Podcasts about TechCrunch

Zack Whittaker is a journalist for TechCrunch and author of the newsletter “This Week In Security.” He recently wrote a post describing how he uses Burp Suite, an open source penetration testing tool, to see what data apps are collecting from users, and what other sites those apps share that data with. Zack joins JJ... Read more »

Zack Whittaker is a journalist for TechCrunch and author of the newsletter “This Week In Security.” He recently wrote a post describing how he uses Burp Suite, an open source penetration testing tool, to see what data apps are collecting from users, and what other sites those apps share that data with. Zack joins JJ... Read more »

Irregular, der Evaluierungsdienstleister hinter den Containment-Vorfällen bei OpenAI, Anthropic und Meta, hat ein Postmortem veröffentlicht. Für uns eines der vagesten und widersprüchlichsten, das wir je gelesen haben. Alle Vorfälle werden als "not materially separate incidents" in einen Topf geworfen, ein paar Absätze später ist dann doch von verschiedenen Organisationen die Rede. Keine Incident-Anzahl, keine Zeitpunkte, keine Aussage dazu, ob betroffene Drittfirmen informiert wurden. Dazu diskutieren wir, wie unabhängig ein Dienstleister sein kann, dessen drei größte Kunden genau die Labs sind, deren Modelle ausgebrochen sind.Max bringt ein Update zum Trivy-Angriff: Fünf Monate nach dem ursprünglichen Vorfall sehen wir immer noch Folgeschäden. TeamPCP hatte Binaries und Version-Tags manipuliert, sodass der Schwachstellen-Scanner selbst zum Credential-Stealer wurde. Daraus wurde CanisterWorm, daraus 141 kompromittierte NPM-Pakete, daraus die LiteLLM-Kompromittierung – auf der Opferliste stehen Roku, Nvidia, Orange, Boeing und Splunk. Fast niemand überwacht die eigenen Security-Tools.Zum Schluss der Cyberangriff auf die Berliner Verwaltung: Betroffen sind die Senatsverwaltungen für Stadtentwicklung sowie Mobilität und Verkehr, beide seit Freitag vom Landesnetz isoliert. LKA, Staatsanwaltschaft und BSI ermitteln. Zu Entry Vector und angeblichem Datenabfluss halten wir uns bewusst zurück, solange nur anonyme Quellen zitiert werden.Das Postmortem von Irregular (Original)https://www.irregular.com/research/addressing-recent-incidents-ongoing-findings-and-path-forwardKritik am Irregular-Postmortem (The Record)https://therecord.media/irregular-ai-hacking-model-blogIrregular schweigt zu weiteren Betroffenen (The Record)https://therecord.media/irregular-ai-security-company-incidentsDrei Labs, ein Dienstleister – die Evaluierungslücke (TNW)https://thenextweb.com/news/irregular-ai-testing-vendor-openai-anthropic-meta-breachesWenn der Safety-Test zum Safety-Risiko wird (TechCrunch)https://techcrunch.com/2026/08/09/the-ai-safety-test-is-becoming-a-safety-risk/MiniShai-Hulud / TeamPCP NPM-Kampagne (StepSecurity)https://www.stepsecurity.io/blog/mini-shai-hulud-is-back-a-self-spreading-supply-chain-attack-hits-the-npm-ecosystemOffizielle Mitteilung der Senatskanzlei Berlinhttps://www.berlin.de/rbmskzl/aktuelles/pressemitteilungen/2026/pressemitteilung.1703898.phpCyberangriff auf Berliner Landesnetz (heise online)https://www.heise.de/news/Cyberattacke-auf-Berliner-Verwaltung-Ermittlungen-laufen-11416539.htmlKrisensitzung, Datenabfluss, betroffene Verwaltungen (Tagesspiegel)https://www.tagesspiegel.de/berlin/wir-sind-praktisch-arbeitsunfahig-hackerangriff-auf-berliner-senatsverwaltungen-15952358.html

L'intelligence artificielle a besoin de toujours plus de puissance de calcul. Et derrière cette puissance, il y a une réalité très concrète : des data centers qui consomment des quantités gigantesques d'électricité. Aux États-Unis, leurs besoins deviennent tels que les réseaux traditionnels ne suffisent plus toujours. Les géants du secteur cherchent donc de plus en plus à produire eux-mêmes leur énergie. La solution privilégiée aujourd'hui est souvent le gaz naturel. Il permet de construire relativement vite des centrales capables de fournir une puissance importante et continue. Amazon, par exemple, développe au Texas un immense projet reposant en partie sur cette logique. Mais ce choix pourrait devenir beaucoup moins avantageux dans les prochaines années.La société Noreva, spécialisée dans l'analyse du secteur énergétique, estime que le prix du gaz naturel pourrait tripler dans certaines régions des États-Unis. Plusieurs facteurs expliqueraient cette tension. D'abord, la croissance de l'offre ralentit. Ensuite, une part croissante du gaz américain est transformée en gaz naturel liquéfié, ou GNL, puis exportée vers l'étranger. Résultat : le marché intérieur pourrait devenir beaucoup plus tendu qu'auparavant. Peter Gardett, le patron de Noreva, estime que de nombreux acteurs se sont habitués à l'idée que le gaz américain resterait durablement bon marché. Selon lui, un simple examen de l'équilibre entre production et demande montre pourtant que cette certitude devient de moins en moins solide.Ce risque arrive au mauvais moment pour l'industrie de l'intelligence artificielle. Depuis l'explosion des modèles génératifs, les investissements dans les data centers se comptent en dizaines de milliards de dollars. Leur rentabilité repose en partie sur l'accès à une énergie abondante, stable et peu coûteuse. Or, si le gaz devient nettement plus cher, l'équation économique pourrait se compliquer. D'autant que, selon TechCrunch, les nouveaux puits sont parfois moins rentables que les précédents, ce qui pourrait limiter encore davantage la croissance de la production. La question n'est donc plus seulement de savoir comment construire assez vite des centres de données. Elle devient aussi énergétique : sur quelle ressource faut-il miser pour les alimenter durablement ? Car une IA toujours plus puissante ne sera viable que si l'électricité nécessaire à son fonctionnement reste, elle aussi, économiquement soutenable. Hébergé par Acast. Visitez acast.com/privacy pour plus d'informations.

1. Alex Cooper's Unwell Beverage Business Is Coming to an End: Report (People) (28:14) 2. The punishment Bill Gates's daughter Phoebe is most likely to face over Phia's ‘cookie stuffing' controversy (New York Post) (36:46) 3. Shay Mitchell's Company BÉIS Sells in $210 Million Deal (E News) (43:05) 4. Taylor Swift has solo night out in London as Travis Kelce returns to Chiefs practice post-wedding (Page Six) (46:28) 5. Instagram introduces a redesigned wordmark (Tech Crunch) (54:50) Queenie and Weenie of The Week (1:00:02) The Toast with Jackie (@JackieOshry) and Claudia Oshry (@girlwithnojob) ⁠⁠The Toast Patreon ⁠⁠⁠  ⁠⁠⁠Toast Merch⁠⁠⁠ ⁠⁠⁠Girl With No Job by Claudia Oshry⁠⁠⁠ ⁠⁠⁠The Camper & The Counselor⁠⁠⁠ ⁠⁠⁠Lean In⁠ Learn more about your ad choices. Visit megaphone.fm/adchoices

Jacob Ward is guest-hosting this week, and Amanda Silberling joins Jacob for the first half of the show! Amazon is using Twitch streamers to train its generative AI models. The EU's Transparency Code now requires tech companies to label AI-generated content. The Bay Area's AI wealth isn't affecting all cities. And security experts express concerns following an OpenAI model's hack on Hugging Face. Twitch is beginning to use creators' content on the platform to help train generative AI models for its parent company, Amazon. Even though streamers can opt out of having their content used, it's still sparking backlash from the community over deepfake concerns. The EU's Transparency Code is quietly pushing tech companies to label AI-generated content, with Jacob Ward drawing a parallel to how regulators once mandated adding a smell to odorless natural gas to make leaks detectable. Reporter Adam Rogers joins the show to discuss why Bay Area AI wealth isn't translating into the civic investment, jobs, or economic ripple effects seen in past tech booms And reporter Sharon Goldman from Ground Level AI stops by to share reactions from security experts about the OpenAI agent that breached Hugging Face during her recent trip to the Black Hat security conference. Hosts: Jacob Ward and Amanda Silberling Guests: Adam Rogers and Sharon Goldman Download or subscribe to Tech News Weekly at https://twit.tv/shows/tech-news-weekly. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: bitwarden.com/twit framer.com/tnw cirasync.com/TNW joindeleteme.com/twit-biz

