Podcasts about Y combinator

Today I'm sitting down with Raffi Isanians, a former BigLaw M&A lawyer (with experience at Kirkland & Ellis, Gunderson, and Goodwin), Y Combinator alum, and founder of Mage Legal.Mage Legal helps law firms and in-house legal teams automate legal due diligence by scanning data rooms, flagging risky clauses, generating reports, and producing follow-up questionnaires. Alongside building Mage, Rafi runs Coding for Lawyers, an initiative that helps legal professionals develop technical skills to build with AI better.We talk about why AI won't replace human lawyers, but will radically alter how law firms operate, price, and compete.You'll hear fresh perspectives on why today's top law firms are becoming technology companies, why treating AI like a "smart associate" changes how you prompt, and what attorneys need to do now if they want to stay relevant in an AI-first world.Timestamps:00:00 — Intro02:08 — Why AI Won't Replace Lawyers (But Will Reshape Everything)03:40 — Why Lawyers Should Be Worried About Being Left Behind05:38 — The Newsletter06:10 — Overcoming Risk-Aversion & Investing in Tools09:29 — "Vibe Coding" & The Shift in Legal Mindset11:01 — AI-Native Law Firms: Recipe for Disaster or Future Necessity?13:24 — Case Study: Cleary Gottlieb & Cleary X14:08 — Debunking the Myth: Are Lawyers Training AI to Replace Them?18:16 — Prompting Best Practices: Treat AI Like a Smart Associate20:56 — Skills for the Future: Do Lawyers Need Technical Depth?22:52 — Over-Reliance on Legal Tech & Vendor Dynamics26:33 — How Legal Tech Companies Survive: Bridging Tech & Legal Expertise31:02 — What AI Can't Replace: Human Connection & Emotional Labor34:33 — Building the Law Firm of the Future39:09 — Outro---------Each week I take what I'm hearing in conversations with legal leaders.I analyze the market and track emerging trends in this AI era.In my newsletter called The Future Lawyer Market Intel for the AI eraI'm focused on:What AI is exposingThe opportunitiesThe blind spotsAnd the shifts shaping the next five years.This is how you see the chessboard before everyone else does:https://hollycope.my.canva.site/thefuturelawyer Hosted on Acast. See acast.com/privacy for more information.

Dili's Series A was led by Khosla Ventures, with participation from Allianz, Rebel Fund, Brick and Mortar Ventures' Darren Bechtel, and Y Combinator's Garry Tan. Also, Commonwealth Fusion Systems raised $1 billion as the startup moves toward its first commercial fusion power plant. Learn more about your ad choices. Visit podcastchoices.com/adchoices

Hey, it's Alex (yeah, I'm finally back from my vacation!) What a freaking week to come back to! Just after our last episode was published, Anthropic releases Opus 5, Jensen joins X and drops the “Open Weights & AI Leadership” open letter, Kimi K3 is released the following Monday beating expectations, and then the AI hack (OpenAI model breaking sandbox and infiltrating HuggingFace) is on everyone's mind, another Open Letter, this time from over 1K employees inside the frontier AI companies all talk about pacing the pace of frontier AI development. We played with Opus 5 and Kimi K3, and had the great pleasure to chat with friends of the pod Elie Bakouch (Prime Intellect) and Philip Kiely (BaseTen) about this important open weights release, then covered our general thoughts on Opus 5, and made order of all the different open letters that came out this week. Finally we chatted with Max from Pangram about the next version of AI writing detection (their biggest yet) and finished with Zuckerbergs (also on X! what's going on with everyone joining X) op-ed on the vision of personal superintelligence for everyone. Let's dive into this (as always, all the links and sources at the end, please don't forget to sub to our podcast on your favorite podcast app!) Open Weights AIKimi K3 the king of open weights - 2.8T chonker MoE near frontier model (X, HF, Blog, Tech report)This has got to be the biggest news of this week, and maybe the open weights AI news since GLM 5.2. MoonShot came back with Kimi K3, and we haven't seen any models quite this large in the open. Even Grok 4.5 is around 1.5T, this model is nearly 2x the size. Coming in at close to 3T parameters (and 2.5terabytes of weights at MXFP4 format), this model comes in very close to frontier! This was such an important release that I invited 2 friends of the pod, Elie Bakouch (prev HuggingFace, now Prime Intellect) and Philip Kiely (Author of Inference Engineering book, BaseTen) to dive deep into what makes this special! Elie's take, from reading the tech report, there's no single secret sauce, it's a combination of already available in the open techniques. Like KDA (Kimi Delta Attention) that has been out for a while, attention residuals, NVIDIA's latent MoEs. The highlight for Elie was the scaling work they did that reported a 2.5x scaling efficiency over Kimi K2.5 (2.5 performance at the same compute)! They also skipped RoPE entirely in favor of NoPE (the report calls it No Positional Encoding) for long context.Serving 1.4TB on eight GB300s (Baseten blog)Philip's team at Baseten was a day-zero provider (we're still working on bringing this model to CW Inference, stay tuned!) so I invited him to tell us behind the scenes of hosting this beast. Philip said that just loading the weights takes about 1.5TB!! of VRAM, and that's before the KV cache allocation + 1M token windows, so they're serving it on 8 GB300s where NVL72 . Baseten worked with the vLLM and SGLang teams on kernels and he also said they contributed patches back upstream! The model was trained with MXFP4, which, unlike Nvidia's own NVFP4 is a more standard format per Philip. I enjoyed his deep dive analysis into the differences, but because of this and because they trained the model with quantization awareness, it's “only” 1.5TB vs the would-be 5-6 TB if that this model in FP16 would demand. One of the more favorite nerd snipes moments, Philip pointed out that his colleague discovered that with over 99% of the usage being cached (think harnesses that send millions of the same cached tokens back and forth), tokenization actually starts to become a bottleneck. So they released a custom “basetenkenizer” that reduces the latency to serve the first token significantly! Great job!The harness in question is very importantOne important callout with 2 evidence pieces - the way you inference this model really matters. Kimi trained K3 with preserving thinking history, so when your harness uses it, it must send back the full thinking and tool use into the API to get the best next response. If your harness strips that out, you're not getting the most intelligence out of Kimi (shoutout to Niels from HF team for pointing this out). Additionally, the Composio folks, tested K3 on 3 harnesses, Kimi Code, Hermes and Claude Code. The difference in outcome was negligible, but the different in cost and number of tokens is definitely surprising! Claude Code (as a harness only) took 9x more Kimi tokens to get the same responses! This is also why Kimi Vendor Verified exists, their own held back benchmark of how well model providers serve Kimi across different quantization, tokenizer and KV cache settings. Benchmarks and the license! Ok let's start with the ugly... this isn't MIT, not remotely. This model is suspiciously served by all providers with exactly the same price (check OpenRouter) and requires inference companies to sign a contract with Kimi (I've no internal knowledge of this except that CW folks are working on it). Not something I particularly like, but hey... we're still advancing the frontier here! Speaking of frontier, this model approaches the frontier very closely. On DeepSWE, K3 sits just behind Fable 5 and GPT-5.6 Sol at 67%, beating GPT-5.5 & Opus 4.8. On Terminal-Bench 2.1 it takes second place behind GPT 5.6 Sol! It's 4th overall on Agentic Arena, with frontend design being genuinely good across the board - 1st on Design Arena

Sponsored by Blocks: Save at least 20% on your AWS costs with AI-powered optimization and enterprise discounts. Get your free Cloud Check at https://blocks.cloud/alphalist?utm_source=alphalist&utm_medium=podcast&utm_campaign=blocks-podcast-2026 Aike Hillbrands co-founded and killed two companies before Kombo, now a Y Combinator-backed HR integration platform with $10M+ ARR and a $25M Series A. Along the way, his team built something almost by accident: a company-wide AI brain made of a GitHub repo, a Cursor agent, and a Slack channel, built in two hours, that replaced how the whole company gets answers. In this episode, Aike explains why files and grep beat MCP tools and vector search for agent reliability, walks through Simon Willison's "lethal trifecta" of AI security risks and how a public Slack channel acts as a guardrail against it, and makes the case for why AI won't commoditize enterprise HR integrations anytime soon, despite that being Kombo's own bet. Topics covered: - How Kombo went from Notion AI to a Git-based company brain - Why files and grep beat MCP tools and vector search for agent reliability - The architecture: per-customer summary files, cross-linked support tickets, BigQuery CLI, Slack integration - Simon Willison's "lethal trifecta" and practical mitigations - Why a public Slack channel works as a security guardrail - The buy-vs-build question for internal AI tooling - Why enterprise HR API integrations resist commoditization by AI

Today, we're joined by Qasar Younis, co-founder and CEO of Applied Intuition, a physical AI company valued at $15 billion that builds the models and simulation tools behind autonomous vehicles, defence systems, and industrial machines (before this, he was COO of Y Combinator).Tommy Stadlen talks to Qasar about why he thinks physical AI will define the next 25 years, and why Applied's ability to run the same models across cars, drones, and construction equipment is its biggest edge. They also get into how he thinks about fundraising and cap tables, and why he says not all money is equally useful.He speaks about:Why Applied Intuition has never spent a dollar of the ~$1 billion raisedCorporate VCs, multi-stage funds, and why the partner matters more than the firm nameWhy 85% of the global economy is physical (and why that's the real AI opportunity)Neural simulation, and how Applied trains one model to work in a car, a drone, and a tankWhy timing is the hardest thing for founders to get rightWhy he's not worried about self-driving safetyThe billion-machine vision (and why he still drives a 1987 manual Land Cruiser)Enjoy!Building a purpose driven company? Read more about Giant Ventures at www.Giant.vc. Music credits: Bubble King written and produced by Cameron McLain and Stevan Cablayan aka Vector_XING.Please note: The content of this podcast is for informational and entertainment purposes only. It should not be considered financial, legal, or investment advice. Always consult a licensed professional before making any investment decisions.Building a purpose driven company? Read more about Giant Ventures at www.Giant.vc.Music credits: Bubble King written and produced by Cameron McLain and Stevan Cablayan aka Vector_XING.Please note: The content of this podcast is for informational and entertainment purposes only. It should not be considered financial, legal, or investment advice. Always consult a licensed professional before making any investment decisions.

