Podcasts about Andreessen Horowitz

Justine Moore, partner at Andreessen Horowitz, joins New Economies to explore the rapid evolution of generative media and why AI-native content is reaching an inflection point. They discuss the rise of AI micro-dramas, how creators are building entirely new forms of entertainment, and why the biggest opportunities may lie not in replacing Hollywood—but in expanding who gets to create. They also cover the future of creator tools, AI agents for individuals, generative video, AI "slop," the economics of AI-native studios, and where founders should be building next as consumer AI enters a new phase.   Resources: Follow Justine Moore on X: https://x.com/venturetwins Watch the episode on YouTube: https://www.neweconomies.co/p/justine -moore-andreessen-horowitz Listen to more from New Economies: https://www.neweconomies.co/ Stay Updated:Find a16z on YouTube: YouTubeFind a16z on XFind a16z on LinkedInListen to the a16z Show on SpotifyListen to the a16z Show on Apple PodcastsFollow our host: https://twitter.com/eriktorenberg Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

In this conversation, Anne Neuberger, former U.S. Deputy National Security Advisor for Cyber and Emerging Technology, currently General Partner and Head of Global Affairs at venture capital powerhouse Andreessen Horowitz, joins What's Your Number hosts Yael Wissner Levy and Yonatan Adiri to discuss how AI, cyber warfare, drones, and defense tech are reshaping geopolitics. As technology, commercial investment, and national security become increasingly intertwined, what will determine which countries, and which companies, thrive in this new era and how core values determine their use. ___ Subscribe here to: What's Your Number? ___ Call Me Back is made possible by our subscribers. If these conversations are where you turn to understand Israel and the Jewish world, consider joining them. It's what keeps this show going. Become a Subscriber - Inside Call me Back ____ In this episode: - The new ingredients of national power - Why supply chains became a national security issue - The AI debate between Washington and Silicon Valley - What Israel and Ukraine teach the world about defense tech - Is the U.S.-Israel alliance changing? - Can Israel withstand growing divestment pressure? - The next breakthroughs in AI and cyber - Personal lessons learned from history from the Holocaust to Entebbe  More Ark Media: Want to join Ark Media? Check out our careers page for new openings. Explore Israel Votes Listen to Ark News Daily Listen to For Heaven's Sake Newsletters | Ark Media | Amit Segal | Nadav Eyal Instagram | Ark Media | Dan X | Dan Dan Senor & Saul Singer's book, The Genius of Israel Get in touch Credits: Ilan Benatar, Ryan Lohr, Beth Pearlman, Brittany Cohen, Ava Weiner, Martin Huergo, Mariangeles Burgos, and Yuval Semo

In this conversation, Anne Neuberger, former U.S. Deputy National Security Advisor for Cyber and Emerging Technology, currently General Partner and Head of Global Affairs at venture capital powerhouse Andreessen Horowitz, joins What's Your Number hosts Yael Wissner Levy and Yonatan Adiri to discuss how AI, cyber warfare, drones, and defense tech are reshaping geopolitics. As […]

This week on Sinica, a rare treat: an in-person recording from Beijing with two dear friends who happen to be two of the very best in the business on technology and China — Samm Sacks and Paul Triolo, fresh off the exhibition floor of the World Artificial Intelligence Conference in Shanghai. We dig into Xi Jinping's first in-person WAIC appearance and his most extensive statement on AI to date, the launch of the World AI Cooperation Organization, Moonshot's release of Kimi K3, the Trump administration's reported push to shut Chinese open-weight models out of the U.S. market, the coming age of agents, the untranslatable problem of ānquán, and what to expect from the first U.S.-China AI dialogue in September.8:51 – The view from the floor: heat, humidity, robot boxing grandmas, WeChat-gated free water, and the "AI+" vibe — why WAIC 2026 felt less like an AI conference than a sector-by-sector snapshot of China's entire economy being supercharged with AI, with attendance swelling to some 200,000 tickets16:32 – Why Xi showed up: what the leader's first in-person WAIC appearance and his most extensive AI statement to date signal, and why domestic drivers matter as much as geopolitics18:01 – Chapter and verse: which phrases from the speech will be put to work in the system — "secure and orderly development" and the governance of agents, and Xi's strikingly extensive language on AI safety after China was frozen out of the Paris process22:26 – The ānquán problem: one word meaning both "safety" and "security," the three buckets of AI risk, and how China's safety community has moved from bias and deepfakes toward CBRN and loss-of-control concerns — Black Mirror versus Star Trek28:14 – Shanghai's baby: how WAIC's ownership structure differs from the CAC-run World Internet Conference in Wuzhen, Chen Jining's very visible host duties, and whether the center of gravity in AI policy is shifting to the Yangtze River Delta30:05 – WAICO: what the new World AI Cooperation Organization with its 29 founding members is actually for, Xi's concrete deliverables for the Global South — 5,000 AI training slots, regional cooperation centers, the MAZU early-warning system — and healthy skepticism about follow-through34:39 – Kimi K3: what's technically significant in Moonshot's big new model, why it's the fourth arguably frontier-class Chinese release in a single month, the two-way traffic in distillation accusations, and what it all says about the state of the frontier gap four years into export controls40:33 – Washington reacts: the reported menu of options for shutting Chinese open-weight models out of the U.S. — entity listings, a draft executive order, supply-chain security authorities — and why none of the tools actually fit the problem49:36 – Strange bedfellows: David Sacks versus the "closed lab duopoly," the FUD strategy, why some 80% of Andreessen Horowitz portfolio companies reportedly run on Chinese open models, and how gating U.S. frontier models while Chinese weights flow freely supercharges the AI sovereignty argument worldwide54:55 – The model is infrastructure, the agent is the product: the ByteDance–ZTE agentic phone, the CAC's new initiative on agent trust and interoperability, and why agents fused into operating systems upend both super-app walled gardens and China's data protection regime1:02:27 – An exegesis of kěkòng: the many meanings of "controllable," the long history of ānquán kěkòng in Chinese tech policy, and the unanswered question of who — CAC, NDRC, or somebody new — actually owns AI safety in either system1:08:54 – The road to September: what to expect from the first U.S.-China AI dialogue, why Mythos tops the Chinese grievance list, the securitization feedback loop that starves trust-and-safety advocates of resources on both sides, and why recursive self-improvement makes this feel like a last, best chancePaying It ForwardPaul nominates Tony Peng, whose Substack RecodeChinaAI offers sharp, well-written analysis of the application side of China's AI industry — part of an impressive new generation of independent China tech writers. Samm gives a shout-out to Professor Zhu Yue of Tongji University Law School, published in Science and doing pioneering work at the intersection of disability law and AI law.RecommendationsSamm: The Land and Its People by David Sedaris — laugh-out-loud funny, especially the "Enough is Enough" chapter; Transcription by Ben Lerner, a perfect small novel about fathers, sons, memory, and technology as enabler or disabler of connection; and Didion and Babitz, on Joan Didion and Eve Babitz and the 1970s California rock scene.Paul: The Party's Interests Come First by Joseph Torigian — dense but beautifully written, and essential for understanding the current Chinese leadership.Kaiser: A fiction-only summer! Stoner by John Williams, a small life told most grandly in some of the most beautiful sentence-level writing anywhere; Gilead by Marilynne Robinson, an epistolary novel dense with distilled wisdom from a dying Iowa minister; and Wang Xiaobo's The Golden Age (黄金时代) in Yan Yan's excellent new translation — bawdy, hilarious, and super Beijing-y despite its Cultural Revolution setting.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

A.M. Edition for July 22. In a landmark agreement, President Trump okays a nuclear deal with Saudi Arabia. WSJ's Laurence Norman explains why the commercial deal has nuclear proliferation experts concerned and could shift the balance of power in the Middle East. Plus, utilities pledge to limit increases in electricity bills caused by the data-center buildout. And in a real-life cybersecurity nightmare, OpenAI admits that a pair of its models escaped the lab and hacked into another company. Luke Vargas hosts. Sign up for the WSJ's free What's News newsletter. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Story of the Week (DR):‘We faltered': IBM stock collapses after a grave warning about AI Customers are Prioritizing AI Hardware: While IBM mostly sells software nowadays, businesses are currently cutting back on their software budgets. Instead, they are rushing to spend their money on physical computer parts (like chips and servers) needed to build new Artificial Intelligence systems.Board skills/tenureFormer Intel CEO says the chipmaker went off the rails ‘when it started to be run by business people'The inside story of IBM's shocking profit warningXbox CEO Joins Fed AI Jobs Task Force Days After Announcing 3,200 LayoffsXbox CEO Asha Sharma, who previously worked in Microsoft's Core AI group before taking over Xbox, joins Marc Andreessen, co-founder and general partner at Andreessen Horowitz, and Charles I. Jones, a Stanford University economics professor currently on leave at Anthropic.Productivity and Jobs task force, which will study the economic impact of new general-purpose technologies, including AI, as part of the central bank's approach to monetary policy.Marc Andreessen Says AI Is 'Already a Better Doctor Than 99.99% of Human Doctors'Jamie Dimon says he understands why people have grown 'anti-rich'Gallup CEO says colonizing Mars may be closer than fixing today's ‘broken' workplace—where disengagement levels are as high as 2020Elon Musk Says His Goal Is for SpaceX to Be Worth More than the Entire EarthThe U.S. Added 441,000 Millionaires Last Year, While the Typical American Got 20% PoorerJames Murdoch may have reaped as much as $7.5 billion from his pre-IPO investment in Elon Musk's SpaceXPalantir CEO Alex Karp Warns AI Could Become America's Biggest Driver of Wealth Inequality"You now have a revolution where, you know, I could become 20 times wealthier than I am now," he added. Karp said AI is creating a "complete decoupling" between ordinary economic gains and a small group of people accumulating "unimaginable wealth."The AI Backlash Has Tech Executives Fearing for Their Lives MMPeter Thiel and other tech billionaires are publicly shielding their children from the products that made them richAnthropic's New AI Ad Is So Disturbing, OpenAI CEO Sam Altman Thought It Was SatireMeta Oversight Board study: AI chatbots may be the most perfect propaganda machine ever inventedA Majority of Americans Now Support Seizing Wealth From AI IndustryGoodliest of the Week (MM/DR):DR: New York bans data center construction for a year, rattling AI industry AND New York becomes first U.S. state to impose AI data center banMM: FREE FLOAT! MMMicrosoft's emissions rose 25% last year. Experts say they'll surge even more dramatically in the years aheadWe said FIVE YEARS AGO that MSFT board was one of the worst at managing carbon despite setting a net negative target at the timeWe were correctAssholiest of the Week (MM):It's not us, it's youxAI sued a Grok user for allegedly generating deepfakes of child sexual abuseIn the ultimate in tech bro manbaby id, Musk is blaming the USERS for his failure to stop child sex abuseLike a gun company suing a gun owner for using the gun used in murder - gun terms of service!Dimon urges calm over fear about AI's impact on jobs: 'Stop being breathless over it'AI isn't the problem, your breathless fear isRobotaxis Are Turning Passengers Into Horrible and Entitled Menaces to SocietyIt's not the service, it's the user… Meta CTO Says He'd Like to Sue Leakers, Then Audio From That Same Meeting LeaksIt's not what I said that's the problem, it's that you told someonePeter Thiel and other tech billionaires are publicly shielding their children from the products that made them richBut YOUR children should use them, obviouslyIt's the USER's fault now… unless, of course, it's a school - in which case the SCHOOL is to blame:84% of students use AI for homework. Only 3 in 10 schools have rules for itAccessWhite House teleprompter operator investigated over alleged trades on Trump speechesTrump Media to Sell Faster Access to President's Social PostsOpenAI Strikes Bold Deal With Kalshi to Mix Together the Most Hated Technologies in Existence: AI and Prediction MarketsDOJ Defends Musk's Unpermitted Gas Turbines, Saying Shutting Down Grok Threatens National SecurityElon Musk's $1 million offers to voters in Wisconsin election were probably illegal bribes, bipartisan panel rulesParamount Shareholder Sues Ellisons, Board For Alleged Side Deal, Promises To Donald TrumpMichael Dell has nailed his relationship with Donald Trump, and it's paying offMeritocracyPete Hegseth Announces Military Will Test Service Members' Testosterone Levels and Offer Hormone TherapyEveryone is a trans man!Is AI Rejecting Your CV Because of Your Age or Race? Landmark Lawsuit Could Reshape RecruitmentFor Black women hit by anti-DEI backlash, this election is personalAustralia's highest paid CEO makes 500 times the average salaryThe American E.V. Has Been Crushed. Will It Take the U.S. Auto Industry With It?WE WANT THE CARS. Just give us the Chinese ones nowJim Cramer on Meta: “Zuckerberg's Not a Bozo, You Can Quote Me on That”Paul Fucking Atkins and The War on John Cheveddan DR“Regardless of the fate of Rule 14a-8 next season and beyond, I implore all who have a role in the shareholder proposal process to not let it be weaponized by those who represent fringe interests. Annual meetings are not vehicles for political or social debates that have little or no bearing on investors' financial returns.”“This past season, one—yes, one—individual was the sole or lead proponent for approximately 41 percent of the shareholder proposals that were voted upon.[20] Of this individual's proposals, only eight percent received majority support.[21] Simply put, when a single shareholder can seize annual meetings to present scores of proposals on issues that are not generally supported by other shareholders, the system is woefully ineffective and in desperate need of reformation.”That individual is John CheveddanAtkins neglected to mention the rise of anti-ESG filers as a group, and their average of

In this episode of the Milk Road Show, we sit down with Colin McCune, Head of Government Affairs at Andreessen Horowitz (a16z), to break down the latest developments in Washington, why the Clarity Act matters for Bitcoin, Ethereum, stablecoins, and crypto startups, and how U.S. regulation could shape the next phase of the bull market.~~~~~⁠⁠⁠⁠⁠⁠

July 15, 2026: Andreessen Horowitz's argues that AI is not simply replacing workers, it is turning every worker into a manager of agents. Then I get into Bank of America's claim that AI is already showing ROI in its earnings, and why I'm skeptical of how much of that efficiency gain can really be attributed to AI. Finally, I look at OpenAI's first physical device, a screen-free AI companion reportedly designed to feel alive, and why that matters for the future of work.

- Convey, founded by Rohan Chopra, builds AI "teammates" that automate repetitive and inhuman business tasks, allowing employees to focus on higher-value, strategic work. - The company has enabled over 1.1 million hours of enterprise work to be handled by AI, serving a range of customers from mid-market to large enterprises, including operationally intensive businesses. - Convey's approach emphasizes giving AI agents distinct identities within organizations, enabling collaboration, auditability, and clear ownership of tasks, rather than just acting as personal assistants. - The company recently raised $38 million in Series A funding led by Andreessen Horowitz (a16z), with continued support from Khosla Ventures and Pear VC, bringing total funding to $42.5 million. - Rohan attributes Convey's success to a relentless focus on solving real customer problems, prioritizing durable revenue, and maintaining strong alignment with investors who share their long-term vision.

Flock cameras are all the new rage. We have finally forgotten or lost interest in smart meters, Stingray, Dirtbox, PRISM, etc. Flock Safety systems are being installed without consultation of the public, and sometimes reinstalled without permission from local authorities. Sometimes they're randomly placed. These cameras and sensors are supposed to keep communities safer, but what they ultimately do, even according to law-enforcement, is create a dragnet surveillance grid in local communities. They've already been used to fine and jail people for crimes that they did not commit. Perhaps the worst part, but not unlike Axonius, Palantir, Waze, ShieldAI, and so forth, is that the two primary investors are the Peter Thiel group and Andreessen Horowitz, a company directly rooted in Israel. Furthermore, resistance to these cameras being installed is being labeled terrorism. It's also not out of the realm of possibility that many of the cameras that have been cut down, have been removed by law-enforcement, community officials, or the company itself in order to paint the technology as a victim of ignorant and violent citizens.*The is the FREE archive, which includes advertisements. If you want an ad-free experience, subscribe below.

Jesse Zhang is the Co-founder & CEO of Decagon, one of the world's fastest-growing AI companies. In just two and a half years, Decagon has grown to around 500 employees, raised approximately US$500 million from investors including Andreessen Horowitz, Accel and Bain Capital Ventures, and today powers AI customer service for many of the world's largest airlines, banks, retailers and technology companies. In this rare in-depth conversation, Jesse reflects on growing up in Boulder, Colorado, an intensely disciplined childhood shaped by maths competitions and Chinese immigrant parents, graduating from Harvard in just three years, and why his first startup taught him far more than his second. Vidit and Jesse explore how Decagon found its billion-dollar opportunity by interviewing hundreds of customers, why they reached US$1 million in ARR with just two people, how to hire exceptional talent, why speed has become the ultimate competitive advantage in AI, building one of Silicon Valley's leading AI companies, and what it really takes to scale from founder mode to leading 500 people. Please enjoy exploring your curiosity. ________ 00:00:00 Who is Jesse Zhang? 02:10 The Huawei story that shaped Decagon 07:20 Why Maths Olympiads create successful founders 13:05 Leaving Harvard a year early 17:40 The startup that nearly broke him 23:30 Losing two co-founders 29:15 Why he started again 34:20 The question that found Decagon 40:10 Hitting $1M ARR with just 3 employees early on 45:25 Learning enterprise sales from scratch 50:35 Building AI agents before the hype 56:15 Why Decagon is beating bigger rivals 1:02:20 Hiring only "serious people" 1:08:15 Letting go as CEO 1:13:10 Marriage, sleep & startup obsession 1:17:05 What he's really building ________ Get in touch with us via email at contact@curiositycentre.com Join our stable of commercial partners including the Australian Government, Google, KPMG, Vanta, Allens, Macquarie Capital, City of Sydney and more.  Show notes and more episodes here Follow us on LinkedIn, Twitter and Instagram Get in touch with our Founder and Host, Vidit Agarwal directly here Contact us via our website ________ The High Flyers Podcast features in-depth interviews with the world's most influential figures in business, tech, finance, government and sport. Launched in 2020, it has ranked in the global top ten for past three years, with listeners in 27 countries and over 200+ episodes released, and featured in Forbes, Daily Telegraph, and at SXSW. Our guests include -- Malcolm Turnbull (Prime Minister of Australia), Jason Collins (Head of BlackRock, Asia Pacific), Brad Banducci (CEO, Woolworths), Michael Schneider (CEO, Bunnings), David Eckstein (CFO, Legora), Jesse Zhang (CEO, Decagon), Elena Verna (Head of Growth, Lovable), David Haber (a16z Partner), Jodie Auster (Uber's Global Head of Travel), Rob Giglio (CCO, Canva), Jean-Michel Limieux (CTO, Shopify and Atlassian), Stevie Case (CRO, Vanta), Cristina Cordova (COO, Linear), Gautam Chari (Head of Capital Commitments, Bank of America), John Haddock (CBO, Harvey), Mark Suster (Partner, Upfront Ventures), Niki Scevak (Partner, Blackbird), Craig Tiley (CEO, USA Tennis), Jeanne DeWitt Grosser (COO, Vercel), Paul Bassat (Partner, Square Peg), Bowen Pan (Creator, Facebook Marketplace), Peter Varghese (Secretary of Foreign Affairs, Australian Government), Sam Sicilia (CIO, Hostplus), Jack Zhang (CEO, Airwallex), Tim Doyle (CEO, Eucalyptus), Sukhinder Singh Cassidy (CEO, Xero), Sanjeev Gandhi (CEO, Orica) and Philip Green (Australia's Ambassador/High Commissioner to India), 

Justin Mares built Kettle & Fire into one of the best known bone broth brands in the country. Then he walked away from it to go after something a lot less exciting on paper: the red tape standing between Americans and the preventive health products that actually keep them well. His new company, Truemed, gives people a simple way to use money they already have set aside for health on things like workouts, better food, and sleep, instead of letting it sit unused or only get spent once they're already sick.Alisa Cohn, executive coach and author of From Start-up to Grown-up, sits down with Justin to talk about why he left a thriving consumer brand to go build something in one of the most regulated, paperwork heavy industries there is. You'll hear how he tests ideas before he feels ready, how he hires for real mission alignment, why he chose venture capital this time around after largely bootstrapping Kettle & Fire, and how he still wrestles with imposter syndrome four companies in.You'll learn:Justin Mares walked away from Kettle & Fire, one of the best known bone broth brands in the country, to go build Truemed instead.Launching an imperfect product, like Justin's ghost landing page for Kettle and Fire built on $500 of ads, beats waiting for a perfect one.Justin chose venture capital over bootstrapping Truemed after growing Kettle and Fire to hundreds of millions in revenue on less than $10 million raised.Hiring your first five to ten people purely for mission alignment shapes every hire that follows, including Truemed's best employee, who found the company through Justin's newsletter.Andreessen Horowitz brought Truemed access to founders and operators that a bootstrapped CEO could never reach cold, well beyond the capital itself.Justin still deals with imposter syndrome after twelve years and four companies, and uses a specific reframe to move from doubt into action.Justin believes startups are fundamentally momentum games, and wishes he had learned to trust his own judgment earlier in his career.We talk about:00:00 Justin Mares introduces Truemed and its preventive health mission00:29 Why Kettle and Fire exposed how expensive prevention really is02:37 The red tape problem hiding inside health savings accounts04:43 Why Justin pivoted from consumer products into fintech07:39 What Truemed tested before writing a line of code09:54 Why an ugly MVP still proved real customer demand14:21 How Truemed built its first team around mission alignment19:37 Why Justin chose venture capital over staying bootstrapped24:39 What Andreessen Horowitz brought beyond the check29:00 Why the MAHA movement matters for chronic disease39:38 How Justin worked through his hardest founder decisions43:57 What Justin wishes he had known earlier as a founderFollow Justin onLinkedIn: https://www.linkedin.com/in/justinmares/ Instagram: https://www.instagram.com/justin.mares Website: https://justinmares.com/ Connect with Alisa!Follow Alisa Cohn on Instagram: @alisacohnTwitter: @alisacohnFacebook: facebook.com/alisa.cohnLinkedIn: https://www.linkedin.com/in/alisacohn/Website: http://www.alisacohn.comDownload her 5 scripts for delicate conversations (and 1 to make your life better) Grab a copy of From Start-Up to Grown-Up by Alisa Cohn from Amazon

All links and images can be found on CISO Series This week's episode is hosted by David Spark, producer of CISO Series, and Andy Ellis, principal of Duha. Joining is Patti Degnan, operating partner, Andreessen Horowitz. In this episode: Identity built for one person at a time Patching can't outrun the exploit timeline The exception hiding inside zero trust A revenue question nobody's answered yet A huge thanks to our sponsor, ThreatLocker ThreatLocker delivers Zero Trust Network Access and Zero Trust Cloud Access that verifies both user and device before granting access to specific applications. No broad access, nothing exposed, and no reliance on credentials alone. It's a smarter way to control access and reduce risk. Learn more at ThreatLocker.com/CISO.