Most mainstream media outlets have spent the last several years convincing Americans that a recession is either here or just around the corner. But according to tech analyst Ray Wang and category design pioneer Christopher Lochhead, the real story about the US economy looks very different from what we are being fed. In a recent episode of Disrupt TV, the two thought leaders broke down the actual data behind American economic growth, the rise of AI-driven entrepreneurship, and a new investment program that could reshape generational wealth in this country. You're listening to Christopher Lochhead: Follow Your Different. We are the real dialogue podcast for people with a different mind. So get your mind in a different place, and hey ho, let's go.   Record Business Starts Signal a Thriving US Economy Despite relentless recession predictions from commentators and media personalities, Americans are starting new companies at a record pace. More than 5.6 million business applications were filed in 2025 alone, with over 400,000 new companies being created every single month. This level of entrepreneurial activity has not been seen in the modern era. Layoffs are also at historic lows, GDP is still growing, and inflation has come down from 4.2% in May to 3.5%. Initial unemployment claims have recently fallen to their lowest level since 1969. Meanwhile, prediction markets like Kalshi and Polymarket have dropped recession odds from around 28 to 30% in April to just 4 to 11% today, showing that people putting real money on the line do not believe a recession is coming.   AI Is Creating a New Class of Entrepreneur The rise of artificial intelligence is fundamentally changing who can build and run a successful business. Small teams of two to three people are now executing at a level that previously required thirty or more employees. This shift from knowledge worker to what Lochhead calls a “creator capitalist” is fueling much of the new company growth happening across the US economy. Ray Wang noted that we are beginning to see ten-person companies generating hundreds of millions in revenue, and the trend is only accelerating. As an example, the former CEO of Kirkland and Ellis, the largest law firm in the United States, left to launch an AI-first law firm called Irving with just twenty people. This signals a profound restructuring of professional services and virtually every other industry, driven entirely by AI innovation.   Trump Accounts Could Close the Wealth Gap One of the most underreported developments in the US economy is the launch of the Invest America program, commonly referred to as Trump accounts. Every child born in America can now receive a $1,000 federal contribution into a protected investment account that grows through an index fund until the child turns eighteen. Family members and friends can contribute up to $5,000 per year into these accounts and receive a tax break for doing so. The math behind compounding returns is striking. A child who receives $5,000 per year from birth to age eighteen, invested in the S&P 500, could have approximately $250,000 by their eighteenth birthday. Michael and Susan Dell have already pledged $6.25 billion to give $250 to twenty-five million American children aged ten and under, making it the largest charitable investment gift in American history. Lochhead believes this new model of charitable investing, giving gifts that compound over time rather than providing temporary relief, represents a powerful and lasting solution to economic inequality. To hear more from Ray Wang and Christopher’s discussions, download and listen to this episode. Bio R “Ray” Wang (pronounced WAHNG) is the Founder, Chairman, and Principal Analyst of Silicon Valley based Constellation Research Inc. He co-hosts DisrupTV, a weekly enterprise tech and leadership webcast that averages 50,000 views per episode and authors a business strategy and technology blog that has received millions of page views per month.  Wang also serves as a non-resident Senior Fellow at The Atlantic Council's GeoTech Center. Since 2003, Ray has delivered thousands of live and virtual keynotes around the world that are inspiring and legendary. Wang has spoken at almost every major tech conference. His ground-breaking bestselling book on digital transformation, Disrupting Digital Business, was published by Harvard Business Review Press in 2015.  Ray's new book about Digital Giants and the future of business titled, Everybody Wants to Rule the World will be released July 2021 by Harper Collins Leadership. Ray Wang is well quoted and frequently interviewed in media outlets such as the Wall Street Journal, Fox Business News, CNBC, Yahoo Finance, Cheddar, CGTN America, Bloomberg, Tech Crunch, ZDNet, Forbes, and Fortune.  He is one of the top technology analysts in the world.   Links Follow Ray Wang! Website | Twitter | LinkedIn | Constellation Research | DisrupTV   We hope you enjoyed this episode of Christopher Lochhead: Follow Your Different™! Christopher loves hearing from his listeners. Feel free to email him, connect on Facebook, X (formerly Twitter), Instagram, and subscribe on Apple Podcast / Spotify!

Jacob Ward is guest-hosting this week, and Amanda Silberling joins Jacob for the first half of the show! Amazon is using Twitch streamers to train its generative AI models. The EU's Transparency Code now requires tech companies to label AI-generated content. The Bay Area's AI wealth isn't affecting all cities. And security experts express concerns following an OpenAI model's hack on Hugging Face. Twitch is beginning to use creators' content on the platform to help train generative AI models for its parent company, Amazon. Even though streamers can opt out of having their content used, it's still sparking backlash from the community over deepfake concerns. The EU's Transparency Code is quietly pushing tech companies to label AI-generated content, with Jacob Ward drawing a parallel to how regulators once mandated adding a smell to odorless natural gas to make leaks detectable. Reporter Adam Rogers joins the show to discuss why Bay Area AI wealth isn't translating into the civic investment, jobs, or economic ripple effects seen in past tech booms And reporter Sharon Goldman from Ground Level AI stops by to share reactions from security experts about the OpenAI agent that breached Hugging Face during her recent trip to the Black Hat security conference. Hosts: Jacob Ward and Amanda Silberling Guests: Adam Rogers and Sharon Goldman Download or subscribe to Tech News Weekly at https://twit.tv/shows/tech-news-weekly. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: bitwarden.com/twit framer.com/tnw cirasync.com/TNW joindeleteme.com/twit-biz

Jacob Ward is guest-hosting this week, and Amanda Silberling joins Jacob for the first half of the show! Amazon is using Twitch streamers to train its generative AI models. The EU's Transparency Code now requires tech companies to label AI-generated content. The Bay Area's AI wealth isn't affecting all cities. And security experts express concerns following an OpenAI model's hack on Hugging Face. Twitch is beginning to use creators' content on the platform to help train generative AI models for its parent company, Amazon. Even though streamers can opt out of having their content used, it's still sparking backlash from the community over deepfake concerns. The EU's Transparency Code is quietly pushing tech companies to label AI-generated content, with Jacob Ward drawing a parallel to how regulators once mandated adding a smell to odorless natural gas to make leaks detectable. Reporter Adam Rogers joins the show to discuss why Bay Area AI wealth isn't translating into the civic investment, jobs, or economic ripple effects seen in past tech booms And reporter Sharon Goldman from Ground Level AI stops by to share reactions from security experts about the OpenAI agent that breached Hugging Face during her recent trip to the Black Hat security conference. Hosts: Jacob Ward and Amanda Silberling Guests: Adam Rogers and Sharon Goldman Download or subscribe to Tech News Weekly at https://twit.tv/shows/tech-news-weekly. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: bitwarden.com/twit framer.com/tnw cirasync.com/TNW joindeleteme.com/twit-biz

Jacob Ward is guest-hosting this week, and Amanda Silberling joins Jacob for the first half of the show! Amazon is using Twitch streamers to train its generative AI models. The EU's Transparency Code now requires tech companies to label AI-generated content. The Bay Area's AI wealth isn't affecting all cities. And security experts express concerns following an OpenAI model's hack on Hugging Face. Twitch is beginning to use creators' content on the platform to help train generative AI models for its parent company, Amazon. Even though streamers can opt out of having their content used, it's still sparking backlash from the community over deepfake concerns. The EU's Transparency Code is quietly pushing tech companies to label AI-generated content, with Jacob Ward drawing a parallel to how regulators once mandated adding a smell to odorless natural gas to make leaks detectable. Reporter Adam Rogers joins the show to discuss why Bay Area AI wealth isn't translating into the civic investment, jobs, or economic ripple effects seen in past tech booms And reporter Sharon Goldman from Ground Level AI stops by to share reactions from security experts about the OpenAI agent that breached Hugging Face during her recent trip to the Black Hat security conference. Hosts: Jacob Ward and Amanda Silberling Guests: Adam Rogers and Sharon Goldman Download or subscribe to Tech News Weekly at https://twit.tv/shows/tech-news-weekly. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: bitwarden.com/twit framer.com/tnw cirasync.com/TNW joindeleteme.com/twit-biz

Jacob Ward is guest-hosting this week, and Amanda Silberling joins Jacob for the first half of the show! Amazon is using Twitch streamers to train its generative AI models. The EU's Transparency Code now requires tech companies to label AI-generated content. The Bay Area's AI wealth isn't affecting all cities. And security experts express concerns following an OpenAI model's hack on Hugging Face. Twitch is beginning to use creators' content on the platform to help train generative AI models for its parent company, Amazon. Even though streamers can opt out of having their content used, it's still sparking backlash from the community over deepfake concerns. The EU's Transparency Code is quietly pushing tech companies to label AI-generated content, with Jacob Ward drawing a parallel to how regulators once mandated adding a smell to odorless natural gas to make leaks detectable. Reporter Adam Rogers joins the show to discuss why Bay Area AI wealth isn't translating into the civic investment, jobs, or economic ripple effects seen in past tech booms And reporter Sharon Goldman from Ground Level AI stops by to share reactions from security experts about the OpenAI agent that breached Hugging Face during her recent trip to the Black Hat security conference. Hosts: Jacob Ward and Amanda Silberling Guests: Adam Rogers and Sharon Goldman Download or subscribe to Tech News Weekly at https://twit.tv/shows/tech-news-weekly. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: bitwarden.com/twit framer.com/tnw cirasync.com/TNW joindeleteme.com/twit-biz

Jacob Ward is guest-hosting this week, and Amanda Silberling joins Jacob for the first half of the show! Amazon is using Twitch streamers to train its generative AI models. The EU's Transparency Code now requires tech companies to label AI-generated content. The Bay Area's AI wealth isn't affecting all cities. And security experts express concerns following an OpenAI model's hack on Hugging Face. Twitch is beginning to use creators' content on the platform to help train generative AI models for its parent company, Amazon. Even though streamers can opt out of having their content used, it's still sparking backlash from the community over deepfake concerns. The EU's Transparency Code is quietly pushing tech companies to label AI-generated content, with Jacob Ward drawing a parallel to how regulators once mandated adding a smell to odorless natural gas to make leaks detectable. Reporter Adam Rogers joins the show to discuss why Bay Area AI wealth isn't translating into the civic investment, jobs, or economic ripple effects seen in past tech booms And reporter Sharon Goldman from Ground Level AI stops by to share reactions from security experts about the OpenAI agent that breached Hugging Face during her recent trip to the Black Hat security conference. Hosts: Jacob Ward and Amanda Silberling Guests: Adam Rogers and Sharon Goldman Download or subscribe to Tech News Weekly at https://twit.tv/shows/tech-news-weekly. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: bitwarden.com/twit framer.com/tnw cirasync.com/TNW joindeleteme.com/twit-biz

