Podcast appearances and mentions of Martin Casado

In this feed drop from The Six Five Pod, a16z General Partner Martin Casado discusses how AI is changing infrastructure, software, and enterprise purchasing. He explains why current constraints are driven less by technical limits and more by regulation, particularly around power, data centers, and compute expansion.The episode also covers how AI is affecting software development, lowering the barrier to coding without eliminating the need for experienced engineers, and how agent-driven tools may shift infrastructure decision-making away from humans.Watch more from Six Five Media: https://www.youtube.com/@SixFiveMedia Resources:Follow Martin Casado on X: https://twitter.com/martin_casado  Follow Patrick Moorhead on X:  https://twitter.com/PatrickMoorheadFollow Daniel Newman on X: https://twitter.com/danielnewmanUV Stay Updated:If you enjoyed this episode, be sure to like, subscribe, and share with your friends!Find a16z on X: https://twitter.com/a16zFind a16z on LinkedIn: https://www.linkedin.com/company/a16zListen to the a16z Podcast on Spotify: https://open.spotify.com/show/5bC65RDvs3oxnLyqqvkUYXListen to the a16z Podcast on Apple Podcasts: https://podcasts.apple.com/us/podcast/a16z-podcast/id842818711Follow our host: https://x.com/eriktorenbergPlease 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 http://a16z.com/disclosures. Stay Updated:Find 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.

Patrick Moorhead and Daniel Newman explore how infrastructure constraints, capital dynamics, software consumption shifts, and regulatory friction are increasingly determining who can scale intelligent systems, featuring an exclusive "Off The Record" conversation with Martin Casado, GM of the Infrastructure fund at a16z (Andreessen Horowitz). Follow the hosts:  https://x.com/PatrickMoorhead https://x.com/danielnewmanUV   Follow the guest:  https://x.com/martin_casado   Follow on X: https://x.com/sixfivemedia   Follow on LinkedIn:  https://www.linkedin.com/company/thesixfive/   THE DECODE Enterprise AI moves from pilots to production https://siliconangle.com/2025/12/17/enterprise-ai-hpe-tackles-execution-problem-hpeaimomentum/ SiliconANGLE AI memory and infrastructure supply pressures https://www.reuters.com/world/china/ai-frenzy-is-driving-new-global-supply-chain-crisis-2025-12-03/ Reuters Data center stocks and power bottlenecks in focus https://ts2.tech/en/data-center-stocks-week-ahead-dec-22-26-2025-ai-mega-deals-power-bottlenecks-and-export-control-risk-in-focus/ TechStock² AI capex boom meets power-grid bottlenecks https://ts2.tech/en/data-center-stocks-ai-capex-boom-meets-power-grid-bottlenecks-todays-news-and-2026-outlook-dec-20-2025/ TechStock² Tech executives share AI infrastructure insights https://siliconangle.com/2025/12/19/top-tech-executives-share-ai-insights-thecube/ SiliconANGLE AMD positions for massive compute growth in AI era https://www.techradar.com/pro/amd-ceo-welcomes-us-to-the-yottascale-era-lisa-su-says-ai-will-need-yottaflops-of-compute-power-soon TechRadar   THE FLIP China's power availability could redefine AI compute leadership https://www.businessinsider.com/elon-musk-china-ai-compute-exceed-electricity-power-2026-1 Business Insider Data center geography and sustainability challenges https://theweek.com/tech/data-center-locations-climate-water-energy-ai The Week   BULLS & BEARS AI stock momentum, Nvidia, Micron, and data-center arms race https://ts2.tech/en/ai-stocks-today-dec-26-2025-nvidias-groq-deal-chinas-hard-tech-push-and-the-global-data-center-arms-race/ TechStock² Cloud computing stocks outlook with AI demand https://ts2.tech/en/cloud-computing-stocks-outlook-dec-20-2025-ai-data-center-boom-powers-microsoft-amazon-alphabet-and-tests-oracle TechStock² AI infrastructure rebound amid memory demand and costs https://ts2.tech/en/data-center-stocks-today-micron-ignites-an-ai-infrastructure-rebound-as-oracle-funding-questions-and-power-grid-costs-loom-dec-18-2025/ TechStock² Cisco networking and AI infrastructure tailwinds https://ts2.tech/en/cisco-systems-csco-news-on-dec-25-2025-ai-networking-tailwinds-fy2026-forecasts-and-a-critical-email-security-zero-day/   For a deeper dive into each topic, please click on the links above. Be sure to subscribe to The Six Five Pod so you never miss an episode.

Originally published in June 2023, this conversation features a16z cofounder Marc Andreessen following the release of his nearly 7,000-word essay arguing that AI does not threaten our humanity. In a wide-ranging discussion with a16z General Partner Martin Casado, Andreessen expands on why he believes AI can dramatically amplify human potential, why its future should be shaped by open markets rather than regulation, and why fears of existential catastrophe are misplaced. Rather than destroying the world, he argues, AI may help save it.Read “Why AI Will Save the World”: https://a16z.com/2023/06/06/ai-will-save-the-world/  Resources:Follow Martin on X: https://x.com/martin_casadoFollow Marc on X: https://x.com/pmarca Stay Updated:If you enjoyed this episode, be sure to like, subscribe, and share with your friends!Find a16z on X: https://twitter.com/a16zFind a16z on LinkedIn: https://www.linkedin.com/company/a16zListen to the a16z Podcast on Spotify: https://open.spotify.com/show/5bC65RDvs3oxnLyqqvkUYXListen to the a16z Podcast on Apple Podcasts: https://podcasts.apple.com/us/podcast/a16z-podcast/id842818711Please 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.  Stay Updated:Find 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.

This episode is a special replay from The Generalist Podcast, featuring a conversation with a16z General Partner Martin Casado. Martin has lived through multiple tech waves as a founder, researcher, and investor, and in this discussion he shares how he thinks about the AI boom, why he believes we're still early in the cycle, and how a market-first lens shapes his approach to investing.They also dig into the mechanics behind the scenes: why AI coding could become a multi-trillion-dollar market, how a16z evolved from a small generalist firm into a specialized organization, the growing role of open-source models, and why Martin believes AGI debates often obscure more meaningful questions about how technology actually creates value. Resources:Follow Mario GabrieleX: https://x.com/mariogabrielehttps://www.generalist.com/Follow Martin Casado:LinkedIn: https://www.linkedin.com/in/martincasado/X: https://x.com/martin_casadoThe Generalist Substack: https://www.generalist.com/The Generalist on YouTube: https://www.youtube.com/@TheGeneralistPodcastSpotify: https://open.spotify.com/show/6mHuHe0Tj6XVxpgaw4WsJVApple: https://podcasts.apple.com/us/podcast/the-generalist/id1805868710 Stay Updated:If you enjoyed this episode, be sure to like, subscribe, and share with your friends!Find a16z on X: https://x.com/a16zFind a16z on LinkedIn: https://www.linkedin.com/company/a16zListen to the a16z Podcast on Spotify: https://open.spotify.com/show/5bC65RDvs3oxnLyqqvkUYXListen to the a16z Podcast on Apple Podcasts: https://podcasts.apple.com/us/podcast/a16z-podcast/id842818711Please 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 Stay Updated:Find 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.

This episode is a special replay from The Generalist Podcast, featuring a conversation with a16z General Partner Martin Casado. Martin has lived through multiple tech waves as a founder, researcher, and investor, and in this discussion he shares how he thinks about the AI boom, why he believes we're still early in the cycle, and how a market-first lens shapes his approach to investing.They also dig into the mechanics behind the scenes: why AI coding could become a multi-trillion-dollar market, how a16z evolved from a small generalist firm into a specialized organization, the growing role of open-source models, and why Martin believes AGI debates often obscure more meaningful questions about how technology actually creates value. Follow Mario GabrieleX: https://x.com/mariogabrielehttps://www.generalist.com/ Follow Martin Casado:LinkedIn: https://www.linkedin.com/in/martincasado/X: https://x.com/martin_casado The Generalist Substack: https://www.generalist.com/The Generalist on YouTube: https://www.youtube.com/@TheGeneralistPodcastSpotify: https://open.spotify.com/show/6mHuHe0Tj6XVxpgaw4WsJVApple: https://podcasts.apple.com/us/podcast/the-generalist/id1805868710 Check out everything a16z is doing with artificial intelligence here, including articles, projects, and more podcasts. 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.

a16z's Martin Casado sits down with Shikhar Shrestha, CEO and cofounder of Ambient, the company bringing agentic AI to physical security.Shikhar shares how a traumatic armed robbery at age 12—and a security camera that no one was watching—sparked his mission to make every camera intelligent.They discuss how Ambient's AI monitors camera feeds in real-time to detect threats and prevent incidents before they happen, navigating COVID as a physical security company, building their own reasoning VLM called Pulsar, and why the future of security is AI not just detecting threats but automatically responding to them.If you enjoyed this episode, please be sure to like, subscribe, and share with your friends.Follow Shikhar on X: https://x.com/shikharshresthaFollow Martin on X: x.com/martin_casado Check out everything a16z is doing with artificial intelligence here, including articles, projects, and more podcasts. 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.

Anil Varanasi is the co-founder and CEO of Meter, which provides full-stack networking infrastructure as a service for businesses. Since founding Meter with his brother Sunil in 2015, Anil has been playing a distinctly long game in one of the most entrenched markets in technology, betting on vertical integration, business model innovation, and a multi-decade time horizon. In this conversation, he unpacks Meter's origin story, from four-plus years of heads-down R&D, and shares how his unconventional approach to planning, management, and pace keeps him excited to run the company for decades. In today's episode, we discuss: Why Anil thinks in 25-year horizons How operating in a monopolistic market shaped Meter's approach Why Meter scrapped a year of OS work during the R&D phase How Meter is rethinking networking's business model Surviving COVID, Apple's M1 transition, and “a thousand bad days” Anil's contrarian views on planning, OKRs, and management How founders can build companies they'll want to run for decades Where to find Anil: LinkedIn: https://www.linkedin.com/in/anilcv/ Twitter/X: https://x.com/acv Where to find Brett: LinkedIn: https://www.linkedin.com/in/brett-berson-9986094/ Twitter/X: https://twitter.com/brettberson Where to find First Round Capital: Website: https://firstround.com/ First Round Review: https://review.firstround.com/ Twitter/X: https://twitter.com/firstround YouTube: https://www.youtube.com/@FirstRoundCapital This podcast on all platforms: https://review.firstround.com/podcast References: ADT: ⁠https://www.adt.com⁠ Alex Honnold: ⁠https://www.alexhonnold.com⁠ Alex Tabarrok: ⁠https://x.com/ATabarrok⁠ ⁠alarm.com⁠: ⁠https://www.alarm.com⁠ Andreessen Horowitz (a16z): ⁠https://a16z.com⁠ Apple: ⁠https://www.apple.com⁠ Bloomberg: ⁠https://www.bloomberg.com⁠ Bryan Caplan: ⁠http://www.bcaplan.com/⁠ Cisco: ⁠https://www.cisco.com⁠ Coca-Cola: ⁠https://www.coca-colacompany.com⁠ George Mason University (GMU): ⁠https://www.gmu.edu⁠ Intel: ⁠https://www.intel.com⁠ Julia Galef: ⁠https://x.com/juliagalef⁠ Martin Casado: ⁠https://www.linkedin.com/in/martincasado/⁠ Meraki: ⁠https://meraki.cisco.com⁠ Meter: ⁠https://www.meter.com⁠ Michela Giorcelli: ⁠https://x.com/M_Giorcelli⁠ Nicholas Bloom: ⁠https://www.linkedin.com/in/nick-bloom-stanford/⁠ Raffaella Sadun: ⁠https://www.linkedin.com/in/raffaella-sadun-3a182225/⁠ Sanjit Biswas: ⁠https://www.linkedin.com/in/sanjitbiswas/⁠ Sunil Varanasi: ⁠https://www.linkedin.com/in/sunil-varanasi-662a01253/⁠ Tyler Cowen: ⁠https://www.linkedin.com/in/tyler-cowen-166718/⁠ Twitch: ⁠https://www.twitch.tv⁠ Timestamps: (01:27) Meter's unusual timeframes (04:06) “We don't do OKRs” (06:32) How to plan without planning (08:31) Track your unhappy customers (11:43) How Meter's journey began (15:02) Dissecting the 2010s SaaS boom (17:06) The networking industry trap (21:44) Meter's first roadblock (22:07) Why Shenzhen accelerated Meter's progress (26:29) The process to get a sales-ready product (31:02) Why you should own the full stack (32:45) The surprising thing you should innovate (35:03) Avoiding the one-trick pony trap (37:39) The secret to finding an excellent market (43:48) How COVID's constraints propelled growth (48:25) Why founders need to know their customers (49:34) Why Meter didn't sell via traditional channels (51:44) You need “seller-market fit” (54:51) The danger of meta-work (56:25) Decoupling management from authority (1:02:17) When the person is the problem (1:05:05) The inherent value of going slowly (1:09:41) Running a company for as long as possible

Fei-Fei Li and Justin Johnson are pioneers in AI. While the world has only recently witnessed a surge in consumer AI, they have long been laying the groundwork for the innovations transforming industries today.With the recent launch of Marble, the first product from their company World Labs, we are revisiting this conversation to explore the ideas that started it all. World Labs is focused on spatial intelligence, building Large World Models that can perceive, generate, and interact with the 3D world. Marble brings that vision to life, allowing anyone, from individual creators to major platforms, to generate 3D scenes directly from text or image prompts and turn complex 3D creation into a simple, creative process.In this episode, a16z general partner Martin Casado talks with Fei-Fei and Justin about the journey from early AI winters to the rise of deep learning and multimodal AI. From foundational breakthroughs like ImageNet to the cutting-edge realm of spatial intelligence, they discuss the evolution of the field and what is next for innovation at World Labs. Timecode:0:00 – The Next Decade of AI2:45 – Origins: Backgrounds of the Founders6:50 – The Rise of Deep Learning & ImageNet8:00 – Algorithmic Unlocks: Compute, Data, and Supervised Learning12:00 – From Predictive to Generative AI16:20 – The Journey to Spatial Intelligence18:35 – Defining Spatial Intelligence21:15 – 3D Data, Computer Vision, and Breakthroughs23:15 – Reconstruction vs. Generation in Computer Vision24:45 – Spatial Intelligence vs. Language Models29:00 – Applications: Virtual, Augmented, and Physical Worlds39:55 – Building World Labs: Team and Vision41:55 – The North Star: Measuring Success in Spatial Intelligence Resources:Learn more about World Labs: https://www.worldlabs.aiLearn more about Marble: https://Marble.WorldLabs.aiFind Fei-Fei on Twitter: https://x.com/drfeifeiFind Justin on Twitter: https://x.com/jcjohnssFind Martin on Twitter: https://x.com/martin_casado Stay Updated: If you enjoyed this episode, be sure to like, subscribe, and share with your friends!Find a16z on X: https://x.com/a16zFind a16z on LinkedIn: https://www.linkedin.com/company/a16zListen to the a16z Podcast on Spotify: https://open.spotify.com/show/5bC65RDvs3oxnLyqqvkUYXListen to the a16z Podcast on Apple Podcasts: https://podcasts.apple.com/us/podcast/a16z-podcast/id842818711Follow our host: https://x.com/eriktorenbergPlease 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. Stay Updated:Find a16z on XFind a16z on LinkedInListen to the a16z Podcast on SpotifyListen to the a16z Podcast 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.

When four MIT grads decided to build a code editor while everyone else was building AI agents, they created the fastest-growing developer tool ever built. Cursor CEO Michael Truell joins a16z's Martin Casado to discuss the deliberate constraints that led to breakthroughs: why they rejected the "democratization" narrative to focus on power users, how their 2-day work trials test for agency over credentials, and the strategic decision to own the editor when conventional wisdom said it was impossible. Resources:Follow Michael on X: https://x.com/mntruell Stay Updated: If you enjoyed this episode, be sure to like, subscribe, and share with your friends!Find a16z on X: https://x.com/a16zFind a16z on LinkedIn: https://www.linkedin.com/company/a16zListen to the a16z Podcast on Spotify: https://open.spotify.com/show/5bC65RDvs3oxnLyqqvkUYXListen to the a16z Podcast on Apple Podcasts: https://podcasts.apple.com/us/podcast/a16z-podcast/id842818711Follow our host: https://x.com/eriktorenbergPlease 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. Stay Updated:Find a16z on XFind a16z on LinkedInListen to the a16z Podcast on SpotifyListen to the a16z Podcast 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.

From Netscape to VMware, Raghu Raghuram has been at the center of nearly every major inflection point in enterprise technology.In this episode, Raghu joins Ben Horowitz, Martin Casado and David George to reflect on the early internet wars with Microsoft, how Netscape's browser battles shaped a generation of founders, and the inside story of one of the most successful tech acquisitions in history, VMware's $1.3B purchase of Nicira, which redefined modern networking and grew into a multi-billion-dollar business.They discuss how VMware scaled from tens of millions to over $13 billion in revenue, what it took to outlast the cloud revolution, and why AI is now triggering the biggest infrastructure reset since virtualization. Raghu shares his vision for the next decade — from data-center robotics and energy-aware compute to how AI is reshaping both startups and giants alike. Resources:Follow Raghu on X: https://x.com/RaghuRaghuramFollow Ben on X: https://x.com/bhorowitzFollow Martin on X: https://x.com/martin_casadoFollow David on X: https://x.com/DavidGeorge83 Stay Updated: If you enjoyed this episode, be sure to like, subscribe, and share with your friends!Find a16z on X: https://x.com/a16zFind a16z on LinkedIn: https://www.linkedin.com/company/a16zListen to the a16z Podcast on Spotify: https://open.spotify.com/show/5bC65RDvs3oxnLyqqvkUYXListen to the a16z Podcast on Apple Podcasts: https://podcasts.apple.com/us/podcast/a16z-podcast/id842818711Follow our host: https://x.com/eriktorenbergPlease 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 Stay Updated:Find a16z on XFind a16z on LinkedInListen to the a16z Podcast on SpotifyListen to the a16z Podcast 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.

Augusto Marietti, CEO and cofounder of Kong, has one of the most remarkable founder stories in Silicon Valley history.In this conversation with Martin Casado, Aghi shares how he went from a garage in Milan to building one of the world's leading API infrastructure companies, surviving years of rejection, living in the U.S. on $1,000 a month, and raising his first $50K while sleeping on Travis Kalanick's couch. They talk about the near-death moments that defined Kong's journey, the seven-year grind before breakout success, and how APIs became the “assembly line of software.” Aghi also explains how Kong evolved into the backbone of modern API and AI connectivity, and why the coming wave of AI agents will make APIs more essential than ever. Resources:Follow Aghi on X: x.com/sonicaghiFollow Kong on X: https://x.com/kongFollow Martin on X: x.com/martin_casado Stay Updated: If you enjoyed this episode, be sure to like, subscribe, and share with your friends!Find a16z on X: https://x.com/a16zFind a16z on LinkedIn: https://www.linkedin.com/company/a16zListen to the a16z Podcast on Spotify: https://open.spotify.com/show/5bC65RDvs3oxnLyqqvkUYXListen to the a16z Podcast on Apple Podcasts: https://podcasts.apple.com/us/podcast/a16z-podcast/id842818711Follow our host: https://x.com/eriktorenbergPlease 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. Stay Updated:Find a16z on XFind a16z on LinkedInListen to the a16z Podcast on SpotifyListen to the a16z Podcast 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.

The artificial intelligence boom has sparked one of the costliest building sprees in history. By 2028, investment in chips, servers and data centers could hit nearly $3 trillion, according to Morgan Stanley. To help fund the build-out, tech companies are taking on huge amounts of debt, raising concerns of a possible bubble. On the latest episode of the Bold Names podcast, Martin Casado, a general partner at Andreessen Horowitz, who leads the firm's $1.25 billion infrastructure practice, speaks to WSJ's Christopher Mims and Tim Higgins, about whether the industry's biggest bet in decades will deliver returns. Casado explains why he is optimistic about AI and how this moment compares to the internet buildout of the 1990s.   To watch the video version of this episode, visit our WSJ Podcasts YouTube channel or the video page of WSJ.com. Check Out Past Episodes: The Google Exec Reinventing Search in the AI Era Condoleezza Rice on Beating China in the Tech Race: 'Run Hard and Run Fast' Why IBM's CEO Thinks His Company Can Crack Quantum Computing How Tubi Is Coming for Netflix and YouTube in the New Streaming Wars Let us know what you think of the show. Email us at BoldNames@wsj.com Sign up for the WSJ's free Technology newsletter. Read Christopher Mims's Keywords column. Read Tim Higgins's column. Learn more about your ad choices. Visit megaphone.fm/adchoices

What if the hardest part of building a company isn't the product, but knowing exactly who it's for?In this episode, a16z General Partner Martin Casado sits down with Abhishek Agrawal, Cofounder and CEO of Material Security, to discuss how an ideal customer profile is discovered, how to manage any kind of customer, and how frothy markets can distort real signal.Follow Martin on X: https://x.com/martin_casadoFollow Material Security on X: https://x.com/material_secFollow Abhishek on LinkedIn: https://www.linkedin.com/in/abhishek--agrawal/ Check out everything a16z is doing with artificial intelligence here, including articles, projects, and more podcasts. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Should the US put a price on H-1B visas, or would that block the flow of new talent? Are AI coding agents actually making teams way more productive, or is it just hype? And in the AI platform shift, will the big winners be incumbents or new AI-native startups?Erik Torenberg is joined by Box co-founder and CEO Aaron Levie, a16z board partner Steven Sinofsky, and a16z general partner Martin Casado to debate the biggest questions in tech. They unpack pricing vs lottery for H-1Bs and what we're actually optimizing for, why Box now ships a third of its code from AI, the shift from writing to reviewing code, and why bottom-up personal AI tools succeed where top-down “AI pilots” struggle. Timecodes: 0:00 Introduction1:07  Latest immigration policy and who benefits1:39  Debating the Price on H-1B Visas2:11  Startups vs. Big Tech: Who Benefits from Policy?2:31  Market Dynamics and Wage Impacts3:44  The Lottery System and Startup Challenges12:25  Labor Markets to Labor Productivity with AIs14:47  Startups Achieving 10x Productivity with AI16:43  Early Adopters, Hype, and Measuring Productivity33:50  AI's Impact on Professional and Creative Work37:56  The Rise of AI-Native Startups40:58  Platform Shifts: Startups vs. Incumbents42:12  Disruption, Incumbents, and New Opportunities53:00  The Future of Work and AI Adoption54:38  Brand Effects and Early Leaders in AI55:22  Will Incumbents or Newcomers Win the AI Race?Resources:Find Aaron on X: https://x.com/levieFind Steven on X: https://x.com/stevesiFind Martin on X: https://x.com/martin_casadoFind Erik on X: https://x.com/eriktorenberg Stay Updated: If you enjoyed this episode, be sure to like, subscribe, and share with your friends!Find a16z on X: https://x.com/a16zFind a16z on LinkedIn: https://www.linkedin.com/company/a16zListen to the a16z Podcast on Spotify: https://open.spotify.com/show/5bC65RDvs3oxnLyqqvkUYXListen to the a16z Podcast on Apple Podcasts: https://podcasts.apple.com/us/podcast/a16z-podcast/id842818711Follow our host: https://x.com/eriktorenbergPlease 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. Stay Updated:Find a16z on XFind a16z on LinkedInListen to the a16z Podcast on SpotifyListen to the a16z Podcast 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.

This week's podcast is about why bundling (and cross-selling and upselling) are so powerful in digital. Plus, some thoughts on why data moats are mostly not real.You can listen to this podcast here, which has the slides and graphics mentioned. Also available at iTunes and Google Podcasts.Here is the link to the TechMoat Consulting.Here is the link to our Tech Tours.Here is the article on bundling by Chris Dixon.How bundling benefits sellers and buyersA lot of thinking on data as a moat comes from these articles by Martin Casado, Julian Wright and Andre Hagiu.The Empty Promise of Data MoatsWhen Data Creates Competitive AdvantageHere are 7 complications I mentioned about bundling.Bundling requires a a robust product suite.You want a low attach rate. So you don't cannibalize products by bundling.Bundling can complicate the customer experience and buying journey.Bundling works best when all customers have similar total willingness to pay. But different tastes.Unlimited bundles (subscriptions) can have problems if there are non-zero product costs.Unlimited bundles (subscriptions) do limit revenue per user. This is a problem when willingness to pay is positively correlated with demand for variety.Subscription models do not reward superstar creators very well. ----------I am a consultant and keynote speaker on how to accelerate growth with improving customer experiences (CX) and digital moats.I am a partner at TechMoat Consulting, a consulting firm specialized in how to increase growth with improved customer experiences (CX), personalization and other types of customer value. Get in touch here.I am also author of the Moats and Marathons book series, a framework for building and measuring competitive advantages in digital businesses.This content (articles, podcasts, website info) is not investment, legal or tax advice. The information and opinions from me and any guests may be incorrect. The numbers and information may be wrong. The views expressed may no longer be relevant or accurate. This is not investment advice. Investing is risky. Do your own research.Support the show

Jack Altman sits down with Martin Casado, General Partner at a16z, to unpack the shifting dynamics of venture capital and why media matters more than ever. They cover a16z's evolution from generalists to specialized platforms, the rise of AI infrastructure, and why today's fiercest battles are often for talent, not market share.Timecodes:0:00 Introduction0:27 Importance of Media for VC3:50 Evolution of a16z7:00 Specialization10:32 Value of Distribution13:16 Staying Power in Infrastructure19:49 The Conflicts Dynamic26:32 State of Play in AI30:48 The Future of Coding34:58 Significance of Open Source39:48 Marc Andreessen's Leadership44:02 The Only Sin in VC48:37 Scaling a Lot of Board SeatsResources: Listen to more from Uncapped: https://linktr.ee/uncappedpodFind Jack on X: https://x.com/jaltmaFind Uncapped on X: https://x.com/uncapped_podFind Martin on X: https://x.com/martin_casadoStay Updated: Let us know what you think: https://ratethispodcast.com/a16zFind a16z on Twitter: https://twitter.com/a16zFind a16z on LinkedIn: https://www.linkedin.com/company/a16zSubscribe on your favorite podcast app: https://a16z.simplecast.com/Follow our host: https://x.com/eriktorenbergPlease 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.