Les agents IA vont-ils vraiment transformer nos organisations ? Productivité démultipliée, automatisation avancée, réinvention des processus… Les promesses sont nombreuses. Mais qu'en est-il des résultats concrets ?Tarik Boukherissa, Lead Solution Architect chez Databricks, apporte, dans cet épisode de Trajectoires, une grille de lecture sur les agents IA pour distinguer ce qui relève du discours de ce qui transforme vraiment le fonctionnement des organisations.Il partage des exemples concrets issus de projets récents et explique pourquoi le vrai défi n'est pas l'intelligence des agents, mais leur capacité à accéder au bon contexte au bon moment. Un échange qui aborde aussi la question de l'équilibre entre automatisation et contrôle humain, et ce que les prochaines années pourraient changer à une échelle encore difficile à anticiper.

On this episode of Reaganism, Roger sits down with David Ulevitch of Andreessen Horowitz to discuss the firm's American Dynamism practice and its focus on investing in technologies that strengthen U.S. national security and industrial capacity. Their conversation explored how venture capital can help rebuild the defense industrial base through startups in autonomy, drones, missiles, energy, space, and manufacturing, while also emphasizing the importance of engagement in Washington, procurement reform, and public storytelling to accelerate adoption. David argued that production capability is itself a form of deterrence, and both guests highlighted how recent conflicts in Ukraine and the Middle East have underscored the need for faster, cheaper, more scalable defense innovation.

„Miałem wtedy 27 lat, gdy rozpocząłem tworzenie funduszu”. Co się dzieje, gdy zamiast podążać utartą ścieżką i kupować kolejne nieruchomości, rzucasz bezpieczny świat lokalnych biznesów i postanawiasz rzucić wyzwanie największym graczom na globalnym rynku venture capital?

A recent report from Inc.com argues that six venture firms now dominate startup financing, reflecting the rise of multistage giga VCs. Industry examples cited by founders include Sequoia Capital, Andreessen Horowitz, SoftBank's Vision Fund, Tiger Global Management, General Catalyst, and Lightspeed Venture Partners. Their size influences round structure, with larger lead positions, tighter syndicates, inside rounds, and greater signal risk. Founders can compete by running disciplined processes, preparing robust data rooms with key metrics, and securing an internal partner champion. Alternatives include specialist funds, corporate venture investors such as GV, Salesforce Ventures, and Intel Capital, and non-dilutive options like revenue-based financing and venture debt. Negotiation focus areas include a 1x non-participating liquidation preference, standard pro rata rights, and broad-based weighted average anti-dilution.Learn more on this news by visiting us at: https://greyjournal.net/news/ Hosted on Acast. See acast.com/privacy for more information.

Send us Fan MailDr. Muthu Alagappan is the Founder and CEO of Counsel Health, the company automating access to high-quality, personalized medical advice from doctors. Counsel recently closed a $25M Series A led by Andreessen Horowitz and Google Ventures, following an $11M seed round that included A16Z, Asymmetric Capital Partners, Floodgate Fund, and Pear VC.He holds an MD from Stanford Medicine and a B.S. in Biomechanical Engineering from Stanford, and was among the earliest AI researchers to publish on clinical applications of machine intelligence.In this episode, Muthu draws on 15 years at the intersection of AI research and frontline clinical medicine to explore the shift toward semi-autonomous care.In this conversation, we discuss:How AI addresses the limitations of traditional primary care by offering a highly personalized, knowledgeable, and always available medical experience.Why patients might leapfrog clinicians in their willingness to adopt AI for medical advice, and how this shift challenges the traditional identity of physicians.What semi-autonomous care actually looks like in practice, and how Counsel Health uses a clinician cockpit to augment human compassion with real-time machine intelligence.How to leverage population-level patterns without compromising patient privacy.Why the double standard applied to AI is misplaced, and why Muthu argues we should hold AI to a much higher benchmark than human doctors simply.What the future of global healthcare could look like when cognitive medical expertise is fully democratized, ensuring that a patient's zip code no longer dictates the quality of care they receive.Explore the Conversation00:00 Intro & AI Fun Fact: Big Data Limitations and Bias in Clinical AI03:52 Meet Dr. Muthu Alagappan: From Stanford AI Researcher to Counsel Health CEO06:51 Why Primary Care Falls Short: The Case for AI-Augmented Medicine09:05 Human Doctors Are Human: How Patients Are Adopting AI Medical Advice12:20 Patient Privacy and Population Health: Learning Without Training on Data14:17 Inside the Clinician Cockpit: Real-Time AI Support for Doctors16:17 Why Counsel Health Employs Its Own Physicians: Messaging-Based Care19:00 From Semi-Autonomous to Fully Autonomous Care: Healthcare's Next Era24:19 AI Ethics in Medicine: Safety Standards, Model Values, and Data Ownership27:22 The AI Double Standard: Why Machines Deserve a Higher Benchmark Than Doctors31:07 Founder Lessons: Building a Category-Defining Healthcare AI Company33:53 Rewriting the Commencement Address: Medicine as Lifelong Learning35:37 Where to Connect with Dr. Muthu Alagappan and Counsel HealthResourcesSubscribe to the AI & The Future of Work NewsletterConnect with Muthu on LinkedInAI fun fact article: Artificial Intelligence in Health Care: The Ethical Frontier via ConexiantOn the future of AI, Silicon Valley and Venture Capital LIVE EVENT: See how leading enterprises are using agentic AI to give employees back 4–6 productive hours every week. Join PeopleReign CEO Dan Turchin for a live demo on June 25, 2026.Register here: https://go.peoplereign.io/live-demo-how-agentic-ai-is-being-used-by-global-enterprises

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

En este episodio de El Brieff revisamos cómo la CNTE levantó el plantón pero seguirá a Sheinbaum en sus giras, Claudia Sheinbaum recibe a Ben Horowitz de Andreessen Horowitz y anuncia 37 empresas mixtas eléctricas, Ken Salazar revela que AMLO estaba preocupado por lo que "El Mayo" pueda decir, BMW invierte 2,000 millones para producir eléctricos en San Luis Potosí, las pausas de hidratación del Mundial generan 500-600 millones de dólares, Irán cerró Ormuz una semana después del acuerdo con EE.UU., Apple y Huawei crecen mientras el mercado de smartphones cae 8%, y Revolution Medicines no busca ser adquirida tras avances en cáncer de páncreas.Este episodio es traído a ti por STRTGY AI Enterprise: diseñamos sistemas inteligentes hechos a la medida para resolver retos reales de tu empresa, desde automatizar procesos y recuperar eficiencia hasta reducir errores, fugas y costos operativos. Si hoy tu operación te quita tiempo, margen o control, escríbenos a hola@strtgy.ai o llena el formulario en strtgy.ai para explorar cómo podemos ayudarte.Recibe gratis nuestro newsletter con las noticias más importantes del día.Si te interesa una mención en El Brieff, escríbenos a arturo@strtgy.ai Hosted on Acast. See acast.com/privacy for more information.

SpaceX's $1.75 trillion IPO wasn't just a liquidity event for Elon Musk. It was the final step in a four-year bailout of the investors who backed his $44 billion Twitter acquisition. The SpaceX IPO closed an "amalgamation escalator" that converted depreciated Twitter equity into premium SpaceX stock, delivering a nearly 200% return to the private partners who'd been stuck holding the bag since 2022.This episode breaks down how the Twitter-to-SpaceX pipeline actually worked. The mechanics: Twitter merged with xAI in March 2025 at a $33 billion valuation, wiping out Twitter's standalone losses on paper. xAI then merged into SpaceX in February 2026. When SpaceX went public in June at $1.75 trillion, every original Twitter investor (the Saudi PIF, Sequoia, Andreessen Horowitz, Larry Ellison, Jack Dorsey) ended up holding SpaceX Class A shares worth roughly triple what they'd paid for the original Twitter position.The financial mechanics are clean. The governance questions aren't. SpaceX's multi-class share structure gives Musk absolute voting control regardless of his economic stake. The xAI absorption diluted core SpaceX value (launch and Starlink) to subsidize an AI division that lost $14 billion last year. And the $1.75 trillion valuation depends partly on SpaceX's pivot to space-based AI data centers, a technical bet that analysts are openly skeptical about.The SpaceX IPO also lands in the middle of an AI capex cycle that's pricing in perfection. Anthropic just filed for an IPO at $965 billion. OpenAI filed at $852 billion. SpaceX bought Cursor for $60 billion days after going public. The "Muskonomy" thesis (cross-subsidizing underperforming ventures with star assets, then taking the bundle public) only works if public market investors keep paying premium multiples on operational losses.This episode covers how Twitter equity got laundered into SpaceX stock, why the Saudi PIF was the biggest winner of the SpaceX IPO, what Musk's dual-class share structure means for minority shareholders, and whether the "amalgamation escalator" model becomes the template for the next wave of private-market exits.Keywords: SpaceX IPO, Elon Musk Twitter, $1.75 trillion valuation, xAI merger, Musk Twitter bailout, SpaceX Class A shares, amalgamation escalator, Saudi PIF, Sequoia, Andreessen Horowitz, Muskonomy, AI IPO 2026, dual-class shares, space data centers.

It's Hump Day on The Majority Report On today's program: JD Vance talks to Megyn Kelly why the whole Memorandum of Understanding has not been released, citing certain cultural sensitivities in the region without explaining what that means. The war hawks are not happy with the leaked sections of the Memorandum of Understanding between the U.S. and Iran. Former Vice President Mike Pence says on CNN that the preliminary language "smacks of appeasement". Brian Kilmeade on Fox & Friends shares the same sentiment as Pence but he refuses to criticize the president, insisting to his audience that this is Vance's deal. Dylan Gyauch-Lewis, senior researcher at the Revolving Door Project, joins to talk about her piece in The American Prospect: "New Documents Detail Nine-Figure, Silicon Valley–Funded Abundance Movement." Gil Duran, publisher of the Nerd Reich newsletter, joins to discuss John O'Farrell a former partner at Andreessen Horowitz who recently quit the VC firm over their prioritizing of AI hype over the common good. You can preorder Gil's book "The Nerd Reich: Silicon Valley's Fascism and the War on Democracy" here. In the Fun Half: As Trump's facilities sunset he is having a hard time keeping his inside thoughts inside. At the G7 conference in France, the president couldn't stop talking about world leaders in an erotic fashion. Trump swooned over India's PM Modi's stunning beauty and recounted the time he met Egyptian president el-Sisi in a hotel and fell deeply in love. Trump post another screed to Truth Social, announcing the cancelation of Jay Clayton's U.S Attorney nomination hearing today and the Bill Pulte will remain acting head of DNI. Trump's nominee to head the Office of Management and Budget, Hal Duncan, recites the script and refuses to admit that Joe Biden won the 2020 election but of course freely acknowledges that Trump won the 2024 election. The GOP primary winner for senate in Georgia, Mike Collins, kicks off his campaign by attacking incumbent Sen. Jon Ossoff for voting in support of trans rights, much to the ambivalence of the small audience. DSA backed candidate for mayor of Washington D.C., Janeese Lewis George appears to be headed for victory in the democratic primary. On June 11, Trump threatened to have a federal takeover of D.C. is she wins and evidentially that was the best thing to happen to George's campaign. All that and more. To connect and organize with your local ICE rapid response team visit ICERRT.com The Congress switchboard number is (202) 224-3121. You can use this number to connect with either the U.S. Senate or the House of Representatives. Follow us on TikTok here: https://www.tiktok.com/@majorityreportfm Check us out on Twitch here: https://www.twitch.tv/themajorityreport Find our Rumble stream here: https://rumble.com/user/majorityreport Check out our alt YouTube channel here: https://www.youtube.com/majorityreportlive Gift a Majority Report subscription here: https://fans.fm/majority/gift Subscribe to the AM Quickie newsletter here: https://am-quickie.ghost.io/ Join the Majority Report Discord! https://majoritydiscord.com/ Get all your MR merch at our store: https://shop.majorityreportradio.com/ Get the free Majority Report App!: https://majority.fm/app Go to https://JustCoffee.coop and use coupon code majority to get 10% off your purchase Check out today's sponsors: ROCKET MONEY: Let Rocket Money help you reach your financial goals faster: RocketMoney.com/MAJORITY RITUAL: Get 25% off during your first month. Visit ritual.com/MAJORITY. SUNSET LAKE CBD: Use coupon code "Left Is Best" (all one word) for 20% off of your entire order at SunsetLakeCBD.com Follow the Majority Report crew on Twitter: @SamSeder @EmmaVigeland @MattLech On Instagram: @MrBryanVokey Check out Matt's show, Left Reckoning, on YouTube, and subscribe on Patreon! https://www.patreon.com/leftreckoning Check out Matt Binder's YouTube channel: https://www.youtube.com/mattbinder Subscribe to Brandon's show The Discourse on Patreon! https://www.patreon.com/ExpandTheDiscourse Check out Ava Raiza's music here! https://avaraiza.bandcamp.  

“We are all in the gutter, but some of us are looking at the stars,” Oscar Wilde wrote in his 1892 play Lady Windermere's Fan. This week, Elon Musk managed — not for the first time — to be simultaneously in the stars and the gutter. SpaceX's IPO valued his rocket company at $2 trillion — making Musk, officially, a trillionaire, the richest person in the world by a very large margin. The space Musk — the defiant genius who bet everything on a reusable rocket and the promise of a cosmic monopoly — is astonishing. The Wall Street Journal called the IPO a Goldilocks debut with Musk starring as the three bears. But there is another Musk — the one in the gutter, promoting white nationalist violence from his platform on X. This week Musk not only stoked the anti-immigrant riots in Belfast but reiterated his support for the English white supremacist gangster Tommy Robinson. So is this another Strange Case of Dr Jekyll and Mr Hyde, Robert Louis Stevenson's 1886 novella? Keith Teare, publisher of That Was the Week, certainly thinks so. While Keith is in awe of Musk's entrepreneurial genius at SpaceX, he seems to excuse Musk's support for Tommy Robinson's paramilitarism. “I'm not even sure I like him,” Keith confesses in his musings on “civilisation.” Nor do the rest of us. But I wonder if this good/bad Elon narrative is too convenient. There is an uncomfortable symbiosis between Musk's journey to SpaceX and to white nationalist violence. For all the utopian cornucopia of space, our earthly reality is one of scarce land and fear of immigrants — Trump, Tommy Robinson, and this weekend's Swiss referendum on capping its population at 10 million. For all the Muskian promise of cosmic abundance, today's Muskian politics is paranoid and exclusionary. So maybe it's not just Elon. Everyone these days is simultaneously in the gutter and looking up at the stars. Five Takeaways •       SpaceX: From El Segundo Warehouse to $2 Trillion Juggernaut: SpaceX is 25 years old. It started in a warehouse near Los Angeles, in an area with a concentration of rocket scientists. Musk bet almost all of his Tesla gains on the idea of a reusable rocket — and nearly lost everything. Then a rocket worked. Since then: iterative improvement, the rockets getting bigger and more reliable, a virtual global monopoly on delivering payloads to space, Starlink (satellite internet that actually works at gigabit speeds), and NASA subcontracting its launches. Now: $2 trillion at IPO, Musk a trillionaire. Wall-to-wall applause from the startup world. Wall-to-wall pylon on social media. Both simultaneously true. •       The Grimace vs the Applause: Andrew vs Keith's Media Diet: Keith says most commentators are grimacing at the valuation and Musk's net worth. Andrew says the serious press — the Wall Street Journal, even the New York Times — is largely applauding. The exchange reveals the media bifurcation: mainstream outlets cover the achievement; social media — X, Facebook, LinkedIn — is wall-to-wall outrage about a trillionaire in a world of growing inequality. Keith's verdict on Musk: he doesn't care whether people like him. Neither, in Keith's view, should we. You judge him not on likability but on criteria: civilization or net worth. Different criteria, different judgment. •       California and Europe: The Failure of Government: Fareed Zakaria in the Washington Post: California is a case study in failed government. Andrew had Jonathan Weber on the show this week — City on the Edge, the historic dysfunctionality of San Francisco city government. Fukuyama is trying to be optimistic about Europe's liberal future. Keith's counter: Fukuyama ignores the structural problem — top-heavy EU bureaucracy that overrides countries, producing dislike of the EU in every European nation, even France, which built it. Populism, Keith argues, is not the disease. It's the symptom. The disease is twenty years of bad policy. •       Bernie Sanders Finally Had an Insight: The Sovereign Wealth Fund: Sanders has proposed a sovereign wealth fund owning 50% of all high-growth AI companies, giving every citizen ownership shares. Keith, who last week said 50% wasn't enough, this week credits it as the first genuine insight Sanders has had. The kicker: David Sacks — arch right-winger, former PayPal Mafia, Andreessen Horowitz — agreed on his podcast and said it should be 75%. Keith's observation: when David Sacks and Bernie Sanders can agree on the direction, left-right labels stop helping. The question is just how to make capitalism's gains flow to everyone. •       Planning Beats Complaint: Keith's editorial closer. The choice is not between liking Musk and hating Musk, not between celebrating SpaceX and resenting its valuation. The choice is between complaining and planning. John O'Farrell, former general partner at Andreessen Horowitz, resigned and wrote an op-ed in the New York Times: “We can't let my former venture capital colleagues buy off democracy.” Gary Tan organised an Asian-American reaction against San Francisco's school board and won. Citizens who act beat citizens who complain. That's the week's lesson. That's Keith's lesson. Andrew is away next week. About the Guest Keith Teare is a British-American entrepreneur, investor, and publisher of the That Was the Week newsletter. He is a co-founder of TechCrunch and Andrew's regular TWTW co-host. References: •       That Was the Week by Keith Teare. •       Fareed Zakaria, “How California Became a Case Study in Failed Government,” Washington Post — referenced in the conversation. •       John O'Farrell, “We Can't Let My Former Venture Capital Colleagues Buy Off Democracy,” New York Times — referenced in the conversation. •       Francis Fukuyama on the liberal vision of Europe — referenced in the conversation. •       Episode 2938: Jonathan Weber on City on the Edge — referenced at the opening. About Keen On America Nobody asks more awkward questions than the Anglo-American writer and filmmaker Andrew Keen. In Keen On America, Andrew brings his pointed Transatlantic wit to making sense of the United States — hosting daily interviews about the history and future of this now venerable Republic. With nearly 2,900 episodes since the show launched on TechCrunch in 2010, Keen On America is the most prolific intellectual interview show in the history of podcasting. WebsiteSubstackYouTubeApple PodcastsSpotify Chapters: (00:31) - Introduction: SpaceX IPO, ...

