Podcasts about Frontier

In 1821, a little-known backwoods hunter named David Crockett won a seat in the Tennessee legislature, despite having little formal education and virtually no political experience beyond his gift for spinning a good story. It marked an unlikely turning point for a man who would become a symbol of the American frontier, which was then rapidly expanding as settlers pushed into land long inhabited by Indigenous peoples. Ultimately, Crockett's frontier persona would prove his greatest political asset, eventually carrying him all the way to Congress. But once there, he would break with his own party over Native removal, before heading to Texas for one final, fateful stand at the Alamo.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Stay informed on current events, visit www.NaturalNews.com  - Kimmy K3 AI Model and Solar Project Updates (0:02) - Recommendations for Solar Equipment and Pekron Solar Well Power Station (8:21) - Global Famine Prediction for 2027 (14:59) - Impact of the War on Food Supply and Global Economy (38:40) - Formation of the World Artificial Intelligence Cooperation Organization (WAICO) (41:54) - Challenges and Opportunities for WAICO (54:35) - Comparison of Western and Eastern AI Development (54:56) - Potential Risks and Benefits of AI Development (1:12:39) - Personal Reflections and Call to Action (1:15:21) - Conclusion and Future Outlook (1:21:05) - Kimi K3 vs. Anthropic: Performance and Cost Comparison (1:21:24) - China's AI Advancements and Collaboration (1:30:15) - Cultural Differences and AI Development (1:32:32) - Economic and Political Implications of AI Development (1:46:52) - Zach Voorhees on AI and Government Influence (2:01:22) - AI Cybersecurity and Ethical Concerns (2:13:07) - The Future of AI and Humanity (2:14:49) - Preparing for the AI Future (2:20:28) - The Role of Open-Source AI Models (2:20:42) - The Impact of AI on Global Competition (2:36:02) Watch more independent videos at http://www.brighteon.com/channel/hrreport  ▶️ Support our mission by shopping at the Health Ranger Store - https://www.healthrangerstore.com ▶️ Check out exclusive deals and special offers at https://rangerdeals.com ▶️ Sign up for our newsletter to stay informed: https://www.naturalnews.com/Readerregistration.html Watch more exclusive videos here:

Gary & Shannon Hour 1 (07/20) - Gary returns from vacation and reunites with Shannon as they swap stories from the week, including Gary's trip through Northern California, an unforgettable sunset bagpipe performance, and their uncanny habit of planning trips to the same places without realizing it.They then discuss the latest developments from the Iran conflict, check in on Big Bear's beloved eagle Jackie after her rescue (and Amy King), and review Christopher Nolan's The Odyssey after its massive opening weekend. The hour wraps with World Cup reactions, Madonna's halftime performance, and a quick #TerrorInTheSkies covering new airline battery rules, Frontier's Starlink rollout, and the latest on the next Air Force One.See omnystudio.com/listener for privacy information.

We continue to discuss how we want the magic system to work and what options are there for different magic

Season 8, Episode 5: How did Madison Realty Capital grow from a $10M fund into one of the most active private credit platforms in real estate? Today, we sit down with Josh Zegen, Co-Founder and Managing Principal of Madison Realty Capital, to break down how MRC built its lending business before private credit became an institutional asset class. Josh shares how the firm survived the GFC, became vertically integrated, and scaled into a major capital source for sponsors when banks pulled back. Whether you're interested in distressed debt, construction lending, office-to-residential conversions, or today's maturity wall, this episode is a must-listen. Join us as we dive into how Madison thinks about risk, rescue capital, borrower relationships, and finding opportunity in a volatile market. Shoutout to our sponsor, Lennar Investor Marketplace. New construction rental investments with comps, returns, and underwriting built in. TOPICS 00:00 – Introduction to Josh Zegen and Madison Realty Capital 05:00 – The Early Private Credit Opportunity 10:53 – Surviving the GFC and Taking Over Assets 15:45 – Becoming a Construction Lending Powerhouse 19:00 – Back Leverage and Lending to Lenders 24:12 – Distress, Rescue Capital, and Loan Workouts 31:24 – Fundraising, Insurance Capital, and Investor Demand 35:44 – The Pfizer Office-to-Residential Conversion 42:40 – West Palm Beach, Florida, Texas, and Hot Markets 48:12 – Recaps, Volatility, and Building Through the Cycle For more episodes of No Cap by CRE Daily visit https://www.credaily.com/podcast/ Watch this episode on YouTube: https://www.youtube.com/@NoCapCREDaily About No Cap Podcast Commercial real estate is a $20 trillion industry and a force that shapes America's economic fabric and culture. No Cap by CRE Daily is the commercial real estate podcast that gives you an unfiltered ”No Cap” look into the industry's biggest trends and the money game behind them. Each week co-hosts Jack Stone and Alex Gornik break down the latest headlines with some of the most influential and entertaining figures in commercial real estate. About CRE Daily  CRE Daily is a digital media company covering the business of commercial real estate. Our mission is to empower professionals with the knowledge they need to make smarter decisions and do more business. We do this through our flagship newsletter (CRE Daily) which is read by 65,000+ investors, developers, brokers, and business leaders across the country. Our smart brevity format combined with need-to-know trends has made us one of the fastest growing media brands in commercial real estate.

In this episode, I'm taking a look at one of the biggest trends I've seen in the overlanding world over the past year: the rapid rise of Alibaba campers. It feels like everywhere you look, more people are importing these campers directly from China—but are sales really exploding, and if so, why?I'll share what I've observed as both an owner and someone who's been closely following the market, including what's driving the demand, how pricing compares to traditional brands, and why more people are willing to take a chance on importing one themselves. We'll also talk about whether this is just a passing trend or if Alibaba campers are permanently changing the truck camper industry.If you've been wondering whether the hype is real, considering buying one yourself, or just want to understand why these campers seem to be everywhere lately, this episode is for you.Want one for yourself? Here's a link to the Alibaba page for this camper (use code ALLTHINGSOVERLANDING147 to get 2 items free, worth up to $170 or mention All Things Overlanding to your rep): https://www.alibaba.com/product-detail/Truck-Camper-Custom-Hard-Shell-Rooftop_1601727228413.html?spm=a2747.product_manager.0.0.380971d2UHgNseA huge thanks to my partners:Top Oak (amazing roof top tents and awnings for budget prices):  https://topoakoverland.com/?sscid=51k9_mt1ba&Nitto (my Terra Grappler G3 tires are great for midwestern winters, wet weather, and all terrain use):  https://bit.ly/41EJhbQZ1 Off Road (pretty much the spot for all things Nissan):  https://www.z1offroad.comAll Dogs Offroad (amazing Nissan specific suspension options which I run on my truck):  https://www.alldogsoffroad.comICECO Fridges (the best fridges for the money, hands down-Use code ALLTHINGSOVERLANDING for 12% off your order):  https://icecofreezer.com/ALLTHINGSOVERLANDINGMoon Fab Awning (super flexible, non-permanently mounted awnings for all kinds of applications. This link will take you to more info on how I have it set up on my 3rd gen Frontier):  https://moonfab.com/pages/experts/jason-fletcherClick here to join the Patreon community for exclusive content and access to the Discord channel:  https://www.patreon.com/allthingsoverlandingClick here to get a patches or stickers:  https://allthingsoverlanding.com/shop/For a full list of my gear, check out this page for quick reference links:  https://allthingsoverlanding.com/gear/Looking for budget light bars, rock lights, and LED strips for your rig? Check out Nilight and use code ATO for 5% off! https://www.nilight.com/?ref=s8svsnv_d9qh&utm_source=ATO&utm_medium=Podcast&utm_campaign=DescriptionFor more great content and info, you can follow me on Facebook, Instagram, or search for All Things Overlanding on all the major podcast channels!YouTube:  https://www.youtube.com/c/AllThingsOverlandingFacebook:  https://www.facebook.com/allthingsoverlandingInstagram:  https://www.instagram.com/allthingsoverlandingPodcast:  https://podcasters.spotify.com/pod/show/allthingsoverlandingWebsite:  www.allthingsoverlanding.comNewbie Overlander Facebook Group:  https://www.facebook.com/groups/367203658420467

Kimi K3 is a massive new open AI model with 2.8 trillion parameters, native vision and a one-million-token context window. Its creator claims it can compete near the frontier of coding, reasoning and knowledge work - but its potential goes far beyond benchmark scores. In this Web News, Matt and Mike discuss what happens when companies can operate powerful AI without depending entirely on OpenAI, Anthropic or another hosted provider. Could open models lower costs and unlock better AI products, or will their customizable guardrails create new safety and regulatory concerns? Show Notes: https://www.htmlallthethings.com/podcast/kimi-k3-brings-frontier-ai-into-the-open

