Podcasts about Citadel

拆解AI股神爆倉完整故事! 阿樂為何前一天就知道股市大反彈? 為什麼基金不能對沖所有風險? Citadel 如何多次成功趁火打劫?

In this episode of Money Matters, brought to you by The Greenberg Financial Group, we unpack one of the strangest weeks the market has handed us in a while. The Dow dropped 1,000 points on Wednesday and took it all back over the next two days, and if you only checked your account on Friday you would think nothing happened at all. The Fed got the blame in the headlines, but the real story was a 25 year old former OpenAI employee whose hedge fund was up roughly 1,500% in two years, ran four times leverage into the memory and data center trade, and got picked apart by other funds once his book became visible. A $40 billion valuation became an $8 billion sale to Citadel. We walk through how forced selling moves an entire market, and why the lesson is not about being right but about staying solvent long enough to find out. Dean opens the show on volatility and why selling into weakness leaves you with the hardest question in investing, which is when you plan to get back in. He also lays out something that separates how we work from most firms. We do not plan for your retirement. We plan for your lifestyle changes, whether that means a $40,000 family vacation every year, a remodel, more travel, or simply never stopping work at all. He walks through what that planning actually covers, from Social Security timing to Medicare costs to how much you can spend each year without running out, and why we hand you the completed plan for free whether or not you ever do business with us. We also get into the capital expenditure fear that has dominated the tape. Meta's free cash flow collapsed and the stock got clobbered 10% despite revenue up 28%. Google went negative on free cash flow for the first time in its history. Then Amazon reported and its CEO talked openly about the profitability of data centers, and suddenly Google popped 7% on Friday. Every hyperscaler CEO is saying go, and investors keep saying stop, which means somebody is wrong. We look at Apple's 9% drop on a memory shortage that is now showing up in laptop and iPad prices, Microsoft's 24.6% month, the buyback restriction lifting at the end of 2026, and why the 30 year Treasury at a 19 year high and mortgage rates at 6.66% may matter more to this market than any earnings report. The second hour takes a different turn. Dean brings in attorney Mike Story, who represents Tucson police officers through their unions and responded at 2:30 in the morning to the mass shooting outside Empire Pizza in downtown Tucson. Mike walks through what actually happened that night, why a single shot from a seven year veteran ended the threat without endangering the crowd behind the gunman, and the question that officer asked him first. Am I going to prison for this. We talk about the shooter's prior gun offense, how that case was handled, what a 30% smaller police force means for a downtown everybody says they want to save, and what it would take to bring it back. We close with SpaceX heading into its first earnings report and the insider lockup expiring days later with 35% of the float sold short, energy leading all S&P sectors with XLE up 35% on the year, an options based income strategy that Todd and Dylan have been researching directly with the portfolio managers for its 60/40 tax treatment, and why Trump accounts may be the most powerful generational wealth tool available to a grandparent right now. Plus a Tucson fun fact about how the University of Arizona ended up here in 1885 because our representative showed up late. Our next free interactive financial planning seminar is Friday, August 21st from 11:30 to 2:00 at La Paloma Country Club. Lunch is included and you will see our full financial planning process start to finish. Register at www.GreenbergFinancial.com under the resource tab. If you have been thinking about taking us up on the free financial plan, this is the kind of clarity it brings. If you would like to contact us to learn more about our firm, our seminars, and our process - call us at 520.544.4909 or go to our website at www.Greenbergfinancial.com or email us at Contact@Greenbergfinancial.com Disclaimer: This show discusses different investment products and strategies. Every product and strategy has some type of inherent risk and we strongly encourage our listeners to properly understand these risks. Past performance is no guarantee of future performance. The information presented on this program is believed to be factual and up-to-date, but we do not guarantee its accuracy and it should not be regarded as a complete analysis of the subjects discussed. The material covered on this program does not involve the rendering of personalized investment advice, but is for general information purposes only. A professional advisor should be consulted before implementing any of the options presented. Greenberg Financial Group is registered as an investment advisor with the SEC and only transacts business in states where it is properly registered, or is excluded or exempted from registration requirements.

On this episode of our new market wrap show Last Call, we examine the hidden rotation beneath calm stock market indexes, including sharp AI and semiconductor volatility, small-cap strength, forced fund liquidations, higher rates and changing Federal Reserve guidance. Jack Forehand and Matt Zeigler are joined by Jim Paulsen, Ben Hunt, Brent Kochuba, Cameron Dawson and Dave Nadig to discuss stock market correction risk, the economics of the AI data center buildout, options flows, market leverage, regulation and what could drive volatility next.Follow Last Call on Spotify⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Follow Last Call on Apple Podcasts⁠Topics coveredWhy market indexes can hide sharp rotation, dispersion and volatility in semiconductors and high-beta technology stocksJim Paulsen's Policy Pain framework linking oil, Treasury yields, dollar strength and lagged effects on stocks, bonds and economic growthWhy technology stocks could enter a bear market while old-economy sectors, small caps and value stocks hold upBen Hunt's World War AI thesis comparing the AI infrastructure buildout with inflation-adjusted World War II spendingHow hyperscalers, equity issuance, private credit and government financing could crowd out consumers and businessesWhy data centers could consume nearly one quarter of U.S. electricity and lead to higher prices, rationing and government interventionWhat the Situational Awareness fund liquidation and Citadel portfolio transaction reveal about forced market flowsHow options correlations and narrow market breadth can separate a technical rebound from a fundamental AI bottomRisks from speculative retail investments, weakened regulators, leverage and cyclical semiconductor profit marginsWhy reduced Fed forward guidance could create surprise policy decisions and greater algorithmic market volatilityTimestamps00:00 Market rotation and AI volatility beneath the indexes04:07 Jim Paulsen on Policy Pain and market vulnerability09:23 Why tightening hurts stocks before helping bonds14:23 Tech bear market risk and a possible leadership shift18:23 Ben Hunt on World War AI, private credit and systemic risk26:00 Data center electricity demand and the energy constraint31:29 Brent Kochuba on the Situational Awareness liquidation36:00 The forced buying behind the AI stock rebound40:00 Why the liquidation bounce may not signal an AI bottom44:00 How forced flows distort fundamental market narratives48:00 Retail investing pitches, liquidity and cycle FOMO52:00 Deregulation by destaffing at the SEC and CFTC56:00 Semiconductor operating leverage and fragile S&P 500 margins01:00:07 Jack's grievance with the YouTube algorithm01:04:29 What happens when the Fed stops giving forward guidance01:08:34 How markets could react to a surprise Fed decisionLearn more about the Excess Returns podcast network:⁠https://excessreturns.co⁠No information discussed in this podcast should be construed as investment advice. Securities discussed may be held by the hosts and guests, their firms or their clients.

“The human part is 99 percent. The AI part is maybe 1 percent — even if 100 percent of the manuscript was dumped onto the page by AI.” — Keith Teare on writing with AI What is “real” authorship in our AI age? For That Was The Week publisher Keith Teare, all authorship, even the most AI-enabled, is real. It's the cave-dwelling Luddites who are the sloppy ones. For Keith, true creativity, in our age of Claude and Gemini, almost requires the use of AI. A couple of weeks ago Keith confessed, or perhaps boasted, that he is writing an AI-assisted book entitled Who Owns Intelligence. This week, publishing discovered what happens when others play the same game without public acknowledgement. So we have the case of the “red-hot” debut novel, fought over by fourteen publishers, dropped overnight when the agent found AI in the mix. “It's a fantastic book,” the book's agent acknowledged, before firing its less than transparent author. Which, for Keith, is precisely the scandal. His position, argued in this week's editorial “AI Detected,” is pretty absolute. Nothing is written by AI, Keith argues, because AI has no will. It doesn't produce a single word unless asked. The human part is 99 percent, he says, even when 100 percent of the manuscript is spat out by the machine. So the real sin isn't using AI but not acknowledging its use. So, on Who Owns Intelligence, Keith makes it crystal clear that “AI was heavily used in the writing of this book.” I'm not so sure. Amazon is now infested with AI slop that requires no authorship. Besides, Keith clearly has no love of reading books. He admits to reading only a single volume in the last couple of years. And he's still smarting from being, in his mind at least, ripped off by the publishing industry for his last published book some 40 years ago. He no longer needs to read books, he pronounces, because his world-view is already set. For a man who thinks he knows everything, AI isn't scary. Maybe we should rename him Claude. Postscript. Full Keith-style disclosure: these shownotes were produced with the help of Anthropic. When I fed Claude my draft, it responded: “‘Maybe we should rename him Claude' is a closer I'm contractually obliged to enjoy.” Nothing, as Keith says, is written by AI. Although some algorithms seem to have cheeky opinions of their own. Maybe Anthropic should rename it Keith. Five Takeaways •       Nothing Is Written by AI. Keith's absolutism starts from the machine's lack of will: AI won't produce a word unless a human asks. His Who Owns Intelligence — fifteen chapters, arguments, and references — was structured in ninety minutes from years of thinking; the AI produced a manuscript he then shaped, like a color grader working sliders in Photoshop. The human part is 99 percent even when the machine types every word. And authorship was never solitary anyway: Plato's contemporaries thought writing itself degraded ideas, and no publisher will touch a book that hasn't passed through an editor and a subeditor. If editing doesn't change authorship, Keith asks, why would AI?•       Transparency, Not Technology. The week's cautionary tale: a debut novel fought over by fourteen publishers, dropped when the agent discovered AI — while conceding, in the same breath, “it's a fantastic book.” For Keith the scandal is concealment, not composition: own the tool, as he now does with a strapline under his byline. The detection regime, meanwhile, is collapsing on its own inaccuracy — universities dropped their AI detectors this week, and Substack's new detection partner scored Keith's human-shaped editorial as 100 percent AI when a fair reading was 40. Cheating exists only against obsolete rules: the question is moving from did you use AI to how well did you use it.•       “The Publishing Industry Is Basically a Scam.” Keith's response to Andy Hunter's ban on AI-generated books — from last week's Keen On interview — was one word: outrageous. There is no such thing as an AI-written book, only good and bad ones; and the true victim of publishing is not the bookstore but the author. Exhibit A: his own 1988 book sold 50,000 copies at £3.99 and earned him roughly £10,000 — about 5p a copy — while Penguin took the rest. The future he wants is direct, author-to-reader, without the middlemen. As for “organic” artisanal literature: real, elite, and tiny — the new vinyl, which is itself cut these days from digital masters.•       Sharks, Playing Their Own Book. Leopold Aschenbrenner — fired young from OpenAI, transfigured by one prophetic essay into the “Nostradamus of AI” — saw his $45 billion, heavily leveraged Situational Awareness fund lose $600 million, get margin-called, and sell to Ken Griffin's Citadel. (He is, Keith suspects, still super rich.) Zuckerberg's sudden conversion to open source as “the distribution of wealth to the people” struck us both as a bit rich; Amodei's “I'm not against open source as long as it's safe” — from a man on record that no AI is safe — translates as: against. Keith's deeper complaint, from an admiring Claude user: you can't trust Dario. And China, hardware-constrained, has made open source its national strategy — undermining American foundation-model revenue one cheap routed prompt at a time.•       Money Won't Matter by 2036? Elon Musk predicts money becomes irrelevant within a decade; Vinod Khosla — our post of the week — thinks he might just have a point. Keith, ever the economics tutor, reaches for tendency and ceteris paribus: money is a means of exchange and a store of value, and if abundance drives the labor-value of things toward zero, its irrelevance is not illogical — though between tendency and reality lies a whole ton of variables. I bet the opposite: that by 2036 money will matter more than ever. The problem, we discovered, is the stake — you can't bet money on money not mattering. Wives and children were proposed and hastily withdrawn (“We'd be losing, Andrew”). Loser buys dinner — at a free restaurant. About the Co-Host Keith Teare is the publisher of That Was The Week, the essential weekly tech newsletter, and founder and CEO of SignalRank Corporation. A serial entrepreneur — co-founder of, among others, EasyNet and RealNames — he was present at the creation of the UK internet and has spent four decades at the intersection of technology, capital, and ideas. He joins Keen On America every Sunday to make sense of the week in tech. His AI-assisted book in progress is titled Who Owns Intelligence. References: •       That Was The Week — Keith's newsletter, including this week's editorial, “AI Detected.”•       The Wall Street Journal — on the red-hot debut novel at the center of publishing's AI mystery: fourteen publishers, one discovery, one dropped author.•      ...