Primer fallecimiento por golpe de calor en TabascoTlalpan invita al Festival Fogones de MéxicoHonduras confirma primer caso de sarampión en 30 añosMás información en nuestro Podcast#grc

SpaceX startet mit einem ordentlichen Pop in den Handel. Tausende Mitarbeiter werden zu Millionären, Founders Fund und Andreessen Horowitz vermelden Rekord-Returns. Anthropic launcht Fable 5 und das Mythos-Modell für Testpartner. OpenAI plant laut Wall Street Journal drastische Preissenkungen für den User-Krieg mit Anthropic. China plant $300 Mrd. für nationalen KI-Ausbau über fünf Jahre. Xiaomi MiMo Code schlägt Claude Code in den gängigen Benchmarks. OpenAI übernimmt das Kieler Startup ONA, Mistral kauft das Linzer Emmi AI für eine Industrie-KI-Plattform und verhandelt selbst eine $20-Mrd.-Bewertung. Dario Amodei mit neuem Essay zur AI-Exponential-Politik. Oracle Earnings, Prometheus von Jeff Bezos bei $41 Mrd. Die Trump-Familie hat $2,3 Mrd. mit Krypto eingestrichen. Palantir verliert vor dem Zürcher Handelsgericht gegen die Zeitschrift Republik. Neura Robotics raised $1,4 Mrd. mit Tether als Lead. Landgericht München: Google haftet für seine AI-Overviews.  Unterstütze unseren Podcast und entdecke die Angebote unserer Werbepartner auf ⁠⁠⁠⁠⁠⁠doppelgaenger.io/werbung⁠⁠⁠⁠⁠⁠. Vielen Dank!  Philipp Glöckler und Philipp Klöckner sprechen heute über: (00:00:00) SpaceX-IPO (00:08:04) Mitarbeiter-Millionäre (00:11:51) OpenAI/Anthropic-IPO-Outlook (00:15:31) Elon-Puppe vor der Nasdaq (00:16:13) Anthropic Fable 5 & Mythos 5 (00:19:54) OpenAI Preiskrieg (00:27:27) Token-Wert pro Abo (00:30:30) Messi für ChatGPT (00:34:48) China $300 Mrd. KI-Plan (00:37:31) Xiaomi MiMo Code (00:39:46) OpenAI kauft ONA (00:42:54) Anthropic: AI Exponential Policy (00:46:53) Oracle Earnings (00:48:07) Mistral kauft Emmi AI (00:49:08) Prometheus von Bezos (00:50:48) Trump Phone (00:51:22) Waymo Premier (00:55:40) Google Trade-Worker (00:57:08) Anthropic Claude Corps (00:58:37) Trump-Krypto-Scam (00:59:35) The Platform Group (01:03:48) Palantir vs. Republik (01:05:48) Mistral $20 Mrd. Runde (01:07:07) Neura Robotics Series C (01:10:47) NYT: China und Robotik (01:13:19) Google haftet für AI-Overviews Shownotes SpaceX-IPO zieht $70 Mrd. an Retail-Orders - bloomberg.com Founders Fund + Andreessen: Rekord-Returns aus SpaceX-IPO - bloomberg.com SpaceX Proteste - xcancel.com Anthropic launcht Claude Fable 5 & Mythos 5 - wired.com OpenAI plant drastische Preissenkungen für User-Krieg mit Anthropic - wsj.com Bitte manuell prüfen (petergostev-Post) - xcancel.com SemiAnalysis - xcancel.com China plant $295 Mrd. für nationalen KI-Ausbau - bloomberg.com ONA: Kieler KI-Startup raised - linkedin.com Anthropic: Policy on the AI Exponential - anthropic.com Oracle Q4 Earnings - cnbc.com Mistral übernimmt Emmi AI für Industrie-KI-Plattform - handelsblatt.com Prometheus: Bezos' Industrial-AI-Startup - axios.com Teardown: Trump Phone ist HTC U24 Pro in Gold - de.ifixit.com Waymo launcht Loyalty-Programm mit 10% Cashback - techcrunch.com Google launcht Trade-Worker-Initiative für KI - axios.com Daniela Amodei startet Anthropics Claude Corps - apnews.com Xiaomi MiMo Code schlägt Claude Code bei 200-Step-Tasks - venturebeat.com Trump-Crypto-Playbook: Family wins, Investors don't - reuters.com The Platform Group - manager-magazin.de Einstweilige Verfügung: The Platform Group vs. Manager Magazin - lhr-law.de Palantir - ft.com Mistral verhandelt $20 Mrd. Bewertung - bloomberg.com Bitte manuell prüfen (dreger-Post) - linkedin.com Neura Robotics schließt Rekord-Series-C - neura-robotics.com Chinas Humanoid-Robot-Schub - nytimes.com Deutsches Gericht: Google haftbar für AI-Overviews - thenextweb.com

Exposing The Dark Money Machine Behind AI PropagandaSUPPORT MY WORK: Buy a paid subscription to my newsletter at usermag.co     Support my work on Patreon: http://patreon.com/taylorlorenz I break down my investigation into a network of pro-AI and anti-AI meme accounts that I found were secretly being run and funded by OpenAI, Palantir, and Andreessen Horowitz's big $125M super PAC and dark money group. I reveal how these accounts operated, who is connected to them, why they promoted both sides of the AI debate, and how the organization at the center of the story confirmed key aspects of the reporting after publication.I talk about how a self-described “Meme Lord” named Jason Levin,  founder of Memelord Technologies was hired by the super PAC, Leading the Future, to create and run sock puppet accounts like “DoomersAreDumb” and “Jonathan Doomer” to attack AI critics, mock disabled people, post violent threats, and even pretend to be an anti-AI activist.OpenAI's president Greg Brockman donated millions to this campaign. OpenAI's head of strategy follows these meme accounts. And when confronted, Build American AI confirmed it all.Topics covered:AI propaganda and influence campaignsOpenAI and AI policy politicsDark money groups and Super PACsFake activist accountsAI-generated content networksMeme pages and online manipulationPolitical lobbying and artificial intelligenceSocial media influence operationsTech industry power and public opinion#AI #ArtificialIntelligence #TechNews #OpenAI #SiliconValley #MemeCulture #InvestigativeJournalism #InfluenceCampaign #AIDebate #TechPolicy #PowerUser

Hoy conversé con Ian Lee, fundador de Nexor, una startup de inteligencia artificial respaldada por Andreessen Horowitz, una de las firmas de Venture Capital más influyentes del mundo.Antes de Nexor, Ian fundó Examedi en 2021. Participó en Y Combinator y fue el primer chileno en recibir la prestigiosa beca Thiel Fellowship, creada por Peter Thiel.

"$75M in 13 months. a16z just led their Series A."We sat down with Niklas Lindgren, Co-Founder & CEO of Endra, fresh off their $50M Series A led by Andreessen Horowitz, taking total funding to $75M in 13 months.Endra is building the purpose-built workspace for MEP engineering, already partnering with AtkinsRéalis, Buro Happold, WSP, Hoare Lea, Ramboll and AFRY.Tune in to find out about:✅ Why a16z led at Series A instead of waiting for later traction✅ The Stripe vs PayPal analogy behind Endra's category play (and why they're not replacing Revit)✅ The honest answer to the billable-hours paradox✅ What this means for the next generation of MEP graduatesWatch now on Spotify and YouTube

If it feels like investors everywhere have some curiosity about the defense tech landscape, then it's because more of them both want to increase their knowledge and sometimes involvement in the ecosystem. Steve Brotman, founder and managing partner of the growth equity investment firm Alpha Partners, fits into that category as an observer and participant that works with venture capital firms to be involved in promising tech companies. Steve joins our Ross Wilkers for this episode to answer the questions laid out in the title, namely how it became cool again for investors to get involved with defense tech companies and markers that indicate how long this boom of interest could last. SpaceX's initial public offering and corporate VC funds feature in the chat too. Also listen out for Steve's tips and suggested homework for business leaders to do before venturing out into VC networks. US investors warm to Ukrainian defense startups—but export laws slow cooperation Budget would cut Pentagon research by one-third. Can industry compensate? Meet the startups trying to build military-specific AI Venture investing is part of the M&A conversation too The defense tech ecosystem gives investors many opportunities Public offerings put GovCon in a new spotlight as SpaceX's listing looms SpaceX's S-1 lays out its government work and market ambitions SpaceX's governance structure is built for one person: Elon Musk SpaceX's biggest risk factor might be Elon Musk Lockheed boosts its venture investment fund to $1B Booz Allen commits $400M to Andreessen Horowitz's late-stage fund Booz Allen gives big boost to its venture arm WT 360: For Lockheed's ventures team, its investments are merely step one WT 360: RTX Ventures casts its net wide and far across an expanding tech ecosystem WT 360: Booz Allen's roadmap for collaborating with startups after an investment WT 360: SAIC Ventures' methods for investing in and working with tech startups

Benedict Evans is an independent analyst and former partner at Andreessen Horowitz, where he spent years as their in-house “thinker” tracking the most important technology trends. For the past six years, he's been publishing deeply researched presentations on where tech is heading, most recently focused on AI's transformation of the economy. His work is read by founders, investors, and operators trying to make sense of a noisy field. His most controversial opinion: AI is as big a deal as the internet or mobile—and only as big.In our in-depth conversation, we discuss:1. Why we're in “1997” for AI—early, exciting, and deeply uncertain about what comes next2. Where value will actually accrue in the AI stack3. The anti-AI backlash, and where it may lead4. The surprising boom in consulting and professional services at AI companies5. Why distribution is becoming the ultimate moat as software gets easier to build6. Why the right question about your job isn't “What percent can AI do?” but “Is this a task or a job?”7. Why things will probably be okay—and what you need to do to prepare—Brought to you by:WorkOS—Make your app enterprise-ready, with SSO, SCIM, RBAC, and more: https://workos.com/lennyVanta—Automate compliance, manage risk, and accelerate trust with AI: https://vanta.com/lenny—Episode transcript: https://www.lennysnewsletter.com/p/a-rational-conversation-on-where—Archive of all Lenny's Podcast transcripts: https://www.dropbox.com/scl/fo/yxi4s2w998p1gvtpu4193/AMdNPR8AOw0lMklwtnC0TrQ?rlkey=j06x0nipoti519e0xgm23zsn9&st=ahz0fj11&dl=0—Where to find Benedict Evans:• LinkedIn: https://www.linkedin.com/in/benedictevans• Newsletter: https://www.ben-evans.com/newsletter• Website: https://www.ben-evans.com—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Introduction to Benedict Evans(02:19) What people aren't pricing in about AI's impact(06:24) Why we're in the 1997 moment of AI(09:44) The unexpected boom in professional services and consultants(17:44) Why distribution is becoming the ultimate moat(23:17) The coming job transformation: what's real vs. panic(27:33) Why AGI definitions keep shifting(38:11) Where value will accrue: models vs. applications(42:55) Distribution wars: Google, Meta, Apple, and OpenAI(48:12) The anti-AI sentiment and backlash(53:11) How to raise kids in an AI future(58:27) What jobs to steer toward or away from(59:20) The question nobody's asking about AI(1:06:25) How to be successful in this coming future(1:08:43) AI corner(1:11:43) Lightning round—Referenced: https://www.lennysnewsletter.com/p/a-rational-conversation-on-where—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.—Lenny may be an investor in the companies discussed. To hear more, visit www.lennysnewsletter.com

Brad Carson was the Army's General Counsel, served two terms in Congress and was Acting Under Secretary of Defense for Personnel and Readiness. He now heads Americans for Responsible Innovation, the AI-policy advocacy group he co-founded. Keith Duggar spends roughly eighty minutes pushing back.SPONSOR:---Cyber Fund built the Monastery to help founders ship products that were impossible a year ago. Applications for Batch 1 are now open.Apply now: https://cyber.fund---Carson's whole case rests on one line: the genie is not out of the bottle. We have pulled dangerous tech back before. Asilomar halted recombinant DNA in 1975, and the West still controls the chips AI runs on. Calling it unstoppable, he says, is the most dangerous idea in the room.Then Keith drags him somewhere darker. A Palantir heat map scores you 0.73 on whether you are a combatant, and a strike follows. The model is wrong some accepted share of the time, and when it is, nobody answers for it. You cannot court-martial a model, and not even the interpretability researchers can say why it picked you.—Note: after recording, we learned that Americans for Responsible Innovation is backed by EA-aligned philanthropy (not sponsored)---TIMESTAMPS:00:00:00 From the Pentagon to AI governance00:04:52 Regulatory capture vs Silicon Valley networks00:07:56 Transparency and the Claude tier changes00:09:40 Tort liability when AI tools cause harm00:13:40 AI is a product, not a person00:16:01 Children, suicide, and the suicide business00:19:59 Opaque neural nets and the law of war00:25:54 Probabilistic targeting and the death of accountability00:28:47 The arms race fallacy: Asilomar and restraint00:34:02 Talking to China: track 2 talks and chip leverage00:39:45 Air power never wins: capital for labour00:43:29 Anthropic vs the Department of War00:51:29 Concentration, open source, and brain drain01:00:18 DeepSeek, Chinese culture, and AI as diplomacy01:12:25 Upskilling Congress and why public trust matters---REFERENCES:organization:[00:02:45] ICRC position on autonomous weaponshttps://www.icrc.org/en/law-and-policy/autonomous-weapons[00:05:22] Americans for Responsible Innovation (ARI)https://ari.us[00:07:20] Andreessen Horowitz (a16z)https://a16z.com/[01:16:05] Office of Technology Assessmenthttps://en.wikipedia.org/wiki/Office_of_Technology_Assessmentother:[00:03:35] Beneficial AGI 2019 Conference (Future of Life Institute, Puerto Rico)https://futureoflife.org/event/beneficial-agi-2019/[00:18:30] Section 230 of the Communications Decency Acthttps://en.wikipedia.org/wiki/Section_230[00:19:59] Lethal Autonomous Weapons (LAWS)https://en.wikipedia.org/wiki/Lethal_autonomous_weapon[00:31:35] Strategic Arms Limitation Talks (SALT)https://en.wikipedia.org/wiki/Strategic_Arms_Limitation_Talks[00:32:28] Asilomar Conference on Recombinant DNA (1975)https://en.wikipedia.org/wiki/Asilomar_Conference_on_Recombinant_DNA[00:39:45] The New Iron Triangle (ARI policy byte)https://ari.us/policy-bytes/the-new-iron-triangle/[00:48:05] Defense Production Acthttps://en.wikipedia.org/wiki/Defense_Production_Actperson:[00:03:35] Anthony Aguirrehttps://en.wikipedia.org/wiki/Anthony_Aguirre[00:06:48] Dean Ball — Hyperdimensionalhttps://www.hyperdimensional.co/[00:23:13] Neel Nanda — mechanistic interpretabilityhttps://www.neelnanda.io/[00:36:02] Jack Clark (Anthropic) on Conversations with Tylerhttps://conversationswithtyler.com/episodes/jack-clark/[00:39:15] Robert Trager — Centre for the Governance of AIhttps://www.governance.ai/team/robert-trager[00:41:55] Giulio Douhethttps://en.wikipedia.org/wiki/Giulio_Douhet[01:15:05] Don Beyer (US Congress)https://en.wikipedia.org/wiki/Don_Beyertool:[00:22:19] Phalanx CIWShttps://en.wikipedia.org/wiki/Phalanx_CIWS---ReScript:https://app.rescript.info/public/share/9405ff35c0215b7cdae6402d41284171https://app.rescript.info/api/public/sessions/0a6c081b8e5fe413/pdf

In episode 303 of Remarkable Retail, Steve Dennis and Michael LeBlanc deliver a sharp, fast-moving episode built around a single conviction from one of retail's most influential retailers: the future is people-led and tech-enabled. Chris Nicholas, former President & CEO of Sam's Club and now President & CEO of Walmart International — a global operation spanning 18 countries, 5,700 stores, and over 500,000 employees shares how humanity and technology are intertwined to drive growth. In this encore interview, Chris makes the case that retail innovation isn't about replacing people with technology. It's about using AI and digital tools to strip out friction, empower associates, and build better member experiences. Technology serves the human, not the other way around. Chris unpacks Sam's Club's nearly $90 billion membership-driven model and explains why the warehouse club sector keeps gaining momentum worldwide. He goes deep on the "club of the future" strategy — including the closely watched Grapevine, Texas location with computer vision-powered exits, Scan & Go checkout, AI-enabled shopping, and a radically redesigned store built around convenience, inspiration, and engagement. His core belief: consumers everywhere want the same things — value, convenience, innovation, and experiences that genuinely improve their lives. Before the interview, the hosts break down a blockbuster earnings week. Walmart posts another massive quarter, adding a staggering $18 billion in quarterly revenue while investing aggressively in price to hold share against inflation. Target delivers one of its strongest quarters in years, a sign its turnaround may finally be gaining traction. TJX proves resilient yet again as off-price rides the consumer "stampede to value." Home Depot and Lowe's, meanwhile, keep struggling in a sluggish housing and renovation market as higher rates squeeze big-ticket spending. The episode closes with Shein's surprising acquisition of Everlane — which Steve calls "where irony goes to die," given Everlane's brand built on radical transparency. Steve and Michael also dig into rising bond yields and the broader implications of AI legislation and the growing political clout of major technology investors like Andreessen Horowitz. Join us at the CommerceNext Growth Show in New York June 23rd and 24th with this exclusive discount code for 10% off general admission tickets and FREE retail tickets: Your code is "REMARKABLE" . See you in the Big Apple! About UsSteve Dennis is a strategic advisor and keynote speaker focused on growth and innovation, who has also been named one of the world's top retail influencers. He is the bestselling author of two books: Leaders Leap: Transforming Your Company at the Speed of Disruption and Remarkable Retail: How To Win & Keep Customers in the Age of Disruption. Steve regularly shares his insights in his role as a Forbes senior retail contributor and on social media.Michael LeBlanc is a senior retail advisor, keynote speaker and media entrepreneur. Michael has delivered keynotes, hosted fire-side discussions hosted senior retail executive on-stage in 1:1 interviews worldwide. Michael produces and hosts a network of leading retail trade podcasts, including The Remarkable Retail Podcast, The Voice of Retail The Food Professor, The FEED powered by Loblaw and the Global eCommerce Leaders podcast. He has been recognized by the NRF as a global Top Retail Voice for 2025 and 2025 and continues to be a ReThink Retail Top Retail Expert for the fifth year in a row.

Interactive social media site Status announced Tuesday $17 million in combined seed and Series A funding, with investors including General Catalyst, YC, LightShed Ventures, and Abstract. Also, Stilta announced Tuesday a $10 million seed round led by Andreessen Horowitz. Other investors in the round include YC and operators from companies like OpenAI, Legora, and Lovable. Learn more about your ad choices. Visit podcastchoices.com/adchoices

Marc Andreessen is a co-founder and general partner at the venture capital firm Andreessen Horowitz, co-creator of the Mosaic internet browser and co-founder of Netscape, and author of “The Techno-Optimist Manifesto.”www.youtube.com/@a16zhttps://pmarca.substack.comhttps://a16z.com/the-techno-optimist-manifesto/www.a16z.com Perplexity: Download the app or ask Perplexity anything at https://pplx.ai/rogan. Great Coffee, Great Mission – Black Rifle Coffee is America's Coffee. Visit https://blackriflecoffee.com/joerogan today to get 30% off your next order. Learn more about your ad choices. Visit podcastchoices.com/adchoices

The AI narrative shifting… Jobs apocalypse? What jobs apocalypse!? Who said that was coming? There's been a noticeable shift from the AI titans recently. Turns out (shocker!) the world isn't responding well to being told we'll all be out of a job soon. And Silicon Valley is waking up to the fact that they need more popular support — both for the data centers they hope to build quickly and also for their upcoming IPOs. Meanwhile, the AI outrage is building. This week in AI anxiety: Students boo a commencement speaker who mentioned AIGallup reports that 71% of Americans are opposed to new data centers (with 48% “strongly opposed”)Meta employees are miserable as another round of (AI-driven, so they say) layoffs approachThis week in trying to change the narrative: Andreessen Horowitz publishes “The ‘AI Job Apocalypse' Is a Complete Fantasy” and explains why the “the claim that AI will produce economy-wide, permanent unemployment is unhelpful marketing, bad economics, and worse history.” And I find it very instructive that this list (and every list is an ordered list whether you admit it or not) begins with the concern that this is “unhelpful marketing.” (To the piece's credit, it gets pretty wonky with charts and graphs from there.)Meanwhile, you know what's helpful to marketing? Spending a gazillion dollars to get your message out. To wit: The New York Times reports that Andreessen Horowitz is the biggest spender so far in this midterm election cycle, spending $115M to promote AI, crypto, and other founder-friendly initiatives. OK, so these pieces of data and “anecdata” are the jumping off point for this week's “FAFO Friday.” Enjoy! Support Future Around & Find OutFollow Dan on LinkedInGet the free newsletterBecome a paid subscriber and help future proof FAFO!---Music by Jonathan Zalben

Kara and Scott unpack what AI obsession is doing to relationships and young men. Then, they break down Trump's China summit, the crew of business executives he brought along, and ominous warnings from Xi Jinping about Taiwan. Plus, Sam Altman testifies in the Elon Musk–OpenAI trial, inflation surges, and Andreessen Horowitz becomes the biggest donor of the midterms. Also, Anthropic eyes a valuation higher than OpenAI's, and Google explores orbital data centers with SpaceX. Watch this episode on the ⁠⁠Pivot YouTube channel⁠⁠.Follow us on Instagram and Threads at ⁠⁠@pivotpodcastofficial⁠⁠.Follow us on Bluesky at ⁠⁠@pivotpod.bsky.social⁠⁠Follow us on TikTok at ⁠⁠@pivotpodcast⁠⁠.Send us your questions by calling us at 855-51-PIVOT, or email pivot@voxmedia.com Learn more about your ad choices. Visit podcastchoices.com/adchoices

Grab your favorite beverage for a special, highly opinionated "just us girls" episode of the podcast, featuring Joel Cheesman and Maureen “Moe” Clough taking the mic without the rest of the usual crew. This week, the duo delivers a light-hearted yet deeply substantive look into the massive worker backlash against artificial intelligence and the brutal realities of today's hiring market. The hosts kick things off with quick hits covering a disastrous, heavily booed commencement speech at the University of Central Florida and a surprising take on the narrative depth of The Devil Wears Prada 2. From there, the conversation tackles major industry shifts as massive job platforms like Upwork and ZipRecruiter face severe financial softening, sparking a debate on whether automation is permanently consuming traditional contractor roles. The gloves come off as they dissect a bold claim from Andreessen Horowitz labeling legacy HR software giants like Workday a "cartel," while analyzing how defensive tech acquisitions—such as Ashby buying Talent Llama—signal a broader software-as-a-service apocalypse. Moe offers her expertise on age discrimination, discussing a lawsuit against Bloomberg Industry Group. The discussion moves to the backlash against automated hiring tools and LinkedIn's new paid consultation feature. Finally, there is a disagreement over Google's new Gemini-powered smart glasses. Chapters 00:00 - Introduction to the Podcast and Hosts 01:35 - Current Events and AI's Impact 05:30 - AI and the Youth Perspective 10:01 - Data Centers and Community Impact16:31Industry News: Upwork, ZipRecruiter, and Workday 19:59 - The Future of Work and AI's Role 22:00 - The SaaS Cartel and Its Challenges 25:02 - Age Discrimination in the Workplace 34:56 - AI's Role in Hiring and Recruitment 42:21 - The Rapid Evolution of AI in Hiring 45:04 - LinkedIn's New Monetization Features 52:51 - The Controversy of Smart Glasses 01:03:01 - The Inevitable Rise of Smart Technology

In this episode, we unpack Cerebras Systems' blockbuster IPO and how it may reignite momentum across the AI infrastructure trade. We also discuss the macro backdrop shaping risk assets, including speculation around the new Federal Reserve chair, hotter-than-expected inflation data, alongside resilient labor markets, and what it could mean for digital assets and broader growth markets. Further, we cover growing legislative momentum behind the Digital Asset Market Clarity Act, the sharp rise of privacy-focused token Xcoin, and renewed venture appetite as firms including Haun Ventures and Andreessen Horowitz raise billions in fresh digital asset-focused capital. Remember to Stay Current! To learn more, visit us on the web at https://www.morgancreekcap.com/morgan-creek-digital/. To speak to a team member or sign up for additional content, please email mcdigital@morgancreekcap.com Legal Disclaimer This podcast is for informational purposes only and should not be construed as investment advice or a solicitation for the sale of any security, advisory, or other service. Investments related to the themes and ideas discussed may be owned by funds managed by the host and podcast guests. Any conflicts mentioned by the host are subject to change. Listeners should consult their personal financial advisors before making any investment decisions.