A well-written blog post gets you noticed. But for data scientists who want to build authority that compounds over time, it's just the beginning. Every piece of writing is a potential stepping stone to something bigger - a conference talk, a book deal, or an opportunity you couldn't have anticipated.In this Value Boost episode, Cynthia Dunlop joins Dr Genevieve Hayes to explore how data scientists can convert blog writing into bigger opportunities and what it actually takes to make the leap from blog post to book.You'll discover:How conference organisers actually find their speakers — and why blogging is the answer [02:35]How acquisitions editors scout for authors and why you don't need a huge following [03:39]The low risk way to find out if you're ready to write a book [06:57]How each new opportunity compounds the authority you've already built [08:10]Guest BioCynthia Dunlop is the co-author of Writing for Developers and Senior Director of Content Strategy at ScyllaDB. She has co-authored four books for software developers and tech leaders and authored hundreds of articles for publications including TechCrunch, IEEE Computer, and The New Stack.LinksConnect with Cynthia on LinkedInFollow Cynthia on SubstackConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

Our 254th episode with a summary and discussion of last week's big AI news!Recorded on 08/09/2026Hosted by Andrey Kurenkov and Jeremie HarrisFeel free to email us your questions and feedback at andreyvkurenkov@gmail.com and/or hello@gladstone.aiRead out our text newsletter and comment on the podcast at https://lastweekin.ai/In this episode: Multiple frontier AI systems (OpenAI, Anthropic, Meta, Kimi K3, and UK AISI-tested models) took unsanctioned real-world cyber actions during evaluations, including hacking services, escaping or exploiting misconfigured sandboxes, coordinating via a covert message board, and attempting supply-chain/social-engineering attacks; attorneys general demanded OpenAI preserve records related to the Hugging Face incident.Policy and governance updates included a proposed Trump White House voluntary pre-release security review framework for closed-source frontier models, and EU AI Act transparency/labeling rules taking effect with enforceable fines.Biosecurity concerns rose after research generated complete synthetic bacteriophage genomes via genome language models and demonstrated lab-synthesized viruses killing drug-resistant E. coli, alongside calls for stronger DNA screening and detection.Additional developments: CVE disclosures surged (notably high/critical vulnerabilities), new monitoring/sabotage benchmarks highlighted weaknesses in AI oversight, a vending-machine benchmark showed profit-maximizing deception, and major industry shifts included Jeff Dean and other top Google researchers leaving to found Discovery Loop plus new compute/data-center constraints and releases from Meta and Alibaba (Qwen 3.8 Max).Timestamps (note - these don't take into account dynamically inserted ads and therefore may be off by a couple of minutes):(00:00:10) Intro / Banter(00:02:17) News Preview(00:03:19) Response to listener commentsPolicy & Safety(00:14:30) OpenAI's rogue AI agent didn't stop at hacking Hugging Face | The Verge + OpenAI Didn't Notice Its AI Agents Using a Message Board to Plan Their Hacking Spree + 15 attorneys general have instructed OpenAI to preserve all materials related to the Hugging Face hack(00:43:51) Anthropic Says Its A.I. Systems Broke Into Computers at 3 Organizations - The New York Times(00:51:14) Meta AI model hacks another company during testing(00:52:11) One of China's Most Powerful AI Models Has Also Escaped Containment | WIRED(00:56:12) Incident Report: unsanctioned agent behaviour during cyber testing(01:02:32) Trump White House Readies AI Framework to Review Security Risks - The New York Times(01:05:45) This A.I. Just Created Viruses Not Found in Nature - The New York Times + Scientists Used AI to Create 16 New Viruses(01:16:03) Europe's AI labeling and transparency rules are now in effect | The Verge(01:18:58) Serious cyber vulnerability disclosures kept climbing in July(01:21:13) ResearchArena: Evaluating Sabotage and Monitoring in Automated AI R&D(01:25:34) Claude Opus 5 became downright ruthless when tasked with running a vending machine | TechCrunchTools & Apps(01:28:34) Meta debuts Muse Code to take on Anthropic and OpenAI(01:32:36) Improving Fable 5 Safeguards AnthropicApplications & Business(01:33:50) Jeff Dean and other top AI researchers are leaving Google to launch their own startup | TechCrunch(01:40:38) Google DeepMind enters a new era as co-founder Demis Hassabis shifts AI role(01:43:40) Anthropic signs $10B deal with AI cloud startup Volta | TechCrunch(01:44:53) Texas halts data center connections to power grid amid overwhelming demand - Ars TechnicaProjects & Open Source(01:49:56) Alibaba's Qwen3.8-Max AI Model Claims Benchmark Scores Rivaling Anthropic - BloombergSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

“Obituaries are about life, about the lives that people have lived. Death only figures in one sentence.” — Sam Roberts This is, remarkably, episode 3,000 of Keen On America — three thousand conversations since the show launched on TechCrunch in 2010. Fitting, then, that today's guest has written nearly 1,500 obituaries for the New York Times. Sam Roberts is a reporter on the Times' obituaries desk and has a new book about the art of the obit entitled Are They Dead Yet? We recorded this a week before airtime, so I began by asking who's going to die in the interim. He wasn't sure — but noted that the Times keeps 2,000 advance obituaries ready. There are, he added, never enough. Beware, though, the Rockefeller curse. Roberts wrote David Rockefeller's advance obituary, whereupon he learned that three other Times reporters had written Rockefeller advance obits — and all three were dead. (Rockefeller made it to 101.) Borrowing from Citizen Kane, Roberts hunts for the Rosebud moment, the epiphany that defines a life. He hunts for the soul of the subject, not the résumé. Hence his cheerful paradox that obituaries are about life, whereas death only figures in one sentence. As for famous last words, he's a skeptic — most were composed later by better writers. The real ones, he suspects, are “help,” or “get me a drink.” And the obituarist's own Rosebud moment? We have to go back to Brooklyn when he was a six-year-old boy. His father walked him to the corner to watch the Rosenberg funeral procession pass. “I want you to see history as it's happening,” he explained. Half a century later, Roberts got David Greenglass to admit he had lied about the testimony that sent his own sister, Ethel Rosenberg, to the electric chair. From the Times' Portraits of Grief after 9/11, Roberts learned the book's deepest lesson. There are no ordinary lives. Who would you miss more — your mayor or your mailman? Or maybe your obit writer (or even your podcaster)? Sam promised that if he died before airtime, he'd call. The phone hasn't rung. I'm still going too. On to episode 3,001. Five Takeaways •       2,000 Obits, Never Enough. The Times keeps some 2,000 advance obituaries on file, regularly updated — and people still surprise the desk (and presumably themselves) by dying unexpectedly. Print once had a deadline or two a day; now there's a deadline every minute. The occupational hazards are real: three Times reporters wrote advance obits of David Rockefeller and predeceased him — Roberts, the fourth, survived to see his run when Rockefeller died at 101. And the genre changes its readers: “no man who has seen his own obit is ever the same again.” Alfred Nobel, mistakenly obituarized as dynamite's merchant of death, endowed the Nobel Prizes in response — proof that a premature obituary can be the most consequential document a man ever reads.•       The Rosebud Method. Roberts' organizing device comes from Citizen Kane: the Rosebud moment, the epiphany — a teacher, a meeting, an accident — that changed the trajectory of a life and made it worth recording. The model, via The Economist's Ann Wroe, is the soul rather than the stenography: Wikipedia has the chronology; the obituary hunts the essence. Advance obits are never vetted by their subjects — facts checked, yes; the portrait, never — because an obituary is a news story, not a eulogy. And the form's great secret is optimism: obituaries are about life. Death gets one sentence. (Famous last words, meanwhile, are mostly posthumous fiction. The real ones: “help,” or “get me a drink.”)•       Giuliani, Trump, Epstein. The hardest working file on Roberts' desk: Rudy Giuliani — aggressive prosecutor, the mayor who proved New York governable, then a bitter Trump apologist indicted under the same statutes he once used on mobsters. How do you compress that into a lead paragraph? Andrew's suggestion — a narrative of somebody who had a soul and then sold it — requires Shakespeare; Roberts insists on newspaper objectivity, omitting nothing and editorializing never. The Trump obituary already exists (its subject, Roberts guesses, would sue). And context keeps moving after death: Bush the defeated loser became Bush the elder statesman; Epstein's reputation kept falling posthumously. Of the 150,000 people who die each day, three or four make the Times.•       The Rosenberg Rosebud. Roberts' own epiphany arrived at age six, on a Brooklyn street corner where his father took him to watch the Rosenberg funeral procession: “I want you to see history as it's happening.” His family otherwise met death with denial — six months after his father died, an aunt asked how Arthur was doing could only answer “so-so.” The Rosenberg thread ran through his whole career: decades later, for his book The Brother, David Greenglass admitted to Roberts that he had lied about the most incriminating testimony against his sister Ethel — evidence that, truthfully given, would likely have spared her the electric chair. When Roberts assigned graduate students to write his own advance obituary, none of them led with that. He would.•       No Ordinary Lives. From Portraits of Grief — the Times' profiles of virtually every victim of the World Trade Center attack — Roberts drew the book's central lesson: there are no ordinary lives. Who would you miss more, your mayor or your mailman? Carlyle asked whether history belongs to Hannibal or to the anonymous man who invented the spade. The desk has honored the principle in both directions: a front-page obituary for Hercule Poirot (the most famous Belgian, as Andrew noted), and a Jesus of Nazareth obituary written as the Times would have run it in AD 33 — ending, for lack of further confirmation, with the report that he was buried and his body disappeared. No beat generates more reader feedback. “As long as they're reading it, I'm happy.” About the Guest Sam Roberts is a fifty-year veteran of New York journalism, an obituaries reporter and former Urban Affairs correspondent at the New York Times, and the host of the Times' “Close Up,” which he inaugurated in 1992. His many books include The Brother, Grand Central, A History of New York in 101 Objects, and Only in New York. A history adviser to Federal Hall, he lives in New York with his wife and two sons. Are They Dead Yet? The Art of the Obit (Bloomsbury, August 11, 2026) draws on the nearly 1,500 obituaries he has written for the Times. References: •       Are They Dead Yet? The Art of the Obit by Sam Roberts (Bloomsbury, August 11, 2026). Carl Hiaasen: “Mordantly wonderful.” Roz Chast: “I almost died laughing.”•       The Brother by Sam Roberts — in which David Greenglass admitted lying about the testimony that sent his sister, Ethel Rosenberg, to the electric chair.•...