Dupree Financial Group Blog  ·  The Tom Dupree Show From This Week’s Episode Retirement Investing  ·  August 1, 2026 Is Your Retirement Portfolio Too Concentrated? A 25-year-old hedge fund manager lost roughly $35 billion in a matter of days this week. Here’s what his leverage and the market’s concentration in seven stocks have to do with your retirement account. By Tom Dupree, Founder, Dupree Financial Group  |  dupreefinancial.com  |  859-233-0400 This week, a 25-year-old former OpenAI researcher named Leopold Aschenbrenner watched roughly $35 billion disappear from his hedge fund in a matter of days. Two years ago, he wrote a 165-page essay predicting the future of artificial intelligence with such confidence that Silicon Valley treated it like scripture. This week, his fund — built on borrowed money layered on top of a handful of AI stocks — got forced into a fire sale to Ken Griffin’s Citadel at a steep discount. It’s a dramatic story. But here’s the direct answer to the question that actually matters for your retirement: if most of your money sits in a plain S&P 500 index fund, you may be more concentrated in a handful of the same stocks than you realize — and that concentration, not any single hedge fund’s collapse, is the real thing worth understanding before your next portfolio review. You don’t need borrowed money or a 165-page manifesto to be exposed to this. You just need to own “the market” and assume that means you’re spread across 500 different companies. Key Takeaways Leverage magnifies both directions. Borrowing money to buy investments can boost gains on the way up, but it can wipe out capital just as fast on the way down. That’s the entire story of this week’s hedge fund collapse. Seven stocks now make up a large share of the S&P 500. Depending on the week you check, the “Magnificent Seven” technology stocks account for somewhere between a third and roughly 40% of the entire index’s value. Owning an index fund is not automatically owning a diversified portfolio. A market-cap-weighted index gives its biggest companies the biggest influence — so when those companies wobble, so does “the market.” Know what you own and why you own it. That’s not a slogan — it’s the single most useful question a retiree can ask before the next headline-grabbing selloff. Why This Week’s Story Is Bigger Than One Hedge Fund Every generation produces an investor who seems untouchable — brilliant, early to a trend, riding a wave everyone else is still arguing about. Aschenbrenner’s fund, Situational Awareness, reportedly grew from roughly $200 million to as much as $45 billion in under two years, largely on concentrated bets in AI infrastructure names. Then, using leverage reported as high as 400% — meaning roughly four borrowed dollars for every dollar of the fund’s own capital — a sharp pullback in a handful of semiconductor and AI stocks triggered margin calls his prime brokers couldn’t ignore. That’s the mechanical part, and it’s worth understanding in plain English: when you borrow against an investment and that investment drops in value, your loan doesn’t shrink with it. At some point the lender requires more collateral — a margin call — and if you can’t provide it, your shares get sold for you, often at the worst possible moment. There’s no easy way around that math. It requires diligence, not confidence. Most retirees reading this aren’t using 400% leverage. But there’s a quieter version of the same concentration problem sitting inside a lot of 401(k)s and IRA rollovers, and it doesn’t require a single dollar of borrowed money to hurt you. What the Numbers Actually Show According to CNBC’s reporting on the collapse, Aschenbrenner’s fund held roughly $45 billion in assets at its peak, before margin calls forced the sale of its leveraged public stock positions — including major holdings like SK Hynix and CoreWeave — to Citadel at a discount, with the fund’s overall assets falling to around $10 billion within about 30 trading days (CNBC). TechCrunch’s coverage confirms Aschenbrenner had no prior professional trading experience before launching the fund in 2024, and that the losses came from both AI stocks falling and short positions in software companies moving the wrong way at the same time (TechCrunch). Meanwhile, the broader market has its own version of this concentration story. Reporting from Forbes notes that the “Magnificent Seven” technology stocks made up roughly a third of the S&P 500’s total market capitalization heading into 2026, with some advisors calling the resulting concentration risk a “legitimate concern” (Forbes). Separate reporting from CNBC put the figure as high as 35% to 40% of the index in recent trading, prompting some strategists to recommend equal-weighted alternatives to reduce that concentration (CNBC). The SEC’s own investor education office has published plain-language guidance on why borrowing to invest carries risks that go beyond the investment itself — including the fact that a broker can sell your securities to meet a margin call without waiting for you to act, and can do so without advance notice (SEC Investor.gov). It’s the kind of guardrail worth reading once, even if you never plan to use margin yourself. “Leverage is a thing to be used very judiciously and very carefully, because if you use it in a way that’s irresponsible, it can cost you everything.” — Tom Dupree The Reframe: This Isn’t a Bet on Whether AI Wins or Loses Dupree Financial Group’s Take Most of the commentary this week has been framed as a debate: Is AI spending going to pay off, or is it a bubble? That’s an interesting argument, and reasonable people disagree about it — Microsoft’s stock jumped double digits on one earnings report this year, while Oracle’s bonds have drawn scrutiny over its own AI-related spending. But that debate is largely beside the point for a retiree building income for the next 40 or 50 years. The actual lesson isn’t “buy AI stocks” or “avoid AI stocks.” It’s that when a market’s returns get concentrated in a small number of companies, your risk gets concentrated right along with it — whether you meant it to or not. That’s exactly why our approach starts with cash flow analysis, not headlines: dividend-paying companies across sectors like insurance, telecommunications, and financials keep generating income whether or not seven technology companies are having a good month. You get paid to wait, in good markets and choppy ones, instead of hoping a narrow slice of the market keeps carrying the whole index. What This Looks Like in Practice We build separately managed accounts around companies with a history of paying and growing their dividends, purchased when they’re out of favor and less expensive — not around chasing whichever seven stocks are dominating the headlines that quarter. Bonds play a role too: current income, lower volatility, and dry powder to buy good companies when the market temporarily marks them down for reasons that have nothing to do with their underlying business. None of this means avoiding growth, and it doesn’t mean the S&P 500’s biggest companies are bad businesses — several of them are genuinely excellent. It means not letting one basket, however impressive, decide the outcome of your retirement. All investing involves risk, including the possible loss of principal, and no strategy removes that risk entirely. The goal is to understand it, size it appropriately, and build income you don’t have to sell into a downturn to access. Five Things to Check in Your Own Portfolio 1Pull up your 401(k) or IRA’s top ten holdings. Most plan providers list this on your statement or online dashboard. If you don’t see it, call and ask — it’s your money, and you’re entitled to know. 2Add up what percentage those top ten represent. If it’s a plain S&P 500 index fund, expect a meaningful chunk of your total to be concentrated in a handful of names, most of them technology companies. 3Ask whether that concentration matches your risk tolerance at your stage of life. A 35-year-old accumulating wealth can absorb more concentration risk than someone drawing income in retirement. 4Check whether you’re using any form of leverage or margin, even indirectly through certain funds or products, and make sure you understand exactly what happens if those positions move against you. 5Get a second set of eyes on the whole picture. It’s easy to know your account balance and much harder to know what’s actually driving it. That’s the gap a complimentary portfolio review is built to close. Frequently Asked Questions What is “concentration risk” in a stock market index? Concentration risk means a large share of an index’s total value — and therefore its performance — comes from a small number of companies. In a market-cap-weighted index like the S&P 500, the biggest companies carry the most influence, so a downturn in just a handful of names can drag down the whole index. Why did Leopold Aschenbrenner’s hedge fund lose so much money so quickly? Reporting indicates the fund used leverage as high as 400% on concentrated AI stock positions. When those stocks declined, the borrowed money amplified the losses, triggering margin calls that forced a distressed sale of the fund’s holdings within about a month. Should retirees stop investing in S&P 500 index funds? Not necessarily — index funds remain a legitimate, low-cost building block. The point is to understand what you actually own inside that fund, including how concentrated it has become, rather than assuming “index fund” automatically means “diversified.” What does “leverage” mean in plain English? Leverage means borrowing money to increase the size of an investment beyond what your own capital could buy. It can amplify gains, but it amplifies losses the same way — and if the investment’s value drops enough, the loan doesn’t shrink to match it. How can I tell how concentrated my own retirement portfolio really is? Start by looking up your fund’s top ten holdings and what percentage of the total they represent — most providers publish this. If you’re unsure how to interpret it, a portfolio review with an advisor can walk through what you actually own and why. The Close By the time you read this, Leopold Aschenbrenner’s fund will likely have faded from the headlines, replaced by whoever’s turn it is next — because, as history keeps showing us, there’s always a next one. But the question his week left behind isn’t really about him. It’s about whether you know what you own, and whether you’d be able to answer calmly if your own portfolio had a bad week. That’s the whole point of retiring on income instead of hope: you don’t need to guess right about which seven stocks win. You need a plan that keeps paying you regardless. Keep Learning Listen to the full episode — hear Tom, James Dupree, and Michael Dawahare walk through the Mag Seven earnings debate and this week’s market moves in more detail. Learn more about Dupree Financial Group — our fee-only, fiduciary approach and the team behind it. Schedule a complimentary portfolio review — see exactly how concentrated your own accounts are today. Tom Dupree Tom Dupree is the founder of Dupree Financial Group, a fee-only, fiduciary Registered Investment Advisory firm based in Lexington, Kentucky. He has spent 48 years in the investment business, starting as a municipal bond salesman in the late 1970s, and hosts The Tom Dupree Show, a weekly radio and podcast program covering the financial topics that matter most to retirees. About The Tom Dupree Show The Tom Dupree Show is hosted by Tom Dupree, founder of Dupree Financial Group and a 47-year veteran of the investment business. Each episode covers the financial topics that matter most to retirees and those approaching retirement — in plain English, without the Wall Street spin. Dupree Financial Group is a fee-only, fiduciary Registered Investment Advisory firm based in Lexington, Kentucky. The firm manages separately managed accounts focused on income-generating, dividend-paying portfolios — no products sold, no commissions, no conflicts of interest. Past episodes are available at dupreefinancial.com under the Radio tab. Schedule a Complimentary Portfolio Review If you’re not sure whether your retirement account is more concentrated in a handful of stocks than you’d like — we’ll take a look. No charge. No pressure. Just an honest conversation about what you own and whether it’s working for you. Call: 859-233-0400  |  Visit: dupreefinancial.com All investing involves risk, including the possible loss of principal. Past market performance discussed above refers to historical index and company data, not to the performance of any Dupree Financial Group account. Dupree Financial Group  ·  Fee-only. Fiduciary. 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A $35B Hedge Fund Lesson | Dupree Financial Group appeared first on Dupree Financial.

Fino a poche settimane fa, Leopold Aschenbrenner era considerato uno dei nuovi geni di Wall Street. Il suo hedge fund, Situational Awareness, aveva guadagnato oltre il 1.000% dalla nascita e circa il 439% nei primi sei mesi del 2026. Poi è arrivata la correzione delle azioni legate all'intelligenza artificiale: margin call, vendite forzate e una perdita del 67% in un solo mese. In questo episodio ricostruiamo la crescita e il collasso del fondo, spieghiamo come funzionano leva finanziaria, collateral e richieste di margine, e analizziamo perché Situational Awareness sia stato costretto a trasferire gran parte del proprio portafoglio pubblico a Citadel. Ma un fondo può perdere due terzi del proprio valore e rimanere comunque positivo da inizio anno? E cosa ci dice davvero una performance eccezionale sulla qualità di un gestore? Le lezioni complete per gli investitori, insieme all'analisi delle vendite forzate, del precedente di LTCM e del successivo relief rally, sono disponibili nel Market Outlook del report di luglio della Membership di Investire Semplicemente. Link: https://www.skool.com/investire-semplicemente-7032/plans Learn more about your ad choices. Visit megaphone.fm/adchoices

This week: Situational Awareness—an A.I.-forward hedge fund led by a former wunderkind and OpenAI employee—was tanking fast until a rival fund stepped in. Felix Salmon, Elizabeth Spiers, and Emily Peck explain the series of events that led to Citadel swooping in to buy the bulk of assets held by 25-year-old Leopold Aschenbrenner's investment firm. Then, it's time to talk about the “crack spread”—aka the reason we aren't seeing relief at the gas stations despite steadying oil prices. And finally, the hosts discuss the backlash to the SEC's proposal to do away with its quarterly report requirement. In the Slate Plus episode: Is Cheaper Gas Worth Your Time?Want to hear that discussion and hear more Slate Money? Join Slate Plus to unlock weekly bonus episodes. Plus, you'll access ad-free listening across all your favorite Slate podcasts. You can subscribe directly from the Slate Money show page on Apple Podcasts and Spotify. Or, visit slate.com/moneyplus to get access wherever you listen. Podcast production by Jessamine Molli. Hosted on Acast. See acast.com/privacy for more information.