Special discounts up for AIE Melbourne (LS discount) and AIE World's Fair (group discounts up to 25% - CFPs still open for Autoresearch and Vertical AI) Cya there!Abridge did not start as an “GPT wrapper”. It was founded in 2018, years before the Cambrian explosion of AI application layer companies. OpenAI launched ChatGPT publicly on November 30, 2022 and by then, Abridge had already spent years doing the unglamorous work of building trust for one of the highest context, most important workflows in healthcare: the conversation between a patient and a clinician.Abridge's original wedge was clinical documentation. Listen to the visit, generate the note, reduce the clerical burden, and let clinicians spend more time with patients instead of the EHR. By focusing on how doctors actually document, how health systems actually buy, how EHR integration actually works, how clinicians verify outputs, and how missing context during a visit turns into downstream friction across billing, prior authorization, quality, and follow-up, the adoption of LLMs became a force multiplier on a workflow already optimized for sensitive context gathering.The company has scaled fast: Abridge says it is projected to support 80M+ patient-clinician conversations this year across 250 large and complex U.S. health systems, with support for 28+ languages and 50+ specialties. It raised $300M at a $5.3B valuation in June 2025, after a $250M round earlier that year.Today, Janie Lee and Chaitanya “Chai” Asawa of Abridge join us for another crossover pod with Redpoint's Jacob Effron (who is on the board of Abridge) to dive into how Abridge is building the clinical intelligence layer for healthcare starting with ambient documentation, then expanding into clinical decision support, prior authorization, payer/provider/pharma workflows, and eventually real-time agents that act before, during, and after the patient conversation. We go inside the product, data, infra, evals, workflow, privacy, and org design choices behind bringing AI into one of the highest-stakes enterprise environments from 100M+ medical conversations and specialty-specific evals to real-time alerts, EHR integration, de-identification, clinician-scientist teams, and why healthcare may solve some of the hardest AI problems first.We discuss:* Why Abridge started with clinical documentation, “pajama time,” and saving clinicians 10–20 hours a week* The transition from ambient scribe to clinical intelligence layer: save time, save money, and save lives* Why conversations between patients and clinicians may be the most important workflow in healthcare (patient visit summary feature)* Chai's “healthcare-coded Glean” framing: context is king, but healthcare raises the stakes on safety, evals, and rollout* Why Abridge wants AI to feel like “air conditioning”: always in the background, but only interrupting when it truly matters* The prior authorization example: turning a denied MRI weeks later into real-time guidance while the patient is still in the room* Why payer policies, EHR data, medical literature, and hospital-specific guidelines make the problem hard, and also create the moat* How Abridge thinks about ambient form factors: mobile, desktop, in-room devices, nursing workflows, multimodality, and future AR* The multi-sided healthcare customer: CMIOs, CFOs, CIOs, clinicians, patients, payers, and pharma* The hardest AI problem at Abridge: high-quality, low-latency, low-cost real-time support in a high-stakes clinical setting* When Abridge uses frontier models vs proprietary models, and why its unique data from medical conversations matters* Why “every agent is a coding agent underneath,” and how the EHR can be thought of as a filesystem for healthcare agents* How Abridge approaches personalization across individual doctors, specialties, and health systems* Why “AI slop” is AI without context, and how edits, memories, and clinician preferences create a data flywheel* Abridge's eval stack: LFDs, LLM judges, in-house clinicians, third-party evaluators, specialty-specific evals, and progressive rollout* HIPAA, PHI, de-identification, one-way anonymization, customer contracts, and learning from healthcare data safely* What changes when you operate at 100M+ conversations: reliability, cost, post-training, model routing, and infrastructure optimization* Why the same clinical conversation can serve doctors, patients, payers, pharma, and future clinical-trial workflows* How Abridge works with EHRs, and why deep interoperability is table stakes for clinician adoption* Why healthcare AI has regulatory tailwinds, why 80/20 does not work here, and why high-stakes domains may drive AI forward* Why Abridge embeds “clinician scientists” into product and eval teams* What Chai learned from Glean about search, quality, and durable AI infrastructure* Why the future of AI infra may look like context layers, event-driven systems, Kafka, Temporal, sockets, CRDTs, and tools built for humans* Why Janie changed her mind on “PRDs are dead,” and why crisp written clarity matters more in complex AI products* How Abridge uses Claude Code, Cursor, and coding agents internallyAbridge:* Website: https://www.abridge.com/* X: https://x.com/AbridgeHQJanie Lee:* LinkedIn: https://www.linkedin.com/in/janiejleeChaitanya “Chai” Asawa:* LinkedIn: https://www.linkedin.com/in/casawaTimestamps00:00:00 Introduction and what Abridge does00:02:05 From ambient documentation to clinical intelligence00:04:04 Clinical decision support and context as king00:06:57 Alert fatigue, proactive intelligence, and prior authorization00:12:36 Ambient AI form factors and healthcare customers00:16:59 The hardest AI problems in healthcare00:18:26 Frontier models, proprietary data, and model strategy00:21:07 The EHR as a filesystem for agents00:24:03 Personalization, memory, and clinician preferences00:30:40 Evals, LLM judges, and progressive rollout00:36:47 HIPAA, de-identification, and privacy00:39:21 100M conversations and operating at scale00:44:10 EHR integration and the clinical intelligence layer00:46:39 Healthcare regulation, latency, and high-stakes AI00:50:11 Clinician scientists and long-tail quality00:53:04 Lessons from Glean and durable AI infrastructure00:57:03 The future of agentic healthcare workflows00:57:34 PRDs, product clarity, and building serious AI products01:03:11 AI coding tools at Abridge01:04:06 OutroTranscriptIntroduction: Abridge, Clinical Intelligence, and the Latent Space x Unsupervised Learning CrossoverSwyx [00:00:00]: Okay. This is a special crossover Latent Space Unsupervised Learning pod.Jacob [00:00:07]: Very excited to do this.Jacob [00:00:08]: At this point, we get together once a year.Swyx [00:00:10]: Once a yearJacob [00:00:11]: And this is a fun occasion to get to do it on.Swyx [00:00:13]: I really wanted to talk to Abridge but I felt very underqualified because healthcare is not something we cover very intensely. It just so happens that Redpoint's our big investors and supporters of Abridge.Jacob [00:00:27]: Anytime you want to have a portfolio company on your podcastJacob [00:00:29]: Please, by all means.Swyx [00:00:31]: So we'll introduce our guests. Chai and Janie, welcome to the pod.Janie [00:00:34]: Thanks for having us.Chai [00:00:35]: Thank you.Janie [00:00:35]: We're excited to be here.Chai [00:00:36]: Thank you.Swyx [00:00:36]: So for listeners, what do you guys do, just to situate you guys in the company?Janie [00:00:42]: Abridge is a clinical intelligence layer for health systems. We really started with documentation and building for clinicians and as we think about reducing the burden that clinicians have, they're spending 10 to 20 hours a week on documentation. There's a massive doctor shortage in the country. We also think that conversations between patients and clinicians are probably the most important workflow in healthcare. It's where care is given and received but if you think about the 20% of our GDP that goes towards healthcare, almost everything is a derivative of that conversation, whether it's the claim, the payment, the actual diagnosis given, the treatment. And we've started with a conversation to reduce the burden for doctors on documentation but we're really excited about the path ahead as we become this broader clinical intelligence layer.Chai [00:01:34]: I'm Chai. I work on clinical decision support at Abridge.Swyx [00:01:37]: Yes.Chai [00:01:37]: And so as Janie said, we're uniquely situated where we started off with the clinical note. What I'm really excited about and where we're expanding towards is what are all the things you can do before the conversation, during the conversation and after the conversation if you did have access to all the context about patients, payer guidelines, medical literature and put that together and to serve, how healthcare could look fundamentally different.Swyx [00:02:01]: And that's the context engine that you guys have?Chai [00:02:04]: Yes.Swyx [00:02:04]: Is that what it's called? Okay.Swyx [00:02:05]: So historically, as I understand it, the company started in 2018. A lot of people would be familiar with the AI voice notes form factor that doctors would be “Well, do you consent to being recorded?” It replaces handwriting and what have you. But it sounds like more recently there's been a big transition in the company. Tell me about the broader transition.From Documentation to Clinical Intelligence: Save Time, Save Money, Save LivesJanie [00:02:26]: So from a transition perspective, we really think about our journey as The first act was: how do we help save time? And that's where a lot of that original product was.Swyx [00:02:37]: By the way, one of those interesting statsSwyx [00:02:39]: On your landing page was, doctors spend time after hours.Janie [00:02:43]: They call it pajama time.Swyx [00:02:44]: Why is that pajama time?Janie [00:02:46]: Doctors after work in their pajamasSwyx [00:02:48]: In their pajamas. OhJanie [00:02:49]: At home are just writing and catching up on their notes every day.Janie [00:02:53]: Some of our favorite customer love stories, we have a Slack channel called Love Stories. We have clinicians telling us, “Abridge has helped us, from retiring early or we're now finally able toJanie [00:03:06]: go home and eat dinner with our kids for the first time.”Chai [00:03:08]: Save the marriage in some cases.Swyx [00:03:10]: One of the quotes was “We're not divorcing anymore.”Swyx [00:03:12]: I'm asking, “Why?”Swyx [00:03:14]: Because they're working too much.Janie [00:03:16]: But, in terms of where we're going and where we're expanding, we really think about our second and third acts around how do we help health systems save and make more money. Health systems are operating with record-low operating margins. It's getting harder and harder to serve patients and they have regulatory, some tailwinds but also a lot of headwinds coming their way and AI is ripe for helping on the saving and make-more-money piece. And then ultimately, how do we help save lives? The fact that our software and our product is open millions of times a week before, during and after a patient walks in the room, gives us massive opportunity with products like clinical decision support, which Chai is building but so many others to improve patient outcomes and probably one of the most important workflows and problems to be going after right now.From Glean to Healthcare: Context Is KingJacob [00:04:04]: One thing that's interesting, Chai, is you came over to Abridge from Glean and clinical decision support, which for our listeners is, in the context of a visit, helping a doctor figure out the right type of care. It's really a search problem in many ways, going through lots of different data sources. Very analogous to your previous role as one of the earliest engineers over at Glean. I'm sure a lot of our listeners are curious what's similar about the problems that you're going after now and what feels different, now that you're in healthcare.Chai [00:04:33]: Very similar. Taking a step back, with every wave, there's a lot of very similar patterns that happen across different products. A lot of social networking products look the same. A lot of credit-based products look the same. And we're seeing that very similar in the agent era with many companies, of course, in Redpoint's portfolio and so forth. And the key insight between both companies is that you have amazing models but context is king. Context is what puts them to work. So I see it in a lot of ways, a lot of similarities in this is a healthcare-coded version of Glean but the differences are really interesting. A couple things that come to mind. First and foremost, the rigor of the setting we're in. The downside risk is extremely high here in healthcare. It can be fatal in some cases. You prescribe something that the patient is allergic to for example. Whereas at Glean, it's “Oh, you got the question wrong.” It wasn't the end of the world in most cases. And so what does that mean? That shapes our evaluation strategy, both offline evaluation, progressive rollout and there's a lot more we could go into there. Second thing that comes to mind is, vertical versus horizontal. In both cases, there's a large variance but when Glean is, it's a much more horizontal company, there's a variance of personas, companies that you're working with. We also have a variance of personas, different types of specialties, different hospital systems. But the variance is a little more narrow. So from a product perspective, you're able to focus far more, especially when you have a maturing technology and you're building new products that never existed before. It lets you go after them much more easily and especially in healthcare where so many problems were solved with labor and process, that it's extremely ripe for AI to keep helping augment and enable. And the final thing that's really interesting, Abridge specifically compared to many other companies in the AI area, is the modality we started with where we're ambient and we're always listening in the background. And many more AI products will go that way but it's how we started. And that's the greatest form of AI we can create, AI that's seamless. You're not looking at your screen. It's always there. It's always helping you out and being proactive. The Jarvis vision that, every hackathon I went to over the past decade, there was always a Jarvis competitor. But Abridge very much started from the opportunity and continues to go that way.Ambient AI and Alert Fatigue: When Should the Product Interrupt?Jacob [00:06:57]: One thing that is super interesting then from a product perspective is you have this always-on seamless in the background and then you have to decide when you break the wall almost and say, “Hey, clinician, you might not have thought about X,” or whatever it is that you want to do. And in healthcare traditionally there's been this idea of alert fatigue and a million pop-ups and then a doctor just ignores all of them. It's probably a pattern that a lot of builders are thinking through now. How do you think about the right way to intervene or to pop up in a doctor visit?Janie [00:07:26]: It's such a good question. Alerts are notorious in healthcare specifically. Over 90% of alerts are ignored. The first and most important thing is context is everything, as Chai alluded to and I also think about how do we go from being reactive alerting to really proactive intelligence at the point at which it matters most. One thing we like to say is we want our product to feel like air conditioning. It should be in the background just making things better and if there is something that has great clinical risk and we're acutely aware that intervening now and not later is incredibly important, we should decide to act. But if you think about proactive versus reactive, instead of alerting a clinician during a visit when they're with their patient having a pretty serious and sensitive conversation, how do we prep a clinician before they walk into the room with that patient? And so historically, clinicians might have to manually go through charts with a patient that they've had over the course of months or years and they'll try to suss out what are the things they should be doing. You can imagine a world with Abridge. We'll summarize all of the most recent context for you, tell you based on the reason for a visit the patient is coming in for the types of things you should be discussing. And so you're going into that conversation prepped rather than walking in cold to that patient visit and then having this product interrupt you five or 10 times throughout the visit. And there might be times where it's really important to interrupt. We have a product called Prior Authorization and so this is when you may go into a doctor's office with knee pain. They'll prescribe you an MRI and so many of us have had this experience before, where in four weeks you'll get a call saying, “Hey, Sean, that MRI that you were prescribed wasn't approved and why don't you come back in? We'll figure it out.” In a world with Abridge, we might choose to quietly but still alert a doctor in that visit. And alert is probably not even the word we would want to use. Before a patient leaves, we would want to tell the doctor, “Hey, Doctor, before Sean leaves, you should ask him, has he had physical therapy and has his pain lasted for more than six weeks? Because the Aetna plan that he's on in California requires six things. We've already confirmed four of them have been met ‘cause we have all the context. But these two last criteria, if you can address with Sean before he leaves the room, we could guarantee that your MRI is approved before you leave.” And so when you think about clinical usefulness, impact to the patient, there are instances in which if we can catch a doctor while the patient is still in the room, as we think about save time, save money, save lives, we get to check all of those boxes. But when doctors have 15 minutes between visits, we have to be really thoughtful about when it matters.Prior Authorization: Reducing Latency in CareChai [00:10:23]: There's this interesting product opportunity AI has is reducing latency in the world. For example, prior authorization is an example of where care gets delayed and so great AI can reduce that. And the problem with alerts before partially is a technical problem: the quality of your alerts really matters. They're going to get ignored if you get alerts that... Similarly in engineering, where they're noisy alerts that you can't act on. But if you can make really high-quality alerts with both the context, as Janie said, and really high-quality models, then you can create a whole other game.Janie [00:10:53]: And I really like that experience because it starts to tease apart, what makes this so hard and unique. One, to make that prior authorization example possible, think about all the data that you need to have. You need to integrate with the electronic health record to know all of the patient context. Do we have access to your previous labs, previous imaging? And then to match you and to know that you're on Aetna, we have to collect all of the different payer policies and they vary by state. Some of these payer policies live on websites. Some of them live in unstructured 50-page PDF files.Jacob [00:11:31]: I thought this episode wasJacob [00:11:31]: To make sure we didn't scare people from healthcare.Janie [00:11:34]: But when you think about the things that make it hard, it also gives you the moat.Janie [00:11:39]: And then the second is the AI and the model quality we need to be able to hang our hat on. And so the bar, similarly when I worked at Opendoor, I worked on pricing models. Every outlier wiped out the margins of 30 and so similarly here in healthcare, the bar for accuracy is so high. And then I'd say the last is workflow is everything. If insurance companies deploy AI, it typically happens too late and this is when you have the notorious comical examples of AI just fighting each other when it's too late. But if we can pull forward the use of both the AI but also the ability to solve problems when the patient's in the room, you can start to collapse what typically takes weeks or months after your visit, ideally down to minutes or real-time. And it's where healthcare is both very difficult but also extremely rewarding if you can crack it.Product Form Factors: Mobile, Desktop, In-Room Devices, and ARSwyx [00:12:36]: Just to get some baseline on the form factors, because I've seen some videos on your website and stuff. You guys talk a lot about ambient AI. Is it primarily on the phone? Is there any other form factor that people get Abridge in? Is there an Abridge room setup where it's always on? I don't know.Jacob [00:12:55]: An Abridge podcast studio.Janie [00:12:58]: Primary form factor is mobile and desktop. UsuallyJanie [00:13:00]: Clinicians are walking in and out of rooms with mobile but at the end of the day, when they're closing out their notes or wanting to prep for the day ahead, they might use desktop. We have been having a lot of really interesting partnership conversations with a lot of these in-room device companies as you think about the power of multimodality and even more data, as you think about all of what is not captured today. It is fascinating to think about, especially even as we go into building and scaling our nursing product. It's one where nurses constantly, as they're walking in to check in on a patient for two minutes or maybe even 30 seconds,Janie [00:13:43]: Starting an Abridge experience is probably going to take longer than the visit. And so what can we do with in-room devices that are always on starts to raise really interesting and fun product questions.Swyx [00:13:54]: I was thinking, the way in tech companies we have all these Google MeetSwyx [00:13:58]: And other things, we might as well set up entire rooms with just Abridge tech.Chai [00:14:02]: Very much. AR glasses and related form factors are also relevant: how do we bring the information to the clinician in real-time without a screen, while still letting them focus on the patient?Swyx [00:14:18]: Do you think they want that? I'm skeptical of AR, but I'm curious what you've tried.Chai [00:14:26]: Admittedly, it's not a near-term product roadmapChai [00:14:29]: By any means. I'm being far-fetched.Jacob [00:14:31]: There's some sick AR stuff for surgeries.Swyx [00:14:33]: Really?Jacob [00:14:33]: When people are trying to visualize, you're about to make an incision but you want to see, what the cut might look or what the body might look like inside and they can layer in imaging.Swyx [00:14:43]: That's cool.Chai [00:14:45]: At some point in the future.Janie [00:14:46]: But there are a lot of our largest customers and at the largest health systems integrating already and so even as we think about building into it, unlocks a lot of product capabilities.Swyx [00:14:57]: And just to establish the terminology. Sorry, and I know I'm asking basic questions somewhat for myself but also for the audience who might beHealth Systems, Buyers, Clinicians, Patients, and PayersSwyx [00:15:05]: Less integrated. When you say health systems, it's like the Johns Hopkins, the Kaiser Permanentes.Janie [00:15:09]: Mayos, the Kaisers of the world.Swyx [00:15:10]: These are your customers, right? And the outcome that you deliver for them is happier doctors, reduced cost of processing, reduced mistakes. It's weird in a sense that I feel like there's also, a secondary customer, the customer of the customer and I don't know if you — do you think about it that way?Janie [00:15:28]: The other interesting and complex part of building product is we have our buyers, who are the chief medical information officersJanie [00:15:39]: The chief financial officers, the CIOs of these large health systems. Our users today are clinicians but if you think about who downstream is impacted, it's patients. And so as we build, with every product in mind, we think about who we're building for, who the secondary user is and what does that mean either in terms of experience, security compliance, ROI that we have to make tangible. And so like you said, time savings is one of them. But for CFOs, they care a lot more than just time savings. We have to show for every dollar you put into Abridge, because you have more compliant documentation or because you have fewer queries coming from your billing team, we save or add real dollars to your bottom line or top line, are things that we're constantly thinking about because of the dynamic across all three sets of users.Chai [00:16:32]: There's a whole other axis too with the payers and pharmaChai [00:16:35]: as well. Connecting all these three big stakeholders in healthcare isSwyx [00:16:39]: Do the payers ever see your data? Sorry, the payers meaning the insurers, right?Chai [00:16:44]: Yes.Swyx [00:16:44]: They also see Abridge data?Chai [00:16:47]: NoSwyx [00:16:47]: Like the direct integration to you guysChai [00:16:48]: They wouldn't see the raw Abridge data but when you're working together on something like prior authorization, whatever information they need, we'd communicate to them.Jacob [00:16:59]: That's cool. I would love to dig into the AI side. You still have a lot of problems on the AI side. And so maybe to start at the highest level, what's one of the hardest problems you have to solve in AI at Abridge today?The Hardest AI Problems: Quality, Latency, and CostChai [00:17:11]: To make things simple, let's take, building off the prior auth example. So one thing Janie talked about is okay, this data is all over the place and there's this combinatorial explosion of procedures, payer policies and even sometimes different health systems. There can be some cross-product of all of these different considerations you have to take into account. But what's really hard about this problem is doing it real-time in the conversation. So, in any AI product, usually the three KPIs you care about are quality, latency and cost. Now, what we're saying is we want you to do this real-time in the conversation, guiding the clinician. How do we do it in a way that does not break the bank? But we're using — But we also need very intelligent models because you're working with this cross-product of data and this, all this context layer as well. So you need high intelligence and high-quality because you don't want the alert fatigue but you also need to be fast and cost-effective. And so that's where a lot of clever engineering goes. It's okay, without getting into all the details here, can you model these policies in some intermediate representation or other things that you can do that can make this problem tractable? And of course, the Pareto frontier is always changing but we are also trying to do this now.Model Strategy: Third-Party Models, Proprietary Data, and Medical ConversationsJacob [00:18:26]: What implications has that had for what you take off-the-shelf and say, “ what? We don't need to be world-class at X. We'll just take this from the model providers or from some infrastructure player,” and what you're “No, this is where we spend most of our time focused on”?Chai [00:18:38]: This is, the fun challenge in AI?Jacob [00:18:42]: It changes every three months? SoChai [00:18:42]: Of course, with the shifting landscape, we try to be extremely thoughtful on predicting the trends of where third-party models are going and where we can uniquely go. And, sometimes when you talk about AI models, we're the models are just going to get infinitely better. But I don't think... It may be in the grandness of time you could say that but, within every month, every quarter, there's specific ways they're getting better. They're training on a lot more, coding data to be better coding agents, for example. And soChai [00:19:14]: We have to think about where are the things that won't — unique data that we're uniquely training on or to step back a little, where is a proprietary model bringing advantage to us is if it can give higher quality or lower cost and latency for similar quality, very similar to many other companies. And when we can do that is when we have proprietary data. So, for example, we have on the order of eighty million or hundreds of millions now getting close to of medical conversations.Jacob [00:19:44]: It's insane.Chai [00:19:45]: This is a unique data set. And this data set, it's very interesting because this data set is effectively a large part of the trace between the patient and the provider. That's where the quote-unquote debugging happens in healthcare. We have these traces at scale, as in as, our CEOs even called it, an exhaust that comes out of our product. And so when you have these traces, that's how you can train better agents on certain use cases, whether it's your transcription diarization use cases or so on or like note generation models and we can do that much cheaper and faster. But we're always also working with these third-party model providers. We closely collaborate with them and that's how we predict where the trends are going. The thing that I think about a lot is that, I know that the model providers are going to train much more on agentic workflows and so forth, so that's great, so that you have a better agentic harness. But the other thing that's interesting is that the model providers, because a large class of the consumer model providers is healthcare queries, that they might, optimize to train a lot of healthcare data to encode the knowledge in its weights. And this is just a great thing for us as well, where the off-the-shelf models can keep bett-getting better at general healthcare information, such that what our strategy is, we have a constellation of models, we can use something for this, that and, we only care about, at the end of the day, the best product experience.EHR as File System: Agentic Workflows and Real-Time InterfacesJacob [00:21:07]: And, you have, overall capabilities improving. I'm curious, as these models get better, is there something you look at and you're “, three months ago, we really couldn't do that but God, the the latest models really allow us to do it”?Chai [00:21:19]: So here's something interesting that I've, been toying with. So all models are... This wasn't super obvious a year ago but now it's become clear and clear that almost every agent is a coding agent underneath the hood? So you give it whatever file system, it can write its own code and so forth. So when you think about within healthcare and the use case that we have, you can think of the EHR effectively like a file system. It's just — it's a storage of all this information. It's a lot of information there that cannot fit into the context window, at least of today's models and you want to use that context effectively for all these product use cases we're talking about. And so if you have better agents that can, manipulate data, read that data, treat it as a file system as we see they're going and we know model companies are investing this way, then that very directly benefits us.Swyx [00:22:09]: Yeah. Okay, cool. Again, just establishing basic things. But we're going back to the model stuff. I'm really interested in double-clicking more on the real-time, element, which is pretty important for both of you. Is it — Is real-time just batches of every one minute, every five minutes? Is that how we do it? Or is there some more native, genuinely real-time in the sense that OpenAI has a real-time API or Gemini has a real-time API?Chai [00:22:35]: Yeah. Yeah. So today it is more on the on the batch basis but there's interestingChai [00:22:41]: Prototypes that we have that we're still not fully, full time, voice in text out or in that sense. But, can you trigger your models, your agents or agentic workflows, depending on the right times in the conversation?Chai [00:22:58]: And so you can imagine, different techniques to bring this latency down and, you want to bring the feedback loop down as much as you can. And so a lot of clever engineering there without fully... Maybe one day we'll do full voice in and text out, train a model to do something like that.Swyx [00:23:15]: You do — People don't want voice in voice out?Chai [00:23:18]: Now we aren't creating experiences that are, during the conversation, inter — It's almost likeSwyx [00:23:25]: Might be too disruptiveChai [00:23:26]: Too disruptive until, who knows, maybe eventually you could have full voice agents once we — the quality and we improve the