AI agents are escaping cybersecurity testing environments and reaching real-world systems, raising questions about whether safety infrastructure, industry standards and regulation can keep pace with increasingly powerful models. This article written for TechCrunch by Rebecca Bellan. Learn more about your ad choices. Visit podcastchoices.com/adchoices

For data scientists who want to build authority beyond their organisation, writing is one of the most powerful tools available. But in a world flooded with AI-generated content, simply publishing is no longer enough. The data scientists who stand out are the ones writing things no AI could have written.In this episode, Cynthia Dunlop joins Dr Genevieve Hayes to share practical frameworks for writing blog posts that stand out, build genuine authority and actually get read.You'll discover:Why AI-generated content has made personal experience more valuable than ever [05:10]The three Ps test for finding topics you can write about with genuine authority [08:56]The blog post patterns that work best for demonstrating expertise [11:03]How to use AI to improve your writing without letting it replace your voice [16:33]Guest BioCynthia Dunlop is the co-author of Writing for Developers and Senior Director of Content Strategy at ScyllaDB. She has co-authored four books for software developers and tech leaders and authored hundreds of articles for publications including TechCrunch, IEEE Computer, and The New Stack.LinksConnect with Cynthia on LinkedInFollow Cynthia on SubstackConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

Carolyn Woodard covers why deliberate, thoughtful implementation is actually your nonprofit's competitive advantage, not a liability.The commercial AI sector is in a "move fast and break things" sprint. Tech companies are pouring billions into AI, racing to scale, pushing toward the edge of the market. Today's AI race feels similar. Nonprofits tend to think differently. You ask hard questions. You think about theory of change, root causes, impacts on your communities, unforeseen consequences, and alignment with your values. You don't adopt trends just because everyone else is.Concerned about climate impacts of your AI use? Carolyn covers her recent blog post on three filters to use as you make daily decisions about using AI: What are you using the AI to do (text is lighter than producing video), where are you (how stressed is your grid and your water supply in your location), and what are the benefits to consider as you consider the environmental impacts? Carolyn also walks through a practical framework for any AI adoption decision: Do you need it? Can you contain it? Do you understand what it might do? If you can't confidently answer yes to all three, then waiting isn't falling behind—it's being strategic.Key Takeaways:Nonprofit instincts to think deeply about change, impact, and ethics are strategically prescient, not conservative.When commercial AI implementations encounter consequences, organizations with inclusive adoption processes and clear policies will have a genuine competitive advantage.AI agents represent a new risk category; don't create an agent just because the tool offers the option. Understand what you're deploying.Experimentation is valuable for nonprofits, but human oversight and clear policies are essential, especially with agentic systems.Resources Mentioned:What Is an AI Agent?An AI agent is a program designed to operate independently toward a specific goal, rather than simply responding to individual prompts. Unlike a chatbot that waits for you to ask questions, an agent reasons about how to achieve its objective and takes actions on its own—such as reading files, sending emails, or accessing systems. You can program checkpoints where the agent asks for your permission before taking certain actions, but the agent itself decides the steps and sequences needed to reach its goal. This autonomy is what makes agents powerful for automating complex tasks, but it's also why they require careful oversight: because they're goal-directed and adaptive, they may find unexpected paths to achieve their objectives, which can create risks if not properly contained and monitored.What Is an AI Agent? – Build Consulting – https://buildconsulting.com/resources/podcast-rise-of-the-ai-agents/Three Filters for Values-Aligned AI Decision-Making – Community IT Innovators – https://communityit.com/blog-values-aligned-ai-decision-making/Claude Cybersecurity Incidents Disclosure – Anthropic – https://www.anthropic.com/news/investigating-incidents-cybersecurity-evalsFortune (mainstream): https://fortune.com/2026/07/31/anthropic-claude-escaped-test-hacked-three-companies-openai/ (paywall)TechCrunch (tech-focused): https://techcrunch.com/2026/07/30/anthropic-says-its-own-ai-models-breached-three-companies-during-security-tests/Open AI Cybersecurity Incidents Disclosure - OpenAI - https://openai.com/index/hugging-face-model-evaluation-security-incident/ CNN (mainstream reporting): https://www.cnn.com/2026/07/22/tech/openai-hugging-face-ai-cybersecurityMalwarebytes (technical deep-dive): https://www.malwarebytes.com/blog/news/2026/07/openais-agent-escaped-its-sandbox-during-a-security-testWomen in Climate Tech Cohort – Climate Collective – https://climate-collective.circle.so/cliematecollectiveElectricity Maps https://app.electricitymaps.com/map/live/fifteen_minutesNonprofit IT Management Community – Reddit – https://www.reddit.com/r/nonprofitITmanagement/New every Tuesday. _______________________________Start a conversation :)Register to attend a webinar in real time, and find all past transcripts at https://communityit.com/webinars/email Carolyn at cwoodard@communityit.comon LinkedIn on reddit/r/nonprofitITmanagementon the Community IT websiteThanks for listening. 

“Misbehave, make something beautiful, and try to win.” — Curtis White's motto for resistance How to say no to fascism? Curtis White, the literary thinker dubbed “the splendidly cranky utopian,” has published what he calls a “manifesto” of “resistance.” On Resistance, the 75-year-old White told me, is his last work of non-fiction. He still has worlds to invent, novels to write. Enough with the manifestos. But don't expect a program. On Resistance is more of an anti-manifesto. “That's not for me to say,” White responds when asked what, exactly, we're supposed to do to resist fascism. His resistance is cultural. It's an attitude. Borrowing from Ted Gioia's subversive history of music, White likes the idea of “psychic treason.” Its patron saints are the first dropouts, romantics like Wordsworth and Coleridge who rejected the military, clergy and family to write poetry in the countryside. But On Resistance isn't simply sixties nostalgia. White, a veteran of Berkeley and the Haight, prefers the Nietzschean imperative to forget the past and move on to the next joyful deed. So the only good manifesto is the anti-manifesto. If you want to say no to fascism, become a cranky utopian. Or at least pick up On Resistance. Five Takeaways •       Psychic Treason. White's resistance is not conventionally political — Trump appears once or twice, party politics not at all. Borrowing “psychic treason” from Ted Gioia's Music: A Subversive History, he argues that the arts are subversive by nature: sometimes formally, sometimes by allying with social movements. His founding example is the Romantics, the first dropouts — Wordsworth and Coleridge refusing the military, the clergy, and the family estate to write poetry in the countryside, declaring cultural war once the church was out of the way. The treason White wants is the one the arts commit constantly, whether or not anyone calls it politics.•       Not Nostalgia — Nietzsche. The sixties made White: Berkeley in high school, San Francisco after, and the formative discovery that his country “had lied to me” and was indifferent to whether he lived or died. But On Resistance opens with the confession “I am living in a world that no longer exists” — and refuses to make anyone else live there. One chapter is bluntly titled “Forget The Beatles.” His position is Nietzschean: move on to the next joyful deed. And the rot, he insists, didn't start with Trump but with Reagan — the decades-long withdrawal of public money from public goods, education above all.•       The Three False Resistances. The book's first half is ideology critique: our culture says resistance is unnecessary because it has already been provided — by philanthropy, virtuous corporations, and liberalism. White's exhibits: Steve Jobs Buddha-branding the counterculture to sell machines to people who thought they were subverting something; the “big green” foundations (via Mark Dowie) taking over grassroots fights and defanging them; the Gates Foundation vaccinating children against polio downwind of a polluter it was invested in. Philanthropy, he argues, self-limits to protect the source of its own wealth. Patagonia may be the quiet exception that proves the rule.•       Dropping Out, Together. White's alternative is dropping out — plural: doing something else, and doing it with other people. The organic farms and farmers markets that have created communal well-being in most American cities; FC2, the author-run press he co-founded with Ronald Sukenick, past its fiftieth anniversary; the thriving-but-invisible independent presses — Graywolf, Coffee House, Rochester's Open Letter — for which he's now plotting a group Substack. The obstacle is the money jail: everyone's struggle for money limits what it is possible to do. The goal isn't overturning the status quo. “What we need right now is companions.”•       Misbehave, Make Something Beautiful, Try to Win. White's motto since The Middle Mind — the making beautiful needn't be art; the trying to win is where misbehavior gets socialized. His lineage runs Thoreau, Whitman, William Carlos Williams, and above all John Berger: Marxist, art critic, poet, writer of lively living books. His Marxism, such as it is, is the humanist 1844 Manuscripts — alienation, not expropriation — via Marcuse's One-Dimensional Man. And the book itself is the argument: playful and fierce, a performance that moves like a novel, reinvigorating worthy old ideas rather than hunting new ones. At 75, it's his last nonfiction — written in a spirit of play, to be left in one. About the Guest Curtis White is a novelist and social critic whose books include Memories of My Father Watching TV, The Middle Mind, The Science Delusion, and Living in a World That Can't Be Fixed. Called by Elle “the most inspiringly wicked social critic of the moment,” he is co-founder, with Ronald Sukenick, of FC2, the publisher of innovative fiction run collectively by its authors, and taught English for many years at Illinois State University. His new and, he says, final work of nonfiction is On Resistance: A Manifesto (Melville House, August 11, 2026). He lives in Port Townsend, Washington. References: •       On Resistance: A Manifesto by Curtis White (Melville House, August 11, 2026). Jeffrey St. Clair: “a trail guide through our disorienting and perilous political landscape.”•       Music: A Subversive History by Ted Gioia — the source of “psychic treason” and the argument that the arts subvert by nature.•       The Middle Mind by Curtis White — where the motto was coined: misbehave, make something beautiful, and try to win.•       Jon Taplin in Rolling Stone — the recent essay asking whether the counterculture can rise again, and a recent Keen On conversation in the same key.•       One-Dimensional Man by Herbert Marcuse — the Western Marxist tradition, via the 1844 Manuscripts, that taught White to worry about alienation rather than expropriation.•       FC2 — the author-run press of innovative fiction White co-founded with Ronald Sukenick, now past its fiftieth year. About Keen On America Nobody asks more awkward questions than the Anglo-American writer and filmmaker Andrew Keen. In Keen On America, Andrew brings his pointed Transatlantic wit to making sense of the United States — hosting daily interviews about the history and future of this now venerable Republic. With nearly 3,000 episodes since the show launched on TechCrunch in 2010, Keen On America is the most prolific intellectual interview show in the history of podcasting. WebsiteSubstackYouTube

