer (CCO) services, messaging audits, and narrative design for startups and mid-market organizations. The consultancy eliminates executive administrative debt by bringing public relations, internal branding, corporate documentation, and digital media pipelines under a single, unified oversight structure. Through science-backed auditing frameworks and hands-on execution playbooks, Beltway Media helps high-growth organizations establish absolute messaging consistency to accelerate investor trust and market share.Links Mentioned in This EpisodeBeltway Media Official Leadership Page: beltway.media/leadershipJoshua Altman on LinkedIn: linkedin.com/in/joshuaialtmanKey Episode HighlightsThe Symphony Analogy of Branding: Understanding why individual department communication channels must be structurally harmonized to prevent brand dilution.The Fractional CCO Advantage: Accessing high-level enterprise messaging governance and PR strategy without the overhead of a full-time executive hire.The Four Languages Model: A comprehensive structural framework to audit and align what your audience reads, sees, hears, and experiences across your entire sales funnel.The Multi-Dimensional Messaging Audit: Practical exercises for founders to benchmark their internal communication maturity and spot brand misalignments.The Long-Tail Media Asset Loop: Leveraging podcast guesting to build permanent, searchable authority assets that drive compounding inbound attention.ConclusionThe conversation with Joshua Altman emphasizes that clear, consistent communication is the ultimate driver of enterprise trust and market differentiation. By treating brand narrative design as a strict structural discipline and leveraging fractional executive frameworks, founders can convert fragmented company data into a powerful, unified story that establishes permanent authority across their entire industry.More from The Thoughtful Entrepreneur

Lucinda talks to HR transformation specialist Sharon Green to demystify the evolving world of independent people professionals. Sharon draws on her extensive 20-year career to clearly distinguish between traditional interim management and the emerging trend of fractional HR, which offers scalable, long-term strategic leadership for growing businesses that don't yet need a full-time executive.  Packed with practical advice, the conversation explores the mindset, adaptability, and relationship-building skills required to thrive as a solo practitioner in a changing corporate landscape. KEY TAKEAWAYS While interim roles are typically full-time, project-driven, or coverage-based for a finite period, fractional roles provide ongoing, long-term strategic leadership on a part-time basis tailored to a company's growth stage. Succeeding as an independent consultant requires a high tolerance for ambiguity, as practitioners must frequently step into unfamiliar corporate cultures and hit the ground running without formal onboarding. The value of a senior interim or fractional professional lies less in highly specialised industry knowledge and more in transferable leadership skills and the ability to view business challenges from an objective, outside perspective. Managing the exit phase of a consultancy contract with a thorough handover is just as critical as the onboarding phase to ensure long-term trust and sustainable change for the client. BEST MOMENTS "You're not really leveraging off your domain knowledge... it's more about the transferable skills and experience that you bring to that organisation and the outside-in perspective." "Clients aren't paying to manage you... they're paying for you to manage the work that they are engaging you to deliver." "Doing a good job at the ending is just as important as doing that good job right at the start to build those trusting relationships." VALUABLE RESOURCES The HR Uprising Podcast | ⁠Apple⁠ | ⁠Spotify⁠ | ⁠Stitcher⁠   ⁠The HR Uprising LinkedIn Group⁠ ⁠How to Prioritise Self-Care (The HR Uprising)⁠ ⁠How To Be A Change Superhero - by Lucinda Carney⁠ HR Uprising Mastermind - ⁠https://hruprising.com/mastermind/⁠   ⁠www.changesuperhero.com⁠ ⁠www.hruprising.com⁠            Get your copy of How To Be A Change Superhero by emailing at ⁠info@actus.co.uk⁠ CONTACT SHARON LinkedIn: https://www.linkedin.com/in/sharongreenchiara/ Chiara Consulting Website: http://chiaraconsultancy.co.uk/ ABOUT THE HOST Lucinda Carney is a Business Psychologist with 15 years in Senior Corporate L&D roles and a further 10 as CEO of Actus Software where she worked closely with HR colleagues helping them to solve the same challenges across a huge range of industries. It was this breadth of experience that inspired Lucinda to set up the HR Uprising community to facilitate greater collaboration across HR professionals in different sectors, helping them to ‘rise up' together. “If you look up, you rise up” CONTACT METHOD Join the LinkedIn community - ⁠https://www.linkedin.com/groups/13714397/⁠ Email: ⁠Lucinda@advancechange.co.uk⁠ Linked In: ⁠https://www.linkedin.com/in/lucindacarney/⁠ Twitter: @lucindacarney Instagram: @hruprising Facebook: @hruprising This Podcast has been brought to you by Disruptive Media. ⁠https://disruptivemedia.co.uk/

