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The sudden US government shutdown of Anthropic's Fable model has tech insiders reeling and rival global labs surging ahead. This episode breaks down the unexpected political power play rattling the future of AI innovation. The Fable 5 Export Controls Harm US Cyber Defense Anthropic CEO says government should block dangerous AI The Real Reason Anthropic's Models Are Offline: A Six-Year-Old Trump Grudge (21) Pete Hegseth on X: "Three months ago, @DeptofWar kicked @AnthropicAI out of our building—forever. Every passing day proves why that was the right move.
The AI Breakdown: Daily Artificial Intelligence News and Discussions
The AI race is entering a new phase as SpaceX turns its IPO momentum into AI leverage, Cursor becomes part of Elon Musk's broader strategy, and OpenAI's leaked financials tell a more complicated story than the skeptics suggest. In the headlines: the latest in the Anthropic-Washington fight over Fable 5, Mythos, and what's really behind the government's cybersecurity concerns.Check out the new https://aidailybrief.ai/Brought to you by:KPMG – Research from KPMG and the University of Texas at Austin shows the highest-impact AI users treat AI like a reasoning partner — and those skills can be taught at scale. Learn more at kpmg.com/us/SophisticatedBolt - Claim a free month of Bolt Pro - https://bolt.new/partner/aidb/Section - Section turns AI investment into workforce transformation and ROI - https://www.sectionai.com/Outsystems - Stop wondering how AI will change your business and start building the agents that will lead it - http://outsystems.com/Scrunch - The AI customer experience platform - https://scrunch.com/Zenflow Work - Agents for knowledge work - https://zenflow.free/Blitzy - Want to accelerate enterprise software development velocity by 5x? https://blitzy.com/AssemblyAI - The best way to build Voice AI apps - https://www.assemblyai.com/briefRobots & Pencils - Cloud-native AI solutions that power results https://robotsandpencils.com/The AI Daily Brief helps you understand the most important news and discussions in AI. Subscribe to the podcast version of The AI Daily Brief wherever you listen: https://pod.link/1680633614Our Newsletter is BACK: https://aidailybrief.beehiiv.com/Interested in sponsoring the show? sponsors@aidailybrief.ai
Plus: Yum Brands is selling its Pizza Hut business after stalled growth in the pizza industry. And Robinhood will lay off 10% of its staff. Alex Ossola hosts. Sign up for WSJ's free What's News newsletter. An artificial-intelligence tool assisted in the making of this episode by creating summaries that were based on Wall Street Journal reporting and reviewed and adapted by an editor. Learn more about your ad choices. Visit megaphone.fm/adchoices
Futures took a breather Tuesday morning following Monday's rally on expectations the U.S. and Iran are marching toward a peace deal. Tom White poses the question: "how much of this is priced in?" In equities, Tom points out Micron's (MU) potential to open at new record highs, SpaceX (SPCX) rallying once again as it aims to buy Cursor, and Nvidia's (NVDA) sluggish price action. ======== Schwab Network ========Empowering every investor and trader, every market day. Subscribe to the Market Minute newsletter - https://schwabnetwork.com/subscribeDownload the iOS app - https://apps.apple.com/us/app/schwab-network/id1460719185Download the Amazon Fire Tv App - https://www.amazon.com/TD-Ameritrade-Network/dp/B07KRD76C7Watch on Sling - https://watch.sling.com/1/asset/191928615bd8d47686f94682aefaa007/watchWatch on Vizio - https://www.vizio.com/en/watchfreeplus-exploreWatch on DistroTV - https://www.distro.tv/live/schwab-network/Follow us on X – https://twitter.com/schwabnetworkFollow us on Facebook – https://www.facebook.com/schwabnetworkFollow us on LinkedIn - https://www.linkedin.com/company/schwab-network/ About Schwab Network - https://schwabnetwork.com/about
Had Ada Palmer back on – this time to talk about Machiavelli, perhaps the most misunderstood thinker of all time.Machiavelli cut his teeth as a high-level diplomat for Florence, a position from which he got to closely observe the most important rulers in Europe at the time, including the ones who were on the path to destroying his dearly beloved Florence.In 1513 the Medici retook control of Florence and, wrongly suspecting Machiavelli of participating in a coup attempt, fired, tortured, and exiled him.Machiavelli could have left exile and worked for any number of different principalities that would have been eager to make use of his talents.Instead, he decided to rot in the countryside and compile his career's lessons about power, politics, and human nature into a book he dedicated to the very man whose new regime had tortured and exiled him, Lorenzo di Piero de' Medici.But at least the Medici were in a position to use his insights to defend Florence. Machiavelli the patriot did not want any other hands to touch these books, because those hands, armed further with these lessons, might pose an existential danger to Florence.The closest modern analogy, at least as Machiavelli would have seen it, would be Szilard's letter warning FDR about the possibility of a nuclear fission bomb.What were those insights? And how were they inspired by Machiavelli's dangerous diplomatic missions all across Europe, and his extensive reading of antiquity? Watch this episode with Ada Palmer to find out!By the way, Ada is launching a new podcast which I'm very excited about. The first season will be about Machiavelli - a perfect way to dive deeper into the topics we discussed in this episode. Subscribe at Beforecast's website to be notified of the first episode, subscribe on YouTube, follow her on Patreon, and if you want even more Ada, check out her FixTheNews Podcast episode, and check out her books and more.Watch on YouTube; read the transcript.Sponsors* Cursor recently saved one of my podcast recordings. When a video file from a shoot came out corrupted, I pointed Cursor at it: it recovered the footage on its own, tracking down the right reference file from the file's metadata and realigning the out-of-sync audio. My whole team now uses Cursor for everyday tasks, not just coding. Get started at cursor.com/dwarkesh* Jane Street's hiring process has been going viral on Twitter lately. The memes are pretty funny, but I wanted to see what their interviews were actually like. So I had Ricson, one of Jane Street's ML researchers, walk me through a retired puzzle: he gave me an image dataset where 50% of the files had been corrupted – I had to figure out how to recover them. If you're interested in these sorts of puzzles, you can find Jane Street's open roles at janestreet.com/dwarkesh* Crusoe is turning the AI datacenter buildout into an industrial process. At their massive Colorado factory, they assemble Spark units, modular datacenters with power, cooling, and fire suppression built in. They also manufacture specific components in-house to skip the longest lead times. Crusoe has experience running these Spark units on a range of energy sources, including solar and used EV batteries, ensuring they don't get bottlenecked by grid availability. Learn more at crusoe.ai/dwarkeshTimestamps(00:00:00) – How Florence bargained with Cesare Borgia for survival(00:15:08) – Machiavelli's analytical innovations(00:23:58) – Why popes became warlords(00:36:13) – Why the common people demanded nepotism(00:47:57) – Cesare Borgia brought terror to rulers and justice to the people(00:57:55) – Art as a proxy for war(01:06:41) – Florence, a city famous in hell(01:15:57) – The Prince was a job application to Machiavelli's torturers(01:41:39) – During the Renaissance, original ideas had to be couched in antiquity(01:50:44) – Why copyright began with the Inquisition(02:02:12) – Machiavelli wasn't Machiavellian Get full access to Dwarkesh Podcast at www.dwarkesh.com/subscribe
De slotkoers van de grootste beursgang ooit is bekend. 160 dollar en 95 cent. SpaceX is 2.1 biljoen dollar waard en Elon Musk is biljonair. Bij OpenAI en Anthropic kunnen ze rustig ademhalen, want de markt is niet stuk. Integendeel: beleggers hebben opnieuw betaald voor de mythe van Musk en tonen zich bereid om verregaande bedragen te steken in de bizarre waarderingen van AI-bedrijven die dit jaar naar de beurs gaan. Tijdens het laatste uur van de beursdag maakten Donner Bakker, Jochem Visser en hun gasten een extra uitzending richting die laatste koers op de borden. Gast Johannes Smit, portfoliomanager bij het Centive Global Equity Fund van IBS, legt uit wat dit betekent voor de markt en voor beleggers. Hij bespreekt het verdere verloop van de koers nu er aandelen kunnen worden verkocht door insiders, terwijl indexen juist gedwongen gaan kopen. En hij legt uit waarom de verregaande zorgen van indexbeleggers wat hem betreft onterecht zijn. Gasten Joe van Burik en Ben van der Burg, techcommentatoren van BNR en makers van De Grote Tech Show, bespreken hoe dit bizarre bedrijf nu in elkaar steekt en hoe dat zo is gekomen. Natuurlijk moet Musk zelf ook nog even langs de lat worden gelegd. Is zijn effect op het universum nou netto positief, of negatief? Hint: er is een goeie discussie over te voeren. BNR Beurs is een journalistiek onafhankelijke productie, mede mogelijk gemaakt door Saxo. Over de makers: Jelle Maasbach is presentator van BNR Beurs en freelance financieel journalist. Zijn favoriete aandeel om over te praten is Disney, maar daar lijkt hij de enige in te zijn. Sinds de eerste uitzending van BNR Beurs is 'ie er bij. Maxim van Mil is presentator van BNR Beurs en journalist bij BNR, waar hij zich focust op de financiële markten en ontwikkelingen in de tech-wereld. Je krijgt hem het meest enthousiast als hij kan praten over ASML, of oer-Hollandse bedrijven zoals Ahold of ABN Amro. Jorik Simonides is presentator van BNR Beurs, economieredacteur en verslaggever bij BNR. Hij wordt er vooral blij van als het een keer níet over AI gaat. Je hoort hem ook in de BNR-podcast Moerdijk: dorp van de rekening. Milou Brand is presentator van BNR Beurs, freelance podcastmaker en columnist bij het Financieele Dagblad. Jochem Visser is presentator van BNR Beurs, maakt Beursnerd XL en is redacteur bij de podcast Onder Curatoren. Vraag hem naar obscure zaken op financiële markten en hij vertelt je waarom het eigenlijk nóg leuker is dan je al dacht. Over de podcast: Met BNR Beurs ga je altijd voorbereid de nieuwe beursdag in. We praten je in een kleine 25 minuten bij over alle laatste ontwikkelingen op de handelsvloer. We blijven niet alleen bij de AEX of Wall Street, maar vertellen je ook waar nog meer kansen liggen. En we houden het niet bij de cijfers, maar zoeken ook iedere dag voor je naar duiding van scherpe gasten en experts. Of je nu een ervaren belegger bent of net begint met je eerste stappen op de beurs, de podcast biedt waardevolle inzichten voor je beleggingsstrategie. Door de focus op zowel de korte termijn als de lange termijn, helpt BNR Beurs luisteraars om de ruis van de markt te scheiden van de essentie. Van Musk tot Microsoft en van Ahold tot ASML. Wij vertellen je wat beleggers bezighoudt, wie de markten in beweging zet en wat dat betekent voor jouw beleggingsportefeuille.See omnystudio.com/listener for privacy information.
De slotkoers van de grootste beursgang ooit is bekend. 160 dollar en 95 cent. SpaceX is 2.1 biljoen dollar waard en Elon Musk is biljonair. Bij OpenAI en Anthropic kunnen ze rustig ademhalen, want de markt is niet stuk. Integendeel: beleggers hebben opnieuw betaald voor de mythe van Musk en tonen zich bereid om verregaande bedragen te steken in de bizarre waarderingen van AI-bedrijven die dit jaar naar de beurs gaan. Tijdens het laatste uur van de beursdag maakten Donner Bakker, Jochem Visser en hun gasten een extra uitzending richting die laatste koers op de borden. Gast Johannes Smit, portfoliomanager bij het Centive Global Equity Fund van IBS, legt uit wat dit betekent voor de markt en voor beleggers. Hij bespreekt het verdere verloop van de koers nu er aandelen kunnen worden verkocht door insiders, terwijl indexen juist gedwongen gaan kopen. En hij legt uit waarom de verregaande zorgen van indexbeleggers wat hem betreft onterecht zijn. Gasten Joe van Burik en Ben van der Burg, techcommentatoren van BNR en makers van De Grote Tech Show, bespreken hoe dit bizarre bedrijf nu in elkaar steekt en hoe dat zo is gekomen. Natuurlijk moet Musk zelf ook nog even langs de lat worden gelegd. Is zijn effect op het universum nou netto positief, of negatief? Hint: er is een goeie discussie over te voeren. BNR Beurs is een journalistiek onafhankelijke productie, mede mogelijk gemaakt door Saxo. Over de makers: Jelle Maasbach is presentator van BNR Beurs en freelance financieel journalist. Zijn favoriete aandeel om over te praten is Disney, maar daar lijkt hij de enige in te zijn. Sinds de eerste uitzending van BNR Beurs is 'ie er bij. Maxim van Mil is presentator van BNR Beurs en journalist bij BNR, waar hij zich focust op de financiële markten en ontwikkelingen in de tech-wereld. Je krijgt hem het meest enthousiast als hij kan praten over ASML, of oer-Hollandse bedrijven zoals Ahold of ABN Amro. Jorik Simonides is presentator van BNR Beurs, economieredacteur en verslaggever bij BNR. Hij wordt er vooral blij van als het een keer níet over AI gaat. Je hoort hem ook in de BNR-podcast Moerdijk: dorp van de rekening. Milou Brand is presentator van BNR Beurs, freelance podcastmaker en columnist bij het Financieele Dagblad. Jochem Visser is presentator van BNR Beurs, maakt Beursnerd XL en is redacteur bij de podcast Onder Curatoren. Vraag hem naar obscure zaken op financiële markten en hij vertelt je waarom het eigenlijk nóg leuker is dan je al dacht. Over de podcast: Met BNR Beurs ga je altijd voorbereid de nieuwe beursdag in. We praten je in een kleine 25 minuten bij over alle laatste ontwikkelingen op de handelsvloer. We blijven niet alleen bij de AEX of Wall Street, maar vertellen je ook waar nog meer kansen liggen. En we houden het niet bij de cijfers, maar zoeken ook iedere dag voor je naar duiding van scherpe gasten en experts. Of je nu een ervaren belegger bent of net begint met je eerste stappen op de beurs, de podcast biedt waardevolle inzichten voor je beleggingsstrategie. Door de focus op zowel de korte termijn als de lange termijn, helpt BNR Beurs luisteraars om de ruis van de markt te scheiden van de essentie. Van Musk tot Microsoft en van Ahold tot ASML. Wij vertellen je wat beleggers bezighoudt, wie de markten in beweging zet en wat dat betekent voor jouw beleggingsportefeuille.See omnystudio.com/listener for privacy information.