In this special episode, HexAI producer Brent Phillips steps behind the microphone to speak with Sean McGunigal, Epic Software's Software Development Lead for AI Research and Development. Drawing from his own daily experience navigating healthcare information management systems at a hospital, Brent guides a technical conversation on the immense role that enterprise software architectures like Epic play in orchestrating, optimizing, and embedding artificial intelligence directly into clinical workflows across global health systems.Bridging the gap between frontier software engineering and clinical realities, Sean and Brent discuss Epic's AI development pipeline while anchoring the conversation around Epic's technical vision where utility, explainability, and trust are paramount. Sean details how Epic's proprietary ambient AI framework, Chart with Art, seamlessly ingests and parses acoustic conversational data to generate structured documentation, effectively alleviating cognitive burden and clinical friction so medical professionals can prioritize hands-on patient care. Sean emphasizes that to achieve user adoption, Epic's generative summaries are rigidly tied to programmatic data lineage and granular citations, forcing black-box models to prove their clinical outputs.Aligning with the mission of the University of Pittsburgh's Health and Explainable AI Research Laboratory and Pitt's Computational Pathology and AI Center of Excellence (CPACE), the episode highlights Epic's commitment to helping advance and democratize ethical and responsible AI engineering through open-source frameworks and massive data initiatives. Sean points to Nebula, Epic's cloud-native platform deployed in Microsoft Azure, which provides a high-throughput, horizontally scalable infrastructure allowing health networks to securely deploy LLMs. Furthermore, he underscores Epic's breakthroughs with Cosmos, their multi-institution de-identified dataset, and Curiosity, Epic's custom-trained Medical Language Model designed to predict patient clinical events using autoregressive, next-token intelligence. By exploring the orchestration of complex agentic AI workflows, runtime evaluation harnesses, and cross-platform communication protocols, Sean closes the episode with a call to action for researchers to get involved and work more closely around safely advancing the frontiers of computational medicine.

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

AI news: Moonshot AI's Kimi K3 is a big AI model and Moonshot's early benchmarks put it surprisingly close to GPT-5.6 Sol and Claude Fable 5. And… Kevin's Opus 5 SCOOP!! Also: OpenAI's reported screenless AI speaker, a Seedance 2.5 preview, the Suno hack, robot fights and AI-built games in Unreal Engine and Blender. On today's AI For Humans, Kevin Pereira and Gavin Purcell unpack Kimi K3's benchmarks, pricing, Flappy Bird and Minecraft tests, and giant-model economics. Then, Kevin DRIPS Opus 5 alpha and says it's VERY good and blows the doors off of Fable but it's… slow.  Plus Demis Hassabis's AI-governance proposal, AI 2040's Plan A, OpenAI's reported screenless speaker, Codex Keyboard, a Seedance 2.5 preview, the alleged sources exposed by the Suno hack, spectacular robot violence, polite office-robot dabbing, and what happens when GPT-5.6 Sol meets Unreal Engine, Blender and two hosts with free time. THE AI FRONTIER IS MOVING AGAIN—AND CHINA IS RIGHT THERE WITH IT. // Show Links // AI FOR HUMANS Survey https://aiforhumans.beehiiv.com/forms/b7c77287-2cfd-4b64-a278-eb1a2ccb5744 Official Moonshot AI Kimi K3 launch video https://x.com/Kimi_Moonshot/status/2077521842080817296 Official Kimi K3 launch and benchmark thread https://x.com/Kimi_Moonshot/status/2077830229968683203 Official Kimi K3 technical launch article https://kimi.com/blog/kimi-k3 Kimi K3 head-to-head with GPT-5.6 Sol https://x.com/chetaslua/status/2077701096924229744 Kimi K3 Flappy Bird test https://x.com/jun_song/status/2077396996865003739 Demis Hassabis on a new framework for AI governance https://x.com/demishassabis/status/2076957440109625718 AI 2040: Plan A https://ai-2040.com/ Bloomberg's report on OpenAI's first device https://www.bloomberg.com/news/articles/2026-07-14/openai-s-first-device-will-be-moveable-screenless-speaker-built-as-ai-companion OpenAI Developers' Codex Keyboard post https://x.com/OpenAIDevs/status/2077425991790870644 BytePlus Seedance 2.5 World Cup preview https://x.com/BytePlusGlobal/status/2077321849806234080 Variety's report on the Suno hack and training data https://variety.com/2026/music/news/suno-hack-youtube-music-deezer-genius-data-trained-ai-music-1236811772/ Ultimate Robot Knockout Legend (UKRL) Fight https://x.com/ErenChenAI/status/2077750358302921029 Soft floating robot demo https://x.com/clankrmedia/status/2076593164744376707 Two NEO robots talk to each other—and then one dabs https://x.com/BerntBornich/status/2077749438630805648 GPT-5.6 Sol plus Unreal Engine experiment https://x.com/NomadsVagabonds/status/2077577815684202960 Gavin's first GPT-5.6 Sol plus Blender attempt https://x.com/gavinpurcell/status/2076736788320927925 Kevin's Find The Cursor Game: CURSED https://us-lax-8710957c.colyseus.cloud/ Gavin's Fig + Moss Watch autonomous studio https://x.com/gavinpurcell/status/2077155825274229122 Fig's stand-up set https://x.com/gavinpurcell/status/2076382092842475948   // Join the AI For Humans community // Join the AI For Humans Discord https://discord.gg/muD2TYgC8f Support AI For Humans on Patreon https://www.patreon.com/AIForHumansShow Subscribe to the AI For Humans newsletter https://aiforhumans.beehiiv.com/ Follow AI For Humans on X: @AIForHumansShow https://x.com/AIForHumansShow Follow AI For Humans on TikTok: @aiforhumansshow https://www.tiktok.com/@aiforhumansshow Speaking and booking https://www.aiforhumans.show/  

In this episode of The Jess Larsen Show on Innovation & Leadership, Jess sits down with Lucas Ngoo, CEO of Cortex AI and co-founder of Carousell, the Southeast Asian marketplace that grew to a $1 billion valuation. Lucas explains why the next major leap in AI may not just be chatbots or software, but general-purpose robotics. While robot hardware is getting cheaper and more powerful, Lucas argues that the biggest bottleneck is still data: the real-world human and robot data needed to train machines to operate safely, precisely, and intelligently in everyday environments. Jess and Lucas dive into how Cortex AI is building the data infrastructure for robot brains, collecting human task data from industries like factories, retail, restaurants, hotels, and other real-world environments. They also explore why robots may follow a path similar to Waymo, starting with human supervision and gradually improving until autonomy becomes invisible and widespread. Lucas also shares the lessons he learned from building Carousell from three founders out of school into a billion-dollar company, including how to hire during rapid growth, survive startup chaos, raise capital, choose the right investors, and recognize technologies that are just becoming possible. This is a forward-looking conversation about robotics, AI, entrepreneurship, Silicon Valley ambition, and what it really takes to build the future before the rest of the world sees it coming. Learn more about your ad choices. Visit megaphone.fm/adchoices

Send us Fan MailThe Seline River Valley in Kansas was once home to immense migrating herds of shaggy buffalo that darkened the plains as far as the eye could see. These animals grazed on succulent buffalo and grama grass, drank from the local creeks and rivers, and wallowed in the hard-packed alkaline soil to lick salt from the ground. While Native Americans historically relied on these massive herds for food and clothing, they were not the only ones who hunted them. By the 1840s, the American Fur Company was purchasing thousands of buffalo robes annually. As more people entered the region following the 1859 gold strike at Cherry Creek, frontier merchants like Charles Rath arrived to establish trading contacts with the Southern Cheyenne, Kiowa, and Northern Comanche bands. Rath even strengthened these alliances by marrying a Cheyenne woman in 1860, the same year he took over a trading post on Walnut Creek following a deadly clash between the Kiowa tribe and the post's former owner, George Peacock.As the decade progressed, tensions escalated between Native Americans and white settlers, leading to multiple raids on Rath's Walnut Creek trading post despite his efforts to maintain peace. For his own safety, his Cheyenne wife eventually convinced him to divorce her. Meanwhile, the expansion of the Butterfield Overland Dispatch freight service and the arrival of the railroad transformed the region's landscape. The military established several forts to secure the area, including Fort Fletcher (later known as Fort Hays). The rapid construction of the railroads brought in thousands of settlers and specialized construction crews, who were fed by professional buffalo hunters. Skilled marksmen, including Jim White, Tom Nixon, AC Myer, and Josiah Wright Moore, were hired by the railroad to hunt the animals. The hunters then sold the buffalo hides to factories to create the leather belts needed to drive the machinery of the emerging industrial age, fundamentally changing the American West.Support the showIf you'd like to buy one or more of our fully illustrated dime novel publications, you can click the link I've included. 

