Podcast appearances and mentions of Bruce Schneier

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

Gaming, Cloud-Abzocke, Android als Lebensretter und warum Bruce Schneier wegen Pokémon beinahe seinen Koffer am Frankfurter Flughafen vergessen hätte – diese Folge ist randvoll mit kuriosen Geschichten und spannenden IT-Sicherheits-Themen. Außerdem sprechen wir darüber, warum Pokémon-Go-Spieler möglicherweise unbeabsichtigt militärische Karten erzeugt haben, weshalb gekaufte digitale Filme plötzlich verschwinden können, wie ein Sonic-Entwickler einen Programmfehler als Cheat-Menü tarnte, warum Android-Smartphones bei Erdbeben Leben retten können, weshalb Cloud-Anbieter den Ausstieg bewusst teuer machen, wie ein offener Server sensible Daten aus Cannabis-Clubs preisgab, warum Wegwerf-E-Mail-Adressen oft die bessere Wahl sind, welche verrückte App Essen verkauft, das niemals geliefert wird, und weshalb angeschriene Server tatsächlich langsamer werden. Natürlich gibt's außerdem Diskussionen über Apples neue Preise, Foldables, alte Spielkonsolen und jede Menge Abschweifungen. -- Links zur Folge immer auf https://podcast.ichglaubeeshackt.de/ Wenn Euch unser Podcast gefallen hat, freuen wir uns über eine Bewertung! Feedback wie z.B. Themenwünsche könnt Ihr uns über sämtliche Kanäle zukommen lassen: Email: podcast@ichglaubeeshackt.de Web: podcast.ichglaubeeshackt.de Instagram: http://instagram.com/igehpodcast

Is a full-blown war with Iran again inevitable, or will President Trump take off the gloves and finish the job? Brian breaks down the broken Middle East ceasefire with naval warfare expert Captain Brent Sadler. Plus, Lawrence Jones exposes California's ongoing primary vote-counting disaster, and Ben Domenech drops by to react to the shocking firing and "pompous" lies of former CBS anchor Scott Pelley. [00:00:00] Lawrence Jones   [00:18:26] Capt. Brent Sadler (Ret.)   [00:27:26] Dylan Nealis   [00:36:50] Andrew McCarthy   [00:50:55] Andrew Giuliani   [00:55:14] Bruce Schneier   [01:13:40] Ben Domenech   [01:32:02] Steve Aschburner Learn more about your ad choices. Visit podcastchoices.com/adchoices

This week's Department of Know is hosted by Rich Stroffolino, with guests Bruce Schneier, chief of security architecture, Inrupt, and Chris Ray, field CTO, GigaOm. Missed the live show? Check it out on YouTube. Huge thanks to our sponsor, Guardsquare Mobile security incidents are no longer the exception—they are the norm. Last year, seventy-two percent of companies suffered a mobile app security incident. As the primary gateway to your APIs and data, your mobile app requires more than just basic encryption; it needs a multi-layered security strategy. Protect your brand and your bottom line with layered mobile app protection. Learn more at Guardsquare.com.  

Neste episódio, Guilherme Goulart e Vinícius Serafim analisam casos reais e tendências que colocam em xeque a segurança digital e física no Brasil. Você vai descobrir como criminosos burlaram um sistema de reconhecimento facial em condomínios de Porto Alegre usando engenharia social, expondo os riscos do teatro da segurança, do solucionismo tecnológico e da hipossuficiência técnica dos consumidores. Em seguida, você vai entender o que está por trás do lançamento do modelo Mitos da Anthropic — classificado como perigoso demais para uso público —, e por que os resultados práticos com o Firefox e o cURL geraram ceticismo no meio da cibersegurança, levantando questões sobre propaganda de IA, governança, regulação e concorrência no mercado de inteligência artificial. Neste episódio, você também acompanha a análise da lei 15.397, que atualizou crimes digitais no Brasil com penas mais severas para furto qualificado digital, cessão de conta laranja e fraude eletrônica — e por que, sem investimento em capacidade investigativa, isso pode ser apenas populismo penal. Além disso, são discutidas duas vulnerabilidades críticas no Linux (CVE Copyfile e Dirty Frag) com exploits já circulando antes da correção, e como a IA pode acabar com o anonimato na internet ao identificar autores por fingerprint de texto com apenas 125 palavras. Os temas de privacidade, proteção de dados, LGPD, segurança ofensiva, pentest e infraestrutura em nuvem permeiam toda a conversa. Assine o Segurança Legal na sua plataforma favorita, siga o perfil nas redes sociais e avalie o podcast para ajudar a ampliar o alcance deste projeto independente de conteúdo sobre segurança da informação. Você também pode apoiar diretamente pelo Apoia.se (apoia.se/segurancalegal) ou simplesmente indicar o podcast para colegas e amigos — cada compartilhamento faz diferença. Entre em contato pelo e-mail podcast@segurancalegal.com ou pelo Mastodon, Instagram, Bluesky, YouTube e TikTok. Esta descrição foi realizada a partir do áudio do podcast com o uso de IA, com revisão humana.  Visite nossa campanha de financiamento coletivo e nos apoie!  Conheça o Blog da BrownPipe Consultoria e se inscreva no nosso mailing Shownotes Polícia prende suspeitos de invadir e furtar apartamentos de alto padrão em Porto Alegre; grupo usava fraude em reconhecimento facial Polícia desarticula grupo de criminosos que furtava apartamentos de luxo via redes sociais Atualização do Código Penal para alguns crimes digitais Will AI end anonymity? I tested it I can never talk to an AI anonymously again Anthropic's most dangerous AI model just fell into the wrong hands Unauthorized group has gained access to Anthropic's exclusive cyber tool Mythos, report claims It’s a myth that you need Mythos to find bugs: Open source models can do it just as well Filme: Quebra de Sigilo (Sneakers) BC Protege Livro – Sob a sombra da suástica: a França ocupada Filme – Viagem ao mundo dos sonhos Artigo – Em louvor ao Teatro da Segurança Imagem do episódio: The Ancient Days, Willia, Blanke

Neste episódio comentamos sobre os desafios e as soluções técnicas para a aferição de idade na internet, um tema que ganhou forte destaque com as novas regras do ECA Digital. Você irá descobrir como funcionam os protocolos de conhecimento zero, também conhecidos como Zero-Knowledge Protocol ou ZKP, e de que forma eles permitem comprovar a maioridade de um usuário sem expor dados pessoais sensíveis. Você entenderá a diferença entre ferramentas invasivas, como a biometria facial, e métodos técnicos que respeitam a privacidade e a proteção de dados, utilizando criptografia aplicada e padrões internacionais de segurança da informação. Além disso, você vai aprender sobre os impactos práticos da regulamentação da ANPD no controle de acesso a conteúdos restritos e como evitar o rastreamento excessivo por grandes empresas de tecnologia. O debate também aborda táticas de engenharia social, destacando uma série educativa sobre phishing baseada na psicologia da fraude, que é um conhecimento essencial para evitar golpes online e vazamento de dados. Ao longo da discussão, você verá que é possível equilibrar a proteção no ambiente digital com a garantia da intimidade, sem adotar modelos de vigilância em massa durante a autenticação de sistemas. Para não perder nenhuma discussão sobre tecnologia, direito e sociedade, assine o podcast na sua plataforma de áudio favorita e siga nossos perfis no YouTube, Mastodon, Blue Sky, Instagram e TikTok. Aproveite para avaliar o programa e compartilhar o conteúdo com outras pessoas interessadas no assunto. Você também pode apoiar o projeto acessando a plataforma de financiamento coletivo indicada no áudio ou enviando suas dúvidas e sugestões diretamente para o nosso e-mail oficial. Esta descrição foi realizada a partir do áudio do podcast com o uso de IA, com revisão humana  Visite nossa campanha de financiamento coletivo e nos apoie!  Conheça o Blog da BrownPipe Consultoria e se inscreva no nosso mailing ShowNotes The Psychology of Fraud, Persuasion and Scam Techniques LEI Nº 15.211, DE 17 DE SETEMBRO DE 2025 – Dispõe sobre a proteção de crianças e adolescentes em ambientes digitais (Estatuto Digital da Criança e do Adolescente) DECRETO Nº 12.880, DE 18 DE MARÇO DE 2026 – Regulamenta a Lei nº 15.211, de 17 de setembro de 2025, que dispõe sobre a proteção de crianças e adolescentes em ambientes digitais, e institui a Política Nacional de Promoção e Proteção dos Direitos da Criança e do Adolescente no Ambiente Digital. Mecanismos confiáveis de aferição de idade – ORIENTAÇÕES PRELIMINARES Radar tecnológico – Mecanismos de aferição de idade

Bei Heise wurde ein Bericht veröffentlich, in dem Bruce Schneier zitiert wird. Bruce Schneier ist ein Sicherheitsexperte und Kryptologe der ersten Stunde. Seine Stimme hat durchaus Gewicht und er hat distanziert sich üblicherweise von Marketing-Blumenwiesen und hat kein Problem damit auch unangenehme Meinungen zu vertreten. eine spannende Ausgangslage also. Unserer Sicherheitsexperten Andreas und Sandro diskutieren den Artikel und ordnen die Aussagen von Bruce Schneier ein.