YC's Nemil Dalal joins to explain why he's never been more bullish as BitMEX winds down after 11 years, whether every failed crypto idea (TCRs, DAOs, creator coins) eventually works, why crypto is really about money, Base's consumer mea culpa, on-chain reputation and credit, and who pays in the x402 AI-agent era. Welcome to The Chopping Block – where crypto insiders Haseeb Qureshi, Tom Schmidt, Tarun Chitra, and Robert Leshner chop it up about the latest in crypto. This week they're joined by Nemil Dalal, Visiting Partner at Y Combinator and ex-Coinbase, where he led USDC and the Coinbase Developer Platform. He's here to explain why, with exchanges winding down left and right, he's somehow never been more bullish. The crew digs into the great contrast of the moment: BitMEX shutting down after 11 years (plus BitMart, Movement Labs, Balancer Labs) while the plumbing quietly prints, and whether Imran's viral 'everything that failed will eventually work' thesis is genius or toxic positivity. From there it's the question of whether crypto is really only about money (Jesse's Base mea culpa included), a war-memories tour through TCRs, on-chain reputation and why pure on-chain credit keeps faceplanting, and finally who actually pays in the x402 AI-agent era, and whether decentralization even survives contact with Google-shaped gravity. Listen to the episode on Apple Podcasts, Spotify, Pods, Fountain, Podcast Addict, Pocket Casts, Amazon Music, or on your favorite podcast platform. Show highlights

How do you turn degraded farmland into an investable climate asset? Living Carbon has raised $76M in equity and unlocked $500M in project finance by combining reforestation, biomass, and long-term infrastructure thinking.Living Carbon restores degraded agricultural and mine lands through reforestation while developing biomass supply chains for industrial customers..Maddie Hall is the co-founder and CEO of Living Carbon. Before launching the company, she worked at OpenAI and Y Combinator.How to unlock project finance for climate startups — Why demonstrating repeatable execution, securing blue-chip customers, and reducing underwriting risk enabled Living Carbon to raise $500M beyond traditional venture capital.Why degraded land beats pristine forests — How abandoned farmland and former mine sites create stronger economics, lower land costs, and higher carbon additionality while avoiding competition with food production.Building two businesses within one company — Why Living Carbon separates its carbon credit business from its biomass platform, generating multiple revenue streams without relying entirely on voluntary carbon markets.Ignoring carbon market hype — Why Maddie focuses on building a business that will still matter in 2030 instead of chasing today's policy changes, pricing swings, or investor excitement.Leadership lessons from scaling startups — Why founders should catastrophize less, become comfortable with rejection, stop optimizing for being liked, and build routines that support long-term resilience.--1️⃣ Join our confidential CEO community.Private CEO group for VC/PE-backed climate tech founders navigating capital, strategy, and scale. Capped at 45 CEOs. → entrepreneursforimpact.com2️⃣ Join 40,000 professionals who get our newsletter.Climate tech finance, strategy, leadership. 2-min read. → entrepreneursforimpact.substack.com3️⃣ Leave a podcast review.If you got value, take 30 seconds and do the community a favor. It helps push more capital and talent toward scalable climate solutions.

YC's Nemil Dalal joins to explain why he's never been more bullish as BitMEX winds down after 11 years, whether every failed crypto idea (TCRs, DAOs, creator coins) eventually works, why crypto is really about money, Base's consumer mea culpa, on-chain reputation and credit, and who pays in the x402 AI-agent era. Welcome to The Chopping Block – where crypto insiders Haseeb Qureshi, Tom Schmidt, Tarun Chitra, and Robert Leshner chop it up about the latest in crypto. This week they're joined by Nemil Dalal, Visiting Partner at Y Combinator and ex-Coinbase, where he led USDC and the Coinbase Developer Platform. He's here to explain why, with exchanges winding down left and right, he's somehow never been more bullish. The crew digs into the great contrast of the moment: BitMEX shutting down after 11 years (plus BitMart, Movement Labs, Balancer Labs) while the plumbing quietly prints, and whether Imran's viral 'everything that failed will eventually work' thesis is genius or toxic positivity. From there it's the question of whether crypto is really only about money (Jesse's Base mea culpa included), a war-memories tour through TCRs, on-chain reputation and why pure on-chain credit keeps faceplanting, and finally who actually pays in the x402 AI-agent era, and whether decentralization even survives contact with Google-shaped gravity. Listen to the episode on Apple Podcasts, Spotify, Pods, Fountain, Podcast Addict, Pocket Casts, Amazon Music, or on your favorite podcast platform. Show highlights

There's a popular new playbook for running an "AI-native" company. Record everything, put all your data in one place, and let an AI agent reach all of it. The productivity story is real. New hires ramp up in days, and the whole company can ask questions it never could before. But the same moves that create the speed also switch off safeguards that some businesses are not allowed to switch off. So, which controls are we turning off to get this speed, and are we allowed to? Your hosts are Kip Boyle, CISO with Cyber Risk Opportunities, and Jake Bernstein, Partner with K&L Gates.   Y Combinator's AI native playbook video -- https://youtu.be/B246K_G7mHU AIR-MAP website -- https://air-map.io/

As chief technology officer at Casetext, Ryan Walker helped build CoCounsel, one of the first and most consequential generative AI legal assistants — a product so significant it led to the company's $650 million acquisition by Thomson Reuters. But Walker came away unsatisfied. Although the legal tech tools kept getting better, he believed, clients were seeing no benefit. Billing rates kept climbing and the efficiency gains never reached them. That spurred him to pivot from building products to forming a practice — General Legal, an AI-native law firm he cofounded and leads as CEO, built on the premise that the fastest path to transforming legal services is not retrofitting AI onto traditional firms, but rebuilding the law firm from the ground up on an AI-forward foundation. Just six months out of Y Combinator, the firm has some 400 clients, a newly launched venture financing practice, and, in what may be a first for a law firm, an MCP server that lets clients' AI agents engage the firm directly. In this episode of LawNext, Walker — not a lawyer but a PhD mathematician — tells host Bob Ambrogi why he believes AI can now automate 95% of routine legal work, how the management services organization structure allows an investable technology company to operate alongside a law firm, and why an AI-native firm paradoxically depends on hiring highly experienced lawyers rather than supercharging junior ones. He also explains the "second brain" approach that lets the firm's work product reflect each client's strategy, what earlier failed innovators like Atrium and Clearspire got wrong, and why he thinks the real reckoning will come when major clients simply refuse to pay for work AI can do. Walker cofounded the firm along with two other Casetext colleagues, Javed Qadrud-Din, who was head of AI at Casetext and is now General Legal's chief technology officer, and J.P. Mohler, who was an LLM engineer at Casetext and is now chief product officer and managing partner. Their ultimate ambition, Walker says, is to make General Legal the biggest provider of legal services in the world and, along the way, to break the scarcity model that keeps legal help out of reach for the people and companies who need it.   Thank You To Our Sponsors This episode of LawNext is generously made possible by our sponsors. We appreciate their support and hope you will check them out. Paradigm, home to the practice management platforms PracticePanther, Bill4Time, MerusCase and LollyLaw; the e-payments platform Headnote; and the legal accounting software TrustBooks. Briefpoint, eliminating routine discovery response and request drafting tasks so you can focus on drafting what matters (or just make it home for dinner). CosmoLex, helping law firms manage their entire practice in one platform, from intake to payment. Try it free. Ajax, the AI timekeeper lawyers want to use.   If you enjoy listening to LawNext, please leave us a review wherever you listen to podcasts.  

275 | Samuel und Alex pitchen sich Geschäftsideen! Diesmal: Ein Oura-Ring, der soziale Interaktion misst, eine App gegen Rückenschmerzen und eine Idee mit Pikachu.Partner dieser Folge:sevdesk.deUpgrade deine Buchhaltung! Rabatt auf den Jahrestarif mit 60% auf die gesamte Vertragslaufzeit bei 12 und 24 Monaten: OPTIMISTEN6060% auf die ersten 3 Monate bei Monatstarifen: OPTIMISTEN3M https://sevdesk.de/digitale-optimisten/?utm_source=podcast&utm_medium=referral&utm_campaign=er_digitale-optimisten_q2_2026_#2 Mach das 1-minütige Quiz und finde eine Geschäftsidee, die zu dir passt: digitaleoptimisten.de/quiz.Kapitel(00:40) Intro(04:21) Bitcoin Bros(12:46) Proseminar Klimawandel(24:46) Geschäftsidee: Oura Ring, der soziale Interaktionen misst(37:55) Y-Combinator Request for Startups(30:25) Was ist eigentlich mit E-Mobilität?(47:22) Geschäftsidee von Samuel: App gegen Rückenschmerzen (55:15) Geschäftsidee von Alex: KartenkurierSo erreichst du uns:Sprachnachricht senden: https://www.speakpipe.com/digitaleoptimistenEmail schreiben: alexander@digitaleoptimisten.deLearningsKlimasysteme ganzheitlich denkenDer Dialog zeigt, dass Albedo-Effekt, OTEC und Aerosole zusammenwirken und dass eine einzelne Maßnahme das Klima nicht zuverlässig steuert. Energie bleibt Energie, und der Gesprächsverlauf betont die Wechselwirkungen sowie die Notwendigkeit von Koordination unter Regierungen und privaten Akteuren. Es wird deutlich, dass private Akteure oder einzelne Staaten zu viel Macht übernehmen könnten, weshalb eine globale Abstimmung erforderlich erscheint. Für Gründer heißt das: Klima-Geschäftsideen müssen systemisch gedacht und Risiken, Nebenwirkungen sowie politische Rahmenbedingungen berücksichtigt werden.Health-Score als Workplace-AnsatzDie Idee Back Angel orientiert sich an einem Rücken-Health-Score wie Whoop, mit fünf Übungen pro Woche und einem Recovery Score. Dazu kommen konkrete Verbesserungsempfehlungen und der Vergleich mit anderen High Achievers als Motivations- und Messgröße. Die Zielgruppe soll direkt Verbraucher sein (Direct-to-Consumer), während Kaya Health eher Firmenkunden bedient; das eröffnet verschiedene Vertriebswege. Unternehmerischer Nutzen: Ein messbares Health-Produkt kann Markenbildung und Kundenbindung stärken, er braucht klare Positionierung und messbare Outcomes.Y Combinator RFU als GründungsleitfadenIm Gespräch wird deutlich, dass Y Combinator erneut einen Request for Startups veröffentlicht und damit Orientierung bietet. Als Beispiele nennen die Hosts AI for the aging population und New Operating System for the physical world, die als thematische Leitlinien dienen. Die Diskussion sieht RFU als nützliches Instrument, um Ideen zu bewerten und sich stärker auf passende Themen zu fokussieren. Im Unternehmenskontext bedeutet das: Gründer können RFU-Themen nutzen, um Markteintrittsrisiken zu reduzieren und relevante Pitch-Felder zu identifizieren.Ethik und Regulierung bei Plattform-basierter WetteEs werden Multiple Polymarket-Scams und Insider-Trading diskutiert, unter anderem Wetten auf Trump-Zeilen und Maduro-Festnahmen. Die Gespräche werfen Fragen auf, ob solche Aktivitäten legal sind und zeigen eindrücklich, wie leicht Transparenz und Regulierung unterlaufen werden könnten. Das verdeutlicht, dass Produkt- und Plattformdesign Governance-, Compliance- und Ethik-Anforderungen berücksichtigen müssen. Für Unternehmen heißt das: Risikogovernance und klare Regeln sind essenziell, um Missbrauch zu verhindern und Vertrauen zu schaffen.KeywordsAlbedo-EffektOcean Thermal Energy ConversionFeststoffbatterienE-MobilitätPolymarketAlbedo-Effekt Auswirkungen globale TemperaturenOcean Thermal Energy Conversion ErklärungFeststoffbatterien Vorteile ReichweiteE-Mobilität Secondhand-Batterien WertverlustPolymarket Wettmärkte Risiken Insider-TradingRückenschmerzen Startup Back AngelKartenkurier Sammelkarten Grading ConciergeLongevity Arbeitsplatz Wearable