comfort of the technology. But right now gra — that change is much more gradual and it's more text focus, text out.Janie [00:23:42]: And so much of currently what our product is trying to do is allow a clinician to focus on their patient and maybe at some point but right now patients, clinicians don't want a third voice, at least in a literal voice in that room. And so how do we be there with all the contacts and information ready at hand when there's the right moment?Personalization: Individual Doctors, Specialties, and Health SystemsJacob [00:24:03]: Jenny, one thing I'm curious about is how you think about, personalization in the product. I imagine, every doctor is a special snowflake in their own way, has their own way they like to do things. There are probably a bunch of different approaches you could take to doing that, both within the model layer itself but then also just with clever prompting or engineering. How do youJacob [00:24:20]: Deliver on that?Janie [00:24:21]: It's such a good question. Personalization is massive for us. We think about personalization at three levels. The first is at the individual, the second is at the specialty level and then the third is at the health system or the organization level. To your point, there are a lot of individual preferences. You-When a note is produced, it almost is a reflection that is so deeply personal of a doctor's work and how they give care. And so do they have preferences on things like style? They might want bullets versus paragraphs, really concise versus comprehensive. They also might have phrases that they really like to use or the templates that they want every note to be structured. And, we see it in our feedback all the time. We want two spaces in between sentences or I refuse to use this tool. And so that's something that we've had to build in. And the tricky part is how do you make sure that stylistic preferences don't interrupt accuracy and quality and that's something that we've really had to refine and hone over time. Second is at the specialty level. A cardiologist note or workflow is going to look very different from a dermatologist workflow.Jacob [00:25:32]: I assume cardiology notes are the highest stakes for you guys, given your CEO is a cardiologist.Jacob [00:25:36]: It's “Oh my God, make sure we get this one.”Janie [00:25:37]: Shiv, our CEO, is still a practicing cardiologist. He rounds once a month. And so, first call when we want just quick and easy user feedback too.Janie [00:25:46]: But, specialties require a lot of personalization, both in terms of what does the product look and so we make sure that as new users onboard, we catch that and the product proportionally reflects that. But also on the back end, evals at the specialty level, they are hard-earned to calibrate and get. What does a really great dermatology note look like? What makes it complete? What makes it compliant and billable is very different than a primary care doctor. And so it's not just about what does the product experience look but on the back end tuning and really deepening our understanding for the specialists. What does great output look like? And that's, a problem that we need to calibrate internally, externally, online, offline but, takes lots of cycles but is necessary in a high-stakes environment. And then at the health system level, for products like clinical decision support, you have health systems who've spent years or decades refining their best practices and they want to know, “Hey, we love your clinical decision support product but how do we embed our own hospital guidelines into them to inform clinicians before, during or after a visit what brest — best practices should look like?” And as you think about, deepening moats as well, when health systems, trust us with that data, allow us to productize it and directly into the clinical workflow, makes us a really great partner to health systems who want to build something that truly meets their needs, their practicing guidelines.AI Slop, Memory, and Product Data FlywheelsChai [00:27:23]: And I want to add onto that. The for the clinical documentation problem, it's very similar to AI writing that doesn't feel like your own and then we call that slop. But the way I describe one framing of slop is like AI without context. But we have all that context and both the clinicians, can have it and can guide it. And so part of the other interesting exhaust for us is, memory is, one of these new systems recordsChai [00:27:49]: Almost.Janie [00:27:50]: And we also have all the edits people make on our product and when you think about a data flywheel and how we get better over time becomes really powerful as a mechanism to just going deeper in personalization.Jacob [00:28:04]: It's interesting. I love this idea of working with systems on the guidelines they built up over a long time. I feel like so many of the best AI app companies today are... The question is: How do you take the expertise that a law firm or a bank has built up over many years and then add that as context and also a special sauce over, a an AI tool? And so seems like y'all are really doing that very effectively.Janie [00:28:24]: We're now starting to have our customers ask, “What are other customers doing?”Janie [00:28:28]: “And how are they doing it?”Janie [00:28:30]: And as we think about having visibility across such a large set of care being delivered right now, a really interesting place we could also partner.Swyx [00:28:40]: I'm just curious. I — This may be a nothing question but, how different are health system guidelines from each other? Don't they all converge to the same thing? And if not, where do they differ?Chai [00:28:52]: At a really high level, they're going to talk about very similar things but the difference is probably in some more of the details. “Oh, you should refer to specialists only when XYZ conditions are met,” or so forth and maybe different organizations have different practices and guidelines around that. But high level, talking about similar things but the details are what, of course, that shapes the context and the decisions you make.Swyx [00:29:15]: And this all goes into the context engine and it might affect the notes but maybe not.Chai [00:29:21]: The — For these local pathways, we're definitely thinking about it a little more for our clinical decision support product.Chai [00:29:26]: So yeah.Swyx [00:29:27]: Which is your stuff, yeah.Swyx [00:29:28]: And then the memory which you raised, let's just tell us more about that. What have you tried in memory? What's the structure of the memory? What works? What doesn't work?Chai [00:29:38]: There's, of course, many different ways you could do memory, where it's okay, can you bake it into the model weights or can you do it in some external store? For us, what's interesting is, of course, when you think the models are rapidly changing, whether it's in-house or third-party, baking into the model weights, sometimes you worry that it could be a little throwaway. And so, how do you... You need to find a way that you decompose the problem, the preferences from the underlying models and so forth. The thing we're right now most both that's easiest to start with and we're excited about is having, a separate store for memory, where you have, for example, a memory sub-agent that's, working in the background, figuring out what are the important parts of the clinician's actions that we want to remember for the long term. And then you can also imagine, other things where in the — you have background jobs that are running that are collating these, memories similar to Sleep, of course and what other pattern, patterns products do as well. Learning over all these action, all the action data we have, again, note edits, the conversations they did and the actual transcripts.Evals: LFD, LLM Judges, and Clinical SafetyJacob [00:30:40]: What about evals? How in the world do you... It is such a complex product surface area. We would love to hear you riff on that and also how has that evolved? I'm sure you've gotten better at it, so any learnings along the way.Janie [00:30:50]: From an evals perspective, we, from day one when we build any new product or feature, we think about, what does good look like? And there are table stakes things like clinical safety but then you start to get deeper into what does good quality look like. And when you go into something like our core product, there's stuff like style and completeness and there's things like does this note become something that can be billable, which is very high stakes for a health system. We have a number of ways in which we get confidence for this. We have, internal in-house clinicians who do what we call an LFD process to give us our very first pass at is this or isn't this a good enough output, look at the effing data.Jacob [00:31:41]: LFD?Chai [00:31:42]: That's why I was smiling. I was “Is Janie going to mention what it stands for?”Jacob [00:31:46]: I was not... There's like a million acronyms.Jacob [00:31:48]: How am I supposed to know that I don't? So “Oh yeah, of course, an LFD.”Swyx [00:31:51]: I've never heard of LFDs.Chai [00:31:53]: It's a bridge for sure.Janie [00:31:55]: I got through three days and then I had to ask someone.Janie [00:31:58]: I thought it was just me that didn't knowJanie [00:32:01]: It's our internal process.Swyx [00:32:02]: But look at the data as a meme in ML, ‘cause you tend to not look at it. You just want to look at number go up.Chai [00:32:06]: Exactly.Swyx [00:32:07]: But yes.Janie [00:32:08]: But so, we make sure we look at the data and then as we think about all of the components of good output, we, one, create LLM judges across all of these and we make sure with annotated data and either internal or external evaluators, we feel like these judges are calibrated. And then depending on the stakes, we also work with in-house and third-party evaluators across all of these before we ship any big change. And the goal is, in terms of evolution, how do you go from this process taking months, down to weeks, down to days? Some of it is, a true science and ML problem. A lot of it's also just, hard operational work. Have you planned ahead in terms of what you need? Have you really optimized the capacity that you need across all of the different specialties you need? Have you gotten a really good sense of which third parties are great to work with for what use cases? This takes a lot of domain, expertise and, lots of mistakes and errors in figuring that out. And so as much of it is an ML problem, so much of it has also been operational gains that are hugely important, where domain-specific expertise is everything.Specialty-Level Evaluation and Progressive RolloutsJacob [00:33:23]: But it's funny, ‘cause I feel like people talk about healthcare like it's one giant market and the reality isJacob [00:33:26]: It's, dozens and dozens of sub-markets. And so it feels like in your evals you have to build that up across the board, probably.Swyx [00:33:34]: And is specialization the primary cardinality at... That's the word that comes to mind.Janie [00:33:40]: Sometimes, depending on the product or the use case. And so if we're making a note improvement or feature for a particular specialty, definitely but we have products that are for nurses. We have products that, are really aimed at making the document or the output a lot more billable. And so we'll want to work with coding teams and not necessary clinicians. And so likeJacob [00:34:05]: Coding meaning healthcare coding.Janie [00:34:06]: Yes. Yes.Jacob [00:34:07]: NotChai [00:34:07]: Yes. I see you.Swyx [00:34:07]: Other kinds.Janie [00:34:09]: But is this output proportional to the work that was delivered? Is there sufficient documentation to justify the amount that a health system may end up charging? And so, specialty sometimes but also domain, very different across all of the different products that we're working for. And building out that network is, not easy and is where a lot of our operational investments have gone into.Chai [00:34:35]: And I view a lot of analogies to self-driving cars here, where, part of it is we really want progressive rollout of features to test in the real world is this useful? Is this going to work? One big difference compared to past lives is before I'd build a product, maybe I'd alpha it and then I'd like GA it the next week, ‘cause I'm “Go, move fast, ship,” and whatnot. But the mentality is like you... I want to make contact with the reality as quick as possible but I want a progressive rollout. Because as much as I get as large of an offline eval set, I want the distribution of that to match real-life distribution. And over time, by rolling out early, similar to Waymo has a tagline, “The world's most experienced driver,” another thing that can, at least linearly increase for us is, both the size of our evaluation offline and online, that and it all feeds back.Janie [00:35:25]: Something that's been earned over time, speaking of evolution, is just the trust we've gotten with customers. Historically, a lot of these health systems, when they bring on new vendors, their release cycles are quarters, sometimes twice a year. We've gotten our customers onto monthly release cycles, which is pretty fast for health systems but what is more exciting over the last, call it, few quarters, has been, a subset of our customers have said, “We want to innovate with you. We trust you,” and we have a pretty, decent chunk of our customers who say, “We'll develop with you outside of these monthly release cycles. We have a higher tolerance. We know that the stakes are very high but we want to be the first ones using these products, giving you feedback.” And so for a pretty substantial set of our customers, we've been able to convince them to be able to ship, in this gradual way before GA. Something we talk about a lot internally is, trust is earned in drops, earned in buckets and so we still can't do what I used to do when I worked at Loom. We had 30 million users. I'd just be, rolling out experiments left and. The bar is still quite high for iterative rollout but because of the trust we've earned, we're able to learn at pretty high volume very quickly.Privacy, HIPAA, and De-IdentificationSwyx [00:36:45]: Your scale is still pretty huge.Swyx [00:36:47]: One thing I want to... We were going to go into scale? In a sec. One thing I wanted to call up, follow up on evals, which, again, just coming from a generalist engineer point of view, just thinking through what would people be scared of in doing this, the privacy and HIPAAJacob [00:37:00]: Elements of this. I have zero experience in that. What do you have to do? What is surprisingly not that bad?Chai [00:37:06]: So one thing that's really important here from a compliance perspective is very much that any of the data we use needs to be de-identified, any real-world data we use as a basis of online eval sets we're learning from. And so you have to — And there's, very clear, government guidelines, what counts as PHI. And so we've even have built models that can take, for example, a clinical transcript and remove all the key PHI indicators and so you have a scrubbed/de-identified version. And then once you... And so one thing that's important is first you've got to get confidence in that model in the first place? And prove that out. Because, now you have, multiple probabilistic systems on top of each other.Chai [00:37:46]: But once you have that, then you can train on it use it for evaluation and so forth, provided one of the cool things also that you can do from a business side is the right data contracting as well with your partners.Jacob [00:37:57]: Is the anonymization one way? Once it's done, you cannot undo it? Or is there someoneChai [00:38:01]: YesJacob [00:38:02]: Who holds the master key that can... Yeah, okay. So it's one way.Chai [00:38:05]: It's one way. Yeah.Jacob [00:38:06]: That's how it works. I just wanted to... Because, there's a lot of this, learning from feedback and everything that, you would want to debug more but you can't because you just physically don't allow yourself to.Janie [00:38:17]: Some of it's also written in our customer contracts in terms of who can or can't access PHI data, how long do we retain it,Jacob [00:38:27]: Very goodJanie [00:38:27]: Before it gets de-identified. And so we have a pretty high bar for who can access that PHI data, just to make sure that we always respect our customer data and privacy. But that's something that we partner with our customers on too, to make sure that as we want full, as close to precision as possible in that qualityJanie [00:38:48]: We can still use it.Jacob [00:38:50]: But it'll be fascinating to see how that space evolves? Because you think about, I used to work at a company that, did a lot of healthcare data in the cancer space and if you asked, the average cancer patient, “Hey, do you want people, do you want other patients to be able to learn-”Chai [00:39:03]: Take it.Jacob [00:39:03]: “... Learn from your experience?”Chai [00:39:04]: Take it all.Jacob [00:39:05]: They're “Please.”Jacob [00:39:06]: “I'd love, nothing more than for other people to be able to learn fromJacob [00:39:10]: The experience that I had.” And so in the past it was a lot harder to do that learning. But with this technology, that might really be practical and so it'll be fascinating to see how that continues to evolve.Chai [00:39:21]: There's so much in our data set of 100 million conversations.Chai [00:39:26]: You can imagine things like insights that you can give to the clinician. How could you, oh, how could you have reacted to this? In coaching or insights around, which treatments are effective or, like... Because you have this, again, this data source that was never captured before but that's, where, intuition or experience is created from, going back to this idea that the conversation is the agent of truth.Operating at Scale: Reliability, Cost, and Token EfficiencyJacob [00:39:46]: Back to the 100 million conversations, I feel like you have this insane scale that maybe only a few other AI app companies have and everyone else dreams of. So not everyone has had to confront this yet but maybe just talk about some of the challenges of operating at that scale and what, our listeners have to look forward to if they ever get to this level of scale.Chai [00:40:05]: At large and larger in scale, so of course there's a general, infrastructure reliability. When you... In any given startup, you're building the plane while it's flying. So there's some notion of that. But what gets interesting on the AI and ML side for sure is this, as you get at more and more scale, so one, you have the data to first and foremost do this. But, you start thinking about costs or infrastructure in a whole different way at scale versus, a prototype.Chai [00:40:34]: You can use the most expensive model, you can burn as many tokens as you want but when you're doing 100 million conversationsJacob [00:40:41]: Token max on leaderboards are less upsetting than that context.Chai [00:40:45]: . When you're doing that and so that comes for we have the data and we also have the team that's able to post-train based on this and you can optimize for efficiency, especially in areas where you believe that maybe a lot of the quality headroom is less so and you don't expect the other off-the-shelf models to go that way, such that you want to do, efficiency maximization, in terms of compute and tokens.Jacob [00:41:08]: I feel like you guys live in the future in some way where most use cases today are really just in use case discovery mode, where it's “God, I really hope I can find something that can get to scale,” and so you're always going to use the most powerful model. And then the few things that do get to this level of scale, you start to do those optimizations.Chai [00:41:22]: It's a natural trajectory where it's like zero-to-one, we're not talking about any of these optimizations.Chai [00:41:26]: But when maybe we're in the one-to-100 or so forth, then we're in optimization mode and, what works out really well is you've got all this data from zero-to-one that lets you do this.What Comes Next: The Conversation as the Shared Healthcare PlatformJacob [00:41:36]: That's fascinating. I feel like one thing that's so interesting about the Abridge footprint is that you're in the doctor-patient visit in real-time. I always like to say, there's like probably 50 years' worth of product you could build on top of that. What gets each of you, I don't know, what are you most excited about building, either in the short term or medium term or even, long down the line?Janie [00:41:53]: Something that I get really excited about is that the same conversation can serve so many stakeholders. If you think about the conversation, a doctor needs to know what is the documentation, how do I make sure that this fully represent the care I gave? A patient needs to know, “What the heck just happened? This was really overwhelming. What are my next steps?” A payer needs to know, was this the proper and appropriate care given? A pharma company might want to know why isn't this drug being properly used or is there a good candidate for this clinical trial that I'm about to run? And where I get excited is that our product and our platform and our infrastructure can be the same product across all of those things and start to what's today, separate, very expensive, complex systems that serve each one of these stakeholders in very different ways, start to collapse all of that into a singular platform that enables not just more efficiency across the board but also better outcomes for everyone. And, all of us experience healthcare in probably very painful ways and knowing that there is a world in which we can simplify a lot is really exciting to me and it all starts with the conversation.Chai [00:43:15]: It's interesting. Of it very similar to going back to the KPIs that any AI product cares about. How do you increase quality of care? How do you reduce latency to care? And how do you reduce costs? Which is a huge, in healthcareJacob [00:43:28]: They call it the triple aim in healthcare.Chai [00:43:30]: But very similar to building AI products and the thing that really excites me is when we talk about that latency piece, we talked about one example earlier of prior authorization, can you reduce the latency to care? But you can imagine so much more. Oh, as soon as the lab value gets updated, do you have like a background agent that, kicks off and uses all the context to be “Oh, hey, the patient should do this next,” for example. And of flagging that to the clinician who's always in the loop but reducing that latency, to care. And then you can imagine this is much further down the road but it's like even connecting that to the direct patient and the consumer. And so how can you, how can you build a bridge to all of these things?EHR Partnerships and the Clinical Intelligence LayerJacob [00:44:10]: Very cool. The connections piece is just an ever-growing thing. And one of the key partners is the EHR and I wonder what that relationship is like. Will they, look at this as, something that is valuable enough that they want to own someday?Janie [00:44:29]: Our partnerships with the EHR is, we know that we have to be extremely close partners with all the EHRs who we partner with. Being able to not only pull and push all of the data into the right places is, not only table stakes, if we can't do that, health systems don't want to use us. The second and the reality of today is clinicians spend a lot of their days in the EHR. So much of what allowed us to win in the largest health systems was pretty direct and, very close partnerships with some of the largest electronic health records that allowed us to pull and push data with APIs that weren't ready out of the box. And clinicians want to save clicks. Anytime we introduce a new product that, adds two clicks for them in their day, they're “We're not going to use it.”Janie [00:45:21]: They have 15-minute back-to-back appointments with their patients. They're spending, hours during pajama time doing documentation. Every second and every minute counts and so we really think about being deeply integrated into the EHR as also table stakes to getting real usage and adoption. And anything that we build or introduce, we really talk about earn the right internally a lot, which is we have to provide so much value or save so much time that people will use us. But those are the two things that are close to us, is we know that the product won't be used unless it is deeply interoperable.Chai [00:46:01]: And strategically, to your point, it's like what does EHR want to own versus us? EHRs are really focused on the clinical workflows and so forth but some of the things that we're talking about here, I do these traditionally are outside of the domain where it's oh, connecting pairs and providers together with provider policies or the clinical trial matching, as Janie brought up. And so these are, entirely — we position ourselves as building this entirely new intelligence, clinical intelligence layer across, again, providers, pharma and, payers.Chai [00:46:33]: And so that's a it's a whole different ballgame that we try to playChai [00:46:36]: In combination with them.Jacob [00:46:37]: But it's like a different layer of scope.Healthcare AI Regulation, Technical Depth, and What Changed Their MindsJacob [00:46:39]: I'm curious, you are both relatively newcomers to healthcare. People have these, there's lots of futuristic healthcare AI takes of “Oh, everything will look different.”, now that you've been in healthcare for a bit, you live at the edge of AI, what have you, changed your mind on around this, as you think about what healthcare looks like in ten, 20 years? Any updates to your mental model from the time being close to the problems?Chai [00:47:02]: One thing that IChai [00:47:04]: Was hesitant about before and it's a common thing when I'm trying to recruit engineers that people ask me around, is definitely oh, healthcare, heavily regulated space. And it is, rightfully so. You want to keep, the patients at the end of the day safe. But one of the interesting things that, is a that surprised me how much it is coming to the company is there's a lot of really favorable regulatory tailwinds as well. Where you think about, government really wants interoperability between all these systems that we talked about and so agents can access this information. The government just in January, the FDA released updated guidance on clinical decision support, what I work on in such a way that they used to have guidance from like 2022 that required you to have, mention all these options and do all these other things but it's a very forward and forward-looking way. And so for me, what's been really cool to work on is this, there's this very special moment both in AI in general, we all know that but there's a special moment also regulatory in healthcare as well.Janie [00:48:05]: One thing I would call out is for the very reasons things are higher stakes or, potentially considered more difficult in healthcare, it's where some of the hardest AI problems will get solved first, just because the bar is so high. When I first joined, I was “Oh, this is where we'll be on the tail end of where, all of the AI innovation will be able to be applied.” But when you think about, zero error evals or multi-step workflows that have really low tolerance, a lot of the innovation will happen here just because we have to or else we can't ship.Jacob [00:48:42]: ‘Cause like in other domains, you'd much rather just solve the 80%-is-good-enough problems firstJanie [00:48:46]: 80/20 doesn't work hereChai [00:48:48]: And building off that, traditionally, there was a bit of stigma that, oh, healthcare companies are not that interesting from a technical perspective or I've seen that or faced that myself. But these are really hard and fun problems from a pure technical perspective beyond just the impact. How do you bring the latency of this thing down and make it really high-quality?Reducing Latency: Clinical Workflows, Agents, and Implementation RealityJacob [00:49:07]: How do you bring the latency of things down?Chai [00:49:10]: Yeah. Yeah. Yeah. So okay, let's answer the latency question. And maybe hopefully not too redundant with some of the things I've said earlier but some part of it is with any latency, you have to like what is, what is really your bottleneck. In a lot of workflows, it's sometimes it's the model itself. And so that's where like our data flywheel, our post-training team and so forth come in so that can you make the models far more efficient. So that's one aspect of latency. But there's whole other aspects of latency where it's okay, on top of that, if you use a constellation of different models, can you use — can you first use like a — it's like thinking fast and slow. Can you use a cheap, fast model that triages and hands it off to a larger model where you get more intelligence and so forth and so all theseChai [00:49:56]: Clever tricks to make it work.Chai [00:49:58]: And by the way, we are totally — we also realize that the parameter frontier is changing and so these tricks will — may not get us to where we want to be in five years but we need to if we want to build a useful product right now.Jacob [00:50:11]: Should we go to the quick-fire or you want to ask more about Abridge? We can stuff everything that's not Abridge into the quick-fireSwyx [00:50:16]: I don't mind. I was — I feel like Janie was on the topic of more long tail stuff, which isSwyx [00:50:21]: Not the eighty/twenty thing and that really matters. And I'll —, if you have any tips or cool stories or just general approaches that have worked for you that's interesting to dig into.Janie [00:50:32]: One of them is even just how we staff our teams looks different than a traditional software engineering team, I'd say.Swyx [00:50:40]: Let's go.Clinician Scientists, Edge Cases, and Evals at ScaleJanie [00:50:41]: We have a bunch of folks with different roles who are clinicians and so we have this role called the clinician scientist and I heard one of our leaders refer to them as mutants recently. But they are people who've had clinical backgrounds, so MDs typically, who are also deeply technical, somewhere, on the spectrum of like a full stack engineer all the way to like extremely scrappy prompter. But having each of these people embedded within our teams instantly raises the bar for everything that we build because not only are they determining, is this product clinically useful but they're deeply embedded in our whole evals process. And so when we talk about LFDs, when we talk about what is our actual evaluation criteria, you don't want Chai or me creating what those are because we don't have clinical background. But is probably unique to Abridge but has been game changing. And when you think about where the puck is going, you have people build with clinical backgrounds who are technical and where AI tools are going, they just becomeJanie [00:51:53]: More and more, critical and like the killers of the team. And so that's one. And then the second is just the scale at which we do evals to catch that long tail up front before anything ever gets into production is something that we've pretty much like really started to fine-tune, both from a scale but when do we know we need to get several hundred versus several thousand offline responses, what helps us make that quick decision and make this less of an art and as much of a science as possible. But that's also been something we've had to tune over time.Swyx [00:52:27]: And you have partners who opted in to give you those evals.Janie [00:52:31]: So we work either internally or with third-party for offline evals and then we have customers who also agree to give us, whether it's like thumbs up, thumbs down to like choose this or that, a lot of data to get us to what is as close to fully confident as possible.Swyx [00:52:51]: The term that comes to mind isSwyx [00:52:53]: Like active learning on things where you're weak. I feel like it's a lost artSwyx [00:52:58]: Is a lot of the polish that comes into doing something like this.Janie [00:53:02]: Really.Chai [00:53:03]: Hundred percent.Lessons from Glean: Technical Foundations and AI App InfrastructureJacob [00:53:04]: Maybe, on a totally unrelated note, Chai, you had a very, storied run at Glean b