Show DescriptionThe Zero Day Clock is ticking — and the numbers should make every security leader uncomfortable. In this episode, I sit down with Sergej Epp, CISO at a leading security firm, who built the Zero Day Clock after a weekend experiment using AI to discover vulnerabilities firsthand. What he found shocked him: with no professional vulnerability research background and just a few hours of work, he was successfully finding zero days across major security projects using AI models and basic scaffolding.Sergej breaks down his concept of the "Verifier's Law" — the idea that offense has the cheapest verifier in cybersecurity because feedback is binary and instant (you either popped a shell or you didn't), while defense operates in a space where validation is expensive, ambiguous, and slow. We dig into what this asymmetry means for the industry, why 20 years of warnings from Ross Anderson, Bruce Schneier, Halvar Flake, and others have gone unheeded, and whether coordinated disclosure models are broken now that AI can reverse engineer a patch into a working exploit in minutes.We also discuss the tension between regulation and deregulation playing out in the U.S. and EU, why the answer might be outcome-based accountability rather than prescriptive compliance, and what a realistic defensible posture actually looks like when the mean time to exploit for actively exploited vulnerabilities is under two days — while most organizations are still operating on 30-day patch cycles.Show NotesSergej shares how a weekend AI experiment led him to discover multiple zero days across major security projects with no professional vulnerability research experience — and why that should alarm the entire industryThe "Verifier's Law" explained: offense has cheap, deterministic validators (pop a shell, exfiltrate data, trigger an XSS) while defense faces expensive, ambiguous validation (parsing SIM alerts, measuring security posture), giving AI-accelerated offense a structural advantageThe Zero Day Clock synthesizes 3,500+ CVE-exploit pairs and shows the mean time to exploit for actively exploited vulnerabilities is now under two days — while organizations still operate on 14-to-30-day patch cycles20 years of ignored warnings: from Ross Anderson's 2001 economics paper through Bruce Schneier, Halvar Flake's "the patch is the advisory" insight, and DARPA's Cyber Grand Challenge — the industry has consistently failed to act on clear signalsAI can now reverse engineer patches to identify underlying flaws and generate working exploits in minutes, potentially breaking coordinated disclosure models and compressing the window between patch release and active exploitation to near zeroThe regulation paradox: the EU risks overregulating AI in ways that hamper defenders while attackers face no such constraints, while the U.S. is pushing deregulation that may remove the only forcing function for vendor accountability — Sergej and Chris discuss outcome-based regulation as a potential middle pathDefenders have a data advantage: by understanding their own environments, infrastructure, and processes, security teams can detect AI-driven attacks through behavioral anomalies like hallucinated API calls, non-existent user accounts, and other artifacts of AI-generated attack playbooksThe Zero Day Clock's real power is as a board-level communication tool — a single slide that translates the patching gap into a number executives and policymakers can't ignore, shifting the conversation from "are we compliant?" to "are we fast enough?"

March 3, 2026 - Sidney Blumenthal | Sina Toossi | Bruce Schneier by Ian Masters

We chat with Bruce Schneier — renowned security technologist and, most recently, co-author of Rewiring Democracy — to discuss the relationship between technology and democracy. We get into how people with money/power use systems like AI to create a flywheel of more money/power. But importantly, as an advocate of public-interest technology, Bruce also lays out how AI is being used to empower citizens and strengthen democracy, and the techno-political conditions needed to build these democratic systems. ••• Check out all of Bruce's work https://www.schneier.com/ ••• Rewiring Democracy | Bruce Schneier and Nathan Sanders https://mitpress.mit.edu/9780262049948/rewiring-democracy/ ••• The Promptware Kill Chain: How Prompt Injections Gradually Evolved into a Multi-Step Malware https://www.schneier.com/academic/archives/2026/01/the-promptware-kill-chain-how-prompt-injections-gradually-evolved-into-a-multi-step-malware.html Standing Plugs: ••• Order Jathan's book: https://www.ucpress.edu/book/9780520398078/the-mechanic-and-the-luddite ••• Subscribe to Ed's substack: https://substack.com/@thetechbubble ••• Subscribe to TMK on patreon for premium episodes: https://www.patreon.com/thismachinekills Hosted by Jathan Sadowski (bsky.app/profile/jathansadowski.com) and Edward Ongweso Jr. (www.x.com/bigblackjacobin). Production / Music by Jereme Brown (bsky.app/profile/jebr.bsky.social)

AI is changing many aspects of our lives, so it's reasonable to expect that it will impact democracy, too. The question is how? Two experts in technology and politics join us to discuss how we can harness AI's power to strengthen democracy. Yes, there will be deepfakes and automated misinformation, but there can also be greater opportunities for the government to serve people and for all of us to have a greater say in our systems of self governance.In their book Rewiring Democracy: How AI Will Transform Our Politics, Government, and Citizenship, Bruce Schneier and Nathan E. Sanders describe how AI could change political communication, the legislative process, bureaucracy, the judiciary, and more. It's a more hopeful argument than you might expect. They discuss how AI's broad capabilities can augment democratic processes and help citizens build consensus, express their voice, and shake up long-standing power structures. As they say in the interview, AI is just a tool; how we use it is up to us.Schneier is a security technologist and the New York Times bestselling author of 14 books, including A Hacker's Mind. He is a lecturer at the Harvard Kennedy School, a board member of the Electronic Frontier Foundation, and Chief of Security Architecture at Inrupt, Inc.Sanders is a data scientist focused on making policymaking more participatory. He has served in fellowships at the Massachusetts legislature and the Berkman-Klein Center at Harvard University.Related EpisodesThe Problem(s) with Platforms (Cory Doctorow)Building Better Bureaucracy (Jennifer Pahlka)Laboratories of Restricting Democracy (Virginia Eubanks)  Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Bruce Schneier and Dr. Nathan Sanders discuss Rewiring Democracy: How AI Will Transform Our Politics, Government, and Citizenship (MIT Press). Bruce and Nathan share how AI becomes viable for governments when the speed, scale, scope, and sophistication of computational systems surpasses human capacity. We also discuss the relationship between AI and the Internet of Things, how AI interacts with lobbying and legislation, and how values can be construed in an automated context. Current examples of AI implementation are shared from political campaigning, public administration, and civil society. 