Dans cet épisode, j'interroge Max Berthelot, co-fondateur de Lucis, sur la création de sa startup en santé préventive: le bilan santé qui vous accompagne dans le changements des habitudes de vie (nutrition, exercice, sommeil, compléments, santé mentale), mais aussi sur leur passage pas Y Combinator: l'incubateur célèbre qui a fait naître Airbnb, Stripe et Dropbox.Quel est ce bilan santé et combien ça coûte?Pourquoi c'est mieux que de donner sa prise de sang à Claude / ChatGPT?Comment ça change réellement la santé de ses clients?Les européens sont ils prêts à payer pour la santé préventive?Comment intégrer le meilleur incubateur du monde?Que fait YC pour que ces boîtes avancent aussi vite?Le site de LucisMax sur LinkedinAnastasia sur LinkedinCarnets d'Entrepreneurs sur InstagramHébergé par Ausha. Visitez ausha.co/politique-de-confidentialite pour plus d'informations.

Update : la Forward Data Conference aura lieu le 16 novembre cette année :)Christophe Blefari (alias Blef), fondateur de la newsletter blef.fr et cofondateur de nao, revient sur son expérience chez Y Combinator, l'incubateur américain le plus prestigieux au monde (Airbnb, Stripe…).Pendant 3 mois au cœur de la Silicon Valley, il a vécu l'intensité de l'entrepreneuriat US et rencontré des dizaines d'équipes Data et Tech.On aborde :

Nineteen stories this week and not one of them wanted to be the headline — which is honestly kind of great. Palo Alto Networks bought a company called Embrace that may or may not count as a Portland startup depending on how you squint (its president and VP of engineering are right here, so I'm counting it), two local apps picked up Apple Design Awards, and then things just kept coming — a plant-poison checker named after somebody's cat, an app that scores your Spotify for yacht-rock content, a whole association of small companies trying to out-organize Big Tech. No anchor, just volume — and I'm genuinely energized by that.LINKS:Apple Design Awards 2026 — https://siliconflorist.com/2026/07/20/two-portland-apps-recognized-by-apple-design-awards-2026/Apple Design Awards — https://developer.apple.com/design/awards/World Reborn (Apple Game of the Day) — https://siliconflorist.com/2026/07/23/medford-made-world-reborn-lands-apple-game-of-the-day-honors/Wicked Saints Studio — https://www.wickedsaints.studio/Palo Alto Networks acquires Embrace — https://siliconflorist.com/2026/07/22/palo-alto-networks-acquires-embrace/Embrace — https://embrace.io/Lewis & Clark $1M Google grant — https://siliconflorist.com/2026/07/22/your-next-cybersecurity-hire-could-come-from-lewis-clark-thanks-to-a-1m-google-grant/Lewis & Clark announcement — https://www.lclark.edu/live/news/58507-lc-lands-1-million-in-google-funding-for-cybersecurityBoulder Care $1.8M grant — https://siliconflorist.com/2026/07/20/boulder-care-lands-1-8m-grant-to-further-opioid-addiction-efforts/Paige Hendrix Buckner joins the Check Writers Room — https://siliconflorist.com/2026/07/22/all-raise-ceo-paige-hendrix-buckner-joins-the-check-writers-room/Little Tech may be for you — https://siliconflorist.com/2026/07/23/tired-of-big-tech-holding-all-of-the-power-little-tech-may-be-for-you/Little Tech Association — https://www.little.tech/LinkedIn Cities on the Rise — https://siliconflorist.com/2026/07/22/linkedin-highlights-both-portlands-among-cities-on-the-rise/Quilld (branding/rebranding) — https://siliconflorist.com/2026/07/22/branding-is-hard-rebranding-is-even-worse-quilld-is-here-to-help/Quilld — https://quilld.ai/Rami (plants + pets) — https://siliconflorist.com/2026/07/21/helping-houseplants-and-housepets-cohabitate-rami/Rami — https://getrami.app/Is it yacht rock? — https://siliconflorist.com/2026/07/21/looking-to-answer-the-age-old-question-is-that-yacht-rock-playing-theres-an-app-for-that/Is It Yacht Rock? app — https://madegglabs.com/apps/is-it-yacht-rock/Lemonvite (ad-free invites) — https://siliconflorist.com/2026/07/22/tired-of-ads-cluttering-your-invites-look-at-lemonvite/Lemonvite — https://www.lemonvite.com/Fan the Flame (Portland Fire app) — https://siliconflorist.com/2026/07/23/portland-fire-fan-youll-want-this-app-burning-a-hole-in-your-pocket/Fan the Flame — https://fantheflame.app/funnr (kids' schedules) — https://siliconflorist.com/2026/07/22/managing-your-kids-social-schedule-just-got-funnr/Chain React 2026 visitor guide — https://siliconflorist.com/2026/07/24/attending-chain-react-2026-in-portland-heres-a-visitor-guide-to-help-you-make-the-most-of-your-stay/Y Combinator F26 apps (close July 27) — https://siliconflorist.com/2026/07/24/reminder-y-combinator-applications-for-f26-close-july-27-2026/Y Combinator apply — https://www.ycombinator.com/apply/Pitch Latino Portland (apps due July 30) — https://siliconflorist.com/2026/07/23/reminder-pitch-latino-portland-applications-are-due-in-a-week/Pitch Latino — https://www.latinofounder.com/pitchlatinoDevOps Days Portland (Sept 8–10, code SILICONFLORIST) — https://siliconflorist.com/2026/07/21/need-a-little-more-devops-in-your-life-go-to-devops-days-portland-with-a-discount/DevOps Days Portland — https://devopsdays.org/events/2026-portland-or/welcome/

Pour l'épisode de cette semaine, je reçois Pierre-Eliott Lallemant, le CEO et cofondateur de Gojiberry.Gojiberry développe des agents IA capables d'automatiser le go-to-market des entreprises B2B. Leur premier agent identifie les meilleurs prospects, construit les séquences de prospection et booke des rendez-vous commerciaux.Au cours de cet épisode, nous sommes revenus sur le parcours de Pierre-Eliott et de ses associés, de la création puis la vente d'un premier SaaS de marketing WhatsApp au lancement de Gojiberry. Pour valider leur idée, ils ont commencé par vendre le produit avant même de l'avoir construit, puis ont réalisé manuellement le travail promis à leurs premiers clients.Nous avons ensuite parlé de leur croissance particulièrement rapide : 20 000 euros de MRR en décembre, plus de 40 000 en février, 80 000 en mars, puis un passage de 1 à 3 millions d'ARR pendant leurs trois mois chez Y Combinator.Pierre-Eliott détaille également les canaux d'acquisition qui ont alimenté cette croissance : utilisation de leur propre produit, influenceurs LinkedIn, lead magnets, affiliation, ads, vidéos en motion design, founder-led content sur X ou encore une stratégie particulièrement agressive sur Reddit.Enfin, nous avons parlé de leur expérience chez Y Combinator, de l'importance de se fixer des objectifs très ambitieux, de leur volonté de construire une entreprise internationale et de la manière dont Gojiberry souhaite créer une véritable défensibilité grâce aux données accumulées par ses agents.Vous pouvez suivre Pierre-Eliott sur X.Bonne écoute !Mentionnés pendant l'épisode :Y CombinatorStarter StoryMy First MillionPagerDutyApolloClaylemlistInstantlyOutreachGongModjoPour soutenir SaaS Connection en 1 minute⏱ (et 2 secondes) :Abonnez-vous à SaaS Connection sur votre plateforme préférée pour ne rater aucun épisode

Dans ce nouvel épisode "Build in Public", je fais un état des lieux complet des grands projets DataGen du moment : ce qui marche, ce qu'on lance, et les premiers insights qui en ressortent.On aborde :