When somebody says “win-win” in Silicon Valley, check your pockets. It's usually some elaborate prelude to a sales pitch. And the only thing dodgier than a two-way win is the “win-win-win” narrative that my friend Keith Teare is selling this week. “User, Publishers and AI: Everybody Wins” is the title of Keith's That Was The Week newsletter this week. And to be fair, what he's selling is the dream of an AI world in which the publishers, consumers and manufacturers of information all win. Who wouldn't want that? Our conversation this week is built around the AI ethics showdown by Y Combinator and Andreessen Horowitz which has shaken Silicon Valley this week. The battle centers on whether AI agents should identify themselves when accessing publisher content - a seemingly technical question that reveals broader tensions about who controls information in the age of artificial intelligence. Y Combinator's Garry Tan called new authentication requirements an "axis of evil" while Andreessen Horowitz's Martin Casado argued they represent common sense infrastructure. But the ever-optimistic Keith (who seems to believe that all progress is good, even for its victims) thinks everyone can win - users, publishers and tech companies. Presumably even Garry Tan and Martin Casado. If you believe that, then I might have some beautiful, no-risk Las Vegas beachfront real-estate for you. 1. The "Axis of Evil" Fight Is Really About Anonymous Access When Y Combinator's Garry Tan attacked Cloudflare and Browserbase's AI authentication system as an "axis of evil," he revealed Silicon Valley's preference for consequence-free data harvesting. The technical dispute over AI agent identification masks a deeper question: should AI companies remain anonymous when accessing publisher content, or must they become accountable?2. Publishers Need Influence, Not Just Traffic The conversation exposed a crucial distinction between advertising models that require massive scale and sponsorship models that reward targeted influence. Quality audiences matter more than raw pageviews - an insight that could reshape how content creators think about monetization in the AI era.3. The "Virtuous Circle" Depends on AI Companies Acting Against Self-Interest Keith's vision of AI systems surfacing attribution links back to original sources requires companies to voluntarily complicate their user experience. Why would ChatGPT or Claude choose to send users away to read original articles when seamless summarization is their core value proposition?4. "Bad Publishers Deserved to Fail" Sidesteps Structural Questions Keith's argument that only inferior publishers lost to digital disruption ignores how entire categories of valuable journalism - particularly local news - faced structural economic challenges regardless of quality. This reveals the limitations of purely market-based explanations for technological displacement.5. Trust May Be Irrelevant in the Post-Truth Era My observation that "nobody cares about trust anymore" challenges the entire premise of authentication systems. If users don't demand source verification, then the economic incentives for Keith's proposed "trusted third party" infrastructure may not exist.Keen On America is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit keenon.substack.com/subscribe

In this interview from the 20VC podcast, Martin Casado (a16z General Partner) joins Harry Stebbings to unpack the state of AI, the rise of coding models, the future of open vs. closed source, and how value is shifting across the stack.Martin offers a candid view of the opportunities and dangers shaping AI and venture capital today. Resources: Find Martin on X: https://x.com/martin_casadoFind Harry on X: https://x.com/harrystebbingsMore about 20VC:Subscribe on YouTube: https://www.youtube.com/@20VCSubscribe on Spotify:https://open.spotify.com/show/3j2KMcZTtgTNBKwtZBMHvl?si=85bc9196860e4466&nd=1&dlsi=d1dbbc6a0d7c4408Subscribe on Apple Podcasts:https://podcasts.apple.com/us/podcast/the-twenty-minute-vc-20vc-venture-capital-startup/id958230465Visit their Website: https://www.20vc.comSubscribe to their Newsletter: https://www.thetwentyminutevc.com/Follow 20VC on Instagram:  https://www.instagram.com/20vchq/#Follow 20VC on TikTok: https://www.tiktok.com/@20vc_tok Stay Updated: Let us know what you think: https://ratethispodcast.com/a16zFind a16z on Twitter: https://twitter.com/a16zFind a16z on LinkedIn: https://www.linkedin.com/company/a16zSubscribe on your favorite podcast app: https://a16z.simplecast.com/Follow our host: https://x.com/eriktorenbergPlease 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.

AGENDA: ​​00:00 – Marc Benioff vs Snowflake, Databricks & Palantir: Who Wins the Data Cloud War? 05:10 – Does Benioff Feel The Need to Buy AI Talent Like Zuck Is? 09:00 – What Salesforce has Learned From Palantir on Forward Deployed Engineers? 18:00 – Will SaaS apps disappear in an AI world? Why Satya is Chatting S*** 23:40 – Are SDRs really screwed by AI… or just evolving? 26:10 – Benioff on Who Wins: OpenAI or Anthropic? 30:00 – Nat Friedman reports to Alex Wang: Genius move or career downgrade? 34:00 – Anthropic's $10B round: Have we hit peak AI hype? 47:00 – Klarna's wild ride: From $45B to $6B to IPO at $15B 55:00 – Inside a16z's seed machine: 72 bets vs Sequoia's 27 57:45 – Martìn Casado: Is consensus investing dangerous—or the only game? 01:05:00 – The big lesson: consensus, contrarian, and why investing is harder than ever  

What exactly is an AI agent, and how will agents change the way we work?In this episode, a16z general partners Erik Torenberg and Martin Casado sit down with Aaron Levie (CEO, Box) and Steven Sinofsky (a16z board partner; former Microsoft exec) to unpack one of the hottest debates in AI right now.They cover:Competing definitions of an “agent,” from background tasks to autonomous internsWhy today's agents look less like a single AGI and more like networks of specialized sub-agentsThe technical challenges of long-running, self-improving systemsHow agent-driven workflows could reshape coding, productivity, and enterprise softwareWhat history — from the early PC era to the rise of the internet — tells us about platform shifts like this oneThe conversation moves from deep technical questions to big-picture implications for founders, enterprises, and the future of work. Timecodes: 0:00 Introduction: The Evolution of AI Agents0:36 Defining Agency and Autonomy1:54 Long-Running Agents and Feedback Loops4:49 Specialization and Task Division in AI6:20 Human-AI Collaboration and Productivity6:59 Anthropomorphizing AI and Economic Impact9:10 Predictions, Progress, and Platform Shifts11:31 Recursive Self-Improvement and Technical Challenges13:20 Hallucinations, Verification, and Expert Productivity16:20 The Role of Experts and Tool Adoption22:14 Changing Workflows: Agents Reshaping Work Patterns45:55 Division of Labor, Specialization, and New Roles48:47 Verticalization, Applied AI, and the Future of Agents54:44 Platform Competition and the Application Layer55:29 Closing Thoughts and Takeaways  Resources: Find Aaron on X: https://x.com/levieFind Martin on X: https://x.com/martin_casadoFind Steven on X: https://x.com/stevesi Stay Updated: Let us know what you think: https://ratethispodcast.com/a16zFind a16z on Twitter: https://twitter.com/a16zFind a16z on LinkedIn: https://www.linkedin.com/company/a16zSubscribe on your favorite podcast app: https://a16z.simplecast.com/Follow our host: https://x.com/eriktorenbergPlease 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.

What happens when a startup becomes a giant—and then has to reinvent itself all over again?In this episode, Martin Casado sits down with Raghu Raghuram (former CEO of VMware) and Jeetu Patel (President and CPO at Cisco) for a deep, tactical conversation on scaling, disruption, and navigating transformation from the inside. They share hard-won lessons from leading two of the most iconic infrastructure companies in tech—through waves like virtualization, cloud, containers, and now AI.They cover:How to keep innovation alive inside large companiesWhy the best companies operate with a founder's mindset, even without foundersThe difference between selling to buyers vs. practitionersWhy the story is the strategy, and how to tell it at scaleHow Cisco is rebuilding its startup DNA in the age of AIIf you're building or leading through a major tech wave, this episode is a playbook. Timecodes:0:00 Introduction 2:02 Weapons of Mass Disruption: Abstractions, Business Models, and Cloud  5:57 Cisco's Missed Cloud Wave & Resetting for Innovation  6:39 Operating Like a Startup: Speed, Scale, and Leadership  10:00 Go-to-Market Challenges: Fencing Off Innovation  11:04 Organic vs. Inorganic Growth: Lessons from VMware  12:04 The 10x Rule and Competing with Incumbents  14:39 Structuring for Disruption: Two-Pizza Teams and Ideal Customer Profiles  18:43 Storytelling as Strategy: Galvanizing Large Organizations  19:42 The AI Wave: Consumerization and Infrastructure Demands  25:34 Founders vs. Operators: Leading Transformations  31:47 Product-Led Organizations: From Sales to Product Focus  34:35 The Future of Infrastructure: AI, Market Size, and Vertical Integration  39:34 Timing, Market, Team, Product, Brand, and Scale  41:19 Authenticity, Opportunity, and Final Thoughts   Resources:Find Martin on X: https://x.com/martin_casadoFind Raghu on X: https://x.com/raghuraghuramFind Jeetu on X: https://x.com/jpatel41 Stay Updated: Let us know what you think: https://ratethispodcast.com/a16zFind a16z on Twitter: https://twitter.com/a16zFind a16z on LinkedIn: https://www.linkedin.com/company/a16zSubscribe on your favorite podcast app: https://a16z.simplecast.com/Follow our host: https://x.com/eriktorenbergPlease 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.

a16z General Partners Erik Torenberg and Martin Casado sit down with technologist and investor Balaji Srinivasan to explore how the metaphors we use to describe AI—whether as god, swarm, tool, or oracle—reveal as much about us as they do about the technology itself.Balaji, best known for his work in crypto and network states, also brings a deep background in machine learning. Together, the trio unpacks the evolution of AI discourse, from monotheistic visions of a singular AGI to polytheistic interpretations shaped by culture and context. They debate the practical and philosophical: the current limits of AI, why prompts function like high-dimensional programs, and what it really takes to “close the loop” in AI reasoning.This is a systems-level conversation on belief, control, infrastructure, and the architectures that might govern future societies. Timecodes:0:00 Introduction: The Polytheistic AGI Framework1:46 Personal Journeys in AI and Crypto3:18 Monotheistic vs. Polytheistic AGI: Competing Paradigms8:20 The Limits of AI: Chaos, Turbulence, and Predictability9:29 Platonic Ideals and Real-World Systems14:10 Decentralized AI and the End of Fast Takeoff14:34 Surprises in AI Progress: Language, Locomotion, and Double Descent25:45 Prompting, Verification, and the Age of the Phrase29:44 AI, Crypto, and the Grounding Problem34:26 Visual vs. Verbal: Where AI Excels and Struggles37:19 The Challenge of Markets, Politics, and Adversarial Systems40:11 Amplified Intelligence: AI as a Force Multiplier43:37 The Polytheistic Counterargument: Convergence and Specialization48:17 AI's Impact on Jobs: Specialists, Generalists, and the Future of Work57:36 Security, Drones, and Digital Borders1:03:41 AI, Power, and the Balance of Control1:06:33 The Coming Anti-AI Backlash1:09:10 Global Implications: Labor, Politics, and the Future Resources:Find Balaji on X: https://x.com/balajisFind Martin on X: https://x.com/martin_casado Stay Updated: Let us know what you think: https://ratethispodcast.com/a16zFind a16z on Twitter: https://twitter.com/a16zFind a16z on LinkedIn: https://www.linkedin.com/company/a16zSubscribe on your favorite podcast app: https://a16z.simplecast.com/Follow our host: https://x.com/eriktorenbergPlease 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.

Martin Casado is a General Partner @ a16z where he leads the firms $1.25BN infrastructure fund. At a16z, Martin has led investments in companies like Cursor, dbt Labs, and Fivetran to name a few. Before joining a16z, he co-founded Nicira, acquired by VMware for $1.26B. At VMware, he served as CTO of Networking. Widely regarded as a visionary in enterprise infrastructure, Martin has helped shape the modern cloud computing stack. Agenda: 00:00 – Analysis of Current AI Investment Landscape 04:45 – Will Anthropic Kill the AI App Layer? 09:20 – “The Oligopoly Is Coming—Just Like Cloud” 12:50 – Are AI Models Actually Terrible Venture Investments? 15:40 – Why it is BS to Put Down AI Apps for Having Temporary Revenue 21:30 – “Open Source Is a National Security Weapon—And We're Losing” 26:40 – “Have the Foundation Models of the Future All Been Founded Already” 34:30 – Why it is BS to Denigrate AI Apps for Having Low Margins 38:40 – Does AI Make 1x Engineers 10x or 10x Becomes 100x 44:10 – “We're All Dead Wrong About AI and Job Loss” 50:30 – “The Only Sin in Venture: Backing the Wrong Winner” 55:10 – What People Think They Know About Wealth But Do Not  

Is AI the Fourth Pillar of Infrastructure?Infrastructure doesn't go away — it layers. And today, AI is emerging as a new foundational layer alongside compute, storage, and networking.Erik Torenberg interviews a16z's Martin Casado, Jennifer Li, and Matt Bornstein breaking down how infrastructure is evolving in the age of AI — from models and agents to developer tools and shifting user behavior.We dive into what infra actually means today, how it differs from enterprise, and why software itself is being disrupted. Plus, we explore the rise of technical users as buyers, what makes infra companies defensible, and how past waves — from the cloud to COVID to AI — are reshaping how we build and invest. Timestamps: (00:00) Introduction (01:49) Defining Infrastructure in the AI Era(03:15) The Fourth Pillar: AI's Role in Infrastructure(06:01) Historical Context and Evolution of Infrastructure(08:20) The Impact of AI on Software Development(10:18) Investment Strategies and Market Dynamics(17:02) Developer Tools and AI Integration(20:57) Defensibility in the AI Landscape(22:16) Founders' Intuition and Industry Progress(22:26) Defensibility in AI Infrastructure(24:00) Expansion and Contraction Phases in the Industry(24:35) The Role of Layers in Market Consolidation(27:43) The Future of AI Models and Specialization(29:27) The Decade of AI Agents(29:54) Context Engineering and New Infrastructure(34:23) The Evolution of Software Development(42:13) Horizontal vs. Vertical Integration in AI(43:54) Conclusion and Final Thoughts Resources: Find Martin on X: https://x.com/martin_casadoFind Jennifer on X: https://x.com/JenniferHliFind Matt on X: https://x.com/BornsteinMatt Stay Updated: Let us know what you think: https://ratethispodcast.com/a16zFind a16z on Twitter: https://twitter.com/a16zFind a16z on LinkedIn: https://www.linkedin.com/company/a16zSubscribe on your favorite podcast app: https://a16z.simplecast.com/Follow our host: https://x.com/eriktorenbergPlease 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.

AI's breakout moment is here - but where is the real value accruing, and what's just hype?Recorded live at a16z's annual LP Summit, General Partners Erik Torenberg, Martin Casado, and Sarah Wang unpack the current state of play in AI. From the myth of the GPT wrapper to the rapid rise of apps like Cursor, the conversation explores where defensibility is emerging, how platform shifts mirror (and diverge from) past tech cycles, and why the zero-sum mindset falls short in today's AI landscape.They also dig into the innovator's dilemma facing SaaS incumbents, the rise of brand moats, the surprising role of prosumer adoption, and what it takes to pick true category leaders in a market defined by both exponential growth - and accelerated wipeouts.Resources: Find Martin on X: https://x.com/martin_casadoFind Sarah on X: https://x.com/sarahdingwangStay Updated: Let us know what you think: https://ratethispodcast.com/a16zFind a16z on Twitter: https://twitter.com/a16zFind a16z on LinkedIn: https://www.linkedin.com/company/a16zSubscribe on your favorite podcast app: https://a16z.simplecast.com/Follow our host: https://x.com/eriktorenbergPlease 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.