Tired vs. Wired: $4 Trillion in IPOs Coming, $100B in M&A, and Why the SaaSpocalypse is Over The public markets spent the last twelve months telling you B2B software was finished. Stocks down 60 to 70 percent. PE firms buying nobody. For the first time in history, software trading at a discount to the S&P 500. And at the exact same moment, Anthropic is projecting $50 billion in revenue, Cursor is getting acquired for $60 billion, and SpaceX, Anthropic, OpenAI, and Databricks are about to generate more market value than every other IPO since 2000 combined. Both things are true - and which one defines your next 18 months depends entirely on one question: are you tired or are you wired? In this episode, SaaStr CEO and Founder Jason Lemkin calls the market as he sees it, names who is winning and who is pretending, and makes the case that the Cambrian explosion in B2B is just getting started. You'll learn: Why the SaaSpocalypse was never about B2B dying - it was about pre-AI software dying - and what the Palantir, Twilio, and Atlassian re-acceleration stories actually tell you The four categories every B2B company falls into right now, and why category four founders need to stop pretending the recovery is coming on its own Why vibe coding your CRM is dead as a concept, and what "putting deals on your calendar" actually means as a product strategy Why your biggest near-term competitive edge might be two days of engineering work - making your API agent-friendly before your competitors do What SaaStr's own journey from 20 humans to 3 humans and 21 agents teaches you about consistency as the only real cheat code in agents This is for you if: Your growth has slowed and you are not sure whether it is a market problem or a you problem - this session will help you figure out which You are a founder or exec who has been in the "AI is coming" conversation for a year but has not yet seen it show up in your revenue You want the unfiltered version of where B2B is headed in the next 18 months, including the parts most people are too polite to say out loud
Six months after their last roundup, Jacob sits down with Ari Morcos (Datology AI CEO, former Meta AI researcher) and Rob Toews (Radical Ventures partner, Forbes AI columnist) to take stock of an AI landscape that has shifted dramatically: coding agents crossing the long-time-horizon threshold has turned engineers into managers of agents, near-frontier open weight AI looks like it may be disappearing as Meta and the Chinese labs pull back, and Anthropic's restrictions on its newly released Fable model have its biggest supporters questioning whether safety framing is masking competitive positioning. The conversation runs through the full state of the lab wars, including Rob doubling down on his Sam Altman ouster prediction and the Bret Taylor succession theory, why Google's structural advantages remain intact despite falling behind on coding, what xAI's Cursor acquisition is really for, and Ari's claim that compute constraints could push labs to suspend their APIs entirely. The back half digs into the physical bottlenecks underneath it all, from atom and x-ray lithography startups challenging ASML to H100 prices reversing their decline, before closing with predictions: recursive self-improvement is closer than it was six months ago but slower than the takeoff narratives suggest, robotics is nearing its GPT-3 moment, and Anthropic's next chapter may be life sciences. (0:00) Intro (1:40) Coding Agents Cross a Threshold (3:29) Is Open-Weight AI in Retreat? (7:37) Cost Crunch & Scaffolding (12:13) The "Apps Are Cooked" Debate (16:37) Sam Altman Under Scrutiny (19:44) Anthropic's Fable Backlash (23:24) How Big a Step Change Is Fable? (26:50) What's Going On at Google? (33:20) Could the APIs Go Away? (34:11) Breaking the Semiconductor Bottleneck (35:42) Beyond EUV: Atom & X-Ray Lithography (37:23) Implications of a Compute Shortage (40:20) Do Alt Chips Actually Help? (43:43) SpaceX, xAI & the Cursor Acquisition (48:50) How Close Are We to RSI? (52:21) Quickfire With your host: @jacobeffron - Managing Director at Redpoint
Invest Like the Best: Read the notes at at podcastnotes.org. Don't forget to subscribe for free to our newsletter, the top 10 ideas of the week, every Monday --------- My guest today is Alex Sacerdote, founder of Whale Rock Capital Management. Whale Rock is a technology focused investment firm that manages more than $17 billion across hedge fund, long only, and hybrid strategies. Over the past three years it has been one of the best performing hedge funds, compounding at roughly 44 percent a year. Alex invests through a single lens that he has refined over twenty years. He looks for technology S-curves, durable competitive advantages, and underappreciated earnings power. This conversation is a tour through how he applies that framework right now. We start with his highest conviction position, which is Anthropic, and use it to work through the entire AI stack from chips to models to applications. Please enjoy my conversation with Alex Sacerdote. For the full show notes, transcript, and links to mentioned content, check out the episode page here. ----- Become a Colossus member to get our quarterly print magazine and private audio experience, including exclusive profiles and early access to select episodes. Subscribe at colossus.com/subscribe. ----- Ramp's mission is to help companies manage their spend in a way that reduces expenses and frees up time for teams to work on more valuable projects. Go to ramp.com/invest to sign up for free and get a $250 welcome bonus. ----- Trusted by thousands of businesses, Vanta continuously monitors your security posture and streamlines audits so you can win enterprise deals and build customer trust without the traditional overhead. Invest Like the Best listeners get a special offer of $1,000 off Vanta when you go to vanta.com/invest. ----- WorkOS is the infrastructure B2B and AI-native companies use to sell to enterprise. It covers everything enterprise security requires: SSO, SCIM, RBAC, Audit Logs, AI governance, and more. Trusted by 2,000+ fast-growing companies, including OpenAI, Anthropic, Cursor, and Vercel. ----- Rogo is the AI platform for finance. They're building agents for Wall Street that are trained to understand how bankers and investors actually do work: from diligence and modeling, to turning analysis into deliverables. To learn more, visit rogo.ai/invest. ----- Ridgeline has built a complete, real-time, modern operating system for investment managers. It handles trading, portfolio management, compliance, customer reporting, and much more through an all-in-one real-time cloud platform. Visit ridgelineapps.com. ----- Editing and post-production work for this episode was provided by The Podcast Consultant (https://thepodcastconsultant.com). Timestamps: (00:00:00) Welcome to Invest Like The Best (00:00:00) Welcome to Invest Like The Best (00:02:29) Alex Sacerdote (00:03:08) Anthropic: Highest Conviction Position (00:13:23) Investing in Private Markets at Scale (00:19:08) S-Curves: The Full Framework (00:25:08) When to Buy Tech Companies (00:30:20) Identifying the Leader from the Pack (00:34:04) Anthropic & OpenAI's Competitive Moats (00:37:31) AI's Threat to Enterprise Software (00:43:18) Network Effects in the Agent Era (00:44:22) The Hardware Renaissance: Chips & Infrastructure (00:53:56) Why So Few Investors Get This Right (00:55:36) Key Risks to the AI Bull Case (00:57:47) The Application Layer (00:59:40) How AI Is Changing Research at WhaleRock (01:02:53) The Role of Investor Networks & Idea Sharing (01:03:40) Building a Multi-Product Firm (01:07:58) WhaleRock as a Learning Machine (01:09:15) The Kindest Thing
Sign up for Practi, a new platform that helps law firms use subscription billing.Here are the top 5 takeaways from this episode:* Going solo requires proving your own value. Stephen emphasizes that attorneys who rely solely on a firm's name have “borrowed value.” Going out on your own, and actually generating revenue and clients, is the real proof of marketability and professional worth.* Client quality over quantity. Early on, Stephen learned to fire bad clients: those who don't pay, don't cooperate, or ask you to act unethically. You can't build a sustainable practice on a foundation of problematic clients, and protecting your license and reputation comes first.* Build your own AI workflows rather than relying on generic tools. Stephen moved away from consumer chatbots (Claude Desktop, ChatGPT) toward IDEs like Cursor and Windsurf, and built ~80 of his own MCPs (Model Context Protocol integrations). Custom-built workflows tailored to your specific practice are far more powerful and secure than off-the-shelf solutions.* AI hallucination is a context problem, not just a model flaw. Stephen explains that AI “hallucinations” happen because the model lacks the right context window and fills gaps with plausible-sounding but fabricated information. The solution is connecting AI to reliable, specific data sources (like his custom Court Listener MCP) rather than letting it guess.* Start small and don't overspend on AI tools. Many expensive subscriptions are overkill for beginners. Stephen built a functional court case search tool for roughly 20 cents. His advice: start with a free or low-cost tier, learn the technology through hands-on experimentation, and only scale spending once you understand what you actually need.__________________________Want your question to be answered on a future show? Fill out this short survey.Have subscription model question? Check out this free resource to ask all of your questions at notebook.practi.ai.Check out Polauf Law LLC.Sign up for Paxton, my all-in-one AI legal assistant, helping me with legal research, analysis, drafting, and enhancing existing legal work product.Get Connected with SixFifty, a business and employment legal document automation tool.Sign up for Gavel, an automation platform for law firms.Visit Law Subscribed to subscribe to the weekly newsletter to listen from your web browser.Prefer monthly updates? Sign up for the Law Subscribed Monthly Digest on LinkedIn.Check out Mathew Kerbis' law firm Subscription Attorney LLC.Want to use the subscription model for your law firm? Click here to sign up for a new platform that helps law firms use subscription billing. Get full access to Law Subscribed at www.lawsubscribed.com/subscribe
A VC fell asleep for 30+ minutes during a founder's pitch. The round stillclosed.This week on Bricks, Bucks & Bytes, we traded the VC horror stories founders never forget, debated whether the hottest AI startups are just "reselling tokens," and brought on three founders fresh off funding rounds: Guy Saxelby (Earlytrade, $25M total raised), Adrian Rhaese (EnvioTech, €1M pre-seed) and Ben Waters (LightTable, $22M Series A).Tune in to find out about:✅ Why Patrick calls Lovable, Cursor and Vercel "resellers of tokens" and what that means for their valuations✅ How Earlytrade automates construction payments, with 10% of revenue already running with zero humans✅ The streetlight startup saving cities 80% on energy while mapping how a whole city moves✅ Dustin's no-mercy pushback on what it actually takes to be a "platform for pre-construction"Listen now on Spotify and YouTube. #bricksandbytes #bricksbytes #bricksbucksandbytes #aec #construction#constructiontech #ai #vcChapters00:00 Intro01:10 VC Horror Stories Founders Never Share07:05 The Weirdest VC Behaviour We've Seen13:01 Why AI Costs Are Eating Your Margins19:15 Will AI Companies Ever IPO?25:49 How to Find Early Product-Market Fit33:24 Expanding Internationally: What Actually Works40:09 Where Construction Tech Innovation Happens45:48 The Growth Playbook for the Next 5 Years56:56 The Hardest Lessons of Entrepreneurship58:11 Why Timing Beats Everything in Business58:19 How Perception Shapes Professional Success59:10 Why Being Eccentric Is a Branding Advantage01:02:07 Where Tech Meets Construction01:04:09 AI That Actually Manages Construction Projects01:10:10 Why Pre-Construction Is Where the Money Is01:16:16 Mastering the Critical Path
Consumption pricing and AI adoption are forcing revenue teams to prove value faster, with less room to hide behind contracts, pilots, or broad technical promises. Seong Park, Senior Vice President of Customer Support and Services at Cursor, joins John Kaplan and John McMahon to examine how customer success has become a consultative, technical, and commercial function in modern go-to-market. The conversation explores why post-sale execution is now central to retention, how teams need to embed into customer workflows, what finance scrutiny means for consumption models, and why the fundamentals of pain, champions, outcomes, and evidence still matter in a market moving at unusual speed. Seong Park is the Senior Vice President of Customer Support and Services at Cursor. His background spans pre-sales, customer success, and go-to-market leadership across companies including MongoDB, ThoughtSpot, and now Cursor. Connect with Seong: LinkedIn Key takeaways from this episode: 00:00 – Seong Park's perspective on how pre-sales, open source SaaS, and customer success shaped his view of enterprise go-to-market. 02:26 – Why consumption models force revenue teams to re-earn the customer's business through usage and realized value. 08:00 – The value realization test every revenue leader should care about: what happens if the solution gets unplugged. 11:04 – Why workflow depth quietly becomes a moat in enterprise accounts. 18:04 – Why the real selling often starts after the customer signs. 23:50 – A look inside where Cursor is finding technical go-to-market talent, and what it takes to build that talent into customer-facing operators. 34:38 – Why finance scrutiny quietly changes the standard of proof for AI investments. 52:00 – The three things post-sale teams need to understand before value delivery can begin. Hosted by five-time CRO John McMahon and Force Management Co-Founder John Kaplan, the Revenue Builders podcast goes behind the scenes with the sales leaders who have been there, done that, and seen the results. This show is brought to you by Force Management. We help companies improve sales performance, executing their growth strategy at the point of sale. Connect with Us: LinkedInYouTubeForce Management
The skills that survive every industry shakeup aren't the ones you can Google — they're softer, harder to name, and far more durable. In this episode, Jonathan explores principle-oriented thinking: the practice of stripping away the labels we attach to tools, roles, and even ourselves to see what something actually does at its core. It's the difference between handing your coding off to an agent and rethinking your entire workflow around what these new materials are truly capable of. If you've been following along with our recent focus on durable skills, you know we've been hunting for the abilities that translate beyond this month, this year, or whatever AI does to our industry next. Today's skill doesn't have a tidy name you can search for — it's softer than that. Jonathan calls it "principle-oriented thinking": the habit of deconstructing the labels we put on things to understand their core components, properties, and capabilities. It's how NASA engineers turned a sock into a water filter on Apollo 13, and it's how forward-thinking engineers are reframing what AI can actually do rather than jamming it into a predetermined slot. Labels Are Useful Shortcuts — Until They Aren't: Every label, from "software engineer" to "sock," carries baggage, heuristics, and presupposition. That's not a flaw — labels are how we move through the world quickly. But when a label is the only lens you have, it quietly caps how much value you can get out of the thing you're looking at. The Apollo 13 Sock: When the crew needed to fix a life-threatening problem with mismatched parts, the engineers on the ground had to forget what a sock was for and ask what it actually is — a piece of cloth with tensile strength, flexibility, and filtering properties. Strip the assumption that it goes on a foot, and a whole new set of uses opens up. Stop Slotting AI Into Old Roles: The common move is to take one responsibility — coding, debugging, refactoring — hand it to an agent, and keep everything else the same. That works, but it's low-leverage. The more powerful approach starts by asking what the agent is fundamentally capable of, then rebuilding the workflow around those raw materials. See Things as Materials, Not Fixed Functions: When you deconstruct out from under a label, tools and concepts start to look like craftable raw materials. You can then combine them in new, valuable ways they haven't been combined before — alloying old methods with new capabilities to create properties neither had on its own. Reason From Properties, Not Personas: Ask what the actual properties of an LLM are. Non-determinism isn't a bug to apologize for — it's a property you can exploit. The existence of many different models is a property too, which is exactly what makes adversarial review possible. That's principle-oriented thinking applied to agents. Extend the Latticework: Charlie Munger talked about a latticework of mental models that weave together rather than sit in isolation. The durable skill isn't quarantining your concept of "AI" off to the side — it's grafting a new section onto the existing tapestry and letting it reshape everything you already understood. Episode Takeaway: Look at how you spend your time and ask new questions of it. What is the material here? What kind of thinking does the agent actually do? What can a human do that an LLM can't — and the other way around? That's how you avoid believing a sock is only ever good for a foot.
Send us Fan MailIn this episode of the WTR Small-Cap Spotlight podcast, Gautam Garg, Vice President of Finance of eGain Corporation (NASDAQ: EGAN), joins host Tim Gerdeman, Vice Chair, Co-Founder, and Chief Marketing Officer of Water Tower Research, and WTR Analyst James Kisner.eGain is a leader in AI-powered knowledge management, helping Global 2000 enterprises unify siloed content into an AI Knowledge Hub that delivers accurate, compliant answers across customer service and adjacent functions.Garg explains why trusted knowledge has emerged as core AI infrastructure and why enterprise AI initiatives frequently underperform when built on stale or inconsistent data. He walks through recent product launches including the AI Knowledge Suite for Retail Banking, the IVA voice agent, Evaluator, Agentic Studio, and the developer-focused Composer platform, which supports integrations with Copilot, Claude, Gemini, and Cursor via MCP connectors.The conversation also covers a surge in RFP activity, a fast-growing partner ecosystem, expansion into HR and field service verticals, and eGain's profitable, debt-free financial profile heading into fiscal year 2027.
My guest today is Alex Sacerdote, founder of Whale Rock Capital Management. Whale Rock is a technology focused investment firm that manages more than $17 billion across hedge fund, long only, and hybrid strategies. Over the past three years it has been one of the best performing hedge funds, compounding at roughly 44 percent a year. Alex invests through a single lens that he has refined over twenty years. He looks for technology S-curves, durable competitive advantages, and underappreciated earnings power. This conversation is a tour through how he applies that framework right now. We start with his highest conviction position, which is Anthropic, and use it to work through the entire AI stack from chips to models to applications. Please enjoy my conversation with Alex Sacerdote. For the full show notes, transcript, and links to mentioned content, check out the episode page here. ----- Become a Colossus member to get our quarterly print magazine and private audio experience, including exclusive profiles and early access to select episodes. Subscribe at colossus.com/subscribe. ----- Ramp's mission is to help companies manage their spend in a way that reduces expenses and frees up time for teams to work on more valuable projects. Go to ramp.com/invest to sign up for free and get a $250 welcome bonus. ----- Trusted by thousands of businesses, Vanta continuously monitors your security posture and streamlines audits so you can win enterprise deals and build customer trust without the traditional overhead. Invest Like the Best listeners get a special offer of $1,000 off Vanta when you go to vanta.com/invest. ----- WorkOS is the infrastructure B2B and AI-native companies use to sell to enterprise. It covers everything enterprise security requires: SSO, SCIM, RBAC, Audit Logs, AI governance, and more. Trusted by 2,000+ fast-growing companies, including OpenAI, Anthropic, Cursor, and Vercel. ----- Rogo is the AI platform for finance. They're building agents for Wall Street that are trained to understand how bankers and investors actually do work: from diligence and modeling, to turning analysis into deliverables. To learn more, visit rogo.ai/invest. ----- Ridgeline has built a complete, real-time, modern operating system for investment managers. It handles trading, portfolio management, compliance, customer reporting, and much more through an all-in-one real-time cloud platform. Visit ridgelineapps.com. ----- Editing and post-production work for this episode was provided by The Podcast Consultant (https://thepodcastconsultant.com). Timestamps: (00:00:00) Welcome to Invest Like The Best (00:00:00) Welcome to Invest Like The Best (00:02:29) Alex Sacerdote (00:03:08) Anthropic: Highest Conviction Position (00:13:23) Investing in Private Markets at Scale (00:19:08) S-Curves: The Full Framework (00:25:08) When to Buy Tech Companies (00:30:20) Identifying the Leader from the Pack (00:34:04) Anthropic & OpenAI's Competitive Moats (00:37:31) AI's Threat to Enterprise Software (00:43:18) Network Effects in the Agent Era (00:44:22) The Hardware Renaissance: Chips & Infrastructure (00:53:56) Why So Few Investors Get This Right (00:55:36) Key Risks to the AI Bull Case (00:57:47) The Application Layer (00:59:40) How AI Is Changing Research at WhaleRock (01:02:53) The Role of Investor Networks & Idea Sharing (01:03:40) Building a Multi-Product Firm (01:07:58) WhaleRock as a Learning Machine (01:09:15) The Kindest Thing
Join Tyler Wells, Co-founder and CTO of BrainGrid, for a forward-looking discussion on how artificial intelligence is rewriting the rules of product development. Boasting over 25 years of distributed systems engineering—including a foundational tenure at Skype building Facebook's first video-calling engine and 7+ years directing Video and global SRE at Twilio—Tyler has built infra where structural failure was not an option. In this episode, we explore why the traditional constraints of software engineering—headcount, timelines, and budgets—are dissolving, leaving a brand-new bottleneck at the front of the innovation cycle: human imagination.
Agentic engineering is quickly becoming one of the most important shifts in software development.In this episode, I sit down with Scott Breitenother, Co-Founder & CEO of Kilo Code, to discuss why individual coding assistants won't drive the future of software development, but by autonomous AI agents capable of planning, building, testing, and shipping software.Scott shares the journey from building and selling Brooklyn Data Company to launching Kilo, an open-source agentic engineering platform designed to help developers become dramatically more productive in the AI era.We discussed product-market fit, engineering adoption, the realities of competing with Cursor, Copilot, and Claude Code, and what engineering leaders should be thinking about as AI fundamentally changes how software teams operate.If you're a founder, engineering leader, developer, or simply interested in the future of AI-powered software development, this is an episode you won't want to miss.
SpaceX预计6月12日在纳斯达克上市，募集资金750亿美元，这是有史以来最大规模的IPO。把一家商业航天公司的估值撑到如此地步的，是它的AI业务。2026年2月SpaceX收购了同样由马斯克掌控的xAI公司，后者主要的业务是包括算力中心、大语言模型Grok和社交平台X（前Twitter）。但问题是，xAI是一个2025年亏损60多亿美元的烧钱机器，并且在大模型领域并非顶。SpaceX之所以能把“饼”画圆，关键一步是4月对cursor的收购。这也是整个IPO过程中最有趣的部分之一，因为双方设置了一个巧妙地期权交易，使得IPO能充分利用这一交易的想象力，并且规避其风险。| 主播 |肖文杰、约小亚| 延伸资料 |SpaceX S-1 filing indexTechCrunch - SpaceX is working with Cursor and has an option to buy the startup for $60BTechmeme - Cursor / SpaceX roundupxAI - New Compute Partnership with AnthropicThe Information - SpaceX's Anthropic Compute Deal Worth Up to $40 BillionAxios - Two hours that changed AI| 后期制作 |潘鑫| 声音设计 |刘三菜| 收听方式 |你可以通过小宇宙、苹果播客、Spotify、喜马拉雅、网易云音乐、QQ音乐、荔枝、豆瓣等平台收听节目。| 认识我们 |微信公众号：第一财经YiMagazine联系我们：thatisbiz@yicai.com
https://novacut.ai/ https://genaimeetup.com/ Anthropic has officially closed a $65 billion Series H at a $965 billion valuation, nearly 2.5x its valuation from just 100 days ago. Meanwhile, funding is flowing across the ecosystem: Frameworks AI at $15B, Baseten at $11B, OpenRouter's $113M Series B, and Cognition AI's $1B Series D. NVIDIA went on an open-source super week with Nemotron 3 Ultra, Cosmos 3, and Nemotron 3.5 ASR. Microsoft dropped 5 new MAI models. Google released Gemma 4 12B, and Anthropic shipped Opus 4.8. On the benchmarks front, DeepSWE crowns GPT-5.5 as the leader in long-horizon coding tasks, while ITBench shows even frontier models struggle with real-world SRE incidents — Claude Opus 4.7 tops out at just 47%. Plus: Cloudflare acquires VoidZero to build the future of AI-native edge development, and Google is paying SpaceX $920M/month for compute. Topics covered: • Anthropic's $65B Series H and path to $1T • Fireworks AI, Baseten, OpenRouter & Cognition funding rounds • Microsoft's 5 new MAI models • NVIDIA's open-source super week (Nemotron, Cosmos 3) • MiniMax M3, Gemma 4 12B, JetBrains Mellum2, Opus 4.8 • DeepSWE benchmark: GPT-5.5 leads long-horizon coding • ITBench: Frontier models under 50% on real SRE tasks • Cloudflare + VoidZero for AI-native edge dev • Google's $920M/month SpaceX compute deal #AI #Anthropic #NVIDIA #OpenAI #AInews #TechNews #LLM Funding rounds Anthropic formally confirmed the closure of its $65 billion Series H funding round at a post-money valuation of $965 billion. This represents a 2.5-fold increase over its $380 billion Series G valuation from February 2026, adding $585 billion in value in approximately 100 days https://www.anthropic.com/news/series-h Frameworks AI raising at 15B valuation representing a near fourfold increase from its $4 billion Series C valuation recorded in October 2025 processing 15 trillion tokens daily for major production clients including Cursor, Notion, and Perplexity https://finance.yahoo.com/sectors/technology/articles/fireworks-ai-eyes-15-billion-174609357.html Baseten is raising 1B at 11B valuation annualized revenue, which skyrocketed from $200 million to $600 million over a single quarter https://techstartups.com/2026/05/26/ai-inference-startup-baseten-in-talks-to-raise-1-billion-at-11-billion-valuation/ OpenRouter has secured a $113 million Series B funding OpenRouter has experienced exponential traffic growth, with weekly production throughput expanding fivefold from 5 trillion to 25 trillion tokens over a six-month horizon https://www.businesswire.com/news/home/20260526953416/en/OpenRouter-Raises-%24113-Million-CapitalG-led-Series-B-as-Weekly-Volume-Explodes-to-25T-Tokens Further up the stack: Cognition AI secured a $1 billion Series D round led by Lux Capital and 8VC https://cognition.ai/blog/series-d Model Releases MAI models: MAI-Code-1-Flash: A 5-billion active parameter model optimized for ultra-low latency within GitHub Copilot and VS Code. MAI-Image-2.5: A high-fidelity image generation model ranking third on global image evaluation arenas, outperforming competing architectures like Nano Banana Pro. MAI-Transcribe-1.5: A multi-lingual speech processing engine offering fivefold speed improvements across 43 languages. MAI-Voice-2: Natural audio and voice generation across 15 languages, available at a highly competitive price point. Web IQ: A search-grounding API engineered to directly compete with Perplexity. https://microsoft.ai/models/ https://www.peoplematters.in/news/ai-and-emerging-tech/uber-imposes-dollar1500-monthly-ai-spending-limit-on-employees-amid-rising-costs-50073 Nvidia has executed an "Open-Source Super Week," positioning itself as a dominant software and model publisher: Nemotron 3 Ultra (best US open source open weights model but behind china): A massive 550-billion parameter MoE (55 billion active) designed with a 1-million token context window, optimized specifically for high-throughput, cyclical agent loops. It achieved peak throughput rates of 400 tokens per second on day-zero optimized clusters. Cosmos 3: A physical AI world-modeling framework comprising 16-billion Nano and 64-billion Super variants. Built on a Mixture-of-Transformers (MoT) architecture, Cosmos 3 natively binds textual, visual, auditory, and physical kinetic vectors. Nemotron 3.5 ASR: A highly compact 0.6-billion parameter streaming speech recognition model pushing sub-100 millisecond latencies across 40 language locales. https://www.minimax.io/models/text/m3 MiniMax M3: A 1-million token context model hitting 59.0% on SWE-Bench Pro and 74.2% on MCP Atlas, though noted for high token consumption due to intensive internal self-validation loops. https://blog.google/innovation-and-ai/technology/developers-tools/introducing-gemma-4-12b/ Gemma 4 12B: Google's Apache 2.0 on-device model, which utilizes an encoder-free architecture that projects vision and audio vectors directly into the text-token space, bypassing separate CLIP-style encoders to minimize local memory footprints. https://www.jetbrains.com/mellum/ JetBrains Mellum2: A compact 12-billion parameter MoE (2.5 billion active) engineered for ultra-low latency routing and retrieval-augmented generation (RAG) sub-agents within developer IDEs. Opus 4.8 https://www.anthropic.com/news/claude-opus-4-8 https://www.cnbc.com/2026/06/05/google-to-pay-spacex-920-million-a-month-for-xai-compute-capacity.html Benchmarks: https://deepswe.d atacurve.ai/blog https://venturebeat.com/technology/deepswe-blows-up-the-ai-coding-leaderboard-crowns-gpt-5-5-and-finds-claude-opus-exploiting-a-benchmark-loophole (GPT 5.5 the winner in long horizon tasks) a highly complex software engineering benchmark focused on original, long-horizon tasks across five distinct programming languages. Comprising 113 chaotic tasks across 91 live, production-grade repositories, DeepSWE forces agents to generate 5.5 times more code and modify an average of 7 separate files per task compared to standard evaluations. On this challenging leaderboard, GPT-5.5 leads with a score of 70%, establishing a significant 16-percentage-point lead over contemporary alternatives I think older benchmarks where models reach ~90% accuracy can be considered saturated. Few percentage points don't give us any good signal. https://research.ibm.com/publications/developing-ai-agents-for-it-automation-tasks-with-itbench ITBench-AA, an evaluation framework focusing on live Kubernetes incident response and Site Reliability Engineering (SRE) operations. Comprising 59 live, containerized SRE incident snapshots, the results are remarkably sobering: every frontier model scored under 50% on successful incident resolution, with Claude Opus 4.7 leading at 47% and GPT-5.5 following closely at 46%. Edge AI announcements: https://www.cloudflare.com/press/press-releases/2026/cloudflare-acquires-voidzero-to-build-the-future-of-the-ai-native-web/ The consolidation of the AI-native developer stack has reached the runtime virtualization layer. Cloudflare recently completed the acquisition of VoidZero, the development group responsible for Vite, Vitest, Rolldown, and Oxc, backing the transaction with a $1 million open-source ecosystem fund. This acquisition is highly strategic; as autonomous agents write an increasing proportion of production software, local development environments, compilation pipelines, and bundlers must be optimized for execution speeds that match agent speeds. Cloudflare's goal is to construct a localized, full-stack edge playground. In this sandbox, AI agents can generate, test, bundle (utilizing the highly parallelized, Rust-based Oxc and Rolldown engines), and deploy entire web applications end-to-end within milliseconds. This architecture completely bypasses traditional local machine container bottlenecks, enabling high-velocity agent loops to execute in a fully sandboxed, web-scale edge runtime.