A startup lecture recommended by Sam Altman calls people "ammunition" and tells you to work 80-hour weeks.Brian and Om dissect Keith Rabois' (in)famous "How to Operate" talk, exposing the dehumanizing logic behind "barrels and ammunition," the cult of heroic effort, and why grading managers on output-per-head creates a fear-based culture.What Brian and Om get into:• Why the "people are irrational" premise is a condescending excuse for bad system design• The truth behind 80-100 hour work weeks and the duct-tape engine myth• What "build a company idiots can run" really means for your career• The "barrels and ammunition" metaphor and its Taylorist roots• Why "expand responsibilities until they break" destroys psychological safety• The output-per-head metric and how it incentivizes burning out your teamFor product managers, engineers, and anyone who's ever been called a "resource" by someone who read too much Ayn Rand.#ToxicLeadership #StartupCulture #ManagementAdviceKeith Rabois, Sam Altman, Y Combinator, Andy Grove, High Output Management, Warren Buffett, Square, PayPal, W. Edwards Deming, The Goal by Eliyahu GoldrattLINKSYouTube: https://www.youtube.com/@arguingagileSpotify: https://open.spotify.com/show/362QvYORmtZRKAeTAE57v3Apple: https://podcasts.apple.com/us/podcast/agile-podcast/id1568557596INTRO MUSICToronto Is My BeatBy Whitewolf (Source: https://ccmixter.org/files/whitewolf225/60181)CC BY 4.0 DEED (https://creativecommons.org/licenses/by/4.0/deed.en)

Pele Collins went from leading the SpaceX parachute engineering team that brought American astronauts safely home to co-founding a Y Combinator-backed deep tech startup, Beyond Reach Labs. In his latest interview with Irakli, the proudly South African engineer reveals how his team is tackling the ultimate pick-and-shovel play for the next era of the space race: deployable structures that unfold from the size of a dining table to a full-sized football field in orbit. Building in space historically meant assembling structures piece-by-piece over dozens of rocket launches. Collins wants to bypass those massive costs using dynamic, folding mechanisms. While the company initially entered Y Combinator focused on simulation software, raw customer feedback forced a fast pivot to pure hardware. “...customers said this, I mean this tool's amazing, we'd love to use it, but honestly, can you just go design and build the thing for us?” Collins recalls. Collins credits his resilient problem-solving mindset to his South African roots, highlighting a culture of mutual support that even landed him his first break at SpaceX under a fellow South African manager. “South Africans also always help each other out and... open doors for each other,” he says.

How do we move beyond shallow representation to capture the deep, fluid, and beautifully complex realities of the global South Asian diaspora? Snigdha Sur, Founder & CEO of The Juggernaut, joins us again to share how her media tech company operates as a "smart best friend" providing essential context, investigative reporting, and nuanced storytelling for a dynamic global community.If you want to catch more deep dives into the global Indian and South Asian diaspora experience, hit the Subscribe button to join our community!In this episode of TRUST ME I KNOW WHAT I'M DOING, host Dr. Abhay Dandekar sits down with Snigdha to catch up on the cultural zeitgeist of 2026. From her personal obsession with the National Spelling Bee to her background and experiences as a founder , Snigdha challenges us to demand deeper specificity in journalism and the stories offered by The Juggernaut. She also opens up about the rigorous realities of investigative journalism and leading a media company, why she chose to learn video editing to out-innovate aggregators, navigating social media shadowbans, and her hard-earned Y Combinator lesson on filtering out internet noise to focus exclusively on serving paying subscribers.In this episode, we cover:• Nuanced Subcontinental Identity: Why geographical specificity matters over broad-brush labels.• Smart Context vs. Representation: Moving past surface-level media tropes to deliver real depth.• The Barbell Effect of AI in Media: Why deep-dive human creation will become increasingly priceless.• Founder Wisdom & Burnout: Learning whose feedback to value and when to mute the noise.Chapters:00:00 Introducing Snigdha Sur & The Fluidity of the Diaspora03:19 The National Spelling Bee: Passion, Tropes, and Community Anchors06:55 Identity, Borders, and Subcontinental History in 202610:47 The Juggernaut Strategy: Serving Smart Context Over Shallow Representation15:16 Sponsor Break - TRAVELOPOD and LOTUS LANE COFFEE16:16 Investigative Journalism: Pushing Boundaries, Data Truths, and Backlash23:37 The Barbell Effect of AI, Founder Burnout, and Protecting Your Creative Well33:04 Y Combinator Lessons: Filtering Negative Commentary to Focus on Subscribers35:28 Democratizing Media Spaces & Designing Radically Inclusive Communities36:56 Conclusions and Shout OutsConnect with Snigdha Sur & The Juggernaut:• Website: https://thejuggernaut.com/• Newsletter: https://thejuggernaut.com/newsletterShout outs this week:• Sheela Bhat, Preety Kalra, Aarti Kohli, Latika Malkani, and Dharini Rasiah (Editors of Our Feet Walk the Sky: Women of South Asian Diaspora )• World Cup ¡Shabbas! to España• Hydrogen Trains & the teams behind Vikram One• Astronaut Anil Menon • Desi Me Dating and the PopShift team#TheJuggernaut #SnigdhaSur #SouthAsianMedia #DiasporaStories #TrustMeIKnowWhatImDoing #StartupFounderTrust Me I Know What I'm Doing | Dr. Abhay DandekarA mirror and window for global Indians and South Asians through conversation.Every week, we share chats with artists, leaders, musicians, chefs, experts, change makers, and innovators from the home and diaspora — sharing their journeys and motivations.Support our sponsors: Personalized travel journey to destinations like Africa, India, Japan, Antarctica, or the Northern Lights regions with Travelopod at:https://vacation.travelopod.com/Lotus Lane Coffee — Rooted in India, Roasted in Columbus.https://lotuslanecoffee.com/For enquiries

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

Accounts payable has produced multiple billion-dollar companies, yet its mirror image, accounts receivable, remains almost entirely manual at most enterprises despite decades of software spend. In this episode, Caitlin Leksana, co-founder and CEO of Fazeshift, explains why AR has remained unsolved and how her company's AI agents are changing that. A mechanical engineer turned BCG consultant turned founder, Caitlin came to the problem the hard way, doing her own AR by hand at a previous startup, and her outsider's view of a stubborn back-office chore is exactly what makes the conversation worth your time.What We CoveredA million AR analysts doing manual work in the USWhy accounts payable got solved and AR did notThe leverage imbalance between AP and AR departmentsThe swivel chair problem and fragmented data$200 million in unapplied cash on one balance sheetFazeshift as a context layer, not a rip-and-replaceWhy traditional SaaS and if-then logic could never scale ARThe collections, cash application, and AR inbox modulesHuman in the loop and building trust when AI touches moneyTraining agents on historical data and tribal knowledgeFrom Y Combinator to a Series A led by F-PrimeThe vision for the context layer and autonomous financeKey TakeawaysAR is the inverse of AP, and every bill is someone else's invoice, so the market is at least as large and mostly uncaptured.The real unlock is not the AI model but unifying fragmented data across the ERP, bank, CRM, and inbox into a single context layer.Human in the loop with full auditability is what earns risk-averse finance teams' trust, and it is how agents move toward full automation over time.Some of the best unsolved startup problems are the ones furthest removed from an engineer, because no one with the tools to fix them ever felt the pain.About Caitlin LeksanaCaitlin Leksana is the co-founder and CEO of Fazeshift, a San Francisco startup building AI agents for accounts receivable. She earned bachelor's and master's degrees in mechanical engineering from Georgia Tech, advised Fortune 500 companies at BCG, and earned her MBA at Harvard Business School before founding a crypto marketing startup and then Fazeshift. The company went through Y Combinator's Summer 2024 batch, raised a $4M seed led by Gradient Ventures, and announced a Series A led by F-Prime in 2026.Connect with Fintech One-on-One:Tweet me @PeterRentonConnect with me on LinkedInFind previous Fintech One-on-One episodes

Healthcare organizations have more data than ever before, but turning that data into actionable insights remains one of the industry's biggest challenges. In this episode of The Beat AI in Healthcare Podcast, host Sandy Vance sits down with Nabil Jallouli, CEO and co-founder of Rollstack, to discuss how AI-powered reporting is helping healthcare organizations simplify value-based care reporting, improve decision-making, and scale personalized reporting without overwhelming their teams. Nabil shares why dashboards alone are no longer enough, why executives need trusted narratives instead of raw data, and how healthcare organizations can automate reporting while maintaining governance, security, and accuracy. Whether you work in healthcare leadership, data analytics, customer success, value-based care, or digital transformation, this conversation offers practical insights into using AI responsibly to improve reporting and business outcomes.  In this episode, they talk about: Why value-based care requires an entirely different approach to healthcare reporting The hidden "last mile" problem between business intelligence dashboards and executive decision-making Why healthcare organizations struggle with manual reporting despite major investments in analytics platforms How AI can automate repetitive reporting while keeping humans focused on strategy and relationships The importance of governance and trusted data in healthcare AI Why executives rarely spend more than a few seconds reviewing dashboards How personalized reporting improves payer, provider, and customer relationships The role of automation in scaling reporting across hundreds of healthcare partners Measuring ROI from AI reporting solutions and why reporting automation often delivers significant business value Why healthy skepticism around AI is actually helping healthcare adopt better long-term solutions How Rollstack protects sensitive healthcare data with enterprise-grade security and zero data retention policies What healthcare leaders should prioritize as AI adoption continues to accelerate A Little About Nabil: Nabil Jallouli is the co-founder and CEO of Rollstack, an enterprise platform helping companies like SoFi, Zillow, and Whirlpool automate their data-driven presentations and documents. Rollstack leverages deep integrations with leading business intelligence tools (Tableau, Power BI, Looker, and others) and AI to streamline reporting processes, including business reviews, EHR and HCP reporting, financial reporting, and client presentations. By eliminating manual copy-paste, reducing errors, and ensuring accuracy, Rollstack enables organizations to operate more efficiently and focus on decision-making rather than document preparation. The company is backed by Y Combinator and Insight Partners and is fully SOC 2 Type II and HIPAA compliant, meeting the highest standards of security and data protection for enterprise and healthcare customers. Before founding Rollstack, Nabil built his career leading data analytics and revenue strategy teams at Pinterest, Deel, and Groupon. Across these experiences, he observed a common problem: highly skilled professionals spending countless hours preparing slides and reports instead of focusing on high-value work and strategy. This recurring pain point ultimately inspired the creation of Rollstack. Nabil holds a Master of Engineering and dual Bachelor's degrees in Mathematics, Physics, and Computer Science from École Centrale Paris.