This week: Situational Awareness—an A.I.-forward hedge fund led by a former wunderkind and OpenAI employee—was tanking fast until a rival fund stepped in. Felix Salmon, Elizabeth Spiers, and Emily Peck explain the series of events that led to Citadel swooping in to buy the bulk of assets held by 25-year-old Leopold Aschenbrenner's investment firm. Then, it's time to talk about the “crack spread”—aka the reason we aren't seeing relief at the gas stations despite steadying oil prices. And finally, the hosts discuss the backlash to the SEC's proposal to do away with its quarterly report requirement. In the Slate Plus episode: Is Cheaper Gas Worth Your Time?Want to hear that discussion and hear more Slate Money? Join Slate Plus to unlock weekly bonus episodes. Plus, you'll access ad-free listening across all your favorite Slate podcasts. You can subscribe directly from the Slate Money show page on Apple Podcasts and Spotify. Or, visit slate.com/moneyplus to get access wherever you listen. Podcast production by Jessamine Molli. Hosted on Acast. See acast.com/privacy for more information.

This week: Situational Awareness—an A.I.-forward hedge fund led by a former wunderkind and OpenAI employee—was tanking fast until a rival fund stepped in. Felix Salmon, Elizabeth Spiers, and Emily Peck explain the series of events that led to Citadel swooping in to buy the bulk of assets held by 25-year-old Leopold Aschenbrenner's investment firm. Then, it's time to talk about the “crack spread”—aka the reason we aren't seeing relief at the gas stations despite steadying oil prices. And finally, the hosts discuss the backlash to the SEC's proposal to do away with its quarterly report requirement. In the Slate Plus episode: Is Cheaper Gas Worth Your Time?Want to hear that discussion and hear more Slate Money? Join Slate Plus to unlock weekly bonus episodes. Plus, you'll access ad-free listening across all your favorite Slate podcasts. You can subscribe directly from the Slate Money show page on Apple Podcasts and Spotify. Or, visit slate.com/moneyplus to get access wherever you listen. Podcast production by Jessamine Molli. Hosted on Acast. See acast.com/privacy for more information.

Oil crashed on peace, stocks crashed anyway, a 557% profit was a "miss," and a hedge fund called Situational Awareness got blindsided. A week of maximum noise — and the three signals underneath that actually matter.Hypernormal Times on Substack. For your capital markets training needs, visit my friends at Finance Talking.The market fell a fifth and rose a fifth in the same week, on no change in the facts — so this episode strains out the churn and holds up what actually changed.We start with the noise: a ceasefire nobody signed, "peace broke out and stocks crashed anyway," and the record round-trip driven by a leverage unwind — including the week's best story, the hedge fund Situational Awareness, run by the ex-OpenAI author of the famous "see-it-coming" AI essay, getting caught spectacularly unaware and dumping its book to Citadel at the bottom, right before those shares ripped. Then the three signals worth keeping: the AI reckoning turned out to be a sorting, not a crash (Microsoft and Amazon proved the return; Meta didn't); the feared AI glut is, at the physical level, a shortage — one now capping Apple's revenue and turning the Bank of Japan hawkish; and the great bifurcation went concrete, with China floating its own memory champion (CXMT, +472%), building its own chip-making machines, and pulling a piece of Tesla across the US–China line. Plus a Fed chair whose silence the bond market repriced as a credibility shock.Never investment advice.In this episodeWhy the week's violent round-trip was noise, not signal — and how to tellSituational Awareness vs Citadel: a thesis meets a balance sheet at the bottomThe 557% profit that counted as a miss — and the bar detaching from realityThe reckoning as a sorting: Microsoft/Amazon prove the return, Meta doesn't; "free cash flow" runs the tapeThe AI glut that's actually a shortage — Apple can't get chips, and the BoJ turns hawkishThe great bifurcation: CXMT +472%, China's own lithography, Tesla splitting off ChinaWarsh holds, the 30-year hits a 19-year high, and the market calls his bluffAI bubble, AI reckoning, is AI a bubble, AI 2008 vs dot-com, Situational Awareness hedge fund, Leopold Aschenbrenner, Citadel, SK Hynix earnings, 557% profit, Microsoft Azure earnings, Amazon cloud, Meta capex, Apple chip shortage, memory shortage 2028, Samsung, CXMT IPO, China semiconductors, ASML lithography, Tesla SpaceX merger, Kevin Warsh Fed, 30-year Treasury yield, Bank of Japan hawkish, macro podcast, markets podcast, HyperNormal Report, Jeremy McKeown.This podcast explores stocks, markets, and capital, examines the role of gold in finance, unpacks tax policy and economics, discusses pathways to financial freedom and retirement, explains how interest rates affect investing, features insights from financial advisers, analyzes inflation, recession, and market volatility, covers the actions of central banks, evaluates different assets, addresses inheritance planning, reviews portfolio construction with bonds and an isa, assesses long-term returns and allocation strategies, explores macro trends, and helps listeners understand risk and pensions.

Amazon reported accelerated cloud growth in the quarter to the end of June, and JPMorgan has walked into another football firestorm as it works on a plan with Fifa. Plus, Citadel bought Situational Awareness equity holdings after the hedge fund suffered steep AI losses, and DR Congo's cobalt boom carries an unwanted cargo: uranium. Mentioned in this podcast:Amazon shares surge higher after cloud business grows 37%How JPMorgan walked into another football firestormEuropean nations to boycott World Cup in protest at Fifa's plansCitadel buys Situational Awareness equity holdings after steep AI lossesDR Congo's cobalt boom carries an unwanted cargo: uraniumSave 10% on tickets with the code FTPodcast. Visit ft.com/festival to find out more.Want to get in touch? Email us at podcasts@ft.comNote: The FT does not use generative AI to voice its podcasts The FT News Briefing is produced by Victoria Craig, Sonja Hutson, Saffeya Ahmed, Katya Kumkova, and Fiona Symon. Our editor is Marc Filippino. Our show is mixed by Sam Giovinco and Alex Higgins. Additional help from Gavin Kallmann, Michael Lello, Peter Barber and David da Silva. Our intern is Cole van Miltenburg. Our executive producer is Topher Forhecz. Flo Phillips is the FT's global head of audio. The show's theme music is by Metaphor Music.Read a transcript of this episode on FT.com Hosted on Acast. See acast.com/privacy for more information.

Joel and Stephen share their thoughts on the new trailer for Avengers: Doomsday, and speculate on what MARVEL fan expectations should be, then dive into Season 2 of the live action Avatar The Last Airbender from Netflix, and look to the starts with a new LEGO set.Show notes for The Citadel Cafe are here:https://thecitadelcafe.com/2026/07/30/the-citadel-cafe-507-avatar-the-last-doomsday/Join The Citadel Cafe Discord community!http://Patreon.com/TheCitadelCafeThe Citadel Cafe YouTube:https://youtube.com/thecitadelcafeMusic for The Citadel Cafe by Kevin MacLeod (incompetech.com) licensed under Creative Commons by Attribution 4.0 Hosted on Acast. See acast.com/privacy for more information.

Signal vs Noise: Jackson Mikalic, Michael Tanguma, Brian Cubellis, and Liam Nelson put four stories on the shot clock. They break down Leopold Aschenbrenner's Situational Awareness fund blowing up and getting scooped by Citadel as South Korea's market sheds roughly $2 trillion in 40 days, Elizabeth Warren and Donald Trump agreeing to scrap the debt ceiling, Fauci pleading the fifth before Rand Paul, and open source tools like Granola and Whoop getting reverse engineered overnight. Where's the signal, and where's the noise?---

The Investing Power Hour is live-streamed every Thursday on the Chit Chat Stocks Podcast YouTube channel at 5:00 PM EST. This week we discussed: (00:00) Introduction (02:31) AI Hedge Fund 'Situational Awareness' Sells Major Holdings (14:44) Amazon's Impressive Quarterly Results and Growth Drivers (17:10) Google Cloud and Cloud Revenue Growth Insights (21:52) Microsoft Earnings: Cloud, Office, and Gaming Performance (26:36) Korean Market Volatility and Degenerate Trading Culture (35:01) Robinhood's Prediction Markets and Trading Incentives (40:21) Meta's Earnings: Spending, AI, and Metaverse Investments (50:23) Luxury Goods Earnings: Hermes, Ferrari, and LVMH (59:25) Insider Trading and Political Connections in Stocks (01:01:21) NVIDIA's Support for OpenAI and Data Center Projects (01:02:06) Market Bubble Indicators and Blow-Off Tops ***************************************************** Subscribe to Emerging Moats Research: emergingmoats.com  ********************************************************************* Chit Chat Stocks is presented by Interactive Brokers. Get professional pricing, global access, and premier technology with the best brokerage for investors today:  https://www.interactivebrokers.com/  Interactive Brokers is a member of SIPC.  ********************************************************************* Fiscal.ai is building the future of financial data. With custom charts, AI-generated research reports, and endless analytical tools, you can get up to speed on any stock around the globe. All for a reasonable price.  Use our LINK and get 15% off any premium plan: ⁠https://fiscal.ai/chitchat  ********************************************************************* Disclosure: Chit Chat Stocks hosts and guests are not financial advisors, and nothing they say on this show is formal advice or a recommendation. Learn more about your ad choices. Visit megaphone.fm/adchoices