This week we talk about social networks, propaganda, and Oracle.We also discuss foreign adversaries, ByteDance, and X.Recommended Book: Rewiring Democracy by Bruce Schneier and Nathan E. SandersTranscriptIn 2021, TikTok, a short-form video platform that's ostensibly also a social network, though which leans heavily toward consuming content over socializing, was ranked the most popular website by internet services company Cloudflare, beating out all the other big tech players, including search engine juggernaut, Google.It was a neck and neck sort of thing, with Google taking the lead some days that year, but 2021 was definitely TikTok's time to shine, as it was already popular with young people and was starting to become popular with the general public, of all ages and across a huge swathe of the planet. It even beat Facebook as the most popular social media website that year, despite, again, being mostly about consuming content rather than interacting—that was actually a prime motivator for Meta, which owns Facebook and Instagram, to redirect its own apps in a similar direction, shifting its focus from communication and interaction between users toward the creation of binge-able content, and feeding users more of that content in a feed optimized for time-losing levels of consumption.2021 was also the first full year that TikTok was coming under scrutiny from the US government. In the preceding year, 2020, then first-term president Donald Trump said he was considering banning the app because it was becoming so popular, with young people in particular, and because it was owned by a Chinese company, ByteDance it represented a potential national security threat.So the idea was that because Chinese companies are forced, by their very nature, to do what the Chinese government tells them—that's just how things work over there—and to do so on the down-low if that's what the governments demands, and to lie about having to do what the government tells them to do, if the government tells them to thus lie, it doesn't matter that ByteDance's leadership swore up and down to the world that the company will never use its popularity, and the data it soaks up from all its users as a result of that popularity, to help the Chinese government, the Chinese military, or Chinese intelligence services.It of course will have to do that, and if it doesn't, its leaders could be black-bagged and disappeared in the night—because again, that's just how things work over there. So the Trump administration decided to make TikTok a sort of bogeyman, representing Chinese companies in general, and to some degree the presence of China in the US and throughout the Western world, and said, nope, we're not gonna let this thing continue to operate over here.It's worth remembering, too, that by 2021 the world was enmeshed in the COVID-19 pandemic, which originated in China, and which Trump and his administration were ardently attempting to tie to the Chinese government—calling Covid the Chinese Flu, and even worse things, as part of that effort.So this move against TikTok and its parent company, while based on genuine concerns about the ownership of the company and how and where the data being collected by said company is handled, it should also be seen as a political maneuver, allowing Trump, during the 2020 election run-up, to look like he was taking a big stand against a big foreign threat, China.What I'd like to talk about today is a deal that was proposed way back then by the Trump administration, as a potential way out for TikTok and ByteDance, allowing it to continue operating in the US despite threats to shut it down, now that said deal, or a version of it, seems to have finally come to fruition—and what we know about the shape of the resulting new, US-based version of TikTok.—On January 18, 2025, TikTok stopped worked in the US. It voluntarily suspended all services in the country in the lead-up to the implementation of the Protecting Americans from Foreign Adversary Controlled Applications Act, which was passed by the US congress and signed into law by then-president Joe Biden in April of 2024. This law gave social networking services controlled by ‘foreign adversaries' 270 days, with the possibility of a 90-day extension, to divest themselves so that they're no longer considered foreign adversary-owned.This law was almost exclusively aimed at TikTok, and the idea was that TikTok, in the US, would no longer be able to legally function following that deadline if it was still owned by China, which for the purposes of this law has been labeled a foreign adversary.ByteDance could keep TikTok in the US going if it sold a majority, controlling stake of its US-based assets to non-adversary owners, but otherwise it would have to shut down.Interestingly, though Trump was the original source of concerns about TikTok and its Chinese ownership during his first administration, when he stepped back into office in January 2025, he signed a new executive order that delayed the enforcement of this Biden-signed law, and then delayed it still-further, three more times after that, saying that he wanted to give American investors the time to negotiate controlling interest of US TikTok, rather than banning it.Those efforts eventually bore fruit in the shape of a new controlling entity called TikTok USDS Joint Venture LLC, which is made up of a bunch of non-Chinese investment entities, including US software behemoth Oracle, an Emirati investment firm called MGX, a US investment firm called Silver Lake, and a personal investment company owned by Michael Dell, the founder of Dell Technologies. There are other, smaller investors also involved, but the red thread that runs through almost all of them is that they're big Trump supporters and funders, funneling a lot of money into Trump's campaigns, and his family businesses.So six years after the initial legal salvo was fired at TikTok in the US, the local assets are now controlled by non-Chinese investors, though the original Chinese owner, ByteDance, still owns just under 20%, compared to about 15% apiece for Oracle, MGX, and Silver Lake.The new company's board is majority-run by those investors, too, which means it's majority-run by ardent Trump supporters. We don't yet know what effect this will have on content within the app, but under full Chinese ownership, topics related to democracy, Tianamen Square, and the LGBTQ community, among others, were significantly downgraded in the algorithm, ensuring they were seldom shown to anyone, which in turn disincentivized content that those owners didn't like while incentivizing content that was pro-China, and pro-Chinese government priorities.It's considered to be likely, by analysts who watch these sorts of maneuverings, that the same will be true of this new entity, but for and against subject matter that the Trump administration is for and against. Which raises the possibility that the new US TikTok, while superficially the same as the previous US TikTok, will slowly go the way X, formerly Twitter, has gone under Elon Musk, which was dramatically pushed in a new direction under its own owner, focusing on his political and ideological priorities and punishing users who spoke against those priorities.TikTok could become more or less an extension of the Trump-verse, in other words, and could thus become something more akin to Trump's own network, Truth Social, or other right-leaning and far-right social networks, like conservative YouTube-clone, Rumble, rather than something less ideological, or maybe I should say less overtly politically ideological, like Meta's Facebook, Threads, and Instagram.Users have already noticed some changes to US TikTok after the change in ownership, though, including what sorts of data are collected.TikTok's new privacy policy, which all users have to agree to before using the app, now that the platform has changed hands, says that TikTok will be using precise location tracking, keeping tabs on exactly where users are located via their device's GPS. That's compared to the app's previous approximate location-tracking effort, which used SIM card and IP address data to understand general proximity—it still uses that data, too, but now, rather than knowing what neighborhood you're probably in, it may also know what room in your house you're scrolling from.The new US TikTok also tracks users' interactions with AI tools, including their prompts, outputs, and metadata attached to said interactions, which includes details about where users are when they're using such tools, and what time they used them.They also collect gobs of marketing data from outside sources, and based on the users' activity within the app. So things you buy, websites and other apps you visit and use, and conversations you have will all be sucked up and agglomerated into a profile that's then used to show you targeted advertising. This isn't unique to US TikTok, but the company does seem to intend to make use of more such data, and to combine it with that other stuff it's now collecting, to increase the price it can charge for ads, because they'll be a lot more specifically targeted than before.Some users are beginning to comb through the new user agreement with a fine-toothed comb, noticing, in addition to those aforementioned major changes, that the company also reserves the right to collect information about your physical and mental health, to use identifying information in the videos and images you might share, and information gleaned from people and their identifying characteristics in images and videos, and to collect biometric data, which usually means eyes and faces and walking gate and things like that, to differentiate and track people across such content. They can keep tabs on your sex life, sexual orientation and gender, your drug usage, your ethnic and racial origins, your citizenship and immigration status, your financial situation and information—all sorts of stuff is collected, and they say in the privacy policy and user agreement that they intend to do gather and store and cross-reference this kind of information whenever possible.Again, much of this isn't novel, as social platforms are gobbling up all sorts of stuff about their users all the time, mostly to refine their ad placements because that allows them to charge advertisers more for better-targeted placements, over time.That said, because of the nature of the group that now owns US TikTok and which is making executive decisions about it, including, potentially, how this data is shared, including with the US government and its many agencies, there's a chance we might see an exodus of sorts from the still younger-than-average user base of this network, because there is a nonzero chance it could become a tool in the Trump administration's utility belt for tracking down people they don't like and spreading messages that are favorable to them and their ideological aims; so basically what was happening under the previous ownership, but for the current US administration's priorities, rather than those of the Chinese government.Show Noteshttps://www.nbcnews.com/tech/tech-news/tiktok-surpasses-google-popular-website-year-new-data-suggests-rcna9648https://www.nytimes.com/2026/01/22/technology/tiktok-deal-oracle-bytedance-china-us.htmlhttps://www.wired.com/story/tiktok-new-privacy-policy/https://archive.is/20260123005655/https://www.bloomberg.com/news/articles/2026-01-23/tiktok-seals-deal-to-create-us-venture-with-oracle-silver-lakehttps://www.axios.com/2026/01/23/tiktok-deal-trump-app-banhttps://www.theverge.com/tech/866868/tiktok-usds-new-owners-algorithm-explainedhttps://www.politico.com/news/2026/01/22/5-things-to-know-about-the-tiktok-deal-00743316https://www.nytimes.com/2026/01/23/business/media/tiktok-us-terms-conditions.htmlhttps://en.wikipedia.org/wiki/TikTokhttps://en.wikipedia.org/wiki/Donald_Trump%E2%80%93TikTok_controversyhttps://en.wikipedia.org/wiki/Efforts_to_ban_TikTok_in_the_United_Stateshttps://en.wikipedia.org/wiki/Protecting_Americans_from_Foreign_Adversary_Controlled_Applications_Act This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit letsknowthings.substack.com/subscribe

As AI increasingly shapes geopolitics, elections, and civic life, its impact on democracy is becoming impossible to ignore. In this episode, Daniel and Chris are joined by security expert Bruce Schneier to explore how AI and technology are transforming democracy, governance, and citizenship. Drawing from his book Rewiring Democracy, they explore real examples of AI in elections, legislation, courts, and public AI models, the risks of concentrated power, and how these tools can both strengthen and strain democratic systems worldwide.Featuring:Bruce Schneier – XChris Benson – Website, LinkedIn, Bluesky, GitHub, XDaniel Whitenack – Website, GitHub, XLinks: Schneier on SecuritySponsors:Framer - The website builder that turns your dot com from a formality into a tool for growth. Check it out at framer.com/PRACTICALAIZapier - The AI orchestration platform that puts AI to work across your company. Check it out at zapier.com/practicalUpcoming Events: Register for upcoming webinars here!