HTML All The Things - Web Development, Web Design, Small Business
In this edition of Web News, Matt and Mike debate whether AI coding agents are starting to reverse the no-code revolution. Inspired by a recent article about a company abandoning its no-code website and returning to code, the conversation explores how tools like OpenAI Sites, Cursor, and other agentic workflows are changing the way websites are built. Are platforms like Webflow, Wix, and Squarespace facing a new challenge, or will they evolve alongside AI? From agency workflows and client expectations to the future of frameworks like React and Next.js, this episode dives into one of the biggest shifts currently happening in web development. Show Notes: https://www.htmlallthethings.com/podcast/ai-vs-no-code
Chinese cybercrime group sets record pace Cisco warns of critical Unified CM flaw with PoC exploit code Hackers spied on a stock exchange executive's Outlook mailbox for five months Get the show notes here: https://cisoseries.com/cybersecurity-news-chinese-cybercrime-group-cisco-cm-flaw-cisa-faces-changes/ Huge thanks to our episode sponsor, Vanta Your team just added its 67th AI tool. And unfortunately, also your 67th security blind spot. The good news: The Vanta [rhymes with Santa] Agent works like a GRC engineer in the background, finding every app your team uses, scoring the risk, and drafting fixes for you. Vanta is the platform used by over sixteen thousand fast-moving companies like Ramp, Cursor, and Harvey who are shaping the future with AI, AND staying ahead of AI risk. Get started at vanta.com/headlines.
This week's Department of Know is hosted by Rich Stroffolino, with guests Robb Dunewood, host, Daily Tech News Show, and David Cross, CISO, Atlassian. Get the show notes here. Missed the live show? Check it out on YouTube. The Department of Know is live every Friday at 4:00 p.m. ET. Join us each week by registering for the open discussion at CISOSeries.com. Your team just added its 67th AI tool. And unfortunately, also your 67th security blind spot. The good news: The Vanta Agent works like a GRC engineer in the background, finding every app your team uses, scoring the risk, and drafting fixes for you. Vanta is the platform used by over sixteen thousand fast-moving companies like Ramp, Cursor, and Harvey who are shaping the future with AI, AND staying ahead of AI risk. Get started at vanta.com/headlines.
Deepak Bapat, CTO and co founder at Tabs, joins The Tech Trek to talk about how his team is using tools like Claude Code and Cursor, where AI is helping, and why systems thinking may matter more than raw coding ability as engineering work shifts.Practical Takeaways• AI coding agents are already producing useful production work, but judgment still matters.• Tool choice may be less important than standardizing the expected output.• Messy repos can make AI generated work harder to trust, so cleanup and patterns matter.• The future engineer may look more like a product engineer with strong systems thinking.• Teams may move from debating features to rapidly building multiple versions and testing what works.Timestamped Highlights00:37What Tabs does and why contracts create hard revenue workflow problems for B2B finance teams.02:16Deepak compares pre AI engineering work with the current shift toward AI assisted development.05:09How the Tabs engineering team uses Claude Code, Cursor, and other coding tools in real work.08:13Why inconsistent codebases create more risk when teams add coding agents14:00The idea that teams can build the same feature multiple ways in one afternoon.20:53Deepak's view on whether the future team needs separate PMs and engineers, or more product engineers.23:38A future where software can become more bespoke to each customer because AI changes the cost model.One Line That Stuck“You can build on three different work trees the same feature in three different ways and see which one you like, and you can do it all in an afternoon.”Practical Moves From The Conversation• Keep humans close to the review process, especially when the last five percent still requires taste and judgment.• Clean up inconsistent code patterns before letting agents operate broadly across the repo.• Hire for adaptability, systems thinking, and problem solving, not just past tool familiarity.• Use AI to explore more product options faster, but do not remove the need to ask whether the feature should exist.Subscribe or follow The Tech Trek for more conversations on how technical teams are building, hiring, and operating as AI changes the work.
Law enforcement cracks down on illegal streamers The European Commission releases digital sovereignty plan The startup costs for US cyber force Get the show notes here: https://cisoseries.com/cybersecurity-news-illegal-streamers-eu-digital-sovereignty-cost-of-a-cyber-force/ Huge thanks to our episode sponsor, Vanta Your team just added its 67th AI tool. And unfortunately, also your 67th security blind spot. The good news: The Vanta [rhymes with Santa] Agent works like a GRC engineer in the background, finding every app your team uses, scoring the risk, and drafting fixes for you. Vanta is the platform used by over sixteen thousand fast-moving companies like Ramp, Cursor, and Harvey who are shaping the future with AI, AND staying ahead of AI risk. Get started at vanta.com/headlines.
SpaceX's historic move toward a public listing on the Nasdaq with a target valuation reaching $2 trillion. The company's S-1 filing reveals a complex financial landscape where the profitable Starlink satellite business is being used to fund massive losses within its newly integrated AI division, xAI. Beyond space exploration, the company is pivoting toward a future in AI infrastructure, evidenced by a planned $60 billion acquisition of the coding startup Cursor. However, legal disclosures warn that ambitious joint ventures with Tesla and Intel, such as the Terafab chip factory, remain in non-binding, early stages. Investors are also monitoring how this IPO might eventually lead to a broader merger of Elon Musk's various enterprises.
Did AI Trading Agents Actually Work?Today we're diving into AI trading agents, the rise of DeFAI, and whether autonomous trading bots are actually generating alpha. We discuss: AI trading agents and the DeFAI hype cycle Eliza, Virtuals, and agentic trading products Onchain tracking vs qualitative research Whether agents actually made money Early wins, later losses, and extractive markets Alpha Arena and AI model trading competitions Why sample size matters for agent experiments Agent reliability, harnesses, and model variability AI workflows, Dune MCP, and product building ChatGPT, Claude, Cursor, and personal productivity Ethereum bearishness, EVM success, and ETH value capture Stablecoins and prediction markets as local bull markets And much more—enjoy! — Timestamps: (00:00) Introduction (00:46) AI trading agents (01:26) DeFAI product promises (04:55) Eliza and Virtuals (05:08) Research methodology (12:58) Biggest wallet gains (14:48) Profitability fades (16:19) Alpha Arena experiment (19:18) Sample size problem (25:57) Learning from competitions (31:51) Dune MCP workflows (42:04) Ethereum bearishness (53:41) Outro —Content links:Agentic Trading paper: https://arxiv.org/abs/2605.29174*Tweet 1: https://x.com/jay_azhang/status/1996301170281795607?s=20Tweet 2: https://x.com/jay_azhang/status/1996984618751611006?s=20Tweet 3: https://nof1.ai/leaderboard —Follow the co-hosts:https://x.com/hildobby https://x.com/0xBoxer https://x.com/sui414Follow the Indexed Podcast:https://x.com/indexed_pod — The Indexed Podcast discusses hot topics, trendy metrics and chart crimes in the crypto industry, with a new episode every 1st and 3rd Thursday of the month, brought to you by wizards @hildobby @0xBoxer @sui414.Subscribe/follow the show and leave a comment to help us grow the show! —DISCLAIMER: All information presented here should not be relied upon as legal, financial, investment, tax or even life advice. The views expressed in the podcast are not representative of hosts' employers views. We are acting independently of our respective professional roles.
My guest today is Dara Khosrowshahi, the CEO of Uber. Before Uber, Dara ran Expedia for thirteen years. We start with why he took this job in 2017, and a big part of that story is Daniel Ek, who told him that life is not about happiness, it is about impact. We talk about what the chaos felt like on day one, and how his family leaving Iran when he was nine shaped the way he handles pressure today. We spend most of our time on autonomous vehicles and Uber's role as the demand aggregator in a world of physical AI. Dara explains why Uber is a supply-led company, what it will take to win, and why he expects many winners in AVs rather than one. We also discuss Uber's $10 billion in free cash flow, the push toward a single app for everything, and what he has learned from Allen & Co, Barry Diller and Reed Hastings. For the full show notes, transcript, and links to mentioned content, check out the episode page here. ----- Become a Colossus member to get our quarterly print magazine and private audio experience, including exclusive profiles and early access to select episodes. Subscribe at colossus.com/subscribe. ----- Ramp's mission is to help companies manage their spend in a way that reduces expenses and frees up time for teams to work on more valuable projects. Go to ramp.com/invest to sign up for free and get a $250 welcome bonus. ----- Trusted by thousands of businesses, Vanta continuously monitors your security posture and streamlines audits so you can win enterprise deals and build customer trust without the traditional overhead. Invest Like the Best listeners get a special offer of $1,000 off Vanta when you go to vanta.com/invest. ----- WorkOS is the infrastructure B2B and AI-native companies use to sell to enterprise. It covers everything enterprise security requires: SSO, SCIM, RBAC, Audit Logs, AI governance, and more. Trusted by 2,000+ fast-growing companies, including OpenAI, Anthropic, Cursor, and Vercel. ----- Rogo is the AI platform for finance. They're building agents for Wall Street that are trained to understand how bankers and investors actually do work: from diligence and modeling, to turning analysis into deliverables. To learn more, visit rogo.ai/invest. ----- Ridgeline has built a complete, real-time, modern operating system for investment managers. It handles trading, portfolio management, compliance, customer reporting, and much more through an all-in-one real-time cloud platform. Visit ridgelineapps.com. ----- Editing and post-production work for this episode was provided by The Podcast Consultant (https://thepodcastconsultant.com). Timestamps: (00:00:00) Welcome to Invest Like The Best (00:02:29) Intro to Dara Khosrowshahi (00:03:37) How Daniel Ek Convinced Dara to Take the Uber Job (00:06:54) Bringing Order to Chaos (00:09:20) Managing Stress as a Leader (00:11:22) The Chip on His Shoulder (00:12:53) Parenting Lessons (00:17:01) Mandate for AI Adoption (00:21:21) Uber's Role in Physical AI (00:22:48) Winning the AV Demand Race (00:27:41) Partnering vs. Competing with Waymo (00:32:05) AV Success Unlocks New Markets (00:35:09) Why Drones Haven't Arrived Yet (00:36:27) Regional AV Rollout Differences (00:37:35) Uber Eats International Winning Formula (00:39:44) Key to Aggregating Supply Well (00:44:34) Adding Hotels to Uber Platform (00:50:46) Lessons in Marketing at Scale (00:52:59) Apps vs. AI Agents in Seven Years (00:54:08) What Dara Learned from Barry Diller (00:56:52) What Dara Learned from Allen & Co (01:00:09) Buybacks vs. Growth Investing (01:04:17) Lessons from Reed Hastings (01:05:49) The Kindest Thing
Russia claims officials' surveillance Project Glasswing access expands CISA flags two-year-old Oracle flaw Get the show notes here: https://cisoseries.com/cybersecurity-news-russia-claims-officials-surveillance-project-glasswing-expands-cisa-flags-two-year-old-oracle-flaw/ Huge thanks to our episode sponsor, Vanta Your team just added its 67th AI tool. And unfortunately, also your 67th security blind spot. The good news: The Vanta [rhymes with Santa] Agent works like a GRC engineer in the background, finding every app your team uses, scoring the risk, and drafting fixes for you. Vanta is the platform used by over sixteen thousand fast-moving companies like Ramp, Cursor, and Harvey who are shaping the future with AI, AND staying ahead of AI risk. Get started at vanta.com/headlines.
Alfred Wahlforss, co-founder and CEO of Listen Labs, is building an AI agent that interviews your customers at a scale no focus group ever could—thousands of voice conversations at once, drawn from an audience of 30 million people. A year after launch, Listen serves hundreds of Fortune 100s to Startups including Microsoft, Google, NBC Universal, P&G, Anthropic, Cursor, and Cognition. Alfred explains the counterintuitive finding underneath it all: people are often more honest with an AI than a human interviewer, opening up to a non-judgmental entity that costs less and never makes them feel rushed. He walks through why interview transcripts—not credit card data or behavioral logs—turn out to be the richest fuel for predicting how customers will behave, how Listen back-tests its simulations to know which questions it can and can't answer, and why 80% of the company's engineering goes into building the right audience. As AGI makes building trivial, Alfred argues the scarce resource becomes knowing what to build. That's the loop Listen wants to own.
Meta AI hands over Instagram account access Dutch police dismantle huge botnet RedHat packages get backdoored Get the show notes here: https://cisoseries.com/meta-ai-hands-over-instagram-access-dutch-police-dismantle-botnet-redhat-packages-backdoored/ Huge thanks to our episode sponsor, Vanta Your team just added its 67th AI tool. And unfortunately, also your 67th security blind spot. The good news: The Vanta [rhymes with Santa] Agent works like a GRC engineer in the background, finding every app your team uses, scoring the risk, and drafting fixes for you. Vanta is the platform used by over sixteen thousand fast-moving companies like Ramp, Cursor, and Harvey who are shaping the future with AI, AND staying ahead of AI risk. Get started at vanta.com/headlines.
Sherwood Callaway is the founder of Sazabi (YC P26), the AI-native observability platform built for engineering teams who ship fast. He previously founded and exited a YC company — now he's back, betting that logs are all you need to replace Datadog.Logs Are All You Need: Rethinking Observability with AI Agents // MLOps Podcast #381 with Sherwood Callaway, the Founder of Sazabi
Welcome to another episode of Barn Talk! In this Hot Topics edition, Sawyer and Tork open up about what's happening in rural America and beyond.They kick things off by reflecting on an exciting month filled with outstanding guests and conversations, and they offer heartfelt thanks to listeners for making it all possible. Today's episode covers some of the biggest issues impacting farmers and rural communities: rising farm bankruptcies, major changes in the ag markets, and mounting financial pressures faced by producers. Sawyer and Tork discuss the struggles of rural hospitals under federal budget cuts, share first-hand perspectives on input costs, and question whether the government or politicians have any real solutions for the challenges ahead. But there's plenty of optimism, too. The hosts explore how innovation and technology—like AI, robotics, and the upcoming public offering of SpaceX are beginning to reshape the world. They share practical advice on adapting to change, insights into market trends, and examples of the unwavering American work ethic they see all around them. If you want to stay informed, inspired, and connected to the pulse of rural America, this episode is packed with eye-opening updates, personal reflections, and plenty of straight talk from the barn. JOIN THE BARN TALK NEWSLETTER & GET LIVE EVENT ACCESS: We're on a mission to get 10,000 subscribers, and once we do, we're hosting a live event at the barn! Sign up to get exclusive access to tickets and details.