Send us Fan MailJosiah Wright Moore, a young New Englander who traveled to Fort Hays, Kansas, in 1870 to make his fortune, recounts his initial struggles and a pivotal meeting with an experienced buffalo hunter named Jim White. After deciding to transition from hauling wood to buffalo hunting, Josiah attempts to purchase a wagon and team, which leads to a rough encounter with a stable owner who turns out to be Jim himself. Jim ultimately takes Josiah under his wing, and together they encounter Tom Nixon and AC Myers, who offer to form a four-man hunting team using a surplus outfit funded by eastern sportsmen. Inspired by a circular advertising a profitable market for buffalo hides through dealers like Charlie Rath, the men prepare to embark on a new hunting enterprise together.Support the showIf you'd like to buy one or more of our fully illustrated dime novel publications, you can click the link I've included. 

Anezka Christovova, Ben Ramsey and Ayomide Mejabi discuss the latest market developments and their impacts for the EM fixed income asset class. This podcast was recorded on 16 July 2026. © 2026 JPMorgan Chase & Co. All rights reserved. This material or any portion hereof may not be reprinted, sold or redistributed without the written consent of J.P. Morgan. It is strictly prohibited to use or share without prior written consent from J.P. Morgan any research material received from J.P. Morgan or an authorized third-party (“J.P. Morgan Data”) in any third-party artificial intelligence (“AI”) systems or models when such J.P. Morgan Data is accessible by a third-party.

Canada produces world-leading science, engineering, and AI research. So why does so much of that research still commercialize outside of Canada?In this episode of TechSurge, host Nic Brathwaite puts that question to four leaders at two of Canada's top research universities: Mary Wells (Dean of Engineering) and Chris Houser (Dean of Science) at the University of Waterloo, and Heather Sheardown (Dean of Engineering) and Gianni Parise (VP Research) at McMaster.At Waterloo, Mary Wells traces how the university's origin produced one of the world's most influential co-op programs and a creator-owned IP policy that lets inventors keep their ideas, making the school a talent engine for global tech. The group digs into Canada's AI paradox, foundational research and talent but far less of the economic value, and what quantum, robotics, and advanced manufacturing show about getting research to market.McMaster runs a different model, built on health sciences, nuclear research, and problem-based learning. Heather Sheardown explains the McMaster Method and why it matters in an AI-shaped future. Gianni Parise argues for commercialization as a core university function, with work spanning AI-assisted drug discovery, inhaled vaccines, critical-mineral-free motors, and a campus nuclear reactor that supplies much of the world's iodine-125 for prostate cancer treatment. They also unpack Fusion Pharmaceuticals, the McMaster spin-out acquired by AstraZeneca, and what it reveals about university commercialization.Together, these conversations ask what universities must become in an era defined by AI, deep tech, national competitiveness, and the urgent need to move ideas from the lab into the world.Sign up for our newsletter at techsurgepodcast.com for updates on upcoming TechSurge Live Summits and future episodes.Speaker Profiles and LinksMary Wells - University of Waterloo Profile: https://uwaterloo.ca/engineering/about/dean-engineerinChris Houser - University of Waterloo profile: https://uwaterloo.ca/earth-environmental-sciences/profile/chouserHeather Sheardown - McMaster Engineering profile: https://www.eng.mcmaster.ca/chemeng/faculty/dr-heather-sheardown/Gianni Parise - McMaster Experts Profile: https://experts.mcmaster.ca/people/parisegChapters: 0:00 Highlights0:56 Welcome 2:39 Waterloo's origin story 4:51 Creator-owned IP and the Waterloo model 7:42 The Co-op Flywheel 8:56 Canada's AI paradox: world-class research, slower domestic value capture 10:21 AI, Regulation, Trust, and Canadian Competitiveness 17:09 Rethinking the PhD for Commercialisation 21:43 Inside Waterloo's labs 30:14 What Waterloo wants to be in ten years: builders of the country 32:39 Meet McMaster: health sciences, nuclear capability, and research intensity 34:12 The McMaster Method 35:11 Research, Health, and Commercialisation 40:45 McMaster Labs: Heat, Motors and Health Innovation 47:13 Bioinnovation, Nuclear Research and Fusion Pharmaceuticals 58:43 The university of 2035: less lecture, deeper societal impact References Mentioned and Further ReadingUniversity of Waterloo Policy 73 - Intellectual Property Rights: https://uwaterloo.ca/secretariat/policies-procedures-guidelines/policies/policy-73-intellectual-property-rightsUniversity of Waterloo - Our IP policy: https://uwaterloo.ca/entrepreneurship/our-ip-policyUniversity of Waterloo Co-op programs: https://uwaterloo.ca/future-students/co-opUniversity of Waterloo - Academy of Research Commercialization: https://uwaterloo.ca/conrad-school-entrepreneurship-business/graduate-students/academy-research-commercialization-arcOpen Quantum Design: https://openquantumdesign.org/Institute for Quantum Computing, University of Waterloo: https://uwaterloo.ca/institute-for-quantum-computing/CIFAR - Pan-Canadian Artificial Intelligence Strategy: https://cifar.ca/ai/Government of Canada / ISED - Pan-Canadian Artificial Intelligence Strategy: https://ised-isde.canada.ca/site/ised/en/pan-canadian-artificial-intelligence-strategyStatistics Canada - Understanding Canada's innovation paradox: https://www150.statcan.gc.ca/n1/pub/36-28-0001/2024007/article/00002-eng.htmCouncil of Canadian Academies - Innovation and Business Strategy: Why Canada Falls Short: https://cca-reports.ca/wp-content/uploads/2018/10/2009-06-11-innovation-report-1.pdfKPMG / University of Melbourne - Trust, attitudes and use of artificial intelligence: https://assets.kpmg.com/content/dam/kpmg/ca/pdf/2025/07/trust-in-ai-en-report.pdfMcMaster - Our approach to teaching and learning / Problem-Based Learning: https://provost.mcmaster.ca/teaching-learning/our-approach/McMaster - Evidence-based medicine: https://fhshrwelcome.mcmaster.ca/did_you_know/evidence-based-medicine/McMaster Nuclear Reactor - Medical Isotopes: https://nuclear.mcmaster.ca/medical-isotopes/McMaster Industry Liaison Office - IP and commercialization FAQ: https://research.mcmaster.ca/mcmaster-industry-liaison-office-milo/ip-education/intellectual-property-guides/faqs/McMaster - AstraZeneca to acquire McMaster-supported Fusion Pharmaceuticals: https://news.mcmaster.ca/astrazeneca-to-acquire-mcmaster-supported-fusion-pharmaceuticals/Fusion Pharmaceuticals - FACIT investment and CPDC spin-out background: https://fusionpharma.com/facit-announces-investment-in-fusion-pharmaceuticals-and-alpha-emitting-radiotherapeutics/PubMed - Evidence-based medicine and problem-based learning at McMaster: https://pubmed.ncbi.nlm.nih.gov/31617018/ScienceDirect - Fifty Years on: The first problem-based learning programme at McMaster:

The World Cup failed to deliver the U.S. tourism bump everyone was counting on, Skift lays out a compelling theory for why Airbnb's best hotel move might be Ennismore's upcoming IPO, and Frontier Airlines announces Starlink — the last major holdout finally goes premium. On today's Skift Daily Briefing, Sarah Dandashy breaks down why June's overseas arrival numbers are sobering even with a global sporting event on U.S. soil, why the Airbnb-Ennismore deal Skift is floating could solve a distribution problem neither company can fix alone, and what Frontier's Starlink announcement signals about the end of the truly bare-bones flying experience. Articles Referenced: Honorable Mention: @AskAConcierge on IGWorld Cup Inbound Travel Decline: JuneAirbnb's Best Hotel Strategy Is Sitting in Ennismore's IPO FilingFrontier Airlines to Install Starlink — Premium Push Connect with Skift LinkedIn: https://www.linkedin.com/company/skift/ WhatsApp: https://whatsapp.com/channel/0029VaAL375LikgIXmNPYQ0L/ Facebook: https://facebook.com/skiftnews Instagram: https://www.instagram.com/skiftnews/ Threads: https://www.threads.net/@skiftnews Bluesky: https://bsky.app/profile/skiftnews.bsky.social X: https://twitter.com/skift Subscribe to @SkiftNews and never miss an update from the travel industry.