Those Long-Term Chip Deals May Not Be as Secure as Investors Are Led to Believe When you listen to memory chip companies like Samsung Electronics, SK Hynix, and Micron Technology discuss their businesses, they often make it sound like customer contracts—some extending as long as five years—are essentially set in stone. Unfortunately, that's not entirely true. Yes, these companies have long-term agreements in place, but contracts in this industry are often renegotiated when market conditions change. If demand for memory chips weakens significantly, chip manufacturers have a strong incentive to work with their customers rather than strictly enforce every contractual commitment. The reason is simple: preserving long-term customer relationships is often far more valuable than maximizing short-term revenue. Imagine a customer that suddenly doesn't need as many chips because its own sales have slowed. If a supplier forces that customer to accept unwanted inventory, those chips may simply sit in a warehouse until demand recovers. By the time the customer needs additional chips, it may choose to reduce future orders or move business to a competitor that proved to be more flexible during difficult times. Competitors are always looking for opportunities to gain market share. If one supplier refuses to work with its customers, another is usually willing to offer better pricing or more favorable terms. Losing a major customer over a rigid interpretation of a contract can cost far more in future profits than making temporary concessions during a downturn. This isn't just theory and it has happened before. During the COVID-era, many long-term agreements were adjusted as demand shifted. Rather than forcing customers to take products they no longer needed, suppliers often renegotiated delivery schedules and purchasing commitments to preserve long-term partnerships. The same principle applies across many industries. Companies frequently modify or delay large commercial agreements when business conditions change. While contracts provide a framework, successful businesses understand that maintaining trust with key customers is often more important than enforcing every clause to the letter. Investors should remember that a signed contract does not necessarily guarantee future revenue will be recognized exactly as originally planned. Management teams often emphasize the value of their long-term agreements during earnings calls, but those agreements can evolve if market conditions deteriorate. At the end of the day, great businesses understand that customer relationships are built over years but can be damaged in a matter of weeks. In many cases, giving a customer flexibility during a downturn is a much better investment than insisting on strict contract enforcement. That's why investors should view long-term chip contracts as valuable, but not invincible.   Why Index Investing Could Leave You Disappointed Long Term I often hear people say, "Just buy the S&P 500 and forget about it. You'll be fine." While that sounds simple, investing is rarely that easy. Many investors don't fully understand how an index works or why it has performed so well in recent years. The S&P 500 has been driven largely by a handful of technology and AI companies. By blindly investing in the index, many people are simply participating in a momentum strategy without realizing it. Very little thought is given to what those 500 companies are actually worth. There is no effort to trim positions that have become extremely expensive or overly concentrated. As valuations climb, the index simply gives those companies an even larger weighting, leaving investors with greater exposure to the stocks that have already gone up the most. Some people respond by saying, "I won't put everything in the S&P 500. I'll diversify into other index funds." But once you go down that road, investing becomes much more complicated and you'll likely underperform the S&P 500. Should you own an international index? A European index? A bond index? A growth index? A value index? Small-cap funds? REITs? There are hundreds of ETFs and mutual funds to choose from. Now you have another challenge: deciding how much to allocate to each one. When your portfolio declines will you understand why? More importantly, will you know what to do next? Many investors don't, and that uncertainty often leads to emotional decisions at exactly the wrong time. This is why I prefer managing a portfolio of individual value-oriented stocks, combined with money market funds and selected real estate investment trusts (REITs). That approach still provides diversification, but I understand what each investment is worth and why I own it. In my view, that's a much better foundation than owning five or ten different index funds without truly understanding what's inside them or how they're valued. Another common argument for index investing is lower fees. While fees certainly matter, they shouldn't be the only factor. The number that ultimately matters is your total return after all fees and expenses. A lower fee doesn't automatically translate into better long-term performance. If you own index funds, take some time to look under the hood. Do you really understand what you own? Do you know which sectors dominate your portfolio, which companies make up the largest holdings, and how expensive those businesses are today? If the answer is no, don't assume you'll be comfortable when the market experiences its next major decline. Investors who don't understand what they own are often the first to panic, and that confusion can lead to costly investment mistakes.   The U.S. economy is still in much better shape than many people think. This week brought three major events for investors: GDP, PCE inflation, and the Federal Reserve meeting. While the headlines may have sounded mixed, the underlying data still paints a healthy consumer. Second-quarter GDP grew at a 1.5% annualized rate, below economists' expectations. At first glance, that may seem disappointing. But when you look under the hood, the economy continues to show resilience. Consumer spending, which accounts for nearly 70% of U.S. GDP, increased 3.2% after a weak first quarter where it only climbed 0.5%. That tells me the American consumer is still in good shape, and that's one of the biggest reasons the economy continues to avoid the recession that so many have been predicting. Major drags on the headline GDP figure included government spending, which reduced growth by 0.14 percentage points, as well as the more volatile components of trade and the change in private inventories, which subtracted 1.01 and 0.67 percentage points, respectively. Inflation remains the biggest challenge. The Fed's preferred inflation measure, core PCE, increased 3.3% over the past year. While that's an improvement from where we've been, it's still well above the Federal Reserve's 2% target. I continue to believe inflation will remain sticky until energy prices become more stable. Energy impacts transportation, manufacturing, and virtually every supply chain, so it's difficult to see inflation falling sustainably while energy costs remain volatile. The Fed, as expected, left interest rates unchanged. What stood out wasn't the decision, it was the growing disagreement among policymakers. The 3 dissents that voted for a 25-basis point increase highlight just how uncertain the economic outlook remains. When inflation is still elevated but the economy continues to grow, there isn't an easy policy answer. One thing I do like so far is Kevin Warsh's changes at the Fed. I like the simplified statement, the encouragement of differing viewpoints, and rather than projecting absolute confidence in economic forecasts, he has acknowledged the uncertainty surrounding them. That's a refreshing change. Economic forecasting has never been an exact science, and I would rather have a Fed Chair who recognizes the limitations of those projections than one who pretends they are precise. What's surprising is how quickly some of the talking heads have claimed Warsh already has a credibility problem. I don't see it that way. Credibility isn't about making bold predictions that later need to be revised. It's about being honest about what we know, what we don't know, and allowing incoming data to guide policy. The takeaway for investors is simple: don't let one headline drive your investment decisions. The economy continues to expand, consumers are still spending, inflation remains stubborn, and the Fed is navigating a difficult policy environment. Looking beneath the surface is often where you'll find the real story.   Leverage Is Fuel... Until It Becomes the Fire The last few weeks have been a reminder that leverage looks like a wonderful tool on the way up... but it's a devastating one on the way down. FINRA's new margin rules have effectively replaced the 25-year-old Pattern Day Trader rule, allowing traders with as little as $2,000 to make unlimited day trades using intraday margin. While this opens the door for more retail participation, it also means more investors have access to leverage, something that has historically magnified both gains and losses. This is a big problem considering FINRA margin debt climbed 49% year over year to another record in June of roughly $1.5 trillion. This comes as investor net credit balances have fallen to a record negative $1.06 trillion. In other words, investors collectively owe more on margin than they have sitting in cash accounts. For comparison's sake, in March 2000 this measure stood at a negative $0.13 trillion. That's an aggressive setup if volatility returns. We also saw this past week the spectacular collapse of Leopold Aschenbrenner's AI-focused hedge fund, Situational Awareness, which shows what can happen when conviction is paired with excessive leverage. The near 25-year-old Aschenbrenner was painted as a genius with strong credentials like being Columbia University's valedictorian at age 19. His fund was launched in July 2024 and he had no experience managing money before that. Before this month's decline the fund had gains of more than 1,000% since inception. The fund used tons of leverage with some saying as much as 400% to build massive positions in AI and semiconductor stocks while shorting stocks in the software space like Adobe. The problem is when names like Coreweave, Nebius, and Sandisk fell more than 50% from their highs and the software stocks rallied, margin calls forced the liquidation of most of its public equity portfolio. The result was staggering considering the fund peaked at above $45 billion in assets and with the selloff they plunged to around $10 billion. This forced a fire sale of assets at a discount to Ken Griffin's Citadel. Some speculate that the forced selling may have helped create the bottom. Once one of the market's largest leveraged sellers had finished liquidating, the selling pressure eased and many AI stocks staged a sharp rebound. Others believe the selling is not over as Michael Burry reportedly used Thursday's powerful rally as an opportunity to increase several of his bearish positions in Micron, Nvidia and the VanEck Semiconductor ETF. Whether he's ultimately right or wrong remains to be seen, but it's a reminder that some experienced investors still believe AI-related valuations and leverage remain stretched.   Here Come the Robots! Robots have been making their way into manufacturing for decades. The first industrial robotic arm, called Unimate, was installed in 1961 on the assembly line at a General Motors plant in Trenton, New Jersey. But today's robots are very different. They're no longer just stationary robotic arms bolted to the factory floor, they're starting to look and move like humans. That reality is beginning to make workers uneasy. At a Hyundai Motor plant in South Korea, employees have gone on a partial strike, with concerns over automation playing a role. Hyundai recently unveiled its humanoid robot, Atlas, which stands 6'2", weighs about 200 pounds, can lift up to 110 pounds, and can continuously carry nearly 70 pounds. It's easy to understand why workers are wondering what these machines could mean for their jobs. South Korea is already the world leader in industrial robot adoption, with approximately 1,220 industrial robots for every 10,000 manufacturing employees. By comparison, the United States has around 307 robots per 10,000 workers. One statistic that surprised me was China, which currently has only about 166 industrial robots per 10,000 manufacturing workers. If Elon Musk has anything to say about it, those numbers could change dramatically over the next several years. Tesla is aggressively developing its humanoid robot, Optimus, with the goal of having it help build vehicles in its factories before long. If that vision becomes reality, other manufacturers will almost certainly follow. The idea of humanoid robots can be unsettling, but the transition is likely to be slower than many people expect. Industry forecasts suggest that global annual production of humanoid robots could reach roughly 1.2 million units by 2030. While that sounds like a large number, it's still a tiny fraction of the global workforce. So, we're probably still a few years away from living like The Jetsons. If you're not familiar with the cartoon, it debuted in September 1962 and imagined a future filled with flying cars and household robots. I guess I will have to wait a few more years to get a maid like the Jetsons had named Rosie the robot.   Financial Planning: Tax Relief Coming for Older Home Sellers? The federal home sale capital gain exclusion has remained unchanged since 1997, allowing homeowners to exclude up to $250,000 of gain if single or $500,000 if married filing jointly when selling a primary residence. With home values rising significantly over the past three decades, particularly in high-cost areas like California, many long-time homeowners now face substantial capital gains taxes when downsizing. A new proposal, the Nest Egg Protection Act, would increase the exclusion to $1 million for homeowners age 65 and older who have owned and lived in their home for at least 25 years. This would allow more seniors to keep the equity they've built over a lifetime. In addition to providing tax relief, the proposal could encourage more older homeowners to sell, increasing housing inventory and making homeownership more attainable for first-time buyers. While the legislation has not yet been enacted and homeowners should continue planning under current law, the proposal reflects a growing recognition that the existing exclusion no longer aligns with today's housing market.   Companies Discussed: International Business Machines Corporation (Ticker: IBM)

Der Hedgefonds des 25-Jährigen Leopold Aschenbrenner, war vierfach gehebelt auf die KI-Rally gesetzt, lag im ersten Halbjahr 450 Prozent im Plus und musste dann innerhalb von Stunden fast alles verkaufen. Ken Griffins Citadel hat die Reste eingesammelt. Pip erklärt, wie Margin Calls funktionieren, warum so ein Blocktrade für den Käufer beinahe risikofreies Geld ist und welche drei Erklärungen es für Aschenbrenners Aufstieg gibt. Danach senkt OpenAI die Preise um bis zu 80 Prozent, was zu der Frage führt, ob es je eine Softwarekategorie gab, die so schnell billiger wurde. Es folgt die große Earnings-Runde mit Apple, Microsoft, Meta, Amazon, Reddit und Robinhood, und die Beobachtung, dass zwei Konzerne für denselben Capex völlig unterschiedlich behandelt werden. In der Schmuddelecke will Josh Kushner Anteile an der Weltmeisterschaft kaufen, und Google Earth lässt jeden ein Atomkraftwerk in den Iran setzen. Unterstütze unseren Podcast und entdecke die Angebote unserer Werbepartner auf ⁠⁠⁠⁠⁠⁠⁠doppelgaenger.io/werbung⁠⁠⁠⁠⁠⁠⁠. Vielen Dank!  Philipp Glöckler und Philipp Klöckner sprechen heute über: (00:00:00) Aschenbrenner und Citadel (00:20:11) OpenAI senkt Preise (00:30:00) Anthropic-Modelle hacken (00:32:55) OpenAI-Umsatz (00:36:00) Tesla und SpaceX (00:39:35) Apple (00:43:58) Microsoft (00:46:18) Meta (00:54:23) Amazon (01:08:25) Reddit (01:09:24) Robinhood (01:12:50) FIFA-Ultimatum (01:15:12) Gefälschte Satellitenbilder (01:17:43) LinkedIn-Slop-Button (01:23:46) Pentagon gegen Anthropic (01:26:25) Durow Shownotes Situational Awareness sucht Kapital nach KI-Ausverkauf - ft.com Citadel kauft Aschenbrenners Aktienportfolio - ft.com OpenAI senkt GPT-5.6-Preise um bis zu 80 Prozent - axios.com Anthropics Modelle hackten drei Firmen im Test - wsj.com Juli-Umsatz uebertrifft das ganze zweite Quartal - cnbc.com Tesla erwaegt Verkauf des China-Geschaefts - wsj.com Apple-Quartalszahlen im Liveticker - cnbc.com Apple bremst wegen Engpaessen in der Lieferkette - ft.com Microsoft-Quartalszahlen, Azure knackt 100 Milliarden - cnbc.com Groesster Kurssprung der Firmengeschichte - finance.yahoo.com Meta-Aktie faellt nach Zuckerbergs Agenten-Vision - ft.com Amazon erhoeht KI-Investitionen auf 220 Milliarden - ft.com Amazon-Quartalszahlen, AWS waechst 37 Prozent - cnbc.com Big Tech investiert mehr als eine Billion in KI - ft.com Reddit-Quartalszahlen, Umsatz plus 61 Prozent - cnbc.com Robinhood mit Rekordumsatz durch Volatilitaet - marketwatch.com Infantino setzt FIFA-Verbaenden eine Frist von 53 Tagen - telegraph.co.uk Wie man ein Atomkraftwerk in den Iran faelscht - digitaldigging.org LinkedIn fuehrt einen Melde-Button fuer KI-Schrott ein - 404media.co Richterin zerlegt den Pentagon-Fall gegen Anthropic - axios.com Durows Reaktion auf den russischen Haftbefehl - xcancel.com

Los mercados encaran el cierre semanal con un claro rebote del apetito por el riesgo. Amazon rema a favor, Apple en contra, pero se le perdona. Empaña las perspectivas por los problemas de suministro, lo que pone el foco en la demanda de sus iPhone. Ya no basta con superar las previsiones. Las empresas también deben tranquilizar a los inversores sobre los motores del crecimiento futuro, comentan los analistas. Y entretenido anda el mercado con la historia del fondo de cobertura Situational Awareness. El primer gran colapso financiero de la IA. Citadel, ¿más zorro que viejo? Lo hablamos con Miguel Ángel Temprano. Hubo, por otro lado, fuertes subidas en Bolsas asiáticas. El foco hoy en activos japoneses tras intervención en yen su banco central no notca tipos. En Europa, datos de inflación y más resultados. En España destacamos Amadeus, IAG, Prosegur y Unicaja. En el frente geoestratégico, Trump anuncia acuerdo con Hamás para su desarme. Es difuso y sin calendario definido. Algo cede el precio del petróleo. Como todos los viernes, repaso a operativa con futuros y posiciones en valores Ibex con Gerardo Ortega.