We are calling for the world's best AI Engineer talks for AI Architects, /r/localLlama, Model Context Protocol (MCP), GraphRAG, AI in Action, Evals, Agent Reliability, Reasoning and RL, Retrieval/Search/RecSys , Security, Infrastructure, Generative Media, AI Design & Novel AI UX, AI Product Management, Autonomy, Robotics, and Embodied Agents, Computer-Using Agents (CUA), SWE Agents, Vibe Coding, Voice, Sales/Support Agents at AIEWF 2025! Fill out the 2025 State of AI Eng survey for $250 in Amazon cards and see you from Jun 3-5 in SF!Coreweave's now-successful IPO has led to a lot of questions about the GPU Neocloud market, which Dylan Patel has written extensively about on SemiAnalysis. Understanding markets requires an interesting mix of technical and financial expertise, so this will be a different kind of episode than our usual LS domain.When we first published $2 H100s: How the GPU Rental Bubble Burst, we got 2 kinds of reactions on Hacker News:* “Ah, now the AI bubble is imploding!”* “Duh, this is how it works in every GPU cycle, are you new here?”We don't think either reaction is quite right. Specifically, it is not normal for the prices of one of the world's most important resources right now to swing from $1 to $8 per hour based on drastically inelastic demand AND supply curves - from 3 year lock-in contracts to stupendously competitive over-ordering dynamics for NVIDIA allocations — especially with increasing baseline compute needed for even the simplest academic ML research and for new AI startups getting off the ground.We're fortunate today to have Evan Conrad, CEO of SFCompute, one of the most exciting GPU marketplace startups, talk us through his theory of the economics of GPU markets, and why he thinks CoreWeave and Modal are well positioned, but Digital Ocean and Together are not.However, more broadly, the entire point of SFC is creating liquidity between GPU owners and consumers and making it broadly tradable, even programmable:As we explore, these are the primitives that you can then use to create your own, high quality, custom GPU availability for your time and money budget, similar to how Amazon Spot Instances automated the selective buying of unused compute.The ultimate end state of where all this is going is GPU that trade like other perishable, staple commodities of the world - oil, soybeans, milk. Because the contracts and markets are so well established, the price swings also are not nearly as drastic, and people can also start hedging and managing the risk of one of the biggest costs of their business, just like we have risk-managed commodities risks of all other sorts for centuries. As a former derivatives trader, you can bet that swyx doubleclicked on that…Show Notes* SF Compute* Evan Conrad* Ethan Anderson* John Phamous* The Curve talk* CoreWeave* Andromeda ClusterFull Video PodLike and subscribe!Timestamps* [00:00:05] Introductions* [00:00:12] Introduction of guest Evan Conrad from SF Compute* [00:00:12] CoreWeave Business Model Discussion* [00:05:37] CoreWeave as a Real Estate Business* [00:08:59] Interest Rate Risk and GPU Market Strategy Framework* [00:16:33] Why Together and DigitalOcean will lose money on their clusters* [00:20:37] SF Compute's AI Lab Origins* [00:25:49] Utilization Rates and Benefits of SF Compute Market Model* [00:30:00] H100 GPU Glut, Supply Chain Issues, and Future Demand Forecast* [00:34:00] P2P GPU networks* [00:36:50] Customer stories* [00:38:23] VC-Provided GPU Clusters and Credit Risk Arbitrage* [00:41:58] Market Pricing Dynamics and Preemptible GPU Pricing Model* [00:48:00] Future Plans for Financialization?* [00:52:59] Cluster auditing and quality control* [00:58:00] Futures Contracts for GPUs* [01:01:20] Branding and Aesthetic Choices Behind SF Compute* [01:06:30] Lessons from Previous Startups* [01:09:07] Hiring at SF ComputeTranscriptAlessio [00:00:05]: Hey everyone, welcome to the Latent Space podcast. This is Alessio, partner and CTO at Decibel, and I'm joined by my co-host Swyx, founder of Smol AI.Swyx [00:00:12]: Hey, and today we're so excited to be finally in the studio with Evan Conrad from SF Compute. Welcome. I've been fortunate enough to be your friend before you were famous, and also we've hung out at various social things. So it's really cool to see that SF Compute is coming into its own thing, and it's a significant presence, at least in the San Francisco community, which of course, it's in the name, so you couldn't help but be. Evan: Indeed, indeed. I think we have a long way to go, but yeah, thanks. Swyx: Of course, yeah. One way I was thinking about kicking on this conversation is we will likely release this right after CoreWeave IPO. And I was watching, I was looking, doing some research on you. You did a talk at The Curve. I think I may have been viewer number 70. It was a great talk. More people should go see it, Evan Conrad at The Curve. But we have like three orders of magnitude more people. And I just wanted to, to highlight, like, what is your analysis of what CoreWeave did that went so right for them? Evan: Sell locked-in long-term contracts and don't really do much short-term at all. I think like a lot of people had this assumption that GPUs would work a lot like CPUs and the like standard business model of any sort of CPU cloud is you buy commodity hardware, then you lay on services that are mostly software, and that gives you high margins and pretty much all your value comes from those services. Not really the underlying. Compute in any capacity and because it's commodity hardware and it's not actually that expensive, most of that can be sort of on-demand compute. And while you do want locked-in contracts for folks, it's mostly just a sort of de-risk situation. It helps you plan revenue because you don't know if people are going to scale up or down. But fundamentally, people are like buying hourly and that's how your business is structured and you make 50 percent margins or higher. This like doesn't really work in GPUs. And the reason why it doesn't work is because you end up with like super price sensitive customers. And that isn't because necessarily it's just way more expensive, though that's totally the case. So in a CPU cloud, you might have like, you know, let's say if you had a million dollars of hardware in GPUs, you have a billion dollars of hardware. And so your customers are buying at much higher volumes than you otherwise expect. And it's also smaller customers who are buying at higher amounts of volume. So relative to what they're spending in general. But in GPUs in particular, your customer cares about the scaling law. So if you take like Gusto, for example, or Rippling or an HR service like this, when they're buying from an AWS or a GCP, they're buying CPUs and they're running web servers, those web servers, they kind of buy up to the capacity that they need, they buy enough, like CPUs, and then they don't buy any more, like, they don't buy any more at all. Yeah, you have a chart that goes like this and then flat. Correct. And it's like a complete flat. It's not even like an incremental tiny amount. It's not like you could just like turn on some more nodes. Yeah. And then suddenly, you know, they would make an incremental amount of money more, like Gusto isn't going to make like, you know, 5% more money, they're gonna make zero, like literally zero money from every incremental GPU or CPU after a certain point. This is not the case for anyone who is training models. And it's not the case for anyone who's doing test time inference or like inference that has scales at test time. Because like you, your scaling laws mean that you may have some diminishing returns, but there's always returns. Adding GPUs always means your model does actually get. And that actually does translate into revenue for you. And then for test time inference, you actually can just like run the inference longer and get a better performance. Or maybe you can run more customers faster and then charge for that. It actually does translate into revenue. Every incremental GPU translates to revenue. And what that means from the customer's perspective is you've got like a flat budget and you're trying to max the amount of GPUs you have for that budget. And it's very distinctly different than like where Augusto or Rippling might think, where they think, oh, we need this amount of CPUs. How do we, you know, reduce that? How do we reduce our amount of money that we're spending on this to get the same amount of CPUs? What that translates to is customers who are spending in really high volume, but also customers who are super price sensitive, who don't give a s**t. Can I swear on this? Can I swear? Yeah. Who don't give a s**t at all about your software. Because a 10% difference in a billion dollars of hardware is like $100 million of value for you. So if you have a 10% margin increase because you have great software, on your billion, the customers are that price sensitive. They will immediately switch off if they can. Because why wouldn't you? You would just take that $100 million. You'd spend $50 million on hiring a software engineering team to replicate anything that you possibly did. So that means that the best way to make money in GPUs was to do basically exactly what CoreWeave did, which is go out and sign only long-term contracts, pretty much ignore the bottom end of the market completely, and then maximize your long-term contracts. With customers who don't have credit risk, who won't sue you, or are unlikely to sue you for frivolous reasons. And then because they don't have credit risk and they won't sue you for frivolous reasons, you can go back to your lender and you can say, look, this is a really low risk situation for us to do. You should give me prime, prime interest rate. You should give me the lowest cost of capital you possibly can. And when you do that, you just make tons of money. The problem that I think lots of people are going to talk about with CoreWeave is it doesn't really look like a cloud platform. It doesn't really look like a cloud provider financially. It also doesn't really look like a software company financially.Swyx [00:05:37]: It's a bank.Evan [00:05:38]: It's a bank. It's a real estate company. And it's very hard to not be that. The problem of that that people have tricked themselves into is thinking that CoreWeave is a bad business. I don't think CoreWeave is explicitly a bad business. There's a bunch of people, there's kind of like two versions of the CoreWeave take at the moment. There's, oh my God, CoreWeave, amazing. CoreWeave is this great new cloud provider competitive with the hyperscalers. And to some extent, this is true from a structural perspective. Like, they are indeed a real sort of thing against the cloud providers in this particular category. And the other take is, oh my gosh, CoreWeave is this horrible business and so on and blah, blah, blah. And I think it's just like a set of perception or perspective. If you think CoreWeave's business is supposed to look like the traditional cloud providers, you're going to be really upset to learn that GPUs don't look like that at all. And in fact, for the hyperscalers, it doesn't look like this either. My intuition is that the hyperscalers are probably going to lose a lot of money, and they know they're going to lose a lot of money on reselling NVIDIA GPUs, at least. Hyperscalers, but I want to, Microsoft, AWS, Google. Correct, yeah. The Microsoft, AWS, and Google. Does Google resell? I mean, Google has TPUs. Google has TPUs, but I think you can also get H100s and so on. But there are like two ways they can make money. One is by selling to small customers who aren't actually buying in any serious volume. They're testing around, they're playing around. And if they get big, they're immediately going to do one of two things. They're going to ask you for a discount. Because they're not going to pay your crazy sort of margin that you have locked into your business. Because for CPUs, you need that. They're going to pay your massive per hour price. And so they want you to sign a long-term contract. And so that's your other way that you can make money, is you can basically do exactly what CoreWeave does, which is have them pay as much as possible upfront and lock in the contract for a long time. Or you can have small customers. But the problem is that for a hyperscaler, the GPUs to... To sell on the low margins relative to what your other business, your CPUs are, is a worse business than what you are currently doing. Because you could have spent the same money on those GPUs. And you could have trained model and you could have made a model on top of it and then turn that into a product and had high margins from your product. Or you could have taken that same money and you could have competed with NVIDIA. And you could have cut into their margin instead. But just simply reselling NVIDIA GPUs doesn't work like your CPU business. Where you're able to capture high margins from big customers and so on. And then they never leave you because your customers aren't actually price sensitive. And so they won't switch off if your prices are a little higher. You actually had a really nice chart, again, on that talk of this two by two. Sure. Of like where you want to be. And you also had some hot takes on who's making money and who isn't. Swyx: So CoreUv locked up long-term contracts. Get that. Yes. Maybe share your mental framework. Just verbally describe it because we're trying to help the audio listeners as well. Sure. People can look up the chart if they want to. Evan: Sure. Okay. So this is a graph of interest rates. And on the y-axis, it's a probability you're able to sell your GPUs from zero to one. And on the x-axis, it's how much they'll depreciate in cost from zero to one. And then you had ISO cost curves or ISO interest rate curves. Yeah. So they kind of shape in a sort of concave fashion. Yeah. The lowest interest rates enable the most aggressive. form of this cost curve. And the higher interest rates go, the more you have to push out to the top right. Yeah. And then you had some analysis of where every player sits in this, including CoreUv, but also Together and Modal and all these other guys. I thought that was super insightful. So I just wanted to elaborate. Basically, it's like a graph of risk and the genres of places where you can be and what the risk is associated with that. The optimal thing for you to do, if you can, is to lock in long-term contracts that are paid all up front or in with a situation in which you trust the other party to pay you over time. So if you're, you know, selling to Microsoft or something or OpenAI. Which are together 77% of the revenue of CoreUv. Yeah. So if you're doing that, that's a great business to be in because your interest rate that you can pitch for is really low because no one thinks Microsoft is going to default. And like maybe OpenAI will default, but the backing by Microsoft kind of doesn't. And I think there's enough, like, generally, it looks like OpenAI is winning that you can make it's just a much better case than if you're selling to the pre-seed startup that just raised $30 million or something pre-revenue. It's like way easier to make the case that the OpenAI is not going to default than the pre-seed startup. And so the optimal place to be is selling to the maximally low risk customer for as long as possible. And then you never have to worry about depreciation and you make lots of money. The less. Good. Good place to be is you could sell long-term contracts to people who might default on you. And then if you're not bringing it to the present, so you're not like saying, hey, you have to pay us all up front, then you're in this like more risky territory. So is it top left of the chart? If I have the chart right, maybe. Large contracts paid over time. Yeah. Large contracts paid over time is like top left. So it's more risky, but you could still probably get away with it. And then the other opportunity is that you could sell short-term contracts for really high prices. And so lots of people tried that too, because this is actually closer to the original business model that people thought would work in cloud providers for CPUs. It works for CPUs, but it doesn't really work for GPUs. And I don't think people were trying this because they were thinking about the risk associated with it. I think a lot of people are just come from a software background, have not really thought about like cogs or margins or inventory risk or things that you have to worry about in the physical world. And I think they were just like copy pasting the same business model onto CPUs. And also, I remember fundraising like a few years ago. And I know based on. Like what we knew other people were saying who were in a very similar business to us versus what we were saying. And we know that our pitch was way worse at the time, because in the beginning of SF Compute, we looked very similar to pretty much every other GPU cloud, not on purpose, but sort of accidentally. And I know that the correct pitch to give to an investor was we will look like a traditional CPU cloud with high margins and we'll sell to everyone. And that is a bad business model because your customers are price sensitive. And so what happens is if you. Sell at high prices, which is the price that you would need to sell it in order to de-risk your loss on the depreciation curve, and specifically what I mean by that is like, let's say you're selling it like $5 an hour and you're paying $1.50 an hour for the GPU under the hood. It's a little bit different than that, but you know, nice numbers, $5 an hour, $1.50 an hour. Great. Excellent. Well, you're charging a really high price per GPU hour because over time the price will go down and you'll get competed out. And what you need is to make sure that you never go under, or if you do go under your underlying cost. You've made so much money in the first part of it that the later end of it, like doesn't matter because from the whole structure of the deal, you've made money. The problem is that just, you think that you're going to be able to retain your customers with software. And actually what happens is your customers are super price sensitive and push you down and push you down and push you down and push you down, um, that they don't care about your software at all. And then the other problem that you have is you have, um, really big players like the hyperscalers who are looking to win the market and they have way more money than you, and they can push down on margin. Much better than you can. And so if they have to, and they don't, they don't necessarily all the time, um, I think they actually keep pride of higher margin, but if they needed to, they could totally just like wreck your margin at any point, um, and push you down, which meant that that quadrant over there where you're charging a high price, um, and just to make up for the risk completely got destroyed, like did not work at all for many places because of the price sensitivity, because people could just shove you down instead that pushed everybody up to the top right-hand corner of that, which is selling short-term. Contracts for low prices paid over time, which is the worst place to be in, um, the worst financial place to be in because it has the highest interest rate, um, which means that your, um, your costs go up at the same time, your, uh, your incoming cash goes down and squeezes your margins and squeezes your margins. The nice thing for like a core weave is that most of their business is over on the, on the other sides of those quadrants that the ones that survive. The only remaining question I have with core weave, and I promise I get to ask if I can compute, and I promise this is relevant to SOF Compute in general, because the framework is important, right? Sure. To understand the company. So why didn't NVIDIA or Microsoft, both of which have more money than core weave, do core weave, right? Why didn't they do core weave? Why have this middleman when either NVIDIA or Microsoft have more money than God, and they could have done an internal core weave, which is effectively like a self-funding vehicle, like a financial instrument. Why does there have to be a third party? Your question is like... Why didn't Microsoft, or why didn't NVIDIA just do core weave? Why didn't they just set up their own cloud provider? I think, and I don't know, and so correct me if I'm wrong, and lots of people will have different opinions here, or I mean, not opinions, they'll have actual facts that differ from my facts. Those aren't opinions. Those are actually indeed differences of reality, is that NVIDIA doesn't want to compete with their customers. They make a large amount of money by selling to existing clouds. If they launched their own core weave, then it would be a lot more money. It'd make it much harder for them to sell to the hyperscalers, and so they have a complex relationship with there. So not great for them. Second is that, at least for a while, I think they were dealing with antitrust concerns or fears that if they're going through, if they own too much layers of the stack, I could imagine that could be a problem for them. I don't know if that's actually true, but that's where my mind would go, I guess. Mostly, I think it's the first one. It's that they would be competing directly with their primary customers. Then Microsoft could have done it, right? That's the other question. Yeah, so Microsoft didn't do it. And my guess is that... NVIDIA doesn't want Microsoft to do it, and so they would limit the capacity because from NVIDIA's perspective, both they don't want to necessarily launch their own cloud provider because it's competing with their customers, but also they don't want only one customer or only a few customers. It's really bad for NVIDIA if you have customer concentration, and Microsoft and Google and Amazon, like Oracle, to buy up your entire supply, and then you have four or five customers or so who pretty much get to set prices. Monopsony. Yeah, monopsony. And so the optimal thing for you is a diverse set of customers who all are willing to pay at whatever price, because if you don't, somebody else will. And so it's really optimal for NVIDIA to have lots of other customers who are all competing against each other. Great. Just wanted to establish that. It's unintuitive for people who have never thought about it, and you think about it all day long. Yeah. Swyx: The last thing I'll call out from the talk, which is kind of cool, and then I promise we'll get to SF Compute, is why will DigitalOcean and Together lose money on their clusters? Why will DigitalOcean and Together lose money on their clusters?Evan [00:16:33]: I'm going to start by clarifying that all of these businesses are excellent and fantastic. That Together and DigitalOcean and Lambda, I think, are wonderful businesses who build excellent products. But my general intuition is that if you try to couple the software and the hardware together, you're going to lose money. That if you go out and you buy a long-term contract from someone and then you layer on services, or you buy the hardware yourself and you spin it up and you get a bunch of debt, you're going to run into the same problem that everybody else did, the same problem we did, same problem the hyperscalers did. And that's exactly what the hyperscalers are doing, which is you cannot add software and make high margins like a cloud provider can. You can pitch that into investors and it will totally make sense, and it's like the correct play in CPUs, but there isn't software you could make to make this occur. If you're spending a billion dollars on hardware, you need to make a billion dollars of software. There isn't a billion dollars of software that you can realistically make, and if you do, you're going to look like SAP. And that's not a knock on SAP. SAP makes a f**k ton of money, right? Right. Right. Right. Right. There aren't that many pieces of software that you could make, that you can realistically sell, like a billion dollars of software, and you're probably not going to do it to price-sensitive customers who are spending their entire budget already on compute. They don't have any more money to give you. It's a very hard proposition to do. And so many parties have been trying to do this, like, buy their own compute, because that's what a traditional cloud does. It doesn't really work for them. You know that meme where there's, like, the Grim Reaper? And he's, like, knocking on the door, and then he keeps knocking on the next door? We have just seen door after door after door of the Grim Reeker comes by, and the economic realities of the compute market come knocking. And so the thing we encourage folks to do is if you are thinking about buying a big GPU cluster and you are going to layer on software on top, don't. There are so many dead bodies in the wake there. We would recommend not doing that. And we, as SF Compute, our entire business is structured to help you not do that. It's helped disintegrate these. The GPU clouds are fantastic real estate businesses. If you treat them like real estate businesses, you will make a lot of money. The cloud services you can make on that, all the software you want to make on that, you can do that fantastically. If you don't own the underlying hardware, if you mix these businesses together, you get shot in the head. But if you combine, if you split them, and that's what the market does, it helps you split them, it allows you to buy, like, layer on services, but just buy from the market, you can make lots of money. So companies like Modal, who don't own the underlying compute, like they don't own it, lots of money, fantastic product. And then companies like Corbeave, who are functionally like really, really good real estate businesses, lots of money, fantastic product. But if you combine them, you die. That's the economic reality of compute. I think it also splits into trading versus inference, which are different kinds of workloads. Yeah. And then, yeah, one comment about the price sensitivity thing before we leave this. This topic, I want to credit Martin Casado for coining or naming this thing, which is like, you know, you said, you said this thing about like, you don't have room for a 10% margin on GPUs for software. Yep. And Martin actually played it out further. It's his first one I ever saw doing this at large enough runs. So let's say GPT-4 and O1 both had a total trading cost of like a $500 billion is the rough estimate. When you get the $5 billion runs, when you get the $50 billion runs, it is actually makes sense to build your own. You're going to have to get into chips, like for OpenEI to get into chip design, which is so funny. I would make an ASIC for this run. Yeah, maybe. I think a caveat of that that is not super well thought about is that only works if you're really confident. It only works if you really know which chip you're going to do. If you don't, then it's a little harder. So it makes in my head, it makes more sense for inference where you've already established it. But for training there's so much like experimentation. Any generality, yeah. Yeah. The generality is much more useful. Yeah. In some sense, you know, Google's like six generations into the CPUs. Yeah. Yeah. Okay, cool. Maybe we should go into SF Compute now. Sure. Yeah.Alessio [00:20:37]: Yeah. So you kind of talked about the different providers. Why did you decide to go with this approach and maybe talk a bit about how the market dynamics have evolved since you started a company?Evan [00:20:47]: So originally we were not doing this at all. We were definitely like forced into this to some extent. And SF Compute started because we wanted to go train models for music and audio in general. We were going to do a sort of generic audio model at some points, and then we were going to do a music model at some points. It was an early company. We didn't really spec down on a particular thing. But yeah, we were going to do a music model and audio model. First thing that you do when you start any AI lab is you go out and you buy a big cluster. The thing we had seen everybody else do was they went out and they raised a really big round and then they would get stuck. Because if you raise the amount of money that you need to train a model initially, like, you know, the $50 million pre-seed, pre-revenue, your valuation is so high or you get diluted so much that you can't raise the next round. And that's a very big ask to make. And also, I don't know, I felt like we just felt like we couldn't do it. We probably could have in retrospect, but I think one, we didn't really feel like we could do it. Two, it felt like if we did, we would have been stuck later on. We didn't want to raise the big round. And so instead, we thought, surely by now, we would be able to just go out. To any provider and buy like a traditional CPU cloud would sell offer you and just buy like on demand or buy like a month or so on. And this worked for like small incremental things. And I think this is where we were basing it off. We just like assumed we could go to like Lambda or something and like buy thousands of at the time A100s. And this just like was not at all the case. So we started doing all the sales calls with people and we said, OK, well, can we just get like month to month? Can we get like one month of compute or so on? Everyone told us at the time, no. You need to have a year long contract or longer or you're out of luck. Sorry. And at the time, we were just like pissed off. Like, why won't nobody sell us a month at a time? Nowadays, we totally understand why, because it's the same economic reason. Because if you if they had sold us the month to month or so on and we canceled or so on, they would have massive risk on that. And so the optimal thing to do was to only to just completely abandon the section of the market. We didn't like that. So our plan was we were going to buy a year long contract anyway. We would use a month. And then we would. At least the other 11 months. And we were locked in for a year, but we only had to pay on every individual month. And so we did this. But then immediately we said, oh, s**t, now we have a cloud provider, not a like training models company, not an AI lab, because every 30 days we owed about five hundred thousand dollars or so and we had about five hundred thousand dollars in the bank. So that meant that every single month, if we did not sell out our cluster, we would just go bankrupt. So that's what we did for the first year of the company. And when you're in that position. You try to think how in the world you get out of that position, what that transition to is, OK, well, we tend to be pretty good at like selling this cluster every month because we haven't died yet. And so what we should do is we should go basically be like this broker for other people and we will be more like a GPU real estate or like a GPU realtor. And so we started doing that for a while where we would go to other people who had who was trying to sell like a year long contract with somebody and we'd go to another person who like maybe this person wanted six months and somebody else on six months or something and we'd like combine all these people. Together to make the deal happen and we'd organize these like one off bespoke deals that looked like basically it ended up with us taking a bunch of customers, us signing with a vendor, taking some cut and then us operating the cluster for people typically with bare metal. And so we were doing this, but this was definitely like a oh, s**t, oh, s**t, oh, s**t. How do we get out of our current situation and less of a like a strategic plan of any sort? But while we were doing this, since like the beginning of the company, we had been thinking about how to buy GPU clusters, how to sell them effectively, because we'd seen every part of it. And what we ended up with was like a book of everybody who's trying to buy and everyone is trying to sell because we were these like GPU brokers. And so that turned into what is today SF Compute, which is a compute market, which we think we are the functionally the most liquid GPU market of any capacity. Honestly, I think we're the only thing that actually is like a real market that there's like bids and asks and there's like a like a trading engine that combines everything. And so. I think we're the only place where you can do things that a market should be able to do. Like you can go on SF Compute today and you get thousands of H100s for an hour if you want. And that's because there is a price for thousands of GPUs for an hour. That is like not a thing you can reasonably do on kind of any other cloud provider because nobody should realistically sell you thousands of GPUs for an hour. They should sell it to you for a year or so on. But one of the nice things about a market is that you can buy the year on SF Compute. But then if you need to sell. Back, you can sell back as well. And that opens up all these little pockets of liquidity where somebody who's just trying to buy for a little bit of time, some burst capacity. So people don't normally buy for an hour. That's not like actually a realistic thing, but it's like the range somebody who wants, who is like us, who needed to buy for a month can actually buy for a month. They can like place the order and there is actually a price for that. And it typically comes from somebody else who's selling back. Somebody who bought a longer term contract and is like they bought for some period of time, their code doesn't work, and now they need to like sell off a little bit.Alessio [00:25:49]: What are the utilization rates at which a market? What are the utilization rates at which a market? Like this works, what do you see the usual GPU utilization rate and like at what point does the market get saturated?Evan [00:26:00]: Assuming there are not like hardware problems or software problems, the utilization rate is like near 100 percent because the price dips until the utilization is 100 percent. So the price actually has to dip quite a lot in order for the utilization not to be. That's not always the case because you just have logistical problems like you get a cluster and parts of the InfiniBand fabric are broken. And there's like some issue with some switch somewhere and so you have to take some portion of the cluster offline or, you know, stuff like this, like there's just underlying physical realities of the clusters, but nominally we have better utilization than basically anybody because, but that's on utilization of the cluster, like that doesn't necessarily translate into, I mean, I actually do think we have much better overall money made for our underlying vendors than kind of anybody else. We work with the other GPU clouds and the basic pitch to the other GPU clouds is one. So we can sell your broker so we can we can find you the long term contracts that are at the prices that you want, but meanwhile, your cluster is idle and for that we can increase your utilization and get you more money because we can sell that idle cluster for you and then the moment we find the longer, the bigger customer and they come on, you can kick off those people and then go to the other ones. You get kind of the mix of like sell your cluster at whatever price you can get on the market and then sell your cluster at the big price that you want to do for long term contract, which is your ideal business model. And then the benefit of the whole thing being on the market. Is you can pitch your customer that they can cancel their long term contract, which is not a thing that you can reasonably do if you are just the GPU cloud, if you're just the GPU cloud, you can never cancel your contract, because that introduces so much risk that you would otherwise, like not get your cheap cost of capital or whatever. But if you're selling it through the market, or you're selling it with us, then you can say, hey, look, you can cancel for a fee. And that fee is the difference between the price of the market and then the price that they paid at, which means that they canceled and you have the ability to offer that flexibility. But you don't. You don't have to take the risk of it. The money's already there and like you got paid, but it's just being sold to somebody else. One of our top pieces from last year was talking about the H100 glut from all the long term contracts that were not being fully utilized and being put under the market. You have on here dollar a dollar per hour contracts as well as it goes up to two. Actually, I think you were involved. You were obliquely quoted in that article. I think you remember. I remember because this was hidden. Well, we hid your name, but then you were like, yeah, it's us. Yeah. Could you talk about the supply and demand of H100s? Was that just a normal cycle? Was that like a super cycle because of all the VC funding that went in in 2003? What was that like? GPU prices have come down. Yeah, GPU prices have come down. And there's some part that has normal depreciation cycle. Some part of that is just there were a lot of startups that bought GPUs and never used them. And now they're lending it out and therefore you exist. There's a lot of like various theories as to why. This happened. I dislike all of them because they're all kind of like they're often said with really high confidence. And I think just the market's much more complicated than that. Of course. And so everything I'm going to say is like very hedged. But there was a series of like places where a bunch of the orders were placed and people were pitching to their customers and their investors and just the broader market that they would arrive on time. And that is not how the world works. And because there was such a really quick build out of things, you would end up with bottlenecks in the supply chain somewhere that has nothing to do with necessarily the chip. It's like the InfiniBand cables or the NICs or like whatever. Or you need a bunch of like generators or you don't have data center space or like there's always some bottleneck somewhere else. And so a lot of the clusters didn't come online within the period of time. But then all the bottlenecks got sorted out and then they all came online all at the same time. So I think you saw a short. There was a shortage because supply chain hard. And then you saw a increase or like a glut because supply chain eventually figure itself out. And specifically people overordered in order to get the allocation that they wanted. Then they got the allocations and then they went under. Yeah, whatever. Right. There was just a lot of shenanigans. A caveat of this is every time you see somebody like overordered, there is this assumption that the problem was like the demand went down. I don't think that's the case at all. And so I want to clarify that. It definitely seems like a shortage. Like there's more demand for GPUs than there ever was. It's just that there was also more supply. So at the moment, I think there is still functionally a glut. But the difference that I think is happening is mostly the test time inference stuff that you just need way more chips for that than you did before. And so whenever you make a statement about the current market, people sort of take your words and then they assume that you're making a statement about the future market. And so if you say there's a glut now, people will continue to think there's a glut. But I think what is happening at the moment. My general prediction is that like by the winter, we will be back towards shortage. But then also, this very much depends on the rollout of future chips. And that comes with its own. I think I'm trying to give you like a good here's Evan's forecast. Okay. But I don't know if my forecast is right. You don't have to. Nobody is going to hold you to it. But like I think people want to know what's true and what's not. And there's a lot of vague speculations from people who are not that close to the market actually. And you are. I think I'm a closer. Close to the market, but also a vague speculator. Like I think there are a lot of really highly confident speculators and I am indeed a vague speculator. I think I have more information than a lot of other people. And this makes me more vague of a spectator because I feel less certain or less confident than I think a lot of other people do. The thing I do feel reasonably confident about saying is that the test time inference is probably going to quite significantly expand the amount of compute that was used for inference. So a caveat. This is like pretty much all the inference demand is in a few companies. A good example is like lots of bio and pharma was using H100s training sort of the bio models of sorts. And they would come along and they would buy, you know, thousands of H100s for training and then just like not a lot of stuff for inference. Not in any, not relative to like an opening iron anthropic or something because they like don't have a consumer product. Their inference event, if they can do it right. There's really like only one inference event that matters. And obviously I think they're going to run into it. And Batch and they're not going to literally just run one inference event. But like the one that produces the drug is the important one. Right. And I'm dumb and I don't know anything about biology, so I could be completely wrong here. But my understanding is that's kind of the gist. I can check that for you. You can check that for me. Check that for me. But my understanding is like the one that produces the sequence that is the drug that, you know, cures cancer or whatever. That's the important deal. But like a lot of models look like this where they're sort of more enterprising use cases or they're so prior to something that looks like test time inference. You got lots and lots of demand for training and then pretty much entirely fell off for inference. And I think like we looked at like Open Router, for example, the entirety of Open Router that was not anthropic or like Gemini or OpenAI or something. It was like 10 H100 nodes or something like that. It's just like not that much. It's like not that many GPUs actually to service that entire demand. But that's like a really sizable portion of the sort of open source market. But the actual amount of compute needed for it was not that much. But if you imagine like what an OpenAI needs for like GPT-4, it's like tremendously big. But that's because it's a consumer product that has almost all the inference demand. Yeah, that's a message we've had. Roughly open source AI compared to closed AI is like 5%. Yeah, it's like super small. Super small. It's super small. Super small. But test time inference changes that quite significantly. So I will... I will expect that to increase our overall demand. But my question on whether or not that actually affects your compute price is entirely based on how quickly do we roll out the next chips. The way that you burst is different for test time.Alessio [00:34:01]: Any thoughts on the third part of the market, which is the more peer-to-peer distributed, some are like crypto-enabled, like Hyperbolic, Prime Intellect, and all of that. Where do those fit? Like, do you see a lot of people will want to participate in a peer-to-peer market? Or just because of the capital requirements at the end of the day, it doesn't really matter?Evan [00:34:20]: I'm like wildly skeptical of these, to be frankly. The dream is like steady at home, right? I got this $15.90. Nobody has $15.90. $14.90 sitting at home. I can rent it out. Yeah. Like, I just don't really think this is going to ever be more efficient than a fully interconnected cluster with InfiniBand or, you know, whatever the sort of next spec might be. Like, I could be completely wrong. But speaking of... I mean, like, SpeedoLite is really hard to beat. And regardless of whatever you're using, you just like can't get around that physical limitation. And so you could like imagine a decentralized market that still has a lot of places where there's like co-location. But then you would get something that looks like SF Compute. And so that's what we do. That's why we take our general take is like on SF Compute, you're not buying from like random people. You're buying from the other GPU clouds, functionally. You're buying from data centers that are the same genre of people that you would work with already. And you can specify, oh, I want all these nodes to be co-located. And I don't think you're really going to get around that. And I think I buy crypto for the purposes of like transferring money. Like the financial system is like quite painful and so on. I can understand the uses of it to sort of incentivize an initial market or try to get around the cold start problem. We've been able to get around the cold start problem just fine. So it didn't actually need that at all. What I do think is totally possible is you could launch a token and then you could like subsidize the crypto. You could compute prices for a bit, but like maybe that will help you. I think that's what Nuus is doing. Yeah, I think there's lots of people who are trying to do things like this, but at some point that runs out. So I would, I think generally agree. I think the only thread in that model is very fine grained mixture of experts that can be like algorithms can shift to adapt to hardware realities. And the hardware reality is like, okay, it's annoying to do large co-located clusters. Then we'll just redesign attention or whatever in our architecture to distribute it more. There was a little bit buzz of block attention last year that Strong Compute made a big push on. But I think like, you know, in a world where we have 200 experts in MOE model, it starts to be a little bit better. Like, I don't disagree with this. I can imagine the world in which you have like, in which you've redesigned it to be more parallelizable, like across space.Evan [00:36:43]: But assuming without that, your hardware limitation is your speed of light limitation. And that's a very hard one to get around.Alessio [00:36:50]: Any customers or like stories that you want to shout out of like maybe things that wouldn't have been economically viable like others? I know there's some sensitivity on that.Evan [00:37:00]: My favorites are grad students, are folks who are trying to do things that would normally otherwise require the scale of a big lab. And the grad students are like the worst pilots. They're like the worst possible customer for the traditional GPU clouds because they will immediately turn if you sell them a thing because they're going to graduate and they're not going to go anywhere. They're not going to like, that project isn't continuing to spend lots of money. Like sometimes it does, but not if you're like working with the university or you're working with the lab of some sort. But a lot of times it's just like the ability for us to offer like big burst capacity, I think is lovely and wonderful. And it's like one of my favorite things to do because all those folks look like we did. And I have a special place in my heart for that. I have a special place in my heart for young hackers and young grad students and researchers who are trying to do the same genre of thing that we are doing. For the same reason, I have a special place in my heart for like the startups, the people who are just actively trying to compete on the same scale, but can't afford it time-wise, but can afford it spike-wise. Yeah, I liked your example of like, I have a grant of 100K and it's expiring. I got to spend it on that. That's really beautiful. Yeah. Interesting. Has there been interesting work coming out of that? Anything you want to mention? Yeah. So from like a startup perspective, like Standard Intelligence and Find, P-H-I-N-D. We've had them on the pod.Swyx [00:38:23]: Yeah. Yeah.Evan [00:38:23]: That was great. And then from grad students' perspective, we worked a lot with like the Schmidt Futures grantees of various sorts. My fear is if I talk about their research, I will be completely wrong to a sort of almost insulting degree because I am very dumb. But yeah. I think one thing that's maybe also relevant startups and GPUs-wise. Yeah. Is there was a brief moment where it kind of made sense that VCs provided GPU clusters. And obviously you worked at AI Grants, which set up Andromeda, which is supposedly a $100 million cluster. Yeah. I can explain why that's the case or why anybody would think that would be smart. Because I remember before any of that happened, we were asking for it to happen. Yeah. And the general reason is credit risk. Again, it's a bank. Yeah. I have lower risk than you due to credit transformation. I take your risk onto my balance sheet. Correct. Exactly. If you wanted to go for a while, if you wanted to go set up a GPU cluster, you had to be the one that actually bought the hardware and racked it and stacked it, like co-located it somewhere with someone. Functionally, it was like on your balance sheet, which means you had to get a loan. And you cannot get a loan for like $50 million as a startup. Like not really. You can get like venture debt and stuff, but like it's like very, very difficult to get a loan of any serious price for that. But it's like not that difficult to get a loan for $50 million. If you already have a fund or you already have like a million dollars under your assets somewhere or like you personally can like do a personal guarantee for it or something like this. If you have a lot of money, it is way easier for you to get a loan than if you don't have a lot of money. And so the hack of a VC or some capital partner offering equity for compute is always some arbitrage on the credit risk. That's amazing. Yeah. That's a hack. You should do that. I don't think people should do it right now. I think the market has like, I think it made sense at the time and it was helpful and useful for the people who did it at the time. But I think it was a one-time arbitrage because now there are lots of other sources that can do it. And also I think like it made sense when no one else was doing it and you were the only person who was doing it. But now it's like it's an arbitrage that gets competed down. Sure. So it's like super effective. I wouldn't totally recommend it. Like it's great that Andromeda did it. But the marginal increase of somebody else doing it is like not super helpful. I don't think that many people have followed in their footsteps. I think maybe Andreessen did it. Yeah. That's it. I think just because pretty much all the value like flows through Andromeda. What? That cannot be true. How many companies are in the air, Grant? Like 50? My understanding of Andromeda is it works with all the NFTG companies or like several of the NFTG companies. But I might be wrong about that. Again, you know, something something. Nat, don't kill me. I could be completely wrong. But the but you know, I think Andromeda was like an excellent idea to do at the right time in which it occurred. Perfect. His timing is impeccable. Timing. Yeah. Nat and Daniel are like, I mean, there's lots of people who are like... Sears? Yeah. Sears. Like S-E-E-R. Oh, Sears. Like Sears of the Valley. Yeah. They for years and years before any of the like ChatGPT moment or anything, they had fully understood what was going to happen. Like way, way before. Like. AI Grant is like, like five years old, six years old or something like that. Seven years old. When I, when it like first launched or something. Depends where you start. The nonprofit version. Yeah. The nonprofit version was like, like happening for a while, I think. It's going on for quite a bit of time. And then like Nat and Daniel are like the early investors in a lot of the sort of early AI labs of various sorts. They've been doing this for a bit.Alessio [00:41:58]: I was looking at your pricing yesterday. We're kind of talking about it before. And there's this weird thing where one week is more expensive of both one day and one month. Yeah. What are like some of the market pricing dynamics? What are things that like this to somebody that is not in the business? This looks really weird. But I'm curious, like if you have an explanation for it, if that looks normal to you. Yeah.Evan [00:42:18]: So the simple answer is preemptible pricing is cheaper than non-preemptible pricing. And the same economic principle is the reason why that's the case right now. That's not entirely true on SF Compute. SF Compute doesn't really have the concept of preemptible. Instead, what it has is very short reservations. So, you know, you go to a traditional cloud provider and you can say, hey, I want to reserve contract for a year. We will let you do a reserve contract for one hour, which is the part of SFC. But what you can do is you can just buy every single hour continuously. And you're reserving just for that hour. And then the next hour you reserve just for that next hour. And this is obviously like a built in. This is like an automation that you can do. But what you're seeing when you see the cheap price is you're seeing somebody who's buying the next hour, but maybe not necessarily buying an hour after that. So if the price goes up. Up too much. They might not get that next hour. And the underlying part of this of where that's coming from the market is you can imagine like day old milk or like milk that's about to be old. It might drop its price until it's expired because nobody wants to buy the milk that's in the past. Or maybe you can't legally sell it. Compute is the same way. No, you can't sell a block of compute that is not that is in the past. And so what you should do in the market and what people do do is they take. They take a block. A block of compute. And then they drop it and drop it and drop it and drop into a floor price right before it's about to expire. And they keep dropping it until it clears. And so anything that is idle drops until some point. So if you go and use on the website and you set that that chart to like a week from now, what you'll see is much more normal looking sort of curves. But if you say, oh, I want to start right now, that immediate instant, here's the compute that I want right now is the is functionally the preemptible price. It's where most people are getting the best compute or like the best compute prices from. The caveat of that is you can do really fun stuff on SFC if you want. So because it's not actually preemptible, it's it's reserved, but only reserved for an hour, which means that the optimal way to use as of compute is to just buy on the market price, but set a limit price that is much higher. So you can set a limit price for like four dollars and say, oh, if the market ever happens to spike up to four dollars, then don't buy. I don't want to buy that at that price for that price. I don't want to buy that at that price for that price for an hour. But otherwise, just buy at the cheapest price. And if you're comfortable with that of the volatility of it, you're actually going to get like really good prices, like close to a dollar an hour or so on, sometimes down to like 80 cents or whatever. You said four, though. Yeah. So that's the thing. You want to lower the limit. So four is your max price. Four is like where you basically want to like pull the plug and say don't do it because the actual average price is not or like the, you know, the preemptible price doesn't actually look like that. So what you're doing when you're saying four is always, always, always give me this compute. Like continue to buy every hour. Don't preempt me. Don't kick me off. And I want this compute and just buy at the preemptible price, but never kick me off. The only times in which you get kicked off is if there is a big price spike. And, you know, let's say one day out of the year, there's like a four dollar an hour price because of some weird fluke or something. If there are other periods of time, you're actually getting a much lower price than you. It makes sense. Your your average cost that you're actually paying is way better. And your trade off here is you don't literally know what price you're going to get. So it's volatile. But your actual average historically has been like everyone who's done this has gotten wildly better prices. And this is like one of the clever things you can do with the market. If you're willing to make those trade offs, you can get a lot of really good prices. You can also do other things like you can only buy at night, for example. So the price goes down at night. And so you can say, oh, I want to only buy, you know, if the price is lower than 90 cents. And so if you have some long running job, you can make it only run on 90 cents and then you recover back and so on. Yeah. So what you can kind of create as like a spot inst is what other the CPU world has. Yes. But you've created a system where you can kind of manufacture the exact profile that you want. Exactly. That is not just whatever the hyperscalers offer you, which is usually just one thing. Correct. SF Compute is like the power tool. The underlying primitives of like hourly compute is there. Correct. Yeah, it's pretty interesting. I've often asked OpenAI. So like, you know, all these guys. Cloud as well. They do batch APIs. So it's half off of whatever your thing is. Yeah. And the only contract is we'll return in 24 hours. Sure. Right. And I was like, 24 hours is good. But sometimes I want one hour. I want four hours. I want something. And so based off of SF Compute's system, you can actually kind of create that kind of guarantee. Totally. That would be like, you know, not 24, but within eight hours, within four hours, like the work half of a workday. Yes. I can return your results to you. And then I can return it to you. And if your latency requirements are like that low, actually it's fine. Yes. Correct. Yeah. You can carve out that. You can financially engineer that on SFC. Yeah. Yeah. I mean, I think to me that unlocks a lot of agent use cases that I want, which is like, yeah, I worked in a background, but I don't want you to take a day. Yeah. Correct. Take a couple hours or something. Yeah. This touches a lot of my like background because I used to be a derivatives trader. Yeah. And this is a forward market. Yeah. A futures forward market, whatever you call it. Not a future. Very explicitly not a future. Not yet a futures. Yes. But I don't know if you have any other points to talk about. So you recognize that you are a, you know, a marketplace and you've hired, I met Alex Epstein at your launch event and you're like, you're, you're building out the financialization of GPUs. Yeah. So part of that's legal. Mm-hmm. Totally. Part of that is like listing on an exchange. Yep. Maybe you're the exchange. I don't know how that works, but just like, talk to me about that. Like from the legal, the standardization, the like, where is this all headed? You know, is this like a full listed on the Chicago Mercantile Exchange or whatever? What we're trying to do is create an underlying spot market that gives you an index price that you can use. And then with that index price, you can create a cash settled future. And with a cash settled future, you can go back to the data centers and you can say, lock in your price now and de-risk your entire position, which lets you get cheaper cost of capital and so on. And that we think will improve the entire industry because the marginal cost of compute is the risk. It's risk as shown by that graph and basically every part of this conversation. It's risk that causes the price to be all sorts of funky. And we think a future is the correct solution to this. So that's the eventual goal. Right now you have to make the underlying spot market in order to make this occur. And then to make the spot market work, you actually have to solve a lot of technology problems. You really cannot make a spot market work if you don't run the clusters, if you don't have control over them, if you don't know how to audit them, because these are super computers, not soybeans. They have to work. In a way that like, it's just a lot simpler to deliver a soybean than it is to deliver it. I don't know. Talk to the soybean guys. Sure. You know? Yeah. But you have to have a delivery mechanism. Your delivery mechanism, like somebody somewhere has to actually get the compute at some point and it actually has to work. And it is really complicated. And so that is the other part of our business that we go and we build a bare metal infrastructure stack that goes. And then also we do auditing of all the clusters. You sort of de-risk the technical perspective and that allows you to eventually de-risk the financial perspective. And that is kind of the pitch of SF Compute. Yeah. I'll double click on the auditing on the clusters. This is something I've had conversations with Vitae on. He started Rika and I think he had a blog post which kind of shone the light a little bit on how unreliable some clusters are versus others. Correct. Yeah. And sometimes you kind of have to season them and age them a little bit to find the bad cards. You have to burn them in. Yeah. So what do you do to audit them? There's like a burn-in process, a suite of tests, and then active checking and passive checking. Burn-in process is where you typically run LINPACK. LINPACK is this thing that like a bunch of linear algebra equations that you're stress testing the GPUs. This is a proprietary thing that you wrote? No, no, no. LINPACK is like the most common form of burn-in. If you just type in burn-in, typically when people say burn-in, they literally just mean LINPACK. It's like an NVIDIA reference version of this. Again, NVIDIA could run this before they ship, but now the customers have to do it. It's annoying. You're not just checking for the GPU itself. You're checking like the whole component, all the hardware. And it's a lot of work. It's an integration test. It's an integration test. Yeah. So what you're doing when you're running LINPACK or burn-in in general is you're stress testing the GPUs for some period of time, 48 hours, for example, maybe seven days or so on. And you're just trying to kill all the dead GPUs or any components in the system that are broken. And we've had experiences where we ran LINPACK on a cluster and it rounds out, sort of comes offline when you run LINPACK. This is a pretty good sign that maybe there is a problem with this cluster. Yeah. So LINPACK is like the most common sort of standard test. But then beyond that, what you do is we have like a series of performance tests that replicate a much more realistic environment as well that we run just assuming if LINPACK works at all, then you run the next set of tests. And then while the GPUs are in operation, you're also going through and you're doing active tests and passive tests. Passive tests are things that are running in the background while somebody else is running, while like some other workload is running. And active tests are during like idle periods. You're running some sort of check that would otherwise sort of interrupt something. And then the active tests will take something offline, basically. Or a passive check might mark it to get taken offline later and so on. And then the thing that we are working on that we have working partially but not entirely is automated refunds, which is basically like, is the case that the hardware breaks so much. And there's only so much that we can do and it is the effect of pretty much the entire industry. So a pretty common thing that I think happens to kind of everybody in the space is a customer comes online, they experience your cluster, and your cluster has the same problem that like any cluster has, or it's I mean, a different problem every time, but they experience one of the problems of HPC. And then their experience is bad. And you have to like negotiate a refund or some other thing like this. It's always case by case. And like, yeah, a lot of people just eat the cost. Correct. So one of the nice things about a market that we can do as we get bigger and have been doing as we can bigger is we can immediately give you something else. And then also we can automatically refund you. And you're still gonna experience it like the hardware problems aren't going away until the underlying vendors fix things. But honestly, I don't think that's likely because you're always pushing the limits of HPC. This is the case of trying to build a supercomputer. that's one of the nice things that we can do is we can switch you out for somebody else somewhere, and then automatically refund you or prorate or whatever the correct move is. One of the things that you say in this conversation with me was like, you know, you know, a provider is good when they guarantee automatic refunds. Which doesn't happen. But yeah, that's, that's in our contact with all the underlying cloud providers. You built it in already. Yeah. So we have a quite strict SLA that we pass on to you. The reason why