Hugging Face CEO Clem Delangue says enterprises increasingly want open models, due to cost, accessibility, and ownership. Do frontier models still matter if most production AI ends up running on open models? Learn more about your ad choices. Visit podcastchoices.com/adchoices

Demis Hassabis proposed a US-based frontier AI standards body modeled on FINRA. IBM's stock cratered 20% on a Q2 miss from chip-spending shifts, Spotify launched a voice-control feature, Kalshi debuted an AI compute forward curve, and Anthropic studied Claude's values. Demis Hassabis proposes a US-based Standards Body for "Frontier-class" AI, modeled after FINRA; labs would share models for review up to 30 days before release (X) Demis Hassabis proposes a US-based Standards Body for "Frontier-class" AI, modeled after FINRA; labs would share models for review up to 30 days before release (The Verge) IBM reports preliminary Q2 revenue up 1% YoY to $17.2B, below $17.9B est., as CEO Arvind Krishna says customers are shifting spending to chips; IBM falls 20%+ (Bloomberg) Spotify launches a Talk to Spotify feature that lets users create playlists and more, rolling out in beta to Premium users 18+ in the US, Ireland, and Sweden (Engadget) Kalshi launches a forward curve tool for AI compute, using event contracts to track the future rental costs of GPUs, storage, and memory (Bloomberg) Simulating everything, sort of: The promise and limits of world models (Ars Technica) Subscribe to the ad-free feed. Learn more about your ad choices. Visit megaphone.fm/adchoices

We are divided over the history of the United States, and one of the central dividing lines is the frontier. Was it a site of heroism? Or was it where the full force of an all-powerful empire was brought to bear on Native peoples? In this startingly original work Heart of American Darkness: Bewilderment and Horror on the Early Frontier (Norton, 2024), historian Robert Parkinson presents a new account of ever-shifting encounters between white colonists and Native Americans. Drawing skillfully on Joseph Conrad's famous novella, Heart of Darkness, he demonstrates that imperialism in North America was neither heroic nor a perfectly planned conquest. It was, rather, as bewildering, violent, and haphazard as the European colonization of Africa, which Conrad knew firsthand and fictionalized in his masterwork. At the center of Parkinson's story are two families whose entwined histories ended in tragedy. The family of Shickellamy, one of the most renowned Indigenous leaders of the eighteenth century, were Iroquois diplomats laboring to create a world where settlers and Native people could coexist. The Cresaps were frontiersmen who became famous throughout the colonies for their bravado, scheming, and land greed. Together, the families helped determine the fate of the British and French empires, which were battling for control of the Ohio River Valley. From the Seven Years' War to the protests over the Stamp Act to the start of the Revolutionary War, Parkinson recounts the major turning points of the era from a vantage that allows us to see them anew, and to perceive how bewildering they were to people at the time. For the Shickellamy family, it all came to an end on April 30, 1774, when most of the clan were brutally murdered by white settlers associated with the Cresaps at a place called Yellow Creek. That horrific event became news all over the continent, and it led to war in the interior, at the very moment the First Continental Congress convened in Philadelphia. Meanwhile, Michael Cresap, at first blamed for the massacre at Yellow Creek, would be transformed by the Revolution into a hero alongside George Washington. In death, he helped cement the pioneer myth at the heart of the new republic. Parkinson argues that American history is, in fact, tied to the frontier, just not in the ways we are often told. Altering our understanding of the past, he also shows what this new understanding should mean for us today. Robert G. Parkinson is professor of history at Binghamton University. Edward J. Blum is a professor of nineteenth-century United States History in the History Department at San Diego State University. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/history

July 14th, 2026:  St Kateri Tekakwitha - The Lily of the Mohawks;  St Kateri & Catholic Native American History;  Do Not Fear - Be Not Afraid;  St Kateri on the Frontier of Faith

In this episode, we talk with Tanner Evans of Frontier Longhorns in Abilene, Texas, about the vision behind Frontier Cattle Company and the program he's building. With a focus on preserving the legacy of the Texas Longhorn while building for the future, Tanner shares how Frontier is producing cattle known for standout color, strong structure, and impressive horn. Tune in as we discuss the importance of quality genetics, long-term breeding goals, and what continues to drive his passion for raising exceptional Longhorns.Frontier Longhorns - https://www.frontierlonghorns.com/The Longhorn Exchange - https://www.thelonghornexchange.com/Send us Fan Mail From the Pasture with Hired Hand:Hired Hand Websites (@hiredhandwebsites): https://hiredhandsoftware.comHired Hand Live (@hiredhandlive): https://hiredhandlive.comInstagram: https://www.instagram.com/hiredhandwebsites/Facebook: https://www.facebook.com/HiredHandSoftwareTikTok: https://www.tiktok.com/@hiredhandwebsitesNewsletter: https://www.hiredhandsoftware.com/resources/stay-informed

We are divided over the history of the United States, and one of the central dividing lines is the frontier. Was it a site of heroism? Or was it where the full force of an all-powerful empire was brought to bear on Native peoples? In this startingly original work Heart of American Darkness: Bewilderment and Horror on the Early Frontier (Norton, 2024), historian Robert Parkinson presents a new account of ever-shifting encounters between white colonists and Native Americans. Drawing skillfully on Joseph Conrad's famous novella, Heart of Darkness, he demonstrates that imperialism in North America was neither heroic nor a perfectly planned conquest. It was, rather, as bewildering, violent, and haphazard as the European colonization of Africa, which Conrad knew firsthand and fictionalized in his masterwork. At the center of Parkinson's story are two families whose entwined histories ended in tragedy. The family of Shickellamy, one of the most renowned Indigenous leaders of the eighteenth century, were Iroquois diplomats laboring to create a world where settlers and Native people could coexist. The Cresaps were frontiersmen who became famous throughout the colonies for their bravado, scheming, and land greed. Together, the families helped determine the fate of the British and French empires, which were battling for control of the Ohio River Valley. From the Seven Years' War to the protests over the Stamp Act to the start of the Revolutionary War, Parkinson recounts the major turning points of the era from a vantage that allows us to see them anew, and to perceive how bewildering they were to people at the time. For the Shickellamy family, it all came to an end on April 30, 1774, when most of the clan were brutally murdered by white settlers associated with the Cresaps at a place called Yellow Creek. That horrific event became news all over the continent, and it led to war in the interior, at the very moment the First Continental Congress convened in Philadelphia. Meanwhile, Michael Cresap, at first blamed for the massacre at Yellow Creek, would be transformed by the Revolution into a hero alongside George Washington. In death, he helped cement the pioneer myth at the heart of the new republic. Parkinson argues that American history is, in fact, tied to the frontier, just not in the ways we are often told. Altering our understanding of the past, he also shows what this new understanding should mean for us today. Robert G. Parkinson is professor of history at Binghamton University. Edward J. Blum is a professor of nineteenth-century United States History in the History Department at San Diego State University. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/american-studies

A beloved classic gets the makeover treatment, and a luxury getaway goes spectacularly off the rails. Jason and Wenlei review Little House on the Prairie and The Five Star Weekend.

Welcome to the Forbidden Frontier with hosts Gary from @nerdrotic , Adam Crigler from @TheCriglerShow and @QTRBlackGarrett from @NegaGarrett Produced by @XrayGirl_ Las Vegas MeetContinue reading

Welcome to the Forbidden Frontier with hosts Gary from @nerdrotic , Adam Crigler from @TheCriglerShow and @QTRBlackGarrett from @NegaGarrett Produced by @XrayGirl_ Las Vegas MeetContinue reading

We are divided over the history of the United States, and one of the central dividing lines is the frontier. Was it a site of heroism? Or was it where the full force of an all-powerful empire was brought to bear on Native peoples? In this startingly original work Heart of American Darkness: Bewilderment and Horror on the Early Frontier (Norton, 2024), historian Robert Parkinson presents a new account of ever-shifting encounters between white colonists and Native Americans. Drawing skillfully on Joseph Conrad's famous novella, Heart of Darkness, he demonstrates that imperialism in North America was neither heroic nor a perfectly planned conquest. It was, rather, as bewildering, violent, and haphazard as the European colonization of Africa, which Conrad knew firsthand and fictionalized in his masterwork. At the center of Parkinson's story are two families whose entwined histories ended in tragedy. The family of Shickellamy, one of the most renowned Indigenous leaders of the eighteenth century, were Iroquois diplomats laboring to create a world where settlers and Native people could coexist. The Cresaps were frontiersmen who became famous throughout the colonies for their bravado, scheming, and land greed. Together, the families helped determine the fate of the British and French empires, which were battling for control of the Ohio River Valley. From the Seven Years' War to the protests over the Stamp Act to the start of the Revolutionary War, Parkinson recounts the major turning points of the era from a vantage that allows us to see them anew, and to perceive how bewildering they were to people at the time. For the Shickellamy family, it all came to an end on April 30, 1774, when most of the clan were brutally murdered by white settlers associated with the Cresaps at a place called Yellow Creek. That horrific event became news all over the continent, and it led to war in the interior, at the very moment the First Continental Congress convened in Philadelphia. Meanwhile, Michael Cresap, at first blamed for the massacre at Yellow Creek, would be transformed by the Revolution into a hero alongside George Washington. In death, he helped cement the pioneer myth at the heart of the new republic. Parkinson argues that American history is, in fact, tied to the frontier, just not in the ways we are often told. Altering our understanding of the past, he also shows what this new understanding should mean for us today. Robert G. Parkinson is professor of history at Binghamton University. Edward J. Blum is a professor of nineteenth-century United States History in the History Department at San Diego State University. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/new-books-network