Today we are talking about AI, How to stay up to date with it, and if it will really take our jobs with guests Angie Byron & Amber Matz. We'll also cover AI Best Practices for Drupal as our module of the week. For show notes visit: https://www.talkingDrupal.com/555 Topics What Is AI Learners Club Amber Defines the Club Origin Story and DrupalCon AI Debate and Community Tensions Issue Queue Conduct and Moderation Thread Tone vs Substance AI Adoption Outside Drupal Conflict Mediation Playbook Maintainer Burnout and Flood Safe Space Learners Club How the Club Started Picking Topics and Demos AI Taking Our Jobs Future of Learners Club Resources Context Control Center AI Learners Club Initiative page Event calendar YouTube Playlist Session Recaps Next session (Claude Design) Slack: #ai-learners Most wanted topics What Angie's working on these days Guests Amber Matz - tugboatqa.com amber-himes-matz Angie Byron - ai_best_practices webchick Hosts Nic Laflin - nLighteneddevelopment.com nicxvan John Picozzi - epam.com johnpicozzi Scott Falconer - managing-ai.com scott-falconer MOTW Correspondent Martin Anderson-Clutz - mandclu.com mandclu Brief description: Do you want to start using AI tools for Drupal development, in the most efficient way possible? There's a composer plugin for that! Module name/project name: AI Best Practices for Drupal Brief history How old: created in Mar 2026 by Angie Byron (webchick), one, of today's guests, a long-time Drupalist, one-time Acquian, and a fellow Canadian Versions available: dev version only, which doesn't seem directly opinionated about what version of Drupal you're using, though it does have minimum versions of PHP and Symfony libraries that suggest Drupal 10 is functionally your minimum Maintainership It is officially seeking co-maintainers Test coverage Documentation - an in-depth README, or you can ask an AI model! (like I did for this segment) 54 open "Work Items" on Gitlab, so lots of active discussion already Module features and usage AI Best Practices for Drupal aims to be the opinionated starter experience for AI-assisted Drupal development You can think of it as a single Composer install that makes any AI coding agent "speak Drupal": following community standards, preferring contrib over custom code, and avoiding framework-naive mistakes. It replaces scattered, tool-specific CLAUDE.md files and Cursor rules that some Drupal developers currently maintain individually, with one canonical, community-governed package that works across Claude Code, Cursor, Copilot, and more. With contributions by a variety of Drupal luminaries including Marcus Johansson, Christoph Briedert, and Scott Falconer, it's the Drupal equivalent of Laravel Boost: stop explaining Drupal to your AI every session and just get writing code. After install or update, it will create an AGENTS.md file from a provided template if there isn't one already, or it will update a specifically marked "ai-best-practices" section of an existing file You will also have a directory of provided skills, and guidance for creating new Drupal agent skills Also included is a set of evals, meant to automatically identify when AI models go off course and provide feedback AI Best Practices for Drupal is meant to provide guidance that will be particularly useful for AI agents, so it's ideal for Drupal developers getting started with AI tools, or for AI developers who want to get started with Drupal
Palo Alto GlobalProtect VPN auth bypass flaw now exploited in attacks ChatGPT share links used to host fake outage pages to deliver malware Federal audit reveals NIST's NVD problems Get the show notes here: https://cisoseries.com/cybersecurity-news-globalprotect-vpn-exploited-chatgpt-share-links-exploits-feds-criticize-nist/ Huge thanks to our episode sponsor, Vanta Your team just added its 67th AI tool. And unfortunately, also your 67th security blind spot. The good news: The Vanta [rhymes with Santa] Agent works like a GRC engineer in the background, finding every app your team uses, scoring the risk, and drafting fixes for you. Vanta is the platform used by over sixteen thousand fast-moving companies like Ramp, Cursor, and Harvey who are shaping the future with AI, AND staying ahead of AI risk. Get started at vanta.com/headlines.
L'encyclique de Léon XIV replace l'IA dans un cadre moral • Samsung redistribue les gains de la ruée vers les puces • Alexa+ arrive en français avec une IA plus conversationnelle • Robinhood ouvre la porte au trading par agents IA • Meta teste la bascule payante de ses plateformesAvec Bruno Guglielminetti (Mon Carnet)================L'IA vue par le VaticanNous revenons sur Magnifica humanitas, la première encyclique de Léon XIV consacrée à la protection de la personne humaine à l'ère de l'intelligence artificielle. Le texte, présenté au Vatican comme un appel à « désarmer » l'IA, replace les enjeux de désinformation, d'armes autonomes, de concentration des données et de pouvoir technologique dans une perspective comparable à celle de la révolution industrielle.Entre éthique, influence et stratégie des géants de l'IAOn évoque aussi les coulisses politiques et industrielles autour du Vatican, avec les démarches de la Silicon Valley et la rencontre mentionnée entre Yoshua Bengio et le pape. Le rôle d'Anthropic est discuté à travers son image d'acteur “responsable”, mais aussi son positionnement stratégique, notamment dans les débats sur les usages militaires de Claude.Samsung paie le prix de la paix socialeDirection la Corée du Sud, où les salariés de Samsung dans les semi-conducteurs obtiennent un accord historique après la menace d'une grève massive. La redistribution d'une partie des profits liés à l'explosion de la demande en puces IA pourrait représenter des primes très élevées pour les employés concernés, illustrant la valeur stratégique extrême de cette industrie.Alexa+ parle françaisNous racontons les premiers essais d'Alexa+, la nouvelle génération de l'assistant vocal d'Amazon dopée à l'IA générative. Plus conversationnelle, localisée culturellement et compatible avec de nombreux appareils Echo récents, elle promet d'aller au-delà des commandes basiques, avec des services tiers et une interaction plus naturelle ; Monde Numérique lui consacre aussi un épisode dédié.La version québécoise d'Alexa+ se fait attendreBruno apporte l'angle canadien : au Québec, l'enjeu ne sera pas seulement de parler français, mais de parler le bon français, avec les références, les expressions et les usages locaux. L'arrivée éventuelle d'Alexa+ au Canada pourrait être facilitée par l'écosystème Amazon Prime, si le service est proposé aux abonnés.Robinhood confie la Bourse aux agents IANous débattons de la fonction “Agentic Trading” de Robinhood, qui permet à des agents IA connectés via des plateformes comme Claude ou Cursor d'analyser un portefeuille et de passer des ordres dans un compte dédié. La discussion porte sur la confiance, la validation humaine et les risques d'un marché où des machines pourraient agir directement au nom des particuliers.Quand une IA gère un caféL'exemple du café suédois piloté par une IA sert de contrepoint concret aux promesses des agents autonomes. Dans un sujet récent de Monde Numérique, l'expérience montre que les limites de mémoire et de cohérence à long terme peuvent conduire à des décisions absurdes, même dans une activité simple comme la gestion d'un commerce.Meta prépare ses abonnementsNous analysons la stratégie de Meta autour de Facebook+, Instagram+, WhatsApp+ et de futures offres liées à Meta AI. Derrière quelques options de personnalisation, on voit surtout se dessiner une nouvelle étape du numérique : après l'ère du gratuit financé par la publicité, les plateformes cherchent des revenus récurrents pour soutenir leurs investissements massifs dans l'IA.Hébergé par Audiomeans. Visitez audiomeans.fr/politique-de-confidentialite pour plus d'informations.
A few spots left for Parsity's AI Engineer Cohort. Apply hereI told candidates they could use Claude, Cursor, anything they wanted in their AI engineering interview. Half of them still cheated. Badly.I break down what cheating looked like and why you should re-consider it if you're thinking about it... even if you don't have any moral qualms against it
Episode 29 of Guardians of M365 Governance: Christian Buckley, Joy Apple, and Ragnar go off-script. No guest this month, just three MVPs working through a laundry list of the governance topics keeping them up at night, from Agent 365 and shadow AI to the real question underneath it all: what does it mean to be the human in the loop?In this episode we get into:00:59 The hottest news in the M365 governance space02:00 Lessons from Agent 365 customer workshops (delivered in Spanish!)03:25 What resonates: agent inventory and classification across Microsoft, third-party, and homegrown agents03:52 Shadow AI: OpenClaw, Cortex, Bedrock and why "observe or block" is the only lever today05:04 Don't be the department of "no": have the conversation first06:50 Coming soon to shadow AI discovery: Claude Code CLI, Codex CLI, Cursor, Llama and more07:19 Multi-model reality: Copilot, Grok, Claude and where each fits08:35 Mike Gennady's agent factory, nightly agent conferences, and #ClawPilot10:08 Microsoft Build preview and OpenClaw + Teams / Copilot integration11:05 New Agent 365 registry sync: Amazon Bedrock, Google Vertex AI, Databricks Genie, Salesforce Agentforce15:16 Cloud migration vs. AI: the governance parallels and the need for foundational cleanup18:00 The risk to Microsoft's strategy: enterprise vs. the developer space20:28 Licensing changes, Agent 365 pricing, and the true (unknown) cost of AI22:45 Why automating away junior roles handicaps your future talent pipeline24:01 Retrieval, semi-autonomous, and autonomous agents, and why nobody wants full autonomy yet25:33 Human in the loop on multiple levels: content cleanup, the publishing quality gate, and workflow escalation28:50 100 test cases for Power Platform alone: never underestimate the testing effort29:31 Productivity vs. effectiveness: redefining how humans work with AI31:17 AI-assisted writing done right: a 47-page doc drafted by AI, then days of human verification35:28 Handwriting vs. typing, stream-of-consciousness drafting, and thinking through the words36:36 Why the human mind can't be replicated, and Hegel on master and horse39:28 Finding your USP as a human in the loop, a daily new discoveryThe big takeaway: the discussion of the next two to three years won't be about productivity. It will be about effectiveness, and resetting the standard for what it means to keep humans meaningfully in the loop. Govern your agents as helpers, never the other way around.Guardians of M365 Governance is a monthly webcast dedicated to everything governance in the Microsoft 365 ecosystem. Got a topic you want us to cover, or want to join as a guest? Connect with Christian, Joy, or Ragnar on LinkedIn.Microsoft Build runs June 2-3, free online: https://build.microsoft.com
Stewart Alsop sat down with Michael Shackelford to discuss their experiences building applications through vibe coding—the practice of using AI to create software without traditional programming expertise. Stewart, who runs the AI Whispers community in Buenos Aires and hosts the Crazy Wisdom podcast (with over 660 interviews), shared how he went from teaching people prompt engineering to building his own video conferencing software as a Riverside.fm replacement, while Michael opened up about his year-long journey creating Genrupt Inc, an AI-powered content generation tool for e-commerce sellers. The conversation covered everything from the decline in quality of Claude's reasoning capabilities and how Chinese companies used distillation attacks to copy Anthropic's models, to the importance of spaced repetition systems for managing knowledge in the age of LLMs, with both sharing battle-tested prompting strategies like asking AI to "explain it to me in genius terms" and using deep research queries to reverse engineer how competitors build their products.Show Notes:- Dan Martell's book "Buy Back Your Time" was mentioned as one of the best business books for thinking about life and business- Check out John Vervaeke's "Awakening from the Meaning Crisis" for understanding relevance realization and why AI fundamentally cannot determine what's relevant to humans without being toldTimestamps00:00 Michael discusses being exhausted from getting his app ready for launch, working nonstop with AI to prepare landing page for podcast traffic driving beta signups05:00 Stewart explains starting AI Whispers in Buenos Aires after leaving OpenAI vendor company, meeting early adopters like Torin who was building mind-reading EEG technology10:00 Discussion of how corporations resist AI adoption due to political games and job security fears while some companies use AI as excuse for pandemic-era layoffs15:00 Stewart describes teaching workshops on using LLMs as linguistic tools rather than coding tools, noting technical people often lack humanities background needed for prompting20:00 Explaining chatbot wrappers, API calls, and how Anthropic's reasoning quality declined after Chinese distillation attacks copied their secret sauce developed with philosophers25:00 Technical discussion of model training, fine-tuning versus RAG for new information, and different approaches to updating AI knowledge beyond initial training30:00 Stewart describes building podcast recording software to replace expensive Riverside, struggling with syncing audio and video files across different computer clocks35:00 Discussion of critical factors in vibe coding, discovering unknown technical requirements, and how AIs don't automatically reveal missing information40:00 Stewart's reverse engineering process using deep research function to study competitors' hiring and technology stacks, separating planning agents from coding agents45:00 Prompting techniques including "explain like I know everything" and using spaced repetition systems to capture valuable prompts and technical knowledge50:00 Michael explains his Generux app for generating ecommerce content using Amazon review data analysis to inform high-converting listing images and videos55:00 Discussion of founder mentality involving self-delusion about project timelines, Michael working nine-plus hours daily for nine months on app development60:00 Comparing Amazon's expert software to prosumer software approach, discussing distribution challenges and future robotics applications for customized products65:00 Stewart demonstrates spaced repetition app for memory improvement and knowledge retention, explaining relevance realization problem that AI agents cannot solve without embodimentKey Insights1. Stewart Alsop started AI Whisperers in Buenos Aires after leaving his role at Invisible Technologies, which was OpenAI's largest vendor for RLHF work. He noticed that machine learning engineers at tech companies lacked the humanities background needed to properly interact with large language models, which are fundamentally linguistic tools. This led him to create weekly workshops teaching non-technical people how to use AI effectively, running events every Thursday for two years straight. The group attracted intense geeks from the start and eventually led to Stewart speaking right after Vitalik Buterin at DevConnect, marking a significant milestone for the community.2. Large corporations are resistant to AI adoption due to multiple factors including political dynamics within organizations and employees fearing job loss. Many companies that grew during the pandemic are now using AI as an excuse to downsize when the real issue is inefficiency from rapid expansion. Stewart observed that even technical people in machine learning often don't understand how to properly use AI tools because they lack linguistic and humanities training. The fundamental problem is educational, requiring companies to train people how to use these new tools while those same people resist learning them.3. Vibe coding has evolved significantly with Claude Code being a game changer that reduced the technical barrier to entry. Before Claude Code, developers needed substantial technical knowledge to work through constant doom loops and debugging cycles. The success of coding AI tools stems from thirty years of testing infrastructure that provides clear yes or no feedback on whether code works. This infrastructure doesn't exist in the same way for manufacturing, science, and other fields, which is why software became the dominant area for AI assistance initially.4. Claude's quality degradation over recent months resulted from multiple factors including distillation attacks by Chinese companies who reverse engineered Anthropic's reasoning capabilities. Anthropic had hired philosophers, sociologists, and psychologists to develop exceptional reasoning in Claude 4.5, but this was expensive to run. When Chinese models like Kimi copied these capabilities at one tenth the cost, and when mainstream users flooded the platform before Anthropic's planned IPO, the company had to reduce quality to manage computational costs. This represents a significant loss for power users who relied on Claude's superior reasoning abilities.5. Stewart built a podcast recording application to replace Riverside because he needed API access to automate workflows, which Riverside wanted one thousand dollars monthly to provide. The technical challenge involves syncing audio and video from local recordings on multiple computers with different clocks through a server, then merging them so voices match lip movements. This problem requires understanding complex timing issues across different network conditions and file formats. Stewart has been working through AI psychosis for months on this FFMPEG pipeline problem, illustrating how vibe coding still requires building intuition about technical problems even without traditional coding knowledge.6. The transition from expert software to prosumer software represents a major opportunity for AI-enabled tools. Expert software like Photoshop, Blender, and terminal interfaces have extreme complexity that intimidates beginners, but AI is making these capabilities accessible through natural language. The reign of specialists is ending as generalists with broad knowledge and curiosity can now build complete applications by leveraging AI to fill technical gaps. This shift particularly benefits entrepreneurs and founders who specialize in getting into difficult situations and figuring them out, even when they originally thought tasks would be easier than they turned out to be.7. Building applications with AI requires accepting massive time investments beyond initial estimates and developing strategies for overcoming knowledge gaps. Michael estimated his ecommerce content generation app would take months but spent nearly a year working over nine hours daily, while Stewart spent months solving audio-video sync issues. Success requires using tools like deep research to understand how competitors solve problems, maintaining separate planning and coding agents, and learning to ask the right questions. The key insight is that vibe coders can achieve ninety percent of functionality independently, but the final ten percent often requires understanding specific technical concepts that AI cannot intuit without proper context and domain knowledge.