What even is AI anyway, and how do we harness it for the good of the people? Host Emily talks technology and public power in this episode with Bruce Schneier and Nathan Sanders, authors of Rewiring Democracy: How AI will Transform our Politics, Government, and Citizenship. Find the book at your local library!   Overdueing It is a project funded by the Rhode Island Office of Library and Information Services and is produced by library staff around the Ocean State. We are proud to be a resident partner of the Rhode Island Center for the Book. The views, thoughts, and opinions expressed are the speakers' own and do not represent those of the Overdueing It podcast, its sponsor organizations, or any participants' place of employment. The content of Overdueing It episodes are the property of the individual creators, with permission for Overdueing It to share the content on their podcast feed in perpetuity. Any of the content from the Overdueing It podcast can not be reproduced without express written permission.    Our logo was designed by Sarah Bouvier and our theme music is by Neura-Flow.   Books The Mechanic and the Luddite: A Ruthless Criticism of Technology and Capitalism by Jathan Sadowski A Drop of Corruption by Robert Bennet Age of Wonder by Richard Holmes Lynch on Lynch edited by Chris Rodley Rewiring Democracy: How AI will Transform our Politics, Government, and Citizenship by Bruce Schneier and Nathan Sanders The Light Eaters by Zoe Schlanger The Hidden Language of Trees by Peter Wohlleben The Benevolent Society of Ill-Mannered Ladies by Alison Goodman A Hacker's Mind by Bruce Schneier Automatic Noodle by Annalee Newitz The Hands of the Emperor by Victoria Goddard   Media Zero Dark Thirty Ella McCay Zenon Girl of the Twenty First Century   Other Song - The Arrogant Worms: Carrot Juice is Murder (I've Heard the Scream of the Vegetables) Video - The Fall  

What even is AI anyway, and how do we harness it for the good of the people? Host Emily talks technology and public power in this episode with Bruce Schneier and Nathan Sanders, authors of Rewiring Democracy: How AI will Transform our Politics, Government, and Citizenship. Find the book at your local library!   Overdueing It is a project funded by the Rhode Island Office of Library and Information Services and is produced by library staff around the Ocean State. We are proud to be a resident partner of the Rhode Island Center for the Book. The views, thoughts, and opinions expressed are the speakers' own and do not represent those of the Overdueing It podcast, its sponsor organizations, or any participants' place of employment. The content of Overdueing It episodes are the property of the individual creators, with permission for Overdueing It to share the content on their podcast feed in perpetuity. Any of the content from the Overdueing It podcast can not be reproduced without express written permission.    Our logo was designed by Sarah Bouvier and our theme music is by Neura-Flow.   Books The Mechanic and the Luddite: A Ruthless Criticism of Technology and Capitalism by Jathan Sadowski A Drop of Corruption by Robert Bennet Age of Wonder by Richard Holmes Lynch on Lynch edited by Chris Rodley Rewiring Democracy: How AI will Transform our Politics, Government, and Citizenship by Bruce Schneier and Nathan Sanders The Light Eaters by Zoe Schlanger The Hidden Language of Trees by Peter Wohlleben The Benevolent Society of Ill-Mannered Ladies by Alison Goodman A Hacker's Mind by Bruce Schneier Automatic Noodle by Annalee Newitz The Hands of the Emperor by Victoria Goddard   Media Zero Dark Thirty Ella McCay Zenon Girl of the Twenty First Century   Other Song - The Arrogant Worms: Carrot Juice is Murder (I've Heard the Scream of the Vegetables) Video - The Fall  

Synopsis Dans l'épisode 0x285, Patrick, Steve, Jacques et Richer se retrouvent pour un tour d'horizon des sujets cyber et vie privée qui ont marqué la semaine. Entre actualités de sécurité, tendances ransomware, et signaux faibles qui deviennent des signaux d'alarme, l'équipe met en contexte ce qui compte vraiment, et pourquoi. On parle aussi d'événements à venir, notamment la Semaine de la protection des données (26 au 30 janvier 2026), des conférences et rendez-vous au Québec, et des enjeux très concrets autour du cloud et de la souveraineté numérique. Côté menaces, on revient sur l'évolution des ransomwares, l'industrialisation des attaques, et ce qui change avec l'IA dans l'outillage et les scénarios. Enfin, l'épisode aborde plusieurs dossiers d'actualité, dont la protection des données, les campagnes de phishing qui s'adaptent, des incidents d'infrastructure, et des débats sur l'accès aux technologies, la régulation et la confiance. Une écoute utile pour rester aligné sur les risques, et sur les décisions qui en découlent. Nouvelles Richer Richer veut savoir Jacques Ransomware activity never dies, it multiplies Cybersecurity spending keeps rising, so why is business impact still hard to explain? Microsoft shuts down RedVDS cybercrime subscription service tied to millions in fraud losses Steve 20260126, Google agrees to pay $68 million to settle voice recording lawsuit 20260126, China hacked Downing Street phones for years 20260122, Phishing kits adapt to the script of callers 20260120, XTC Mobile, Le pari audacieux de la souveraineté numérique avec le LynX Phone 20260120, VoidLink, Evidence That the Era of Advanced AI-Generated Malware Has Begun 20260126, Europe Prepares for a Nightmare Scenario, The U.S. Blocking Access to Tech 20260124, Cartes de crédit, attention à cette faille de sécurité chez Visa et Mastercard 20260123, Des voitures espionnes de la Chine, mythe ou réalité? 20260126, Massive Data Leak, 48M Gmail and 6.5M Instagram Entries Found in Open Database 20260126, Russian state hackers likely behind wiper malware attack on Poland's power grid 20260123, ESET Research, Sandworm behind cyberattack on Poland's power grid in late 2025 20260126, Data center power outage took out TikTok first weekend under US ownership 20260121, EU unveils new plans to tackle Huawei, ZTE as China alleges protectionism 20260123, Microsoft Gave FBI Keys To Unlock Encrypted Data, Exposing Major Privacy Flaw Crew Patrick Mathieu Steve Waterhouse Richer Dinelle Jacques Sauvé Shamelessplug Join Hackfest/La French Connection Discord #La-French-Connection Join Hackfest us on Masodon POLAR - Québec - 29 Octobre 2026 Hackfest - Québec - 29-30-31 Octobre 2026 Événements 26-30 janvier 2026, Semaine de la protection des données Privacy Day 2026, Ne laissez pas le cloud décider pour vos données, 28 janvier 2026 CICC, The Coming AI avec Bruce Schneier, 29 janvier 2026 DEL, Défense, saisir les opportunités d'un marché stratégique, 20 février 2026 ALTSECCON, 9-10 avril 2026 CYBERECO, 28-29 avril 2026 NorthSEC, 11-17 mai 2026 ITSEC Devolution, 2026 Matinée conférence CPQ, 29 mai 2026 Conférence 2026 du Consortium national pour la cybersécurité, 16-19 juin 2026 Crédits Montage audio par Hackfest Communication Music par Dynamic Range – Acid - Acid Locaux virtuels par Streamyard

Show host Gene Tunny speaks with cybersecurity expert Bruce Schneier of the Harvard Kennedy School about his new book, Rewiring Democracy, which explores the profound and often underappreciated ways AI is already reshaping democratic institutions. From AI-powered political campaigns and legislative drafting to citizen engagement and court systems, Schneier lays out both the potential and the peril of this technological transformation.Gene would love to hear your thoughts on this episode. You can email him via contact@economicsexplored.com. TimestampsIntroduction (0:00)Bruce Schneier's New Book "Rewiring Democracy" (1:44)Impact of AI on Democracy and Humanity (4:25)AI in Government Administration and Courts (9:12)Examples of AI in Citizen Assemblies and Public AI (12:02)Challenges and Opportunities with AI in Democracy (18:10)Regulation and Accountability of AI (22:04)TakeawaysAI is already transforming democracy. It plays roles in political campaigning, lawmaking, courtrooms, and public service—even if we don't always notice it.The real danger is corporate control. Schneier stresses that AI's trajectory is largely shaped by a small group of powerful tech companies and calls for “public AI” as a counterbalance.AI is a tool, not a force. Whether AI supports democracy or authoritarianism depends entirely on how humans use it.Citizens can be empowered by AI. Projects from CalMatters and make.org show how AI can help amplify civic voices and improve transparency.Urgent regulation is needed. Schneier argues that AI, like cars or planes, must be regulated for safety, transparency, and accountability—especially to prevent manipulation and abuse.Links relevant to the conversationBruce's book - Rewiring Democracy: How AI Will Transform Our Politics, Government, and Citizenshiphttps://mitpress.mit.edu/9780262049948/rewiring-democracy/Lumo Coffee promotion10% of Lumo Coffee's Seriously Healthy Organic Coffee.Website: https://www.lumocoffee.com/10EXPLOREDPromo code: 10EXPLORED 

Guest: Bruce Schneier Topics: Do you believe that AI is going to end up being a net improvement for defenders or attackers?  Is short term vs long term different? We're excited about the new book you have coming out with your co-author Nathan Sanders "Rewiring Democracy".  We want to ask the same question, but for society: do you think AI is going to end up helping the forces of liberal democracy, or the forces of corruption, illiberalism, and authoritarianism?  If exploitation is always cheaper than patching (and attackers don't follow as many rules and procedures), do we have a chance here?  If this requires pervasive and fast "humanless" automatic patching (kinda like what Chrome does for years), will this ever work for most organizations? Do defenders have to do the same and just discover and fix issues faster? Or can we use AI somehow differently? Does this make defense in depth more important? How do you see AI as changing how society develops and maintains trust?  Resources: "Rewiring Democracy" book "Informacracy Trilogy" book Agentic AI's OODA Loop Problem EP255 Separating Hype from Hazard: The Truth About Autonomous AI Hacking AI and Trust AI and Data Integrity EP223 AI Addressable, Not AI Solvable: Reflections from RSA 2025 RSA 2025: AI's Promise vs. Security's Past — A Reality Check  

Author of "Rewiring Democracy: How AI Will Transform Our Politics, Government and Citizenship" Learn more about your ad choices. Visit podcastchoices.com/adchoices

On this episode of the Plutopia News Network podcast, security technologist and author Bruce Schneier joins the hosts to discuss his new book Rewiring Democracy: How AI Will Transform Our… The post Bruce Schneier: Rewiring Democracy first appeared on Plutopia News Network.