This episode is sponsored by Notion. Learn more about Notion's Developer Platform today at https://notion.com/mlstBritain's most capable coding model can't be exported, and that ban is the whole reason Cosine set out to build one from scratch. Alistair Pullen, CEO and co-founder of Cosine, sits down with Tim Scarfe to explain how a frontier system he calls Fable, locked behind US export controls, became the founding case for a UK sovereign model trained on the Isambard supercomputer in Bristol.The bet underneath it is economic. Pullen argues that an inference company, rather than a training-first lab, doesn't need billions to compete: millions, a national compute allocation, and a consortium feedback loop can be enough. From there it gets into the machinery, why open-weight models still trail the frontier on size, active parameters and data, the mixture-of-experts versus dense trade-off and why active params dominate how a model actually feels, and the edge that real coding trajectories confer.The back half is about making agents trustworthy. Pullen makes the case for beating "slop" by rewarding the process instead of the final answer, reframes code review as runtime proof (spin the bug up in a VM and force the agent to actually exploit it), and walks through Swarm, Cosine's system running hundreds of sub-agents in one shot. It ends on why memory is still an unsolved hack, how synthetic graders let you run RL on tasks with no built-in test, and why Pullen reads US export controls as an accidental gift, with a supply-chain sting in the tail.---TIMESTAMPS:00:00:00 The sovereign mandate and the Fable ban00:04:02 Millions vs billions: the inference-company model00:07:19 The consortium feedback loop00:07:40 Why open models lag the frontier00:14:59 MoE vs dense, and why active params matter00:16:29 Trajectories: the process-data advantage00:19:48 Beating slop: reward the process, not the answer00:26:06 Reusable abstractions and the epistemic wall00:29:56 Code review becomes runtime proof00:37:32 Do agentic harnesses still matter?00:40:35 Swarm: orchestrating hundreds of sub-agents00:45:14 Why memory is still unsolved00:48:25 Synthetic data and graders for RL00:53:09 The US export gift and supply-chain risk---REFERENCES:organization:[00:01:15] Cosinehttps://cosine.sh[00:04:14] Mistral AIhttps://mistral.ai[00:05:50] Anthropichttps://www.anthropic.com[00:07:42] Coherehttps://cohere.com[00:08:36] DeepSeekhttps://www.deepseek.comtool:[00:02:52] Isambard-AIhttps://isambard.ac.uk[00:05:56] Colossus (xAI)https://en.wikipedia.org/wiki/Colossus_(supercomputer)[00:07:52] GLM (Z.ai)https://z.ai[00:11:52] NVIDIA B300https://www.nvidia.com/en-us/data-center/dgx-b300/[00:15:37] gpt-oss-120bhttps://huggingface.co/openai/gpt-oss-120b[00:15:52] Devstral 2https://mistral.ai/news/devstral[00:16:01] Llama 70bhttps://www.llama.com[00:17:05] Claude Codehttps://www.anthropic.com/claude-code[00:26:23] ARC-AGI (Francois Chollet)https://arcprize.org[00:40:38] Swarm (Cosine)https://cosine.sh[00:40:50] OpenAI Codexhttps://github.com/openai/codex[00:41:16] Lumen Outpost (Cosine)https://cosine.sh[00:41:18] Kimi K2 (Moonshot)https://huggingface.co/moonshotai/Kimi-K2-Instruct[00:49:55] SWE-benchhttps://www.swebench.com[00:52:40] SystemVeriloghttps://en.wikipedia.org/wiki/SystemVerilogperson:[00:23:40] Andrej Karpathyhttps://karpathy.aipaper:[00:27:10] GRPO (DeepSeekMath)https://arxiv.org/abs/2402.03300[00:27:13] GSPOhttps://arxiv.org/abs/2507.18071Incompressible Knowledge Probes, Bojie Lihttps://arxiv.org/pdf/2604.24827Estimating the Size of Claude Opus 4.5/4.6https://unexcitedneurons.substack.com/p/estimating-the-size-of-claude-opus---ReScript:https://app.rescript.info/session/5852d2b884c4ce4b?share=10b9799160845bb11779f8ac6cd3124f

We are divided over the history of the United States, and one of the central dividing lines is the frontier. Was it a site of heroism? Or was it where the full force of an all-powerful empire was brought to bear on Native peoples? In this startingly original work Heart of American Darkness: Bewilderment and Horror on the Early Frontier (Norton, 2024), historian Robert Parkinson presents a new account of ever-shifting encounters between white colonists and Native Americans. Drawing skillfully on Joseph Conrad's famous novella, Heart of Darkness, he demonstrates that imperialism in North America was neither heroic nor a perfectly planned conquest. It was, rather, as bewildering, violent, and haphazard as the European colonization of Africa, which Conrad knew firsthand and fictionalized in his masterwork. At the center of Parkinson's story are two families whose entwined histories ended in tragedy. The family of Shickellamy, one of the most renowned Indigenous leaders of the eighteenth century, were Iroquois diplomats laboring to create a world where settlers and Native people could coexist. The Cresaps were frontiersmen who became famous throughout the colonies for their bravado, scheming, and land greed. Together, the families helped determine the fate of the British and French empires, which were battling for control of the Ohio River Valley. From the Seven Years' War to the protests over the Stamp Act to the start of the Revolutionary War, Parkinson recounts the major turning points of the era from a vantage that allows us to see them anew, and to perceive how bewildering they were to people at the time. For the Shickellamy family, it all came to an end on April 30, 1774, when most of the clan were brutally murdered by white settlers associated with the Cresaps at a place called Yellow Creek. That horrific event became news all over the continent, and it led to war in the interior, at the very moment the First Continental Congress convened in Philadelphia. Meanwhile, Michael Cresap, at first blamed for the massacre at Yellow Creek, would be transformed by the Revolution into a hero alongside George Washington. In death, he helped cement the pioneer myth at the heart of the new republic. Parkinson argues that American history is, in fact, tied to the frontier, just not in the ways we are often told. Altering our understanding of the past, he also shows what this new understanding should mean for us today. Robert G. Parkinson is professor of history at Binghamton University. Edward J. Blum is a professor of nineteenth-century United States History in the History Department at San Diego State University. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/native-american-studies

This is a short piece taken from our podcast "Walking the Margins: Mental Health & Housing Precarity Along Admiral" which you can listen to on demand at KOSU.org, the NPR app, NPR.org, or wherever you get your podcasts.A motel room. The interstate. Winter wind. Days of walking with no plan but to witness life on the street. Nick Alexandrov set out to report on mental health along an extended-stay motel corridor in Tulsa. What he found was a quieter, more elusive, more human story.Unfolding on sidewalks, overpasses, church steps, and in fleeting conversations with people living outside. This quarterly feature asks: How does this environment produce its own kind of mental strain? How do people cope with that stress? And what if, rather than the other way around, housing insecurity itself helps drive mental distress and addiction?This special episode of Focus: Black Oklahoma is part of a larger quarterly effort from Oklahoma media addressing mental health. Find the rest of the quarterly and more stories and coverage from Tulsa Flyer, The Oklahoma Eagle, KOSU, La Semana, and The Frontier at https://tulsaflyer.org/snapshot/mental-health/.Focus: Black Oklahoma is produced in partnership with KOSU, Tulsa Flyer, & Tri-City Collective.Our theme music is by Moffett Music.The production team for this special quarterly edition of Focus: Black Oklahoma are Quraysh Ali Lansana, Bracken Klar, & Jesse Ulrich.You can visit us online at or FocusBlackOklahoma.com, & on YouTube @TriCityCollectiveOK.You can follow us on Instagram @FocusBlackOK & on Facebook at Facebook.com/FocusBlackOK.You can hear Focus: Black Oklahoma on demand at KOSU.org, the NPR app, NPR.org, or wherever you get your podcasts.https://linktr.ee/focusblackok

We finished with the campaign world.  Now how does magic work?

As frontier AI models become more powerful, concerns about cybersecurity risks are moving to the forefront of policy and business discussions. In this episode of Current Account, Clay is joined by Martin Boer, General Manager and Chief Representative for Europe at the IIF, to examine how advances in AI could reshape the cyber threat landscape. They discuss why security experts are paying close attention to the latest generation of AI systems, how financial institutions are preparing for emerging risks, and whether regulators and standard setters can keep pace with the speed of innovation. The conversation also explores the growing debate over AI governance and what future legislation could mean for innovation, competition, and financial stability. This IIF Podcast was hosted by Clay Lowery, Executive Vice President, Research and Policy, with production and research contributions from Christian Klein, Digital Graphics and Production Associate, and Miranda Silverman, Senior Program Assistant.