SUMMARY:Aaron and Terryn tackle one of the most expensive decisions entrepreneurs get wrong: hiring full time when fractional would serve them better. They run through a rapid-fire breakdown of common roles including executive assistants, finance team, customer support, and tech, and call out exactly when each one makes sense as a fractional hire versus an internal employee. They also get into the EA versus VA debate, why hemisphere matters more than most people think, and why piloting with a fractional expert first almost always sets you up for a smarter full-time hire down the road. Grab the free recruiting templates and interview questions at recruiting.opsexpertsacademy.com. Minute By Minute: 00:00 Introduction to Recruiting Challenges 03:05 The Benefits of Fractional Hiring 05:51 Understanding Executive Assistants vs. Virtual Assistants 08:48 When to Hire an Internal Finance Team 12:00 Customer Support: Internal vs. Fractional 14:58 Tech Roles: Fractional Hiring Insights 17:55 Final Thoughts on Hiring Strategies

What does it look like to have a world-class CMO in your corner without the full-time price tag? In this episode, Alloy founder Rick Mayo sits down with Erin Levzow, CMO at CapitalSpring, to talk about the real value of fractional marketing leadership. Erin has held CMO roles at Wingstop, Freebirds, Museum of Ice Cream, and Marcus Hotels, and now works across the full CapitalSpring portfolio to help franchise brands build stronger marketing systems and better internal teams. She and Rick dig into how she mentors marketers inside growing companies, why most franchisees make the mistake of betting everything on one marketing channel, and what the faucet-and-bucket analogy tells you about why your spend is or is not working. They also talk about the human connection at the core of the Alloy model and why that matters more than ever in an AI-driven world. If you are building a franchise brand or trying to get more out of your marketing team, this episode is worth your time. Listen in to learn what great franchise marketing leadership actually looks like. Key Takeaways: 00:00 There Is No Silver Bullet 02:48 From Vegas Dead-Body Apartment to CMO 05:00 Wingstop, Museum of Ice Cream and the Road to CapitalSpring 07:32 Why CapitalSpring Recruited Erin onto Their Own Team 09:26 What a Fractional CMO Actually Does Day to Day 13:04 The Cyclical Relationship Between Franchisees and Customers 16:42 Why Alloy Stood Out as a Marketing Story 17:36 Mentoring Marketers Who Want to Do Everything Themselves 21:06 The Faucet and Bucket: Why Dripping Does Not Work 23:36 Do Not Get Drunk on Digital 25:16 The Karaoke Question Additional Resources: - Alloy Personal Training - Learn About The Alloy Franchise Opportunity --------- You can find the podcast on Apple, Google, Spotify, Stitcher, or wherever you listen to podcasts. If you haven't already, please rate and review the podcast on Apple Podcasts! To learn more about the Alloy Personal Training Franchise Opportunity, visit

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

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Meet Kevin Deasy, Fractional Director and Senior Management Consultant in IT Digital Transformation. To Kevin, leadership is the art of influencing and guiding a group toward a shared destination, standing in stark contrast to the "command and control" nature of managers. He believes true leaders don't need a title; instead, they rely on deep self-awareness and the wisdom to surround themselves with people more talented than they are. Kevin views communication as the ultimate “differentiator” in the age of AI, prioritizing the ability to listen and maintain respect even when opinions clash. Outside of the digital world, he finds balance and joy in Irish history, hot yoga, and the stress-relieving power of the sea. A quote that resonates with him: A leader is best when people barely know he exists. When his work is done, his aim fulfilled, the group will say they did it themselves – Lao Tzu.