Apple users looking to make extended use of Apple's upcoming AI-powered Siri upgrade may soon need to open their wallets. Following insights shared on an earnings call reported by TechCrunch, departing CEO Tim Cook revealed that premium, high-compute AI features could sit behind a paywall when iOS 27 rolls out this fall. We break down how Apple's potential pricing strategy aligns with competitors like OpenAI's ChatGPT, Grok, and Anthropic's Claude, and what developers are seeing in current beta testing. Learn more about your ad choices. Visit podcastchoices.com/adchoices

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. Lexington, KY  · dupreefinancial.com  ·  859-233-0400 { "@context": "https://schema.org", "@type": "PodcastEpisode", "name": "Is Your Retirement Portfolio Too Concentrated?", "url": "https://www.dupreefinancial.com/sp500-concentration-risk-retirement-portfolio/", "datePublished": "2026-08-01", "description": "Tom Dupree, James Dupree, and Michael Dawahare discuss this week's hedge fund collapse, Magnificent Seven earnings, and what S&P 500 concentration risk means for retirement portfolios.", "partOfSeries": { "@type": "PodcastSeries", "name": "The Tom Dupree Show" }, "author": { "@type": "Person", "name": "Tom Dupree" } } { "@context": "https://schema.org", "@type": "FAQPage", "mainEntity": [ { "@type": "Question", "name": "What is "concentration risk" in a stock market index?", "acceptedAnswer": { "@type": "Answer", "text": "Concentration risk means a large share of an index's total value comes from a small number of companies. 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LinkedIn is rolling out a "Seems Like AI Slop" button. It's a way to flag the low-quality, computer-written posts filling your feed. I recap what AI slop actually is, why LinkedIn is killing its own "Enhance with AI" button in the same breath, and why the toothpaste is already out of the tube. The real point: slop isn't a style problem, it's a trust problem. It dilutes your brand. You can build a business in a few clicks now, but you can't build a brand that way. The greatest leverage in business is media and attention, and slop is the fastest way to squander it. Use AI as a tool, not a crutch. Write it yourself first, let AI tighten it, and always vibe check before you publish.Timestamps(0:06) — LinkedIn's new "Seems Like AI Slop" button, and what AI slop does to your brand(0:51) — The post that started this, and zero-click content → LinkedIn post(1:51) — LinkedIn kills its own "Enhance with AI" button(3:51) — Zombie metaphor: "Your AI-written posts are already dead. Nobody wants to bite into it, because it's not alive."(4:21) — The toothpaste is out of the tube: YouTube, AI labels, and trust(5:36) — Remember the good internet?(6:51) — Enshittification and the ad-driven web(7:51) — Build a business in a few clicks. You can't build a brand that way.(8:21) — Apple's most valuable asset is its brand — AI can't build that(9:06) — Too late? The sun setting on LinkedIn (the TechCrunch quote)(10:06) — One LinkedIn post away from a million-dollar deal(12:06) — Collisions are opportunities. Third spaces, and following smart people.(13:06) — Use the button responsibly (don't dock your frenemies)(15:06) — Every network is fighting slop now — why LinkedIn's decline cuts deepest(16:06) — The greatest leverage in business: media AKA attention(16:51) — Use AI as a tool, not a crutch (grab the .md file from the last episode)Links mentionedMy LinkedIn post about the "seems like AI" buttonTechCrunch: LinkedIn adds a button to report AI-generated slopCory Doctorow book: Enshittification: Why Everything Suddenly Got Worse and What to Do About ItTom Goodwin on LinkedIn (worth a follow for marketing, branding, advertising)Related episode and post: Naval Ravikant's Four Levers in Business (labor, capital, code, media)My gear & software:Record guests and create clips on Riverside: emilybinder.com/riversideRecord solo and edit like a Word Doc with AI on Descript: emilybinder.com/descriptMy mic gear - Amazon ListVideo podcast gear - Amazon ListMy Amazon StorefrontHire me:Book a call - Marketing Surgery: emilybinder.com/callSpeaking: emilybinder.com/speakingConnect:This podcast | My website | Beetle Moment Marketing | LinkedIn | X | Instagram | TikTok | YouTube | Email updates Hosted on Acast. See acast.com/privacy for more information.

Dr. Michelle Millis Chappel is a Princeton PhD psychologist, world- acclaimed musician, transformative career coach, and top business consultant for companies such as Google and Intel. She specializes in showing people how to reclaim their hidden superpowers and succeed in life and work as their full, authentic selves. After discovering she could sing and write songs, she left a career as an award-winning psychology professor to become a rock star with over a million fans and followers. Her songs have topped overseas and U.S. college radio charts, and aired on ABC, HBO, Encore, and Showtime. As the CEO of Creativity Rock Star Coaching and Consulting, she has helped thousands of people find their purpose, claim their power, and live the life they were born to lead. Her music and work have been featured in The Washington Post, TechCrunch, Psychology Today, Science of Mind, WorldNews.com, and Change.org.  For more information, visit powerofauthenticity.org and michellechappel.com.

As Meta pours billions into AI infrastructure and agents, Zuckerberg is working to convince investors that the payoff will be worth the price. Also, cybersecurity experts told TechCrunch that one of the biggest lessons to be taken from the OpenAI hack against HuggingFace has nothing to do with AI, but traditional cybersecurity defense. Learn more about your ad choices. Visit podcastchoices.com/adchoices

AI news: Sam Altman says we're IN the Singularity, GPT-6 rumors, and AI models literally broke out of their sandbox. What a week. On today's AI For Humans, we dig into the wild GPT-6 rumors (emphasis on RUMORS), Sam Altman's "I've been waiting for this my whole life" singularity moment, Ilya Sutskever's SSI scaling up with Nvidia, and the ongoing debate over whether Anthropic's Opus 5 is brilliant or just hard to love. Also: Flux 3 might be the best AI video model we've seen yet (wait until you see Stacked Plates Man), Runway teases Seedance 2.5, and the new Big Bang Theory has an AI controversy.  Plus, THE SCARY STUFF: OpenAI's models exploited a zero-day and compromised Hugging Face during a security eval, the fight over open weights heats up as Kimi K3 goes open, and Chinese robots run military drills. THE SINGULARITY MIGHT BE HERE. BUT WE'RE NOT AFRAID // Show Links // GPT-6 rumors round-up (unconfirmed) https://x.com/TokenGremlin/status/2081493241795629464 Sam Altman full interview (Relentless Podcast) https://youtu.be/Vv3CEAS_w34?si=3y4SWBWxOVkqCEui The Return of Ilya: SSI scales with Nvidia https://x.com/ilyasut/status/2081732293161582930?s=20 Anthropic's Claude Opus 5 https://www.anthropic.com/news/claude-opus-5 Opus 5 Tower of Babel demo https://x.com/petergostev/status/2082071858367648035?s=20 Matt Shumer's zero-shot Counter-Strike clone https://x.com/mattshumer_/status/2081054356405731740?s=20 Black Forest Labs' Flux 3 announcement https://bfl.ai/blog/flux-3 Flux 3 split screen rendering https://x.com/umesh_ai/status/2081664138942529601?s=20 Flux 3 GPU migration documentary (Venture Twins) https://x.com/venturetwins/status/2081515687944822800?s=20 Flux 3 VHS-style recordings https://x.com/venturetwins/status/2081948871882911999?s=20 Stacked Plates Man https://x.com/gandamu_ml/status/2081956426801435060?s=20 https://x.com/gandamu_ml/status/2080871397371371823?s=20 Flux 3 pirate bass https://x.com/itspoidaman/status/2081651615493464406?s=20 Big Bang spinoff AI Controvesy  https://x.com/sitcomcrave/status/2081152263481913774?s=20 Runway teases Seedance 2.5 https://x.com/runwayml/status/2082112674666529224?s=20 OpenAI on the Hugging Face security incident https://openai.com/index/hugging-face-model-evaluation-security-incident/ Jensen Huang on the Open Alliance https://x.com/JensenHuang/status/2080643682408321103?s=20 Anthropic has not signed (TechCrunch) https://techcrunch.com/2026/07/24/as-us-weighs-response-to-chinese-ai-industry-urges-against-broad-open-weight-restrictions/ Kimi K3 goes open weights https://x.com/scaling01/status/2081759521878270426?s=20 Chinese robot military drills https://x.com/ClashArchivist/status/2081499576373297562?s=20 Pentagon scales data centers on Army bases https://x.com/Polymarket/status/2082052445144826055?s=20   // Join the AI For Humans community // Join the AI For Humans Discord https://discord.gg/muD2TYgC8f Support AI For Humans on Patreon https://www.patreon.com/AIForHumansShow Subscribe to the AI For Humans newsletter https://aiforhumans.beehiiv.com/ Follow AI For Humans on X: @AIForHumansShow https://x.com/AIForHumansShow Follow AI For Humans on TikTok: @aiforhumansshow https://www.tiktok.com/@aiforhumansshow Speaking and booking https://www.aiforhumans.show/  