W 4. odcinku "Raportu o sztucznej inteligencji" opowiemy o tym, gdzie kończy się bezpieczna AI, a gdzie zaczyna zagrożenie dla nas wszystkich. I kto powinien być odpowiedzialny za kontrolę sztucznej inteligencji oraz jej wprowadzanie w różnych branżach i sferach życia.

Democracy faces challenges worldwide, and artificial intelligence has become an increasing part of that. In their book Rewiring Democracy: How AI Will Transform Our Politics, Government, and Citizenship, cybersecurity technologist Bruce Schneier and data scientist Nathan E. Sanders methodically unpack the ways AI is changing every aspect of democracy, while making the case that we can harness the technology to support and strengthen these systems. Neither fear-mongering nor utopian, Rewiring Democracy aims to present a clear-eyed and optimistic path for putting democratic principles at the heart of AI development — highlighting how citizens, public servants, and elected officials can use AI to expand access to justice and inform, empower, and engage the public.On October 23, the authors discussed their book with Data & Society's Director of Research Alice Marwick, walking us through their roadmap for understanding how AI is changing power and participation and what we can do to shape that change for the better.

This and all episodes at: https://aiandyou.net/ . How should AI change democracy? That's the topic of Rewiring Democracy: How AI Will Transform Our Politics, Government, and Citizenship, and I am continuing my talk with its authors. Bruce Schneier is an internationally renowned security technologist and the bestselling author of fourteen books, including Data and Goliath and A Hacker's Mind.  Nathan Sanders is a data scientist who has served in fellowships and the Massachusetts legislature and the Berkman-Klein Center at Harvard. He writes in The New York Times and The Atlantic. We talk about whether wealthy entities might subvert the use of AI in democracy, how smaller countries are engaging with AI in government, the utility of open weight and open source models, digital twins in government, the future of surveillance, and what makes Bruce and Nathan optimistic about the future. All this plus our usual look at today's AI headlines. Transcript and URLs referenced at HumanCusp Blog.        

The discussion centers on the book "Rewiring Democracy," authored by Bruce Schneier and Nathan E. Sanders, which explores the implications of artificial intelligence (AI) on governance, power distribution, and democratic principles. The authors highlight the risks associated with AI, particularly the concentration of power among a few corporations, primarily in Silicon Valley, which can undermine democratic values and lead to inefficiencies in government and business. They advocate for a vision of AI that democratizes power and enhances the efficiency of governance, emphasizing the need for transparency, fairness, and accountability in AI systems.Schneier and Sanders argue that the democratization of AI technology is already underway, as the costs of developing AI models decrease, allowing smaller organizations to create their own systems. However, they caution that the opacity of these models poses significant challenges. They suggest that regulation and competition can play crucial roles in ensuring that AI systems are transparent and accountable to both the public and clients. The conversation also touches on the importance of diverse participation in policymaking, asserting that individuals bring valuable lived experiences that can inform AI governance.The episode further addresses the issue of bias in AI systems, emphasizing that while complete neutrality is unattainable, transparency about inherent biases is essential. The authors discuss the legal implications of biased AI implementations, referencing a case involving a pharmacy chain that faced accountability for racially biased facial recognition technology. They argue for a systemic approach to governance that considers the roles of both technology providers and the organizations that implement these systems.For Managed Service Providers (MSPs) and IT service leaders, the insights from this episode underscore the importance of actively testing AI systems for bias and ensuring compliance with evolving regulations. The authors encourage IT providers to engage in the development of governance frameworks that prioritize transparency and accountability, ultimately fostering a more equitable technological landscape. As AI continues to evolve, the need for informed participation and robust regulatory frameworks will be critical for maintaining democratic values and addressing the challenges posed by emerging technologies.

This and all episodes at: https://aiandyou.net/ . How should AI change democracy? That's the topic of Rewiring Democracy: How AI Will Transform Our Politics, Government, and Citizenship, and I am talking today with its authors. Bruce Schneier is an internationally renowned security technologist and the bestselling author of fourteen books, including Data and Goliath and A Hacker's Mind. He is a lecturer at the Harvard Kennedy School, and a board member of the Electronic Frontier Foundation, and Chief of Security Architecture at Inrupt. Nathan Sanders is a data scientist researching machine learning, astrophysics, public health, environmental justice, and more. He has served in fellowships and the Massachusetts legislature and the Berkman-Klein Center at Harvard. He writes on AI and democracy in The New York Times and The Atlantic. We talk about this fascinating and scary intersection of AI and government, of AI being used in making legislation, the concept of democracy as an information system, ways AI can transform how citizens engage their governments, regulatory responses to AI from the US and around the world, and how the judicial branch can use AI.  All this plus our usual look at today's AI headlines. Transcript and URLs referenced at HumanCusp Blog.        

Bruce Schneier is a security technologist at Harvard's Kennedy School. Nathan Sanders is a data scientist at Harvard's Berkman Klein Center. They've been studying AI's impact on democratic institutions. Their new book is Rewiring Democracy.

Keywordscybersecurity, technology, AI, IoT, Intel, startups, security culture, talent development, career advice  SummaryIn this episode of No Password Required, host Jack Clabby and Kayleigh Melton engage with Steve Orrin, the federal CTO at Intel, discussing the evolving landscape of cybersecurity, the importance of diverse teams, and the intersection of technology and security. Steve shares insights from his extensive career, including his experiences in the startup scene, the significance of AI and IoT, and the critical blind spots in cybersecurity practices. The conversation also touches on nurturing talent in technology and offers valuable advice for young professionals entering the field.  TakeawaysIoT is now referred to as the Edge in technology.Diverse teams bring unique perspectives and solutions.Experience in cybersecurity is crucial for effective team building.The startup scene in the 90s was vibrant and innovative.Understanding both biology and technology can lead to unique career paths.AI and IoT are integral to modern cybersecurity solutions.Organizations often overlook the importance of security in early project stages.Nurturing talent involves giving them interesting projects and autonomy.Young professionals should understand the hacker mentality to succeed in cybersecurity.Customer feedback is essential for developing effective security solutions.  TitlesThe Edge of Cybersecurity: Insights from Steve OrrinNavigating the Intersection of Technology and Security  Sound bites"IoT is officially called the Edge.""We're making mainframe sexy again.""Surround yourself with people smarter than you."  Chapters00:00 Introduction to Cybersecurity and the Edge01:48 Steve Orrin's Role at Intel04:51 The Evolution of Security Technology09:07 The Startup Scene in the 90s13:00 The Intersection of Biology and Technology15:52 The Importance of AI and IoT20:30 Blind Spots in Cybersecurity25:38 Nurturing Talent in Technology28:57 Advice for Young Cybersecurity Professionals32:10 Lifestyle Polygraph: Fun Questions with Steve

We discuss how AI can both serve the public interest and advance the goals of our democracy, despite the misgivings about the current state of AI. Bruce's civic action toolkit recommendations are: 1) Use the tools of AI! 2) Use assistive tech to write to your elected representatives Bruce Schneier is an internationally renowned security technologist, chief of security architecture at Inrupt Inc, a lecturer at the Harvard Kennedy School, and the co-author of Rewiring Democracy: How AI Will Transform Our Politics, Government, and Citizenship. Let's connect! Follow Future Hindsight on Instagram: https://www.instagram.com/futurehindsightpod/ Discover new ways to #BetheSpark: https://www.futurehindsight.com/spark Follow Mila on X: https://x.com/milaatmos Follow Bruce on X: https://x.com/schneierblog Sponsor: Thank you to Shopify! Sign up for a $1/month trial at shopify.com/hopeful. Early episodes for Patreon supporters: https://patreon.com/futurehindsight Credits: Host: Mila Atmos Guests: Bruce Schneier Executive Producer: Mila Atmos Producer: Zack Travis

What will happen when dictators realise just how useful big tech and the data it gathers could be for them? How is this already happening – and how could it expand, as A.I. ramps up the way draconian governments can use tech? Bruce Schneier, a renowned cyber security expert, best-selling author and co-author of Rewiring Democracy How AI Will Transform Our Politics, Government, and Citizenship joins Gavin Esler to discuss.   • This episode of This Is Not A Drill is supported by Incogni the service that keeps your private information safe, protects you from identity theft and keeps your data from being sold. There's a special offer for This Is Not A Drill listeners – go to https://incogni.com/notadrill  to get an exclusive 60% off your annual plan.      • Support us on Patreon to keep This Is Not A Drill producing thought-provoking podcasts like this.     Advertisers! Want to reach smart, engaged, influential people with money to spend? (Yes, they do exist). Some 3.5 MILLION people download and watch our podcasts every month – and they love our shows. Why not get YOUR brand in front of our influential listeners with podcast advertising? Contact ads@podmasters.co.uk to find out more      Written and presented by Gavin Esler. Produced by Robin Leeburn. Original theme music by Paul Hartnoll – https://www.orbitalofficial.com. Executive Producer Martin Bojtos. Managing Editor Jacob Jarvis. Group Editor Andrew Harrison. This Is Not A Drill is a Podmasters production.   Learn more about your ad choices. Visit podcastchoices.com/adchoices