Hiring the wrong fractional CFO will cost you more than not hiring one at all. In this episode, David Richter breaks down exactly how to know when you're ready for a fractional CFO, what questions to ask before you hire one, and the secret question most business owners never think to ask that reveals everything about whether someone is actually worth trusting with your finances.Whether you're at $100K and feeling the cash crunch for the first time or already past seven figures and wondering where it all went, this episode gives you a clear framework for finding the right financial leader for your business — and avoiding the wrong one.Timeline Highlights[0:26] Why hiring the wrong fractional CFO costs more than hiring none at all[1:03] What a CFO is actually there to help you do — and why your bookkeeper and CPA can't fill that role[1:41] How to know if you're even ready to look for a fractional CFO[2:02] Why the same cash flow problems show up at $100K and $1M+ — and what that tells you[3:06] The scaling trigger: when deals and complexity outgrow your spreadsheet[3:24] What a short-term CFO engagement looks like and who it's built for[4:39] Under $500K: why a short-term engagement beats a long-term one[5:16] Why getting good financial habits early means those habits scale with your business[6:10] Question #1 to ask a fractional CFO: do you work with businesses at my revenue level?[6:33] Question #2: do you have experience in my specific industry?[6:53] Question #3: how many clients have you worked with and what's your track record?[7:33] The secret question: are you part of any masterminds or member communities — and how long?[8:38] Why financial freedom is about what you do with the money once it's in the door[9:33] If you're over $1M in revenue, a fractional CFO is no longer optional[10:59] The revenue roadmap: fractional CFO at $100K+, required at $1M+, consider full-time at $10M+Key TakeawaysHiring the wrong fractional CFO is more costly than not hiring one — know what to look for before you commit.If you're making money but feel broke, a bookkeeper and CPA can't solve that problem — a CFO can.You don't need to be at seven figures to benefit from fractional CFO support — $100K in revenue is a reasonable starting point.Under $500K, look for a short-term engagement to build your financial foundation first.Good financial habits built early scale with your business — bad habits at seven figures are far harder to undo.Ask a fractional CFO about their industry experience, client track record, and how long they've been part of professional communities.The secret question — how long have they been in a mastermind or member group — reveals whether they have a real reputation to protect.Links & ResourcesBook a free discovery call to find your path to financial clarity and freedom: profitrei.comClosingThanks for spending time with me today. If this episode gave you clarity or a new perspective on how to find the right financial partner for your business, be sure to like, subscribe, and comment below. If you're ready to apply what we talked about today with real guidance and accountability, visit profitrei.com to schedule a free discovery call and create your path to financial clarity and freedom.

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/

Most small-to-mid manufacturers know they've under-invested in marketing, but where do you even begin? Javier Lozano, Founder of Bolder Media and a fractional CMO, joins Carman and Jeff to lay the foundation. He explains why your founder's origin story falls flat, how to mine real differentiation from customer interviews, and why your brand should be more Yoda, the guide, than Luke, the hero. Plus: how to find a wedge in a “red ocean” without making yourself unfindable, and what a fractional CMO actually does that a consultant or full-time hire can't.

Stella Han shares her journey from growing up in the Bay Area with software engineer parents who flipped houses, to becoming a real estate entrepreneur and founder of Fractional. After starting with out-of-state single-family investments in Atlanta, she attempted to raise $1M at age 22 for a 22-duplex portfolio but lost $55K due to securities and fundraising challenges. That failure inspired her to create Fractional, a platform helping investors form investment clubs as an alternative to traditional syndications. Backed by Y Combinator, Stella discusses failing forward, building in public, and embracing the identity shift required to become a startup founder.