In this episode, Ray Cochrane breaks down AI distillation, the teacher-student technique frontier labs now lean on to train smaller, cheaper models. He also covers GPT-5.6’s government-vetted rollout, Claude Sonnet 5 landing on AWS, Maryland’s two-year data center pause, and Microsoft’s climbing carbon numbers. Finally, he wraps with Apple’s $30 billion Broadcom deal, Meta’s tamper-proof recording light, Michigan’s parasite outbreak, and a simulation that erased a super El Niño. – Want to start a podcast? Its easy to get started! Sign-up at Blubrry – Thinking of buying a Starlink? Use my link to support the show. Subscribe to the Newsletter. Email Ray if you want to get in touch! Like and Follow Geek News Central’s Facebook Page. Support my Show Sponsor: Best Godaddy Promo Codes Get 1Password Full Summary Cochrane opens with a quick personal update. Longer days have him outdoors, including a float trip on the Sandy River at Dabney State Park, where he found clearer water, clay-like sand, and easy footing. Next week brings both a move and a trip home, so he is stocking up on Trader Joe’s “Power Berries” and IKEA bags at his mom’s request. Then he turns to the lead story. AI Distillation Explained: How Frontier Models Teach Each Other Cochrane’s featured story comes from Hugging Face engineer Sergio Paniego. Distillation is teacher-student training for AI: a capable model generates the training signal, and a smaller student learns to match it. The classic off-policy version compresses giant models into cheap students, either through soft labels or piles of worked answers. Google’s Gemma models and DeepSeek’s R1-Distill line were built exactly this way. However, the industry is now converging on multi-teacher on-policy distillation, or MOPD. Labs build reinforcement-learning specialists for math, coding, and agentic work, then have them grade a single student, word by word, as the student generates its own answers. DeepSeek-V4, MiMo-V2-Flash, and NVIDIA’s Nemotron 3 Ultra all run versions of the recipe, and the Qwen3 team reported better results at roughly a tenth of the GPU hours of raw reinforcement learning. Finally, self-distillation lets models like Cursor’s Composer 2.5 learn from better-prompted versions of themselves. Sponsor: GoDaddy Economy hosting $6.99/month, WordPress hosting $12.99/month, domains $11.99. Website builder trial available. Use codes at geeknewscentral.com/godaddy to support the show. GPT-5.6 Arrives With a Government-Vetted Rollout OpenAI shipped GPT-5.6 as a three-tier family: Sol, Terra, and Luna. Sol costs five dollars in and thirty dollars out per million tokens, half of Claude Fable 5’s rate. The benchmarks split: Sol Ultra wins Terminal-Bench at 91.9 percent, while Claude Fable 5 still leads SWE-Bench Pro. Notably, the API launched in limited preview to roughly 20 partners vetted by the U.S. government, though the model went live in Microsoft 365 Copilot on day one. Claude Sonnet 5 Lands on AWS, Plus Quick AWS Wins Claude Sonnet 5 arrived on AWS through Bedrock, pitched as top-tier intelligence at Sonnet pricing. Additionally, Amazon WorkSpaces for AI agents reached general availability, enabling agents to drive full desktop applications securely. OpenSearch gained a log-analytics engine claiming four times the price-performance, and SageMaker now scales inference about twice as fast. Cochrane also flags that Kendra and Q Business move to maintenance mode at the end of July. Anthropic Wants You to Reflect on Your Claude Habits Anthropic launched Reflect, a beta feature that analyzes your past Claude conversations and visualizes how you actually use the assistant. It requires Memory, excludes incognito and health-related chats, and keeps its insights inside the tool. Cochrane loves the idea. He reviews his own transcripts to extract prompt patterns and turn them into reusable skills, and he suggests listeners simply ask their AI to do the same. AlphaEvolve Goes GA on Google Cloud Google made AlphaEvolve generally available to Google Cloud customers on the Gemini Enterprise Agent Platform. The agent acts as an evolutionary collaborator: provide a baseline algorithm and your goals, and it searches for better, human-readable code. BASF, JetBrains, and Kinaxis are the named early adopters. Meanwhile, Cochrane renews his standing wish that DeepMind release AlphaGo as a playable teacher. Google Adds “How This Ad Was Made” AI Labels Google is adding a “How this ad was made” section to My Ad Center across Search, YouTube, and Discover. Ads built with Google’s own AI tools automatically get the disclosure, backed by invisible watermarks. However, ads made with outside tools rely on advertiser self-declaration. Cochrane points out the limits of voluntary disclosure in an AI-flooded content economy. Microsoft’s Carbon Emissions Climb 25 Percent Microsoft’s new sustainability report shows emissions up 25% in 2025, driven by a data center construction spree. The gross figure is 34 million metric tons before offsets, while other coverage puts the net figure at around 20 million. Water consumption also jumped thirty-four percent, even as Microsoft claims its first water-positive year. Cochrane argues regulation needs to catch up, since Google and Amazon report similar increases. Prince George’s County Pauses Data Centers for Two Years Prince George’s County adopted a two-year moratorium on new data center development, the longest pause in Maryland so far. The resolution blocks new applications, including hyperscale projects, until the council passes real regulations. Water and energy impacts remain open questions the county intends to study. Cochrane gives kudos to residents for making their voices heard. Apple and Broadcom Ink a $30 Billion U.S. Chip Deal Apple is expanding its partnership with Broadcom with a multiyear agreement expected to exceed $30 billion. The deal covers custom silicon and wireless components, with more than fifteen billion chips to be made on American soil. Broadcom’s Fort Collins, Colorado plant anchors the work with a $1.5 billion equipment expansion. Tim Cook framed the deal as accelerating Apple’s commitment to American manufacturing. MSI and Intel Ship the First Arc G3 Extreme Handheld Intel detailed how it co-engineered the MSI Claw 8 EX AI+, the first handheld on the Arc G3 Extreme processor. Highlights include a heat-spreading board layout and game-tuning loops that Intel says run Cyberpunk 2077 up to thirty-seven percent faster. The device is on sale now in void purple for around $1,500. At that price, Cochrane jokes he would rather buy a computer. Meta’s Glasses Get a Tamper-Proof Recording Light Meta answered the most common privacy questions about its AI glasses. Photos stay private on the device until the wearer imports or shares them, and a white capture LED blinks during any recording with no off switch. Moreover, newer glasses disable the camera if the LED is blocked, tampered with, or destroyed. Cochrane reminds listeners these claims are Meta grading its own homework, but the blink signal is worth recognizing in public. Michigan’s Parasite Outbreak Tops 1,200 Cases Michigan’s cyclosporiasis outbreak reached 1,251 cases since June 22, with roughly forty hospitalizations along the way. Northwest Ohio adds more than five hundred cases. The parasite typically spreads through contaminated fresh produce, and investigators still have not found the source. Cochrane’s advice: wash your produce, and get tested if your symptoms fit. AI Finds the San Andreas Fault’s Silent Slips Researchers paired AI with borehole strainmeters to detect dozens of hidden slow-slip events beneath the San Andreas Fault’s Parkfield section. Each silent slip releases stress within hours and is reliably followed by low-frequency earthquakes. Together, the findings support a continuous spectrum from silent creep to destructive quakes. The study appears in Nature Communications, and Cochrane hopes it will lead to better earthquake prediction. Cloud Brightening Erased a Super El Niño, in a Simulation Finally, a Science Advances study simulated marine cloud brightening in response to the 1997 and 2015 super El Niño events. Seeding clouds over the eastern Pacific erased the events entirely inside the model. Real deployment would take roughly 2,400 ships spraying continuously, and the simulations showed side effects like extra warming over Europe and Asia. Cochrane finds the weather-machine concept fascinating, yet he questions the consequences of altering cycles the planet runs for a reason. The post AI Distillation: How Frontier Models Teach Each Other #1870 appeared first on Geek News Central.

Nicola Scafetta discusses his book, "The Frontier of Climate Science," a review based on about 650 peer‑reviewed papers arguing that IPCC global climate models have serious limitations because they fail to capture multi‑scale natural variability (oceanic, solar, astronomical, and tidal cycles). He says model claims that nearly all warming since 1850 is anthropogenic are not experimentally validated, climate sensitivity remains highly uncertain, and many models run too hot, with possible surface-record warm bias from urban heat islands. He cites past warm periods and correlations between climate and solar/cosmic-ray proxies, proposes empirical/semi‑empirical cycle-based modeling, and concludes warming risk is moderate, SSP2 could meet Paris targets, net zero is unnecessary, and adaptation should be prioritized.00:00 Introducing Scafetta's New Book06:03 Earth's Deep-Time Climate Swings11:06 IPCC Attribution to Humans14:44 Why Model Proof Falls Short17:27 Paris Targets and Net Zero Logic24:33 Are IPCC Scenarios Realistic29:04 Model Uncertainty and Sensitivity34:57 Satellites vs Surface Warming38:42 Missing Past Warm Periods43:21 Millennial Solar Climate Cycles46:00 Forests Beneath Glaciers46:59 Solar Records vs Climate49:18 TSI Reconstructions Debate52:09 Cosmic Rays and Clouds56:07 Empirical Models Challenge IPCC59:18 Policy Implications and Adaptation01:01:12 Planetary Cycles Climate Theory01:05:37 Matching Climate Spectral Cycles01:12:51 Future Hiatus and Sensitivity01:19:43 Next Glaciation Long View01:24:01 Book Wrap Up and ThanksThe Frontier of Climate Science: Solar variability, natural cycles and model uncertainty: https://a.co/d/0flzOYJ3=========Brochure for Nicola's “The Frontier of Climate Science” book, along with slides, summaries, references, and transcripts of my podcasts: https://tomn.substack.com/p/podcast-summariesMy Linktree: https://linktr.ee/tomanelson1

On this edition of Hayden's History Hour, Federalist Staff Editor Hayden Daniel shares the history of the American Revolution west of the Appalachian Mountains, including the great leaders and brutal tactics involved in the war, and how victory in the west began America's manifest destiny. The Federalist Foundation is a nonprofit, and we depend entirely on our listeners and readers — not corporations. If you value fearless, independent journalism, please consider a tax-deductible gift today at TheFederalist.com/donate. Your support keeps us going.