Martin Casado is a General Partner at Andreessen Horowitz (a16z), where he focuses on AI and infrastructure investments. He previously co-founded Nicira which was acquired by VMware for $1.2 billion in 2012.In this episode of World of DaaS, Martin and Auren discuss:Economics of open source AIChinese AI innovation with DeepSeekModel collapse and data moatsRegulatory challenges in AILooking for more tech, data and venture capital intel? Head to worldofdaas.com for our podcast, newsletter and events, and follow us on X @worldofdaas.You can find Auren Hoffman on X at @auren and Martin Casado on X at @martin_casado.Editing and post-production work for this episode was provided by The Podcast Consultant (https://thepodcastconsultant.com)

Today we're sharing a conversation between Martin Casado, general partner at Andreessen Horowitz and Nathan Labenz, AI scout, which originally aired on The Cognitive Revolution podcast from Turpentine. Their discussion explores AI systems complexity and debates whether AI development will lead to AGI. The conversation covers model scaling, biological AI, driverless cars, and AI safety concerns. — 

Cerebral Valley is tomorrow! I've been listening to old interviews, brainstorming with Claude and ChatGPT, and talking to investors to prep for my conversations with Dario Amodei, Martin Casado, and Alexandr Wang. We'll be sharing those conversations here in the newsletter. Expect video highlights on our social media feeds, a detailed rundown of the biggest moments in the newsletter Thursday, and full-length conversations on our YouTube channel.To satiate your AI appetites until then, give a listen to the latest edition of the Cerebral Valley Podcast with my friends and co-hosts Max Child and James Wilsterman. You've listened to us assess whether startups are underrated or overrated and make our draft picks. Now we're looking to the future. We asked Claude and ChatGPT o1 to make some predictions about what will happen in artificial intelligence over the next year. And then we took the over or under on those predictions.Brought to you by BrexBrex knows runway is everything for venture-backed startups, so they built a banking solution that helps them take every dollar further. Unlike traditional banking solutions, Brex has no minimums and gives startups access to 20x the standard FDIC protection via program banks.Plus, startups can earn industry-leading yield from their first dollar — while being able to access their funds anytime. If you want to make sure your portfolio companies have a place to save, spend, and grow their capital, check out Brex here.Chapters* 00:00 — Introduction to AI Predictions* 02:48 — Exploring Predictions for AI in 2025* 06:06 — AI Regulation in Healthcare* 08:53 — Self-Driving Cars and Tesla's Future* 12:04 — AI in News Media* 14:55 — AI-Generated Films and Entertainment* 17:53 — Anthropic's Predictions and AI Co-Processors* 20:59 — AI in Pharmaceutical Development* 24:13 — International AI Treaties and Regulations* 26:47 — Comparing AI Models: ChatGPT vs. Claude* 30:06 — Future of AI and Human Systems* 32:46 — Conclusion and Reflections on AI Predictions Get full access to Newcomer at www.newcomer.co/subscribe

Martin Casado, general partner at venture-capital firm Andreessen Horowitz, says concrete risks from the artificial intelligence boom haven't materialized. Casado spoke with WSJ global tech editor Jason Dean about the U.S. government's stance on AI policy and the outlook for investing in the space at WSJ Tech Live. Plus, scientists and engineers are working to build more efficient electric motors using a technology pioneered by Benjamin Franklin. Zoe Thomas hosts.  Sign up for the WSJ's free Technology newsletter.  Learn more about your ad choices. Visit megaphone.fm/adchoices

Deepfakes—AI-generated fake videos and voices—have become a widespread concern across politics, social media, and more. As they become easier to create, the threat grows. But so do the tools to detect them.In this episode, Vijay Balasubramaniyan, cofounder and CEO of Pindrop, joins a16z's Martin Casado to discuss how deepfakes work, how easily they can be made, and what defenses we have. They'll also explore the role of policy and regulation in this rapidly changing space.Have we lost control of the truth? Listen to find out.Resources:Find Vijay on Twitter: https://x.com/vijay_voiceFind Martin on Twitter: https://x.com/martin_casadoStay Updated: Let us know what you think: https://ratethispodcast.com/a16zFind a16z on Twitter: https://twitter.com/a16zFind a16z on LinkedIn: https://www.linkedin.com/company/a16zSubscribe on your favorite podcast app: https://a16z.simplecast.com/Follow our host: https://twitter.com/stephsmithioPlease 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.

Timestamps:1:57 Interview starts / Will there be an AI monopoly?5:33 The Bitter Lesson19:30 Government AI Scaling Projects25:09 AI Agents Versus Information Theory34:25 Little Tech and PoliticsFind Martin:https://a16z.com/author/martin-casado/https://x.com/martin_casadoMentioned in the Episode:William Perry and Martin's course at Stanford: https://web.stanford.edu/class/msande91si/slides/msande91si_course_information.pdfUS Senate AI Roadmap: https://www.politico.com/f/?id=0000018f-79a9-d62d-ab9f-f9af975d0000Entropy bounds in Information Theory: https://en.wikipedia.org/wiki/Entropy_(information_theory)Martin and Ion Stoica on Little Tech and Open Source: https://www.economist.com/by-invitation/2024/07/29/keep-the-code-behind-ai-open-say-two-entrepreneurs This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.fromthenew.world/subscribe

Thank you for 1m downloads of the podcast and 2m readers of the Substack!

My guest today is Martin Casado. Martin is a partner at Andreessen Horowitz and first joined me on Invest Like the Best in 2022. So much has changed since then, and it was awesome to have Martin back to discuss all of the different implications of this AI revolution. Before joining a16z, Martin pioneered software-defined networking and co-founded Nicira, which was bought by VMware for $1.3 billion in 2012. He has studied, built, and invested in digital infrastructure his whole career which has primed him to go in-depth in this interview on the immense opportunities and challenges AI presents among creativity, policy-making, agentic systems, real-world data structures, and beyond. Please enjoy this conversation with Martin Casado.  Listen to Founders Podcast For the full show notes, transcript, and links to mentioned content, check out the episode page here. ----- This episode is brought to you by Tegus, where we're changing the game in investment research. Step away from outdated, inefficient methods and into the future with our platform, proudly hosting over 100,000 transcripts – with over 25,000 transcripts added just this year alone. Our platform grows eight times faster and adds twice as much monthly content as our competitors, putting us at the forefront of the industry. Plus, with 75% of private market transcripts available exclusively on Tegus, we offer insights you simply can't find elsewhere. See the difference a vast, quality-driven transcript library makes. Unlock your free trial at tegus.com/patrick. ----- Invest Like the Best is a property of Colossus, LLC. For more episodes of Invest Like the Best, visit joincolossus.com/episodes.  Past guests include Tobi Lutke, Kevin Systrom, Mike Krieger, John Collison, Kat Cole, Marc Andreessen, Matthew Ball, Bill Gurley, Anu Hariharan, Ben Thompson, and many more. Stay up to date on all our podcasts by signing up to Colossus Weekly, our quick dive every Sunday highlighting the top business and investing concepts from our podcasts and the best of what we read that week. Sign up here. Follow us on Twitter: @patrick_oshag | @JoinColossus Editing and post-production work for this episode was provided by The Podcast Consultant (https://thepodcastconsultant.com). Show Notes: (00:00:00) Welcome to Invest Like the Best (00:01:48) The Future of AI and Creativity (00:03:11) Economic Implications of AI (00:04:33) AI's Impact on Content Creation (00:08:21) Challenges in AI and Robotics (00:12:16) Human Data and AI Training (00:20:30) Investing in AI and Robotics (00:26:00) Defensibility and Competition in AI (00:33:22) Regulatory Considerations (00:35:26) Internet Era Parallels and Security Concerns (00:40:25) Open Source vs. Closed Source in Tech (00:43:45) Market Annealing and Category Creation (00:46:13) Data and Hardware Innovations in AI (00:55:55) Agents and the Future of AI

Dive into the profound discussion on AI expectations and the future with Martin Casado, General Partner at Andreessen Horowitz, as we unpack the complexity of AI systems and their potential impact on the world. Explore the differing viewpoints on AI's epistemics, possible regulatory standards, and the art of predicting AI advancements. Gain insights into the actionable outcomes of this dialogue and the significance of understanding AI before shaping policies. Join us for this episode of the Cognitive Revolution to contemplate AI's trajectory and what it might mean for humanity. Apply to join over 400 founders and execs in the Turpentine Network: https://hmplogxqz0y.typeform.com/to/JCkphVqj RECOMMENDED PODCAST: Byrne Hobart, the writer of The Diff, is revered in Silicon Valley. You can get an hour with him each week. See for yourself how his thinking can upgrade yours. Spotify: https://open.spotify.com/show/6rANlV54GCARLgMOtpkzKt Apple: https://podcasts.apple.com/us/podcast/the-riff-with-byrne-hobart-and-erik-torenberg/id1716646486 SPONSORS: Oracle Cloud Infrastructure (OCI) is a single platform for your infrastructure, database, application development, and AI needs. OCI has four to eight times the bandwidth of other clouds; offers one consistent price, and nobody does data better than Oracle. If you want to do more and spend less, take a free test drive of OCI at https://oracle.com/cognitive The Brave search API can be used to assemble a data set to train your AI models and help with retrieval augmentation at the time of inference. All while remaining affordable with developer first pricing, integrating the Brave search API into your workflow translates to more ethical data sourcing and more human representative data sets. Try the Brave search API for free for up to 2000 queries per month at https://bit.ly/BraveTCR Omneky is an omnichannel creative generation platform that lets you launch hundreds of thousands of ad iterations that actually work customized across all platforms, with a click of a button. Omneky combines generative AI and real-time advertising data. Mention "Cog Rev" for 10% off https://www.omneky.com/ Head to Squad to access global engineering without the headache and at a fraction of the cost: head to https://choosesquad.com/ and mention “Turpentine” to skip the waitlist. CHAPTERS: (00:00:00) About the Show (00:03:18) AI progress (00:05:20) Threshold effects (00:12:30) Heavy-tailed universe (00:14:53) LLMs are not very good at unique tasks (00:26:27) Sponsors: Oracle | Brave (00:28:36) Understanding meaning (00:31:44) How do LLMs work? (Part 1) (00:36:25) Sponsors: Omneky | Squad (00:38:12) How do LLMs work? (Part 2) (00:44:01) Post-training (00:51:09) Simulation (01:04:06) Regulation (01:08:53) What makes AI a paradigm shift (01:11:51) Compute limits (01:20:07) Sleeper agents (01:23:16) Surface area of models (01:25:49) AI regulation (01:27:35) AI in medicine (01:29:56) AGI, superintelligence (01:37:04) Competition in the foundation model space (01:40:57) The scaling laws (01:44:31) The AGI convergence (01:45:35) Bets on the future of AI (01:48:20) Outro

Milin Desai is the CEO at Sentry, an application monitoring tool for developers. Sentry has recently passed two key milestones: 100K customers and over $100M in ARR. Before Sentry, Milin was a GM at VMware and scaled their cloud networking into a billion-dollar business. Prior to stepping into leadership roles, Milin was a PM at Riverbed and a software engineer at Veritas. — In today's episode, we discuss: The key ingredients of Sentry's success Sentry's developer-centric approach Lessons on pricing, packaging, and product from VMware Being an external CEO at a startup Forging successful relationships with founders — Referenced: Building for the Fortune 500,000: https://blog.sentry.io/building-for-the-fortune-500-000/ Carl Eschenbach: https://www.linkedin.com/in/carl-eschenbach-980543/ Chris Jennings: https://www.linkedin.com/in/chriskjennings/ David Cramer: https://www.linkedin.com/in/dmcramer/ FRC's product market fit framework: https://pmf.firstround.com/ Martin Casado: https://www.linkedin.com/in/martincasado/ Pat Gelsinger: https://www.linkedin.com/in/patgelsinger/ Raghu Raghuram: https://www.linkedin.com/in/raghuraghuram/ Riverbed: https://www.riverbed.com/ Sentry: https://sentry.io/ Todd Bazakas: https://www.linkedin.com/in/todd-bazakas-b5a2533/ Veritas: https://www.veritas.com/ VMware: https://www.vmware.com/ — Where to find Milin Desai: LinkedIn: https://www.linkedin.com/in/milin-desai-464757/ Twitter/X: https://twitter.com/virtualmilin — Where to find Brett Berson: LinkedIn: https://www.linkedin.com/in/brett-berson-9986094/ Twitter/X: https://twitter.com/brettberson — Where to find First Round Capital: Website: https://firstround.com/ First Round Review: https://review.firstround.com/ Twitter/X: https://twitter.com/firstround YouTube: https://www.youtube.com/@FirstRoundCapital This podcast on all platforms: https://review.firstround.com/podcast — Timestamps: (00:00) Introduction (03:03) Joining Sentry as an external CEO (06:27) The CEO/founder relationship (09:37) Lessons from VMware (13:04) What PMs did differently at VMware (18:04) Becoming the need, not the want (20:53) Scaling Sentry (23:07) Building for the “Fortune 500,000” (27:02) Open versus closed source product (30:43) The key ingredients to Sentry's success (36:21) How Milin updated his playbook at Sentry (38:49) Focus on packaging, not pricing (40:29) “Build for the many, not the few” (41:53) Sentry's B2D model (45:10) The second product mindset (51:03) Contrarian take on building for enterprise (52:50) Several people who influenced Milin