Send us a textQuantum threats aren't waiting politely on the horizon, and neither should we. We kick off with Signal's bold move to deploy post-quantum encryption, unpacking the “belt and suspenders” approach that blends classical cryptography with quantum-resistant algorithms. No jargon traps—just clear takeaways on why this matters for privacy, resilience, and the pressure it puts on other messaging platforms to evolve. We point you to smart reads from Ars Technica and Bruce Schneier that make the technical guts approachable and actionable.From there, we switch gears into a focused CISSP Domain 8 walkthrough: how to weave security into every phase of the software development lifecycle. We talk practical integration across waterfall, agile, and DevOps; show why change management, continuous monitoring, and application-aware incident response are non-negotiable; and explain how maturity models like CMMI and BSIMM help teams move from reactive to repeatable. We also break down the developer's toolbox—secure language choices, vetted libraries with SCA, hardened runtimes, and IDE plugins that surface issues in real time—so teams can ship faster without trading away safety.Speed meets rigor in the CI/CD pipeline, where shift-left security comes alive with SAST, DAST, and SOAR-driven checks. We cover repository hygiene, secret scanning, and how to measure effectiveness with audit trails and risk analysis that map code issues to business impact. You'll get a clear view of third-party risk across COTS and open source, the shared responsibility model for SaaS, PaaS, and IaaS, and the daily practices that keep APIs from leaking data: least privilege, strict authorization, input validation, and rate limiting. We close with software-defined security—policies as code—bringing consistency, versioning, and automation to your defenses. Subscribe, share with a teammate who owns your pipeline, and leave a review to tell us the next Domain 8 topic you want us to deep-dive.Gain exclusive access to 360 FREE CISSP Practice Questions at FreeCISSPQuestions.com and have them delivered directly to your inbox! Don't miss this valuable opportunity to strengthen your CISSP exam preparation and boost your chances of certification success. Join now and start your journey toward CISSP mastery today!

Author of 'Rewiring Democracy: How AI Will Transform Our Politics, Government, and Citizenship Learn more about your ad choices. Visit podcastchoices.com/adchoices

AI is changing democracy. We still get to decide how.AI's impact on democracy will go far beyond headline-grabbing political deepfakes and automated misinformation. Everywhere it will be used, it will create risks and opportunities to shake up long-standing power structures.In this highly readable and advisedly optimistic book, Rewiring Democracy: How AI Will Transform Our Politics, Government, and Citizenship (MIT Press, 2025), security technologist Bruce Schneier and data scientist Nathan Sanders cut through the AI hype and examine the myriad ways that AI is transforming every aspect of democracy—for both good and ill.The authors describe how the sophistication of AI will fulfill demands from lawmakers for more complex legislation, reducing deference to the executive branch and altering the balance of power between lawmakers and administrators. They show how the scale and scope of AI is enhancing civil servants' ability to shape private-sector behavior, automating either the enforcement or neglect of industry regulations. They also explain how both lawyers and judges will leverage the speed of AI, upending how we think about law enforcement, litigation, and dispute resolution.Whether these outcomes enhance or degrade democracy depends on how we shape the development and use of AI technologies. Powerful players in private industry and public life are already using AI to increase their influence, and AIs built by corporations don't deliver the fairness and trust required by democratic governance. But, steered in the right direction, AI's broad capabilities can augment democratic processes and help citizens build consensus, express their voice, and shake up long-standing power structures.Democracy is facing new challenges worldwide, and AI has become a part of that. It can inform, empower, and engage citizens. It can also disinform, disempower, and disengage them. The choice is up to us. Schneier and Sanders blaze the path forward, showing us how we can use AI to make democracy stronger and more participatory. Nathan E. Sanders is a data scientist focused on making policymaking more participatory. His research spans machine learning, astrophysics, public health, environmental justice, and more. He has served in fellowships at the Massachusetts legislature and the Berkman-Klein Center at Harvard University. Caleb Zakarin is editor of the New Books Network. 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

AI is changing democracy. We still get to decide how.AI's impact on democracy will go far beyond headline-grabbing political deepfakes and automated misinformation. Everywhere it will be used, it will create risks and opportunities to shake up long-standing power structures.In this highly readable and advisedly optimistic book, Rewiring Democracy: How AI Will Transform Our Politics, Government, and Citizenship (MIT Press, 2025), security technologist Bruce Schneier and data scientist Nathan Sanders cut through the AI hype and examine the myriad ways that AI is transforming every aspect of democracy—for both good and ill.The authors describe how the sophistication of AI will fulfill demands from lawmakers for more complex legislation, reducing deference to the executive branch and altering the balance of power between lawmakers and administrators. They show how the scale and scope of AI is enhancing civil servants' ability to shape private-sector behavior, automating either the enforcement or neglect of industry regulations. They also explain how both lawyers and judges will leverage the speed of AI, upending how we think about law enforcement, litigation, and dispute resolution.Whether these outcomes enhance or degrade democracy depends on how we shape the development and use of AI technologies. Powerful players in private industry and public life are already using AI to increase their influence, and AIs built by corporations don't deliver the fairness and trust required by democratic governance. But, steered in the right direction, AI's broad capabilities can augment democratic processes and help citizens build consensus, express their voice, and shake up long-standing power structures.Democracy is facing new challenges worldwide, and AI has become a part of that. It can inform, empower, and engage citizens. It can also disinform, disempower, and disengage them. The choice is up to us. Schneier and Sanders blaze the path forward, showing us how we can use AI to make democracy stronger and more participatory. Nathan E. Sanders is a data scientist focused on making policymaking more participatory. His research spans machine learning, astrophysics, public health, environmental justice, and more. He has served in fellowships at the Massachusetts legislature and the Berkman-Klein Center at Harvard University. Caleb Zakarin is editor of the New Books Network. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/political-science

AI is changing democracy. We still get to decide how.AI's impact on democracy will go far beyond headline-grabbing political deepfakes and automated misinformation. Everywhere it will be used, it will create risks and opportunities to shake up long-standing power structures.In this highly readable and advisedly optimistic book, Rewiring Democracy: How AI Will Transform Our Politics, Government, and Citizenship (MIT Press, 2025), security technologist Bruce Schneier and data scientist Nathan Sanders cut through the AI hype and examine the myriad ways that AI is transforming every aspect of democracy—for both good and ill.The authors describe how the sophistication of AI will fulfill demands from lawmakers for more complex legislation, reducing deference to the executive branch and altering the balance of power between lawmakers and administrators. They show how the scale and scope of AI is enhancing civil servants' ability to shape private-sector behavior, automating either the enforcement or neglect of industry regulations. They also explain how both lawyers and judges will leverage the speed of AI, upending how we think about law enforcement, litigation, and dispute resolution.Whether these outcomes enhance or degrade democracy depends on how we shape the development and use of AI technologies. Powerful players in private industry and public life are already using AI to increase their influence, and AIs built by corporations don't deliver the fairness and trust required by democratic governance. But, steered in the right direction, AI's broad capabilities can augment democratic processes and help citizens build consensus, express their voice, and shake up long-standing power structures.Democracy is facing new challenges worldwide, and AI has become a part of that. It can inform, empower, and engage citizens. It can also disinform, disempower, and disengage them. The choice is up to us. Schneier and Sanders blaze the path forward, showing us how we can use AI to make democracy stronger and more participatory. Nathan E. Sanders is a data scientist focused on making policymaking more participatory. His research spans machine learning, astrophysics, public health, environmental justice, and more. He has served in fellowships at the Massachusetts legislature and the Berkman-Klein Center at Harvard University. Caleb Zakarin is editor of the New Books Network. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/national-security

AI is changing democracy. We still get to decide how.AI's impact on democracy will go far beyond headline-grabbing political deepfakes and automated misinformation. Everywhere it will be used, it will create risks and opportunities to shake up long-standing power structures.In this highly readable and advisedly optimistic book, Rewiring Democracy: How AI Will Transform Our Politics, Government, and Citizenship (MIT Press, 2025), security technologist Bruce Schneier and data scientist Nathan Sanders cut through the AI hype and examine the myriad ways that AI is transforming every aspect of democracy—for both good and ill.The authors describe how the sophistication of AI will fulfill demands from lawmakers for more complex legislation, reducing deference to the executive branch and altering the balance of power between lawmakers and administrators. They show how the scale and scope of AI is enhancing civil servants' ability to shape private-sector behavior, automating either the enforcement or neglect of industry regulations. They also explain how both lawyers and judges will leverage the speed of AI, upending how we think about law enforcement, litigation, and dispute resolution.Whether these outcomes enhance or degrade democracy depends on how we shape the development and use of AI technologies. Powerful players in private industry and public life are already using AI to increase their influence, and AIs built by corporations don't deliver the fairness and trust required by democratic governance. But, steered in the right direction, AI's broad capabilities can augment democratic processes and help citizens build consensus, express their voice, and shake up long-standing power structures.Democracy is facing new challenges worldwide, and AI has become a part of that. It can inform, empower, and engage citizens. It can also disinform, disempower, and disengage them. The choice is up to us. Schneier and Sanders blaze the path forward, showing us how we can use AI to make democracy stronger and more participatory. Nathan E. Sanders is a data scientist focused on making policymaking more participatory. His research spans machine learning, astrophysics, public health, environmental justice, and more. He has served in fellowships at the Massachusetts legislature and the Berkman-Klein Center at Harvard University. Caleb Zakarin is editor of the New Books Network. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/politics-and-polemics