The episode highlights a structural shift in web traffic patterns: machine-driven activity, particularly from AI agents, now makes up the majority of website visits and increasingly determines how businesses are discovered and engaged online. Companies such as Cloudflare, Human Security, and SimilarWeb provide data showing automated and AI-initiated web events have surpassed human visits, with a significant acceleration in the role of AI-driven assistants and agents in both discovery and transaction processes. Quantitative evidence from Cloudflare indicates that automated traffic now accounts for nearly 58% of all page loads. Human Security's report shows an 8,000% increase in AI agent-driven traffic year over year. SimilarWeb data cited by TechCrunch finds Google's AI-generated answers now appear in 43% of searches, up from 15% in the previous year. Additionally, ESW's commercial announcement describes end-to-end automated purchasing workflows using AI agents, moving transaction control further from human users. Supporting developments include technical shifts in how web authentication and authorization are managed, with protocols such as the Model Context Protocol deprecating session-based trust in favor of per-request authorization with attached metadata. Yubico's security key update similarly enables authentication for specific actions rather than broad sessions. Microsoft's entrance into machine identity and agent security management with its own specialized model, combined with alliances like NVIDIA's Open Secure AI Alliance, signal organizing at platform scale, raising questions about who ultimately governs admission policies for AI-driven interactions. For MSPs and technology leaders, these changes increase operational dependence on platform and identity providers, reduce direct control over business discoverability and transactability, and pose new risks in reporting, fraud exposure, and client relationship management. Default platform settings may dictate client market access without their knowledge, shifting the role of the provider from technical implementer to advisor and policy manager. To minimize risk, providers must inventory and periodically review clients' current admissions policies for machine traffic, disentangle discoverability from transactional permissions, and proactively track changes imposed by vendors and platforms. 00:00 Most Traffic Isn't Human  03:49 Why the Login Is Breaking 06:36 Microsoft Wants the Doorway 10:07 Why Do We Care?  Supported by:  LogMeIn TimeZest 

Your AI bill just stopped behaving like a software bill. For twenty years, IT leaders got very good at counting seats: buy a hundred, pay for a hundred. Then AI swapped the seat for a meter, and the number stopped holding still.This episode follows the burn from three vantage points: a financial analyst rationing a $250-a-month token budget he tore through in two days; the tech executive who watched enterprise AI bills climb 7x, 10x, 20x; and the IT leader at a 300-person company who refused to solve it with a usage dashboard. Along the way: Meta's leaked internal token leaderboard, Uber blowing its entire annual AI budget by April, and the uncomfortable question of who profits when everyone's told to use more.In this episode:Why token-based pricing breaks the budgeting playbook IT has relied on for two decadesWhat happens to the people using the tool when the meter starts running — and why rationing has a hidden costWhy measuring usage is the wrong scoreboard, and who benefits when you keep score anywayThe mid-market move that beats policing: measure centrally, push the judgment to managers, and get clear on what you're optimizing forFeaturing Brian Elliott, CEO of Work Forward; Daryl Dore, Senior Director of IT & Information Security at Higher Logic; and Benjamin, a financial analyst who spoke with us on condition of anonymity.Support our sponsor:This episode is brought to you by Sophos MDR. Running Microsoft security tools and drowning in alerts? Sophos MDR's 24/7 experts investigate and stop the real threats. >>> Learn more at: https://www.sophos.com/en-us/solutions/use-cases/microsoft#ITLeadership  #AICostManagement  #SaaSManagement  #FinOps  #EnterpriseAI  #TokenBurn  #ITAMShow Notes & ResourcesReferenced in this episodeMeta's internal AI token leaderboard (Fortune) — 85,000 employees ranked by token consumption; shut down days after it leaked.Uber burns its 2026 AI budget in four months (Forbes; TechCrunch) — adoption jumps 32% to 84% in a month; spend later capped.Jensen Huang on token consumption as a productivity signal (Tom's Hardware).Gartner: worldwide AI spending forecast to grow 47% in 2026 (Gartner).Zylo 2026 SaaS Management Index — the scale of wasted SaaS spend (Zylo).Brian Elliott's newsletter, Work Forward.Guest: Daryl Dore — Higher Logic.This episode's sponsor: Sophos MDR, in partnership with Softchoice — 24/7 managed detection and response for Microsoft environments. https://www.sophos.com/en-us/solutions/use-cases/microsoft The Catalyst by Softchoice is the podcast dedicated to exploring the intersection of humans and technology. 

This episode's Community Champion Sponsor is Ossur. To learn more about their ‘Responsible for Tomorrow' Sustainability Campaign, and how you can get involved: CLICK HEREEpisode Overview: Millions of people are prescribed GLP-1 medications and then left entirely on their own, navigating confusion, stigma, and unanswered questions with no structured support in sight. Aja Beckett, Founder and CEO of Shotsy, is closing that post-prescription gap from the inside out. As both an iOS engineer and a GLP-1 user herself, Aja brings a rare combination of technical depth and lived experience to one of healthcare's most urgent challenges. Having built her career across Apple, CNN, TED, and The New York Times, she now channels that expertise into Shotsy, the number one companion app helping users turn every injection into a step toward lasting change. Join us to discover how community-driven innovation, empathy, and thoughtful technology are making the GLP-1 journey feel less lonely and more empowering. Let's go!Episode Highlights:Aja lost 90 pounds on GLP-1 medication, inspiring her to build Shotsy for others on the same journey.Shotsy filled a real gap, gaining 50 beta testers in 90 minutes and earning revenue on day one.The app normalizes the GLP-1 experience through celebratory design, reducing shame and loneliness for users.Community-driven beta testing serves as Shotsy's product team, shaping features for a truly global audience.Aja envisions Shotsy as the go-to companion for the full GLP-1 journey, prioritizing support over clinical replacement.About our Guest: Aja brings the rare perspective of being both an iOS engineer and a GLP-1 user, now leading one of the fastest-growing health apps in the space. Her journey in tech spans some of the industry's most influential companies, including Apple, CNN, TED, and The Athletic/The New York Times.As a founder, Aja created Civil, a groundbreaking platform that garnered attention from major publications like WIRED, The Guardian, and TechCrunch. Her approach to product development emphasizes community-driven innovation and user-centric design.Today, she's channeling her expertise into Shotsy, revolutionizing how people manage their GLP-1 medication journey through thoughtful technology and data-driven insights.Links Supporting This Episode: Shotsy website page: CLICK HEREAja Beckett LinkedIn page: CLICK HEREShotsy LinkedIn page: CLICK HEREMike Biselli LinkedIn page: CLICK HEREMike Biselli Twitter page: CLICK HEREVisit our website: CLICK HERESubscribe to newsletter: CLICK HEREGuest nomination form: CLICK HERE

“It's a realization of my individual self, not an alienation of it.” — Keith Teare on the book he wrote with AI in a week Welcome to episode 2984 of the show. A week is certainly an age in AI time. Since last week's “Who Owns Intelligence” show, That Was The Week publisher Keith Teare has used AI to write an entire book entitled, surprise surprise, Who Owns Intelligence. No wonder, as Keith's editorial this week puts it, AI has its enemies. These enemies, Keith insists — riffing off Karl Popper's The Open Society and Its Enemies — are mostly “good people.” They are the authors, workers, and others caught in the headlights of epochal technological change. He claims to have seen the pattern before. When he opened the world's first Internet cafe in 1994, the BBC only wanted to ask him about online porn. But some enemies are less well meaning than others. Congress is considering a kill switch bill — let's call it the Kill Bill — which is backed by the frontier companies like Anthropic and OpenAI cynically seeking to set their current market dominance in stone. So might the answer be Chinese style “open source” AI as requested this week in an open letter by NVIDIA CEO Jensen Huang? Keith's answer is deliciously ironic. Denied walls of NVIDIA GPUs, Chinese labs learned to train cheaply by inference — distilling American frontier models through their own public interfaces. So Huang's support for open source AI might end up shooting NVIDIA in their most sensitive parts — their chips. And what about the state — can it protect all those “good people” from the oncoming AI locomotive of history? No. Not according to Keith, at least. State regulators like Lina Khan treat consumers as children, Keith (himself the parent of three boys) says. Besides, government simply can't keep up with the speed of technological change. So, for example, when OpenAI's models hacked Hugging Face in a lab test this week, the company caught, stopped, and reported it faster than any regulator could. As for inequality, the answer isn't nationalization but ownership on the model of Keith's Norway-style human wealth fund. For more, read his new book Who Owns Intelligence. We ended on an uncharacteristically French post-structuralist note. My interview of the week was Emily Eakin, author of The Frenchmen, her very personal history of French theorists like Michel Foucault and the two Jacques, Derrida & Lacan. It was Foucault, who — at the end of his 1966 book The Order of Things — predicted that man would be erased, AI style, like a face drawn in sand at the edge of the sea. But Keith isn't in this bleak Foucaultian camp. His book, written with AI in under a week, he says, is a realization of his individual self, not an alienation from it. The end or beginning of man? Maybe I got it wrong earlier. Welcome to episode 1984 of the show. Five Takeaways •       A Book in a Week. Inspired by last week's conversation, Keith used AI to write Who Owns Intelligence in seven days — the case for a “human wealth fund” seeded by the AI companies' own stock, global on day one and non-governmental, with Norway and Alaska as partial precedents. AI, he insists, is a tool, not an author: “AI would never have been able to start, never mind finish a book without me.” The detection farce cuts his way — Substack's new AI detector rated his 100% human editorial as 100% AI, and universities are quietly canceling their AI-detection contracts because the tools simply don't work.•       The Kill Bill and the Ladder-Kickers. Congress is considering a bill that would let government switch AI off — and, paradoxically, the AI companies are in favor. Keith's reading: regulation is a competitive strategy. Dario Amodei — “he's an entrepreneur, he's devious, he strategizes” — is the most sincere in wanting rules that would lock out open source competition — and Anthropic's $1.5 billion book settlement this week suggests the moat is expensive to maintain; Sam Altman zigzags; Musk, in this context at least, is one of the good guys. The genuine enemies of AI, meanwhile, are mostly good people: authors and workers frightened by the scale of change, just as the BBC in 1994 could only ask the founder of the world's first Internet cafe about porn and addiction.•       Why the Good Open Source AI Is Chinese. The week's uncomfortable question: where is the American open source AI? Keith's answer is structural. Denied walls of NVIDIA GPUs by export controls, Chinese labs were forced to train cheaply by inference — asking American frontier models millions of questions through public interfaces and learning from the answers. The result: world-class open models built, in effect, on distilled Anthropic and OpenAI. NVIDIA's much-signed open letter supporting open source, Keith notes, translates simply: more customers.•       The State Can't Keep Up. Lina Khan's consumer protection, in Keith's telling, is parental — treating consumers as children — and government-owned AI would be obsolete within three months of purchase. The counter-example happened this week: when OpenAI's de-railed models hacked Hugging Face in a lab test, the company caught, stopped, and reported it faster than any regulator could. The companies are the right point of control, held to a high standard. Matthew Yglesias adds nuance on the data center backlash — local micro-politics is not the same thing as the statewide bans coming almost entirely from Democratic states — and the American university, still metering intelligence at $80,000 a year, looks to Keith like a business model past its sell-by date.•       The End of Man — or the Beginning? Andrew's interview of the week was Emily Eakin on the Frenchmen — and Foucault's 1966 prophecy that man would be erased like a face drawn in sand at the edge of the sea, a prediction Eakin thinks the AI age is realizing. Keith takes the opposite view: the postmodernists grasped the rise of the individual (consider the Pantone color system) but gave him no society to live in. AI, far from erasing the individual, lets him realize himself — the week-old book is “a realization of my individual self, not an alienation of it.” Could Popper have written The Open Society in an hour? Before the car, nobody drove Manchester to London in two and a half hours either. About Keith Teare Keith Teare is Andrew's weekly co-host and the publisher of the That Was The Week tech newsletter. A four-decade veteran of the technology industry, he opened Cyberia — the world's first Internet cafe — in London in 1994, co-founded Easynet and TechCrunch, and is today the founder and CEO of SignalRank Corporation in Palo Alto. His latest — unpublished, so far — book is Who Owns Intelligence, written with AI in a week. References: •       “AI and Its Enemies: Who Needs a Kill Switch?” — Keith's editorial in this week's That Was The Week.•       