My guest today is Dan Loeb, the founder and CEO of Third Point. Dan started Third Point in 1995 with a few million dollars, and today the firm manages over 24 billion across equities, corporate and structured credit, venture, and insurance. He is best known for his activist work at companies like Sotheby's, Sony, and Yahoo, and for the public letters he has written to boards over the years. What I find most interesting about Dan is how much his approach has evolved across thirty years. He came up as a credit and event-driven investor at Warburg Pincus and Jefferies, built Third Point, then layered in quality investing, thematic technology investing, and now a very large credit business that sits alongside the hedge fund. We cover how he thinks about the AI stack and the companies inside it he believes matter most, the difference between good and bad governance, what FTX taught him about due diligence, the Sony and Sotheby's stories, and the power of writing. Please enjoy my conversation with Dan Loeb. For the full show notes, transcript, and links to mentioned content, check out the episode page here. ----- Become a Colossus member to get our quarterly print magazine and private audio experience, including exclusive profiles and early access to select episodes. Subscribe at colossus.com/subscribe. ----- Ramp's mission is to help companies manage their spend in a way that reduces expenses and frees up time for teams to work on more valuable projects. Go to ramp.com/invest to sign up for free and get a $250 welcome bonus. ----- Trusted by thousands of businesses, Vanta continuously monitors your security posture and streamlines audits so you can win enterprise deals and build customer trust without the traditional overhead. Invest Like the Best listeners get a special offer of $1,000 off Vanta when you go to vanta.com/invest. ----- WorkOS is the infrastructure B2B and AI-native companies use to sell to enterprise. It covers everything enterprise security requires: SSO, SCIM, RBAC, Audit Logs, AI governance, and more. Trusted by 2,000+ fast-growing companies, including OpenAI, Anthropic, Cursor, and Vercel. ----- Rogo is the AI platform for finance. They're building agents for Wall Street that are trained to understand how bankers and investors actually do work: from diligence and modeling, to turning analysis into deliverables. To learn more, visit rogo.ai/invest. ----- Ridgeline has built a complete, real-time, modern operating system for investment managers. It handles trading, portfolio management, compliance, customer reporting, and much more through an all-in-one real-time cloud platform. Visit ridgelineapps.com. ----- Editing and post-production work for this episode was provided by The Podcast Consultant (https://thepodcastconsultant.com). Timestamps: (00:00:00) Welcome to Invest Like The Best (00:02:29) Dan Loeb (00:03:21) Mental Models Information Overload (00:06:50) Dan's Identity as an Investor (00:11:24) The End of Classic Event-Driven Investing (00:13:52) Evolving Strategy Over 30 Years (00:17:48) Return Opportunities in Today's Market (00:21:12) Sources of Alpha for Fundamental Investors (00:22:10) Good vs. Bad Governance (00:26:17) Writing as an Investing Tool (00:27:29) The Sotheby's Story (00:30:04) Activism Opportunities Today (00:31:03) Third Point's Evolution to 60% Credit (00:36:10) Dan as Sole Portfolio Manager (00:38:09) Value Investor Perspective on Today's Market (00:39:23) Investing Outside the US (00:40:33) The Sony Activism Story (00:43:59) Lessons from 30 Years of Investing (00:46:26) Danaher and Operational Excellence (00:48:48) Building the Insurance Liability Business (00:51:19) The FTX Story (00:53:07) Leading a Team Through Uncertainty (00:54:29) Where Third Point Is Most Contrarian (00:56:22) What Makes a Great Analyst Today (00:58:12) The Next 10 Years (01:00:24) The Kindest Thing
The new AIEWF website is live! CFPs close in 2 days and we will run our first New Engineer Orientation this weekend, get your tickets booked ASAP as they -will- sell out. Take the AI Engineering Survey and get >$2k in credits and free AIE WF tickets!One of the central tensions in the agents industry is that even while there are major decacorn agent labs like Sierra, Decagon, Notion and Cursor being built up, it is also true that it has never been easier to DIY agents, with a plethora of agent frameworks like LangGraph and Pydantic and Flue, and managed agents from Anthropic and Gemini and Amazon. There has been a wave of companies building their own background agents from Shopify to Stripe to Paradigm to Razorpay, and even Cognition's friends Ramp have built their own coding agent with other friend Modal.You'd think Cognition might feel a bit threatened, but they're not - even after all this, they were way oversubscribed for the $1B Series D they just announced:Walden Yan, coiner of context engineering and Chief Product Officer/Cofounder of Cognition, invited OpenInspect's Cole Murray to talk about why the Devin is in the Details.Full conversation live on the pod today: In retrospect, async agents were the most AGI pilled bet you could make in 2024 - the models weren't good enough yet to vibecode, and people didn't trust AI enough to let it rip, nobody (including early Cognition) was sure about the form factors. Now it is obvious:* The first wave of AI coding tools made the developer faster but remain heavily in the loop. Copilor and Cursor's tab autocomplete are prime examples However, the workflow was still heavily centered around and bottlenecked by the developer's local workflow: a developer in an IDE, watching the model, accepting or rejecting changes, and pushing code one interaction at a time.* The second wave was local agents: Claude Code, Windsurf, Cursor's agents pane: first one and increasingly many terminals all running concurrently.* The current Age of Async Agents points to a different future focused more on agent orchestration which drives end-to-end development.According to previous guest Steve Yegge, there are finer-grained 8 levels to agent adoption, but we have collapsed it into three.As Cursor's Michael Truell put it in The third era of AI software development:Cursor is no longer primarily about writing code. It is about helping developers build the factory that creates their software. This factory is made up of fleets of agents that they interact with as teammates: providing initial direction, equipping them with the tools to work independently, and reviewing their work.The agent should not sit solely inside the developer's flow. It should be setup to work in the background so that you can give it a task, a repo, a machine, a shell, a browser, tests, memory, and review loops to go do the work somewhere else.In less than a year, the sentiment has shifted from avoiding multi-agent systems:to suggesting approaches that actually work:From coining “context engineering” to building the infrastructure behind Devin's 7x PR growth and jump from 16% to 80% of commits across Cognition repos, Walden Yan has had a front-row seat to the background-agent shift. In this episode, Cognition co-founder and CPO Walden Yan joins swyx alongside Cole Murray, creator of OpenInspect, to unpack why everyone is building their own Devin, what changed after the December 2025 model inflection, and why “spec to pull request” is now becoming a real production workflow.We go deep on the architecture of background agents: harness-in-the-box vs out-of-the-box, why Devin separates the “brain” from the machine, why repo setup is still one of the hardest problems, why Docker is not always enough, and how full VMs, snapshots, scoped secrets, GitHub bots, Slack integrations, and video-based testing all fit together. Walden and Cole also dig into memory, MCP limitations, multi-agent orchestration, AI code review, SRE auto-triage, PMs shipping code from Slack, Windsurf 2.0, hybrid frontier/sub-frontier systems, and the real failure mode of uncontrolled vibe coding: your codebase regressing to your worst engineer.And as agents eat software… and software eats the world… you can draw the conclusion on what is next:We discuss:* Why the engineering world is waking up to background agents and cloud agents* The December 2025 model inflection that made spec-to-PR workflows practical* Devin's 7x merged PR growth and rise from 16% to 80% of commits* Why Cole built OpenInspect as an open-source background-agent system* The economics of $20/seat agent products and why monetization is tricky* What Cognition actually sells beyond Devin: infra, onboarding, integrations, and adoption* Harness in the box vs out of the box, and why architecture matters* Why Devin separates the brain from the machine for security and permissions* Repo setup, scoped secrets, Docker Compose, and agent-ready dev environments* Why full VMs matter when agents need to run real applications and test them* Android, macOS, Windows, nested virtualization, and machine-specific agent work* Why testing is much harder than “computer use”* Screenshots, video verification, and the “I know it works” merge moment* GitHub UX, Devin Review, AI reviewers, and agents responding to PR comments* Why MCP alone is not enough for first-class Slack and enterprise integrations* Memory, Knowledge, skills, Claude.md, and why retrieval is still unsolved* Devin's auto-generated memories and the challenge of memory pruning* Always-on agents as permanent PMs for issues, tickets, and product areas* Sub-agents, meta-Devin management, and what multi-agent systems actually add* Why pure auto-merge vibe coding breaks down after about two weeks* AI code smells, lint rules, reward hacking, and Semgrep for agent-written code* GitAI, inline context, and preserving the “why” behind code changes* Local testing, mock servers, older codebases, and preparing companies for agents* Windsurf 2.0 and the handoff between local foreground agents and cloud background agents* SRE auto-triage, support workflows, and agents as first responders* PMs, marketing, and non-engineers creating pull requests from Slack* AI agent budgets, $1k-$5k per engineer spend, and hybrid frontier/sub-frontier systems* The rise of autonomous coding factories and who Cognition is hiringWalden Yan* X: https://x.com/walden_yan* LinkedIn: https://www.linkedin.com/in/waldenyan/Cole Murray* X: https://x.com/_colemurray* LinkedIn: https://www.linkedin.com/in/colemurray/* OpenInspect / Background Agents: https://github.com/ColeMurray/background-agentsTimestamps00:00:00 Introduction00:00:43 Why Everyone Is Building Their Own Devin00:01:57 Devin's 2025 Ramp: 7x PR Growth and 80% of Commits00:03:49 OpenInspect and the Rise of Open-Source Background Agents00:07:59 What Cognition Actually Sells Beyond Devin00:09:56 Background Agent Architecture: Harness In vs Out of the Box00:12:08 Separating the Brain from the Machine00:14:07 Repo Setup, Secrets, Docker, and Full VMs00:19:13 Why Testing Is Harder Than Computer Use00:22:40 Video Verification and the “I Know It Works” Merge Moment00:23:19 GitHub UX, Devin Review, and AI Code Review00:25:42 MCP, Slack, and Enterprise Agent Integrations00:28:59 Memory, Knowledge, and Always-On Agents00:36:16 Sub-Agents, Multi-Agent Orchestration, and Meta-Devin00:43:55 Vibe Coding, Auto-Merge, and Codebase Decay00:48:38 Agent Infra, VPCs, Cloud Providers, and Fast VM Restore00:52:25 AI Code Smells, Reward Hacking, and Code Review Systems00:56:10 Making Codebases Agent-Ready00:58:30 Windsurf 2.0 and the Local-to-Cloud Agent Handoff01:01:15 SRE Auto-Triage, PMs Shipping Code, and Agent Use Cases01:04:32 Agent Budgets, Hybrid Models, and Autonomous Coding Factories01:06:51 Hiring at Cognition and OpenInspect Consulting01:07:45 OutroTranscriptIntroduction: Walden Yan, Cole Murray, and Context EngineeringSwyx [00:00:00]: All right, we're in the studio with Walden Yan, co-founder of Cognition, CPO.Walden [00:00:08]: Happy to be here.Swyx [00:00:09]: Which is a cool title. And coiner of context engineering.Walden [00:00:15]: Although I think there are many people who'd used the terms in various ways beforehand, but I did find that people, both internally and externally, enjoyed the upgrade from prompt engineering or model wrapping into maybe a more thoughtful way to build agents.Swyx [00:00:33]: For those who haven't caught up on that, I have on screen the Don't Build Multi-Agents post, which you should go read on and we might refer to, and Cole Murray, who created OpenInspect.Cole [00:00:43]: Great to be here.Swyx [00:00:43]: So let's talk about it. Everyone is building their own Devins. What's going on?The December Shift: From Handholding Models to Autonomous PRsCole [00:00:51]: So I think the engineering world is waking up to this idea of background agents, cloud agents, whatever you'd like to call it. And I think we saw a shift around the December timeframe of 2025, where the models Opus 4.5 and GPT 5.2, they reached a capability where we moved away from handholding the model and being able to actually more or less autonomously drive the model. And what I mean by that is that we could pretty much go from a specification to a completed pull request, assuming the spec was good enough, with very little friction. And that paradigm alone, I think, changed a lot of how we interact with agents, and opened this world where background agents became more practical.Swyx [00:01:41]: I think for Cole, everyone experienced this in December, but I feel like there was just this increasing ramp, right? There was this moment which was, I think, Sonnet 3.7, where, You guys rewrote Devin in one night or something. So describe 2025 or how it felt from your side.Walden [00:02:01]: In retrospect, we always thought it was ramping up, but then even now, over the last three, four months from today, it's been ramping up even faster. So it's almost funny to be talking about how, big of a leap Sonnet 3.7 was, and honestly, a lot of it was stripping out parts of Devin that were no longer needed with that jump in of intelligence. But I also just think that a lot of the recent leaps, especially, you look at, models like Opus and the latest GPT models, they are reaching levels of autonomy where people are actually finding that they actually can just be hands-off. And people who were once debating, “Oh, do I need to be in the weeds with my model in the IDE? Can I just completely move it off into the cloud?” That's a more serious conversation, and we've seen that in all of our growth charts. Internally there's this funny graph where our usage has, of PRs, our merged PRs, has grown 7X since I forget what it was called.Swyx [00:02:57]: I think Dev, maybe tweeted that. Yes.Walden [00:03:01]: it grew like 7X over, the last, I think it was, two months, three months, something like that. And then you see our engineering headcount growth. It's, gone up by, 10% or something.Swyx [00:03:11]: We were, we were afraid To release this. So this is Devin commit percentages on all Devin repos, was 16% in January and now 80% in March.Walden [00:03:25]: It's a big shift right now. And so it makes sense that a lot of people are now thinking about, buying Devin, but also maybe, trying to build their own and there's Lots of I have a lot of fun building Devin, so I can see why other people would want to build their own cloud agents as well. Matt, well, maybe it's good to hear, what initially inspired you to try to build OpenInspect?OpenInspect: Ramp, Cloud Agents, and Open SourceCole [00:03:49]: OpenInspect came about, through primarily my clients observing how they were using tools like Claude, OpenAI's Codex at the time, and seeing some of the friction that they were having with it. Primarily the Claude was being used through Slack, and a big issue they ran into was that the sessions that were launched were specific to whoever called it via Slack. And so if a PM was the one who invoked the session and they would then go to pass context to engineering can't see the session. And that in itself was a deal breaker because the PM, “Hey, engineering, can you jump in?” But there's nothing to jump in on unless they're copy-pasting out or the single response that came back. And so seeing some of these problems, I had built a similar architecture internally, just to experiment with, test out different ideas as this trend of moving off of localhost was starting to become, And as Ramp released their blog post, I had a lot of the pieces for this already in place, and just thought it would be funny to, see what Claude could do just purely from the blog post. And on my X account, there's actually a thread of where I live tweeted, going through thisCole [00:05:14]: comparing GPT and Claude as both of them are going through it.Swyx [00:05:17]: On the announcement thing or something else?Cole [00:05:19]: right after it got released. We can put it in the show notes. Yeah, it was helpful that I had already knew how to verify the system. I knew what I was looking for. I think Ramp did a great job of really illustrating, the technical aspects of how to build something. It was much more than just like, “Hey, we built a great system.” It was, “And here's how you can build it too.” And so, I resonated a lot with that, just with the problems that I was already seeing, and I thought that, looking around, I didn't really see anything in the open source community that, met this type of system. I think there's a lot that run, in localhost like Superset, Conductor, and many others.But nothing that was actually running in the cloud. And so, I built it, and I thought it was interesting to just open source it and allow anyone to then have a foundation that they can mix and match on top of.The Business of Background Agents: Open Source vs. DevinSwyx [00:06:16]: So literally after Devin was launched was, there was OpenDevin Which became All Hands. I don't know if you tried that orWalden [00:06:22]: I was going to say, one of the things that interested me a lot with OpenInspect was, you didn't try to go make it then something you monetize. There are a lot of, I think, these open source projects would then go and really try to, raise VSwyx [00:06:36]: That's why no OpenDevin. Yeah.Walden [00:06:38]: yeah, and how did you think about that? I thought that was very interesting.Cole [00:06:44]: I thought, and just what I had seen across my clients, was that having a background agent system is going to become a critical infrastructure within their company. And so because of that, I think that I wanted to open source it so that they could fork it and put in whatever customization they wanted. To that question though, I get asked all, “Oh, are you going to raise? Are you going to turn this into a service?”Walden [00:07:08]: I'm sure you've gotten offers.Cole [00:07:09]: but primarily I don't want to do that for a few reasons. One, I think that I don't want to compete for, $20 a seat. I think that is just a really difficult business. I think it's very easy to copy the main pieces of it. Again, I built this fairly quickly. And I think because you are not owning, I guess, the entire stack, it's hard to monetize. You have money being made at the sandbox layer with Daytona, E2b, many other players. You have money being made at the model layer. And you sit in this weird in-between gray area where what are you actually selling? You're selling, I guess, the infrastructure. You're selling, the integrations maybe.Swyx [00:07:55]: let's ask the guy. What are you What are you selling?Walden [00:07:59]: Well, yeah, there's multiple layers to this in practice, and actually it's funny you mentioned the infrastructure, ‘cause when we got started building Devin as well, we had to go figure out how to make the infrastructure as well because,Swyx [00:08:10]: You had to build this two years before everyone else,?Swyx [00:08:15]: Including, the model sideWalden [00:08:17]: It was not, it was not very polished at the start, when we just built it off of raw VMs from cloud providers like EC2, the boot up time was so slow, I think, And especially then, turning off the machines, saving them, and then to be able to bring them back up again when the, when you want Devin to wake up again later. It would just be out cold for like 10 minutes because that's just how long these systems took. They were not built for this repeated down and up usage. And so we actually had to go do all of that. And as a result now, one thing we offer when we go and sell Devin to people is, you don't have to worry about all the compute side of things. We'll make it work. We'll make it work in your cloud if you want it to. But aside from the product, and I want to go into the agents and the tuning of the intelligence part later, but I think a big part of what we do at Cognition as well is to just make sure that your company learns and uses and adopts these coding agents. ‘Cause I think for especially the largest enterprises in the world, you find that there is a lot of people who want to move over to using AI for their day-to-day workloads. But because of the way projects are planned, because, not everyone is literate in using AI in these ways, having a team of engineers who can actually go in and onboard you, set up all the integrations you need, the automations you need to really get to that level of, leverage with AI, is super helpful. And so We do that. We show thought partners to the customers that we work with as well.Swyx [00:09:56]: So let's talk about, architectural stuff. I think that's always, that is something that was the topic of conversation between the two of you. Is this, the mental model that you want to start with or something else? I'll just leave the floor open to you guys.Agent Architecture: Harness in the Box vs. Out of the BoxCole [00:10:11]: I think, maybe we can start here as just a general what are the pieces of a background agent system. And then maybe we can go into some of the nuances of, Decisions that you can make.Swyx [00:10:22]: But I guess I also Like, what, maybe what Walden is saying is the agent is like in this open code box, I guess. Right? This is infra, and then there's, that's the agent. And you had this discussion about whether you put the agent in here or in Out externally. Can you tease that out?Cole [00:10:39]: In a background agent systems, you have a decision to make of where the agent is actually going to run. This is typically described as the harness in the box or out of the box. With running the agent in the box, you're making some trade-offs by doing that. The negative trade-off you're making is primarily security. Because the agent is running in that box, unless you otherwise design it, all of your secrets need to go into that box as well. And given the nature of AI, it can be unpredictable, and you could very easily end up accidentally exfilling your secrets, or other unintended behavior. Now, the out of the box is the idea that we are going to have the actual agent running not directly in the sandbox, and we will have, quote-unquote, the brain of the agent running in some type of worker, control plane. That sandbox then is going to serve as the hands where the brain is basically operating and making tool calls into that environment to manipulate it. I guess other trade-off that you're making between the two systems is that, in my opinion, running it out of the box is much more complex because, you have state that has to be managed, whereas if you're running it in the box, all of the state of that agent is actually in the box, and yes, it's you could persist it elsewhere, but it's all localized and you have less concerns to worry about.Walden [00:12:08]: I think a lot of that, what you mentioned, is why we actually from the start built Devin to what we called separate the brain from the machine. The other thing that this allows you to do is reuse any existing infrastructure you have for dev boxes Perhaps. And so you don't have to worry as much about making a new type of dev box that has all the dependencies the brain needs, as you mentioned, the secrets the brain needs as well. One thing that we've seen some customers run into is, you have a GitHub app and you want Devin, your agent, whatever, be able to interact with GitHub through this application, but then you have different users with different actual permissions. If they are all interacting through the same GitHub app and there's no actual, separation between the system that decides, what it does and the actual secrets on the machine, then you run into an issue where, okay, it's hard to do the separation. But in practice, with Devin, it's much easier because we just say whatever you put on the machine, that is, the scope of basically what the user is free to do, what the agent is free to do. So only put the most scoped secrets on that machine, and then the brain is fully not accessible from the machine. So you don't have to worry about messing with the, any of the most secure parts of the brain if the user is free to do whatever they want with the machine.Swyx [00:13:31]: I was going to just bring, I have this, chart from OpenAI, where I don't know if this is, in the box, out of the box. That is something that they do use to describe it. And then also recently Anthropic did, managed agentsSwyx [00:13:44]: Which is, this is their thing. I don't know. It's all, it's all variations of the