AI is changing democracy. We still get to decide how.AI's impact on democracy will go far beyond headline-grabbing political deepfakes and automated misinformation. Everywhere it will be used, it will create risks and opportunities to shake up long-standing power structures.In this highly readable and advisedly optimistic book, Rewiring Democracy: How AI Will Transform Our Politics, Government, and Citizenship (MIT Press, 2025), security technologist Bruce Schneier and data scientist Nathan Sanders cut through the AI hype and examine the myriad ways that AI is transforming every aspect of democracy—for both good and ill.The authors describe how the sophistication of AI will fulfill demands from lawmakers for more complex legislation, reducing deference to the executive branch and altering the balance of power between lawmakers and administrators. They show how the scale and scope of AI is enhancing civil servants' ability to shape private-sector behavior, automating either the enforcement or neglect of industry regulations. They also explain how both lawyers and judges will leverage the speed of AI, upending how we think about law enforcement, litigation, and dispute resolution.Whether these outcomes enhance or degrade democracy depends on how we shape the development and use of AI technologies. Powerful players in private industry and public life are already using AI to increase their influence, and AIs built by corporations don't deliver the fairness and trust required by democratic governance. But, steered in the right direction, AI's broad capabilities can augment democratic processes and help citizens build consensus, express their voice, and shake up long-standing power structures.Democracy is facing new challenges worldwide, and AI has become a part of that. It can inform, empower, and engage citizens. It can also disinform, disempower, and disengage them. The choice is up to us. Schneier and Sanders blaze the path forward, showing us how we can use AI to make democracy stronger and more participatory. Nathan E. Sanders is a data scientist focused on making policymaking more participatory. His research spans machine learning, astrophysics, public health, environmental justice, and more. He has served in fellowships at the Massachusetts legislature and the Berkman-Klein Center at Harvard University. Caleb Zakarin is editor of the New Books Network. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/science-technology-and-society

AI is changing democracy. We still get to decide how.AI's impact on democracy will go far beyond headline-grabbing political deepfakes and automated misinformation. Everywhere it will be used, it will create risks and opportunities to shake up long-standing power structures.In this highly readable and advisedly optimistic book, Rewiring Democracy: How AI Will Transform Our Politics, Government, and Citizenship (MIT Press, 2025), security technologist Bruce Schneier and data scientist Nathan Sanders cut through the AI hype and examine the myriad ways that AI is transforming every aspect of democracy—for both good and ill.The authors describe how the sophistication of AI will fulfill demands from lawmakers for more complex legislation, reducing deference to the executive branch and altering the balance of power between lawmakers and administrators. They show how the scale and scope of AI is enhancing civil servants' ability to shape private-sector behavior, automating either the enforcement or neglect of industry regulations. They also explain how both lawyers and judges will leverage the speed of AI, upending how we think about law enforcement, litigation, and dispute resolution.Whether these outcomes enhance or degrade democracy depends on how we shape the development and use of AI technologies. Powerful players in private industry and public life are already using AI to increase their influence, and AIs built by corporations don't deliver the fairness and trust required by democratic governance. But, steered in the right direction, AI's broad capabilities can augment democratic processes and help citizens build consensus, express their voice, and shake up long-standing power structures.Democracy is facing new challenges worldwide, and AI has become a part of that. It can inform, empower, and engage citizens. It can also disinform, disempower, and disengage them. The choice is up to us. Schneier and Sanders blaze the path forward, showing us how we can use AI to make democracy stronger and more participatory. Nathan E. Sanders is a data scientist focused on making policymaking more participatory. His research spans machine learning, astrophysics, public health, environmental justice, and more. He has served in fellowships at the Massachusetts legislature and the Berkman-Klein Center at Harvard University. Caleb Zakarin is editor of the New Books Network. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/law

AI is changing democracy. We still get to decide how.AI's impact on democracy will go far beyond headline-grabbing political deepfakes and automated misinformation. Everywhere it will be used, it will create risks and opportunities to shake up long-standing power structures.In this highly readable and advisedly optimistic book, Rewiring Democracy: How AI Will Transform Our Politics, Government, and Citizenship (MIT Press, 2025), security technologist Bruce Schneier and data scientist Nathan Sanders cut through the AI hype and examine the myriad ways that AI is transforming every aspect of democracy—for both good and ill.The authors describe how the sophistication of AI will fulfill demands from lawmakers for more complex legislation, reducing deference to the executive branch and altering the balance of power between lawmakers and administrators. They show how the scale and scope of AI is enhancing civil servants' ability to shape private-sector behavior, automating either the enforcement or neglect of industry regulations. They also explain how both lawyers and judges will leverage the speed of AI, upending how we think about law enforcement, litigation, and dispute resolution.Whether these outcomes enhance or degrade democracy depends on how we shape the development and use of AI technologies. Powerful players in private industry and public life are already using AI to increase their influence, and AIs built by corporations don't deliver the fairness and trust required by democratic governance. But, steered in the right direction, AI's broad capabilities can augment democratic processes and help citizens build consensus, express their voice, and shake up long-standing power structures.Democracy is facing new challenges worldwide, and AI has become a part of that. It can inform, empower, and engage citizens. It can also disinform, disempower, and disengage them. The choice is up to us. Schneier and Sanders blaze the path forward, showing us how we can use AI to make democracy stronger and more participatory. Nathan E. Sanders is a data scientist focused on making policymaking more participatory. His research spans machine learning, astrophysics, public health, environmental justice, and more. He has served in fellowships at the Massachusetts legislature and the Berkman-Klein Center at Harvard University. Caleb Zakarin is editor of the New Books Network. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/technology

***Please subscribe to Matt's ⁠Substack⁠ at https://worthknowing.substack.com/***This isn't just another breathless AI conversation. Cybersecurity expert Bruce Schneier joins host Matt Robison to discuss the truly transformative things that are not just on the horizon, but actually starting to happen today, as artificial intelligence bleeds into politics and government. They look at the hidden upsides for fixing many of our deepest problems, but also some of the staggering problems we could increasingly encounter. They explore some of surprising ways AI is already being used in opinion polls, political campaigns, and voter engagement. Schneier's new book is Rewiring Democracy: How AI Will Transform Our Politics, Government, and Citizenship02:21 AI in Opinion Polls04:50 AI Voting Assistance13:32 AI as a Consensus Builder17:00 The Dark Side of AI in Democracy23:33 Concerns About AI and Dystopia29:48 AI Avatars in Politics33:28 AI in Fundraising and Campaign Efficiency38:19 Challenges and Ethical Considerations of AI44:03 Public AI vs. Corporate AI46:48 Conclusion: The Future of AI in Democracy

In this episode, we discuss Bruce Schneier and Nathan E. Sander's upcoming book,  Rewiring Democracy: How AI Will Transform Our Politics, Government, and Citizenship.