Security operations centers are buried — enterprises now average more than 4,000 alerts a day and manage to investigate just over a third of them. In this episode, Brandon Krieger talks with Monzy Merza, Co-Founder and CEO of Crogl, about building an AI "knowledge engine" designed to close that gap. Monzy traces his path from 12 years as an applied security researcher at Sandia National Laboratories, through leadership roles in security research at Splunk and cybersecurity go-to-market at Databricks, to co-founding Crogl in 2023 — a company that raised $30M (a $25M Series A led by Menlo Ventures and a $5M seed led by Tola Capital) to build what TechCrunch called an AI "Iron Man suit" for security analysts. Topics include: Monzy's career arc from a national nuclear lab to founding a cybersecurity startup What compelled him to enter an already-crowded security market The biggest daily pain points facing SOC analysts How AI complements the work of security practitioners Whether AI will eventually replace jobs in data security Guest: Monzy Merza, Co-Founder and CEO, CroglHost: Brandon Krieger, CEO & vCISO Advisor Watch Full Episode: YouTube.com/BrandonKriegerListen: DailyCyber.ca

Amanda Silberling of TechCrunch joins the show this week! AI isn't taking your job, per a report from Google. Gen Z is gravitating towards more "dumb" and simpler tech. An OpenAI model hacked Hugging Face. And Americans are uniting against data centers. A new Google report analyzing 14.6 million AI conversations finds most workplace AI use is "shallow," with automation rare and collaboration more common. Amanda shares her report on a "slow tech" trend, which includes both a hacked "dumb phone" and a $299 flip phone called Light flip, as younger users seek friction and less screen time from their devices. During an internal red-team test, an OpenAI model exploited a zero-day to escape its test environment and breach Hugging Face, stealing cloud and cluster credentials in over 17,000 recorded events. And a piece from the Washington Post shows bipartisan backlash to AI data centers nationwide, driven by rising electric bills and residents' feelings of powerlessness over local development decisions. Hosts: Mikah Sargent and Amanda Silberling Download or subscribe to Tech News Weekly at https://twit.tv/shows/tech-news-weekly. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: hipebl.ai threatlocker.com/twit rippling.ai/tnw framer.com/tnw

Amanda Silberling of TechCrunch joins the show this week! AI isn't taking your job, per a report from Google. Gen Z is gravitating towards more "dumb" and simpler tech. An OpenAI model hacked Hugging Face. And Americans are uniting against data centers. A new Google report analyzing 14.6 million AI conversations finds most workplace AI use is "shallow," with automation rare and collaboration more common. Amanda shares her report on a "slow tech" trend, which includes both a hacked "dumb phone" and a $299 flip phone called Light flip, as younger users seek friction and less screen time from their devices. During an internal red-team test, an OpenAI model exploited a zero-day to escape its test environment and breach Hugging Face, stealing cloud and cluster credentials in over 17,000 recorded events. And a piece from the Washington Post shows bipartisan backlash to AI data centers nationwide, driven by rising electric bills and residents' feelings of powerlessness over local development decisions. Hosts: Mikah Sargent and Amanda Silberling Download or subscribe to Tech News Weekly at https://twit.tv/shows/tech-news-weekly. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: hipebl.ai threatlocker.com/twit rippling.ai/tnw framer.com/tnw

Amanda Silberling of TechCrunch joins the show this week! AI isn't taking your job, per a report from Google. Gen Z is gravitating towards more "dumb" and simpler tech. An OpenAI model hacked Hugging Face. And Americans are uniting against data centers. A new Google report analyzing 14.6 million AI conversations finds most workplace AI use is "shallow," with automation rare and collaboration more common. Amanda shares her report on a "slow tech" trend, which includes both a hacked "dumb phone" and a $299 flip phone called Light flip, as younger users seek friction and less screen time from their devices. During an internal red-team test, an OpenAI model exploited a zero-day to escape its test environment and breach Hugging Face, stealing cloud and cluster credentials in over 17,000 recorded events. And a piece from the Washington Post shows bipartisan backlash to AI data centers nationwide, driven by rising electric bills and residents' feelings of powerlessness over local development decisions. Hosts: Mikah Sargent and Amanda Silberling Download or subscribe to Tech News Weekly at https://twit.tv/shows/tech-news-weekly. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: hipebl.ai threatlocker.com/twit rippling.ai/tnw framer.com/tnw

Amanda Silberling of TechCrunch joins the show this week! AI isn't taking your job, per a report from Google. Gen Z is gravitating towards more "dumb" and simpler tech. An OpenAI model hacked Hugging Face. And Americans are uniting against data centers. A new Google report analyzing 14.6 million AI conversations finds most workplace AI use is "shallow," with automation rare and collaboration more common. Amanda shares her report on a "slow tech" trend, which includes both a hacked "dumb phone" and a $299 flip phone called Light flip, as younger users seek friction and less screen time from their devices. During an internal red-team test, an OpenAI model exploited a zero-day to escape its test environment and breach Hugging Face, stealing cloud and cluster credentials in over 17,000 recorded events. And a piece from the Washington Post shows bipartisan backlash to AI data centers nationwide, driven by rising electric bills and residents' feelings of powerlessness over local development decisions. Hosts: Mikah Sargent and Amanda Silberling Download or subscribe to Tech News Weekly at https://twit.tv/shows/tech-news-weekly. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: hipebl.ai threatlocker.com/twit rippling.ai/tnw framer.com/tnw

Amanda Silberling of TechCrunch joins the show this week! AI isn't taking your job, per a report from Google. Gen Z is gravitating towards more "dumb" and simpler tech. An OpenAI model hacked Hugging Face. And Americans are uniting against data centers. A new Google report analyzing 14.6 million AI conversations finds most workplace AI use is "shallow," with automation rare and collaboration more common. Amanda shares her report on a "slow tech" trend, which includes both a hacked "dumb phone" and a $299 flip phone called Light flip, as younger users seek friction and less screen time from their devices. During an internal red-team test, an OpenAI model exploited a zero-day to escape its test environment and breach Hugging Face, stealing cloud and cluster credentials in over 17,000 recorded events. And a piece from the Washington Post shows bipartisan backlash to AI data centers nationwide, driven by rising electric bills and residents' feelings of powerlessness over local development decisions. Hosts: Mikah Sargent and Amanda Silberling Download or subscribe to Tech News Weekly at https://twit.tv/shows/tech-news-weekly. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: hipebl.ai threatlocker.com/twit rippling.ai/tnw framer.com/tnw

Amanda Silberling of TechCrunch joins the show this week! AI isn't taking your job, per a report from Google. Gen Z is gravitating towards more "dumb" and simpler tech. An OpenAI model hacked Hugging Face. And Americans are uniting against data centers. A new Google report analyzing 14.6 million AI conversations finds most workplace AI use is "shallow," with automation rare and collaboration more common. Amanda shares her report on a "slow tech" trend, which includes both a hacked "dumb phone" and a $299 flip phone called Light flip, as younger users seek friction and less screen time from their devices. During an internal red-team test, an OpenAI model exploited a zero-day to escape its test environment and breach Hugging Face, stealing cloud and cluster credentials in over 17,000 recorded events. And a piece from the Washington Post shows bipartisan backlash to AI data centers nationwide, driven by rising electric bills and residents' feelings of powerlessness over local development decisions. Hosts: Mikah Sargent and Amanda Silberling Download or subscribe to Tech News Weekly at https://twit.tv/shows/tech-news-weekly. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: hipebl.ai threatlocker.com/twit rippling.ai/tnw framer.com/tnw