AGENDA: 05:00 Washington Just Put Frontier AI on a Leash 06:30 Sam Altman's Wild 5% Government Stake Idea 19:00 The AI Funding Bubble: Why Founders No Longer Fear Dilution 28:00 Alex Karp's Brutal Warning: Enterprises Don't Trust Frontier AI 33:00 Meta's Shock Pivot: Has Zuck Accidentally Built the Next CoreWeave? 41:00 Nvidia's Dangerous New Game: "Compute Now, Pay Later" 45:00 Anthropic & DeepSeek Go After Nvidia's Crown 48:00 Kling vs Sora: Did China Just Win AI Video? 52:00 Is China Secretly Winning the Open Source AI War? 01:02:00 Microsoft & Amazon's $6B Bet: AI Still Needs Humans 01:11:00 Ashton Kutcher Walks Away From Sound Ventures 01:16:00 The New Startup Talent War: No Liquidity, No Chance 01:20:00 Final Thoughts: Who Wins the AI Endgame?

In this episode of the Data Center Frontier Show, DCF Editor in Chief Matt Vincent sits down with Data Center Frontier Contributing Editor Bill Kleyman, CEO and co-founder of Apolo, to preview the Data Center Frontier Trends Summit 2026, taking place August 4–6 in Reston, Virginia. This year's Summit arrives at a defining moment for the data center industry. After two years of massive AI infrastructure announcements, the conversation has shifted from projection to execution. The question is no longer whether AI will reshape digital infrastructure. It is whether the industry can actually build, power, cool, finance, commission, and operate the capacity now being promised. Kleyman frames the moment around one of the key realities shaping the market: announced megawatts are not the same as energized megawatts. As power constraints, utility delays, supply chain friction, capital risk, rack density, liquid cooling, permitting, and community opposition converge, the winners will be the companies that convert intent into operational capacity. The conversation previews major themes across the 2026 Trends Summit agenda, including the new geography of AI development, power-first site selection, the rise of the AI factory, high-density design, liquid cooling, behind-the-meter generation, supply chain execution, investment discipline, and the growing importance of earning social license with communities. Highlights include a look ahead to the opening keynote fireside chat featuring Data Center Frontier founder Rich Miller and EdgeCore Digital Infrastructure CEO Lee Kestler; the Day Two keynote on scaling the AI factory with leaders from NVIDIA, Meta, and the Open Compute Project; sessions on AI power architecture, density, cooling, site selection, supply chain risk, and the final bottlenecks before go-live; and the closing keynote on stewardship, sustainability, and community acceptance. As Kleyman notes, the AI infrastructure race is moving from announcements to accountability. The industry does not need more theoretical capacity. It needs energized, commissioned, operational capacity. Listen now for a preview of the conversations shaping Data Center Frontier Trends Summit 2026—and join us in Reston this August for the full discussion.

This is America's Favorite Car Talk Show! Under The Hood. Call us to get free car repair advice. We can help.  13 Frontier has low miles but should fluids be changed? 48 Studebaker Positive Ground fix 02 Acura can't turn the key in ignition 02 F150 Ford dome lights won't shut off 17 grand Cherokee misfire 05 Avalanche runs rich

Andrew "Boz" Bosworth is the chief technology officer of Meta. Bosworth joins Big Technology to discuss why Meta fell behind in the frontier AI race and how it plans to turn its models, products, and distribution into an advantage. Tune in to hear his candid explanation of what went wrong with Llama, why the best AI products will use multiple models, and what it will take for consumer agents to break through. We also cover Meta's AI glasses, the future of augmented reality, employee tracking and training programs, AI companions, and the painful process of adapting a company to a technological revolution. Hit play for a revealing conversation about Meta's AI comeback and the products that could shape how we interact with computers. --- Enjoying Big Technology Podcast? Please rate us five stars ⭐⭐⭐⭐⭐ in your podcast app of choice. Watch the full documentary here: https://www.gravitee.io/ai-agent-documentary Want a discount for Big Technology on Substack + Discord? Here's 25% off for the first year: https://www.bigtechnology.com/subscribe?coupon=0843016b Learn more about your ad choices. Visit megaphone.fm/adchoices

In today's Cloud Wars Minute, I explain why Microsoft's newest AI initiative could reshape enterprise engineering and customer success. Highlights 00:09 — Some huge news from Microsoft today. The company has launched Microsoft Frontier Company, a brand-new business that's entirely focused on helping customers achieve frontier transformation with AI. 00:28 — Now, Microsoft, despite not coining the term [Frontier Firm] itself, has been using it extensively to really outline its strategy in terms of how it sees its AI tools transforming companies, essentially enabling them to become frontier firms. This Frontier Company, to me, feels like the culmination of all that forethought and clarity around Microsoft's enterprise AI mission. 00:56 — Microsoft is investing $2.5 billion into the initiative, which will see 6,000 industry specialists and AI engineers embedded into customer organizations to help them co-design, deploy, and continuously improve AI systems. You can think about it as forward-deployed engineering, but on a much broader scale. 01:19 — Judson Althoff, CEO of Microsoft Commercial Business, calls it the "largest, most capable, outcome-driven engineering organization in the industry." Ultimately, Althoff explained the aim of Microsoft Frontier Company is to focus on end-to-end frontier transformation and enable customers to "amplify their IQ with AI while refining their differentiated value in the markets that they serve." 01:51 — Microsoft has said it will be working closely with its partner ecosystem, particularly with partners including Accenture, Capgemini, EY, KPMG, and PwC, to scale the company, extend its capabilities to organizations across many sectors globally. It's an incredibly interesting and strategic move from Microsoft, and one that I'll be following up with a deeper analysis in a written article publishing shortly. Visit Cloud Wars for more.

Software-defined warfare is today's reality for national security, shifting the emphasis in military operations from hardware to software. In the latest podcast from the Carnegie Mellon University Software Engineering Institute, SEI director Paul Nielsen recently sat down with Matthew Butkovic, technical director of Risk and Resilience in the SEI's CERT Division, to discuss the evolution of software-defined warfare and the ways in which software engineering practices can meaningfully address the challenges of implementing software on the battlefield.  

Keegan Spittle asks whether the Charlotte Mason method should adapt to the demands of college, or colleges should adapt to the demands of the Charlotte Mason method.

LOUNGE LIZARDS PRESENTED BY FABRICA5 - Brilliant Honduran Cigars - Visit Fabrica005.com and use code LIZARDPOD at checkout for 10% off THE ENTIRE STORE! Free worldwide shipping from Miami on all orders over $125. See website for more information and terms.SMALL BATCH CIGAR - SAVE 15% - Exclusive Cigar Retail Partner of the Lizards - Visit SmallBatchCigar.com and use code LIZARD15 for 15% off your order. Free shipping and 5% rewards back always. Standard exclusions apply. Simple. Fast. Small Batch Cigar.Fixed audio! Recorded at Ten86 Cigars in Hawthorne, New Jersey, the Lizards pair Aladino 250th with ten years aged Bulleit Frontier Kentucky Straight Bourbon Whiskey. The guys share their interview from PCA 2026 with Ariana Eiroa from JRE Tobacco and Aladino, they debate the peaks and valleys of aging cigars, and they share their favorite places in the world to smoke a cigar.PLUS: Choosing Your Last-Ever Cigar, RIP Glenn Wolfson (Drew Estate CEO), Cuban Cigar Shipments Down Significantly, Who is Responsible for Cigar Consistency?, Listener Email Catch-Up, Phoenicia Shipping Surcharge, How Long Would Current Lizard Collections Last? & 'Bam Explains it All'Join the Lounge Lizards for a weekly discussion on all things cigars (both Cuban and non-Cuban), whiskey, food, travel, life and work. This is your formal invitation to join us in a relaxing discussion amongst friends and become a card-carrying Lounge Lizard yourself. This is not your typical cigar podcast. We're a group of friends who love sharing cigars, whiskey and a good laugh.website/merch/rating archive: loungelizardspod.comemail: hello@loungelizardspod.com to join the conversation and be featured on an upcoming episode!instagram: @loungelizardspodGizmo HQ: LizardGizmo.com

The guys kick off the Fourth of July week by arguing that high agency culture is still America's edge, then dive into why AI is officially too big to fail. Marty and John break down FERC forcing grid operators to fast track data center connections, OpenAI floating a five percent stake to the Trump administration, and Marc Andreessen landing on the Pentagon's defense policy board. They also dig into the memory bottleneck squeezing the chip buildout, why frontier models are not getting commoditized by open source, and what Saudi oil flows back at ninety percent mean for Iran's leverage. To close, they look at Trump's fifty million dollar Bitcoin stash, Strategy selling coin to fund dividends, and whether Washington might already be building a strategic position through the public markets.