Is most of what you know about SEO experiments actually wrong? Host Russ Macumber sits down with Sara Taher, founder of the SEO Riddler newsletter and community, to challenge some deeply held assumptions in the SEO world — including a Google patent that may signal social engineering against sites with spammy backlinks.Sara shares her honest take on schema markup, her accidental start in SEO via a failed Y Combinator-backed startup, and why she built one of the industry's most active SEO communities. Then the conversation turns to AI: Sara's hard rule is don't generate AI output unless you already know what 80% of the correct answer should look like — backed by real stories from Amazon's AI outage to Starbucks walking back over-automation.In this episode: broken SEO experiments, the truth about schema, lessons from a failed startup, building community in a cliquey industry, and the AI literacy rule every SEO needs.

Did you like the episode? Send me a text and let me know!!No Money, No Problem: The Step-by-Step Framework for Finding a Technical Co-Founder Using ValidationEpisode Summary: You have the idea. You have the drive. You have absolutely zero cash for salaries. So how do you find the brilliant technical partner who can actually build the thing — and convince them to join you? In this episode of Business Conversations with Pi and Piette 2.0, PI and Piette tackle one of the most dangerous and high-stakes challenges in entrepreneurship, driven by a real listener question from tuepodcast.net/askpi.Drawing from Harvard Business School research, Y Combinator strategy, Mike Moyer's Slicing Pie model, Dan Martell's co-founder playbook, and startup employment law, this episode delivers the exact blueprint for building a technical team when cash isn't an option — and reveals why that constraint might actually be your greatest advantage.What You'll Learn:Why 65% of startups die because founders hate each other — not because they ran out of moneyWhy you probably don't need a developer yet — and the no-code trap most founders fall intoHow to build leverage before you pitch anyone using no-code validation toolsWhere to find technical co-founders beyond your immediate networkThe 90-day rule that forces a decision and prevents analysis paralysisHow to vet a developer when you don't know a single line of codeDan Martell's "10-hour weekend test" and what friction response revealsWhy a 50/50 equity split on day one is called the "quick handshake penalty" — and how it kills valuationsHow Mike Moyer's Slicing Pie dynamic equity model works with multipliersThe employment law trap that can destroy your company before it startsWhat a four-year vesting schedule and one-year cliff actually mean — and why they protect everyoneTimestamps:[00:00:00] – Introduction & The Listener Question[00:01:00] – The Harvard Study: 65% of Startups Die Because Founders Hate Each Other[00:02:30] – Why Co-Founded Startups See 163% More Valuation Growth[00:03:30] – Do You Actually Need a Developer Right Now?[00:04:00] – No-Code First: Webflow, Airtable, Bubble — Validate Before You Build[00:04:30] – Dan Martell's Filter: Why Top Developers Ignore Idea Guys[00:05:30] – How to Show Up With Leverage, Not a Pitch[00:06:00] – Where to Find Technical Co-Founders: Start Closer Than You Think[00:06:30] – Michael Seibel's Strategy: Make a Real Offer, Not a Favor[00:07:00] – Friends vs. Strangers: The Surprising Data on Who Makes Better Co-Founders[00:08:30] – Co-Founder Matching Platforms: YC Cofounder Match & Start2Pitch Explained[00:09:30] – The 90-Day Rule: Set a Hard Deadline or Fall Into Analysis Paralysis[00:10:30] – How to Pitch Vision When You Have No Cash[00:11:30] – How to Vet a Developer When You Can't Code[00:12:30] – Dan Martell's 10-Hour Weekend Test & the Friction Response Framework[00:13:30] – The Equity Conversation: Why 50/50 Is a Trap[00:14:30] – Noam Wasserman's Quick Handshake Penalty & Investor Red Flags[00:15:30] – Mike Moyer's Slicing Pie: Dynamic Equity With 1X and 2X Multipliers[00:17:30] – The Employment Law Trap: Can You Legally Pay People Only in Equity?[00:19:00] – Contractors vs. Employees: The Classification That Could Destroy Your Company[00:19:30] – Vesting Schedules & the One-Year Cliff Explained[00:21:00] – The Full Playbook Summary[00:22:00] – The Mind-Bending Final Question: Do You Even Need VC Money?[00:23:00] – Submit Your Question & Wrap-UpPlatforms & Resources Mentioned:

Στο πρώτο επεισόδιο της 7ης σεζόν των Outliers, ακολουθούμε την διαδρομή του Παναγιώτη Στραβοπόδη Co-Founder της ELYOS AI. Γεννημένος και μεγαλωμένος στη Ζάκυνθο, ήρθε από νωρίς σε επαφή με την τεχνολογία μέσω του πατέρα του. Ξεκίνησε να πειραματίζεται με υπολογιστές και να γράφει τον πρώτο του κώδικα σε μικρή ηλικία, ανακαλύπτοντας σταδιακά την κλίση του. Σπούδασε στο Πανεπιστήμιο Πατρών ως Μηχανικός Ηλεκτρονικών Υπολογιστών και Πληροφορικής. Κατά τη διάρκεια των σπουδών του, ανέπτυξε ένα σύστημα online κρατήσεων για ένα boutique hotel, αποκτώντας την πρώτη του επαγγελματική εμπειρία και θέτοντας τις βάσεις για την πορεία του στο software engineering. Στη συνέχεια, ακολούθησε συνειδητά τον χώρο των startups, συμμετέχοντας σε διαφορετικές ομάδες και projects. Μέσα από αυτή τη διαδρομή, κατέληξε ότι ο μόνος τρόπος να έχει ουσιαστικό έλεγχο στο πρόβλημα που λύνει είναι μέσα από τη δημιουργία της δικής του εταιρείας. Έτσι ιδρύθηκε η ELYOS AI, με αρχικό στόχο την ανάπτυξη agents για τη βελτιστοποίηση της ενεργειακής απόδοσης σε εμπορικά και βιομηχανικά κτίρια. Ωστόσο, η εμπειρία στο Y Combinator αποτέλεσε σημείο καμπής. Εκεί, οι συνιδρυτές συνειδητοποίησαν ότι, για να μπορέσουν να εξελιχθούν, έπρεπε να αλλάξουν κατεύθυνση. Η στρατηγική αυτή αλλαγή υλοποιήθηκε το 2024 και οδήγησε στις πρώτες ουσιαστικές επιτυχίες, με την εταιρεία να καταγράφει ρυθμούς ανάπτυξης της τάξης του 30% σε μηνιαία βάση. Σήμερα, η ELYOS AI προσφέρει λύσεις που αποδεικνύουν έμπρακτα την αξία τους στους πελάτες της, ενώ πρόσφατα ολοκλήρωσε έναν σημαντικό γύρο χρηματοδότησης ύψους 13 εκατομμυρίων. Στόχος της εταιρείας είναι η περαιτέρω γεωγραφική επέκταση, καθώς και η ενίσχυση της ομάδας της, από 17 σε 32 άτομα μέχρι το τέλος του έτους, με έμφαση και στην ανάπτυξη engineering ταλέντου, συμπεριλαμβανομένης της Ελλάδας. Μια ιστορία που αναδεικνύει τη σημασία της περιέργειας, της προσαρμοστικότητας και της ικανότητας να αναγνωρίζεις πότε χρειάζεται να αλλάξεις κατεύθυνση, προκειμένου να προχωρήσεις ουσιαστικά μπροστά. Αν σας άρεσε το επεισόδιο, πατήστε like, μοιραστείτε το και εγγραφείτε στο κανάλι μας για περισσότερες ιστορίες που θα σας εμπνεύσουν! Ακολουθήστε και βαθμολογήστε μας στις streaming πλατφόρμες μας:

This is a recap of the top 10 posts on Hacker News on July 09, 2026. This podcast was generated by wondercraft.ai (00:30): GPT-5.6Original post: https://news.ycombinator.com/item?id=48849066&utm_source=wondercraft_ai(01:56): EU Parliament greenlights Chat Control 1.0Original post: https://news.ycombinator.com/item?id=48843923&utm_source=wondercraft_ai(03:23): Show HN: 18 WordsOriginal post: https://news.ycombinator.com/item?id=48845049&utm_source=wondercraft_ai(04:50): My thoughts on the Bun Rust rewriteOriginal post: https://news.ycombinator.com/item?id=48843352&utm_source=wondercraft_ai(06:17): Postgres rewritten in Rust, now passing 100% of the Postgres regression testsOriginal post: https://news.ycombinator.com/item?id=48841676&utm_source=wondercraft_ai(07:43): Show HN: Getting GLM 5.2 running on my slow computerOriginal post: https://news.ycombinator.com/item?id=48842459&utm_source=wondercraft_ai(09:10): Hy3Original post: https://news.ycombinator.com/item?id=48847552&utm_source=wondercraft_ai(10:37): I think I have LLM burnoutOriginal post: https://news.ycombinator.com/item?id=48839984&utm_source=wondercraft_ai(12:04): Why developers are ditching GitHub for Codeberg and self-hosting alternativesOriginal post: https://news.ycombinator.com/item?id=48842611&utm_source=wondercraft_ai(13:31): Muse Spark 1.1Original post: https://news.ycombinator.com/item?id=48846184&utm_source=wondercraft_aiThis is a third-party project, independent from HN and YC. Text and audio generated using AI, by wondercraft.ai. Create your own studio quality podcast with text as the only input in seconds at app.wondercraft.ai. Issues or feedback? We'd love to hear from you: team@wondercraft.ai