If markets no longer price value, then what's actually setting the price?Raise Your Average hosts Pierre Daillie and Adam Butler sit down with Michael Green, Chief Strategist and Portfolio Manager at Simplify Asset Management, for a deep dive into the passive investing thesis he has spent over a decade researching, defending, and stress testing.Green argues that trillions of dollars flowing automatically into index funds via 401(k)s, RSPs, and defined contribution plans have created a market where price no longer reflects judgment about value. He walks through the mechanics of the "inelastic market hypothesis," the outsized role of leveraged and levered sector ETFs like SOXL, the Grossman-Stiglitz framework and why its core assumptions no longer hold, and why active and value investing have become structurally disadvantaged in the current regime.The conversation also covers the 2026 macro backdrop of a US-Iran conflict, an oil shock, and equities at all-time highs despite it, the risk of a passive "end stage," and where genuine diversification (like managed futures) still fits. It's a candid, occasionally combative, and consistently illuminating discussion for anyone trying to understand why markets are behaving in ways that don't match historical patterns.Chapters00:00 – Introduction: has the market stopped pricing risk?08:00 – Welcome to Michael Green; setting up 2026's contradictions09:00 – The 50-year shift into "all equities all the time"10:00 – How ETF mechanics reduce market elasticity12:00 – Why pod shops and passive flows ignore fundamentals entirely13:00 – Leveraged sector ETFs (SOXL) aren't really passive15:00 – Echoes of the dot-com bubble: 1999 vs. today18:00 – Circular funding and Mag Seven earnings41:00 – Momentum, autocorrelation, and portfolio construction under passive dominance44:00 – Pushback from the Financial Times and mainstream finance media44:30 – Malkiel's Paradox of Skill and the Grossman-Stiglitz framework, unpacked47:00 – Why the "equal endowment" assumption is false49:00 – The large-stack player sets the terms of the market50:00 – The Inelastic Market Hypothesis (Gabaix and Koijen) and Green's updated multiplier estimates52:00 – Facilitators vs. correctors: why Citadel and Jane Street are thriving55:00 – The Newtonian vs. quantum physics analogy for market scale57:00 – GameStop, Michael Saylor, and self-liquidating vehicles1:13:00 – Market cap concentration data and transaction cost asymmetries1:15:00 – Why cap weighting has flipped from historically losing to structurally winning1:17:00 – Stein's Law and the coming correction1:18:00 – Why value investing is a "negative selection criteria" right now1:21:00 – Where active investors can still add value: becoming facilitators1:22:00 – Managed futures as liquidity provision and portfolio ballast1:25:00 – Capacity constraints and closing thoughts#MichaelGreen #PassiveInvesting #RaiseYourAverage #SimplifyAssetManagement #ETFs #IndexFunds #MarketStructure #InelasticMarketHypothesis #ActiveManagement #ValueInvesting #ManagedFutures #Macro #InvestingPodcast #StockMarket #FinancePodcast #WallStreet #PortfolioManagement #MarketBubble #AdvisorAnalyst

P.M. Edition for July 30. The U.S. economy grew just 1.5% last quarter, lower than the previous quarter and falling short of economists' expectations. WSJ economics reporter Harriet Torry explains why the details in the report, particularly around consumer spending, suggest things aren't as bad as the headline number makes it seem. Plus, the buzzy AI-focused hedge fund Situational Awareness, founded by AI whiz kid Leopold Aschenbrenner, sold most of its stock portfolio to investment firm Citadel. We hear from WSJ special writer Greg Zuckerman about why this happened and where the company goes from here. And a big rally in tech companies sent U.S. stocks soaring today. Alex Ossola hosts. See the new fronts in the Iran war.  Sign up for the WSJ's free What's News newsletter. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

July 30, 2026: I look at Mark Zuckerberg's argument that AI superintelligence should be for everybody, and why Meta's falling operating margin and massive AI spending tell a more complicated story. Then I get into Leopold Aschenbrenner's AI hedge fund selling its public portfolio to Citadel after a brutal reversal in AI infrastructure stocks. Finally, I unpack the Financial Times report on PwC publishing fake AI citations and why fabricated sources may become one of the biggest credibility risks in the AI era.

Matty A. and Ryan Breedwell dive into a packed week for the financial markets, starting with predictions for the upcoming FOMC rate decision and the impact of the ongoing Iran conflict on global oil prices. They explore how inflation and geopolitical tensions are keeping the S&P 500 range-bound, while highlighting crucial earnings reports from major AI and semiconductor companies like SanDisk, Seagate, and Nvidia.The hosts also analyze the recent spike in United States real estate foreclosures, breaking down why record-high homeowner equity and supply shortages mean a housing crash is highly unlikely. Finally, the conversation shifts to digital assets, discussing the Crypto Clarity Act, the regulatory threat to meme coins, and how tokenization could soon reshape institutional finance.KEY TOPICS DISCUSSEDFOMC rate hike probabilities and Citadel's surprise hike prediction.Impact of the Iran conflict on global oil prices and WTI trends.Semiconductor stock pullbacks and AI data storage investments.Q2 tech earnings expectations for Meta, Apple, and Microsoft.Analysis of rising United States real estate foreclosures compared to 2019.Record homeowner equity and the national housing supply shortage.The Crypto Clarity Act and the future of real world asset tokenization.Regulatory crackdowns on meme coin markets and platforms like PumpFun.KEY TAKEAWAYSA surprise FOMC rate hike is highly unlikely given current market conditions, despite some hawkish institutional forecasts.Geopolitical energy shocks are being digested faster by the market, with oil prices retreating sharply after recent spikes.Semiconductor and memory storage companies present strong buy opportunities as they continue to beat earnings despite broader tech sector pullbacks.The current real estate market is insulated from a crash due to a massive 11 trillion dollars in tappable equity and pervasive sub-6 percent mortgage rates.The impending Crypto Clarity Act will likely eliminate unregulated meme coin exchanges while attracting trillions in institutional capital to legitimate tokenization projects.CONNECT & TAKE ACTIONImagos Income Fund: Text "INCOME" or "DEALS" to 844-447-1555 to learn more about Matty A's private debt fund targeting 10% fixed returns paid out monthly.

It was a pleasure to welcome Alec Litowitz, the Founder of Magnetar Capital and QStar Capital, to the Alpha Exchange. Central to our discussion is an exploration of the ideas in Alec's new book, The Adaptability Quotient. Here, he draws on more than thirty years of investing across multiple market regimes. We begin with Alec's three decades in financial markets, from his early years at Citadel through the founding of Magnetar. Looking back across multiple market cycles, he argues that long-term investing success is driven by more than intelligence alone. Instead, he introduces the concept of Adaptability Quotient, or AQ, emphasizing the ability to revise views, respond to changing conditions, and distinguish between environments defined by risk, uncertainty, and black swans. A central theme throughout the discussion is decision-making under uncertainty. Alec explains why markets spend much of their time in environments where outcomes are possible, but probabilities remain difficult to estimate. He outlines a framework centered on metacognition, simulation, experimentation, and continuous feedback, encouraging investors to develop "strong opinions, weakly held" while remaining willing to revise conclusions as new information emerges. The conversation then turns to practical investing examples drawn from Alec's career. He reflects on building Citadel's risk arbitrage business by developing proprietary research processes around regulatory uncertainty, and later discusses Magnetar's emphasis on sourcing, structuring, and risk management in areas undergoing structural change. Examples include investments tied to energy infrastructure and AI-related computing capacity, illustrating how the firm approached evolving industries through the lens of uncertainty rather than prediction. I hope you enjoy this episode of the Alpha Exchange, my conversation with Alec Litowitz.

Join Pitt Girl, Commish, Corn Correspondent Andy and Beth, along with our VP of Podcast Production, Arthur. We talk about the sold out $5,200 cruise from Tulsa to Memphis and other options offered, Lane Kiffin said he has given up social media and it's been refreshing (REFRESHING FOR WHO), also Lane Kiffin said we're all gonna die. We start a new company that helps with PR called Getting a Dog, the Big Sky has Nevada surrounded, couch burning commitment video, we applaud Jon Sumrall for getting pedicures, MAYO COUNTY wins Gaelic football for the first time in 75 years, then we're on to season previews in the SUPER SICKO SHAKING SPECULATION SEASON PREVIEW FORECAST: SSSSSPF aka the 5SPF this time with DICE ROLLS, we attempt to preview the Southland and SoCon, we have snippets of interviews with Tennessee Tech and The Citadel's coaches along with WR AJ Colombo from Western Carolina, we roll to see how do the dice see your season shaping out, the squirrels have returned and oh so much, much more!Join our Patreon for just $3 or $5 a month. https://www.patreon.com/cw/SickosCommitteeBuy some of our officially licensed merch here https://thesickoscommittee-shop.fourthwall.com/NEW SICKOS FC JERSEYS FOR SALE https://oliveandyork.com/products/sickos-fcCheck out our Linktree for all our discount codes https://linktr.ee/sickoscommitteeSubscribe to our blog at https://sickos-newsletter.beehiiv.com/Subscribe to our YouTube at https://www.youtube.com/@sickoscommitteeDonate here to help with the West Virginia floods/tornados:http://www.parishhouse.org/donate.htmlhttps://communityengagement.wvu.edu/programs/dollars-for-disasterhttps://www.unitedwayofglu.com/donateSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

27/7 Futures in rosso: l'onda del sell-off sui chip si estende in Asia in attesa delle trimestrali Big Tech. Mix di fattori, timori di autosufficienza cinese tra nuovi chip (CXMT), macchine a litografia (The Information) e i finanziamenti circolari di Nvidia. Siamo entrati in una nuova fase AI. Per MS è un buy the dip sui semiconduttori. Steve Eisman (Big Short): il mercato sottostima i rischi Ai. JPM: segnale buy S&P500 da nostro modello proprietario. SpaceX -50% da massimo Ipo. Settore finanziario nuovi massimi. Fed: per Citadel potrebbe alzare domani. Chances mercato al 38%. Brent ancora in calo su stallo raid, giù anche dollaro, Treasuries, oro argento e Bitcoin. Apple nuovo record storico, supera Nvidia in market cap. Nvidia lancia l'alleanza industriale per la cybersecuirty Ai, Microsoft un nuovo modello cyber che fa concorrenza ad Anthropic, Amazon fa concorrenza a Starlink. ***Questo episodio è offerto da ⁠Scalable Capital ⁠Apri un conto con Scalable Capital e inizia a ricevere il 2,5% di interessi* sui tuoi risparmi:  https://it.scalable.capital/broker-online?utm_medium=affiliate&utm_source=qualityclick&utm_campaign=broker&c_id=QC59486e7f67706c777b517d435049607362766c747c5aS7541p&utm_term=983 Messaggio pubblicitario. Tasso lordo annuo variabile sulla liquidità depositata nel conto deposito non vincolato, composto da tasso base collegato al Tasso di Deposito BCE e tasso bonus discrezionale. Liquidità allocata presso banche partner e fondi monetari riconosciuti. Foglio informativo e condizioni su scalable.capital. Investire comporta dei rischi*** Sell-off in Asia. Nikkei -4% con Softbank e Kioxia, Kospi -10% con ribassi doppia cifra SK Hynix e Samsung. Attesa per Politburo cinese e BOJ. Europa in rosso, attesa trimestrali e inflazione venerdì. Governo: sconto 17 centesimi su gasolio. Costa 125 milioni di euro. Focus su risiko e possibile componente cash nella fusione per incorporazione tra Mps e Bpm. LVMH: salgono le vendite, gli Usa compensano Europa e Paesi del Golfo.  Learn more about your ad choices. Visit megaphone.fm/adchoices