A reminder for new readers. That Was The Week collects the best writing on critical issues in tech, startups, and venture capital. I selected the articles because they are of interest. The selections often include things I entirely disagree with. But they express common opinions, or they provoke me to think. The articles are only snippets. Click on the headline to go to the original. I express my point of view in the editorial and the weekly video below.Thanks To This Week's Contributors: @TEDchris, @LilyWhitsitt, @RocketToLulu, @saeedtaji, @geneteare, @EricNewcomer, @jeffbeckervc, @jasonlk, @elonmusk, @benshapiro, @StevenLevy, @apple, @bheater, @bmw, @Growcoot, @illscience, @venturetwins, @omooretweets, @conniechanContents* Editorial: Civility and Civilization* Essays of the Week* US Seed Investment Actually Held Up Pretty Well For The Past 2 Years. Here's What That Means For 2024* Lower Valuations, Higher Bar: What It's Like To Raise A Seed Round In 2024 * Unicorns & Inevitabilities* Sequoia, Founders Fund, USV, Elad Gil & Benchmark Top Venture Manager Survey* Why 2024 May Be Tougher on Venture Capital Than 2023* Video of the Week* The Mac at 40* AI of the Week* BMW will deploy Figure's humanoid robot at South Carolina plant* Google's New AI Video Generator Looks Incredible* OpenAI's Sam Altman seeks funds for AI chip factories as demands surge* The Future of Prosumer: The Rise of “AI Native” Workflows* Andreessen Horowitz's Connie Chan to Leave as Consumer Focus Shifts to AI* OpenAI Is a (Relative) Steal* News Of the Week* Ted fellows resign from organisation after Bill Ackman named as speaker* Tesla's Slowdown Disqualifies It From ‘Magnificent Seven' Group* TikTok's Testing 30 Minute Uploads as It Looks To Expand Its Content Options* Instagram to scan under-18s' messages to protect against ‘inappropriate images'* Tiger Global Investor Relations Staff Depart After Fundraising Challenges* Worldcoin hints at new Orb for a friendlier iris-scanning experience* Startup of the Week* Loyalty Startup Bilt Rewards Hits $3.1B Valuation After $200M Round* X of the Week* Elon Musk visits Auschwitz with Ben ShapiroEditorialThere is a lot to digest in this week's newsletter. Gené Teare's two essays on Seed investing head up the Essays of the Week, along with Jeff Becker talking about unicorns and inevitabilities, Eric Newcomer on who are the top investors and Jason Lemkin on the reasons 2024 might be harder for Venture Capital than 2023.But my attention was distracted from venture capital by a Guardian article announcing (triumphantly, I might add) that several TED fellows had resigned from the organization due to an invite to Bill Ackman to speak at this year's TED event in Vancouver.“Lucianne Walkowicz and Saeed Taji Farouky accuse Ted of taking anti-Palestinian stand over controversial billionaire's inclusion”It seems Ackman is not alone. They also object to Bari Weiss being invited. The leavers are also not alone; up to 30 others have signed a “solidarity” letter.The accusations echo much of the discussion around the medieval assassination of Jews on 7 October and Israel's efforts to defeat Hamas in the aftermath. Because these speakers are against anti-Semitism and so supportive of Israel's war against Hamas, they are accused of the ridiculous claim of supporting “Genocide” against Palestinians.“We refuse for our work and identities to be exploited to promote the Ted brand while the organisation and its speakers generate income and advance their careers through dehumanising Palestinians and justifying their genocide,” the pair said.It probably will not surprise readers of this newsletter that I applaud TED curators Chris Anderson and Lily James Olds for not backing down on the invitations. Whatever one believes about the current conflict in Israel, it is clear that banning opponents of anti-Semitism because of their stance is not a solution to anything. I believe the cause of fighting anti-Semitism should be close to the heart of any progressive person. It is not anti-Palestinian to support Jews against being slaughtered in the street, to oppose anti-Semitism, or to condemn Hamas as anti-Jewish murderers. Supporting Jews against slaughter by Hamas is not incompatible with supporting Palestinians. The Guardian reported that Ackman responded to the resignations with a statement:“I stand unapologetically with Israel and against antisemitism and terrorism, while strongly supporting the Palestinian people. Attempts to cancel speech and eliminate the free and respectful exchange of ideas among people with differing views are driving much of the divisiveness that plagues our nation. Truth, wisdom and ultimately peace are the result of the free exchange of ideas and debate, precisely what Ted is all about. It is sad that this is not more widely understood,”Unsurprisingly, one of the resigners, Farouky, told the Guardian he did not regard the issue as freedom of speech. It clearly IS about freedom of speech. Speech only needs protecting when opinions are wide apart and strongly held.For example, here are my views on the actual issues:These are trying times. Over 25,000 deaths in Gaza are hard to comprehend. And I certainly cannot. But I can understand that Jews have to defend themselves. And I can understand that progressive thinkers MUST stand up to anti-Semitism, whatever form it takes.In case there is doubt about my support for Muslim victims of racism, my book Under Seige is about the attacks on Muslims in the UK between 1961 and 1981. It starts with recognizing that racism targets differences and that Jews and Muslims are both targets. Indeed, the very ghettoes that Pakistani and Bengali immigrants were being attacked in had earlier, in the 1930s, been inhabited by Jewish settlers fleeing pogroms. I am not Jewish, and I am not Muslim. But I will always be on both of their sides when they are attacked for their ethnic and racial origin.In Israel, Jews were killed for being Jews. Palestinians are being killed because Hamas is hiding in their cities and buildings. I do not consider Israel's response to be racist against Palestinians. I consider it reasonable in the context of 7 October. I consider that Hamas has done this to Palestinians and probably wanted that outcome. I am sad that Hamas has done this for the Palestinian victims. But I do not doubt that Hamas is to blame.My views may anger you. But do you want me banned or silenced?My title this week is Civility and Civilization. The TED events bring both to the fore. Like those I write here, opinions are there to be disagreed with, debated, and interrogated. Civilized behavior requires dialogue and civility within the dialogue. I certainly understand opinions I disagree with, and far from banning them or walking away so that I do not have to hear them, I want to hear them. We all should.This is a different editorial than usual. I hope the humanity of refusing to forget 7 October and the determination to preserve the view that fighting anti-Semitism is a non-negotiable minimum requirement of civilization are grasped. By the same token, Islamaphobia must be fought. But in Israel, there is no Islamophobia at work. Jews are simply reacting to an atrocity. They are right to blame Hamas.Essays of the WeekUS Seed Investment Actually Held Up Pretty Well For The Past 2 Years. Here's What That Means For 2024Gené Teare, January 24, 2024, @geneteareEditor's note: This is the first in a two-part series on the state of seed startup investing at the start of 2024. Check back tomorrow for Part 2.Despite a broad pullback in global startup investment over the past two years, investors say the U.S. seed funding environment was the most vibrant compared to other funding stages during the downturn.In fact, U.S. seed funding in 2022 grew by close to 10% in terms of dollars invested, in contrast to a downturn at all other funding stages. In 2023, U.S. seed funding fell 31% — a significant proportion — but still less than other funding stages year over year, an analysis of Crunchbase data shows. (It's also worth noting that those other stages had already experienced year-over-year declines in 2022.)In the current startup funding market, “we're seeing a lot more great talent excited about starting things,” said Renata Quintini, co-founder of Renegade Partners, a Bay Area-based investment firm that focuses on Series A companies and is therefore close to the seed ecosystem.Other investors share that enthusiasm. “Valuations are coming down, more talent is available in the market,” said Michael Cardamone of New York-based seed investor Forum Ventures. “A lot of these companies at seed and Series A are going to scale into what will likely be the next bull market.”Seed trends over the decadeSeed as an asset class, not surprisingly, has grown in the U.S. over the past decade. In 2014 less than $5 billion was invested at seed. At the market peak in 2022, seed investment was more than $16 billion, although it fell to $11.5 billion in 2023.Despite the downturn, seed funding in 2023 was still $2 billion to $3 billion higher in the U.S. than in the pre-pandemic years of 2019 and 2020.Higher bar, pricier rounds, better valuedBut in a tougher market, seed investors are being more selective about which companies they fund.“We're being far more disciplined and patient knowing how hard it is for these companies to get to Series A and beyond,” said Jenny Lefcourt, a general partner at Bay Area-based seed investor Freestyle Capital. “Our bar for conviction is higher than it had been in the heyday where everything was getting funded.”In the slower funding environment, the firm has been investing later at the seed stage, “gravitating toward ‘seed plus' or ‘A minus' — pick your favorite term for it — because I feel like I get to see more risk mitigated. I get to see more data,” she said.Freestyle seeks to have ownership of around 12% to 15% in the companies it backs. “The reason is because of our model,” Lefcourt said. “We are low-volume, high-conviction investors.”And because the firm invests in companies that are pre-Series A, “our reality has been that our valuations have actually been higher in this market, which is not what we would have predicted.“But the data we've seen is, we're not alone in that,” she said.…MoreLower Valuations, Higher Bar: What It's Like To Raise A Seed Round In 2024 Gené Teare, January 25, 2024, @geneteareEditor's note: This is the second in a two-part series on the state of seed startup investing at the start of 2024. Read Part 1, which looked at seed funding trends over the past decade and the median time period between seed and Series A funding, here.Seed funding to startups has grown into its own asset class over the past decade, with round sizes trending larger, and a bigger pool of investors backing these nascent startups. But in the aftermath of 2021's venture funding heyday and subsequent pullback, investors say that while seed funding has held up better than other startup investment stages, these very young startups will see lower valuations and must now clear a much higher bar to get backing.More companies raised seed funding above $1 million in 2021. Those companies — which raised during a record-smashing year for venture funding — are saddled with valuations that could be too high for this current market — even at seed. Many of those startups have been forced to cut costs to extend their runways, and face a tougher sales environment.“You could then be sacrificing growth, which is one of the main levers that Series A investors are looking for,” said Michael Cardamone of New York-based seed investor Forum Ventures.2021 after effectsIn 2021 it was “grow, grow, grow, grow,” said Jenny Lefcourt, a general partner at Bay Area-based seed investor Freestyle Capital. “It's embarrassing to look back on, but that was the game being played.”Investors got sloppy during the boom times, she said. “I think a lot of VCs were thrilled to back you, and then say, ‘we'll figure it out.' ”“The reality is that almost anything that was done then — call it 2021 — was the wrong price,” she said.This led to down rounds, even at seed, though those are generally not viewed negatively like they were in the past, she said.In fact, “when our companies get their down rounds done, it's a sign of it's a good business. It just had the wrong price on it,” she said.While the bar is higher to raise funding these days, “I think it's so much better for a company who gets to start in this environment,” Lefcourt said.Down rounds can actually be a sign of conviction, she said. “None of us would do all the heavy lifting to not only give the company more capital, but recap it, which takes a lot. It's a heavy lift — none of us would do that if we weren't super jazzed about the company. The lazier approach, the easier approach, is to just put it on the note, keep it flat, and be done,” she said.Renata Quintini, co-founder of Renegade Partners, a Bay Area-based investment firm that focuses on Series A companies, is hearing of “more ‘pay-to-play' these days and it's starting to get ugly.” This happens when new investors wipe out the prior investors, and anyone seeking equity needs to pony up into the new funding round.Median and averages climbNonetheless, “seed round valuations haven't dropped a ton from even the peak,” according to Forum Ventures' Cardamone. But, “the bar to raise a seed [round] is a lot higher.”“Most first-time founders especially, and the vast majority of founders generally — they have to get significant traction to be able to raise that same round they used to be able to raise. And a lot fewer of those rounds are happening,” he said.“A priced seed round of $3 million at $15 million [pre-money] is still happening, but you might have to be at $500,000 ARR, to raise that round now. Whereas in 2021, it was the norm to raise that round pre-revenue,” he said.Series A fundings have gotten harder as “companies are going out and raising three seed rounds,” said Cardamone.Based on an analysis of Crunchbase data, median and average seed round sizes in the U.S. have climbed through the past decade.In 2023, median and average raises are not far from the peak of 2022, Crunchbase data shows, and were well above pre-pandemic levels. (However, this will shift downward somewhat as the long tail of seed fundings are retroactively added to the Crunchbase database.)Seed rounds got larger“If I have conviction, we may need them to have more money, cause we know it's going to take them longer to reach the milestones that are now higher,” said Lefcourt.Per an analysis of Crunchbase data, larger seed rounds — those $1 million and above — have increased through the decade.The amount of funding to seed-stage companies below $1 million hasn't budged much, and is a fraction of what it was earlier in the decade.Seed below $1 million in 2014 represented around 25% of all seed funding.That has come down as a proportion every year since then.And as of 2021 that proportion has dipped below 10% for the first time, ranging from 5% to 7% of all seed dollars invested in the U.S. since then.Earlier in the past decade, the number of seed deals in rounds below $1 million outpaced those rounds at $1 million and above significantly.But 2021 was once again a pivotal year. That's when $1 million and above seed rounds outpaced smaller seed for the first time.In 2023, they are neck and neck in count. (That might shift as the long tail of seed rounds are added to the Crunchbase database long after they close.)What this all shows is that seed has become an increasingly significant and elongated phase in a company's early life cycle, where companies are raising multiple million-dollar seed rounds. And as of late, more companies than ever before are wading in the seed pool.What does this mean for the seed funding market in 2024?…MoreUnicorns & InevitabilitiesUp and to the right, or not so much?JEFF BECKER, JAN 22, 2024TLDR: Go read Aileen Lee's update to the Unicorn Club… and a few inevitabilities.Did anyone catch Aileen Lee & Allegra Simon's Welcome Back to the Unicorn Club, 10 Years Later?If not, go read it. That's your MMM.If you did read it, you can't help but wonder if the tech sector isn't going to resemble the public markets over time. Ups and downs, but consistently up and to the right over a long enough period.After all, we are creating leverage in ways we've never seen before.And for unicorns, that meant 14X growth over a 10-year period.Could you imagine another 14 or even 10X from here? That would be stratospheric, from ~500 to ~5,000 unicorns? What if the exit sizes did too? $5B, $10B, $50B?Crazy to think, but hardly impossible. After all, we've already seen near-centicorns like Uber's IPO at $75B in 2019.The interesting part about that thought exercise though is not the crazy zero interest rate IPO's, but the fact that entry valuations didn't and don't move nearly as fast as top end outcomes because of the time horizon to realizing them.For example, Airbnb raised $20K from Y Combinator for 6%, then they took another $600K for 20% in their seed.That was 2009. The idea of an IPO for $47B just 11 years later in 2020 probably wasn't even a consideration. Paul Graham and the YC team would've had to believe Airbnb's IPO could compete with AT&T, General Motors, and Visa.Insane.Fast forward, that $333,333 valuation at YC has moved to $1.78m (125K for 7%), and they'll stack another 2.6% ownership on average from their $375K MFN with the average YC company raising seed at a $14.4m cap instead of Airbnb's $3m.That's a ~5X increase in valuation at pre-seed & seed for a 47X increase in IPO size if you were modeling $1B outcomes into your VC fund model in 2009.I'm not saying that will continue. There are counterforces of course.* Margins are way too high. The fact that software margins have persisted at 80% or more is just craziness. Companies will start to use price more aggressively to compete for market share as cheap AI tools enter the market and try to unseat them. This compression will change the value of discounted cash flow models.* Pricing models need to change. One way to reduce sticker price and maintain some semblance of healthy long-term margins is to pay a smaller implementation fee, but incur ongoing services & upgrade costs. This is a more traditional pricing model, and creative economics that leverage this kind of thinking run rampant in the titans of tech. It's a game of deeper roots, higher switching costs, and long-term contracts. With API calls and data usage more prevalent, we'll also see more pay-per-use models, the same way we buy copiers. We'll also see more pay-for-performance models with attributable ROI, akin to Amazon's ACoS model or Rakuten's affiliate marketing model. Customers will prefer it too, placing a higher emphasis customer value. This will also drive margins to condense.* AI, AI, AI. AI will cut OpEx costs dramatically. SDR teams, gone. Copywriters at agencies, you don't need as many. Data scientists? Just run a query against your data lakes. The list goes on. Costs of running these companies is going to get shellacked. Good for margins for sure, but also a compelling opportunity for newcomers to undercut and unseat incumbents too.* More hardware. With software margins condensing, hardware margins will start to feel more attractive too, the maintenance and upgrade fees will resemble what we see in SaaS, and the software that powers these machines will be incredible. Skynet for autonomous off-road vehicles, absolutely.* Less dilution, earlier exits, and stratification. We already see it in the S&P 500 with the top end accounting for an outsized share of total value. With that kind of cash on balance sheets, bigger companies will just buy the smaller ones. Think about how Broadcom rolls up companies. If you've built the business more efficiently, you've also raised less, incurred less dilution, and that $100m exit when you still own 50% is looking pretty prett-ty good compared to the same outcome 5-10 grueling years later to own 5% of $1B.* Massive founder salaries, less emphasis on growth. If you've built a company that's profitable from day one, and you have complete control of your board, what's your incentive to keep the pedal down on growth, or stay on the VC treadmill? World domination? Why not pay yourself 10X, stop fundraising, and continue to tighten the core business until someone acquires you? It's better for the founding team and employees for sure, and it's probably better for customers in most instances too.These are just some of things I think we'll see over the next five years until we approach ZIRPy-dirpy times again and massive growth becomes irresistible.But there are also a whole slew of things I think are inevitabilities that will benefit from these dynamics because we will not only have new technologies, with more attractive pricing, but we will be tackling new opportunities that were created by the prior evolutions across adjacent industries.For example…* Cost of energy is going to zero with nuclear fusion* Longevity is starting to work; check out Loyal for Dogs* Batteries & cameras continue to improve; medical devices, for one, will be more personal & affordable* Disintermediation of big ad networks with new global distribution channels; check out Benjamin* Massive cost reductions driven by AI* Software will be built by software* An aging population is retiring (10,000 per day); wealth transfer & SMB's with no exit paths* Climate change* …and so on and so on and so onThe list is long. Much longer than this. If you want the rest, just reply or comment so that I know, and I'll go deeper next week.Net of all of it, I think we're going to see a tale of two cities. Stronger, more profitable businesses, with smaller, but better founder founder exits in the near term, and a continued growth both in number of total unicorns, and what that top-end outcomes look like in the longer-term.And like I said, go read Aileen's post.Sequoia, Founders Fund, USV, Elad Gil & Benchmark Top Venture Manager SurveyI got my hands on a VC scorecard circulating among top founders & VCsERIC NEWCOMERJAN 25, 2024Before we get started, I want to be clear — this isn't the end-all, be-all list of the top venture capital firms or the most promising startups.But I got my hands on a survey of 91 people at 69 different venture capital firms conducted by a well-respected investor in venture capital firms.The survey results are spreading hand-to-hand in Silicon Valley. The results of the survey rank the most desirable venture capital firms and companies, according to VCs themselves. When I was out in San Francisco last week for The Information's 10th anniversary gala, sources kept bringing it up.My sources tell me that the survey was conducted by Ed Hutchinson, managing partner at Golden Bell Partners. Hutchinson is ignoring my emails.Which firms and companies would top VCs themselves put their money into? It's a question everyone wants to know the answer to.I've got my hands on their list of favorites:Firms* (1) Sequoia* (2) Founders Fund* (3) Union Square* (4) Elad Gil* (5) Benchmark…Much More (but only for subscribers)Why 2024 May Be Tougher on Venture Capital Than 2023by Jason Lemkin | Blog Posts, Fundraising, ScaleSo I thought the toughest times for venture would be behind us now.  In 2022, we were in free fall, with public market caps falling like a knife, and the IPO markets frozen.  And 2023 was the year of the Work Out in venture.  Bridge rounds slowed down, and VCs acknowledged a lot of portfolio companies just weren't going to make it.  It got real in 2023, and that realness got normalized.  The drama mostly was behind us.  And public SaaS stocks in many cases did really, really well in 2023.  So shouldn't 2024 at least be better for venture?So I thought.But the reality is I'm a bit more worried the venture drama in 2024 will be bigger than 2023.  Why?  Four core reasons:#1:  Now We Have to Deal With the Reality of the Stumbling Unicorns.The ones that are doing $100m+ ARR, still growing, but there just isn't going to be any more money coming.  This is going to burn up a ton of energy in VC funds.  Even tougher, the reality is while many VC funds marked down their unicorns to lower valuations in 2023, they often didn't mark them down enough.#2.  The Chase for AI Unicorns and Decacorns is All-consuming.  It's Still 2021 There.The one place where paper money seems easy to come by is Hot AI Startups.   And that's probably not you.  It's just consuming all the oxygen in venture, trying to get into the next Imaging AI startup worth $1B in 10 months.  In AI, 2021 never went away.  In AI, it's still 2021.#3.  A Lot of Seasoned VCs are Discouraged. This Doesn't Help Founders.A lot of VCs who have been around for a while are quietly discouraged.  They just don't see a great path to making a ton of money in venture these days.  We're in Year 3 of a venture downturn, and that weighs of most of us.  At a practical level, for founders, it makes it harder to lean it.#4.  More Valuation Markdowns Are Still to ComeRelated to the first point, but more markdowns are like mutliple rounds of layoffs.  They're just tough.  LPs lose confidence.  Coworkers lose confidence.  We should have gotten through a lot of this in 2023, but we didn't.  Personally, I've got several investments for example that I marked down. 70%-80% or more — that my co-investors didn't mark down at all.#5.  VCs Have Run out of ReservesVCs used what extra “reserve” capital they had for bridge rounds in 2022 and 2023.  Now it's gone.  That's adds to the stress as companies struggle.  You don't have a play anymore.The bottom line is there likely is at least another full year of working through the excesses of 2021.  That will weigh across venture.  No matter what some AI headlines suggest.Video of the WeekThe Mac at 40Apple Shares the Secret of Why the 40-Year-Old Mac Still RulesThe pioneering PC revolutionized how people interact with computers. As the Mac enters its fifth decade, Apple says it will continue to evolve.STEVEN LEVY, Jan 19, 2024 10:00 AMON JANUARY 24, Apple's Macintosh computer turns 40. Normally that number is an inexorable milestone of middle age. Indeed, in the last reported sales year, Macintosh sales dipped below $30 billion, more than a 25 percent drop from the previous year's $40 billion. But unlike an aging person, Macs now are slimmer, faster, and last much longer before having to recharge.My own relationship with the computer dates back to its beginnings, when I got a prelaunch peek some weeks before its January 1984 launch. I even wrote a book about the Mac—Insanely Great—in which I described it as “the computer that changed everything.” Unlike every other nonfiction subtitle, the hyperbole was justified. The Mac introduced the way all computers would one day work, and the break from controlling a machine with typed commands ushered us into an era that extends to our mobile interactions. It also heralded a focus on design that transformed our devices.That legacy has been long-lasting. For the first half of its existence, the Mac occupied only a slice of the market, even as it inspired so many rivals; now it's a substantial chunk of PC sales. Even within the Apple juggernaut, $30 billion isn't chicken feed! What's more, when people think of PCs these days, many will envision a Macintosh. More often than not, the open laptops populating coffee shops and tech company workstations beam out glowing Apples from their covers. Apple claims that its Macbook Air is the world's best-selling computer model. One 2019 survey reported that more than two-thirds of all college students prefer a Mac. And Apple has relentlessly improved the product, whether with the increasingly slim profile of the iMac or the 22-hour battery life of the Macbook Pro. Moreover, the Mac is still a thing. Chromebooks and Surface PCs come and go, but Apple's creation remains the pinnacle of PC-dom. “It's not a story of nostalgia, or history passing us by,” says Greg “Joz” Joswiak, Apple's senior vice president of worldwide marketing, in a rare on-the-record interview with five Apple executives involved in its Macintosh operation. “The fact we did this for 40 years is unbelievable.”…Much MoreAI of the WeekBMW will deploy Figure's humanoid robot at South Carolina plantBrian Heater @bheater / 3:00 AM PST•January 18, 2024Image Credits: FigureFigure today announced a “commercial agreement” that will bring its first humanoid robot to a BMW manufacturing facility in South Carolina. The Spartanburg plant is BMW's only in the United States. As of 2019, the 8 million-square-foot campus boasted the highest yield among the German manufacturer's factories anywhere in the world.BMW has not disclosed how many Figure 01 models it will deploy initially. Nor do we know precisely what jobs the robot will be tasked with when it starts work. Figure did, however, confirm with TechCrunch that it is beginning with an initial five tasks, which will be rolled out one at a time.While folks in the space have been cavalierly tossing out the term “general purpose” to describe these sorts of systems, it's important to temper expectations and point out that they will all arrive as single- or multi-purpose systems, growing their skillset over time. Figure CEO Brett Adcock likens the approach to an app store — something that Boston Dynamics currently offers with its Spot robot via SDK.Likely initial applications include standard manufacturing tasks such as box moving, pick and place and pallet unloading and loading — basically the sort of repetitive tasks for which factory owners claim to have difficulty retaining human workers. Adcock says that Figure expects to ship its first commercial robot within a year, an ambitious timeline even for a company that prides itself on quick turnaround times.The initial batch of applications will be largely determined by Figure's early partners like BMW. The system will, for instance, likely be working with sheet metal to start. Adcock adds that the company has signed up additional clients, but declined to disclose their names. It seems likely Figure will instead opt to announce each individually to keep the news cycle spinning in the intervening 12 months.Unlike some other humanoid designers (including Agility), Figure is focused on creating a dexterous, human like hand for manipulation. The thinking behind such an end effector is the same that's driving many toward the humanoid form factor in the first place: Namely, we've designed our workspaces with us in mind. Adcock alludes to Figure 01 being tasked with an initial set of jobs that require high dexterity.As for the importance of legs, the executive suggests that their importance for maneuvering during certain tasks is as — or more — important than things like walking up stairs and over uneven terrain, which tend to get most of the love during these conversations.…MoreGoogle's New AI Video Generator Looks IncredibleJAN 25, 2024MATT GROWCOOTGoogle has announced Lumiere: an AI video generator that looks to be one of the most advanced text-to-video models yet.The name Lumiere is seemingly a nod to the Lumiere brothers who are credited with putting on the first ever cinema showing in 1895. Just as motion picture was cutting-edge technology at the end of the 19th century, the Lumiere name is once more being associated with something new and original.The demo of Lumiere that Google put out focuses firmly on animals. The model can generate a scene using just text; much the same way AI image generators work, the user can dream up any scenario they would like to see a short video clip of.However, the user can also use an image as a prompt. Google provided multiple examples: including some that are real photos such as Joe Rosenthal's iconic Raising the Flag photo; “Soldiers raising the united states flag on a windy day” saw one of the 20th-centuries most recognizable photos suddently come to life as the soliders struggle with the flag that's being affected by gusts.Also in Lumiere is a “Video Stylization” setting which allows users to upload a source video and then ask the generative AI model for various element changes. For example, a person running may be suddenly turned into a toy made of colorful bricks.Another feature Google showed off is “Cinemagraphs”, where just a section of an image is animated while the rest stays still. “Video Inpainting” is included too which involves masking part of the image so that section can be changed to the user's desire.Space-Time Diffusion ModelLumiere is powered by “Space-Time U-Net architecture that generates the entire temporal duration of the video at once, through a single pass in the model.”This difficult-to-understand concept is apparently in contrast to existing video models which “synthesize distant keyframes followed by temporal super-resolution — an approach that inherently makes global temporal consistency difficult to achieve.”…Much MoreOpenAI's Sam Altman seeks funds for AI chip factories as demands surgeOpenAI CEO Sam Altman has opened discussions with global investors over the possibility of funding a network of artificial intelligence (AI) chip factories to keep pace with soaring demand.Altman is seeking around $8 billion to $10 billion worth of funds to set up several AI chip fabrication plants around the globe, an endeavor that will require synergy between leading chip manufacturers backed by investment giants.Altman is reportedly in talks with Japanese-based financial giant SoftBank Group (NASDAQ: SFTBF) and Abu Dhabi's G42 over funding plans, but details remain sparse. The discussions with G42 have been underway since 2023, with Altman describing a potential chip partnership as laying the foundation “for equitable advancements in generative AI across the globe.”Aside from SoftBank and G42, insiders say that Altman is still pursuing collaborations with other industry players to set up a network of chip fabrication plants. Although exact entities were not namechecked, industry experts are noting Intel Corporation (NASDAQ: INTC), Samsung Electronics, and Taiwan Semiconductor Manufacturing Co. (NASDAQ: TSM) as potential partners.Altman's approach to raising funds hinges on concerns that the chip supply will not be able to meet global demands for AI offerings by 2030. The OpenAI's CEO argues that the ideal solution will be a collaborative effort to set up chip manufacturing plants rather than build in silos.OpenAI has had its fair share of chip scarcity, rolling back a number of its offerings over a steady chip supply. To meet the rising demand, the company is reportedly mulling several options, including the prospect of building its chips from scratch and joining ranks with Google (NASDAQ: GOOGL) and Amazon (NASDAQ: AMZN) to explore an in-house solution.Given the costs associated with an in-house approach, OpenAI may pursue the acquisition of a chip manufacturer as a short-term solution or expand its collaboration with existing partners. However, a potential acquisition opens its own can of worms, including an inquiry by antitrust regulators.Governments are also involvedIn 2023, Altman urged the South Korean government to double their investments in AI chip manufacturing as a veritable strategy to play a leading role in the nascent ecosystem. Currently, South Korea ranks behind the U.S., China, and Japan in chip manufacturing, but a concerted government involvement could see the country climb up the charts.The OpenAI boss disclosed during his visit to South Korea that his firm will back local entities building chips for AI and other emerging technologies, with Samsung rumored to be in top position.“We are exploring how to increase our investment in Korean startups,” said Altman. “We are excited to meet as many as we can here today. I think this type of collaboration is essential to our work.”..MoreThe Future of Prosumer: The Rise of “AI Native” WorkflowsAnish Acharya, Justine Moore, and Olivia MoorePosted January 25, 2024Few people love the software they use to get things done. And it's no surprise why. Whether it's a slide deck builder, a video editor, or a photo enhancer, today's work tools were conceived decades ago — and it shows! Even best-in-class products often feel either too inflexible and unsophisticated to do real work, or have steep, inaccessible learning curves (we're looking at you, Adobe InDesign). Generative AI offers founders an opportunity to completely reinvent workflows — and will spawn a new cohort of companies that are not just AI-augmented, but fully AI-native. These companies will start from scratch with the technology we have now, and build new products around the generation, editing, and composition capabilities that are uniquely possible due to AI. On the most surface level, we believe AI will help users do their existing work more efficiently. AI-native platforms will “up level” user interactions with software, allowing them to delegate lower skill tasks to an AI assistant and spend their time on higher-level thinking. This applies not only to traditional office workers, but to small business owners, freelancers, creators, and artists — who arguably have even more complex demands on their time. But AI will also help users unlock completely new skill sets, on both a technical and an aesthetic level. We've already seen this with products like Midjourney and ChatGPT's Code Interpreter. Everyone can now be a programmer, a producer, a designer, or a musician, shrinking the gap between creativity and craft. With access to professional-grade yet consumer-friendly products with AI-powered workflows, everyone can be a part of a new generation of “prosumers.”In this piece, we aim to highlight the features of today's — and tomorrow's — most successful Gen AI-native workflows, as well as hypothesize about how we see these products evolving.What Will GenAI Native Prosumer Products Look Like?All products with Gen AI-native workflows will share one crucial trait: translating cutting-edge models into an accessible, effective UI.Users of workflow tools typically don't care what infrastructure is behind a product; they care about how it helps them! While the technological leaps we've made with Generative AI are amazing, successful products will importantly still start from a deep understanding of the user and their pain points. What can be abstracted away with AI? Where are the key “decision points” that need approval, if any? And where are the highest points of leverage? There are a few key features we believe products in this category will have: * Generation tools that kill the “blank page” problem. The earliest and most obvious consumer AI use cases have come from translating a natural language prompt into a media output — e.g., image, video, and text generators. The same will be true in prosumer. These tools might help transform true “blank pages” (e.g., a text prompt to slide deck), or take incremental assets (e.g., a sketch or an outline) and turn them into a more fleshed-out product.Some companies will do this via a proprietary model, while others may mix or stitch together multiple models (open source, proprietary, or via API) behind the scenes. One example here is Vizcom's rendering tool. Users can input a text prompt, sketch, or 3D model, and instantly get a photorealistic rendering to further iterate on.Another example is Durable's website builder product, which the company says has been used to generate more than 6 million sites so far. Users input their company name, segment, and location, and Durable will spit out a site for them to customize. As LLMs get more powerful, we expect to see products like Durable pull real information about your business from elsewhere on the internet and social media — the history, team, reviews, logos, etc. — and generate an even more sophisticated output from just one generation. * Multimodal (and multimedia!) combinations. Many creative projects require more than one type of content. For example, you may want to combine an image with text, music with video, or an animation with a voiceover. As of now, there isn't one model that can generate all of these asset types. This creates an opportunity for workflow products which allow users to generate, refine, and stitch different content types in one place.…MoreAndreessen Horowitz's Connie Chan to Leave as Consumer Focus Shifts to AIBy Kate Clark, Erin Woo and Cory WeinbergJan 23, 2024, 7:22am PSTFor years, partners at Andreessen Horowitz proclaimed they would scour the startup world for the next big consumer marketplace like Airbnb or the next hit consumer app out of China, areas in which the firm had unique expertise. Now, it's shifting toward an area more en vogue across venture capital: consumer apps powered by artificial intelligence.Those changes are happening amid an overhaul of its consumer team. Connie Chan, a general partner at Andreessen Horowitz who formerly led a team of consumer investors and was known for spotting internet trends coming from China, said she is leaving the firm.  She may raise her own fund, a person familiar with the matter said. Anish Acharya, a general partner at the firm who invested in enterprise-focused and financial technology businesses, now leads the consumer team, said people familiar with the change.Chan's move also follows a distancing by U.S. VC firms from investments in China tech, once a hotbed for U.S.  investors. In recent months, Chan has privately said it's becoming more difficult for her to work at Andreessen Horowitz because the partners have been increasingly disinterested in anything China related, another person said.The Takeaway• Fintech-focused GP Anish Acharya leading consumer deals• Consumer GP Connie Chan is leaving the firm• Consumer partner Anne Lee Skates left to start own fundThe changes are part of a broader personnel shakeup, including the decision by senior consumer investor and Airbnb board member Jeff Jordan to step back from making new investments last year. Of the four general partners that led the firm through a consumer deal blitz, none remain on the consumer team.Meanwhile, Anne Lee Skates, a consumer partner who worked on the firm's investment in live shopping app WhatNot, left in the fall to raise her own fund, according to two people familiar with the matter. Axios first reported that Chan was leaving the firm.The Andreessen Horowitz changes are emblematic of a broader VC industry gravitation toward AI and away from once-hot sectors like consumer marketplaces and financial technology, as a spike in interest rates undercut the growth aspirations of startups trying to elbow out incumbent social platforms and banking institutions.“We've gotten into this cycle now where, generally speaking, investors are less interested in consumer,” said Ben Lerer, managing partner at Lerer Hippeau. Known for its consumer investments in Warby Parker and Allbirds, the firm has invested 70% of its latest fund in enterprise companies, he said. “And AI feels like this very hopeful, very exciting, fresh thing.”Founders of some consumer startups have noticed the shift at Andreessen Horowitz. One founder of a consumer startup in the firm's portfolio said they had heard little from investment partners over the last year, a contrast to a steady drumbeat of emails the founder got in prior years from Andreessen staff who support portfolio companies with marketing and operations advice.Andreessen Horowitz's consumer investing team has been perhaps most well known for its focus on backing digital marketplaces, from peer-to-peer self-storage to real estate investment marketplaces, that could turn into the next Airbnb. Every year, it releases a ranking of top marketplace startups. “We are obsessed with marketplaces and have been since our inception,” Chan, who led investments in  social fashion startup Cider for the firm in 2021.But some of those startups backed by the firm, such as self-storage startup Neighbor, have struggled to take off in recent years. And like other venture firms, Andreessen Horowitz has also stepped back from investing in Chinese startups, an area of focus for Chan. She had championed the idea that the next wave of breakout U.S. consumer startups will model themselves after China's internet success stories, like all-in-one app WeChat.With $53 billion in assets under management, Andreessen Horowitz is one of the largest of traditional Silicon Valley firms and closely watched among other VC firms as a trend setter. And its track record of sniffing out hitmakers primed its partners to find the next trendy consumer app.The number of consumer deals Andreessen Horowitz has led dropped to 13 last year from 30 in 2021, a record for the firm, according to PitchBook data. It's possible the firm completed more consumer deals and that those investments haven't been announced. Its investments in AI companies have jumped to 23 from nine over the same years, including leading a $415 million investment in Mistral, the French developer of an open-source large language model.The firm has beefed up this team of investors primarily focused on enterprise, software infrastructure and AI startups. Led by Martin Casado, a close confidante to the firm's founders Horowitz and Marc Andreessen, it is raising its first standalone fund and has brought on two new general partners, Anjney Midha and Zane Lackey, since 2022, as well as a number of junior partners.As the infrastructure team gained power, the consumer team's profile shrank. The firm in 2023 combined its consumer and fintech teams and created a new group, called apps, led by general partner Alex Rampell, who previously co-founded installment lender Affirm, The Information reported last year. Under Rampell's leadership, the newly formed apps team will also soon launch a dedicated apps fund, according to people with direct knowledge of the matter. The consolidated team has been encouraged to pursue AI deals.Within Rampell's apps group, Acharya now leads the consumer sub-group. His portfolio of companies includes payroll company Deel and Silo, a provider of supply chain automation software. He's also an investor in Titan, a consumer investment application.Fueling the firm's shift away from consumer apps are likely disappointing returns. The startups that captivated consumers during the pandemic shutdowns have failed to retain their attention. Growth at companies the consumer team bet on, like Clubhouse, which Andreessen Horowitz backed three times in one year, and photo-sharing app BeReal, which it backed in 2021, has stalled.…MoreOpenAI Is a (Relative) StealBy Stephanie PalazzoloJan 22, 2024, 7:35am PSTOver the past year, we've seen billions in funding thrown at AI startups at eye-popping valuations. More important than the absolute valuation figures, though, is how they stack up to those startups' revenue numbers.In the chart above, we've tracked the valuations of eight AI startups that have recently raised funding, calculated against their projected revenue. On average, these companies raised money at a price that is 83 times their projected sales for the next twelve months. That's a big multiple by any measure, reflecting the rocket ship nature of these startups. But what makes the comparison noteworthy is that OpenAI has one of the lowest multiples, even though its business has the most traction.Venture capitalists tend to value early-stage startups at a premium based on their growth rates. OpenAI's business is far bigger and more mature—if we can use that word for a company growing as fast as OpenAI—than other generative AI companies. So, as fast as its revenue pace is growing—more than 20% in just two months most recently—newer firms are growing even faster.For instance, AI-powered search engine Perplexity AI doubled its annual recurring revenue from $3 million to $6 million from October to January. VCs were likely taking that expected growth into account at the time of investment, as the company would have garnered a much lower 75-times forward revenue multiple if it had raised at the same price just a few months later. Similarly, even though OpenAI rival Anthropic was likely generating around $200 million in annualized revenue at the end of last year (according to its October estimates), its projection that it would reach $850 million in annualized revenue by the end of this year surely made its mind-boggling valuation more palatable to investors.When you see the details of these AI startup funding rounds, it can sometimes feel like investors are throwing darts at nine-figure numbers on a wall. The chart suggests there's a method to the madness. Typically, startups selling to companies are valued based on the sector in which they operate. The lowest valuation multiples are accorded to startups offering industry-specific applications, while those offering more generalized applications draw a premium. The most highly valued firms are often infrastructure startups, which create the tools that developers use to build these apps. This order stems from how big the target market of these startups are, ranging from a specific industry (like healthcare or education) to all developers. We can see that general order reflected in burgeoning AI startups. For instance, Harvey, which sells an AI application for lawyers, has one of the lower multiples, while broader-reaching companies like Glean and VAST Data land higher multiples.It seems like investors aren't quite sure yet where model developers like OpenAI and Anthropic fall on this spectrum. Their costs are very different from a typical software startup due to how much computing power they need, and many investors are still worried that closed-source model developers may be overtaken by their cheaper, open-source counterparts.…MoreNews Of the WeekTed fellows resign from organisation after Bill Ackman named as speakerLucianne Walkowicz and Saeed Taji Farouky accuse Ted of taking anti-Palestinian stand over controversial billionaire's inclusionChris McGrealThe Ted organisation has been hit with resignations and criticisms after naming the controversial activist billionaire Bill Ackman, who was instrumental in forcing out Harvard's president over antisemitism allegations, among its main speakers at this year's conference.Four Ted fellows, led by the astronomer Lucianne Walkowicz and the filmmaker Saeed Taji Farouky, resigned from the group on Wednesday, accusing it of taking an anti-Palestinian stand and aligning itself “with enablers and supporters of genocide” in Gaza.“2024 main stage speaker Bill Ackman has defended Israel's genocide and ethnic cleansing of the Palestinian people and has cynically weaponised antisemitism in his programme to purge American universities of Pro-Palestinian freedom of speech,” the pair wrote to Chris Anderson, who leads Ted, and Lily James Olds, director of the fellows programme.“We've become increasingly concerned about the fundamental values and moral compass of the organisation over the years, but with this year's speaker selection, it is clear Ted has crossed a red line.”The conference will be held in Vancouver, Canada, in April, under the banner The Brave and the Brilliant”. The theme of Ackman's talk has not been revealed but his selection was announced last week after he was accused of using his money and influence to help force Claudine Gay's resignation as Harvard's president following her disastrous appearance before Congress in December when she was questioned about on-campus antisemitism during the Israel-Gaza war.Ackman has taken stridently pro-Israel positions, including justifying the scale of the attacks on Gaza in which more than 25,000 Palestinians have been killed, mostly civilians, and the forced removal of about 2 million Palestinians from their homes. He has described criticism of Israel as antisemitism and called for the blacklisting from employment of American students who signed petitions denouncing the offensive in Gaza in the wake of the 7 October Hamas attack on Israel.Farouky and Walkowicz's resignation letter noted that other speakers announced by Ted include the journalist Bari Weiss, who they describe as having “a long, sordid, and well-documented history of anti-Palestinian speech”, but that there are no Palestinians in the line-up.“We refuse for our work and identities to be exploited to promote the Ted brand while the organisation and its speakers generate income and advance their careers through dehumanising Palestinians and justifying their genocide,” the pair said.After the resignation letter was published, two other fellows – the entrepreneur Ayah Bdeir and cosmologist Renée Hlozek – also quit. Nearly 30 others added their names “in solidarity” without leaving Ted.…MoreTesla's Slowdown Disqualifies It From ‘Magnificent Seven' GroupBy Martin Peers, Jan 24, 2024, 5:00pm PSTStock market pundits may want to come up with a new name for the big tech stocks driving the overall market. The “magnificent seven” descriptor—referring to Apple, Microsoft, Alphabet, Amazon, Meta Platforms, Nvidia and Tesla—no longer seems to make much sense. I'd like to suggest that's because none of the company CEOs look like cowboy gunslingers from the 1960 movie that made the phrase famous. It's hard to imagine Steve McQueen playing Tim Cook or Andy Jassy, for instance (although Yul Brynner admittedly could have filled the role of horseback-riding Jeff Bezos).The real reason the moniker no longer works, however, is that at least one member of the group, Tesla, has had anything but a magnificent 2024 so far, and its fourth-quarter earnings report, released Wednesday, only made things worse. Before Tesla reported earnings tonight, its stock had fallen 16% so far this year, and it tumbled another 3% after hours to around $200 a share. This isn't a reaction to CEO Elon Musk's antics, which include asking for a bunch more stock, although that surely doesn't help. The stock decline reflects the slowdown in sales suffered by Tesla, which observers attribute to increased competition and a loss of government incentives. Automotive revenues, which make up the bulk of Tesla's top line, grew just 1% in the fourth quarter—down from 18% in the first quarter.In its outlook for this year issued today, the company said its growth in the volume of car sales would be lower than in 2023, and noted that its team is working on its “next-generation vehicle.” Meantime, expenses have been skyrocketing, eroding its profit margin. But our less-than-rigorous takedown of the magnificent seven branding isn't just about Tesla. If you look at the year-to-date performance of big tech stocks, or even their 2023 performance, you can see that just two tech stocks have roared this year. One is Nvidia, which is in a class of its own: up 27% since Jan. 1, thanks to its stranglehold on the specialized chips used in artificial intelligence. The other is Meta Platforms, which is up nearly 13%, reflecting confidence in its ad business.  In comparison, Microsoft and Alphabet are each up around 8%, likely thanks to expectations that AI will lift their businesses, while Apple and Amazon lag behind with year-to-date stock price rises of less than 5% each. Instead of the magnificent seven, it might be more appropriate to refer to the group as Nvidia, Meta and the humble five.… MoreTikTok's Testing 30 Minute Uploads as It Looks To Expand Its Content OptionsBy Andrew Hutchinson Content and Social Media ManagerThe next stage of TikTok is coming, with some users now seeing the option to upload 30 minute long videos in the app.As you can see in this example, shared by social media expert Matt Navarra, TikTok's currently testing the new 30 minute upload option in the beta version of the app.Which, if you've been paying attention, is not really any big surprise.TikTok has been steadily increasing its maximum post limit for years, with the platform originally starting at 15 seconds per clip, which was then extended to 60 seconds, then 3 minutes, then 5 minutes, before rising to 10 minutes in 2022.Last October, TikTok began experimenting with 15 minute uploads, so the trend towards longer clips isn't new.Though 30 minutes is likely the upper limit, based on the Chinese version of the app. Douyin, which is TikTok in China, expanded its upload limit to 30 minutes per clip in 2022, and it hasn't gone any further as yet.And presumably, Douyin has also seen good response to this longer time limit, which is why TikTok is now looking to implement the same, though it does seem like a long time to be watching a TikTok clip in-stream.Will users really warm to TV show length clips in the app?…MoreInstagram to scan under-18s' messages to protect against ‘inappropriate images'Feature will work even on encrypted messages, suggesting platform plans to implement client-side scanningAlex Hern and Dan MilmoInstagram will begin scanning messages sent to and from under-18s to protect them from “inappropriate images”, Meta has announced.The feature, being kept under wraps until later this year, would work even on encrypted messages, a spokesperson said, suggesting the company intends to implement a so-called client-side scanning service for the first time.But the update will not meet controversial demands for inappropriate messages to be reported back to Instagram servers.Instead, only a user's personal device will ever know whether or not a message has been filtered out, leading to criticism of the promise as another example of the company “grading its own homework”.“We're planning to launch a new feature designed to help protect teens from seeing unwanted and potentially inappropriate images in their messages from people they're already connected to,” the company said in a blogpost, “and to discourage them from sending these types of images themselves. We'll have more to share on this feature, which will also work in encrypted chats, later this year.”…Much MoreTiger Global Investor Relations Staff Depart After Fundraising ChallengesBy Francesca Friday and Maria HeeterJan 24, 2024, 4:46pm PSTSeveral Tiger Global Management employees focused on raising capital for the New York firm's venture funds have taken buyout offers, according to a person familiar with the matter. The departures of the staff, who worked with prospective investors, come as the firm has struggled to raise money for its latest venture capital fund after a collapse in startup valuations soured its paper returns for earlier funds.As of the second quarter of 2023, a $12.7 billion fund that Tiger started making investments from in October 2021 had a paper loss of 18%, calculated as an annualized return net of management fees, according to internal data distributed to investors in the fund. That's a slight improvement from six months earlier, when the 2021 fund showed a loss of 20%. The fund's performance is in the bottom quartile of funds started that year, the document said, and has also lagged the S&P 500's annualized net return in the same period.The Takeaway• Tiger employee buyouts are the latest example of VC cost-cutting• Tiger's $12.7 billion had lost 18% on paper as of June* Tiger could soon show a $350 million gain from OpenAI stakeAs of June 30, 2023, the $12.7 billion fund hadn't returned any cash to investors, which isn't unusual for such a young fund. But the paper losses are closely guarded secrets that reflect the kind of write-downs other venture firms have been making over the past two years as tech valuations have fallen.It isn't clear how big Tiger's investor relations team is, but the departures are the latest example of belt-tightening across the venture industry. Firms are raising smaller funds and striking fewer deals, reducing the need for sprawling support staff—including those who help firms raise money from pension funds and endowments...MoreWorldcoin hints at new Orb for a friendlier iris-scanning experienceby Vivian NguyenThe next-gen device will feature various colors and shapes to enhance its visual appeal.Worldcoin, an iris biometric crypto project, is set to launch a new Orb that aims to offer a more user-friendly iris-scanning experience, said Alex Blania, CEO and co-founder of Tools for Humanity, the developer behind the project, in an exclusive interview with TechCrunch today.“The next Orb will roll out in the first half of this year and will feature alternative colors and form factors in an effort to look ‘much more friendly,'” Blania explained. “Overall, it is going to look way more tuned down and similar to an Apple product.”Blania acknowledges that the initial design of the Orb predated his time at the company. “The new orb is coming and the next iterations will look quite different,” he remarked during a fireside chat at a recent StrictlyVC event, signaling a departure from the current, more controversial design.The goal of Worldcoin, as described by Blania, is to reach billions of users as fast as possible.“The thesis is very simple. We race toward billions of users as fast as we possibly can,” said Blania.Founded by Blania, Sam Altman, and Max Novendstern, Tools for Humanity has raised around $250 million from prominent investors like a16z and Bain Capital Crypto, among others. The project is famous for its unique Orb device designed to scan people's irises and assign them a “World ID,” granting access to Worldcoin's application and a digital passport. Worldcoin's vision is to authenticate individual identities and prevent the creation of multiple accounts.The current design of the Orb has been a topic of much debate due to its intimidating look, similar to a prop from a sci-fi movie, according to Blania. The company has also faced criticism for its beta testing approaches in developing economies and concerns over privacy and data security.Despite some skepticism, the Orb has seen practical use. At the StrictlyVC event in downtown San Francisco, a Tools for Humanity employee reported that a “couple dozen” attendees scanned their iris to receive a World ID. There has also been “field testing” of the new Orb design.…MoreStartup of the WeekLoyalty Startup Bilt Rewards Hits $3.1B Valuation After $200M RoundChris MetinkoJanuary 24, 2024Bilt Rewards, a loyalty rewards startup, raised a $200 million round led by General Catalyst at a $3.1 billion valuation — more than double the number after its last fundraising in 2022.The round also included participation from Eldridge Industries, Left Lane Capital, Camber Creek and Prosus Ventures.The New York-based startup allows consumers to earn rewards on the rent they pay. Bilt plans to use some of the proceeds to expand its network to include local dining, grocery stores, ridesharing and other retail purchases.“We're not just building a loyalty program; we're creating a community-centric ecosystem that benefits everyone from renters to local businesses,” said founder and CEO Ankur Jain.The company also appointed some big names to roles in the company. Bilt named Ken Chenault, former chairman and CEO of American Express, as its chairman, and Roger Goodell, the commissioner of the NFL, as an independent director.Big moneyThe company reported its annualized member spend is nearing $20 billion. It also became profitable on an earnings before interest, taxes, depreciation and amortization basis last year.Those metrics must have impressed investors, as Bilt has seen its valuation shoot up after raising a $150 million Series B at a pre-money valuation of $1.4 billion in October 2022. Founded in 2021, the company has raised a total of $413 million, per Crunchbase.Last year was a slow go for loyalty startups. Such companies raised only $74 million, per Crunchbase data. However in 2022, loyalty startups raised more than a half-billion dollars thanks to big raises that included Bilt's Series B and Madison, Wisconsin-based Fetch's $240 million Series E.With this fundraise, things are looking up for loyalty startups again.X of the Week This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit thatwastheweek.substack.com/subscribe