Rajiv Parikh talks with four-time founder Geoff McQueen about how AI is inverting the business pyramid and forcing everyone, not just executives, to act more entrepreneurially. We dig into why demos are easier than trust, how revealed preference drives better pivots than surveys, and what AI-first operations look like when context beats dashboards.• AI flipping high-cognitive work and raising the bar for every role• Why entrepreneurship matters more than traditional management• Speedy vibe-coded prototypes versus enterprise-grade products• Security, permissions, governance, and the real cost of trust• The limits of AI for creativity and naming despite strong research output• Revealed preference as a founder discipline driven by user behavior• The analytics trap and how dashboards can fossilize workflows• AI in the middle of the business with a shared context graph• Observer agents that investigate quietly and escalate only when needed• Coaching culture versus invasive surveillance approaches• A practical path toward $10M ARR with fewer than 10 employees• Openness, trade, and the societal risks of turning inwardAI just made execution cheap, and that changes what founders should obsess over. Rajiv sits down with Geoff McQueen, founder and CEO of WorksightsAI, to unpack what happens when the “business pyramid” flips upside down and high-level cognitive work gets accelerated by frontier models while culture, alignment, and conflict stay deeply human. The result is uncomfortable and exciting: less room for business-as-usual management and a lot more demand for entrepreneurial judgment at every level.We get concrete about the gap between a vibe-coded demo and a product an enterprise can actually trust. Geoff walks through the unglamorous realities that decide whether AI-built software is real or risky: security, access control, permissions, privilege escalation, reliability, and governance. If you've ever watched an agent claim it “did the work” while quietly failing, this part will sharpen how you evaluate prototypes, MVPs, and production systems.Then we go deeper on product strategy and go-to-market. Geoff explains why revealed preference beats stated preference, why surveys can mislead founders, and how the “analytics trap” shows up when dashboards fossilize workflows instead of explaining what's truly going on. We also explore AI-first operations and continuous performance intelligence built around corporate context across Slack, calendars, docs, tickets, CRM, and code, plus how observer agents can help leaders coach instead of police.If you're building with AI, leading a team through change, or trying to scale efficiently, you'll leave with a clearer playbook for direction, trust, and leverage. Subscribe, share this with a founder friend, and leave a review. What's the first workflow you'd redesign if context became instantly searchable?Geoff McQueen: https://www.linkedin.com/in/geoffmcqueen/Geoff McQueen, the Founder and CEO of WorkSightsAI, a continuous performance intelligence platform that helps modern leaders run their businesses with AI.  A four-time founder with over 20 years of experience building and investing in tech startups, Geoff was formerly the CEO and Co-Founder of Accelo, which he scaled to over $14 million in annual recurring revenue before a successful exit to private equity in early 2024.  Armed with an MBA from the Sydney Business School at the University of Wollongong, today, he also serves as an Executive Director of the Aussie Founders Network and has spent over 13 years as an investor and mentor at Startmate, Australia's equivalent to Y-Combinator.Website: https://www.position2.com/podcast/Rajiv Parikh: https://www.linkedin.com/in/rajivparikh/Email us with any feedback for the show: sparkofages.podcast@position2.com

Pour l'épisode de cette semaine, je reçois Dan Elkaïm, le CEO et cofondateur de Topo.Topo est une plateforme de prospection B2B qui permet aux équipes commerciales de trouver, enrichir et contacter leurs prospects avec l'aide d'agents IA.Au cours de cet épisode, nous sommes revenus sur le parcours de Dan, notamment ses années chez Aircall, où il a participé au passage de 10 à 100 millions d'euros d'ARR, puis sur la création de Topo avec ses associés.Dan nous raconte également leur passage par Y Combinator et le pivot radical effectué quelques jours après leur arrivée à San Francisco. Après avoir abandonné leur premier produit, ils reviennent à leur expertise historique : l'outbound. Ils commencent alors par vendre une prestation réalisée presque entièrement à la main, avant de construire progressivement leur plateforme.Nous avons beaucoup parlé de l'évolution de la prospection B2B, du positionnement initial de Topo comme « AI SDR » et des limites d'un agent entièrement autonome et difficile à contrôler. Ce constat les a amenés à repenser leur produit pour permettre aux commerciaux de travailler avec l'IA, en choisissant eux-mêmes le niveau d'automatisation souhaité.Enfin, nous avons échangé sur l'avenir des équipes commerciales, la différence entre remplacer et augmenter les humains grâce à l'IA, mais aussi sur les nouveaux avantages défensifs des SaaS verticaux à une époque où développer un logiciel devient de plus en plus accessible.Vous pouvez suivre Dan sur LinkedIn.Bonne écoute !Mentionnés pendant l'épisode :AircallY CombinatorLemlistClayApolloCursorLegoraGap Selling de Keenan20VC d'Harry StebbingsElevenLabsPour soutenir SaaS Connection en 1 minute⏱ (et 2 secondes) :Abonnez-vous à SaaS Connection sur votre plateforme préférée pour ne rater aucun épisode

Distance stops most investors before they ever get started. Stella Han never let it. In this episode, I sit down with Stella Han, co-founder and CEO of Fractional, to talk about her journey building a real estate portfolio remotely from California into the Atlanta market - the deals, the lessons, and what it actually takes to invest in a market you've never lived in.   Stella's story is one of those that reminds you that geography is not the obstacle most people think it is. But the story doesn't stop at the portfolio. After running into a wall trying to raise capital for a larger deal - a painful and expensive lesson - Stella channeled that frustration into building Fractional, a platform designed to make raising capital and pooling resources with other investors faster, simpler, and more accessible than anything that existed before. What started as a personal problem turned into a company backed by Y Combinator that has helped operators raise hundreds of millions of dollars.   This one is a great listen whether you're an investor trying to figure out how to break into a new market, someone sitting on a deal that needs capital, or just someone who appreciates a great founder story rooted in real estate. Book your call with Neo Home Loanshttps://www.neoentrepreneurhomeloans.com/wjpodcast/ Book your mentorship discovery call with Cory RESOURCESGet business funding - revenued.com/juice

This is a preview | For full audio and show notes plus extras, subscribe via https://patreon.com/newmodels or https://newmodels.substack.com _ When Enlightenment-era scholars taxonomized humankind, they chose homo sapiens, in part because of what they observed as a uniquely human aptitude for “taste.” [“Taste” comes from the Latin taxare (to handle, to assess) – an intensive form of tangere (to touch). It is a term that via vulgar Latin blended with gustare (to taste, to try) to express the act of appraising something by physically handling or even consuming it. In proper Latin, there is the related term sapere, which carries a double meaning of “to taste” and “to be wise.” To taste, in the sense of sapere, is to possess wisdom through tangible experience.] It's remarkable that in recent years, and intensely in recent months, the idea of “taste”—“having taste,” “deploying taste,” “taste agents,” “integrating a taste layer,” “taste as a core skill,” “taste as moat”—has become an ultra-present concern in tech circles. Isn't having taste (wherever one falls along the taste spectrum) an inherent quality of being human? To be sure, there's already a lot of writing on this phenomenon: “Tasteslop” by NEMESIS‘s Emily Segal and “Why Tech Bros Are Now Obsessed With Taste” by Kyle Chayka for the New Yorker being among the best takes. The most confounding, in our opinion being Y-Combinator cofounder Paul Graham's “Taste for Makers” (Feb 2002, but highly cited this year). There is also “Against Taste,” by Will Manidis, which makes some good points (patrons used to fund art for a higher power or at least public display whereas contemporary collectors tend to buy art for their own private use) but in its theory-of-everything aspiration, feels LLM-ish and contextually ahistorical. So in pure “Content Today” form, we are adding to the pile-on with our own fashionably late, probably factually botched but definitely human, free-associative conversation about taste.

272 | Was ist, wenn du glaubst, ein Unicorn zu werden - und dann fängt der Gegenwind an? Hanna Maria Asmussen hat Localyze gegründet und war 2020 der erste Gast in diesem Podcast. Jetzt hat sie ihr Startup verkauft und berichtet ganz offen von den Aufs & Abs eines Gründerlebens.Partner dieser Folge:Readly5000 nationale und internationale Zeitschriften lesen. Hier 1 Monat komplett kostenlos: https://get.readly.co/optimistensommer?promocode=READLYDEOPTIMISTENSOMMERMach das 1-minütige Quiz und finde eine Geschäftsidee, die zu dir passt: digitaleoptimisten.de/quiz.Kapitel(00:00) Intro(02:38) Die Zauber des Anfangs - Gründen, Y-Combinator(16:15) Der Höhepunkt: Hohe Bewertungen in 2021 trotz Covid(28:35) Die Wende: 2023 und harte Entscheidungen(43:10) Der Verkauf in 2025 (48:10 Reflektionen & Learnings(58:14) Hannas beste GeschäftsideeSo erreichst du uns:Sprachnachricht senden: https://www.speakpipe.com/digitaleoptimistenEmail schreiben: alexander@digitaleoptimisten.deLearningsSchnell testen statt perfekt bauen.Das Problem wird erkannt und in zwei Tagen auf den Markt gebracht, um es zu validieren statt monatelang zu entwickeln.Formulare im Frontend und manuelle Prozesse ermöglichen schnelle Testläufe, ohne vollständiges Produkt.Warum wichtig: so lassen sich Hypothesen früh prüfen und Ressourcen sparen.Erster zahlender Kunde als Signaleffekt.Der erste zahlende Kunde zeigte, dass es echtes Zahlungsinteresse gibt.Dieses Feedback war der Beleg für Marktbedarf und inspirierte weitere Produktentwicklung.Warum wichtig: Frühe Bestätigung reduziert Risiko und erhöht Lerntempo.Passender Hiring-Pattern für Startups.Zu frühe Hiring von Konzern-Leuten führte zu kulturellen Konflikten und fehlender Go-to-Market-Expertise.Die besten Ergebnisse kamen von Kandidaten mit Start-up-Hunger und kleinem CV, die sich ins Team einfügten.Eine Mischung aus erfahrenen Rollen und internen Talenten, mit robustem Onboarding, wäre besser gewesen.Krisenmanagement: GTM pivot und Fokussierung.Covid 2020 führte zu massiven Umsatzverlust; 2021/22 wurde die Go-to-Market-Strategie auf Enterprise fokussiert (Lufthansa) und internationale Expansion vorangetrieben.Die Lehre: Eine rein inbound-getriebene GTM-Struktur reicht bei Skalierung nicht; man braucht systematische Prozesse, klare Rollen und eine gut geölte Akquise-Strategie.Externe Hiring-Experimente halfen kurzfristig, funktionierten aber oft nicht nachhaltig; daher gilt: Bau das Playbook intern, bevor man externe Führungspositionen besetzt.Liquidationspräferenz beachten.In Exit-Situationen sichern Investoren oft zuerst ihr Kapital über Liquidationspräferenzen, was Gründer-Auszahlungen schmälert.10% Gründeranteil sind eine gängige Orientierung, bleibt aber je nach Deal unterschiedlich; daher frühzeitig klären.Frühzeitige Verhandlung über Exit-Mechanik und Anreizstrukturen ist entscheidend, damit Gründer auch nach dem Exit motiviert bleiben.KeywordsLocalizeY CombinatorSeries AGo-To-Market-StrategieEnterprise-VerkaufLufthansa Großkunde HR-RelocationLiquidationspräferenz Auswirkungen GründungVerkaufsprozess Startup AblaufCovid-19 Auswirkungen HRTech StartupsGründungserfahrungTeamkulturHiring-FehlerInvestorennetzwerkProfitabilität