Halifax  Part 2 of 2   Welcome to the  Dr. Mary Travelbest Guide podcast. The FAQ is: What is the best messaging app in Asia for Dr. Travelbest women?   Answer: For Dr. Travelbest, women traveling in Asia, the best answer is: use WhatsApp as your home base, but add the local app for the country you're visiting. Use WhatsApp as your main travel messaging app, but download the local app for the country you are visiting. In Japan, Taiwan, and Thailand, that may be LINE. In South Korea, it is KakaoTalk. In China, it is WeChat. Set these up before you leave home, while you still have easy access to your phone number, email, passwords, and two-factor codes. Asia does not use one single messaging app everywhere. The best app depends on where you are going. Before your trip, create a small phone folder called Asia Travel Apps and put these inside: WhatsApp, LINE, KakaoTalk, WeChat, Google Translate, Google Maps or Apple Maps, and your airline app. For most Dr. Travelbest women, the safest and simplest strategy is to keep WhatsApp for home, use the local app for local connections, and not wait until you land to download everything. 60-second confidence challenge Your challenge today, Confidence Challenge Halifax Part 2     See Book A for addressing this concern. I'm an expert at messaging apps. Just ask. Find it on the website​​ at https://www.5stepstosolotravel.com/ or on Amazon. It's a several-part series.   Today's itinerary is Halifax, Nova Scotia, Canada. Part 2 of 2 This episode is a three-day itinerary for a first-time visitor to Halifax, especially a solo woman traveler from the USA who wants history, waterfront views, local food, public transportation, and a relaxed pace. Halifax is a city where the harbor shapes everything. You will see ferries, cruise ships, navy activity, kayaks, boardwalks, seafood restaurants, and historic sites. My advice is to balance the serious history with time outside, because Halifax is best experienced both on land and near the water. Day One: Start with the waterfront and maritime history. Begin your first day with a walk along the Halifax waterfront boardwalk. This is the best way to feel the city. You may hear music, see cruise ships, watch boats moving across the harbor, and find places to sit outside. In summer, the waterfront can feel festive, especially if there are events or buskers. Look for Theodore the Tugboat, one of the cheerful harbor icons. It is a fun stop, especially if you like seeing the lighter side of a working port city. Then visit the Maritime Museum. This is one of the best first-day stops because it helps you understand Halifax's identity as a port city. Pay special attention to the history of the Halifax Explosion. In 1917, two ships collided in the harbor, causing a devastating explosion. The blast was massive, and the next day brought a blizzard. It is a sobering story, but it explains a lot about the city's strength and memory. After the museum, have lunch near the waterfront. This is a good time to try fish and chips, scallops, clams, or another seafood dish. If you are on a budget, balance one restaurant meal with simple food from a market or grocery store later in the day.   https://www.tripadvisor.com/AttractionProductReview-g154976-d23939015-Historic_Halifax_by_Foot-Halifax_Halifax_Regional_Municipality_Nova_Scotia.html In the afternoon, take the ferry to Dartmouth. The ferry is inexpensive; one of my notes said it costs about $2.50. Keep your ticket and enjoy the short ride across the harbor. You do not need to make this complicated. Sometimes the ferry ride itself is the experience. It gives you harbor views, a rest for your feet, and a different look back at Halifax. In the evening, return to the waterfront. Halifax is pretty at night, and if the weather is good, outdoor eating can be delightful. Stay aware of your surroundings, especially near nightlife areas. A local warning I heard was that areas with many bars can get rowdy. As a solo traveler, I would enjoy the evening but avoid getting pulled into a crowded late-night drinking scene. Day Two: Gardens, Citadel Hill, churches, and city views. Start day two at the Halifax Public Gardens. The main gate near South Park Street is a lovely entrance point. The gardens are peaceful, colorful, and a good contrast to the harbor. Go in the morning if you can, when the pace is slower. From there, head toward Spring Garden Road, one of the city's lively areas. You can find shops, cafes, and transit connections. If you like libraries, visit the newer Halifax Central Library and go up to the rooftop area for views and a sense of local life. I noted spring gardening and the rooftop as part of the library experience, and it is a good stop for travelers who like free or low-cost places. Next, walk or take transit toward the Citadel. The Citadel is up the hill, so wear comfortable shoes. Plan about two hours if you want to understand the military and cultural history. The views from the area also help you understand Halifax's shape. After the Citadel, visit St. Paul's Anglican Church downtown. It is an important landmark and one of the oldest buildings in Halifax, dating back to 1749. It is close enough to combine with other downtown stops and gives you a sense of the city's early colonial history. If you enjoy art, consider the Art Gallery of Nova Scotia, especially for Maud Lewis's paintings. Her work is colorful, personal, and deeply connected to Nova Scotia. This is a good afternoon choice if the weather turns rainy or if you need a quieter indoor stop. For dinner, try seafood again or sample poutine if you have not already. Poutine is fries, cheese curds, and gravy. It may be unfamiliar to American visitors, but it is worth trying at least once in Canada. Day Three: Point Pleasant Park, universities, swimming, or a harbor adventure. On your third day, choose a pace that fits your energy.   My travel tip: combine one paid attraction each day with free or low-cost experiences like the Public Gardens, waterfront walks, the ferry, and Point Pleasant Park. Today's destination is My missteps: Leaving my cellphone behind. My mistake for this episode: Leaving my phone at the swimming pool in Toronto. I changed back into my clothes and left the phone on the bench. The phone is my lifeline when I travel. I was with a friend that day, so perhaps more distracted than normal. I called the pool, picked up the phone, and all was well. Don't leave your phone unattended when you travel. My travel tip on eSims: My Airalo discount code is:  MARY2856 for your savings of $3.00 or more. Install before you leave: Always download and install the eSIM while you are still at home on your secure, reliable Wi-Fi network. You don't want to activate it until the day before you arrive in the country you will use it, so have it downloaded already.   For most Dr. Travelbest women, the safest and simplest strategy is to keep WhatsApp for home, use the local app for local connections, and not wait until you land to download everything. AI was used to select some of the suggestions for this episode.   Connect with Dr. Travelbest 5 Steps to Solo Travel website Dr. Mary Travelbest X Dr. Mary Travelbest Facebook Page Dr. Mary Travelbest Facebook Group Dr. Mary Travelbest Instagram Dr. Mary Travelbest Podcast Dr. Travelbest on TikTok Dr.Travelbest on YouTube In the news  

To reach the Flying Citadel, the party must survive a mounted flight on dragonnels above a raging battle.Welcome to Patron DnD, where Platinum-level patrons and I get together to play Dungeons & Dragons via Discord and Roll20. Dragonlance: Shadow of the Dragon Queen is published by Wizards of the Coast, and set in the world of Krynn. We are using the updated 2024 5e rules.Recap at RogueWatson.comStarring:Cere, level 10 dwarf Cleric of the Light DomainDarryl, level 10 human Berserker BarbarianEllowyn, level 10 kender Bard College of LoreKazra, level 10 human Champion Fighter/Paladin Oath of DevotionKorl, level 10 dwarf Bard College of DanceShop for tabletop games, CCGs, miniatures, RPG supplies and more at our sponsor, Noble Knight Games: https://www.nobleknight.com?awid=1553Music by Kevin MacLeod https://incompetech.com/music/royalty-free/music.htmlLicensed under Creative Commons: By Attribution 4.0https://creativecommons.org/licenses/by/4.0/Character art by DemnixChat with us in the Official Discord Server: https://discord.gg/AjvtemjSupport the channel at https://www.patreon.com/Roguewatson

Attacked by His Own Police Agency for a Cover-Up That Never Happened: A Police Lieutenant Who Lost Everything, Then Fought His Way Back. A career built over decades can be destroyed in a matter of seconds. For many people, the greatest threat to a police officer comes from violent criminals. The Podcast is available for free on the Law Enforcement Talk Radio Show and Podcast website, also on Apple Podcasts, Spotify, YouTube, iHeartradio and most major podcast platforms. #LawEnforcementTalk #Free #Podcast #Radio Former Charleston Police Lieutenant Arthur "Rusty" Myers learned that sometimes the greatest threat comes from within the very organization an officer has dedicated a lifetime to serving. The Law Enforcement Talk Radio Show and Podcast social media like their Facebook , Instagram , LinkedIn , Medium and other social media platforms. Accused of participating in a cover-up that he says never happened, Myers watched his reputation, career, and identity collapse almost overnight. Supporting articles about this and much more from Law Enforcement Talk Radio Show and Podcast in platforms like Medium , Blogspot and Linkedin. Yet years later, every charge against him was gone, his law enforcement certification was fully reinstated by the state, and another police agency immediately hired him. He left that agency and was eventually promoting him to Deputy Chief of Police. His remarkable story is shared during an emotional interview on the Law Enforcement Talk Radio Show and Podcast website, also available on Facebook, Instagram, YouTube, Apple Podcasts, Spotify, and across social media platforms where audiences continue following compelling stories from the front lines of policing. Attacked by His Own Police Agency for a Cover-Up That Never Happened: A Police Lieutenant Who Lost Everything, Then Fought His Way Back. The conversation is available on the Law Enforcement Talk Radio Show and Podcast website, Facebook, Instagram, YouTube, Apple Podcasts, Spotify, iHeartRadio, and most other major podcast platforms, where audiences continue discovering firsthand accounts from those who have lived them. A Foot Pursuit That Changed Everything The incident that altered Myers' life appeared routine at first. While serving as a lieutenant with the Charleston Police Department, several officers became involved in a foot pursuit. Myers responded to assist his officers as supervisors routinely do. Within days, however, the focus shifted away from the arrest itself. Investigators alleged that Myers had participated in covering up officers' use of force. He was charged with filing a false police report. The accusations stunned the veteran officer. A career built on leadership, ethics, and officer development suddenly became overshadowed by allegations that challenged everything he had spent decades building. The episode is available across major platforms including their website, Apple Podcasts, Spotify, YouTube, with highlights shared across their Facebook, Instagram, and LinkedIn profiles. When Your Own Agency Turns Against You For police officers, the badge often represents more than employment. It becomes identity. Purpose. Family. Myers describes how administrative leave quickly turned into isolation. The officers he once supervised disappeared from his daily life. The camaraderie vanished. The uncertainty became overwhelming. In his book "Tattered: When the Blue Line Frays," Myers writes about the devastating emotional impact that follows when an officer becomes the subject of an investigation. Attacked by His Own Police Agency for a Cover-Up That Never Happened: A Police Lieutenant Who Lost Everything, Then Fought His Way Back. Available for free on the Law Enforcement Talk Radio Show and Podcast website, also on Apple Podcasts, Spotify, Youtube and most major Podcast networks. "Administrative leave becomes isolation. Silence replaces camaraderie." The investigation wasn't simply about criminal allegations. It became a battle for his reputation, career, and future. Cleared... But Never Given His Career Back Eventually, Myers fought the accusations before the state board responsible for his law enforcement certification. According to Myers, prosecutors presented no case. The charges were dropped. His law enforcement license was fully reinstated. Legally, his name had been cleared. But despite that vindication, the Charleston Police Department never brought him back. The Podcast is available for free on the Law Enforcement Talk Radio Show and Podcast website, also on Apple Podcasts, Spotify, YouTube, iHeartradio and most major podcast platforms. For many officers, being cleared would represent the end of a painful chapter. For Myers, it marked the beginning of another. From Fired Officer to Internal Affairs Detective Instead of leaving law enforcement, Myers chose to continue serving. Almost immediately after his vindication, another South Carolina police agency hired him. Ironically, one of his new assignments became serving as an Internal Affairs Detective, the very type of investigator whose work had dramatically affected his own life. Attacked by His Own Police Agency for a Cover-Up That Never Happened: A Police Lieutenant Who Lost Everything, Then Fought His Way Back. The Law Enforcement Talk Radio Show and Podcast continues bringing listeners real conversations from the front lines of crime, policing, trauma, survival, and healing. His career continued to grow. Today, Myers serves as Deputy Chief of Police at a South Carolina university Department of Public Safety, proving that one agency's decision did not define his future. Turning Pain Into Purpose Rather than allowing bitterness to consume him, Myers transformed his experience into leadership lessons. His memoir, "Tattered: When the Blue Line Frays," explores far more than one controversial investigation. It examines: Institutional betrayal. Leadership during crisis. Officer wellness. Professional accountability. Mental health. Faith. Resilience. Rebuilding purpose after devastating loss. The book has resonated with police officers, supervisors, and leaders who understand how quickly careers can change. A Career Dedicated to Leadership Long before his public controversy, Myers built an impressive law enforcement career spanning more than three decades. A graduate of The Citadel and holder of a Master's degree in Biblical Studies from Liberty University, he served in virtually every level of policing. Attacked by His Own Police Agency for a Cover-Up That Never Happened: A Police Lieutenant Who Lost Everything, Then Fought His Way Back. Supporting articles about this and much more from Law Enforcement Talk Radio Show and Podcast in platforms like Medium , Blogspot and Linkedin. As Training Commander for the Charleston Police Department, he instructed officers on: Ethics Leadership Procedural Justice Professional Accountability Use-of-Force Review Officer Development Later, while serving with the Summerville Police Department, he became the Internal Affairs Inspector and created an innovative police leadership program designed to educate officers from rookie patrol officers to police chiefs. Today, his mission extends beyond policing. He advocates for stronger leadership, healthier police organizations, and honest conversations surrounding officer wellness and institutional responsibility. When the Uniform Comes Off Perhaps the most powerful question Myers asks isn't about policing. It's about identity. Who are you after everything you've built disappears? For officers forced into retirement... Wrongfully accused... Terminated... Or publicly criticized... That question becomes deeply personal. Myers believes resilience begins by understanding that a career can end, but purpose does not. More Than One Story In addition to Tattered, Myers is also the author of "Confession of Justus: A Tale of the Christ," a historical fiction novel following the Roman centurion traditionally associated with the crucifixion of Jesus Christ. Attacked by His Own Police Agency for a Cover-Up That Never Happened: A Police Lieutenant Who Lost Everything, Then Fought His Way Back. The episode is available across major platforms including their website, Apple Podcasts, Spotify, YouTube, with highlights shared across their Facebook, Instagram, and LinkedIn profiles. While vastly different in subject matter, both books explore themes of sacrifice, redemption, faith, and personal transformation. Listen to the Full Interview Arthur "Rusty" Myers shares his remarkable journey from respected police lieutenant, to suspended, fired, to complete vindication, and finally to Deputy Chief of Police, during an unforgettable episode of the Law Enforcement Talk Radio Show and Podcast. His story challenges assumptions about justice, leadership, and loyalty while offering hope to anyone whose career, reputation, or purpose has unexpectedly fallen apart. Whether you're interested in policing, leadership, mental health, resilience, or inspiring true stories, this conversation offers rare insight into one man's determination to rebuild when everything seemed lost. Follow the Law Enforcement Talk Radio Show and Podcast on their website, on Facebook, Instagram, YouTube, Apple Podcasts, Spotify, and other social media platforms for more compelling interviews with police officers, investigators, authors, and first responders who reveal the realities behind the badge. If you've ever wondered what happens when a police officer becomes the target instead of the investigator, Arthur "Rusty" Myers has lived the answer, and survived to tell the story. Be sure to follow us on X , Instagram , Facebook, Pinterest, Linkedin and other social media platforms for the latest episodes and news. Learn and get access to money saving tips and how to increase your net worth at www.LetSavings.com Download the Free Ebook about ways and tips to improve your health. You can get the ebook for free at www.LetHealthy.com Get the Free Clubhouse App, it is Drop In Social Audio. Think of it as your own talk radio show on your phone, and best of all it is free. Be sure to look for me and follow me, that's John J Wiley or @letradioshow you can do all that here. The Law Enforcement Talk Radio Show and Podcast social media like their Facebook , Instagram , LinkedIn , Medium and other social media platforms. You can contact John J. “Jay” Wiley by email at Jay@letradio.com , or learn more about him on their website . Find a wide variety of great podcasts online at The Podcast Zone Facebook Page , look for the one with the bright green logo. Be sure to check out our website . Listen to the Law Enforcement Talk Radio Show and Podcast on their website, Facebook, Instagram, YouTube, Apple Podcasts, Spotify, iHeartRadio, and most major podcast platforms. Attacked by His Own Police Agency for a Cover-Up That Never Happened: A Police Lieutenant Who Lost Everything, Then Fought His Way Back. Attributions Amazon Wikipedia Rusty Myers Facebook Facebook Group   Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Hosts Steve Lowery and Yvonne Godfrey interview trial lawyers Taylor Powell and Whitney Harrison about DHD Jessamine LLC v. Florence County, a Fair Housing Act case over a proposed 60-unit LIHTC affordable housing community in an unzoned “donut hole” parcel in Florence County.   CASE SUMMARY: After county officials initially supported the development, neighborhood opposition and a country club meeting preceded withdrawal of support, shifting objections (traffic, drainage, sidewalks), a special meeting to fast-track a development moratorium that was enforced before becoming law, and later rezoning to R-1 to bar multifamily housing. Plaintiffs pursued disparate treatment and disparate impact claims; the court granted summary judgment on the prima facie disparate impact prong. A jury trial featured streamlined exhibits, visual timelines, and expert testimony on disparate impact. On Nov. 5, 2025, the jury awarded $8.219M compensatory and $4M punitive damages; the case later settled for $10M.    GUEST BIOS Taylor Powell:  Originally from Charlotte, N.C., Taylor Powell brings more than a decade of legal experience to the Lesemann & Associates team. After graduating from The Citadel in 2006 with a B.A. in Criminal Justice and a Minor in U.S. History, Taylor attended Charleston School of Law and graduated in 2011. After law school, Taylor spent two years serving as the law clerk to South Carolina Circuit Judge Larry B. Hyman, Jr. in Conway, S.C. During his eight years at Lesemann & Associates, Taylor has helped his clients achieve successful results in wrongful death and catastrophic injury cases involving tractor trailer accidents, motor vehicle accidents, accidental shootings, drunk driving accidents, dram shop litigation against bars and restaurants, products liability cases against vehicle manufacturers and car dealerships, and cases involving negligent private security. Taylor has also secured significant settlements and verdicts for clients who suffered injuries resulting from improperly paved roads, dangerous homemade zip lines, dog bites, fireworks accidents, and more. Taylor has been directly responsible for securing and collecting more than $20 million in settlements on behalf of his clients. (READ MORE)   Whitney Harrison: Whitney delights in nuance, complexity, and unsettled law.  Having clerked in both of South Carolina's appellate courts, Whitney's seasoned instincts inform her appellate strategy from the start of every case.  As a key member of our trial teams, she anticipates and addresses legal issues at each stage of litigation while preserving the record for an appeal.  By treating every case as one that will involve a trial and an appeal, Whitney provides comprehensive courtroom advocacy. Whitney has tried multiple cases to verdict, as well as handled landmark cases involving civil, criminal, family, utility, and administrative law.  Firms across the state associate her to assist with complex motions, trials, and appeals.  Whitney has handled over fifty appeals—with issues ranging from constitutional challenges to corporate governance to novel law—before the Supreme Court of South Carolina and the South Carolina Court of Appeals. In January 2020, Whitney became the first woman to receive the South Carolina Bar's Trial and Appellate Advocacy Award.  The award—not given annually—“recognizes a member of the Bar who has demonstrated substantial dedication to the furtherance of the art and techniques of trial and appellate advocacy in South Carolina, outstanding and exemplary skill and conduct in the practice of advocacy, and has devoted substantial time and effort to the education and training of lawyers.” (READ MORE)   FIND A FAVORITE SPOT IN THIS EPISODE: 00:00 Podcast Cold Open 00:29 Meet The Hosts 01:13 Introducing The Guests 01:54 Taylor Powell Bio 03:04 UCLA Office And Softball 04:41 Whitney Harrison Bio 06:11 Prizewinning Pound Cake 07:11 Case Setup And Timeline 09:41 Fair Housing Case Overview 14:10 Verdict And Damages 15:08 Crafting The Opening 18:08 Explaining FHA Theories 21:06 Sponsor Break 21:51 Donut Hole Moratorium 26:56 Ordinance Readings And Enforcement 27:48 Trial Team And Appellate Strategy 29:30 Summary Judgment Strategy 31:39 Expert Testimony Impact 34:05 Humanizing The Development 35:50 Punitive Damages Surprise 39:32 Rare Jury Trial Stakes 45:02 Witness Order And Exhibits 49:40 Trial Tech And Impeachment 52:19 Klan Comment Sidebar 58:58 Closing Argument Masterclass 01:01:07 Wrap Up And Next Steps