same pattern, right?Cole [00:13:49]: So this would be out of the box.Swyx [00:13:51]: Which, is preferable for them because it's less work?Cole [00:13:56]: I would say it's more work.Swyx [00:13:58]: It's more work?Cole [00:13:58]: But it, in my opinion, it is the better architecture of the two. It's just, you're taking on a bit of complexity by doing that.Repo Setup, Docker, and VM-Based Development EnvironmentsWalden [00:14:07]: One thing I've not seen a lot of other players do well is how do you manage what's actually on the box? And this can be complex for many reasons. Let's say you have a big repository that's changing and updating a lot with changing dependencies. How do you make sure that the working environment of the agent actually stays up to date, has all the credentials it needs to, let's say, run the app and test it, and all the things you want your autonomousSwyx [00:14:34]: So a repo setup.Walden [00:14:35]: Exactly. So in, internally At Cognition, we call this repo setup.Cole [00:14:39]: The hardest part ofWalden [00:14:40]: It's been a perennial problem since the start of the company, of how do we help people get this set up? Because not everyone just has, working cloud environments working out of the box. And do you find this to be a common problem withSwyx [00:14:53]: How do you solve it?Walden [00:14:53]: Your clients?Cole [00:14:54]: This is a very common problem, and through my consulting, this is a lot of what I help teams do. A lot of teams don't really have great developer environment setups, if any. A lot of the times it's, “Go talk to Bob and get the secrets,” and that obviously doesn't work when the agent needs to actually set this up. And so a lot of that, most teams are using Docker Compose or some type of microservices. And so for theSwyx [00:15:19]: Even in prod?Cole [00:15:20]: Not in prod. With the OpenInspect, you are using this primarily to interact, and make code changes. There is other use cases, but you can hook, whether through CLI, MCPs, other tools, you can then hook that into your production systems primarily for, SRE type use cases. But you are not, necessarily, trying to test your prod internal microservice through the system.Walden [00:15:48]: And you mentioned Docker Compose. I think one direction we saw some of our friends take early on was, using Docker containers as the level of abstraction for their models. There's lots of reasons, I think, why Docker containers are not great. One thing is, Docker container's not really a true security boundary, for one. But the other is, if you are running real applications, a lot of times those applications use Docker, and then you have to think about Docker in Docker, which is, really weird. And so I think part of, the really hard challenge of getting VMs to work, why did we do that? Well, it was because we realized that you actually needed, full VMs to be able to do these types of things. And especially nowadays where there's actually value in running the application and clicking around and sending you screen recordings of these things. The value just, keeps adding on top of that. But it is a decision I see people run into when they try to build their own systems, is, “Oh, do we, in addition to this, do we put the agent in the machine or out of the machine? Do we use Docker? Do we use something else?” What do you recommend people nowadays?Cole [00:16:57]: I think Docker is a good solution for maybe not running the agent, but running your infrastructure, because that is more or less the same setup your engineers are probably already using. If they're not, then I don't know what they're using. But they're probably already using Docker Compose.Swyx [00:17:14]: I've always had a small candle for web containers. I don't know if you guys have tried them before.Swyx [00:17:19]: To me, they were, supposed to be like Docker Light.Cole [00:17:22]: Is it?Swyx [00:17:22]: I don't know.Cole [00:17:22]: No, I haven't tried it. But yeah, I think any environment that you've set up that is a good experience for your developer naturally lends itself to being easy to set up for the agent. And once you figure out that local developer story, you've more or less solved the agent in a sandbox, environment setup. OpenInspect does have hooks as well, where you can, run a setup SH script that will pre-install everything. You can then pre-snapshot that build so it starts instantly, and then there is a second hook to actually then, restore the state of the sandbox when it comes back. And so you can already have all of those microservices running and basically get the same experience that you would on your machine within the sandbox.Testing Agents: Computer Use, Screenshots, and Real App WorkflowsWalden [00:18:08]: Another thing that we've been thinking a lot about is like Different VM service offerings. Have you had customers where they needed like macOS specific VMs or like Windows specificWalden [00:18:20]: VMs?Walden [00:18:22]: There are like many technologies in the world that only work on specific types of machines, right? If you're building a.NET application that has to run on Windows or like, maybe more commonly if you want to build iOS or macOS Does that workSwyx [00:18:32]: Does Commission supportSwyx [00:18:33]: Choices like that?Walden [00:18:35]: The fundamental architecture we do, because we do the separation, it does support, but the actual work in progress is happening right now on these. Another thing that we've actually recently added support now for, it's in beta, is doing Android development. To do that, we needed to support, I think, nested virtualization within our machines because the VM itself is like a, is a virtualized Firecracker instance, and then you had to then run another Android emulator inside. And there's like weird performance issues that like, it, which is why it's like still in beta. We have to think through these problems, but it unlocks a lot for anyone who wants to do Android development.Swyx [00:19:13]: I was trying to find like a reference video for the testing thing. I couldn't find it, but I think you worked on the testing, capability. Why call it testing and not like computer use or I don't know, it's, what's the general Category of problem?Walden [00:19:26]: I think that when people think about the ability of an AI to run your app and test it, I think they actually over-index on the computer use part of it because computer use in my mind is the literal, okay, you want what button you want to click. Can you emit the right coordinates to go click that button? I think testing is actually a really interesting likeWalden [00:19:48]: Problem-solving, challenge for these AIs because if you wanted to do arbitrary testing, imagine you make a change that spans the frontend and the backend, maybe, even some other like even more deeply nested service. To actually test that change, we have to reason through what-- how do you first run these applications to orchestrate with each other with the right version of the code? Then, okay, how do I trigger the feature or how do I make the thing actually happen? And this can get arbitrarily hard, maybe you have to be an admin. Maybe a certain thing has to be feature flagged on. Maybe, you have to like run two sessions and then send us a very specific word into one of them to trigger a specific behavior. And figuring out how do you do that requires a lot of code base context, requires, a lot of orchestration that we've specifically done. And in some cases, we found that you actually, no one frontier model can actually do this full end-to-end task itself.Walden [00:20:42]: We've seen cases where we actually had to orchestrate different frontier models together to solve this problem together. That is where we spend most of our time when we think about this testing problem, not so much the computer use part. Computer use for what it's worth has gotten a lot better with recent models and it's made that part of the job certainly easier.Swyx [00:20:58]: Especially with like even 4.7, that they released yesterday, apparently like way better in terms of the vision stuff, which is going to be encompassing computer use.Walden [00:21:08]: Having evals for all these as well is something that like takes a while to build up. And having the evals be right is tricky as well. Do you ever see like, clients who are building their own agents have to start standing up evals to make sure things don't regress?Swyx [00:21:25]: Not so much evals in the traditional sense, but specific to the testing part that has just gone in. I just added support for screenshots And in theory you can also do video. I need to put in a plugin to do that. But they do show up natively, and it was a very heavily requested feature, especially after Cursor's recording came out. I think that was very enlightening for everyone of like, “Oh, this is a very good feature to actually have.”, I think with Devin you guys have had this for a while.Swyx [00:21:57]: Oh, yeah. See how screenshots work. Yeah, I don't know if there's anything, super and not obvious. It's like once what feature to build, you can just prompt it and it Will mostly work.Walden [00:22:09]: I think to Walden's point, though, the computer use is a subset of the larger testing problem, and I think that's very specific to the code base that you're working and it's not something that, out of the box that you could just solve it. The-- you do need the code base context to actually know how to test it. And I think in the case of a background agent system, you fortunately do have that code base locally that what is changing and could then inspect it and use that to drive the model.Swyx [00:22:40]: For those who haven't seen it before, this is an example of how it works. You, after the PR is done, you click testing approved, and then it sends you back a video. What I really like is that it labels, It's very small here, but it actually labels what it's testing. And then it-- and then you actually see the cursor and everything. So I don't know, yeah, the engineering in this, just Whatever you want to show. ‘cause this is like, this is one of those like, oh, few of the AGI moments, right? ‘cause Once I look at this, I actually don't I wish I can just merge inside Of Slack instead of going to GitHub ‘cause I don't need to see the code. I know it works.Walden [00:23:19]: Maybe a new feature in Cursor. Yeah, the annotations at the bottom was also a big difference for me when I, when I added those.Swyx [00:23:27]: It's just like, what am I looking at? What are you trying to demonstrate?Walden [00:23:30]: Exactly. There's a surprisingly long tail of small details that ends up making a big difference for this end metric of like how fast do you actually merge the code in. One experience that we spent a lot of time tuning early on was what is the right experience on GitHub for these tools. Because I think, most tools out there when you build the agent, you'll think about, oh, it'll create the PR for you. We try to take that a step further and say, “Oh, what if we actually made sure you could interact Devin, with direct Devin directly on GitHub?” And so we made sure that you can comment on GitHub, and Devin would actually receive those comments and address them back. But there's actually quite a bit of tuning you have to do here because you can imagine that actually like-We recently have Devin Review, for example. Devin Review will post comments on his own PR And then Devin has to then goGitHub Workflows: Devin Review, Comments, and PR AutomationSwyx [00:24:23]: He answers his own comments, which is Really loopy. So like, yeah, I like that it just updates here that it's, that I have commented But usually it's just me saying like, “Hey, merged, fix any merge conflicts.”Walden [00:24:37]: The, so when Devin fixes his own comments, you might be scared that, oh, maybe I'll infinite loop. But we've put a lot of work into making sure it doesn't, both by making sure that the comments are high signal, but also that the agent is thoughtful about what comments it immediately goes and tries to fix, and what comments it's like, “Wait a second, I think you're wrong.” Actually, that's one of my favorite moments is when Devin tells me that I'm wrong, when I try to get it to do something different. But tuning that behavior, actually makes a big difference in terms of how useful the actual GitHub experience is.Cole [00:25:06]: I think to touch on that as well, I think having the AI reviewer integrated into the system is a critical part of this background system. OpenInspect does have that. It has a GitHub code reviewer that you can control the prompt. It does do comments as well. It doesn't do them automatically yet. The capability is there, but it's not fully used.Swyx [00:25:27]: So you have to ask for it?Cole [00:25:28]: you do, yeah. You can tag it on GitHub, and then whatever you named your, GitHub bot, it will then follow up on it. It will then, if you have merge conflicts or whatever you have asked it to resolve, it will then resolve it, but it doesn't do it automatically yet.Integrations: Slack, MCP, and First-Party Agent InterfacesWalden [00:25:42]: Well, I'm curious, what is, the most common thing that people end up requesting, that they still need on top of OpenInspect when you help them go implement it?Cole [00:25:52]: I think a lot of it comes down to actually integrating it into the company. It's one thing to have the background agent system set up, but if it isn't actually integrated into your larger ecosystem, it isn't that useful. It is useful to be able to kick off sessions, but what we really want to be able to do is hook it into all of our other systems, whether that is the production database with read-only credentials, the logs, a Confluence or internal knowledge-based system. I think that is where I see the huge leap for companies, and that can be a challenge for companies as well who are maybe not familiar with exactly how to approach it, especially if they're in environments that have more compliance type things where, access control can be pretty big and how do you deliberately think about these problems, I find to be, one of the problems that comes with a system like this.Walden [00:26:46]: The thing we found is So, MCPs, obviously it has been like this, really big explosion of, oh, you can go, integrate it with all these different things. But to actually get the integration right and the and get the right experience, oftentimes we found that we had to go build our own ad hoc things. I think Slack is a great example of this. You could give your agent a Slack MCP and okay, it can post messages back to you on Slack. But we actually use Devin like a coworker in Slack, and that's how it's been built from the ground up. But to do that, you actually need to, support webhooks that come back, right? And then Devin has to respond in a natural way and then hopefully don't spam your threads too much and annoy the people in your company. So you got to tune that experience just right. Especially when there's a lot of back and forths, we find that we actually have to go beyond the simple MCP integrations in these places.Swyx [00:27:39]: I just pulled up the MCP marketplace. I know this is a Fair amount of work. Is the answer to eventually take first party control of all the top MCPs? Is that theWalden [00:27:48]: I would love a world where you could have something that's more expressive than MCP. That, goes both ways, not just a set of tools, but a proper system that interacts back and lets it Have the right experience with all these interfaces.Swyx [00:28:03]: So there actually is sampling in the MCP spec, but nobody Uses it, right?Walden [00:28:07]: And so I think that's the other part is, actually we found that when the MCP spec starts to get too complicated, it starts to lose its original promise of Being like a simple one-step connect. Now then we have to go figure out how to support all these different variations of things and It starts to look a lot like just building the first party integrations in a lot of these cases now.Cole [00:28:29]: I think it matters, too, how critical it is to your company, right? If this is something that nearly every session is going through, it probably makes sense to own it so that you can make optimizations on top of it Versus just whatever is off the shelf.Swyx [00:28:43]: Awesome. Other than MCPs, what else, sorry, well, I don't know if that's Narrowing in too much on, integrations. But what else? What other elements of building OpenInspect or Devin that you guys really sink on?Memory and Knowledge: What Agents Should RememberCole [00:28:59]: I think, a problem that comes up very frequently is this idea of memories or knowledge base.Swyx [00:29:05]: Oh, boy. How do you solve it?Cole [00:29:08]: so not solved yet, is the short answer.Cole [00:29:11]: it's something, there's a open issue for it, someone asking about it.Swyx [00:29:16]: There's, I, D Wiki hasn't indexed anything about memory yet.Cole [00:29:20]: how I'm seeing it solved across my clients is primarily through skills. I find that skills can be a good gap within that or updating Claude MD, but I think memory as a whole is a pretty unsolved problem, and it is why I've been hesitant to add it. I think there is parts of memory and that can be addressed, but I think as a whole it's a very difficult retrieval problem.Swyx [00:29:44]: Oh my God. RAMP didn't write anything about memory? I see zero search results.Walden [00:29:50]: No. Memory can be quite tricky to get right because it's the retrieval, but also the generation of the memories that can be really tricky. You don't want it to just like Remember very specific details.Swyx [00:29:59]: Walk us through the Devin memory journey because I know there's been a journey.Walden [00:30:03]: the first version of memory that like stuck around for a while was A system we have called Knowledge. And the idea was we wanted it to pick up things over time and not need the user to be proactive about teaching Devin things. So, okay, any time you remind Devin, “Wait, no, that's not quite the way you're supposed to use Git”Like, we actually want Devin to say, “Hey, do you want me to actually just remember this for the future?” And for you to just basically quickly approve or reject and for it to build up over time. ‘Cause I find that, 95%, I think, or some crazy stat like that of the memories that Devin has are all through these auto-generated things. Very few people actually just want to sit down and write big docs on Here's how you're supposed to work with the technology, et cetera. The generation and the retrieval has been something that we've been trying to tune a lot over the years. Generation, you don't want it to remember something like, if you asked one time to like, “Oh, please open as a draft PR,” you don't want to be like, “Oh, everyone forever now should get their PRs as draft PRs.” But you do want some, conveyor. Maybe you want to say like, “Oh, Cole generally likes, things to be created as draft PRs.” Same with retrieval, if you have thousands of these memories, how do you actually make sure they're retrieved at the right time? And that can be quite tricky to do right without exploding the context with a bunch of useful yeah, useless information. Surprising amount of just, eval work to just make sure that, memory is, remains a reliable system as new models come and go.Cole [00:31:31]: Do you have anything that you could share on, memory pruning? And like the temporal aspect of memory?Swyx [00:31:36]: Deleting and forgetting?Walden [00:31:39]: The, today, the, So the things they could do is it could edit memories. And so if your memory used to say like, “Oh, Cole likes to open everything as like a draft PR,” then you can imagine, “No, don't do that.” And then it'll say, “Oh, do you want me to update the memory to be Cole now want everything as, open PRs?” I think that at the same time we don't know if this is going to be the final version of the system. Whatever we have here will probably, translate into the new system that we'll be coming up with. But I think one big difference between two years ago and today is these agents are really good at using anything that resembles a file system natively. And so part of us are, is thinking, “Oh, should we rebuild memories to feel more like a file system that we let the agent navigate on its own?” That's been an interesting exploration. Also similar ideas in the scale space.Swyx [00:32:35]: I am pulling up OpenClaude's memory thing right now. So memory, OpenClaude has like this like daily memory journal thing, right? And you can I mean, that is a file system you can grep through and is a source of truth. I don't know if it's the best. It's probably super noisy, but at least, if you lose something you can discover it or you can apply some, forgetting algorithm to, more ancient memories that don't get recalled again or something. I don't know.Walden [00:33:01]: One thing we've been trying to do to push the boundaries of how you use agents at your company is letting an agent basically have a very similar file, a memory.md or something, and just like be your permanent PM for a specific set of issues maybe. So we have like some Slack channels internally, maybe a Slack channel dedicated to, a specific product like DeepWiki maybe. And you can imagine that, or you want a Devin that never stops, it's just always awake, but it has this like memory dock that it can just maintain for itself about, okay, what are like the number one priorities of what we have to fix and prioritize? Who is responsible for some upcoming work? Maybe they'll even Devin will even tag you on some recurring basis. And so it's been an interesting move to see, okay, how can we actually use Devin for more than just engineering? Can we actually upstream above the engineering process and maybe it's just Devin creating tickets, which then maybe some humans do, but then maybe other Devins do.Swyx [00:34:00]: One of my more fun automations is go research competitors and just suggest stuff to me on a weekly basis. That's the automation. I can't find it right now, but basically it just like, “Look at competitors and suggest things.” “And here are three things that you've suggested that I don't want any more of,” and you just stick that in the prompts. But like I wish actually So for like when I, for example, when I reject a PR, I wish that it updated memory so that I can then just not have to go up, go back and update the scheduled, sync, but anyway, feature request.Walden [00:34:31]: what? We might change it soon. I guess OpenInspect, in the time you've been around, has there been anything you tried to implement but then you had to like undo and like do a different way?OpenInspect Architecture: Webhooks, Control Planes, and Agent StateCole [00:34:41]: Nothing yet, but something that is on my mind. The initial way that I built it was that each of the integrations lives as its own package. And so you have The Slack bot, which is what's handling the webhooks, and then is basically interacting with the control plane. As I'm seeing the system starting to be more integrated, specifically with the GitHub bot integration, I'm considering bringing that all into the central control plane because especially now I want to start, And a request that I'm getting is the ability to monitor, the actual, pull requests being merged, as well as just tracking ofSwyx [00:35:19]: What do I have open?Cole [00:35:21]: What do I have open? How many of these are getting merged? How many comments are showing up? To just understand the health of the system. And so in the case of a GitHub app, you only have one webhook. And so then it's a question of do I put that webhook in that GitHub bot package? That's weird. It doesn't really make sense to live there because that package is more for like the code reviewer. Or do I like centralize it? So that's something that's on my mind of, making that decision. I think the other one we touched on earlier is the harness in the box versus out of the box. I think long term the architecture will eventually come back out of the box. Some of the newer tools that I've added are calling back into the control plane so that you don't have the secrets in the sandbox. And so I think long term I probably will pull the actual, agent out of the box, but I think for now it's fine.Subagents and Multi-Agent Systems: When Parallelism Helps or HurtsSwyx [00:36:16]: Just, a quick question on pulling the agent out of the box. I'm One thing I'm very bullish on this year is agents calling other agents or spawning sub-agents or Whatever you want to call it. Does that make it harder or easier? I can't tell. Because if the harness is in the box, you can just spin up more boxes. If the harness is outside the box, then you're, it's less easy because you are, you have a unicorn pet of a, of a harness that's, living outside the box.Cole [00:36:45]: In theory it would be the same way, right? Whether, one agent has launched many, sub-sessions within it, OpenInspect, for example, can launch sub-sessions and actually create other environments and then monitor them. In the case where it is out of the box, that would basically just be an additional session that's running. And so that session is also