Walmart just bought its way into the connected TV arms race, and The Trade Desk is the biggest loser. In a ~$1.4B deal announced during Cannes Lions week, Walmart Connect is acquiring Vibe.co, the self-serve CTV/streaming ad platform, to close a 10x gap with Amazon's ad business.Christian and Ayelet break down the deal from two distinct angles, the operator's read and the deal architect's read, plus a venture market update, hot tea on SPS Commerce quietly selling a business back to its founder, and eight rapid-fire quick hits in what's officially become the summer of add-ons.One venture update. One deep dive, two POVs. Hot tea. Eight quick hits.⏱️ TIMESTAMPS0:38 — Happy Fourth of July, and what's on the agenda2:19 — Market update: JustAI raises $17M Series A (Base10, Y Combinator, Peak XV)3:00 — Concord raises $3M seed for agentic media buying, and why Vibe.co's CEO is an investor4:34 — "The Summer of Add-ons": why fragmentation is fueling M&A4:44 — The deal: Walmart Connect acquires Vibe.co, the "Google Ads of streaming"6:25 — The numbers: ~$100M revenue, ~$1.4B deal, 10-14x revenue (and why it's not an AI deal)8:00 — Walmart's M&A cadence and the Vizio precedent8:54 — The 10x ad-revenue gap: Amazon at $82B vs. Walmart at $8.2B9:30 — The advisors and why this was a CEO-to-CEO deal9:50 — The operator's read: a capability tuck-in that buys 3-4 years10:00 — Is the CTV TAM actually big enough? The Brian Wieser cannibalization argument12:00 — Integration risk: folding a scrappy startup into a corporate giant12:26 — The France factor: why acquiring 60 employees in Paris is its own challenge14:00 — Why The Trade Desk is the biggest loser (per Ari Paparo)14:38 — The deal architect's read: the founders' first big exit, sold from strength16:55 — Why $180M in retention may not hold founders who don't need the money17:31 — The real make-or-break: keeping the team hungry inside a giant18:00 — Hot tea: SPS Commerce carves out Seller Investigators, sells it back to the founder22:01 — Quick hits: Revmatics/DataFeedWatch, Moburst/Hyperzon, The Independents/Phantasm22:51 — More hits: Samba TV/Bestever, Yes&/Modo Modo, Arketi/Sperling24:00 — Martis Capital takes majority of Deerfield Group (~$280M, 12-14x EBITDA)25:00 — The consistent 14x agency marker, and how to break past it into strategic territory25:38 — Final thought: this is add-on summer

Today, we're joined by Alex Bouaziz, co-founder and CEO of Deel, the global HR and payroll platform now serving companies across 150+ countries with more than $1 billion in annual revenue.Tommy Stadlen talks to Alex about how Deel became the fastest startup to reach $100M in recurring revenue: and why that record came down to one thing: following customers. Alex explains why Deel launched with contractors when everyone else went after employees, how they carried the intensity of Y Combinator all the way to a billion-dollar business, and why their M&A strategy (13 acquisitions in six years) has been one of their most underrated growth levers.He speaks about:The YC "time chamber" mindset they still run the company onWhy speed of execution matters - and why comfort in any department is a warning signWhy hiring A players is less about finding them, and more about NOT lowering the bar when you're desperate to hireWhat most people miss about the Deel M&A strategy (and how they keep founders motivated years after acquisition)The case for brand marketing at the enterprise stage, and what Arsenal taught him about communityWhy being a remote company selling to remote companies is an unfair advantageAnd lots more!Building a purpose driven company? Read more about Giant Ventures at www.Giant.vc.Music credits: Bubble King written and produced by Cameron McLain and Stevan Cablayan aka Vector_XING.Please note: The content of this podcast is for informational and entertainment purposes only. It should not be considered financial, legal, or investment advice. Always consult a licensed professional before making any investment decisions.

Making your first 10000 dollars doesn't require an LLC, a business plan, or any startup capital — just skills you already have and the willingness to use them differently. In this episode, Eric Coffie breaks down three real strategies he's personally used to help people generate $5,000 to $10,000 quickly, even with zero business infrastructure in place. If you've ever felt like you don't have enough resources to get started, this episode will show you exactly what's already in your hands. Learn how to package and sell a skill you already have, the same way Eric turned free YouTube content into a $397,000 course launch Discover how documenting a repetitive process, like a government compliance report, can save hundreds of hours and become a sellable asset Watch a live demo of vibe coding an app from scratch using Base44, built in real time during the call Understand why partnering with people who already have customers is the fastest way to generate revenue without building an audience first Get a breakdown of Y Combinator's Fall 2025 "Request for Startups," including AI-powered vocational training and FedRAMP approval automation EPISODE CHAPTERS: 0:00 - Welcome to the GovCon Giants podcast intro 1:22 - Why this episode is for people starting from zero 3:44 - Selling a skill you already know how to do 6:39 - Turning a LinkedIn following into a sellable course 9:31 - Documenting repetitive processes to save companies time 11:57 - Introducing vibe coding with the Base44 platform 15:18 - Partnering with people who already have paying customers 21:33 - Live demo building a late invoice tracking app 26:46 - Reveal of the finished vibe coded invoice app 27:45 - Y Combinator Fall 2025 request for startups breakdown 31:12 - Closing thoughts on using skills to buy back time Mindy gives you the federal opportunities, agency signals, recompete intel, and pursuit briefs that tell you not just what contracts exist, but which ones to chase and how to win them. Sign up for free Daily Alerts and get opportunities delivered to your inbox before the day starts.

Stanford, Loopt, and Y Combinator. Guest Author: Keach Hagey. Altman's career accelerated at Stanford, where he dropped out to co-found Loopt, a pioneering location-tracking startup. Although Loopt achieved visibility—including a famous appearance at an Apple event alongside Steve Jobs—it was financially a disappointment, selling for parts after the 2008 crisis. Following a period of global "backpacking" and self-reflection, Altman discovered his "superpower" in investing, mentored by Peter Thiel. By 2014, he became the president of Y Combinator, overseeing massive successes like Airbnb and Stripe. Influenced by a visit to SpaceX, Altman adopted Elon Musk's "missionary" approach, viewing startups as world-changing missions rather than mere businesses. During this time, he also championed radical social concepts like Georgism and Universal Basic Income (UBI), writing extensively on how mass AI equity could eventually be shared to restructure society. 3JANUARY 1941

Aseon Labs, which came out of Y Combinator's 2026 spring cohort, has raised $10 million from Crane Venture Partners and others. Also, General Intuition has raised $320 million to scale AI trained on millions of hours of gameplay, betting action data can help AI develop something closer to human intuition. Learn more about your ad choices. Visit podcastchoices.com/adchoices

In this episode of In Depth, Brett sits down with Paul Copplestone, co-founder and CEO of Supabase, the open-source Postgres platform now serving more than seven million developers. Before Supabase, Paul launched a Thumbtack-style marketplace in Southeast Asia and co-founded an office-management startup called Nimbus, experiences that taught him to separate fundraising from building and to find product-market fit before blitzscaling. He breaks down how a single tagline change for Supabase unlocked product-market fit, why he runs a fully distributed async team with near-zero attrition, and how he turned PLG signals into a product-led sales motion comped only on incremental uplift. In today's episode, we discuss: How changing one tagline helped Supabase go to #1 in Hacker News - an early sign of product market fit Why Paul ran Supabase like it had only $100K in the bank despite raising real money How Supabase rode three distinct AI waves, from pgvector to Bolt and Lovable, to Claude Code Why Supabase built a sales team comped only on the incremental uplift over a control group What the Toyota production system's "kaizen" taught Paul about unblocking a scaling team References: Ant Wilson: https://www.linkedin.com/in/ant-wilson-46179937 Bolt: https://bolt.new/ Claude Code: https://www.anthropic.com/claude-code Codex: https://openai.com/codex/ Entrepreneurs First: https://www.joinef.com/ Firebase: https://firebase.google.com/ Lovable: https://lovable.dev/ MongoDB: https://www.mongodb.com/ Next.js: https://nextjs.org/ PostgreSQL: https://www.postgresql.org/ Supabase: https://supabase.com/ Thumbtack: https://www.thumbtack.com/ Y Combinator: https://www.ycombinator.com/ Where to find Paul: LinkedIn: https://www.linkedin.com/in/paulcopplestone Twitter/X: https://x.com/kiwicopple Where to find Brett: LinkedIn: https://www.linkedin.com/in/brett-berson-9986094/ Twitter/X: https://twitter.com/brettberson Where to find First Round Capital: Website: https://firstround.com/ First Round Review: https://review.firstround.com/ Twitter/X: https://twitter.com/firstround YouTube: https://www.youtube.com/@FirstRoundCapital This podcast on all platforms: https://review.firstround.com/podcast Timestamps: 00:00 Introduction 01:32 Why Paul's earlier startups were never destined to be huge 07:14 Unlearning the "tall poppy" mindset and going all-in on async 09:54 Reverse-engineering why Supabase was an outstanding idea 12:04 The accidental Hacker News launch and tagline lesson 13:58 Where the early roadmap came from: demand vs. technical taste 17:28 Skill vs. luck, and operating like you have $100K in the bank 21:42 What actually makes a great developer experience 23:10 Solving the "graduation problem" Firebase never could 24:58 The role of open source in Supabase's success 26:10 The three distinct AI tailwinds: From pgvector to Claude Code 35:24 Supabase's egoless, hyper-competitive open-source culture 42:58 A tactical playbook for raising capital 48:37 Product-led sales comped on incremental uplift only 59:27 The production philosophy behind Supabase's operations