Connect with Early Riders — https://www.earlyriders.com/contactConnect with Onramp — https://onrampbitcoin.com/contact-us/Presented collaboratively by Early Riders & Onramp Media…Final Settlement is a weekly podcast covering capital markets, dealmaking, early-stage venture, bitcoin applications and protocol development.This week Michael, Liam, and Brian break down Moonshot's Kimmy K3 release and what a more open, cheaper Chinese frontier model means for the race against Claude Fable 5 and GPT 5.6, from cyber guardrails and export controls to the Trump administration weighing a ban on Chinese models. They dig into the AI capital markets: Anthropic and DeepSeek's IPO plans, Nous Research's $75 million raise at a $1.5 billion valuation, Gavin Baker's intelligence-per-dollar thesis, Liquid AI, and OpenShip's self-hostable app platform. The guys run through the payments story: the $53 billion Stripe, Advent, and Block bid for PayPal, Visa's new OUSD stablecoin platform, and Amazon Japan's move into a yen-backed stablecoin. They cover a stack of digital asset headlines: IBIT options limits rising to 1 million contracts, Citadel's $400 million investment in Crypto.com at a $20 billion valuation, the ECB's digital euro pilot, Velocity's $38 million Series A, Tether's Genius Act countdown, and Lynn Alden's new Bitcoin-focused PE firm. They close on where the Clarity Act stands, Early Riders' mid-year letter, Onramp's back-to-basics promo, and AI's arrival in film and music.Chapters00:00 - Introduction and Weekly Recap01:26 - Kimmy K3 Release and Open Source AI Models05:43 - Meta-level Analysis of AI Race and AGI08:04 - AI Development: Capabilities and Guardrails09:44 - AI as a Commodity and Data Strategies11:08 - Global AI Race and Export Controls13:13 - US-China AI Power Dynamics16:46 - US Regulatory Posturing and Competition21:55 - US and Chinese AI Model Competition24:58 - AI Infrastructure and Market Share Shifts31:40 - AI and Financial Markets: IPOs and Capital Flows36:29 - Open Source AI Projects and Sovereignty37:16 - Fintech and Payments: Stripe, PayPal, and Crypto49:05 - Digital Asset Headlines: Tether, Stablecoins, and Regulation52:58 - Crypto Market Dynamics and Capital Flows55:25 - Bitcoin and Digital Asset Strategies01:00:16 - AI and Bitcoin: The Future of Capital and Innovation01:12:50 - Closing Remarks and Future OutlookIf you found this valuable, please subscribe to Early Riders Insights for access to the best content in the ecosystem weekly: https://www.earlyriders.com/researchKeep up with Michael:https://x.com/MTangumaKeep up with Liam:https://x.com/Lnelson_21Keep up with Brian:https://x.com/BackslashBTC

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

 Halifax Part 1 of 2  Dr. Mary Travelbest Guide podcast.  Find my book series on the website​​ at https://www.5stepstosolotravel.com/ or on Amazon. It's a several-part series. Today's destination is Halifax,  Nova Scotia, Canada. Part 1 of 2 Eastern Canada is understated. This episode is about what I actually did there in August 2025, as a solo woman traveler. Halifax surprised me. It is a harbor city, a university city, a military city, a cruise-ship city, and a place where history seems to rise from the water. One of the first things I noticed is that Halifax is surrounded by water. The harbor is not just scenery. It is part of the city's identity. Ships, ferries, kayaks, cruise passengers, navy vessels, and waterfront walkers all share the same sense of place. So I walked, I spent a lot of time along the waterfront. There was music, people eating outside, and a lively summer feeling. I was there around August 4, which was a holiday, and I ate poutine at the buskers' event. Poutine was new to me. It is French fries with cheese curds and gravy, topped with ketchup. It was a different flavor for me, and that is part of travel: trying something even when you are not sure it will become your favorite. Halifax has a strong maritime history, and I visited the Maritime Museum. That was one of the highlights. I learned more about the Halifax Explosion of December 6, 1917, when two ships collided in the harbor. The explosion was enormous — often described as the world's largest pre-atomic explosion. I heard that the anchor landed miles away, the water was displaced, and a tsunami followed. The next day, there was a blizzard. That story stayed with me because it showed how much tragedy this city has survived. The Maritime Museum also connects you to other parts of Halifax history, including shipyards, dockyards, and the city's role as a port. If you are a first-time visitor, this is a good place to start because it helps you understand why Halifax matters. Immigration museum https://pier21.ca/ Look up your family history here, including whether you have US relatives. It had a 15-minute wait, but it was free. I also visited the Citadel, up on the hill. I spent about two hours there learning about the military and cultural changes in the area. It was informative, and I am glad I made the climb. Halifax has hills, so wear good walking shoes. You may think you are just going "up the hill to the museum," but your legs will know you are in a real city with elevation. Another special experience was my kayak tour around St. George's Island. It was a two-and-a-half-hour tour, and we learned about tunnels and armaments from the time when the British were responsible for the harbor's defense and security. It was expensive, but I was really glad I did it. Sometimes a higher-cost experience is worth it because it gives you a memory you would not get any other way. I also made time for beauty and quiet. I took bus number 1 to the Public Gardens, and they were beautiful. The main gate is near South Park Street and Spring Garden Road. That area is a good part of the city to explore. I wandered, took buses, and let myself explore different parts of Halifax rather than staying in just one tourist zone. One morning, I went swimming at the Centennial Pool at 8:30 AM. The water was chilly, but not freezing, and I swam for about 45 minutes. It felt fantastic. I love finding ordinary things in a new city — a public pool, a bus route, a grocery store, a coffee shop — because those places help me feel less like a tourist and more like a temporary resident. I noticed Tim Hortons everywhere. Strong coffee, bathrooms, and big business. For a traveler, especially one moving around by bus, knowing where you can get coffee and find a bathroom matters. I rode public buses and appreciated that they announced the next street before arriving. That was helpful when I was trying to figure out where I was going. At one point, I jumped on a 10C bus, not completely sure where it was going, but I was on South Park Street and Spring Garden Road and seeing more of Halifax. That is one of my travel styles: I like to wander, but I also pay attention. I passed St. Mary's University, Dalhousie University, Gorsebrook Park on Inglis Street, and other neighborhoods. Halifax has a strong student presence, which gives parts of the city energy. Quinpool Road felt like a main drag for shops. I also hopped at the Mic Mac Mall. I went to the Art Gallery, where Maud Lewis's paintings are an important draw. I saw Point Pleasant Park, one of my favorite parts of the city, especially around Young Avenue. Halifax has one gated street with six houses on the ocean, and that area felt peaceful and special. Point Pleasant Park also carries reminders of storms and resilience. I heard about a hurricane in 2010 that downed a huge number of trees. Nature is beautiful here, but also powerful. I also visited downtown Halifax, including St. Paul's Anglican Church, the city's oldest building, dating back to 1749. It is close to downtown and gives you a sense of how old Halifax is by North American standards. A few small details stayed with me. Some red traffic lights in Nova Scotia were square. The city has a Commons area for winter skating at the Emera Oval. There is an armory, the Scotiabank Center for events, cruise ships at the harbor, Irving gas stations, and a pretty waterfront at night. I even saw or heard about Theodore the Tugboat, which brought a lighter, cheerful side to the harbor. My food experiences included fish and chips, scallops, clams, poutine, and outdoor eating. I remember Olive and Rudy, scallops and clams, and the pleasure of eating outside when the weather is right. Halifax is a good place for seafood and simple travel meals. One day, I had a ham sandwich, an apple, a pear, an orange, and toast. That helped me manage costs while spending money on experiences. My caution for listeners: someone mentioned the "Dome" neighborhood as a place with many bars and a possible recipe for a bar fight. I would say this more gently: as a solo traveler, especially at night, be aware of nightlife districts. You do not have to avoid fun, but you do need to know your surroundings. The shownote includes a link to crime mapping in Halifax. https://www.halifax.ca/safety-security/police/crime-mapping   My mistake for this episode: assuming Halifax would be only a small waterfront stop. It was much more layered than that. My lesson: Halifax is not just a pretty Canadian harbor. It is a city of resilience, history, students, seafood, public spaces, and water views. Next up will be Part 2 of 2 on Halifax.    Travel Mistakes  My mistake for this episode: assuming Halifax would be only a small waterfront stop. It was much more layered than that. My travel tip: take the buses, walk the waterfront, and do at least one water-based experience — a ferry, harbor cruise, or kayak tour.     AI was used to select some of the suggestions for this episode.   Connect with Dr. Travelbest 5 Steps to Solo Travel website Dr. Mary Travelbest X Dr. Mary Travelbest Facebook Page Dr. Mary Travelbest Facebook Group Dr. Mary Travelbest Instagram Dr. Mary Travelbest Podcast Dr. Travelbest on TikTok Dr.Travelbest on YouTube In the news  