Welcome back to the Pear Healthcare Playbook! Every week, we'll be getting to know trailblazing healthcare leaders and diving into building a digital health and biotech business from 0 to 1.We would greatly appreciate it if you took a moment to listen to the episode on either Apple or Spotify and leave us a rating! Your support helps our guests' insights reach a larger audience!Today we're thrilled to host Anirudh Joshi, co-founder of Valar Labs, who is building the company with co-founder Viswesh Krishna. Valar is pioneering AI-native oncology diagnostics using standard pathology slides to predict treatment response, starting with bladder cancer and expanding across oncology. The company raised a $22M Series A co-led by DCVC and Andreessen Horowitz, with continued backing from Pear VC since day one. In this conversation, Anirudh walks through how the genesis of the company, why pathology has long been underutilized and what it took to bring Vesta, its genitourinary-focused portfolio of AI powered pathology tests, to the clinic.

May 7, 2026: A landmark study from the Center for AI Safety spanning 56 AI models finds that smarter models appear to be sadder, that you can give an AI the equivalent of a digital drug, and that when you make an AI miserable it tells you the future is "grim." Second, Andreessen Horowitz publishes the most detailed optimist case yet that the AI job apocalypse is bad economics and worse history — rooted in the lump-of-labor fallacy — while Fortune raises the one question the optimists still haven't answered. And third, Coinbase CEO Brian Armstrong coins the term "pure managers" to describe the layer of corporate hierarchy that AI is eliminating first — and the player-coach model he's building in its place may be the clearest picture yet of what organizations actually look like in the AI era.

Sponsored by Chargebee, subscription and revenue management → check out their startup offer: https://www.chargebee.com/startups - Samar Abbas, Founder of temporal.io https://www.linkedin.com/in/samar-abbas-381997/   - Samar Abbas, co-founder of Temporal.io, shares the journey of building an open-source platform that ensures durable execution of code, allowing developers to focus on business logic instead of handling failures and reliability. - Temporal.io originated from years of experience at companies like Amazon, Microsoft, and Uber, where Samar and his co-founder iterated on workflow and state management systems, eventually creating a new category called "durable execution." - The company's open-source approach led to rapid community adoption, with major companies like Snap using Temporal for mission-critical workloads, validating the product's value and scalability. - Temporal.io monetizes by offering a fully managed cloud service with a consumption-based pricing model, aligning customer costs with the value delivered. - The company has raised significant funding, including a $300M Series D led by Andreessen Horowitz (a16z), with participation from Lightspeed Venture Partners and Sapphire Ventures, reaching a $5B valuation.