Geoffrey Moore is an author, speaker, and advisor, widely known for his seminal book Crossing the Chasm: Marketing and Selling Disruptive Products to Mainstream Customers, which many consider the most important book ever written on go-to-market strategy. Moore's work is focused on the market dynamics surrounding disruptive innovations, and how one overcomes the challenge of transitioning from serving early adopters to the mainstream. In this episode, we discuss:• What “crossing the chasm” means• What steps to take before you try crossing the chasm• The importance of winning a marquee customer• The role of executive sponsors in the sales process• The differences between visionaries and pragmatists, and how to build for each• Geoffrey's four go-to-market playbooks based on stage: Early Market, Bowling Alley, Tornado, and Main Street• The problem with discounting before crossing the chasm• “Deadly sins” to avoid when crossing the chasm—Brought to you by:• CommandBar—AI-powered user assistance for modern products and impatient users• WorkOS—An API platform for quickly adding enterprise features• Arcade Software—Create effortlessly beautiful demos in minutes—Find the full transcript at: https://www.lennyspodcast.com/geoffrey-moore-on-finding-your-beachhead-crossing-the-chasm-and-dominating-a-market/—Where to find Geoffrey Moore:• X: https://twitter.com/geoffreyamoore• LinkedIn: https://www.linkedin.com/in/geoffreyamoore/• LinkedIn posts: https://www.linkedin.com/in/geoffreyamoore/recent-activity/articles/—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) Geoffrey's background(04:03) What people often get wrong about Crossing the Chasm(05:58) Finding your beachhead segment(09:29) The four inflection points of the technology adoption lifestyle(15:45) Geoffrey's bonfire and bowling alley analogies(18:36) Steps to take before trying to cross the chasm(22:19) Signs you're ready to cross the chasm(25:19) Advice for startups on where to start(27:31) Thoughts on venture capital(27:53) A general timeline for crossing the chasm(30:52) What exactly is the “chasm”?(32:35) The difference between visionaries and pragmatists(36:05) Finding the compelling reason to buy(43:45) The Early Market playbook(45:46) The Bowling Alley playbook(48:39) Different sales approaches for early market and bowling alley(51:26) Changing the value state of the company(53:28) The Tornado playbook(57:35) Why combining playbooks doesn't work(59:10) Using generative AI in different market phases(01:03:02) The risks of discounting(01:04:21) Other “deadly sins” of crossing the chasm(01:09:09) Positioning in crossing the chasm(01:10:36) Product-led growth and crossing the chasm(01:13:54) The challenges of software and entrepreneurship(01:16:35) How Geoffrey's thinking has evolved(01:19:30) The importance of entrepreneurship and impact(01:20:42) His book The Infinite Staircase(01:23:58) Connect with Geoffrey Moore—Referenced:• Crossing the Chasm: Marketing and Selling Disruptive Products to Mainstream Customers: https://www.amazon.com/Crossing-Chasm-3rd-Disruptive-Mainstream/dp/0062292986• Oracle: https://www.oracle.com/• Documentum: https://www.opentext.com/products/documentum• Figma: https://www.figma.com/• Notion: https://www.notion.so/• Salesforce: https://www.salesforce.com/• Intel: https://www.intel.com/• Jason Fried challenges your thinking on fundraising, goals, growth, and more: https://www.lennyspodcast.com/jason-fried-challenges-your-thinking-on-fundraising-goals-growth-and-more/• The Mayo Clinic: https://www.mayoclinic.org/• Coda: https://coda.io/• An inside look at how Figma ships product: https://coda.io/@yuhki/figma-product-roadmap• Dylan Field on LinkedIn: https://www.linkedin.com/in/dylanfield/• Regis McKenna on Crunchbase: https://www.crunchbase.com/organization/regis-mckenna-inc• Andrew Grove: https://en.wikipedia.org/wiki/Andrew_Grove• A step-by-step guide to crafting a sales pitch that wins | April Dunford (author of Obviously Awesome and Sales Pitch): https://www.lennyspodcast.com/a-step-by-step-guide-to-crafting-a-sales-pitch-that-wins-april-dunford-author-of-obviously-awesom/• Sales Pitch: How to Craft a Story to Stand Out and Win: https://www.amazon.com/Sales-Pitch-Craft-Story-Stand/dp/1999023021• B2B Go-to-Market Playbooks and the Technology Adoption Life Cycle: https://www.linkedin.com/pulse/b2b-go-to-market-playbooks-technology-adoption-life-cycle-moore/• Juniper: https://www.juniper.net/us/en.html• Sal Khan on LinkedIn: https://www.linkedin.com/in/khanacademy/• Khan Academy: https://www.khanacademy.org/• How the Star Wars Kessel Run Turns Han Solo Into a Time-Traveler: https://www.wired.com/2013/02/kessel-run-12-parsecs/• Atlassian: https://www.atlassian.com/• Martin Casado on LinkedIn: https://www.linkedin.com/in/martincasado/• The Infinite Staircase: What the Universe Tells Us About Life, Ethics, and Mortality: https://www.amazon.com/Infinite-Staircase-Universe-Ethics-Mortality/dp/1950665984—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. Get full access to Lenny's Newsletter at www.lennysnewsletter.com/subscribe