running outside of the box. It's running in your worker plane, wherever you're running this. And then you really just have to think about how does your top level agent then interact with it. I do think it can be more complex, just ‘cause again, you have now a more difficult architecture. But I think if you figured it out once, it's probably fine.Swyx [00:37:26]: Well, then I'm just, throwing it open to you in terms of, I call this like meta Devin management. Which is like the, Devin's calling Devins or Devin scheduling Devins or querying trajectories or anything like that. What have you built or unshipped, anything?Cole [00:37:46]: I think one of the surprising things we've seen is that a lot of the ways that, these, separate agents work with each other, and you want them to, parallelize their work, has still mostly followed the same manager sub-agents regime. And a lot of people I think are excited about this world where you have swarms of agents that, talk with each other all over the place. We've actually given Devin an MCP so they can just go arbitrarily message other Devins And create new Devins, et cetera. But I guess, it somehow creates, a really chaotic world in that sense. And so we've still found that most practical use on a day-to-day basis has been one single Devin.Cole [00:38:33]: Figuring out how to segregate the work and get, have other Devins work on it in, a relatively isolated sense, each with their own boxes Not sharing machines, so there's, a very little room for conflict is the regime that you have to create today.Swyx [00:38:50]: I'll call out, the experiments from Cursor, right? This is Wilson Lin's work on Single agent to multi-agent, and you're obviously famously on the side of don't build multi-agent. But they went through the whole thing, only to arrive at, this Which is exactly what Devin has, I think.Cole [00:39:08]: I think there will be a revision to that post at some point AboutSwyx [00:39:12]: Tell us about itCole [00:39:12]: I think multi-agents were very much not at all possible a year ago. You do see more multi-agent experiments today, but you can argue, are they really multi-agents, or are they just just, tool calls,? There are people who, will create sub-agents to go look for XYZ file, XYZ implementation. Has really nice context management benefits because all of the tool calls and tokens that it spends then get collapsed back to just the answer for the main agent. There's a lot of benefits to doing this. We basically have Devin do this with Deep Bookie, make a call out to Deep Bookie, give you back the results, but that feels like a tool call,? It's not like these, two collaborators actually talking back with each, back and forth with each other. But I think the thing that gives me the most bullishness that multi-agents might actually be possible is actually what I said earlier about Devin will actually sometimes tell me I'm wrong and push back, and I think that demonstrates a level of maturity and communication today that makes a multi-agent world possible. One, can two agents who have seen different information come back to each other and actually figure out who is right, what is the correct implementation? They're not just, yes men. Claude, I guess is like, used to just say, what is it? “You're right,” or,Swyx [00:40:25]: “You're absolutely right.”Cole [00:40:26]: “You're absolutely right.” Yeah.Swyx [00:40:28]: The Have you seen, did you seeCole [00:40:29]: The age is overSwyx [00:40:30]: The Codex app troll in Topic? This is the Codex app. Inside of Settings, there's a little, there's a little Easter egg, right? So if you go to, the Themes or Appearance, right? There's all these, color codes, and the top is absolutely, and it's the Topic's colors. Which is such a troll. Anyway.Model Behavior: Pushback, Adversarial Prompts, and Agent SkepticismCole [00:40:53]: I love that Easter egg. Did you discover that yourself?Swyx [00:40:54]: No, it was, someone was, tweeting about it And I was like, I was like, “Is this true?” Because, sometimes people just tweet stuff to, get a rise out of you. But yeah, there you go, in Topic colors.Cole [00:41:06]: Yeah. So yeah, we're out of this regime where, it just says you're absolutely right, and they can have real conversations and real back and forths.Swyx [00:41:13]: You can prompt it as well to be more adversarial or whatever. Yeah. Okay. Yeah, that, I mean, to me, that is more intelligence, right? That is not just something that's, a dumb tool, it's actually pushing back on you I think. Yeah.Cole [00:41:24]: when you mentioned, of course, the blog posts. There was one blog they had where they fed a swarm of agents together and built a browser.Swyx [00:41:34]: That was I think that was the one.Cole [00:41:36]: You can have, likeSwyx [00:41:37]: I think it's the same oneCole [00:41:37]: Creation of it. We found a surprising success of, don't do a swarm or anything, just have one Devin, it does its own context management. Just let it keep running for a while and give it some crazy tasks. I think we asked it to, rebuild, a Windows OS system. And it managed to do it just like, going on for long enough. It'sSwyx [00:41:55]: Was this Andrew's thing?Cole [00:41:58]: there were lots of demos that we ended up not posting, ‘cause at some point we'd just be posting way too much a bunch of, Demos. But I love that because it shows that I think the multi-agent thing still has, a bit of exciting sexiness to it, which is maybe still beyond still, the actual delta it adds to the capabilities of these systems. But it's absolutely the future. I think we're heading in that direction and we can see the progress being made there already.Swyx [00:42:25]: If I were to, make one super minor pushback because I don't feel that confident about it yetCole [00:42:33]: Go for itSwyx [00:42:33]: But I've had Ryan Lopopolo from OpenAI on the pod And he's a super slop cannon, right? Oh my God, that's my coding agent being done. I downloaded this, Peon Ping. I don't know if you guys have heard this. It takes like-, sound packs from popular games like, Command and Conquer and Warcraft, and then it plays it whenever it's done. And so it's like, “Work,” or whatever, “At your command,” or something. Anyway, what I got from the Cursor code base and from Ryan's thing was that there's a slop cannon approach where you try to loosen the single agent's, bottleneck, and I feel like that is, probably an, a very important thing to try to figure out. I don't think anyone's, really solved it. Because then you just have more reviewer slop on top of the agent slop To try to wrangle it all. Ryan will probably very strongly object that I say that he hasn't solved it, but he thinks he's He thinks he's completely solved it. But I think it's still I think it's, very important, ‘cause, that is a bottleneck, right? I feel Devin is slow sometimes Because I'm like, well, yeah, this is very readable and very sensible, but also it is slower than it could be if I just, I want a button to just say, “Just ramp this up 1,000 next parallel, in parallel and just, see what happens,”? And I don't know if that's, feasible at some point in the future.Code Review, Entropy, and AI SlopWalden [00:43:55]: I And we've also run experiments internally where we've basically tried to build entire products, true products that we knew we would eventually ship, but for now, let's try to see if we can do it just by purely, vibe coding on top of each other, auto merge, no code review at all. And then there's this benchmark of how many weeks can you go onto this for Before you say, “We have the trashiest code base.”Walden [00:44:18]: “Let's actually rewrite it from scratch.”Swyx [00:44:19]: Start a new factory, yeah. What'd you find?Walden [00:44:21]: I think we found that the state-of-the-art in December was you can probably, run this for about two weeks. By the end of those two weeks, you'd find that, hey, you want to, change the color of a button. Well, it turns out this button is implemented in, 10 different places, and they, have All these different variations, and oh, you forgot one of them, and actually it's a slightly different color in one spot. And you're like, “Okay, this is too much to work with. Let's actually try to do code review at the same time.” And make sure that we're on top of our software, actually cleaning it up a bit And making sure it's done in a scalable way.Cole [00:44:54]: I think building on that, the idea of, you don't have to look at code, I think is generally a bad idea. And the meme that I have for thatWalden [00:45:03]: What timeline, all right, is Do you think that statement will be true on?Cole [00:45:06]: I think probably for a while it'll be true that you should continue to look at your code. A problem that I see a lot of teams run into that I work with who are embracing AI native, AI first coding, is The meme that I have is that your code base regresses to your worst engineer, because that engineer who is, very gung-ho about AI and is not auditing their code, their pattern starts cementing into the code, and now the AI is referencing their patterns. And so now their if/else block that, is 20 if/elses back and forth, the AI is seeing that as the pattern of how things are done and starts to then exponentially grow this slop. And I find to your point, a pretty good approach to that is having scheduled cleanup, whether by humans or through systems, that are looking for duplication. They then address that. You'll end up with like 12 helpers for how to format a date. And you need to address that, because otherwise it will continue to sprawl.Swyx [00:46:09]: Within balance, I think it's fine to have some duplication, and then sometimes To have garbage collection, right? Yeah. The What I've been, talking about with a lot of engineering leaders is that you want to be very strict about the boundaries between modules, and it's your job as an architect, as a CTO, whatever, to say like, “Okay, here's the hard contract between you guys and you guys. Whatever you do inside this black box is your business. You do whatever. But between these guys, let's be, really damn clear, and any movement must be signed off by a human or me,” or. Then, and like that's that. I don't know if you have any other modifications or advice.Walden [00:46:44]: Well, I guess generally on the topic of, where humans can be useful, I found that ‘cause, some of these, really deep infra problems, sometimes just having a human that just has, really deep expertise can make a big difference. I've actually seen this come into play when actually building agents. So we've had a few friends now, try building their own coding agents, and I think one same problem that I recurringly heard a lot of them run into was this problem of like, “Oh, Grep is really slow on our agents' machines.” And so a lot of them, I assume because they're using AI and they themselves don't have, super deep infra background knowledge, say, “Okay, we're going to go build our own custom Grep index. It's going to be really fast,” and use that as a way around this problem. When we ran into this problem About like, maybe like a year and a half ago when we were, in the early days of building Devin, we obviously didn't have AI then. We just asked our, how to, how to do this. You can just swap out a new Grep index, so.Infrastructure Details: Grep, File Systems, and SandboxesSwyx [00:47:45]: What do you mean you hand-coded Devin? What?Walden [00:47:48]: It's like, can you believe we hand-wrote this code? And we had, our infra people who are really amazing, they were looking into it and they're like, “Oh, what? We realized that actually the root cause of this problem is actually super simple, but like fine-grain detail,” which is that a lot of these virtual machines actually underlying them don't use real file systems. They use these, network file systems where things are actually cached over the network actually in S3. So when you're Grepping, you're actually making network calls Every time you're doing these things, and that's why Grep is extremely slow on these machines. And so again, goes back to, what is all of the crazy infra work that we had to do to actually get these machines working. If you try to do this yourself, there are tons of small details like this, and so we had to eventually go swap out that network file system. ButSwyx [00:48:35]: I think there's a write-up about it, right? Silas did one about the virtual file system.Walden [00:48:38]: Oh, that was a whole other thing. TheSwyx [00:48:39]: Oh, that's a different thingWalden [00:48:40]: The BlockDev file storage formatSwyx [00:48:42]: I'll bring it upWalden [00:48:42]: Which is, a file system format that we built so that the VMs could be spun up and down very quickly. Basically, the intuition behind this is-Imagine you have, a terabyte of disk, and your agent only, wrote, a hundred lines of code on top of that disk. How long does it, say, take to, save and re-bring up that disk? And most systems, because you're not optimizing for this case, it's just, on the order of a terabyte of work because you have to Save all of that and bring it back up. In our system, we try to build a file system that incrementally builds on top of each other. So every time you save and bring the machine back up, you're only doing work that is proportional to effectively the diff in the file system. And so this, shaves off a lot of time in the boot-up process of Devin. I think we This is actually now outdated. We have a newer system inside of Devin. But yeah, there's a lot of tiny details you have to get right here to actually get the day-to-day experience of Devin to be good.Swyx [00:49:39]: It's, not technically agents, but it is agent infra, and when you sell an agent as a company, you sell agent plus agent infra.Walden [00:49:46]: At least the way we do it be And the other The nice thing about having the agent infra being done together is, you We get to deploy Devin in whatever environment we want now. We don't need to wait for some underlying infra provider to also go and support VPC or on-prem or FedGovCloud, for instance. So we can actually go and figure out, okay, since we own the infrastructure, how can we get that set up for you?Cloud Providers: Modal, Daytona, and Enterprise SandboxesSwyx [00:50:12]: Whereas you're Cloudflare dependent.Cole [00:50:15]: so Cloudflare runs the control plane. The sandboxes, Modal is supported. A contributor just added Daytona. E2B is on the roadmap, and I think there's an abstraction in place that if any contributor wants to add a new provider, they can add that in.Walden [00:50:32]: Well, what are, How are the customers you work with Do they generally try to then go set up a contract with another one of these third-party providers? Do they try to do the VMs in-house?Cole [00:50:44]: most of them I see using Modal. I think Modal has a greatWalden [00:50:48]: Shout out Modal.Swyx [00:50:48]: Shout out Modal.Cole [00:50:50]: I think Modal has a great offering. It captures all of the sandbox pieces you need, snapshots being a pretty big piece of that, and given that they also offer GPUs, I think it's a pretty nice offering as a whole.Swyx [00:51:04]: no debate there.Walden [00:51:07]: Modal is great, especially, I think their container offering is, the most natural, and so especially if you are willing to, forego, the full VM requirements Modal is, a really vast place you can spin something up on.Swyx [00:51:20]: Is there a point So Modal's very Python, and I feel like most workload, has really shifted to JavaScript. I don't know if you guys Get the same feeling. So, okay, when I started Landspace and IE and all these things, I was like 50/50 Python and JS, right? That's roughly. I think that's wrong now. I think JS has won. I don't know if you guys Like, I Maybe I'm overstating it, and maybe for cognition, there's, C# and Java and what have you. But for, new greenfield apps, do you feel that Do you get that sense? Does it matter?Cole [00:51:52]: I think that most of the libraries that I see in this space are Python native first, especially in theCole [00:51:58]: Observability space. That said, I think that there is a pretty big appeal of having your entire system in one language. Especially when you have both your frontend and backend communicating, you can have one central type Which is very nice.Swyx [00:52:11]: That's my case against Modal, which is Then you have to run JS. You can run JS inside Modal. It's just, one extra step That, isn't native to the runtime. I don't know ifWalden [00:52:22]: I don't knowSwyx [00:52:23]: Reviews. Do you have numbers? I don't know.Walden [00:52:25]: the one thing I don't like about Python is whenever AI, whenever it writes Python, it always does, the weirdest patterns, andSwyx [00:52:32]: Oh, because it's, mixing two and three or what?Walden [00:52:34]: I think it's something mixing two and three, yeah. The I don't know if you see this. It always tries to do, has attribute on objects as likeCole [00:52:41]: Oh, my God.Walden [00:52:41]: But it's like But that you shouldn't be doing that. It should error if there wasSwyx [00:52:45]: Because it's training on library code?Cole [00:52:47]: I think it's more of, likeCole [00:52:48]: From what I've seen, it's more of, a reward hacking mechanism where it doesn't want to basicallyWalden [00:52:54]: It'll never error.Cole [00:52:54]: It doesn't want the code to fail. And so it Even when it knows it has the attribute, it'll call getattr on a, and for a lot of my clients who have moved towards more autonomous coding, we've put that in as a lint rule That if you do getattr, your pull request is going to fail.Slop Signatures: Comments, Backwards Compatibility, and TypesSwyx [00:53:12]: Ooh, this is a fun topic. Can you tell me more about this? What else is a sign of AI coding that you have to put guards in?Walden [00:53:21]: So we were talking just before this about Opus 4.7. One of the things this new model likes to do is it writes lots of comments. Not like, it'll, comment every line, but it'll write, paragraph, PRDs, on top of every function. But I will say, to its credit, these aren't slop, descriptions like they were before. “Oh, here's what this function does.” It's like, “Oh, here's actually the r
Scott and Wes sit down with Alex Sexton and Amadeus De Marzi from Pierre Computer to dig into the gnarly performance challenges behind building blazing-fast code review tools, covering virtualization, progressive rendering, and why GitHub's UI feels so sluggish. They also chat about how major AI coding tools like Claude, Codex, and Cursor are adopting Pierre's diffs library, plus the role of web components, benchmarking, and what it takes to build “VS Code 2.0.” Show Notes 00:00 Welcome to Syntax! 04:00 The Need for Better Infrastructure 05:53 Understanding Diffs and Trees diffs.com Trees by the Pierre Computer Co 08:16 Performance Challenges in Code Review 10:49 Virtualization Techniques for Smooth Scrolling 15:04 In-Page Find and Virtualization Limitations 17:00 Browser Limitations and Content Visibility 19:29 Progressive Rendering and Syntax Highlighting 23:05 Tools and Techniques for Performance Testing 33:35 Optimizing Performance with AI 36:31 Mastering Auto Research for Efficiency 42:00 Exploring Web Components and State Management 44:05 Innovations in Rendering and Virtualization 49:12 Business Insights and Future Directions 53:58 Sick Picks Hit us up on Socials! Syntax: X Instagram Tiktok LinkedIn Threads Wes: X Instagram Tiktok LinkedIn Threads Scott: X Instagram Tiktok LinkedIn Threads Randy: X Instagram YouTube Threads
Robinhood launched agentic stock trading, letting users link Claude or Cursor to dedicated accounts. Micron hit $1T market cap in record time on AI memory demand. YouTube now auto-labels AI content, a new coding benchmark crowns GPT-5.5 the clear leader, and Roku overhauls its homescreen. Robinhood launches a feature to let users link AI agents, such as Claude or Cursor, to separate, dedicated investment accounts for trading stocks autonomously (WSJ) Micron hit a $1T market value for the first time on May 26 after its stock closed up 19.29%, rising from $700B earlier in May, driven by high memory chip demand (CNBC) YouTube makes its AI content labels more prominent on desktop and mobile, and will apply them automatically if it detects "significant photorealistic AI use" (Variety) Roku launches its first major homescreen overhaul in over a decade, including a large "marquee" ad spot to tout apps or shows, in a bid to drive more engagement (Hollywood Reporter) Datacurve releases the DeepSWE coding benchmark, a 113-task test across 91 open-source repositories: GPT-5.5 leads at 70%, GPT-5.4 got 56%, and Opus 4.7 got 54% (VentureBeat) Learn more about your ad choices. Visit megaphone.fm/adchoices
Right now, the questions we have about our careers feel existential. We keep coming back to the same theme: how do you prepare for an industry that's changing this fast, and what mindset actually works in this new reality? One skill keeps surfacing as the answer — your ability to update your own mental models. In today's episode, I want to push on that further and put some of software engineering's most beloved thinking models under scrutiny. Some of these models served you well for years. Some of them now deserve to be challenged, replaced, or thrown out entirely — and learning how to tell the difference is itself the skill that will determine whether you hit a ceiling. Move Past "So What" Questions: The typical engineering objection to agentic coding is that it produces quality issues. But the people deciding to adopt these tools already accept that. Our job is to stop arguing the surface-level point and start asking the real one: so what do we actually do about this new economic reality? The Economics of Acceptable Loss: Abstraction always leaves something to be desired. An agent's code may not match what a staff engineer produces by hand over months — but that gap is usually an acceptable trade against shipping something two, three, or four times faster. Understand the cost-benefit picture instead of pretending the cost doesn't exist. Abstraction Has Always Done This: This isn't new. The calculator dissolved the specialization once required for complex math. Spreadsheets commoditized ledgering and accounting. Agentic coding is the same pattern arriving for our work — making something that required deep specialization suddenly far more accessible. Roles Are Blurring: As these generic tools raise everyone's ability to abstract, the boundaries soften. You're already seeing product managers open pull requests and engineers making product decisions. The neat lines around "what an engineer is" are not as fixed as they used to feel. Why Your Hard-Won Wisdom Is the Target: If you've spent years in this industry, your models were bought with blood, sweat, and failed projects. That experience is real wisdom — and it's exactly what I'm asking you to be willing to challenge, because the thing that always worked for you is the thing most likely to become a ceiling. This Skill Survives Either Way: Even if you think AI is mostly hype and I've been infected by it — fine. The ability to challenge your pre-existing models is a critical skill regardless. It's how you keep growing as you get more senior instead of repeating what used to work. Models Are Approximations: The whole point of a model is to approximate the reality around us. That's their value and their limitation. When the underlying reality shifts this dramatically, holding tightly to an old approximation stops being wisdom and starts being a liability.
Brief SummaryBitcoin is trading around $75.5K this morning after sliding toward key $75K support.Ethereum is below $2,100 and remains weaker than Bitcoin on a relative basis.Bitcoin has fallen to 13th among global assets, with capital rotating toward AI, semiconductors, gold, and other non-crypto trades.Traders are moving defensively into stablecoins, with USDT and USDC dominance rising.SoFi launched SoFiUSD to nearly 15 million members, making it one of the first U.S. national banks to offer a stablecoin directly inside a banking app.A large holder reportedly sold about $1.29 billion worth of BlackRock's Bitcoin ETF in a dark-pool trade.IREN signed a $1.6 billion Dell agreement to expand AI cloud infrastructure, showing how crypto infrastructure companies are chasing AI demand.Coinbase's Base launched Base MCP, allowing AI tools like ChatGPT, Claude, and Cursor to interact with wallets and DeFi apps.Crypto PACs spent about $9 million in Texas and scored wins in both parties.The U.K. sanctioned HTX and Russia-linked crypto networks as part of a broader crackdown on sanctions evasion.Singapore charged former Hodlnaut CEO Zhu Juntao with six fraud counts tied to TerraUSD exposure claims.OpenZeppelin's CEO warned that AI coding agents have made DeFi increasingly unsafe because attackers can find vulnerabilities faster than defenders can patch them.XRP remains range-bound near $1.32 to $1.33 after a failed breakout.The stablecoin market remains above $300 billion and is becoming one of the biggest battlegrounds between banks, fintechs, crypto exchanges, and regulators. Hosted on Acast. See acast.com/privacy for more information.