Dr Adam Koontz and Col Willie Grills talk about an icon of western films, "The Searchers" Visit our website - A Brief History of Power Thanks to our sponsors, Ad Crucem, Memento, and Gnesio Health Dr Adam Koontz - Redeemer Lutheran Church Pr. Willie Grills - Zion Lutheran Church Music thanks to Verny

Clay Bavor is the Co-Founder of Sierra, one of the world's fastest-growing enterprise AI companies. Sierra is valued at approximately $15.8 billion, has raised more than $1.5BN from leading investors including Sequoia, Benchmark, Greenoaks, GV and Tiger Global, and today serves more than 40% of the Fortune 50. The company recently surpassed $150 ARR, making it one of the fastest-growing enterprise software businesses in history. AGENDA: 00:00 – Why Frontier AI Demand Will Be Unlimited 08:00 – Open Models vs Frontier Models: Who Actually Wins? 17:00 – China's AI Advantage & The Distillation Debate 20:30 – Inside Sierra: The AI Agents Running the Entire Company 24:00 – The $100,000 Token Budget Every Engineer Will Soon Need 29:00 – Building AI for 40% of the Fortune 50 37:00 – Why Forward-Deployed Engineers Are the Future of Enterprise AI 43:00 – Sierra's Unusual Board Meetings & Billion-Dollar Company Playbook 48:00 – The Four Values Behind a $16B Startup: Craftsmanship, Intensity & Family 56:00 – Clay Bavor's Hiring Philosophy, AI-First Teams & What's Coming Next  

As America nears its 250th birthday, our Global Head of Fixed Income Andrew Sheets looks back at the early republic as a volatile frontier market, and what its path from credit risks to durable institutions can teach investors today.Read more insights from Morgan Stanley.----- Transcript ----- Andrew Sheets: Welcome to Thoughts on the Market. I'm Andrew Sheets, Global Head of Fixed Income Research at Morgan Stanley.Today, markets are closed for the observance of 4th of July. But as America approaches its 250th anniversary, we take a look back to look forward at early America as a frontier market.It's Friday, July 3rd at 9am in Seattle.If you were a global investor at the end of the 18th century looking for a stable, low-risk home for your capital, it would have been entirely reasonable to avoid the newly minted United States of America. By the standards of modern finance, the young republic was not a developed market in waiting. It was a frontier economy: volatile, debt-burdened, institutionally fragile, resource-rich, politically combustible, and astonishingly unequal.Its currency had collapsed. Its public finances were suspect. Its citizens resisted taxation, and its growth prospects were extraordinary. In 1810, 70 percent of the country was under the age of 25.That is one of the revelations of Gordon Wood's Empire of Liberty, which focuses on the early days of the new country from 1789 to 1815.Wood's America is not the marble republic of statues and myth. It is speculative, messy, and full of motion. The United States succeeded not by escaping the dysfunctions that we associate with emerging or frontier markets, but by turning them into sources of strength.Start with capital. Early America needed it desperately. Roads, canals, land purchases, and government all required credit, and there was never enough of it. The country was rich in land and poor in liquidity, a classic emerging market mismatch.What the young country couldn't borrow or invent, it misappropriated, lifting intellectual property from its former masters in Britain. What Alexander Hamilton understood was the importance of confidence given this challenge; that debts would be honored, contracts enforced, and taxes, however unpopular, collected.His financial program was an attempt to solve the emerging market problem before the phrase existed. How to persuade investors that a new state, born in revolution and nearly bankrupted by war, could be trusted. To Hamilton, public credit was the foundation of independence.To many Jeffersonians, however, this system looked like an attempt to smuggle a British financial order back into the country that had just fought to expel it.The early republic's debates over debt, banks, speculation, and taxation sound contemporary because the underlying question is perennial in frontier markets: Can a society embrace credit and foreign capital without being captured by it?The U.S. was not starting from zero. It inherited legal traditions, habits of self-government, and a culture of contract and property. Those foundations gave confidence that disputes could be adjudicated, debts pursued, and rules would not be arbitrary.Early America was risky, but it was not lawless. And still, it did not go smoothly. There was no Federal Reserve, FDIC, or even a uniform national currency. Business was conducted with foreign coins, notes issued by private banks, IOUs, and blind optimism.Bank failures were common. In 1808, the Farmers Exchange Bank of Rhode Island issued over $600,000 of notes against less than $90 of gold in its vaults. You almost have to admire the audacity.Yet the same instability that made early America risky also made it unusually open. Land was the country's great asset class, a source of migration, ambition, speculation, and opportunity, at least for white settlers. It also produced bubbles, administrative strain, the expansion of slavery, and the violent dispossession of Native peoples.The Louisiana Purchase in 1803 was a risky, leveraged acquisition of distressed real estate, doubling the scale of the American experiment before anyone had quite figured out how the original version was supposed to work. Wood is especially good on the familiar energy unleashed by this world.The engine of U.S. growth was not an aristocracy of polished grandees, but the "middling sort." Shopkeepers, artisans, tavern owners, mechanics, farmers, merchants, and speculators – many convinced that in America, birthright mattered less than hustle.Commentators of the time complained about the degraded press, political polarization, hostility to expertise, and the vulgarity of a society obsessed with getting ahead. None of this sounds especially distant.What saved America from the usual traps of frontier economies was not immaculate stability. It was adaptability. Its constitution was amended. Political power changed hands despite animosity.Bankruptcy laws allowed for failure. Competition was ferocious, and economic power was generally too diffuse to be easily monopolized. The early republic's genius lay less in solving its contradictions than in creating ways to fight over them without destroying the whole.That is a useful lesson for America at 250. We tend to look backwards for reassurance, imagining that the country once possessed a unity, prudence, and institutional solidity that we have since lost. Wood suggests something different, that the United States was turbulent from the start.Its legacy was contested, its finances distrusted, its politics venomous, its expansion intertwined with slavery and Native dispossession, and its future uncertain. Emerging markets become developed markets not because they stop having crises, but because they build credibility through them. They learn which institutions matter, which bargains endure, which debts must be paid, and which moral liabilities compound when deferred.America was not born orderly, rich, or secure. It was born in the mud, financed on fragile credit, driven by speculation, and sustained by an almost irrational confidence in the future.So, enjoy the fireworks – and let them be a reminder that national maturity is not the absence of volatility. It's the capacity to turn that volatility into renewal.A postscript: Gordon S. Wood died in early June of this year. As a professor, author, and one of the preeminent scholars of the American Revolution, he brought fresh insight and deep humanization to the country's founding. For anyone looking for a better understanding of America as it celebrates a big anniversary, we'd wholeheartedly recommend his workThank you, as always, for your time. If you find Thoughts on the Market useful, let us know by leaving a review wherever you listen and also tell a friend or colleague about us today.

On today's episode, we'll see how the 5x airline card saga continues, Nick will give us more reasons to use the Southwest app, we'll hear some exciting news about Frontier, and we'll dive deep into Hyatt's devaluation.Giant Mailbag(01:06) - More comments about our comments about there being "No airline cards offer 5x or more"Crazy Thing: Chase(03:41) - Points Boost is sometimes different between Chase Sapphire Reserve® Card & Sapphire Reserve for Business℠ CardBonvoyed(07:22) - Lifemiles+ eliminates free cancellation benefit for new subscribersAwards, Points, and More(11:35) - Alaska Wallet funds can now be used for AS companion fares again(12:20) - Rove adds Frontier as a transfer partnerRead more about Frontier being added as a Rove transfer partner here(15:30) - Nick's Southwest notesMain Event: 12 things to know about Hyatt's devaluation(21:18) - Hyatt recently introduced new award charts. We dug into the numbers...Read more about the changed value of Hyatt points here(22:20) - 1. Median point value dropped ~10%(23:10) - 2. Cherry-picked value dropped

Custers Trials -A Life on the Frontier of a New America