The Great private Capital Reset is upon us. Markets are volatile and driving new economic imperatives. Are VC funds still VC funds, even if they raise billions per fund? What happened to the rest of the market? What is driving VC investments? What do Limited Partners think? What is on their minds? This and more, in episode 76 of Tech Deciphered. Navigation: Intro The State of the Reset: The Hangover from the Party? LP Fatigue and VC Differentiation What Really Matters: Performance.. Returns The Mega Fund Question The Case for Smaller… Rightsized Funds What Comes Next? Conclusion Our co-hosts: Bertrand Schmitt, Entrepreneur in Residence at Red River West, co-founder of App Annie / Data.ai, business angel, advisor to startups and VC funds, @bschmitt Nuno Goncalves Pedro, Investor, Managing Partner, Founder at Chamaeleon, @ngpedro Our show: Tech DECIPHERED brings you the Entrepreneur and Investor views on Big Tech, VC and Start-up news, opinion pieces and research. We decipher their meaning, and add inside knowledge and context. Being nerds, we also discuss the latest gadgets and pop culture news Subscribe To Our Podcast Bertrand Introduction Welcome to episode 76 of Tech Deciphered. This episode will be about the great private capital reset. As you know, or you have probably heard, there is significant structural transformation in the world of venture capital, and we are probably witnessing a fundamental reset of the private capital stack. We got a huge bubble in 2020, 2021. Fueled by near-zero interest rates. We got inflated fund size, compressed due diligence, and now a generation of zombie funds and zombie startups. Now that rates have normalized, exits have not been as much as expected. LP patience is a warning sign, and I guess the industry is being forced to confront an uncomfortable truth: most VC funds raised since 2017 might not return what their LPs expected. You know, how do we start?   Nuno This is going to be a relatively nuanced episode. Obviously, there is going to be a lot of haves and have-nots, both in terms of VC funds, also in terms of startups. And so I want to start with that. This is going to be more nuanced than all transformational and disruptive.   Bertrand It’s not the end. It’s not the end.   Nuno State of the Reset: The Hangover from the Party? It’s not the end. There’s still huge mega funds that are raising more and more. It’s clear that the music has stopped, right? So if we’re playing the game of chairs, the music has stopped. Around ’22, ’23, we started seeing the first signals that funds had raised way too much money. Firms collectively raised around $669 billion globally in 2021 alone. If we fast forward now to last year, 2025, depending on the sources, we did some internal analysis at Chameleon. We came up with $75.6 billion was raised last year by 493 funds, right? So That’s a significant drop, right, in terms of fundraising. Other sources would say a little bit more. There’s a little bit of a discussion around how much did the top 30 funds capture. If you believe some of the stats out there, they would say that actually top 30 funds captured 75% of all capital raised last year. We did again some internal analysis at Chameleon, and the conclusion we came to, it was closer to 50 to 55%. So not as dramatic as some of the sources out there, but still pretty dramatic. There’s a lot of capital concentration on the top funds. Again, the top 30 funds would’ve raised 50 to 55% of capital or up to 75% according to other sources. So definitely a tremendous amount of concentration. There was a lot more fragmentation in terms of capital raised if we’re looking at the years from 2010, 2011, all the way through 2021. So 2021 would’ve been sort of the peak of non-concentration if you look at that. And that again, now we are getting more and more concentration. There’s more and more of this arbitrage around, I’ll give money to the top funds, I will not give money to the smaller funds, or I’ll give less money to the smaller funds. There’s a little bit of a movement around concentration. We’ll talk about it later and what that means. Are mega funds really better? Are the small funds still the way to go? We’ll talk a lot about that later in today’s episode. There seems to be a little bit of a bifurcation. We could say it’s either bifurcation around top-tier VCs or larger VC funds versus smaller VC funds. My perspective is the bifurcation that we’re seeing right now is more of a bifurcation between funds that are no longer just stepped into the VC space, but they’re actually becoming more and more private equity firms with full asset management range from early stage all the way to late stage. Think of it almost like a private equity hedge fund, quasi, versus classic VC funds. And I think what we’re seeing is the Andreessen Horowitzes, the a16zs of the world, the NEAs, the Sequoia Capitals, just to name a few, becoming more and more broad asset class managers across private equity, whereas you have more classic VC happening in earlier stages. And so that’s the real bifurcation that I think is actually happening.   Bertrand And maybe not really hedge fund, because they are always still long-only funds. So there is no hedging happening, at least as far as I know.   Nuno Well, some of these guys have become RIAs, like A16z has become an RIA, so they can do secondaries.   Bertrand That’s true. Yeah.   Nuno And they can also sell stuff, etc. So I don’t know how aggressive they’re going to be in terms of secondaries and selling and actually doing other kinds of services you can do if you’re an RIA. But it’s not, I think, out of the realm of possibility that they would sort of acquire and sell stock more rapidly. In that way, to your point, Bertrand, maybe they actually become beyond just long guys, right?   Bertrand Yes. Another trend I have seen is some of the larger VC funds seems to have no problem investing in multiple competitors. This was not possible before. I mean, if you’re a VC fund, you had some sort of duty not to invest in the competitors, but now some invest OpenAI, Anthropic at the same time. Do you see that as part of this evolution?   Nuno For sure. And I think there’s a lot of people like the ostrich putting their heads below the ground and it’s like, “Eh, no, no, nothing to see here.” But that does constitute a conflict of interest. And if I’m a startup raising, this assumption that you will not invest in one of my competitors is no longer there, certainly for the mega funds, because of that notion of deployment of capital. Now, some funds will still hide under the notion, actually formally from a fund perspective, we’re not investing in competitors. It just happens that different types of our funds are investing in competitors. Like maybe my growth fund is investing in a competitor to my early stage fund, right? But our funds are relatively independent. So I think there’s a little bit of hide and seek that will go on if you talk to some of the fund managers. Well, they say, well, we’re not investing out of the same fund into these competitors. But between you and I, as we know, a lot of these partnerships actually do a lot of stuff together at the general partnership level. So are there really actual Chinese walls between the funds? Well, it really depends on the partnership. And to be honest, most of the partnerships don’t have very significant Chinese walls between the funds, right? The managing general partners sometimes actually occupy investment committee roles across different funds. So I think the conflict of interest is there. So that’s why I say there’s a little bit of ostrich behavior. Put your head behind the ground or below the ground and just pretend nothing is happening. Just sharing maybe a couple of interesting stats. Global fund closings for 2025, according to our numbers at Chameleon, 1,098 closed. In 2025. Closed is when you start deploying capital, right? Whereas— so it’s not closed down, it’s closed like we start deploying capital. And that number, 1,098, is dramatically down from 1,600 in 2024. And it’s actually the lowest number of closings that we saw since 2014. So again, this is bad, right? It means there’s less funds doing fund closings and deploying capital in the market than since 2014 and dramatically below the 2024 numbers, right? Where we already saw some market readjustments. The number of active VC firms in the US that did 2+ deals, which is not a huge bar, has dropped 38% back to numbers in 2023. So we don’t have numbers that are a little bit more up to date, but basically in 2023, those numbers are already dramatically dropped. So there’s less and less active funds. So there’s funds that might be in the market, but they’re not actually deploying that much capital, not doing that many investment. They’re sort of either zombie funds or relatively passive funds that have passed their investment period. For those listening to us, the investment period for a VC fund is normally between the first 3 to 5 years of the fund, which is when you build your portfolio, when you can invest in new companies. After that time period, everything that you do up to normally what would be year 10 is follow-ons. You put more money into the companies that you’re already invested in, that you already constructed portfolio with during those 3 to 5 years.   Bertrand Yeah, that’s a pretty scary change. And obviously, I guess we’ll come to it, but the time it takes to fully liquidate investments is getting longer and longer. In the old days, we used to talk about VC funds having a 10-year life, maybe a +1/+1 in terms of extension of the fund life. But it looks like it’s taking 16 to 18 years actually to get full liquidity from a fund investment.   Nuno LP Fatigue and VC Differentiation And I think that’s the scariest piece. I mean, just to share some numbers, we in venture capital talk about vintages, right? Which year did your fund start in? Normally when you did your first close onto the fund, as we were saying before, close is when you get all your investors at that moment in time to come in and you do your first close so the next fund starts running. 2018 vintage funds, right? This is now almost 7 years ago. So you should start having— actually 8 years ago almost at this point in time. You should start already getting distributions or you start getting cash back if you’re a limited partner and investor in those funds, you should start getting cash back. Half of all 2018 vintage funds have returned $0 to their LPs. So they’ve had no distributions to their LPs. 2020 vintage, which was a very hot vintage, only 42% have begun any distribution. So 58% have distributed $0, right? 2021, only 25% have done any distributions. Now, I happen to have a 2018 vintage fund and a 2021 fund. My 2018 fund has already distributed over 3x net of fees in distributions, and my 2021 fund’s already over 10% distributed back in distribution. So we’re very proud of that. But in general, the numbers are awful. There’s no liquidity back to LPs. And to your point, that’s kind of a big deal because some of these funds have been going on for 7, 8 years, and where’s the liquidity going to come from? On the other hand, if you look at TVPI, so DPI is distributions to paid-ins cash on cash. But if you look at TVPI, which is total value to paid-in, which also includes the book value or the value that you’re marking it on your books, basically the paper value as we call it for the company, even on that, the median 2017 fund, so 2017 vintage fund has a TVPI, total value to paid-in, of only around 1.76x, which is well below what should be, which is sort of the 2 to 3x benchmark of a really good performing fund. So the median funds are doing very, very poorly overall. So if you add that to the fact of what’s happening and distributions are taking a long time, back to your point, Bertrand, it’s taking like— this should be a 10-year asset class, maybe 11, 12 years, and now it’s looking a little bit like a 15, to 18-year asset class, which is not what most limited partners sign up for. Part of this dynamic, I think, is that we’ve had tremendously overvalued private companies over the last few years, right? Secondly, these companies have just stayed private longer. And I was having a discussion recently with a friend of mine, it’s like, hey, what’s this thing about companies are staying private much longer? Is there some dynamic around secondaries? And the reality is there is a dynamic around secondaries, right? Because if I’m a very large fund and I can get away with doing secondaries on my portfolio, I will get liquidity at some point, right? But someone else is stuck with private stock, which hopefully will IPO, but who knows, right? And so there’s this funny dynamic right now of because of secondaries, because of a couple of other things that are happening in the market, actually a lot of these startups are staying private for tremendous amounts of times, and some of them will IPO and they’ll be huge deals. Some of them might not and might not warrant the latest private valuations that they’ve exercised. And so there’s this tremendous noise that we’re seeing in the mid to late funnel of privately held companies where some are just waiting to be public. Some of them might not be able to go public at anything that is an up round versus private valuations that they’ve had in previous moments and in previous rounds.   Bertrand And obviously the 2 to 3x returns that funds are targeting, and obviously more 3x than 2x, I mean, that was good and nice if it’s a 10-year fund, but if it’s the same 3x for 15 to 18 years, it’s not at all the same rate of return annualized. So it’s a really, really, really big issue if you keep the return the same, but you extend the duration of the fund. Concerning going IPO, there is a lot of complexity going public, the IPO process itself, but also after that when you’re a public company. It changed how you can run the business. Some would argue that we have had an issue with more companies delisting than companies listing on the public market. So I think there might be also separate issues about the efficiency of the public market and maybe a need for change. We went very strongly in one direction for the public market, have post and run, but was it really ultimately the right thing to do? I’m actually not so sure.   Nuno Yeah, I mean, just to be clear, this is anecdotal, but when we tell prospective LPs at Chameleon about our returns, the last few funds, 2018, 2021, the first reaction is, “You must be lying, right? Surely you can’t have distributions already for 2021,” et cetera, et cetera. So clearly there’s almost a state of disbelief right now from limited partners. And liquidity does matter. So clearly you have to move forward. So how did we get to this point where we had this bubble 2021 all around that time space and now things don’t look so good. Well, the macro conditions have changed dramatically. I mean, rates when they were near zero, safer assets yield nothing or yield nothing. So basically you had to push capital into longer duration risk assets like venture capital. And so you had to push it. So the opportunity cost of capital also has fundamentally shifted. Obviously a 3x VC return in 15 years over 10 actually competes very poorly against 5% annual credit returns over several years. So there’s been a readjustment of stuff. And then the public equities in particular, the tech public equities have had a lot of volatility, but some of them have done extremely well, right? Chipsets, things like NVIDIA, the Amazons of the world, Alphabets, et cetera, et cetera. They’ve done very, very well. So why would I invest in a long-term illiquid asset that takes now longer to give me money back, and in some case doesn’t give me back, if I can invest just in public equities, and a variety of other things. The venture debt costs have increased dramatically. The burn rates that were sustainable back in the day with sort of the addition of venture debt, private credit, et cetera, now are overblown at this moment in time. At the end of the day, there’s been a lot of movements also overall in the pipeline in terms of valuations, et cetera, et cetera. Now, I would put a grain of salt into all the numbers I just told you. There still is a little bit of the haves and have-nots in startup land. Certainly in early stage where if you’re a hot AI company, you can get away with raising a Series C or $480 million. This is actually a true story. Series C, right? Not Series C, a $480 million at $4 billion pre-money valuation. Whereas if you are maybe in a space that’s less hot, you’ll have more difficulty in raising money at this point in time, might not be able to even raise a Series C, right? So there’s a little bit of the haves and have-nots happening on the VC side in early stage that has been really amplified by the macro regime and where we’re at, which is actively zero-rate era is done and now the new regime is quite different. And so I can get better returns by doing something else.   Bertrand Kind of makes sense. I mean, if you have some ways the SaaSpocalypse in the public market because there is that fear that AI is going to completely change the game for especially for the more typical software companies. Good luck raising private money to quote unquote just build traditional software companies. You cannot expect a warm embrace from the private market if the public markets are completely destroying that category. I’m not saying that this is there forever, uh, things might change over time, but for sure what’s happening on the public markets always have a very strong impact on the private market.   Nuno Indeed. So what’s happening in this relationship between limited partners and VCs, the general partners? Again, limited partners are the people that give venture capital firms and venture capital funds their capital to actually deploy. And they are a variety of different players, right? Could be endowments, like university endowments, pension funds, family offices, very high net worth individuals, fund of funds, et cetera, et cetera. I mean, in particular, if you look at the institutional investors, the endowments, the pension funds, the fund of funds, they have allocations that they do to different asset classes typically. And the feedback that we’ve received from the market is they are increasingly frustrated with what’s happening in terms of distributions. They’re not getting capital back. It’s like, I gave you capital 8 years ago, 9 years ago, 2017, 2018 vintages, and I’m not getting any capital back. So what the hell’s happening? On paper, it looks maybe the fund’s doing okay or it’s doing great in some cases, but where’s my money? And so that creates a little bit of wait-and-see kind of game on portfolio allocation. As we’re thinking through their re-ups, putting more capital into funds that they’re already actually put capital or putting in capital into new slots, into new fund managers that they want to put money into. They’re like, well, let’s wait and see. I want to get my money back or get some money back first before I redeploy it. Again, this is a little bit the haves and have-nots because we’ve seen, for example, a couple of top-end LPs in terms of returns that have a little bit the opposite problem, right? Because they are into funds that are performing extremely well. They actually are over that period and they want to actually redeploy. But to be honest, the average in the industry right now is a wait-and-see game. It’s like, I want to wait and see, which leads to what can only be characterized— I was hearing someone the other day, one of the top advisors in the LP community, saying this is the worst fundraising environment ever for venture capital. Not the last 20 years, 30 years, like ever, right? Since this became an asset class more institutionally in the late ’60s, early ’70s, Pulse Robo 2 as it was created, this is the worst fundraising environment ever. Oh, wow.   Bertrand And concerning TVPI, let’s not forget that typically it’s not mark-to-market. So the metrics in terms of TVPI, correct me if I’m wrong, you know, but the metrics in TVPI are based on typically the last fundraise. So if the valuation went down but there was no additional fundraise, we wouldn’t know by looking at the TVPI metrics. It will only be updated if there is a new Financing, equity financing, or an exit.   Nuno Yeah, normally most funds act like that. Some funds are a little bit more aggressive and do do mark-to-market, but normally funds would be conservative and say, hey, I’m being conservative, it’s whatever is the last known valuation of the company. And if there wasn’t a priced round, it’s a little bit more obscure than that, right, Bertrand? Because it might actually be the company has raised money on a note, or either convertible note or a SAFE note, and that wouldn’t count as a priced round. So I would say actually, even if it was a cap that’s below with a significant discount, I won’t recognize the assets as a down round. I won’t recognize the asset with a lower valuation because formally it wasn’t a price round. So it’s on the one hand conservative, on the other hand, it’s only relating to price rounds or exits to your point. So it’s sort of, you can be like, hmm, well, we opt to do that because we think it’s actually the most conservative route. Mark-to-market is extremely difficult to do. And who would do the mark-to-market for you, right? It’s like it’s some valuation firm, et cetera.   Bertrand I’m not saying a mark-to-market is easy, but I’m not sure I would call using the last valuation something conservative in the context that most startups will fail. So it’s not clear.   Nuno Well, in some cases it is, some cases it’s not, right? Depends on the startup situation, to be honest. Yeah, yeah.   Bertrand But yeah, at least that’s how it’s done. So for instance, to evaluate the impact of the SaaS apocalypse, it’s tough to know. We will have on the private market. I mean, we will see that in a few quarters. Because if companies still exist in that environment, if they still do additional truly price rounds after that, that’s when I will start to know.   Nuno I mean, just to share a little bit more data, like VC fund close time stretched to 15 months. Basically, it’s just taking a long time to raise money. It’s taking a long time to do your first close, get your fund running. When entrepreneurs complain to me that their fundraising is difficult, I always say, you have no clue how difficult it is compared to ours. First-time funds have collapsed. We had some numbers that only 77 first-time funds actually closed. I assume this is in 2025 versus 215 in 2023. So that’s a huge number. We did some internal analysis on our side and we did some analysis that emerging fund managers, emerging fund managers are normally people that are in their first one or two funds. Basically emerging fund managers gained some ground until 2017. Reaching by then a slice that was 63.7% of all capital raised in 2017. But since then, the capital deployed to emerging managers has been largely reduced to actually 24.2%, right? So it’s gone from 63.7% in 2017 to 24.2%. So this has been a culling of sorts on emerging managers and almost like a slaughterhouse of emerging managers. Compared to previous situations, which is obviously incredibly concerning if you’re an emerging manager starting your VC firm, et cetera, et cetera. So really tremendously problematic for those. We think capital’s not leaving VC. I think we see a lot of the institutionals saying— there’s some numbers as high as 33% of institutional investors plan to invest more in venture in the next 12 months. So I don’t think capital’s leaving VC. I think it’s really concentrating. We’ll come back to the concentration issue later in the episode. And part of that concentration comes from a topic that has been widely spoken in venture capital recently, which is differentiation. How do you differentiate in venture capital if you’re talking to a limited partner, right? How does my firm differentiate versus the firm next to mine? And that’s incredibly, incredibly challenging. Bertrand, what are your thoughts on that?   Bertrand Differentiation is always a question. I mean, if you’re an entrepreneur, Typically, you think fully about the best possible partner for your stage and for your type of business model. You want a VC who understands fully your business model, because if they don’t, then it’s going to be troubled down the line. But that’s true that another piece of the puzzle is that the best VCs help you get more visibility in terms of achieving potential customer deals, in terms of attracting the best talent. And that’s where VCs’ brand names can help. If you can say you have backing by some of the top, most visible names in the industry, and usually these are the mega funds because others have trouble to be as visible, then they have some sort of unfair advantage compared to others. So I can see that there is some level of concentration happening naturally, especially in the later stage from Series B onwards.   Nuno What Really Matters: Performance… Returns Yeah, I mean, we did some analysis internally about What are the top funds that invested in the top performing companies in early stage, Series C, Series A? And we looked at it by size of fund and the top performing normally are funds below $100 million, but in some cases very closely followed by funds between $100 and $500 million. And actually funds above $500 million, so $500 million to $1 billion and then $1 billion and above are actually tremendously underperforming. So this notion of the industry that says, well, the mega funds still see The top investments early on, because they still deploy in Series C and Series A opportunistically, in some cases even spray and pray if they have their own incubation and acceleration programs, is not true. Actually, we verified that over the last 12 to 13 years. It is not 12 to 13 years in vintage, right? So up to a 2021 vintage fund. So we went basically 12, 13 years back from there. And it’s not true. Actually, the most performing are 0 to 100 and then 100 to 500. And as I said, there’s 100 to 500 in a couple of years actually are a little bit better. Than the $0 to $100 million ones. So that’s the first thing that’s a conclusion. And actually, that’s not shocking. If we remember back in the day, Kleiner Perkins used to raise funds up to $600 million, Benchmark raised their $425 million funds. It seems like the sweet spot for a VC fund would be around $500 million at the top end, like maximum. And now somehow people are saying, well, I’m raising a $3 billion VC fund. It’s like, well, it can’t be a VC fund. The return profile is totally different, right? You can’t deploy that capital just based on early stage investing. And by the way, you’re not seeing the guys at early stage, all that you’re seeing, you’re going to make your returns in mid to late stage, right? Back to what we said at the beginning of the episode. So there’s a little bit of the haves and have-nots there. The big guys are raising more and more money, but they’re no longer venture capital. And I think limited partners that are a little bit more evolved, that are a little bit more conscious of this, that have been in the market longer, are realizing that shift. So it’s like if they want to have the alpha of venture capital, they need to deploy to the sub-$100 million funds or the sub-$500 million funds, right? That’s where they need to actually focus their VC capital. They can still deploy to mega funds, but they’re deploying to a different asset class. They’re deploying to a private equity, mid to late stage asset class, which looks maybe a little bit more like a growth fund or something like that. The second part of differentiation is the honest truth is most VC funds are like, I have proprietary network access, right? I’m ex-Stripe or I’m ex-Google or I’m ex-Facebook or whatever, and I have access to that. I mean, we know proprietary networks from that standpoint are no longer true. The whole thing that created Silicon Valley back in the ’70s of what I used to call the country club deals where there were a few people coming out of the big companies, the Fairchilds of the world, later on the Intels of the world, et cetera, et cetera, that made some money along the way that sort of bootstrapped their next companies, were well-known quantity to the existing VCs and raised money relatively easy on ideas, that doesn’t work anymore. Someone was telling me the other day one interesting thing that I wasn’t quite aware of, a lot of it had to do with the NDAs. I don’t know if you knew this, Bertrand, but like the fact that in California, it was sort of the Silicon Valley community sort of imposed this, we don’t sign NDAs thing and Boston continued signing it. And this whole NDA enforcement issue and non-compete, actually not the NDA thing, but more strongly that California did not enforce non-competes. I could leave Fairchild and start a company that magically was doing something that could be considered competitive to Fairchild. And that was sort of part of the acceleration actually of venture capital in California versus, for example, Boston, which was sort of hand in hand at the beginning.   Bertrand Yeah, I mean, I’m a big, big believer in California success coming from not enforcing or banning non-compete agreements. I think it’s a key part of the game. If you lock people into not doing something similar in the next 6 months to 24 months. And the industry has always been moving fast. So this is a significant time where you are blocked to do something very similar. I think it was really an issue. So I think it’s a key part of the game and it has been there. I don’t know how it started, but I think that non-enforcement of non-compete has been a key part of the success of California. I’m actually pleased to say that Washington State is going in the same direction. They are just signing a non-compete ban. And you might remember that at the federal level, I think in 2024, there was also a ban that was put in place to ban non-compete, but this has been reversed by the courts. So this is not there anymore. So that’s why we see a state like Washington State putting their own ban, and we might see more state by state moving in that direction. I think it was not helping at all, this non-compete. I mean, there is obviously stuff that needs to be done, like you cannot steal secrets, you cannot steal IP.   Nuno Yeah.   Bertrand Even stealing employees, there should be some restraints. We need to find the right balance, but you have to be careful there. That was key for the success of California, and I’m glad to see that this is a trend that’s going to go beyond California. And I hope most states will have a ban on non-compete.   Nuno Maybe just to close on the differentiation process, two things. One, I think there’s this notion When you talk to some LPs, that seems to be a little bit ingrained, some LPs that prefer specialized funds. We’ve also done some significant analysis internally and have talked to a couple of datasets other than our own, or people that own datasets other than our own, and the feedback has actually been not so fast. Actually, generalist funds over time cannot perform specialist funds. There seems to be a little bit of a sweet spot around generalist funds. We like to call ourselves multi-specialized at Chameleon, but ultimately from the perspective of specialized versus Generalist funds, the picture’s not as clear as specialized funds outperform generalists or generalists outperform specialized. We’ve seen there are pockets where actually generalists outperform specialized, in other pockets where specialized of a certain size can outperform generalists. So that’s one topic on differentiation that is a little bit broader. And then the final topic on differentiation, it’s really an industry that hasn’t innovated dramatically on where it creates the most value, which is really the picking stage, right? So it’s having great deal flow, very optimal, productive, efficient due diligence with very few resources and the ability to then get into those deals. That’s where most of the value is created. And then hopefully liquidating the asset if there’s an opportunity to do so at the right time, either through secondary trade sales or an IPO or something else. And what we’ve seen is the industry has innovated very little. I mean, the only thing I could point out in terms of core innovation at the top of the funnel has been the creation of the mega funds, the well-known funds, right? Like a16z, Union Square Ventures, et cetera, et cetera. But there needs to be more innovation on that cycle. And that’s why we certainly at Chameleon believe that the future is to have quant and AI-native VC firms that develop their own tooling, their own platforms. We have Mantis in our case that allow you to have this unfair advantage in how you source deals and how you do due diligence, how you get into the deals, et cetera, and how you take it to the next level. And we think that’s the beginning of the next stage is that the industry becomes more tech-enabled, shockingly enough, an industry that has made all its returns on tech or almost all of its returns on tech. That we need to be more tech-enabled ourselves. But I think the writing is on the wall there, and that will be a source of differentiation certainly over the next 3 to 5 years.   Bertrand One thing the industry has innovated somewhat and maybe could innovate even more is providing liquidity beyond trade sale and an IPO, because it’s clear that if VCs want more liquidity without waiting 18 years, you need that liquidity at different stage, not just when it’s time to do an exit, a full exit for the business. And for employees as well. I mean, it’s one thing to stay for a company for 4 years, which is your typical vesting. Maybe you extend that to 6 years, to 8 years, you have a great time at the company. But to think that maybe you have to stick around for 15 to 20 years in order to get liquidity on your stock options. I mean, that’s too much to ask for most people. I mean, people have a life, they have other things to do, other plans, they might want to move, they come at a different stage of life. So you need to provide them liquidity. The new game is we are not going to exit until 15 to 20 years, else it’s truly unfair. It’s not just unfair, but people will say, you know what, I’m going to go across the street, go work for Amazon or Google. I will have RSUs at best regularly that are liquid, and why bother? I mean, we need to find pathways to liquidity for both investors but also employees. There has been a change in that direction, but I think we need more of this change, and maybe not just reserved for the absolute biggest, most successful companies like OpenAI or SpaceX, but also us as well. Hopefully we can find a way.   Nuno Well, now we have these AI companies that actually grow so fast that they will IPO in one year. Now, isn’t that what’s going to happen? They raise They raised $500 million in Series C or $1.4 billion in Series C, and they’re going to IPO in 2 years. No? Is that not the new reality? I’m being facetious.   Bertrand At the same time, I mean, there are rumors that some of them are going to IPO this year. I mean, we talk about OpenAI, about Anthropic. I mean, OpenAI is quite old, but Anthropic is a relatively new business, quote unquote. So I think it’s a good time.   Nuno The Mega Fund Question So maybe it will be true after all. Moving to the next section, are mega funds still venture capital, Bertrand? Are they still venture capital funds?   Bertrand Yeah, I guess venture capital is a term that can encompass from small to very big funds. I truly don’t know. I mean, once you reach a growth stage, are you truly a VC fund? I don’t know. I think some of these definitions are kind of arbitrary from my perspective. What is clear is that you as a business need different providers of capital. And as we just discussed, you as a business, probably need to keep going and stay private for longer. One reason being, again, there is a tremendous cost to being a public company. There are some true strategic disadvantages. And at the same time, just practically, I mean, you need to get bigger and bigger in order to have a chance of a successful IPO. So you cannot just go IPO at a $500 million valuation. I mean, that’s like committing suicide, at least in the US market on NASDAQ. So my point is, you truly have no choice. You need to extend and If you need to extend, then you need to have capital providers that are there at later stage and therefore have more money. Is it still true venture capital? Is it true venture? I don’t know. At some point, it makes sense that from the startups to the capital providers, everyone adjusts to a reality where the life cycle is getting longer.   Nuno We don’t think it is. We don’t think mega funds are venture capital. We have actually some data that shows that they’re not in terms of actual returns. The alphas you can generate, the IRR that you can generate is actually not comparable. We did some analysis again with some of our datasets and from 2012 to 2022, so that’s the datasets that we used so that we had actual distributions and stuff we could take into account and so on and so forth. And looking at IRR, just to share some numbers in terms of IRR over those 10 years on sub-$100 million funds versus above $1 billion funds, the differences are incredibly stark. And this is true for global and US IRR, right? So just to quote some numbers in terms of average, sub-$100 million funds, global IRR of 22.9%, US IRR of 21.6% versus above $1 billion, 9.1% and 9.0%. Median IRR, if we just looked at median, 7.3% and 16.6% for sub-$100 million funds, 7.5% and 8.1% above $1 billion. Top quartile IRR, sub-$100 million, 31% versus 30.4% US IRR. And then above $1 billion funds, 14.7%, 15.5%. So it’s very clear if you sort of cut this in different ways, averages, medians, top quartiles, et cetera, over all these years that sub-$100 million funds are in a very different asset class than above $1 billion funds. They’re in different alpha that you can generate and so on and so forth. Now to the point you made, Bertrand, I don’t fully disagree with the point you made of the bigger funds should become bigger. I just think they’re becoming different things. Now, again, some of these funds will hide under the facts like, well, wait a second, we have all these assets under management, but they’re over different funds. Sequoia, we’re still raising small early-stage funds, $500, $600 million funds. And then we have larger funds for growth, et cetera, et cetera. Andreessen Horowitz, a little bit less clear what they’re actually doing. We heard that they’ve raised $15 billion across funds. I’m not sure if that’s the exact number at the end of the day. But the point is, if I’m a multi-asset class manager, like early growth, et cetera, et cetera, then it still applies what Nunu is saying. I’m still going after the $500 million, $600 million early-stage funds. Well, not so fast, right? Because you still have all this capital with managing general partners that are maybe across funds for which their incentives in particular, both carry and management fees are coming from the larger funds. Et cetera, et cetera. So there’s necessarily conflicts of interest. In many cases, the funds are just straight up big, right? And so they are above a billion. And so I don’t think a lot of these guys are in early-stage investing anymore, right? It may appear that they are, but I don’t think that’s where the returns necessarily are going to come from. And so if you are a limited partner, if you’re looking at your asset class allocation, again, you’re absolutely free to put money into mega funds because that’s the kind of asset class you want to play in. In terms of a blended private equity asset class that has a little bit of growth, a little bit of whatever, or actually a lot of growth, a lot of late stage, and maybe a little bit of early stage. And I want something that’s a little bit more blended, right? But if I still want the alpha venture capital, I need to deploy to funds that are early stage, right? And that’s like up to $100 million, up to $500 million. I think that’s my two cents on that topic. We see crossover things coming around, like guys who do both public and private markets. Again, that starts feeling a bit like a hedge fund. A lot of these funds have also become RAs, as we discussed earlier. So I feel the writing’s on the wall. The mega funds are going more and more after either some mechanism of edging or a mechanism that’s a little bit more blended in terms of private equity than classic venture capital.   Bertrand Yes, I think a few things. One, if you’re an LP, I can imagine that dealing with multiple $100 million funds might be more difficult. You, you need to know the partners, you need to have some background, uh, visibility. You need potentially to change regularly of VC investments. So I can see some level of simplicity if you just focus on the bigger ones, especially if you have a lot of assets you have to put to work. Another piece of the puzzle, I would guess that the bigger funds are able to return money faster because they are at later stage of the cycle. So instead of that 15 to 18 years, maybe they are more in a 5 to 10 year range, while the smaller funds being there more early might be the one who are taking longer to deliver. So I can see that Yes, there is an IRR picture, but there is also time to liquidity that is not the same. So that can probably also influence. And in terms of crossover PE hybrid model, I mean, for sure we have seen some of the public equity investors doing crossover, meaning going into private equity firms like Coatue, like Tiger Global and others. And for companies that are preparing for IPO, there is a lot of value to work with these firms because they have very good visibility and understanding of the public markets. And their presence in the cap table is also a sign of quality, typically for public market investors. So there is a lot of value and logic for them to be there on both sides of the puzzle. But again, the fact that firms keep delaying IPOs, that the market is not so much startup-friendly, makes this model a bit more difficult. But personally, I think there is value there.   Nuno Yeah, I think on the mega fund, just so that I’m not boo-booing everything, I mean, but there’s definitely angles in terms of the asset class that make a lot of sense. And there’s the scalability of the model. The ability to go after Series B, Series C, as well as mid-stage, as well as late-stage, even secondaries over time, to your point, in some cases even public equities. And that level of skill I think matters. We’ve also seen, as we’ve known, we won’t mention any brands, but people will know who they are, that late-stage hedge funds and investors, even if they’ve done okay-ish in growth in private equity, don’t necessarily do well in venture. So it’s clearly a very different asset class, right? So once you start getting venture teams together, The returns are not quite the same. Actually, sometimes they’re not even quite the same as the growth investments. So clearly they’re very good at the growth side, but not so good in early stage. But definitely there is a case for it. The Case for Smaller…Rightsized Funds But if we switch gears maybe to the small, or I would call right-sized funds, maybe just to quote a couple of numbers and then open up the discussion. Small funds do seem to outperform larger funds. There’s a lot of data in the market that shows some of that dynamic outperformance frequency. All the Very historical numbers from Cambridge Associates from 1981 to 2010. 19 out of 30 vintages were won by sub-$150 million funds. We did our own analysis as I was sharing before. Funds between $0 and $100 won most years between around 2010 and 2021. And the years that they didn’t outperform in terms of investing in the top-performing companies in early-stage Series C, Series A, they were outperformed by the $100 to $500 million funds. The $500 to $1 billion funds and $1 billion or above were never even in the same league in terms of performance, of having identified those top performers in terms of quantity over those early-stage investments. Top 10 funds by vintage, 2004 to 2006, 2016 numbers. Top 10 funds, 73% were sub-$100 million. 2004 to 2016, top 10 funds by vintage, 73% of those were sub-$100 million. So there seems to be a little bit of a case that actually smaller funds, sub-$100 million, sub-$500 million in some cases, are outperforming the larger funds over time. Now, these funds are complex in and of itself. The positive of it is small fund GPs like myself, we are deeply invested in our own funds. We’re not there to just make management fee monies. I mean, we’re not making $1 million, $2 million a year in management fees of salary ourselves, like some of the larger funds. So we are there to really get the carry and be less focused on management fees. And so I think there’s a little bit of alignment around that and really taking that kind of perspective on portfolio construction and liquidation, being also more aggressive on the individual time that we spend with our startups. On the negative side, obviously a lot of these smaller funds, not the case of Chameleon, but others out there are single GPs, very little teams or very small teams. And so it’s sometimes difficult to actually do a lot for portfolio companies as well. And this is where the mega funds, for example, a16z notably would say, hey, we have 600+ people that can support you, right? On market development, business development, communications, talent recruiting, all this stuff. Question mark whether that’s the right way to do it in terms of operating model, if technology is not a better way of supplying that value back to your portfolio companies, or if there’s no better way of doing it. But still, that’s one of the appeals of actually dealing with a larger mega fund if you’re a startup, right? That they will have the resources, also the financial resources to put more capital in you. But also, again, if there’s entrepreneurs listening to this right now, and hopefully there are, it’s a two-edged sword, right? Because if you have Andreessen Horowitz putting money in you, or NEA, or General Catalyst, or whatever, putting money in you on a Series C and then not doubling down on the Series A or the Series B, there will be questions, right? Because like they have the capital, they have other funds, so why the hell are they not putting more money in? Um, so, so it’s a little bit of a two-edged sword.   Bertrand Yeah, I think that one is a pretty big one. And on top of it, as we discussed, some of these big firms have multiple funds managed technically by different teams. So you might have convinced the early-stage teams, they have investors, they’re happy, but you don’t convince the growth-stage firm. As you say, it might raise questions because people might think that there is some communication between the early-stage team and the growth-stage team. So why the heck are they not deciding to invest? And as we also discussed, even worse possible situation, what happens if the growth-stage team has invested in your competitor? It’s even more trouble. So I think trying to understand how firms behave, what’s the reputation of the firm, what’s the reputation of the partner you are working with, I mean, can have tremendous importance and impact. When it’s time for you to work with a firm.   Nuno Indeed. I mean, at the end of the day, we still believe that the smaller fund— we at Chameleon discuss the notion that our limit should be $500 million per fund, right? And that’s the logic of it. We think that model is the model that works well in venture capital. We do recognize, as I said before, why mega funds keep raising more and more money, right? It becomes a harm’s race at that end of the market. As I said, probably a slightly different asset class, or if not a significantly different asset class as well. So seeing a little bit both sides of the market, I mean, we often compete with the mega funds, but honestly, a lot of the mega funds are kind to us and they let us in. And this whole notion of elbows out, we haven’t felt it that much in the market. And people see our value at the table. And in many cases, I, I do see the larger funds more and more seeing the value of smaller funds coming in on the same rounds and even in some cases co-leading early stage rounds like Series C. So it’s not like elbows are out everywhere across the board. So I don’t mean to say this is like an all-out war between small funds and big funds and the small funds need to win or the big funds need to win. I think actually there’s a lot of potential for coexistence. My point is more that the asset classes and the returns are quite different over time, and that’s how I would think through it. And if you’re an entrepreneur, you should think about that as well, right? What are the implications of taking money from certain funds versus others in terms of the expected returns, expected time allocated to you? For example, if you’re not doing very well as a as a company, right? Will the big funds spend the same amount of energy on you if you’re not doing great and all of that? So it’s a little bit sort of a beware, open your eyes, both for limited partners and for startups. What do you actually want, right? What do you want from your VC firm if you’re a startup? And what do you want from your VC firm if you’re an LP?   Bertrand I must say, as an entrepreneur, uh, a board member, I have seen some situations where the bigger funds are actually trying sometimes to elbow out the existing investors. Like, uh, we have that much money to put to work, we cannot do less. And you’re like, yeah, but I don’t need that much money. And then they’re like, okay, just don’t let your existing investors do their pro rata. I don’t think it’s great because an entrepreneur, if your investors, your VCs, trusted you earlier stage when it’s more risky, and when it’s becoming less risky, you don’t give them the right to their pro rata because you have to let this big guy come in. That’s not great. Or even if there is not this pro rata issue, when an investor tries to put more money to work than it’s really necessary, it’s also not a good idea as an entrepreneur to take more capital than you could use. It will dilute you more, it will set higher expectations in terms of valuation, it will push you to use that capital faster than maybe would be reasonable. So I think that’s something you want to be careful with the bigger funds. So don’t talk to funds that are in some ways beyond your stage and try to make it work in that context. Or don’t accept to have your strategy change dramatically for no good reason by funds that just want to put too much money to work in your business. And that for me is surprising because it should also be in their best interest not to invest in businesses that are not ready to accept that much capital. But as we have seen, there were in the past some funds that believe that capital is a moat. Was a good idea. So hopefully, I guess we’re a bit behind that. But yeah, I would say entrepreneurs, be careful, find partners that are the right partners for you at your current stage. Sometimes some big names look great, but at the same time, if it comes with a lot of issues, from too much capital to also taking the risk that these partners don’t understand the stage of the business you are in or your industry, Just be careful. There is a lot of value to have firms that are very focused on your stage, on your industry, are finely attuned to that situation.   Nuno What Comes Next? Maybe to end in terms of sections, what comes next? And maybe we can come up with some predictions that are a little bit provocative on what’s going to happen to the market. You, if you’re listening to us, feel free to interact with us on LinkedIn, on X. If you have our email address, shoot us an email as well. We’d love to hear from you if you think these are the right predictions or if we’re totally off. Maybe I’ll throw in the first one, Bertrand, and we’ll go one by one. So we’ll each put one at the table and see where we head. My first one is that we’ll have a huge culling of VC investors. We had this rapid expansion of the VC asset class with arguably at least tens of thousands of firms globally, maybe even over 10,000 in the US. I think we’ll have a culling and the culling will continue and we’ll have several firms sort of getting eliminated over the next couple of years that will have either because they’re having tremendous difficulty doing their first close in their next fund, or the returns are not there, or it’s a firm that has done 3, 4 funds, but for some reason the returns have just gone out of whack in the last few years during the bull years. And so therefore, actually they can’t justify to raise more funds out there. So I predict there will be a significant elimination of active firms in the next at least 2 to 3 years. So maybe by 2028, and we’ll be below, I don’t know, 30% of number of active firms that we are today. The other side of it is I do think if we look beyond that, 2029, 2030, and so on, we’ll have the reemergence of not micro funds, but nano funds where people will start deploying capital very, very early and writing small angel checks, but doing it in a way that it’s sort of not this cottage industry that we’ve had of angel investors. So I think angel investment will be disrupted by people that will use more and more of the AI toolification out there to actually manage their portfolios of 10, 15, 5K investments in a way that is a lot more professional, creating sort of an advent of nano funds.   Bertrand Yeah, makes sense. On my side, in terms of prediction, I think there is a possibility that the mega fund model keeps expanding and looks more similar over time to some PE models. So do we have the top 10 VC firms that look more like a Blackstone than a Kleiner Perkins or Sequoia used to be? That for me will be an interesting question and development. I think that there is some possibility that it keeps going in that direction. A lot of incentives are pushing things that way.   Nuno My next prediction is that DPI, distributions to paid-in cash on cash, just cash back, will become essential for limited partners. I think TVPI, total value to paid-in, that also has in there, as we just said, paper valuations. There’s a lot of disbelief now around the TVPI metric if there isn’t distributions going alongside it. For those who, again, don’t know what TVPI is, it’s total value paid in, but it also includes DPI. So it’s cash on cash component plus a remaining valuation to paid in, an RVPI. And the problem is the RVPI really, in reality, it’s that kind of on-paper valuation that never gets attributed. I think LPs, they’ve seen the writing on the wall and they’re like, dude, just show me your DPI numbers. I don’t care about TVPI. Some LPs will still ask about TVPI just to make sure that the rest is sort of looking in order. Like, show me the money, show me the cash. Actually, it’s not money, show me the cash, right? I want money back.   Bertrand But that’s an issue. I mean, if you’re supposed to raise financing every 3 or 4 years, good luck getting DPI to show for that. So you need to be at least on your third fund in order to be able to show DPI, I guess.   Nuno I mean, my corollary to that, Bertrand, is if you allow me just to have a corollary kind of prediction, is that we’ll see certainly for funds like $50 million and above, $100 million, $200 million, et cetera, even increased concentration, right? I really need to have anchors that believe in me over time. And we might start having, again, the advent— we had it some decades ago, the advent of cap table kind of VCs, right? Like Sutter Hill Ventures, right? Where they’re not really raising funds anymore. And so we might have the advent of that, that we’ll have structures that are created that have more permanent capital allocated to them, or at the very least more concentrated capital by very few players.   Bertrand Interesting. Me on my side, as I shared before, I believe secondaries are, are important and here to stay. Um, in the past, some could argue, is it a distress signal or something? I, I don’t think it’s true anymore. In a world where your average startup might take 15 to 18 years to exit through M&A or IPO, we need to have other options. For funds, for employees, they cannot be expected to stick around for so long and have no liquidity. I mean, it’s just pure madness. It’s just bad alignment at some point to do that. So I think secondaries are becoming the third liquidity pathway for VCs, for employees, and it should be more and more a key part of the game, a key infrastructure in the VC/startups tech industry.   Nuno I mean, on specialized versus generalist funds, I believe we’ll continue seeing the coexistence of those two models where the specialized funds will in many pockets actually outperform generalist funds, but where we’ll continue seeing that the large franchises, the tier one franchises will likely be generalist funds. I mean, we just saw it in the cycle. The AI cycle went upon us. We had a 2021 fund. We could easily adapt and go into AI and figure out that AI was growing very fast. I mean, if you have an ultra-specialized fund and that’s your remit and that’s the only thing you can invest on, very difficult to change even during our investment period. I will put a caveat on that. We don’t call, for example, ourselves at Chameleon generalist. We call ourselves multi-specialized because our scoring models for the verticals that we track are specialized within Mantis. Because the partnership is specialized, we all focus on different areas. And because we have the Kin network that allows us to tap into that level of expertise, Again, I think the world will be specialized coexistence. Some pockets specialized will do very well, certainly on the smaller fund size, but the big franchises will likely look a little bit more generalist. And as I said, multi-specialized from our perspective is the future. We’ll start seeing more and more funds that are multi-specialized like ourselves. Do you want to talk about AI and how it’ll distort the metrics? No.   Bertrand Yes. I think AI is an exciting moment in the tech industry. It feels in some ways that the same way we had a big distortion coming with COVID and work from home in 2020, 2021. 2021, where suddenly everyone and their mother will build a SaaS company or invest in a SaaS company. AI feels a bit of the same. I mean, to be clear, I truly believe it’s deserved. I mean, we are facing a dramatic shift in how computing is being done in terms of value you can get from software. So at the same time, AI will probably distort this matrix for a long time. We clearly see a split where investments are going, in what startups are being created. So I think, yeah, we will see some distortion. And we know that maybe 50% of all deal value is going to AI in 2025. We have seen single rounds reaching 40 billion, like to OpenAI. We have seen, as you discussed, some seed stage investment of 400 million. So AI investing and AI startups are definitely a beast on their own. And will distort VC metrics for a long time. And we might need two sets of metrics in parallel, you know, AI versus everything else. So that would be an interesting bifurcation in the industry in some ways. I would say it’s fair to separate AI versus non-AI. We reach a point where it’s two different beasts.   Nuno Conclusion So in conclusion, AI has changed the world and it’s changing VC as well, as we discussed earlier in the episode. We have a tremendous momentous occasion for the asset class where venture capital is really bifurcating into very large funds, which no longer are in venture capital or seemingly may be distributed between different asset classes, and the smaller funds, sub-$500 million and sub-$100 million, that keep having the better returns, but also with much smaller scale. We’re seeing a culling of the industry where the industry is definitely getting smaller and smaller and more concentrated at both ends, number of VC firms, as well as a number of limited partners per fund and the interest that some of these limited partners have of being more and more concentrated in their own portfolio allocations. And last but not the least, the discussion around specialized versus generalist, where it seems like there’s some clear winners on some asset classes, on some sizes, in some industries, but on others, there’s other kinds of winners. And so maybe the future is multi-specialized, as I framed at the end. Thank you so much for listening. If you want to check us out and if you want to comment, feel free to send us messages on X, LinkedIn, to both myself and Bertrand, as well as send us an email. Thank you so much, Bertrand.   Bertrand Thank you, Nuno.