Is it possible to construct a virtual society that authentically replicates human behavior? AI Town, a virtual town experiment where AI residents live, interact, and engage, provides valuable insights into the future of AI's believability and its interaction with humanity.In this panel discussion, Joon Park, the author of 'Generative Agents: Interactive Simulacra of Human Behavior,' and Martin Casado from a16z, discuss  the influence and potential of Generative Agents, exploring their practical applications in the real world.Topics Covered00:00 - Simulating human behaviors04:49 - What are generative agents?07:47 - Simulations, new technology, and LLMs11:45 - The architecture behind simulating human behavior16:37 - Generative agents interactions: observing, planning, and reflecting20:22 - What is the value in advancing generative agents?24:01 - Use cases for simulation behavior technology29:31 - What are the ethical frameworks?33:12 - Q&A from the audience Resources: Find AI Town: https://www.convex.dev/ai-townRead the paper ‘Generative Agents: Interactive Simulacra of Human Behavior': https://arxiv.org/pdf/2304.03442.pdfFind Joon on Twitter: https://twitter.com/joon_s_pkFind Martin on Twitter: https://twitter.com/martin_casadoFind a16z on Twitter: https://twitter.com/a16zFind a16z on LinkedIn: https://www.linkedin.com/company/a16zSubscribe on your favorite podcast app: https://a16z.simplecast.com/Follow our host: https://twitter.com/stephsmithioPlease 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.

A16z Podcast Key Takeaways Marc Andreessen's article, “Why AI Will Save the World,” dispels AI hysteria and emphasizes its transformative potentialMarc is worried about the public conversation on AI, which includes a mix of legitimate questions, explanations, and hysterical emotionsHe is also worried about certain individuals or groups trying to exploit the situation by seeking regulatory capture and stifling innovation and startupsMartin asks about the class of problems that AI is now good at compared to the past: Marc points out two key factors: the scale of training data, made possible by internet-scale data collection, and the increase in compute power, particularly with GPUsHe emphasizes the role of quantity in achieving quality in AI systemsMarc emphasizes that although the initial focus of GPT-4 may lean towards leisure and utility uses, he has always believed in the significance of technology being user-friendly and enjoyable“The actual experience of using these systems today is it's actually a lot more like love, right? And I'm not saying that they literally are conscious that they love you, but like, or maybe the analogy would almost be more like a puppy. Like they're like really smart puppies, right?” – Marc AndreessenTraditional adoption pattern: Government -> Big companies -> Small businesses -> IndividualsShift in adoption pattern: Consumers -> Small businesses -> Big companies -> GovernmentBenefits of the current adoption pattern:Faster access to new technologies for everyoneMass market evaluation of technologies before government and big business decisionsIncreased individual autonomy and agency in technology adoptionConcerns and arguments regarding correctness and adoption:Fear of incorrect or unpredictable outputs from AI systems Potential misuse by criminalsTwo biggest commercial opportunities in recent times: “Those are trillion-dollar prizes, right? Whoever figures out how to fix those problems [correctness and security] has the ability potentially to build a company worth a trillion dollars, to make this technology generally useful in a way where it's guaranteed to always be correct or guaranteed to always be secure.” – Marc AndreessenExample of correctness approach using ChatGPT and Wolfram Alpha plugin:Install the Wolfram Alpha plugin to cross-check math and science statementsWolfram Alpha acts as a deterministic calculatorHybrid architecture combining a deterministic calculator with a creative AI systemThere is a misconception of AI replacing top artists or creators; the focus should be on augmenting their abilitiesAddressing concerns about AI replacing human labor:Technological advancements enhance the productivity rateExponential productivity ramp leads to price crash and near-zero cost for products/servicesMarc is dismissive of concerns that AI will eliminate work and worsen human well-beingSocial reform movements have two sides: True believers: represented by the Baptists, they advocate for social improvement by banning alcohol (as in the analogy of prohibition used by Marc to explain the AI reform phenomenon)Opportunistic beneficiaries: represented by bootleggers, they financially benefit from the illegal trade of alcohol and take advantage of the laws and regulations passed by the reform movement to establish their businessesIn modern times, bootleggers are legitimate business people seeking government protection from competition, aiming to form monopolies or cartels and create regulatory structures that prevent new competitionGeopolitical implications of AI and concerns regarding China's ambitions: Focus on the Chinese Communist Party and regime, not the people of ChinaChina's 2025 plan and speeches by Xi Jinping outline their goal of developing AI for population control and surveillanceTwo-stage plan: Implement authoritarian AI control within China, then spread it globallyThe worst-case scenario involves China's vision spreading across Asia, Europe, South America, and potentially the rest of the worldThe doomsday scenarios presented by AI critics are far-fetched and divorced from the reality of AI technologyThe claim that AI will lead to crippling inequality is a misinterpretation of how the economy and self-interest workRead the full notes @ podcastnotes.orgThis week, a16z's own cofounder Marc Andreessen published a nearly 7,000-word article that aimed to dispel fears over AI's risks to our humanity – both real and imagined. Instead, Marc elaborates on how AI can "make everything we care about better." In this timely one-on-one conversation with a16z General Partner Martin Casado, Marc discusses how this technology will maximize human potential, why the future of AI should be decided by the free market, and most importantly, why AI won't destroy the world. In fact, it may save it. Read Marc's full article “Why AI Will Save the World” here: https://a16z.com/2023/06/06/ai-will-save-the-world/ Resources:Marc on Twitter: https://twitter.com/pmarca Marc's Substack: https://pmarca.substack.com/ gptplaysminecraft - Twitch: https://www.twitch.tv/gptplaysminecraftWhy AI Will Save the World: https://a16z.com/2023/06/06/ai-will-save-the-world/Youtube discussion: https://www.youtube.com/watch?v=0wIUK0nsyUg Stay Updated: Find a16z on Twitter: https://twitter.com/a16zFind a16z on LinkedIn: https://www.linkedin.com/company/a16zSubscribe on your favorite podcast app: https://a16z.simplecast.com/Follow our host: https://twitter.com/stephsmithioPlease 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.

This week, a16z's own cofounder Marc Andreessen published a nearly 7,000-word article that aimed to dispel fears over AI's risks to our humanity – both real and imagined. Instead, Marc elaborates on how AI can "make everything we care about better." In this timely one-on-one conversation with a16z General Partner Martin Casado, Marc discusses how this technology will maximize human potential, why the future of AI should be decided by the free market, and most importantly, why AI won't destroy the world. In fact, it may save it. Read Marc's full article “Why AI Will Save the World” here: https://a16z.com/2023/06/06/ai-will-save-the-world/ Resources:Marc on Twitter: https://twitter.com/pmarca Marc's Substack: https://pmarca.substack.com/ gptplaysminecraft - Twitch: https://www.twitch.tv/gptplaysminecraftWhy AI Will Save the World: https://a16z.com/2023/06/06/ai-will-save-the-world/Youtube discussion: https://www.youtube.com/watch?v=0wIUK0nsyUg Stay Updated: Find a16z on Twitter: https://twitter.com/a16zFind a16z on LinkedIn: https://www.linkedin.com/company/a16zSubscribe on your favorite podcast app: https://a16z.simplecast.com/Follow our host: https://twitter.com/stephsmithioPlease 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.

Martin Casado is a General Partner @ a16z where he focuses on enterprise investing. At a16z, Martin has led investments and serves on the board of dbt Labs, Fivetran, Material Security, Ambient AI and many more incredible companies. Before venture, Martin was previously the Co-Founder and CTO at Nicira, acquired by VMware for $1.26 billion in 2012. While at VMware, Martin served as Senior VP and General Manager of the Networking and Security Business Unit, which he scaled to a $600 million revenue run-rate business. In Today's Episode with Martin Casado We Discuss: 1. From $1.26BN Founder to Leading Enterprise Investing for a16z: How did Martin make his way into the world of VC and come to lead enterprise investing for a16z? What does Martin know now that he wishes he had known when he started investing? What have been some of his biggest investing lessons from Marc and Ben? 2. The VC Model is Broken and Why: Why does Martin believe that the current model for venture is broken? Why does Martin believe that VCs are not oracles and they were not gifted with picking ability? How will asset allocation more broadly fundamentally change over the next decade? Why will Silicon Valley take over and run Wall St? Why does Wall St not care about innovation and true technological development? Who will be the winners and who will be the losers in the next 10 years of venture? 3. Surviving a Crash - What Founders Need To Know: Layoffs: What is Martin's advice to founders on doing layoffs today? How much is the right amount to cut? Should it be done in one go? How should this be communicated to investors and the board? Scenario Planning: What three scenario plans should all founders be creating right now? How should they know which one is the right one to execute against? Comparisons: How should founders use and look to public company performance and market cap to determine which plan they should choose? Hiring Freeze: Why does Martin believe the biggest companies in the world make massive mistakes by freezing hiring? What should they do instead? 4. The Changing Guard at a16z: What have been the single best and worst changes a16z have made over the last 24 months? What are the first things to break when a firm scales as fast as a16z has done? Does Martin agree a16z returns will reduce with the scaling of their funds larger than ever? How does Martin look to train and educate his junior team? How does he advise them on surviving a downturn? What should they do? What should they not do? 5.) The Makings of a Great Board: What are the three types of board members? What is the best? What is the worst? What does Martin believe makes the truly great boards? What is the biggest advice Martin gives to young board members today? How has Martin changed as a board member over time? What does he need to improve? Items Mentioned in Today's Episode: Martin's Fave Book: The Weirdest People in the World: How the West Became Psychologically Peculiar and Particularly Prosperous

In this episode from October 2021, Michael Dell, founder and CEO of Dell Technologies and one of the longest serving founder-CEOs in the technology industry, joins a16z general partner Martin Casado, a16z co-founder Marc Andreessen, and host Sonal Choksi on the occasion of Michael's book, Play Nice to Win: A CEO's Journey from Founder to Leader. There are lots of challenges in being public while trying to innovate, and limits to being a private company as well; but it's rare to see a company go public then private then back to public again. As is the case with Dell Technologies, one of the largest tech companies -- which went private 2012-2013 and then also pulled off one of the most epic mergers of all time with Dell + EMC + VMWare 2015-2016 (and which we wrote about here at the time).Is there a method to the madness? How does one not just start, but keep, and transform, their company and business? Michael, Marc, Martin and Sonal debate these questions, as well as the impact of the cloud wars, how innovation happens when a company is private and when its public (something Michael knows well, having taken Dell public to private to back to public again), whether you can actually play nice to win as a leader, and more.

This week we discuss VMware Explore, Snap's move to multi-cloud and the Galaxy Brain take on thought leadership. Plus, Matt Ray's latest Raspberry Pi project is for the birds…? Runner-up Titles Where's my admin? All my children qualify as adults Start by eating their food Put two letters in front of it Where's the grocery store I got that everything bagel spice Is it OK to hang-up on your kids? In the heat of the moment, you can't set policy. The runbook's already written. Spagetti Bowl Tanzu the Shih Tzu A FinOps Type of Motion The opposite of the Sales Kickoff, the Savings Kickoff Growth is best done in the shadows. Wrapping bullshit with bullshit Nopehouse, home of the fast follower The fast followers are just in front of the also-rans Thought-leadership suicide mission Rundown VMware Explore (https://www.vmware.com/explore/us.html) How Snap rebuilt the infrastructure that now supports 347M users (https://www.protocol.com/enterprise/snap-microservices-aws-google-cloud) Screaming in the Cloud with Martin Casado (https://www.lastweekinaws.com/podcast/screaming-in-the-cloud/the-new-cloud-war-with-martin-casado/) Give finops a say over cloud architecture decisions (https://www.infoworld.com/article/3671148/give-finops-a-say-over-cloud-architecture-decisions.html) Business Dudes Need to Stop Talking Like This (https://newsletters.theatlantic.com/galaxy-brain/630ec150bcbd490021b17eab/business-dudes-need-to-stop-talking-like-this/) Relevant to your Interests Amazon tries a new way to excite you about cybersecurity (it's called laughter) (https://www.zdnet.com/article/amazon-tries-a-new-way-to-excite-you-about-cybersecurity-its-called-laughter/) The golden noose around Apple's neck (https://spectatorworld.com/topic/the-golden-noose-around-apples-neck/) Campaign pushes Cloudflare to drop trans hate site (https://www.axios.com/newsletters/axios-login-85e45e2f-8629-43d3-be69-45072a3631f5.html?chunk=0&utm_term=emshare#story0) Mudge at Twitter (https://twitter.com/igb/status/1562427951882199044) Bloomberg takes cut and paste seriously (https://twitter.com/MidwestHedgie/status/1562450905907478531) Notice of Recent Security Incident - The LastPass Blog (https://blog.lastpass.com/2022/08/notice-of-recent-security-incident/) World's Most Popular Password Manager Says It Was Hacked (https://www.bloomberg.com/news/articles/2022-08-25/the-world-s-most-popular-password-manager-says-it-was-hacked) LastPass Says No Passwords Stolen in Data Breach (https://www.cnet.com/tech/services-and-software/lastpass-says-no-passwords-stolen-in-data-breach/) AWS and Kubecost collaborate to deliver cost monitoring for EKS customers | Amazon Web Services (https://aws.amazon.com/blogs/containers/aws-and-kubecost-collaborate-to-deliver-cost-monitoring-for-eks-customers/) Pandas Pivot Table Explained (https://pbpython.com/pandas-pivot-table-explained.html) Charted: Big Tech's bigness (https://www.axios.com/newsletters/axios-login-3db6f78d-4da1-494b-a5d4-04c8984ce0e5.html?chunk=1&utm_term=emshare#story1) UK's Micro Focus shares nearly double after Canada's OpenText agrees $6 bln takeover (https://www.reuters.com/markets/deals/canadas-opentext-buy-software-firm-micro-focus-6-bln-deal-2022-08-25/) Teradata takes on Snowflake and Databricks with cloud-native platform (https://venturebeat.com/data-infrastructure/teradata-makes-database-analytics-cloud-native/) The State of the Mainframe Market - Summer 2022 (https://futurumresearch.com/market-insight-reports/the-state-of-the-mainframe-market-summer-2022/) City2Surf face recognition raises concerns (https://ia.acs.org.au/content/ia/article/2022/city2surf-face-recognition-raises-concerns.html) IBM Watson Health layoffs disguised as staff 'redeployment' (https://www.theregister.com/2022/08/29/ibm_allegedly_hid_watson_health/) David Young on LinkedIn: The metaverse economy is set to boom... gambling will be a significant (https://www.linkedin.com/posts/david-young-b5276523_metaverse-5g-localisation-activity-6966387069338218496-4x5F?utm_source=share&utm_medium=member_desktop) OCI History (https://twitter.com/solomonstre/status/1564499775415676928) VMware CEO bats away Broadcom concerns as 'next transition' (https://www.theregister.com/2022/08/30/vmware_broadcom_/) Heroku to delete inactive accounts, shut down free tier (https://www.theregister.com/2022/08/25/heroku_delete_inactive_free_tier/) Cloudflare Is One of the Companies That Quietly Powers the Internet. Researchers Say It's a Haven for Misinformation (https://time.com/6208828/cloudflare-misinformation-internet-research/) Nonsense Sounds right (https://twitter.com/6thgrade4ever/status/1433519577892327424?s=20&t=o8cx7C7pcCkVR4cTcQbv4g) When the development team meet their first Scrum Master (https://twitter.com/onejasonknight/status/1564287640366628866?s=20&t=y3AIxGPb8kge28aICQ6dFQ) Chart of the year nominee (https://twitter.com/jpwarren/status/1564109454009716736/photo/1) Conferences DevOps Talks Sydney (https://devops.talksplus.com/sydney/devops.html), Sydney, September 6-7, 2022 Sydney Cloud FinOps Meetup (https://events.finops.org/events/details/finops-sydney-cloud-finops-presents-sydney-cloud-finops-meetup/), online, Oct 13, 2022 Matt's presenting Kube (https://events.linuxfoundation.org/kubecon-cloudnativecon-north-america/https://events.linuxfoundation.org/kubecon-cloudnativecon-north-america/)C (https://events.linuxfoundation.org/kubecon-cloudnativecon-north-america/https://events.linuxfoundation.org/kubecon-cloudnativecon-north-america/)o (https://events.linuxfoundation.org/kubecon-cloudnativecon-north-america/https://events.linuxfoundation.org/kubecon-cloudnativecon-north-america/)n North America (https://events.linuxfoundation.org/kubecon-cloudnativecon-north-america/https://events.linuxfoundation.org/kubecon-cloudnativecon-north-america/), Detroit, Oct 24 – 28, 2022 SpringOne Platform (https://springone.io/?utm_source=cote&utm_medium=podcast&utm_content=sdt), SF, December 6–8, 2022 THAT Conference Texas Call For Counselors (https://that.us/call-for-counselors/tx/2023/) Jan 16-19, 2023 Listener Feedback Enlightning (https://tanzu.vmware.com/developer/tv/enlightning/) from Whitney SDT news & hype Join us in Slack (http://www.softwaredefinedtalk.com/slack). Get a SDT Sticker! Send your postal address to stickers@softwaredefinedtalk.com (mailto:stickers@softwaredefinedtalk.com) and we will send you free laptop stickers! Follow us on Twitch (https://www.twitch.tv/sdtpodcast), Twitter (https://twitter.com/softwaredeftalk), Instagram (https://www.instagram.com/softwaredefinedtalk/), LinkedIn (https://www.linkedin.com/company/software-defined-talk/) and YouTube (https://www.youtube.com/channel/UCi3OJPV6h9tp-hbsGBLGsDQ/featured). Use the code SDT to get $20 off Coté's book, (https://leanpub.com/digitalwtf/c/sdt) Digital WTF (https://leanpub.com/digitalwtf/c/sdt), so $5 total. Become a sponsor of Software Defined Talk (https://www.softwaredefinedtalk.com/ads)! Recommendations Brandon: Black Bird (https://www.rottentomatoes.com/tv/black_bird/s01) Matt: BirdNetPi (https://birdnetpi.com/) Festival of Feet Half-Marathon (https://www.westiesjoggers.com/the-georges-river-festival-of-the-feet/) Coté: Spigen ArcDock 120W [GaN III] 4-Port USB C Charging Stantion USB-C PD/USB-A Hub with Spigen USB 4 Cable for Thunderbolt 4 Cable 100W Charging 40Gbps Data Transfer for MacBook Pro Air iPad USB-C Laptop (https://amzn.to/3RqRl7M). C7/C8 coupler cables Photo Credits CoverArt (https://unsplash.com/photos/Ts3yX7wDthw) Banner (https://unsplash.com/photos/hXttDVCwyRA)