Today we're talking about what it really takes to align brand, marketing, and revenue in a world where channels change daily but fundamentals still matter. Our guest, Jinnie Austin, is a Marketing and Ecommerce Strategic Advisor and fractional CMO who helps growing brands connect the dots between who they are, how they show up, and how they make money. Jinnie has worked with organizations like 361º USA and Exponent Edge, advising on strategy, ecommerce, and digital growth, and she's built a reputation for stepping into leadership gaps and bringing clarity to messy marketing situations. Based in the Charlotte area, she partners with founders and leadership teams to create practical, sustainable marketing systems that actually move the needle instead of adding more noise. We're going to dig into what she's seeing in the market right now, how brands can get unstuck, and why a strategic advisor or fractional CMO might be exactly what a scaling company needs.  

In this Command Control Power episode, host Joe and guests discuss standards, policies, certification, and compliance with Michael Thomsen of Origin 84 in Sydney, continuing an ISO 27001 deep dive. Michael explains how policies are written to solve specific control problems (e.g., MFA) and can be reusable, while areas like data classification require tailoring based on a client's industry, legislation, contracts, and workflows; key discovery questions include where data is stored and shared, and what obligations contracts impose. The conversation contrasts frameworks (NIST, Essential Eight) and notes auditors verify that policies drive processes and are followed, emphasizing continual improvement through audits, risk/incident tracking, and iterative remediation. Jerry and Sam share healthcare/SOC 2 experiences and discuss shifting solo consultants from tactical support to higher-value strategic advisory/account management, using fractional roles and partners. Michael outlines Origin 84's fractional model (financial controller, HR, strategy officer, plus legal/CFO) and sourcing via professional networks, LinkedIn, and conferences like ACEs, where Michael will present on account management

Episode Summary: In this episode of the Work at Home Rockstar Podcast, Tim Melanson chats with Kerri Roberts, Founder of Salt & Light Advisors. Kerri shares her journey from a 20-year corporate career to launching her own HR consulting business, breaking down the real lessons behind tech overwhelm, pricing mistakes, and building a business that actually supports your life. Who is Kerri Roberts? Kerri Roberts is the founder of Salt & Light Advisors, a People Operations and HR consulting firm helping small to mid-sized businesses build strong, practical HR foundations. With over 20 years of experience in corporate HR and operations, she now helps companies clarify roles, expectations, and systems so their teams can succeed. Connect with Kerri Roberts: Website: https://saltandlightadvisors.com Website: https://kerrimroberts.com Instagram: https://www.instagram.com/kerrimroberts LinkedIn: https://www.linkedin.com/in/kerrimroberts Host Contact Details: Website: https://workathomerockstar.com Facebook: https://www.facebook.com/workathomerockstar Instagram: https://www.instagram.com/workathomerockstar LinkedIn: https://www.linkedin.com/in/timmelanson YouTube: https://www.youtube.com/@WorkAtHomeRockStarPodcast X / Twitter: https://twitter.com/workathomestar Email: tim@workathomerockstar.com In this Episode: 00:00 Welcome and Guest Intro 00:21 Big Leap to Entrepreneurship 00:57 Early Mistakes and Lessons 02:29 Essential Tools and Tech 05:03 Home Office and Self Care 07:07 Client Boundaries and Value 08:32 Project Pricing Over Hourly 12:40 Marketing Beyond Your Network 14:20 First Client and Contractor Shift 16:42 Pricing Reality and Revenue Growth 21:03 Money Systems Taxes and AI 24:46 Why Leave a 300K Job 29:06 Business Refinement and Alignment 30:51 Connect and Closing Questions 31:33 Rock Star Picks and Wrap Up  

In this episode, Neil Cohen, Founder/CEO, Quantum CFO Advisory and Services LLC, shares insights on the rising demand for fractional CFOs and how they help companies become fundable, scalable, and sellable.