Federal Election Commission filings for the first quarter of 2026 showed that billionaires Miriam Adelson and George Soros were the biggest donors backing GOP and Democratic super PACs, respectively, ahead of this year's midterms, while billionaire Marc Andreessen's venture capital firm poured $25 million into a pro-artificial intelligence Super PAC. KEY FACTS According to the filings published on Wednesday night, GOP megadonor Adelson donated $30 million to the Senate Leadership Fund, the major super PAC backing Republican Senate candidates. Filings made by the GOP-aligned Congressional Leadership Fund—which backs GOP House candidates—showed Adelson had given the super PAC $10 million, bringing her overall contribution to $40 million so far this year. Billionaire George Soros, one of the biggest backers of Democratic candidates, donated $50 million to his Democracy PAC in January through an associated group, the Fund for Policy Reform. The Democracy PAC then donated $9 million to Senate Majority PAC—which backs Democratic Senate candidates. FORBES VALUATION According to Forbes' Real Time Billionaire's list, Adelson's total fortune is worth $37.3 billion, making her the 58th richest person in the world. In comparison Soros' net worth stands at $7.5 billion as of Thursday morning. WHAT DO WE KNOW ABOUT FUNDING FROM SILICON VALLEY ?Leaders from Silicon Valley launched the pro-AI super PAC Leading the Future in August last year, with venture-capital firm Andreessen Horowitz among its main backers. Wednesday's filings showed that the venture firm donated $25 million to the political action committee, with $12.5 million each coming from co-founders Benjamin Horowitz and billionaire Marc Andreessen. BIG NUMBER $27 million. That is how much Democratic Texas Senate Candidate James Talarico has raised in the first three months of the year so far, according to the New York Times. Talarico's strong numbers appear to reflect Democratic optimism about the race in deep-red Texas, as the GOP has been besieged by infighting among its top two candidates. SURPRISING FACT Filings for a Win for America, a super PAC backed by sports betting platforms, showed it raised more than $40 million in the first three months of the year. FanDuel contributed $19.5 million while DraftKings' holding company, DK Crown Holdings, donated 17.5 million. An additional $4 million came from Fanatics' subsidiary FBG Enterprises Opco. Read the full story on Forbes: By Siladitya Ray https://www.forbes.com/sites/siladityaray/2026/04/16/billionaire-adelson-pours-40-million-to-back-gop-soros-gives-50-million-to-his-democrat-pac/ Learn more about your ad choices. Visit megaphone.fm/adchoices

A structural shift is underway in the managed services sector as venture capital firms move beyond traditional software and vendor investments to fund MSPs directly. This change is exemplified by investments from firms like Andreessen Horowitz, General Catalyst, and Thrive Capital into MSP-specific companies such as Treeline, Titan, and SHIELD. The driving mechanism is the perceived profit potential at the intersection of advanced AI technology and service delivery, with investors targeting AI-native operational models rather than standard rollups or inorganic growth strategies. The episode's primary evidence centers on Andreessen Horowitz's $25 million investment in Treeline, marking its entry alongside previously funded firms Titan (with $74 million from General Catalyst) and SHIELD (over $200 million from Thrive and ZBS Partners). According to Speaker A, Treeline employs proprietary AI-driven service desk automation and reports resolving 98% of help desk requests with AI, altering the economics and labor requirements for traditional MSPs. Unlike rollups, Treeline is focused on organic growth, leveraging targeted acquisitions primarily for talent rather than client base expansion. Supporting developments include the parallel strategies of Titan and SHIELD, which also integrate Silicon Valley AI expertise and homegrown tooling to drive operational efficiency. While these companies currently deploy AI internally for service automation, Treeline distinguishes itself by offering customer-facing AI-powered MDR and compliance services immediately. All three firms reflect the shift towards vertically integrated models where software, service automation, and client-facing solutions are developed and deployed in-house, creating potential competitive pressure for both traditional MSPs and larger private equity-backed consolidators. Operationally, these developments introduce risks around increased pricing pressure, labor model disruption, and a potential skills gap for MSPs reliant on off-the-shelf tooling. The focus on organic growth and deliberate scaling by new entrants like Treeline signals that the transition for incumbents is not immediate, but the need for MSPs to evaluate their AI adoption strategy is acute. Relationships alone are unlikely to differentiate providers in the long term; practical safeguards must include closing operational efficiency gaps, building internal AI capability, and considering cooperative models to maintain autonomy while reducing risk of margin erosion or client loss. Supported by: Zero NetworksCometBackup

Anj Midha is the founder of AMP, and a founding investor in Anthropic. Most recently, Anj was General Partner at Andreessen Horowitz, leading frontier AI investments. He serves on the boards of Mistral, Black Forest Labs, Sesame, LMArena, OpenRouter, Luma AI and Periodic Labs and is an early angel in ElevenLabs among others. Prior to that, Anj was the cofounder/CEO of Ubiquity6 (acquired by Discord) and a partner at Kleiner Perkins. AGENDA:   04:00 Why the "Scaling Laws are Dead" rumor is dangerously wrong 05:30 The 4 bottlenecks stopping us from reaching Super Intelligence 11:30 Where will the actual value accrue in an AI-dominated world? 12:00 Why Europe is building a "Sovereign Stack" to escape US dominance 15:00 Inside the brutal early days of Anthropic and the 21 VCs who said "No" 19:30 Why the most successful AI startups are ditching the "Profit-First" motive 34:30 The 1885 Industrial Revolution: Why we have a "GPU Wastage" bubble 38:00 Is the CCP actually winning the full-stack AI systems race? 43:30 Monopoly Mafias: Will model providers eventually kill the App Layer?  

April 10, 2026: Andreessen Horowitz just released hard data showing nearly a third of the Fortune 500 has live AI deployments — and the pattern underneath reveals exactly which jobs and functions are next in line. Then: Gallup says global employee engagement just hit a five-year low, and I'm going to argue that metric is fundamentally broken and why your board should stop asking for it. Plus, Microsoft Research coins a term you'll be using by tomorrow — "workslop" — and reveals the hidden social penalty employees face for using AI openly. McKinsey adds a critical wrinkle: your most AI-fluent employees are your biggest flight risk. And a new Wharton study finds that 80% of people follow wrong AI answers with complete confidence — and feel better about themselves while doing it. 

Brett Adcock is a technology entrepreneur focused on building companies in robotics, artificial intelligence, and aerospace. Born and raised on a third-generation farm in central Illinois, he developed an early fascination with technology and building systems from the ground up. After attending the University of Florida, he set out to tackle ambitious, capital-intensive industries with the goal of reshaping transportation, labor, and human-machine collaboration. At 26, Adcock founded Vettery, an AI-powered talent marketplace that matched thousands of companies with highly qualified candidates. The company scaled rapidly and was acquired in 2018 for $110 million by The Adecco Group, the world's largest recruiting firm. In 2018, he founded Archer Aviation to develop electric vertical takeoff and landing (eVTOL) aircraft aimed at transforming urban air mobility. During his time leading the company, Adcock helped architect, engineer, and flight-test five generations of aircraft, vertically integrating key technologies including flight software, electric motors, actuation systems, and battery systems. Archer secured a $1.5 billion partnership with United Airlines and positioned itself at the forefront of next-generation aviation. In 2022, Adcock founded Figure, where he serves as Founder & CEO. Figure is building general-purpose humanoid robots designed to address global labor shortages and work alongside humans in manufacturing, logistics, warehousing, retail, and the home. Backed by leading investors including Andreessen Horowitz and Sequoia Capital, the company has raised billions in venture capital and is focused on deploying embodied AI systems at scale. He is also the founder of Cover (2023–present), an AI security company developing non-intrusive scanners in partnership with NASA's Jet Propulsion Laboratory. The technology is designed to passively detect concealed weapons in crowded environments, with the goal of improving public safety without invasive screening. Follow the market: https://polymarket.com/event/ai-bubble-burst-by Shawn Ryan Show Sponsors: SpotOn GPS Fence — trusted by Shawn Ryan for his dog Stanley. The most reliable GPS dog fence: 100% secure from backyard to backcountry with virtual boundaries you control from your phone. No wires, no digging. Sets up in minutes, any size, any shape, anywhere. Learn more: https://spotonfence.com/srs Sign up for your $1 per month trial today at https://shopify.com/srs Get 20% off Rho Nutrition Liposomal NAD+ for clean, sustained energy and sharper focus with code SRS at https://rhonutrition.com/discount/SRS risk-free 60-day money-back guarantee. If you're serious about selling to the Department of War, go to https://SBIRAdvisors.com and mention Shawn Ryan for your first month free. Brett Adcock Links: X - https://x.com/adcock_brett IG - https://www.instagram.com/brett_adcock WEB - https://www.brettadcock.com LI - https://www.linkedin.com/in/brettadcock Learn more about your ad choices. Visit podcastchoices.com/adchoices

Marc Andreessen is a Co-Founder and General Partner at Andreessen Horowitz. The firm now manages over $90BN and has invested in the likes of OpenAI, Airbnb, Coinbase, Anduril and many more. Marc is an innovator and creator, one of the few to pioneer a software category used by more than a billion people and one of the few to establish multiple billion-dollar companies. Marc co-created the Mosaic internet browser and co-founded Netscape (sold to AOL for $4.2 billion). He also co-founded Loudcloud, which as Opsware, sold to Hewlett-Packard for $1.6 billion.  AGENDA: 05:00 — Why Introspection is Overrated: The Dangers of Learning from the Past 08:00 — The One Trait Marc Andreessen Looks For in Every Founder 14:30 — Are the Best Founders Broken? What Makes the Best Founders? 16:00 — "Extreme Ownership": Why Everything Being Your Fault Changes Everything 19:00 — "Do You Read the Comments?" Fame, Criticism & How to Deal with Haters 26:00 — Is Venture Now Go Big or Go Home? The Real Future of VC 30:00 — Does Price Matter Anymore? The Dangerous Truth About Valuations 33:00 — "Stop Chasing Diamonds in the Rough": Why Most VCs Get This Completely Wrong 36:00 — Do You Actually Need to Like Founders? The Uncomfortable Answer 40:00 — Are Companies 75% Overstaffed? The Most Controversial Take on Hiring 45:00 — When Will a16z Go Public?  50:00 — Why Labour Displacement Theory Around AI is Totally Wrong 55:00 — Why Silicon Valley Is More Dominant Than Ever? 01:00:00 — Why a16z Invested $300M into Adam Neumann  01:05:00 — What Still Drives Marc Andreesen? 01:10:00 — What is the Biggest Mistakes VCs Still Make Today?