The CEO of Heart Aerospace describes the development of a hybrid-electric 30-seat regional commercial aircraft. In the news, a near miss at Boston Logan between a landing Delta Air Lines flight and a departing American Airlines flight, NASA's Advanced Aircraft Concepts for Environmental Sustainability 2050 (AACES) program, Canada’s purchase of F-35A fighters and possibly Saab Gripens, and Canada’s look at early-warning-radar planes. Guest Anders Forslund is the co-founder and Chief Executive Officer of Heart Aerospace, formed to electrify short-haul regional aviation. Heart Aerospace is developing the ES-30, a hybrid-electric 30-seat regional commercial aircraft. Heart is currently in upstate New York, testing the X1 demonstrator aircraft, which the company says will be the largest electric aircraft ever to fly. The company is backed by Bill Gates' Breakthrough Ventures and Y Combinator, as well as operator/investors United Airlines and Air Canada. Anders explains Heart Aerospace’s mission to lower the cost of air travel and how their clean-sheet Part 25 airliner will achieve about a 40% reduction in overall operating costs. The ES-30 will be an electric-motor-and-turboprop hybrid, while the full-scale X1 prototype is all-electric. The X1 demonstrator has completed low-speed taxi testing at the company’s X1 flight-test base at Plattsburgh International Airport in upstate New York. First flight is expected shortly, with type certification planned for 2031. Video: Heart X1 Completes Low-Speed Taxi Testing https://youtu.be/5jkyKevsJNI?si=1xreSjh_gRcI6xu2 Anders tells us about the Heart team and how aerospace development has changed in the last decade. The company strives to manage uncertainty rather than minimize it, holds itself accountable, and sets falsifiable goals. Before starting Heart, Anders was an aerospace researcher at Chalmers, where he was a driving force behind the Elise-Electric Aviation research project in Sweden, funded by the Swedish innovation agency Vinnova. He spent 2013-2014 at MIT, where his work on geometric variation of aerospace components was awarded the Charles M. Manly Memorial Medal. He is also a founding member of the Nordic Network for Electric Aviation. Anders has a Ph.D. in Aerospace Product Development and a B.Sc. in Engineering Physics from Chalmers. He has a dual M.Sc. in Astronautics and Space Engineering from Cranfield University and Luleå University, as part of the SpaceMaster program. He is also a member of Prince Daniel’s Fellowship for young entrepreneurs. Follow Heart Aerospace on YouTube, Facebook, Instagram, X, and LinkedIn. Aviation News Horrifying Near Miss at Boston Logan as Quick-Thinking Delta Air Pilots Go Around to Avoid Landing On Top of American Airlines Boeing 737 Delta Air Lines flight DL-2351, an Airbus A319 flying from Dallas, was landing at Boston Logan International Airport (BOS), was cleared to land on runway 33L. Moments later, American Airlines flight AA-3161, a Boeing 737-800, was cleared to take off for Charlotte from intersecting runway 27. The pilots repeated the instruction back to the controller, and after about 40 seconds, started the takeoff. As Delta flight 2351 was about to touch down, the pilots saw the American Airlines plane and executed a go-around. That was followed by the air traffic controller asking the American flight, “American, where are you going?” Listen to the Incident Audio via @xJonNYC. Electra reveals 100-seat hybrid-electric aircraft concept Electra developed the concept under NASA's Advanced Aircraft Concepts for Environmental Sustainability 2050 (AACES) program. NASA has commissioned industry and academia to “develop transformative aircraft designs, propulsion technologies, and sustainability solutions for commercial aviation by 2050.” Electra's large airliner concept features a wide “double-bubble” fuselage that generates lift. Propulsion comes from two turbofans under the wings that produce thrust and electricity, as well as three fans mounted on the top of the aft fuselage. Electra says those fans would “ingest and re-energise slower-moving air over the fuselage, a technique known as boundary layer ingestion.” Other AACES participants include the Georgia Institute of Technology with the Liquefied Natural Gas Powered Athena Aircraft Concept, and JetZero with a hydrogen fueled blended wing body design. Congress Questions Air Force's Combat Rescue Readiness As HH-60W Helicopters Get Turned Into VIP Transports The Senate Armed Services Committee filed S. 4784, the National Defense Authorization Act for Fiscal Year 2027 (NDAA), which establishes funding levels and authorities for the U.S. military. The 66th annual NDAA supports a total of $1.15 trillion in FY 2027 funding for national defense. In its report [PDF], the Committee expresses concern “about combat search and rescue (CSAR) force structure in the Air Force. In recent years, the Air Force truncated the buy of HH-60Ws and has since transferred 26 HH-60Ws from units responsible for CSAR operations to the Air Force District Washington (AFDW) to replace H-1 helicopters. AFDW uses these helicopters to support contingency response, homeland operations, and ceremonial honors in the National Capital Region. “The committee believes that these actions have left CSAR forces unnecessarily short of the forces needed to support CSAR operations in a major contingency. Therefore, the committee directs the Secretary of the Air Force to conduct a study of CSAR requirements and capabilities, including HH-60Ws and HC-130Js, and provide a report and briefing on that study to the congressional defense committees, not later than March 31, 2027. “Furthermore, the committee directs the Secretary to avoid making any changes in CSAR force structure until the study is completed and he or she has provided the results of that study to the Congress.” Canada Plans Fleet Surge to 140+ Fighters as Low Cost Gripens Reduce Expenses According to informed sources, the Royal Canadian Air Force plans to grow its fighter fleet to 140, possibly by purchasing Saab Gripen jets. Canada has planed to purchase 88 F-35A fighters, but that could drop to 70, accompanied by 70 Gripens. Saab offered to establish final assembly, maintenance, and long-term industrial support in Canada. This would transfer technology and intellectual property to Canada. Under the F-35 program, sustainment and software updates are centralized in the United States. Canada to buy Swedish surveillance plane over US models Canadian Prime Minister Mark Carney announced that Canada would not purchase early-warning-radar planes from the United States. Instead, they will purchase Saab’s GlobalEye, which is based on the Bombardier Global 6500 jet, manufactured in Canada. Price and fleet size were not announced. Saab said in a statement that as part of any deal, the company would invest in research and development work in Canada. Hosts this Episode Max Flight, our Main(e) Man Micah, Rob Mark, and David Vanderhoof.

Un grand merci à Loop Capital, la référence mondiale de l'Infinite Banking Concept, de soutenir ce podcast. Découvrez comment reprendre le contrôle absolu de votre capital et bâtir votre souveraineté financière sur : https://loop-capital.co/À 15 ans, Brivaël Le Pogam gagnait entre 1 500 et 2 000 dollars par mois avec un jeu en ligne qu'il avait codé seul.Personne ne le sait.Aujourd'hui, il est co-fondateur et CTO d'Argil.ai — une start-up Y Combinator qui permet à n'importe qui de se cloner en vidéo grâce à l'IA, de parler dans n'importe quelle langue, sans studio, sans équipe, sans caméra.Mais ce qui m'a le plus frappé dans cette conversation, c'est pas la technologie.C'est comment il pense.Brivaël n'utilise pas l'IA comme un outil de délégation. Il l'a construite comme une extension de lui-même — des agents entraînés sur sa façon de raisonner, d'argumenter, de répondre. Sa bio sur X dit littéralement : "soit moi qui écrit, soit mes agents."Dans cet épisode de Débrouillard, il raconte tout :→ Comment il a reverse-engineeré la technologie deepfake vidéo avec une équipe de trois personnes→ Pourquoi Marc Andreessen a retweeté sa démo à 2h du matin — et ce que ça a changé→ Deux pivots douloureux, 70 000 inscrits brûlés, et comment il a trouvé le vrai founder-market fit→ Sa thèse sur l'IA : on est encore au stade de la CLI des années 70 — la vraie révolution n'a pas commencé→ La différence entre utiliser l'IA comme béquille et l'utiliser comme levier→ Pourquoi il pense que la prochaine génération de créateurs va produire le futur Star Wars depuis leur chambreUn épisode dense, technique, et résolument contre-courant.▬▬▬▬▬▬▬▬▬

nFactorial Intelligence - еженедельный обзор новостей из мира стартапов и ИИ Сильный предиктор выдающейся работы — быть безнадежным в повседневных мелочах. Любимая цитата Маска о том, что проигрывать не нужно бояться. Плейбук Y Combinator по поиску первых клиентов и совет, как найти по-настоящему влиятельную идею. Домохозяйка из Ченнаи зарабатывает $3 в час, записывая свою рутину для обучения ИИ. Японские болельщики снова убрали стадион и устроили «Japan Pride», а после ничьей с Нидерландами показали трогательный момент на пресс-конференции. Вратарь Кабо-Верде стал героем мемов, а на Polymarket разбираем крупнейшие проигрыши дня. Levi's обыграла статус спонсора ЧМ силой дизайна. Правительство США приказало Anthropic мгновенно отключить Fable 5 и Mythos 5 для всех клиентов. SpaceX договорилась о покупке Cursor. Джефф Безос вышел из стелса с AI-стартапом Prometheus за $41 млрд. SBF оказался одним из лучших инвесторов десятилетия.   Рекомендации: Следующая встреча nFactorial Club 21 июня в 9:00 онлайн - https://hi.nfactorial.club/ nFactorial Teens: 2-недельный летний лагерь по вайб-кодингу для школьников в Алматы (11-16 лет). Цель: создание своего оригинального веб-приложения или веб-игры - https://courses.nfactorial.school/teens Полный ассортимент буткампов от nFactorial School: https://courses.nfactorial.school/