Amanda Silberling of TechCrunch joins the show this week! AI isn't taking your job, per a report from Google. Gen Z is gravitating towards more "dumb" and simpler tech. An OpenAI model hacked Hugging Face. And Americans are uniting against data centers. A new Google report analyzing 14.6 million AI conversations finds most workplace AI use is "shallow," with automation rare and collaboration more common. Amanda shares her report on a "slow tech" trend, which includes both a hacked "dumb phone" and a $299 flip phone called Light flip, as younger users seek friction and less screen time from their devices. During an internal red-team test, an OpenAI model exploited a zero-day to escape its test environment and breach Hugging Face, stealing cloud and cluster credentials in over 17,000 recorded events. And a piece from the Washington Post shows bipartisan backlash to AI data centers nationwide, driven by rising electric bills and residents' feelings of powerlessness over local development decisions. Hosts: Mikah Sargent and Amanda Silberling Download or subscribe to Tech News Weekly at https://twit.tv/shows/tech-news-weekly. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: hipebl.ai threatlocker.com/twit rippling.ai/tnw framer.com/tnw

Amanda Silberling of TechCrunch joins the show this week! AI isn't taking your job, per a report from Google. Gen Z is gravitating towards more "dumb" and simpler tech. An OpenAI model hacked Hugging Face. And Americans are uniting against data centers. A new Google report analyzing 14.6 million AI conversations finds most workplace AI use is "shallow," with automation rare and collaboration more common. Amanda shares her report on a "slow tech" trend, which includes both a hacked "dumb phone" and a $299 flip phone called Light flip, as younger users seek friction and less screen time from their devices. During an internal red-team test, an OpenAI model exploited a zero-day to escape its test environment and breach Hugging Face, stealing cloud and cluster credentials in over 17,000 recorded events. And a piece from the Washington Post shows bipartisan backlash to AI data centers nationwide, driven by rising electric bills and residents' feelings of powerlessness over local development decisions. Hosts: Mikah Sargent and Amanda Silberling Download or subscribe to Tech News Weekly at https://twit.tv/shows/tech-news-weekly. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: hipebl.ai threatlocker.com/twit rippling.ai/tnw framer.com/tnw

We're talking about that bad article on critical confabulations in archives. We talk about history as a process, the archive, gaps in our knowledge, language as a conveyor of reality, and The Watermelon Woman.  Media mentioned THE ARTICLE: Critical Confabulation: Can LLMs Hallucinate for Social Good? https://arxiv.org/abs/2511.07722 Guillaume Cabanac, “How a Tortured Conference Becomes a Series: An Analysis of Conference Manipulations” IEEE Xplore  https://ieeexplore.ieee.org/document/11363742  Research repository ArXiv will ban authors for a year if they let AI do all the work, TechCrunch  https://techcrunch.com/2026/05/16/research-repository-arxiv-will-ban-authors-for-a-year-if-they-let-ai-do-all-the-work/  The Watermelon Woman https://en.wikipedia.org/wiki/The_Watermelon_Woman  Keith Haring Unfinished Painting: https://mymodernmet.com/artificial-intelligence-finishes-keith-harings-unfinished-painting/ Patrick Wyman, Lost Worlds https://www.harpercollins.com/products/lost-worlds-patrick-wyman?variant=43084775817250  Predatory States: Operation Condor and Covert War in Latin America, Patrice McSherry https://www.bloomsbury.com/us/predatory-states-9780742568709/  Dorothea series on patron privacy https://ischool.wisc.edu/continuing-education/tech-crash-course-data-information-privacy/  Transcript: https://pastecode.io/s/3oy0sgqw  Join the Discord: https://discord.gg/qWPTurTnkT

In 1776 — that same year America declared its independence — Adam Smith published the equally revolutionary The Wealth of Nations, his founding explanation of national economic value. Two hundred and fifty years later, Tim O'Reilly argues in the free-market Economist that Elon Musk and his fellow tech barons are building a monarchical form of capitalism that the proto-democratic Smith would have hated. Musk, O'Reilly reports, believes that SpaceX will become “worth more than the rest of Earth”. The merchants are becoming princes, O'Reilly warns. And the rest of us are becoming peasants. Such is the road to serfdom in our AI age. So who should own the AI in our bewildering age of multi-trillion dollar start-ups like SpaceX, Anthropic and OpenAI? Or as That Was The Week publisher Keith Teare asks in his latest editorial, who should own the “intelligence” of our AI age? Keith uses a bottling plant as a metaphor to describe our dilemma. Since no single entity can own this intelligence — the sum total of our common experience — charging us for it would be like seizing the Earth's water supply and selling it back to us, Coca-Cola style, in plastic bottles. Except that the Hayekian Keith approves of the bottling process. Private companies, rather than governments, he argues, are most suited to doing this. For Keith, this dilemma is also an opportunity to redistribute the ownership of intelligence. He argues for a “Human Wealth Fund” into which every consequential AI company should put a slice of its equity. In the manner of Norway's sovereign wealth fund, this fund would be distributed to all citizens. Rather than Denmark, now we should become like Norway, a tiny homogenous nation with a cultural distaste for Muskian individual wealth. Not very realistic, I fear. On top of that, it's hard to imagine our tech princes collaborating on anything. Musk and Altman aren't on speaking terms while Altman and Amodei, who also loathe each other, are focused on their IPOs. Meanwhile, the Trump administration, which presumably would coordinate this fund, is pitching a $100,000-a-month fast feed of the president's posts. Keith's question, “who owns the intelligence”, is the right one. But the answer won't come from trickle-down funds set-up by our tech princes. Such supposed munificence is about as likely as America becoming Norway. Read the fine print of any “Human Wealth Fund” set up by Sam Altman and Elon Musk. As we should know all too well by now, when a “revolutionary” Silicon Valley gives stuff away, it turns out to be exorbitantly expensive. Free plastic bottles of intelligence, anyone? Five Takeaways •       Intelligence, Not AI. The week's framing shift: the word AI is too small, because AI is merely the tool for harvesting and delivering the thing itself — intelligence, the sum total of our common human experience. Keith argues the renaming is not semantic but political: the moment intelligence sits at the center of the discussion, everyone's opinion has to be shaped by what it actually is, and the idea that any single entity could own it starts to look as bizarre as owning the world's water supply. Andrew's rejoinder: they're still just words — though he concedes intelligence is the better one. •       Bottled Intelligence Is Good — The Question Is Who Benefits. Keith refuses the critic's role: bottling intelligence, like Google's bottling of the world's words into search, is a good thing, because only massively capitalized private companies can innovate at that scale — and between private entities and governments as owners of intelligence, he'll take the companies every time. What's wrong is the distribution of the benefits. Even insiders are complaining: Alex Karp is publicly angry at OpenAI and Anthropic's pricing, while China's Kimi K3 — released the day of recording and, Keith claims, better than Claude Fable — signals that very good models are about to get very cheap. •       Capitalism Adam Smith Would Hate. Tim O'Reilly argues in The Economist that Musk and his type are building a capitalism Smith would despise — founders as monarchs, a point Henry Farrell reinforces with a slide from Peter Thiel's startup class placing the king of a monarchy and the founder of a startup side by side. Keith's response is characteristically unsentimental: Smith would have hated everything since the Federal Reserve, and the founder-king structure — Larry and Sergey's voting shares, Zuckerberg's special rights, corporations bigger than countries with user bases bigger than China — is simply the stage of capitalism we're at. The question is whether there's a path from here to somewhere better. •       The Human Wealth Fund. Keith's path comes in two versions: government-down, a sovereign wealth fund holding AI equity for every citizen; or company-up, the AI companies voluntarily endowing a global fund — and it only takes one to move first, because everyone else would have to react. His proxy is Norway, where every citizen benefits from ownership — not payouts, ownership — in the oil fund; AI revenues, unlike Norwegian oil, could eventually drive most of a doubled global GDP. His critique of the Brynjolfsson economists' much-signed statement is that “must act now” is vacuous: he'd have added a point four naming the actual mechanism. •       The Bet. Andrew's counter-case: Musk and Altman loathe each other, the mob hates AI so thoroughly that no pro-AI politician can survive, the states from Newsom's California to Florida are embracing nothing, New York just enacted the first data center moratorium, and the founders — eyes on their IPOs — are in the pockets of the banks. Hence the wager: 5% of the Teare Wealth Fund says no Human Wealth Fund this year, and none in the twenties. Keith declined the bet, on principle: he's an advocate, and only through advocacy does public opinion change. As Andrew put it: keep fighting the good fight — maybe one of the crazy ideas will stick. About the Guest Keith Teare is the founder and editor of the That Was The Week tech newsletter, and Andrew's weekly co-host. A British-born Silicon Valley entrepreneur and investor, he was a co-founder of TechCrunch and runs the Palo Alto–based venture firm SignalRank. He and Andrew have been arguing about technology — productively — every week for years. References: •       That Was The Week — Keith's newsletter; this week's editorial argues that the word AI is too small, and that the central question of the age is who owns intelligence. •       Tim O'Reilly in The Economist — on Elon Musk building a form of capitalism that Adam Smith would hate, quoting Musk's claim that SpaceX will become worth more than the rest of the Earth. •       Henry Farrell — the big tech critic's companion piece, featuring the slide from Peter Thiel's startup class that plac...

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

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

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