Have a question, comment, idea or suggestion? Send us a text.Security expert Bruce Schneier joins Campaign Trend host Eric Wilson to discuss his upcoming book "Rewiring Democracy" and how AI is already transforming politics behind the scenes. From AI-written laws in Brazil to chatbots helping local candidates engage voters, Schneier explores AI's potential as both a democratizing force and power concentrator. He introduces his "four S's" framework for understanding when AI replaces humans and addresses why deepfakes aren't the biggest concern. The conversation covers AI polling, regulation challenges, and the concept of AI-enhanced citizenship while rejecting both utopian and dystopian extremes. Visit our website: CampaignTrend.com

If Geoffrey Hinton is the Godfather of AI, then Bruce Schneier might be described as the Godfather of Security. A celebrated cryptographer and computer security expert, Schneier's latest co-authored (with Nathan Sanders) book is entitled Rewiring Democracy and speculates on how AI might transform our politics, government and citizenship. American democracy, Schneier notes, runs on archaic 1776 technology in today's digital 2025 world. Rather than fighting against AI then, he suggests, Americans should adapt this new technology to update how they do politics in the 21st century. But Schneier offers the crucial caveats that AI can neither solve fundamental human problems nor transcend ideology. "A value is just a bias we like," he warns about the impossibility of a “valueless” AI system. While cautiously optimistic about AI's potential to democratize power—from helping local politicians without resources to enabling mass citizen assemblies—he warns that without fixing underlying political and economic structures, AI will simply radically empower the already powerful. Trust the Godfather of Security on this one. AI might well turn out to be reassuringly less revolutionary than both its critics and supporters promise. 1. You're Already Using AI More Than You Think Schneier distinguishes between generative AI (ChatGPT, Claude) and the AI that's already embedded everywhere - from Google searches to map apps to spell checkers. While he rarely uses generative AI himself, he points out we're all using AI constantly without realizing it.2. AI Can't Solve Democracy's Core Problems "A value is just a bias we like," Schneier argues. AI won't transcend human ideology or provide objective answers to political questions. Democracy isn't about getting the "correct" answer - it's about the messy human process of figuring things out together.3. Trust No One with Too Much Power - Including AI Leaders When asked about trusting Sam Altman or other tech leaders, Schneier is clear: "I don't want anyone to have that sort of power, no matter who they are." The problem isn't the individual but the system that allows such concentration of power.4. Politics and Economics Matter More Than Technology AI will either democratize power or make the rich richer, but technology alone won't determine which. "If you don't have the agency politically, no amount of tech can change that," Schneier insists. Fix the political and economic structures first.5. AI-Run Government Would Be Dystopia, Even If It Worked Even if an AI could make perfect decisions about climate policy or monetary supply, Schneier argues it would be fundamentally dystopian. Democracy is the process of deciding, not just the outcome. Lose that process, and we're no longer in control of our future.Thanks for reading Keen On America! This post is public so feel free to share it. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit keenon.substack.com/subscribe

My conversation with Bruce begins at 33 mins in today after my headlines and clip show Stand Up is a daily podcast. I book,host,edit, post and promote new episodes with brilliant guests every day. This show is Ad free and fully supported by listeners like you! Please subscribe now for as little as 5$ and gain access to a community of over 750 awesome, curious, kind, funny, brilliant, generous souls Learn more about my guest Bruce Schneier Buy his books!  REWIRING DEMOCRACY: How AI Will Transform Our Politics, Government, and Citizenship I am a public-interest technologist, working at the intersection of security, technology, and people. I've been writing about security issues on my blog since 2004, and in my monthly newsletter since 1998. I'm a fellow and lecturer at Harvard's Kennedy School, a board member of EFF, and the Chief of Security Architecture at Inrupt, Inc. This personal website expresses the opinions of none of those organizations. Join us Monday's and Thursday's at 8EST for our Bi Weekly Happy Hour Hangout's !  Pete on Blue Sky Pete on Threads Pete on Tik Tok Pete on YouTube  Pete on Twitter Pete On Instagram Pete Personal FB page Stand Up with Pete FB page All things Jon Carroll  Follow and Support Pete Coe Buy Ava's Art  Hire DJ Monzyk to build your website or help you with Marketing Gift a Subscription https://www.patreon.com/PeteDominick/gift  

3pm: The Foreword:  Honesty and Honor System Farm Stands // Guest – Bruce Schneier – Author at Schneier.Com, public speaker and lecturer at Harvard’s Kennedy School and Chief of Security Architecture at Inrupt, INC. // Honesty, trust and morality in 2025 // This Day in History // 1493 - The King of England bans kissing // Noem teases liquid size changes for TSA at Hill Nation Summit

6pm: The Foreword:  Honesty and Honor System Farm Stands // Guest – Bruce Schneier – Author at Schneier.Com, public speaker and lecturer at Harvard’s Kennedy School and Chief of Security Architecture at Inrupt, INC. // Honesty, trust and morality in 2025 // This Day in History // 1493 - The King of England bans kissing // Noem teases liquid size changes for TSA at Hill Nation Summit

Please enjoy this encore of Career Notes. Chief Security Officer, Chief Analyst, and Senior Fellow at the CyberWire, Rick Howard, shares his travels through the cybersecurity job space. The son of a gold miner who began his career out of West Point in the US Army, Rick worked his way up to being the Commander of the Army's Computer Emergency Response Team. Rick moved to the commercial sector working for Bruce Schneier running Counterpane's global SOC. Rick's first CSO job was for Palo Alto Networks where he was afforded the opportunity to create the Cybersecurity Canon Hall of Fame and the Cyber Threat Alliance. Upon considering retirement, Rick called up on the CyberWire to ask about doing a podcast and he was hired on to the team. Rick shares a proud moment through a favorite story. We thank Rick for sharing his story with us. Learn more about your ad choices. Visit megaphone.fm/adchoices

"There's almost no story of the future going well that doesn't have a part that's like '…and no evil person steals the AI weights and goes and does evil stuff.' So it has highlighted the importance of information security: 'You're training a powerful AI system; you should make it hard for someone to steal' has popped out to me as a thing that just keeps coming up in these stories, keeps being present. It's hard to tell a story where it's not a factor. It's easy to tell a story where it is a factor." — Holden KarnofskyWhat happens when a USB cable can secretly control your system? Are we hurtling toward a security nightmare as critical infrastructure connects to the internet? Is it possible to secure AI model weights from sophisticated attackers? And could AI might actually make computer security better rather than worse?With AI security concerns becoming increasingly urgent, we bring you insights from 15 top experts across information security, AI safety, and governance, examining the challenges of protecting our most powerful AI models and digital infrastructure — including a sneak peek from an episode that hasn't yet been released with Tom Davidson, where he explains how we should be more worried about “secret loyalties” in AI agents. You'll hear:Holden Karnofsky on why every good future relies on strong infosec, and how hard it's been to hire security experts (from episode #158)Tantum Collins on why infosec might be the rare issue everyone agrees on (episode #166)Nick Joseph on whether AI companies can develop frontier models safely with the current state of information security (episode #197)Sella Nevo on why AI model weights are so valuable to steal, the weaknesses of air-gapped networks, and the risks of USBs (episode #195)Kevin Esvelt on what cryptographers can teach biosecurity experts (episode #164)Lennart Heim on on Rob's computer security nightmares (episode #155)Zvi Mowshowitz on the insane lack of security mindset at some AI companies (episode #184)Nova DasSarma on the best current defences against well-funded adversaries, politically motivated cyberattacks, and exciting progress in infosecurity (episode #132)Bruce Schneier on whether AI could eliminate software bugs for good, and why it's bad to hook everything up to the internet (episode #64)Nita Farahany on the dystopian risks of hacked neurotech (episode #174)Vitalik Buterin on how cybersecurity is the key to defence-dominant futures (episode #194)Nathan Labenz on how even internal teams at AI companies may not know what they're building (episode #176)Allan Dafoe on backdooring your own AI to prevent theft (episode #212)Tom Davidson on how dangerous “secret loyalties” in AI models could be (episode to be released!)Carl Shulman on the challenge of trusting foreign AI models (episode #191, part 2)Plus lots of concrete advice on how to get into this field and find your fitCheck out the full transcript on the 80,000 Hours website.Chapters:Cold open (00:00:00)Rob's intro (00:00:49)Holden Karnofsky on why infosec could be the issue on which the future of humanity pivots (00:03:21)Tantum Collins on why infosec is a rare AI issue that unifies everyone (00:12:39)Nick Joseph on whether the current state of information security makes it impossible to responsibly train AGI (00:16:23)Nova DasSarma on the best available defences against well-funded adversaries (00:22:10)Sella Nevo on why AI model weights are so valuable to steal (00:28:56)Kevin Esvelt on what cryptographers can teach biosecurity experts (00:32:24)Lennart Heim on the possibility of an autonomously replicating AI computer worm (00:34:56)Zvi Mowshowitz on the absurd lack of security mindset at some AI companies (00:48:22)Sella Nevo on the weaknesses of air-gapped networks and the risks of USB devices (00:49:54)Bruce Schneier on why it's bad to hook everything up to the internet (00:55:54)Nita Farahany on the possibility of hacking neural implants (01:04:47)Vitalik Buterin on how cybersecurity is the key to defence-dominant futures (01:10:48)Nova DasSarma on exciting progress in information security (01:19:28)Nathan Labenz on how even internal teams at AI companies may not know what they're building (01:30:47)Allan Dafoe on backdooring your own AI to prevent someone else from stealing it (01:33:51)Tom Davidson on how dangerous “secret loyalties” in AI models could get (01:35:57)Carl Shulman on whether we should be worried about backdoors as governments adopt AI technology (01:52:45)Nova DasSarma on politically motivated cyberattacks (02:03:44)Bruce Schneier on the day-to-day benefits of improved security and recognising that there's never zero risk (02:07:27)Holden Karnofsky on why it's so hard to hire security people despite the massive need (02:13:59)Nova DasSarma on practical steps to getting into this field (02:16:37)Bruce Schneier on finding your personal fit in a range of security careers (02:24:42)Rob's outro (02:34:46)Audio engineering: Ben Cordell, Milo McGuire, Simon Monsour, and Dominic ArmstrongContent editing: Katy Moore and Milo McGuireTranscriptions and web: Katy Moore

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