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What separates the investors and founders who thrive in moments of radical change from those who don't? According to Alec Litowitz, it isn't intelligence or emotional maturity - it's adaptability.Alec is the founder of Magnetar Capital, one of the most respected multi-strategy hedge funds in the world, and the founder and managing partner of QStar Capital, his single family office and investment platform. Over a 30-year career that began at J.P. Morgan, continued as a founding partner and global head of equities at Citadel, and culminated in building Magnetar from scratch, Alec developed a framework he calls the Adaptability Quotient - AQ - for making decisions under genuine uncertainty. His book, The Adaptability Quotient, publishes September 15th.Today, through QStar, Alec invests with no fund mandate and no LP constraints - thematically across both public and private markets, in everything from CoreWeave and SpaceX to top-tier VC and PE managers. That unconstrained vantage point, combined with three decades of pattern recognition across market regimes, gives him a distinctive lens on where venture capital sits inside the current moment of change.Nick and Beezer dig into the core distinction Alec draws between risk and uncertainty - a difference he argues most investors collapse at their peril - and how the AQ framework maps directly onto how founders build, how VCs back them, and how the venture ecosystem itself needs to adapt. They also get into what he calls the second cognitive revolution: why AI isn't just a new tool but a system-level regime change, what that means for the capital stack and liquidity timelines in venture, and why the answer for smaller players isn't resistance - it's remapping.Quotes"What entrepreneurs get paid for is not risk. They get paid for uncertainty, for resolving the uncertainty. People may stay at some stranger's house or they may not, but I don't know the probability. If it's high, I have a business. If it's zero, I don't have a business. Let's go resolve that probability. And when someone does a startup and tests it, raises money, probes around it, and gets feedback loops - the answer is yes. That's what they get paid for, for resolving that uncertainty."Time Stamps00:00 What Entrepreneurs Actually Get Paid For00:31 Introducing Alec Litowitz: Citadel, Magnetar, and QStar02:49 Three Career Chapters and the Through Line: A Systematic Approach to Uncertainty06:09 The Book: Why Alec Wrote The Adaptability Quotient07:29 AQ Defined: Why IQ and EQ Aren't Enough When the Frame Itself Changes9:40 Why QStar: No Constraints, No Mandates, Just Mapping the Moment12:22 QStar's Investment Framework: Thematic, Top-Down, Technocentric and Anthrocentric14:51 Why Venture Still Matters: The Venture 20, the Mag Seven, and Where Disruption Lives17:03 Risk vs. Uncertainty vs. Black Swan: The Framework Most Investors Get Wrong22:30 Applying AQ in Venture: MVPs as Probes, Pivots as Feedback Loops22:51 A Case Study in Failing Without Feedback Loops24:33 The Second Cognitive Revolution: Why AI Is a Regime Change, Not a Tool29:20 Is SaaS Uninvestable? What Becomes Abundant and What Becomes Scarce32:53 Mapping the Venture Ecosystem: Capital Intensity, New Entrants, and IRR Pressure37:08 The Liquidity Problem Reframed: DPI, TDPI, and Timeline Mismatch40:12 Secondary Markets as a Structural Response43:48 Final Advice: Upgrade Your Operating SystemLinksConnect with the guest and hosts on LinkedIn!Alec LitowitzBeezer ClarksonNick ChirlsLearn more about:The Adaptability Quotient (pre-order on Amazon)QStar CapitalMagnetar Capital⁠⁠Early Adapters Newsletter⁠⁠Asylum Ventures⁠⁠OpenLP

Jim and A.Ron gather a conspiracy of ravens this week to answer your feedback and questions about all things House of the Dragon. Then, from the Citadel at Oldtown, please welcome Maester Anthony! He unveils a secret scroll of his own design and joins A.Ron and Max to break down the spoiler questions. Theme song: Game of Thrones (80's TV Theme) by Highway Superstar Maester Anthony's Double Dragon on Spotify and Apple Podcasts  Support Bald Move:  Club Bald Move Leave Us A Review on Apple Podcasts Join the discussion:  Email  |  Discord  |  Reddit  |  Forums Follow us: Twitch | YouTube | Twitter  |  Instagram  |  Facebook Learn more about your ad choices. Visit megaphone.fm/adchoices

Jim and A.Ron gather a conspiracy of ravens this week to answer your feedback and questions about all things House of the Dragon. Then, from the Citadel at Oldtown, please welcome Maester Anthony! He unveils a secret scroll of his own design and joins A.Ron and Max to break down the spoiler questions. Theme song: ⁠Game of Thrones (80's TV Theme)⁠ by ⁠Highway Superstar⁠ Maester Anthony's Double Dragon on ⁠Spotify⁠ and ⁠Apple Podcasts⁠  Support Bald Move:  ⁠Club Bald Move⁠ ⁠Leave Us A Review on Apple Podcasts⁠ Join the discussion:  ⁠Email⁠  |  Discord  |  Reddit  |  ⁠Forums⁠ Follow us: ⁠Twitch⁠ | ⁠YouTube⁠ | ⁠Twitter⁠  |  ⁠Instagram⁠  |  ⁠Facebook⁠ Learn more about your ad choices. Visit megaphone.fm/adchoices

It was a pleasure to welcome David Silber, Head of Institutional Equity Derivatives at Citadel Securities, to the Alpha Exchange to discuss the evolution of listed options markets, institutional liquidity, and the technology reshaping modern derivatives trading. We begin with Dave's early career on the floor of the Chicago Board Options Exchange during the transition to multi-listed options, where market making, open outcry, and physical proximity to order flow defined liquidity provision. He reflects on the evolution of the options market from paper tickets and fractional pricing to today's electronic ecosystem, highlighting how advances in technology have fundamentally changed both price discovery and risk management. We then turn to the creation of Citadel Securities' institutional derivatives business. Dave explains how his experience across multiple firms led him to identify opportunities to reduce friction in institutional options execution by combining technology, quantitative research, and broad access to liquidity. He describes how automation, electronic execution, and competitive pricing have transformed the institutional trading experience while expanding access to listed options. The discussion also examines recent growth in listed options markets, including increasing contract volumes, shorter-dated expirations, and the expanding use of listed options by institutional investors for hedging, leverage, and portfolio management. Dave shares his perspective on liquidity provision, risk management, and the importance of maintaining resilient markets during periods of elevated activity. We conclude with a discussion on recruiting talent, developing strategy and data products for clients, and aligning sales, trading, and technology teams around creating a more efficient experience for institutional investors. I hope you enjoy this episode of the Alpha Exchange, my conversation with David Silber.

Ken Griffin, the founder and CEO of Citadel, expects agentic artificial intelligence (AI) to enable a “golden age” of entrepreneurship and eliminate some corporate moats, even as the cost of using AI creates a deep moat around other companies. And while some jobs may be replaced by technology, Griffin says he has found that productivity increases from AI have, instead of reducing headcount, allowed his company to pursue new opportunities.   Griffin shares his views on geopolitical tensions between the US and China, the need for domestic data center construction in the US, and a range of other factors rippling through global markets in this episode of Goldman Sachs Exchanges: Great Investors, recorded at Goldman Sachs's Apex Symposium.  This episode was recorded on June 2, 2026. The opinions and views expressed herein are as of the date of publication, subject to change without notice, and may not necessarily reflect the institutional views of Goldman Sachs or its affiliates. The material provided is intended for informational purposes only, and does not constitute investment advice, a recommendation from any Goldman Sachs entity to take any particular action, or an offer or solicitation to purchase or sell any securities or financial products. This material may contain forward-looking statements. Past performance is not indicative of future results. Neither Goldman Sachs nor any of its affiliates make any representations or warranties, express or implied, as to the accuracy or completeness of the statements or information contained herein and disclaim any liability whatsoever for reliance on such information for any purpose. Each name of a third-party organization mentioned is the property of the company to which it relates, is used here strictly for informational and identification purposes only and is not used to imply any ownership or license rights between any such company and Goldman Sachs. A transcript is provided for convenience and may differ from the original video or audio content. Goldman Sachs is not responsible for any errors in the transcript. This material should not be copied, distributed, published, or reproduced in whole or in part or disclosed by any recipient to any other person without the express written consent of Goldman Sachs. Disclosures applicable to research with respect to issuers, if any, mentioned herein are available through your Goldman Sachs representative or at ⁠http://www.gs.com/research/hedge.html⁠ Goldman Sachs does not endorse any candidate or any political party. Views of the interviewee do not necessarily reflect the views of Goldman Sachs.  Copyright 2026. All rights reserved. Learn more about your ad choices. Visit megaphone.fm/adchoices

Gullah-Geechee Diasporas: Knowledge, Culture, and Black Lowcountry Legacies (University of South Carolina Press, 2026) counters romantic portrayals of Gullah-Geechee culture as a static, geographically isolated remnant of the past. Across eight interdisciplinary essays, the book's contributors trace an arc, described in time and space, from pre-Middle Passage Africa through the Caribbean and coastal United States into the interior South and beyond. They consider how Gullah-Geechee cultural traditions are simultaneously rooted in the physical Lowcountry homeland and represent a dynamic cultural ethos that is not bounded by geography and has shaped Black life across North America and the Caribbean Basin. Together, these essays reveal the resilience and adaptability of people whose history defies myths of isolation and immobility. Gullah-Geechee Diasporas is a fresh framework for understanding African American cultural origins, migrations, and transformations. Dr. Muhammad Fraser-Rahim is associate professor of Intelligence and Security Studies at The Citadel. He is the author of America's Other Muslims and Gullah Geechee Muslims in America. You can find him on Instagram and LinkedIn.Dr. Elizabeth J. West is professor of English and the John B. and Elena Diaz-Verson Amos Distinguished Chair in English Letters at Georgia State University. Her books include Finding Francis and African Spirituality in Black Women's Fiction. She can be found online at Instagram and LinkedIn. Subscribe, like, follow, and rate Additions to the Archive with Sullivan Summer on Instagram, Substack, and wherever you get your podcasts. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/african-american-studies