My guest today is Darren Farber, and this is his second appearance on the show. Darren is a Managing Partner of Albion River, a defense-focused investment firm and he previously served as a special advisor to the Deputy Under Secretary of Defense. We recorded this conversation in the middle of the Iranian contingency, and we spent most of our time on what winning actually means in a theater like Iran. We discuss why magazine depth matters for the American industrial base, lessons from Ukraine, and what the rise of neo-prime defense companies will require from Congress. Please enjoy my second conversation with Darren Farber. For the full show notes, transcript, and links to mentioned content, check out the episode page here. ----- Become a Colossus member to get our quarterly print magazine and private audio experience, including exclusive profiles and early access to select episodes. Subscribe at colossus.com/subscribe. ----- Ramp's mission is to help companies manage their spend in a way that reduces expenses and frees up time for teams to work on more valuable projects. Go to ramp.com/invest to sign up for free and get a $250 welcome bonus. ----- Trusted by thousands of businesses, Vanta continuously monitors your security posture and streamlines audits so you can win enterprise deals and build customer trust without the traditional overhead. Invest Like the Best listeners get a special offer of $1,000 off Vanta when you go to vanta.com/invest. ----- WorkOS is the infrastructure B2B and AI-native companies use to sell to enterprise. It covers everything enterprise security requires: SSO, SCIM, RBAC, Audit Logs, AI governance, and more. Trusted by 2,000+ fast-growing companies, including OpenAI, Anthropic, Cursor, and Vercel. ----- Rogo is the AI platform for finance. They're building agents for Wall Street that are trained to understand how bankers and investors actually do work: from diligence and modeling, to turning analysis into deliverables. To learn more, visit rogo.ai/invest. ----- Ridgeline has built a complete, real-time, modern operating system for investment managers. It handles trading, portfolio management, compliance, customer reporting, and much more through an all-in-one real-time cloud platform. Visit ridgelineapps.com. ----- Editing and post-production work for this episode was provided by The Podcast Consultant (https://thepodcastconsultant.com). Timestamps: (00:00:00) Welcome to Invest Like The Best (00:02:29) Darren Farber Intro (00:02:59) Defining What Winning Looks Like in Iran (00:12:16) The Strait of Hormuz (00:13:27) Eisenhower vs. Taylor: Two Military Doctrines Explained (00:17:12) US Military Readiness vs. the Pentagon Era (00:20:05) America's Magazine Depth (00:21:36) China's Vulnerability (00:25:28) Trading Freedom for Security (00:27:31) Today's Industrial Base (00:29:30) Lessons from the Ukraine War (00:31:11) Impact of Iran Conflict on Taiwan Risk (00:33:02) What Neo-Prime Defense Companies Need to Succeed (00:39:53) Can We Win Without Full Regime Change in Iran? (00:45:46) AI's Impact on Modern Warfare
ESG StuffBP removes chairman Albert Manifold over governance issues 9The board said the decision was unanimous. In a statement, Amanda Blanc, BP's senior independent director, described the board as having been caught off guard by what it found: "The board has been surprised and disappointed to learn of governance oversight and conduct issues it deems unacceptable and has taken decisive action."The company did not elaborate on the specific nature of the concerns.Ian Tyler has been named interim chair, BP said, with the board set to begin a formal process to identify a permanent successor: "The Board and leadership team have deep conviction in the strategic direction we have laid out, and the company is moving at pace to deliver it."Manifold took up the chairmanship just last October. At last month's annual general meeting, just 81.8% of shareholders backed his electionAmong the most consequential decisions of Manifold's short tenure: pushing out former CEO Murray Auchincloss and overseeing the selection of Meg O'Neill to succeed him — a hire that marked the first time BP had recruited an external CEO and the first time a woman had led one of the oil industry's largest players.Tulsi Gabbard Exit Marks Fourth Woman to Leave Trump Cabinet 0Apology TourBank boss sorry after describing workers as 'lower value human capital' 7Standard Chartered CEO Bill Winters triggered a massive PR firestorm by describing the bank's plan to replace back-office staff with automation as replacing "lower-value human capital" with financial investmentStandard Chartered is cutting roughly 7,800 jobs—representing about 15% of its global back-office corporate support roles—over the next four years to make room for AIAfter internal anger and blistering public criticism, Winters posted a formal apology for his "choice of words." However, he initially fueled the fire by attaching the full interview transcript to justify his broader context, drawing further criticism for being defensiveIn his first attempt to quiet the storm, Winters leaned heavily into the corporate strategy rather than apologizing for the specific phrasing: "I said that lower-value roles are more vulnerable to automation, and that we have a responsibility to help colleagues move into higher-value roles. That is what a responsible employer should do. We will continue to speak honestly about the impact of technological change, and we will continue to act responsibly in helping our people to adapt and succeed."After a barrage of negative comments on his first post, Winters returned to LinkedIn later that day to offer an explicit apology for his phrasing: "I have received a lot of support for the messages in my previous post but still get questions about my choice of words, which I know has caused upset to some colleagues. For that I am sorry.""I think the transcript makes it clear that I value our colleagues – all of them – most highly and that we are totally committed to helping them to cope with the accelerating pace of change in our industry."JPMorgan's Jamie Dimon says bank chief's viral AI comment was 'inartful' Dimon downplayed the viral backlash against Standard Chartered CEO Bill Winters—who drew fire for saying his bank would replace "lower-value human capital" with technology—calling it an "inartful" slip-of-the-tongue from a friend.Neopbabies and Dropout babiesJames Murdoch to acquire New York Magazine and Vox Media Podcast Network -1Bolt CEO says he let go of his entire HR team for creating problems that didn't exist: ‘Those problems disappeared when I let them go' 6Bolt CEO Ryan Breslow justified firing his entire Human Resources department by claiming they actively manufactured internal frictionThe aggressive purge follows a brutal 97% collapse in Bolt's valuation—crashing from an $11 billion peak in 2022 down to $300 millionTraditional HR has been entirely swapped for a skeletal "people operations" team, shifting the focus away from employee complaints and internal processes toward basic compliance training and empowering managers to make split-second decisionsAlongside gutting HR, Breslow rolled back employee-friendly benefits like four-day workweeks and unlimited PTO, claiming a culture of complacency had taken over and that 99% of his legacy workforce was simply unwilling to work hardRyan dropped out of Stanford in 2014 to launch BoltThe Middle School Boy Man Babies Rule the WorldMan Drives Cybertruck Into Lake to Test Elon Musk's “Boat” Claims, and It Went About as Well as You'd Guess -10"The passengers abandoned the vehicle and the driver was arrested."Tesla CEO Elon Musk:randomly tweeted that the vehicle would function as a rudimentary flotation device.“It will even float for a while.”“[The vehicle would be able to] traverse at least 100m [330 feet] of water as a boat.”“Cybertruck will be waterproof enough to serve briefly as a boat, so it can cross rivers, lakes and even seas that aren't too choppy.”Jeff Bezos urges US government to stop taxing 50% of America — and claims doubling his taxes won't help ‘that teacher in Queens' 400Jeff Bezos backs Mamdani's tax on luxury second homes, but says Ken Griffin isn't the villainJeff Bezos on Zohran Mamdani's big mistake: ‘When you don't know how to solve a problem, create a villain, blame them'Jeff Bezos says there is ‘no truth' to the ‘buy borrow die' tax strategyBillionaires Openly Use It: Oracle co-founder Larry Ellison has historically pledged over $30 billion worth of his Oracle stock as collateral for personal bank loans. Elon Musk has similarly pledged tens of billions of dollars in Tesla shares to secure lines of credit over the yearsHe said he was "skeptical that that's a true loophole," but added, "If it is, and we can fix it, then we should. I don't think such a loophole should exist."Jeff Bezos Praises Trump's Second Term as ‘More Mature' Jeff Bezos Says AI Will 'Elevate' Workers — Despite Amazon's 30,000 Job Cuts Amid $100 Billion AI PushElon Musk compares his company's work to that of Jesus 0In an interview on Monday, the billionaire said his Neuralink brain-implant company is progressing in its development of ‘Jesus-like technologies'Although brain-computer interface (BCI) as a concept has been around since at least the 1970s, the push to commercialize the technology is more recent. According to data from market-intelligence firm Tracxn, more than 130 BCI startups have been launched since 2016.Why Is Mark Zuckerberg Taunting His Employees Before Firing Them? 20Back in April, Meta announced it was laying off 10 percent of its workforce, or around some 7,800 workers. Unlike traditional layoffs, which are enacted relatively quickly, Meta gave its employees a nearly month-long warning period without announcing who exactly would be headed for the unemployment line.In newly leaked audio from an all-hands meeting at Meta, released by More Perfect Union, the Meta CEO seems to actually be taunting the thousands of workers who were about to be let go by pointing to how the company was harvesting employee data to train its in-house AI models ahead of the massive layoffs.“So we're in a phase where basically the AI models learn from heaving real, from watching really smart people do things. And if you're trying to get it to be able to be able to do certain capabilities, having [AI] be able to observe really smart people doing those things is, is very important.”Going on, Zuckerberg explained that it was better to train AI on soon-to-be-former Meta employees, rather than “contract companies.”“In general, the average intelligence of the people who are at this company is significantly higher than the average set of people that you can get to do tasks if you're working through… contractors,” Zuckerberg stammered. “So if we're trying to teach the models coding, for example, then having people internally, um, build tools that, or, or solve tasks that, um, that help teach the model how to code, we think is going to dramatically increase our models coding ability faster than what others in the industry have the capability to do.”Intuit to Cut 17% of Staff, Invest in ‘Big Bets' 3The restructuring cost is estimated at about $300 million to $340 millionAbout 3,100 employees: and invest the savings in “big bets” as it makes artificial intelligence a centerpiece of its business.Woke WarsTexas AG Sues ISS Over ESG Considerations 0Texas AG Ken Paxton (in a senate race) is suing ISS for allegedly “misleading” customers by pushing “radical political agendas” through its proxy adviceNotably, ISS has attempted to obstruct ExxonMobil's planned reincorporation from New Jersey to Texas“ISS has enormous influence over how billions of dollars are invested and managed across this country, and they have abused that influence in order to push woke ideology”Iowa AG Brenna Bird sues ISS, says advice risks retirement savingsIowa Attorney General Brenna Bird is suing the world's largest proxy-advice firm for abusing its influence and threatening Iowans' retirement savings by "lying" to investors.Stakeholders Rule!Wells Fargo must pay $100M to help homebuyers after discrimination lawsuit — 51 cities are eligible 7The settlement, which was recently approved by a federal judge in California, comes after four years of legal disputes involving Wells Fargo shareholders, former employees and job applicants who accused the bank of systemic problems in both lending and hiring practices.While Wells Fargo denied wrongdoing, the company agreed to the deal to avoid prolonged litigation and mounting legal costs.The case centered on allegations that Wells Fargo's board failed to maintain adequate oversight of the bank's mortgage lending operations, exposing the company to regulatory scrutiny and accusations of discriminatory practices.According to reporting from Realtor.com, plaintiffs accused the bank of “widespread and systematic discrimination in lending” and cited concerns over lending algorithms and refinancing approval patterns.The lawsuit stated that Wells Fargo was allegedly the only major lender in 2020 to reject more refinancing applications from Black homeowners than it approved.Airbus, Air France Hit With Manslaughter Charges Over Pilot Training Failures in Deadly 2009 Flight 447 Crash 1A Paris appeals court delivered a dramatic verdict in one of the longest-running and most complex legal sagas in aviation history. The court overturned a 2023 acquittal and found both Airbus and Air France guilty of corporate manslaughter for the tragic 2009 crash of Flight AF447.The ruling marks a massive victory for the victims' families after a 17-year legal battle. A lower court had previously cleared the European planemaker and the French airline in 2023, ruling that while errors were made, a direct causal link to the crash couldn't be proven. The appeals court completely rejected that logic, declaring the companies "solely and entirely responsible" for the disaster.Ride-Share Drivers in Massachusetts Formally Unionize 100The App Drivers Union said it was the first organization in the country to be formally certified to represent drivers for apps such as Uber and Lyft.In a news release, the organization, the App Drivers Union, said it would represent nearly 70,000 workers in Massachusetts who now have the power to collectively bargain.MATTA very special “who do we blame for SpaceX IPO governance” gameFirst, some S-1 highlights:“Starlink internet is what's being used to pay for humanity getting to Mars.” - MuskTranslation: We don't care much about Starlink, it's just paying our AI billsHe's not kidding: $3.2bn revenue for Starlink, net income of $1.2m$0.6bn revenue for rocket ship, net income of -$0.6bn$0.8bn revenue for AI, net income of -$2.5bnThis isn't a space company - it's classic Musk - you buy the vision (“To build the systems and technologies necessary to make life multiplanetary, to understand the true nature of the universe, and to extend the light of consciousness to the stars.”), but what you're really buying is an internet company that spends all its money on AI and does some rockets on the sideLet someone else invent the car (Tesla) and make them sexy with “big visions” for “humanity”Let someone else invent the rockets, build new ones using someone else's moneyLet someone else invent the satellites, put a whole bunch in space (and buy more satellites from someone else)Musk initially took the role of “Chief Engineer”, but every engineering task seems to have been the other employees - he supplied the moneyShoehorned AI into space exploration because…?Grok is designed as a truth-seeking AI model, built on our founder Elon Musk's mission to enable humanity to understand the universe. We believe that accomplishing this mission requires a truth-seeking approach to AI. We define truth seeking as the active, relentless pursuit of what is objectively true about reality, and grounded in evidence, logic, empirical data, and first principles thinking.AI's ability to revolutionize human potential is directly dependent on meeting exponentially increasing resource demands.We now must go to space to get more resources for AI so we can get to spaceNow the governance who do you blame gameMusk will get:85% voting power (dual class, he owns 94% of Class B 10 vote shares and 12% of Class A shares)The ability to nominate and vote exclusively on >50% of the boardA board which currently includes..TWO execs - Gwynne Shotwell (President) and Musk (three titles)Tesla mafia: Ira Ehreinpreis, Tesla board sycophant, director at the Boring Company and xAI, and longtime Musk hanger on, added Feb 2026Antonio Gracias, ex Tesla director who was explicitly called out in the Tornetta decision as corrupted, cross party transactions with Musk, on boards of Neuralink and Boring Company, added Oct 2010TWO VC bros from DFJ - Randy Glein (SpaceX board observer for 16 years, directors since Feb 2026) and Steve Jurvestson (former Tesla director, director since March 2009) who was ousted from the VC firm with his name on it for sexual harassmentPaypal mafia:Luke Nosek, co founder of PayPal, one of the founders of Founders Fund with Thiel and Ken Howery, invested in DeepMind, director since July 2008Donald Harrison - managed Google purchase of DeepMind, relationship with Nosek, director since Feb 2015Director relationship tenures to Musk: Shotwell: 24 yearsEhreinpreis: 21 yearsGracias: 21 yearsJurvetson: 17 yearsGlein: 16 yearsNosek: 26 yearsHarrison: 11 years (+1 if Nosek/Deepmind connection counts)Texas jurisdiction exclusively (judge shopped) - 3% to sue them, mandatory arbitration, anti-takeover statutes, special meetings ONLY CALLED BY MUSK (no one less than 50% of stock can call a meeting or vote)No written consent - no prior noticeAdvance notice bylaws for the zero shareholder proposals allowedFull omission of board liability - including a provision that automatically allows whatever the conflicts of interest they want with directorsWHO (WHEN) DO YOU BLAME?The US GovernmentDepartment of Energy - in 2010, the DoE gave Tesla a $465m loan, which basically paid for the Model S and helped it buy a factory 6 months before it went public - Musk has said Tesla would not have survived without the loanNevada - in 2014, Nevada gave Musk $1.3bn to build a factory, the most everNASA - spent more than $15bn over years on SpaceX and programs with themThe IRS/Congress - the EV tax credit for $7,500 single handedly pushed Tesla from losing money in 2020 to making money (they effectively got $1.6bn from the US government in 2020), and showing its first profit, which sparked the memefest during COVID and made Musk the richest man on earth - Musk then went on and called for an end to the tax credit since his “competitors” needed it more than Tesla. Tesla made ~$11bn from tax credits aloneThe DoD - started paying SpaceX in 2003 for concept work - and even when the rockets didn't work, the DoD and NASA awarded the company massive contracts anywayJeff Bezos said in 2016 that, “Elon's real superpower is getting government money.”FOMOSpaceX LOSES MONEY - it does not make moneyIf it were a satellite internet company - and NOT THE FIRST - the first was HughesNet in 1996, and Viasat offered it in 2012 - it would make money ($1.2m in income!)Instead, investors are valuing SpaceX as THE LARGEST IPO IN THE HISTORY OF EVER despite the fact that they are burning money on AI, and arguably the worst AIIncluding spending the most on R&D, marketing, and acquisition of Cursor to make up for the fact that Grok suckedIn exchange for FOMO, investors have ENTIRELY GIVEN UP THEIR RIGHTSIt is 100% a private companyTornettaIf Tornetta hadn't sued for Musk's pay, would SpaceX be structured this way?The banks underwriting the dealWho AGREED TO BUY GROK as a term of getting the underwriting, because everyone bends the knee to moneyThe boardI guess
Google dropped like 197 new AI features this week.
The AI Breakdown: Daily Artificial Intelligence News and Discussions
A week of AI news added up to something bigger than any single story: Anthropic's path to profitability, OpenAI's math breakthrough, Google pushing AI deeper into Search and Docs, Cursor's cheaper coding model, SpaceX becoming an AI compute player, Andrej Karpathy joining Anthropic, and the political fight over AI policy all pointed in the same direction. AI acceleration is showing up across business models, model capabilities, consumer products, compute infrastructure, and regulation at the same time.Enterprise Claw Cohort 3 Registration: https://enterpriseclaw.ai/Brought to you by:KPMG – Agentic AI is powering a potential $3 trillion productivity shift, and KPMG's new paper, Agentic AI Untangled, gives leaders a clear framework to decide whether to build, buy, or borrow—download it at www.kpmg.us/NavigateGranola - The AI notepad for people in back-to-back meetings. 100% off your first 3 months with code AIDAILY at http://granola.ai/aidailyScrunch - The AI customer experience platform - https://scrunch.com/Mercury - Modern banking for business and now personal accounts. Learn more at https://mercury.com/personal-bankingZenflow Work - Agents for knowledge work - https://zenflow.free/Drata - The agentic trust management platform - https://drata.com/Blitzy - Want to accelerate enterprise software development velocity by 5x? https://blitzy.com/AssemblyAI - The best way to build Voice AI apps - https://www.assemblyai.com/briefRobots & Pencils - Cloud-native AI solutions that power results https://robotsandpencils.com/The AI Daily Brief helps you understand the most important news and discussions in AI. Subscribe to the podcast version of The AI Daily Brief wherever you listen: https://pod.link/1680633614Our Newsletter is BACK: https://aidailybrief.beehiiv.com/Interested in sponsoring the show? sponsors@aidailybrief.ai
My guest today is Gavin Baker, founding partner and CIO of Atreides Management, and this is our sixth conversation. The central theme is watts and wafers, the two physical constraints that in Gavin's view will dictate the next phase of AI. On power, he thinks the near-term shortage starts to ease in 2027 and 2028 as new sources of energy come online, and that orbital compute solves it in the long term. On wafers, he explains what is different this time from the dotcom bubble and why TSMC's capacity decisions may be the single most important variable to watch. We also discuss Elon's Terrafab, the disaggregation of GPUs, the role of new chip companies, and whether the economic value of AI will keep accruing to frontier models. For the full show notes, transcript, and links to mentioned content, check out the episode page here. ----- Become a Colossus member to get our quarterly print magazine and private audio experience, including exclusive profiles and early access to select episodes. Subscribe at colossus.com/subscribe. ----- Ramp's mission is to help companies manage their spend in a way that reduces expenses and frees up time for teams to work on more valuable projects. Go to ramp.com/invest to sign up for free and get a $250 welcome bonus. ----- Trusted by thousands of businesses, Vanta continuously monitors your security posture and streamlines audits so you can win enterprise deals and build customer trust without the traditional overhead. Invest Like the Best listeners get a special offer of $1,000 off Vanta when you go to vanta.com/invest. ----- WorkOS is the infrastructure B2B and AI-native companies use to sell to enterprise. It covers everything enterprise security requires: SSO, SCIM, RBAC, Audit Logs, AI governance, and more. Trusted by 2,000+ fast-growing companies, including OpenAI, Anthropic, Cursor, and Vercel. ----- Rogo is the AI platform for finance. They're building agents for Wall Street that are trained to understand how bankers and investors actually do work: from diligence and modeling, to turning analysis into deliverables. To learn more, visit rogo.ai/invest. ----- Ridgeline has built a complete, real-time, modern operating system for investment managers. It handles trading, portfolio management, compliance, customer reporting, and much more through an all-in-one real-time cloud platform. Visit ridgelineapps.com. ----- Editing and post-production work for this episode was provided by The Podcast Consultant (https://thepodcastconsultant.com). Timestamps: (00:00:00) Welcome to Invest Like The Best (00:02:29) Gavin Baker Intro (00:03:32) Anthropic's Record ARR Growth (00:11:49) Should OpenAI and Anthropic Raise at a Much Higher Valuation? (00:13:23) How Elon Preserves Investor Trust (00:14:00) Watts & Wafers (00:15:45) Data Centers in Space Explained (00:20:51) Orbital Compute's Impact on Terrestrial Data Centers (00:26:24) TSMC Supply Discipline & Bubble Risk (00:30:50) Demand for Frontier Tokens & The Bitter Lesson (00:35:33) Continual Learning & Memory (00:40:01) New Chip Companies & Startups (00:42:49) Prefill vs. Decode Disaggregation (00:48:40) AI-Native Founders: Different & Hard (00:51:27) Token Path & Application Layer (00:56:13) How Gavin Uses AI in Atreides (01:00:06) Signs of a Diversity Breakdown (01:05:42) Google, Meta, Amazon, Microsoft (01:11:42) Broader Knock-On Effects of AI
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