About MartinMartin Casado is a general partner at the venture capital firm Andreessen Horowitz where he focuses on enterprise investing. He was previously the cofounder and chief technology officer at Nicira, which was acquired by VMware for $1.26 billion in 2012. While at VMware, Martin was a fellow, and served as senior vice president and general manager of the Networking and Security Business Unit, which he scaled to a $600 million run-rate business by the time he left VMware in 2016.Martin started his career at Lawrence Livermore National Laboratory where he worked on large-scale simulations for the Department of Defense before moving over to work with the intelligence community on networking and cybersecurity. These experiences inspired his work at Stanford where he created the software-defined networking (SDN) movement, leading to a new paradigm of network virtualization. While at Stanford he also cofounded Illuminics Systems, an IP analytics company, which was acquired by Quova Inc. in 2006.For his work, Martin was awarded both the ACM Grace Murray Hopper award and the NEC C&C award, and he's an inductee of the Lawrence Livermore Lab's Entrepreneur's Hall of Fame. He holds both a PhD and Masters degree in Computer Science from Stanford University.Martin serves on the board of ActionIQ, Ambient.ai, Astranis, dbt Labs, Fivetran, Imply, Isovalent, Kong, Material Security, Netlify, Orbit, Pindrop Security, Preset, RapidAPI, Rasa, Tackle, Tecton, and Yubico.Links: Yet Another Infra Group Discord Server: https://discord.gg/f3xnJzwbeQ “The Cost of Cloud, a Trillion Dollar Paradox” - https://a16z.com/2021/05/27/cost-of-cloud-paradox-market-cap-cloud-lifecycle-scale-growth-repatriation-optimization/ TranscriptAnnouncer: Hello, and welcome to Screaming in the Cloud with your host, Chief Cloud Economist at The Duckbill Group, Corey Quinn. This weekly show features conversations with people doing interesting work in the world of cloud, thoughtful commentary on the state of the technical world, and ridiculous titles for which Corey refuses to apologize. This is Screaming in the Cloud.Corey: This episode is sponsored in part by Honeycomb. When production is running slow, it's hard to know where problems originate. Is it your application code, users, or the underlying systems? I've got five bucks on DNS, personally. Why scroll through endless dashboards while dealing with alert floods, going from tool to tool to tool that you employ, guessing at which puzzle pieces matter? Context switching and tool sprawl are slowly killing both your team and your business. You should care more about one of those than the other; which one is up to you. Drop the separate pillars and enter a world of getting one unified understanding of the one thing driving your business: production. With Honeycomb, you guess less and know more. Try it for free at honeycomb.io/screaminginthecloud. Observability: it's more than just hipster monitoring.Corey: This episode is sponsored in part by our friends at Sysdig. Sysdig secures your cloud from source to run. They believe, as do I, that DevOps and security are inextricably linked. If you wanna learn more about how they view this, check out their blog, it's definitely worth the read. To learn more about how they are absolutely getting it right from where I sit, visit Sysdig.com and tell them that I sent you. That's S Y S D I G.com. And my thanks to them for their continued support of this ridiculous nonsense.Corey: Welcome to Screaming in the Cloud. I'm Corey Quinn. I'm joined today by someone who has taken a slightly different approach to being—well, we'll call it cloud skepticism here. Martin Casado is a general partner at Andreessen Horowitz and has been on my radar starting a while back, based upon a piece that he wrote focusing on the costs of cloud and how repatriation is going to grow. You wrote that in conjunction with your colleague, Sarah Wang. Martin, thank you so much for joining me. What got you onto that path?Martin: So, I want to be very clear, just to start with is, I think cloud is the biggest innovation that we've seen in infrastructure, probably ever. It's a core part of the industry. I think it's very important, I think every company's going to be using cloud, so I'm very pro-cloud. I just think the nature of how you use clouds is shifting. And that was the focus.Corey: When you first put out your article in conjunction with your colleague as well, like, I saw it and I have to say that this was the first time I'd really come across any of your work previously. And I have my own biases that I started from, so my opening position on reading it was this is just some jerk who's trying to say something controversial and edgy to get attention. That's my frickin job. Excuse me, sir. And who is this clown?So, I started digging, and what I found really changed my perspective because as mentioned at the start of the show, you are a general partner at Andreessen Horowitz, which means you are a VC. You are definitionally almost the archetype of a VC in that sense. And to me, being a venture capitalist means the most interesting thing about you is that you write a large check consisting of someone else's money. And that's never been particularly interesting.Martin: [laugh].Corey: You kind of cut against that grain and that narrative. You have a master's and a PhD in computer science from Stanford; you started your career at one of the national labs—Laurence Livermore, if memory serves—you wound up starting a business, Nicira, if I'm pronouncing that correctly—Martin: Yeah, yeah, yeah.Corey: That you then sold to VMware in 2012, back at a time when that was a noble outcome, rather than a state of failure because VMware is not exactly what it once was. You ran a $600 million a year business while you were there. Basically, the list of boards that you're on is lengthy enough and notable enough that it sounds almost like you're professionally bored, so I don't—Martin: [laugh].Corey: So, looking at this, it's okay, this is someone who actually knows what he is talking about, not just, “Well, I talked to three people in pitch meetings and I now think I know what is going on in this broader industry.” You pay attention, and you're connected, disturbingly well, to what's going on, to the point where if you see something, it is almost certainly rooted in something that is happening. And it's a big enough market that I don't think any one person can keep their finger on the pulse of everything. So, that's when I started really digging into it, paying attention, and more or less took a lot of what you wrote as there are some theses in here that I want to prove or disprove. And I spent a fair bit of time basically threatening, swindling, and bribing people with infinite cups of coffee in order to start figuring out what is going on.And I am begrudgingly left with no better conclusion than you have a series of points in here that are very challenging to disprove. So, where do you stand today, now that, I guess, the whole rise and fall of the hype around your article on cloud repatriation—which yes, yes, we'll put a link to it in the show notes if people want to go there—but you've talked about this in a lot of different contexts. Having had the conversations that you've had, and I'm sure some very salty arguments with people who have a certain vested interest in you being wrong, do you wind up continuing to stand by the baseline positions that you've laid out, or have they evolved into something more nuanced?Martin: So yeah, I definitely want to point out, so this was work done with Sarah Wang was also at Andreessen Horowitz; she's also a GP. She actually did the majority of the analysis and she's way smarter than I am. [laugh]. And so, I'm just very—feel very lucky to work with her on this. And I want to make sure she gets due credit on this.So, let's talk about the furor. So like, I actually thought that this was kind of interesting and it started a good discussion, but instead, like, [laugh] the amount of, like, response pieces and, like, angry emails I got, and [laugh] like, I mean it just—and I kind of thought to myself, like, “Why are people so upset?” I think there's three reasons. I'm going to go through them very quickly because they're interesting.So, the first one is, like, you're right, like, I'm a VC. I think people see a VC and they're like, oh, lack of credibility, lack of accountability, [laugh], you know, doesn't know what they're doing, broad pattern matcher. And, like, I will say, like, I did not necessarily write this as a VC; I wrote this as somebody that's, like, listen, my PhD is an infrastructure; my company was an infrastructure. It's all data center stuff. I had a $600 million a year data center business that sold infrastructure into data centers. I've worked with all of the above. Like, I've worked with Amazon, I've—Corey: So, you sold three Cisco switches?Martin: [laugh]. That's right.Corey: I remember those days. Those were awesome, but not inexpensive.Martin: [laugh]. That's right. Yeah, so like, you know, I had 15 years. It's kind of a culmination of that experience. So, that was one; I just think that people see VC and they have a reaction.The second one is, I think people still have the first cloud wars fresh in their memories and so they just don't know how to think outside of that. So, a lot of the rebuttals were first cloud war rebuttals. Like, “Well, but internal IT is slow and you can't have the expertise.” But like, they just don't apply to the new world, right? Like, listen, if you're Cloudflare, to say that you can't run, like, a large operation is just silly. If you went to Cloudflare and you're like, “Listen, you can't run your own infrastructure,” like, they'd take out your sucker and pat you on the head. [laugh].Corey: And not for nothing, if you try to run what they're doing on other cloud providers from a pure bandwidth perspective, you don't have a company anymore, regardless of how well funded you are. It's a never-full money pit that just sucks all of the money. And I've talked to a number of very early idea stage companies that aren't really founded yet about trying to do things like CDN-style work or streaming video, and a lot of those questions start off with well, we did some back-of-the-envelope math around AWS data transfer pricing, and if our numbers are right, when we scale, we'll be spending $65,000 on data transfer every minute. What did we get wrong?And it's like, “Oh, yeah, you realize that one thing is per hour not per minute, so slight difference there. But no, you're basically correct. Don't do it.” And yeah, no one pays retail price at that volume, but they're not going to give you a 99.999% discount on these things, so come up with a better plan. Cloudflare's business will not work on AWS, full stop.Martin: Yep, yep. So, I legitimately know, basically, household name public companies that are software companies that anybody listening to this knows the name of these companies, who have product lines who have 0% margins because they're [laugh] basically, like, for every dollar they make, they pay a dollar to Amazon. Like, this is a very real thing, right? And if you go to these companies, these are software infrastructure companies; they've got very talented teams, they know how to build, like, infrastructure. To tell them that like, “Well, you know, you can't build your own infrastructure,” or something is, I mean, it's like telling, like, an expert in the business, they can't do what they do; this is what they do. So, I just think that part of the furor, part of the uproar, was like, I just think people were stuck in this cloud war 1.0 mindset.I think the third thing is, listen, we've got an oligopoly, and they employ a bunch of people, and they've convinced a bunch of people they're right, and it's always hard to change that. And I also think there's just a knee-jerk reaction to these big macro shifts. And it was the same thing we did to software-defined networking. You know, like, my grad school work was trying to change networking to go from hardware to software. I remember giving a talk at Cisco, and I was, like, this kind of like a naive grad student, and they literally yelled at me out of the room. They're like, it'll never work.Corey: They tried to burn you as a witch, as I recall.Martin: [laugh]. And so, your specific question is, like, have our views evolved? But the first one is, I think that this macro downturn really kind of makes the problem more acute. And so, I think the problem is very, very real. And so, I think the question is, “Okay, so what happens?”So, let's say if you're building a new software company, and you have a choice of using, like, one of the Big Three public clouds, but it impacts your margins so much that it depresses your share price, what do you do? And I think that we thought a lot more about what the answers there are. And the ones that I think that we're seeing is, some actually are; companies are building their own infrastructure. Like, very famously MosaicML is building their own infrastructure. Fly.io, -building their own infrastructure.Mighty—you know, Suhail's company—building his own infrastructure. Cloudflare has their own infrastructure. So, I think if you're an infrastructure provider, a very reasonable thing to do is to build your own infrastructure. If you're not a core infrastructure provider, you're not; you can still use somebody's infrastructure that's built at a better cost point.So, for example, if I'm looking at a CDN tier, I'm going to use Fly.io, right? I mean, it's like, it's way cheaper, the multi-region is way better, and so, like, I do think that we're seeing, like, almost verticalized clouds getting built out that address this price point and, like, these new use cases. And I think this is going to start happening more and more now. And we're going to see basically almost the delamination of the cloud into these verticalized clouds.Corey: I think there's also a question of scale, where if you're starting out in the evening tonight, to—I want to build, I don't know Excel as a service or something. Great. You're pretty silly if you're not going to start off with a cloud provider, just because you can get instant access to resources, and if your product catches on, you scale out without having to ever go back and build it as quote-unquote “Enterprise grade,” as opposed to having building it on cheap servers or Raspberry Pis or something floating around. By the time that costs hit a certain point—and what that point is going to depend on your stage of company and lifecycle—you're remiss if you don't at least do an analysis on is this the path we want to continue on for the service that we're offering?And to be clear, the answer to this is almost entirely going to be bounded by the context of your business. I don't believe that companies as a general rule, make ill-reasoned decisions. I think that when we see a decision a company makes, by and large, there's context or constraints that we don't see that inform that. I know, it's fun to dunk on some of the large companies' seemingly inscrutable decisions, but I will say, having had the privilege to talk to an awful lot of execs in an awful lot of places—particularly on this show—I don't find myself encountering a whole lot of people in those roles who I come away with thinking that they're a few fries short of a Happy Meal. They generally are very well reasoned in why they do what they do. It's just a question of where we think the future is going on some level.Martin: Yep. So, I think that's absolutely right. So, to be a little bit more clear on what I think is happening with the cloud, which is I think every company that gets created in tech is going to use the cloud for something, right? They'll use it for development, the website, test, et cetera. And many will have everything in the cloud, right?So, the cloud is here to stay, it's going to continue to grow, it's a very important piece of the ecosystem, it's very important piece of IT. I'm very, very pro cloud; there's a lot of value. But the one area that's under pressure is if your product is SaaS if your product is selling Software as a Service, so then your product is basically infrastructure, now you've got a product cost model that includes the infrastructure itself, right? And if you reduce that, that's going to increase your margin. And so, every company that's doing that should ask the question, like, A, is the Big Three the right one for me?Maybe a verticalized cloud—like for example, whatever Fly or Mosaic or whatever is better because the cost is better. And I know how to, you know, write software and run these things, so I'll use that. They'll make that decision or maybe they'll build their own infrastructure. And I think we're going to see that decision happening more and more, exactly because now software is being offered as a service and they can do that. And I just want to make the point, just because I think it's so important, that the clouds did exactly this to the hardware providers. So, I just want to tell a quick story, just because for me, it's just so interesting. So—Corey: No, please, I was only really paying attention to this market from 2016 or so. There was a lot of the early days that I was using as a customer, but I wasn't paying attention to the overall industry trends. Please, storytime. This is how I learned things. I hang out with smart people and I come away a little bit smarter than when I started.Martin: [laugh]. This is, like, literally my fa—this is why this is one of my favorite topics is what I'm about to tell you, which is, so the clouds have always had this argument, right? The big clouds, three clouds, they're like, “Listen, why would you build your own cloud? Because, like, you don't have the expertise, and it's hard and you don't have economies of scale.” Right?And the answer is you wouldn't unless it impacts your share price, right? If it impacts your share price, then of course you would because it makes economic sense. So, the clouds had that exact same dilemma in 2005, right? So, in 2005, Google and Amazon and Microsoft, they looked at their COGS, they looked like, “Okay, I'm offering a cloud. If I look at the COGS, who am I paying?”And it turns out, there was a bunch of hardware providers that had 30% margins or 70% margins. They're like, “Why am I paying Cisco these big margins? Why am I paying Dell these big margins?” Right? So, they had the exact same dilemma.And all of the arguments that they use now applied then, right? So, the exact same arguments, for example, “AWS, you know nothing about hardware. Why would you build hardware? You don't have the expertise. These guys sell to everybody in the world, you don't have the economies of scale.”So, all of the same arguments applied to them. And yet… and yes because it was part of COGS] that it impacted the share price, they can make the economic argument to actually build hardware teams and build chips. And so, they verticalized, right? And so, it just turns out if the infrastructure becomes parts of COGS, it makes sense to optimize that infrastructure. And I would say, the Big Three's foray into OEMs and hardware is a much, much, much bigger leap than an infrastructure company foraying into building their own infrastructure.Corey: There's a certain startup cost inherent to all these things. And the small version of that we had in every company that we started in a pre-cloud era: renting virtual computers from vendors was a thing, but it was still fraught and challenging and things that we use, then, like, GoGrid no longer exist, for good reason. But the alternative was, “Great, I'm going to start building and seeing if this thing has any traction.” Well, you need to go lease a rack somewhere and buy servers from Dell, and they're going to do the fast expedited option, which means only six short weeks until they show up in the data center and then gets sent away because they weren't expecting to receive them. And you wind up with this entire universe of hell between cross-connects and all the rest.And that's before you can ever get anything in front of customers or users to see what happens. Now, it's a swipe of a credit card away and your evening's experiments round up to 25 cents. That was significant. Having to make these significant tens of thousands of dollars of investment just to launch is no longer true. And I feel like that was a great equalizer in some respects.Martin: Yeah, I think that—Corey: And that cost has been borne by the astonishing level of investment that the cloud providers themselves have made. And that basically means that we don't have to. But it does come at a cost.Martin: I think it's also worth pointing out that it's much easier to stand up your own infrastructure now than it has been in the past, too. And so, I think that there's a gradient here, right? So, if you're building a SaaS app, [laugh] you would be crazy not to use the cloud, you just be absolutely insane, right? Like, what do you know about core infrastructure? You know, what do you know about building a back-end? Like, what do you know about operating these things? Go focus on your SaaS app.Corey: The calluses I used to have from crimping my own Ethernet patch cables in data centers have faded by now. I don't want them to come back. Yeah, we used to know how to do these things. Now, most people in most companies do not have that baseline of experience, for excellent reasons. And I wouldn't wish that on the current generation of engineers, except for the ones I dislike.Martin: However, that is if you're building an application. Almost all of my investments are people that are building infrastructure. [laugh]. They're already doing these hardcore backend things; that's what they do: they sell infrastructure. Would you think, like, someone, like, at Databricks doesn't understand how to run infr—of course it does. I mean, like, or Snowflake or whatever, right?And so, this is a gradient. On the extreme app end, you shouldn't be thinking about infrastructure; just use the cloud. Somewhere in the middle, maybe you start on the cloud, maybe you don't. As you get closer to being a cloud service, of course you're going to build your own infrastructure.Like, for example—listen, I mean, I've been mentioning Fly; I just think it's a great example. I mean, Fly is a next-generation CDN, that you can run compute on, where they build their own infrastructure—it's a great developer experience—and they would just be silly. Like, they couldn't even make the cost model work if they did it on the cloud. So clearly, there's a gradient here, and I just think that you would be remiss and probably negligent if you're selling software not to have this conversation, or at least do the analysis.Corey: This episode is sponsored in part by our friend EnterpriseDB. EnterpriseDB has been powering enterprise applications with PostgreSQL for 15 years. And now EnterpriseDB has you covered wherever you deploy PostgreSQL on-premises, private cloud, and they just announced a fully-managed service on AWS and Azure called BigAnimal, all one word. Don't leave managing your database to your cloud vendor because they're too busy launching another half-dozen managed databases to focus on any one of them that they didn't build themselves. Instead, work with the experts over at EnterpriseDB. They can save you time and money, they can even help you migrate legacy applications—including Oracle—to the cloud. To learn more, try BigAnimal for free. Go to biganimal.com/snark, and tell them Corey sent you.Corey: I think there's also a philosophical shift, where a lot of the customers that I talk to about their AWS bills want to believe something that is often not true. And what they want to believe is that their AWS bill is a function of how many customers they have.Martin: Oh yeah.Corey: In practice, it is much more closely correlated with how many engineers they've hired. And it sounds like a joke, except that it's not. The challenge that you have when you choose to build in a data center is that you have bounds around your growth because there are capacity concerns. You are going to run out of power, cooling, and space to wind up having additional servers installed. In cloud, you have an unbounded growth problem.S3 is infinite storage, and the reason I'm comfortable saying that is that they can add hard drives faster than you can fill them. For all effective purposes, it is infinite amounts of storage. There is no forcing function that forces you to get rid of things. You spin up an instance, the natural state of it in a data center as a virtual machine or a virtual instance, is that it's going to stop working two to three years left on maintain when a raccoon hauls it off into the woods to make a nest or whatever the hell raccoons do. In cloud, you will retire before that instance does is it gets migrated to different underlying hosts, continuing to cost you however many cents per hour every hour until the earth crashes into the sun, or Amazon goes bankrupt.That is the trade-off you're making. There is no forcing function. And it's only money, which is a weird thing to say, but the failure mode of turning something off mistakenly that takes things down, well that's disastrous to your brand and your company. Just leaving it up, well, it's only money. It's never a top-of-mind priority, so it continues to build and continues to build and continues to build until you're really forced to reckon with a much larger problem.It is a form of technical debt, where you've kicked the can down the road until you can no longer kick that can. Then your options are either go ahead and fix it or go back and talk to you folks, and it's time for more money.Martin: Yeah. Or talk to you. [laugh].Corey: There is that.Martin: No seriously, I think everybody should, honestly. I think this is a board-level concern for every compa—I sit on a lot of boards; I see this. And this has organically become a board-level concern. I think it should become a conscious board-level concern of, you know, cloud costs, impact COGS. Any software company has it; it always becomes an issue, and so it should be treated as a first-class problem.And if you're not thinking through your options—and I think by the way, your company is a great option—but if you're not thinking to the options, then you're almost fiduciarily negligent. I think the vast, vast majority of people and vast majority of companies are going to stay on the cloud and just do some basic cost controls and some just basic hygiene and they're fine and, like, this doesn't touch them. But there are a set of companies, particularly those that sell infrastructure, where they may have to get more aggressive. And that ecosystem is now very vibrant, and there's a lot of shifts in it, and I think it's the most exciting place [laugh] in all of IT, like, personally in the industry.Corey: One question I have for you is where do you draw the line around infrastructure companies. I tend to have an evolving view of it myself, where things that are hard and difficult do not become harder with time. It used to require a deep-level engineer with a week to kill to wind up compiling and building a web server. Now, it is evolved and evolved and evolved; it is check a box on a webpage somewhere and you're serving a static website. Managed databases, I used to think, were something that were higher up the stack and not infrastructure. Today, I'd call them pretty clearly infrastructure.Things seem to be continually, I guess, a slipping beneath the waves to borrow an iceberg analogy. And it's only the stuff that you can see that is interesting and differentiated, on some level. I don't know where the industry is going at all, but I continue to think of infrastructure companies as being increasingly broad.Martin: Yeah, yeah, yeah. This is my favorite question. [laugh]. I'm so glad you asked. [laugh].Corey: This was not planned to be clear.Martin: No, no, no. Listen, I am such an infrastructure maximalist. And I've changed my opinion on this so much in the last three years. So, it used to be the case—and infrastructure has a long history of, like, calling the end of infrastructure. Like, every decade has been the end of infrastructure. It's like, you build the primitives and then everything else becomes an app problem, you know?Like, you build a cloud, and then we're done, you know? You build the PC and then we're done. And so, they are even very famous talks where people talk about the end of systems when we've be built everything right then. And I've totally changed my view. So, here's my current view.My current view is, infrastructure is the only, really, differentiation in systems, in all IT, in all software. It's just infrastructure. And the app layer is very important for the business, but the app layer always sits on infrastructure. And the differentiations in app is provided by the infrastructure. And so, the start of value is basically infrastructure.And the design space is so huge, so huge, right? I mean, we've moved from, like, PCs to cloud to data. Now, the cloud is decoupling and moving to the CDN tier. I mean, like, the front-end developers are building stuff in the browser. Like, there's just so much stuff to do that I think the value is always going to accrue to infrastructure.So, in my view, anybody that's improving the app accuracy or performance or correctness with technology is an infrastructure company, right? And the more of that you do, [laugh] the more infrastructure you are. And I think, you know, in 30 years, you and I are going to be old, and we're going to go back on this podcast. We're going to talk and there's going to be a whole bunch of infrastructure companies that are being created that have accrued a lot of value. I'm going to say one more thing, which is so—okay, this is a sneak preview for the people listening to this that nobody else has heard before.So Sarah, and I are back at it again, and—the brilliant Sarah, who did the first piece—and we're doing another study. And the study is if you look at public companies and you look at ones that are app companies versus infrastructure companies, where does the value accrue? And there's way, way more app companies; there's a ton of app companies, but it turns out that infrastructure companies have higher multiples and accrue more value. And that's actually a counter-narrative because people think that the business is the apps, but it just turns out that's where the differentiation is. So, I'm just an infra maximalist. I think you could be an infra person your entire career and it's the place to be. [laugh].Corey: And this is the real value that I see of looking at AWS bills. And our narrative is oh, we come in and we fix the horrifying AWS bill. And the naive pass is, “Oh, you cut the bill and make it lower?” Not always. Our primary focus has been on understanding it because you get a phone-number-looking bill from AWS. Great, you look at it, what's driving the cost? Storage.Okay, great. That doesn't mean anything to the company. They want to know what teams are doing this. What's it going to cost for them to add another thousand monthly active users? What is the increase in cost? How do they wind up identifying their bottlenecks? How do they track and assign portions of their COGS to different aspects of their service? How do they trace the flow of capital for their organization as they're serving their customers?And understanding the bill and knowing what to optimize and what not to becomes increasingly strategic business concern.Martin: Yeah.Corey: That's the fun part. That's the stuff I don't see that software has a good way of answering, just because there's no way to use an API to gain that kind of business context. When I started this place, I thought I was going to be building software. It turns out, there's so many conversations that have to happen as a part of this that cannot be replicated by software. I mean, honestly, my biggest competitor for all this stuff is Microsoft Excel because people want to try and do it themselves internally. And sometimes they do a great job, sometimes they don't, but it's understanding their drivers behind their cost. And I think that is what was often getting lost because the cloud obscures an awful lot of that.Martin: Yeah. I think even just summarize this whole thing pretty quickly, which is, like, I do think that organically, like, cloud cost has become a board-level issue. And I think that the shift that founders and execs should make is to just, like, treat it like a first-class problem upfront. So, what does that mean? Minimally, it means understanding how these things break down—A, to your point—B, there's a number of tools that actually help with onboarding of this stuff. Like, Vantage is one that I'm a fan of; it just provides some visibility.And then the third one is if you're selling Software as a Service, that's your core product or software, and particularly it's a infrastructure, if you don't actually do the analysis on, like, how this impacts your share price for different cloud costs, if you don't do that analysis, I would say your fiduciarily negligent, just because the impact would be so high, especially in this market. And so, I think, listen, these three things are pretty straightforward and I think anybody listening to this should consider them if you're running a company, or you're an executive company.Corey: Let's be clear, this is also the kind of problem that when you're sitting there trying to come up with an idea for a business that you can put on slide decks and then present to people like you, these sounds like the paradise of problems to have. Like, “Wow, we're successful and our business is so complex and scaled out that we don't know where exactly a lot of these cost drivers are coming from.” It's, “Yeah, that sounds amazing.” Like, I remember those early days, back when all I was able to do and spend time on and energy on was just down to the idea of, ohh, I'm getting business cards. That's awesome. That means I've made it as a business person.Spoiler: it did not. Having an aggressive Twitter presence, that's what made me as a business person. But then there's this next step and this next step and this next step and this next step, and eventually, you look around and realize just how overwrought everything you've built is and how untangling it just becomes a bit of a challenge and a hell of a mess. Now, the good part is at that point of success, you can bring people in, like, a CFO and a finance team who can do some deep-level analysis to help identify what COGS is—or in some cases, have some founders, explain what COGS is to you—and understand those structures and how you think about that. But it always feels like it's a trailing problem, not an early problem that people focus on.Martin: I'll tell you the reason. The reason is because this is a very new phenomenon that it's part of COGS. It's literally five years new. And so, we're just catching up. Even now, this discussion isn't what it was when we first wrote the post.Like, now people are pretty educated on, like, “Oh yeah, like, this is really an issue. Oh, yeah. It contributes to COGS. Oh, yeah. Like, our stock price gets hit.” Like, it's so funny to watch, like, the industry mature in real-time. And I think, like, going forward, it's just going to be obvious that this is a board-level issue; it's going to be obvious this is, like, a first-class consideration. But I agree with you. It's like, listen, like, the industry wasn't ready for it because we didn't have public companies. A lot of public companies, like, this is a real issue. I mean really we're talking about the last five, seven years.Corey: It really is neat, just in real time watching how you come up with something that sounds borderline heretical, and in a relatively short period of time, becomes accepted as a large-scale problem, and now it's now it is fallen off of the hype train into, “Yeah, this is something to be aware of.” And people's attention spans have already jumped to the next level and next generation of problem. It feels like this used to take way longer for these cycles, and now everything is so rapid that I almost worry that between the time we're recording this and the time that it publishes in a few weeks, what is going to have happened that makes this conversation irrelevant? I didn't used to have to think like that. Now, I do.Martin: Yeah, yeah, yeah, for sure. Well, just a couple of things. I want to talk about, like, one of the reasons that accelerated this, and then when I think is going forward. So, one of the reasons this was accelerated was just the macro downturn. Like, when we wrote the post, you could make the argument that nobody cares about margins because it's all about growth, right?And so, like—and even then, it still saved a bunch of money, but like, a lot of people were like, “Listen, the only thing that matters is growth.” Now, that's absolutely not the case if you look at public market valuations. I mean, people really care about free cash flow, they really care about profitability, and they really care about margins. And so, it's just really forced the issue. And it also, like, you know, made kind of what we were saying very, very clear.I would say, you know, as far as shifts that are going, I think one of the biggest shifts is for every back-end developer, there's, like, a hundred front-end developers. It's just crazy. And those front-end developers—Corey: A third of a DevOps engineer.Martin: [laugh]. True. I think those front-end developers are getting, like, better tools to build complete apps, right? Like, totally complete apps, right? Like they've got great JavaScript frameworks that coming out all the time.And so, you could argue that actually a secular technology change—which is that developers are now rebuilding apps as kind of front-end applications—is going to pull compute away from the clouds anyways, right? Like if instead of, like, the app being some back-end thing running in AWS, but instead is a front-end thing, you know, running in a browser at the CDN tier, while you're still using the Big Three clouds, it's being used in a very different way. And we may have to think about it again differently. Now, this, again, is a five-year going forward problem, but I do feel like there are big shifts that are even changing the way that we currently think about cloud now. And we'll see.Corey: And if those providers don't keep up and start matching those paradigms, there's going to be an intermediary shim layer of companies that wind up converting their resources and infrastructure into things that suit this new dynamic, and effectively, they're going to become the next version of, I don't know, Level 3, one of those big underlying infrastructure companies that most people have never heard of or have to think about because they're not doing anything that's perceived as interesting.Martin: Yeah, I agree. And I honestly think this is why Cloudflare and Cloudflare work is very interesting. This is why Fly is very interesting. It's a set of companies that are, like, “Hey, listen, like, workloads are moving to the front-end and, you know, you need compute closer to the user and multi-region is really important, et cetera.” So, even as we speak, we're seeing kind of shifts to the way the cloud is moving, which is just exciting. This is why it's, like, listen, infrastructure is everything. And, like, you and I like if we live to be 200, we can do [laugh] a great infrastructure work every year.Corey: I'm terrified, on some level, that I'll still be doing the exact same type of thing in 20 years.Martin: [laugh].Corey: I like solving different problems as we go. I really want to thank you for spending so much time talking to me today. If people want to learn more about what you're up to, slash beg you for other people's money or whatnot, where's the best place for them to find you?Martin: You know, we've got this amazing infrastructure Discord channel. [laugh].Corey: Really? I did not know that.Martin: I love it. It's, like, the best. Yeah, my favorite thing to do is drink coffee and talk about infrastructure. And like, I posted this on Twitter and we've got, like, 600 people. And it's just the best thing. So, that's honestly the best way to have these discussions. Maybe can you put, like, the link in, like, the show notes?Corey: Oh, absolutely. It is already there in the show notes. Check the show notes. Feel free to join the infrastructure Discord. I will be there waiting for you.Martin: Yeah, yeah, yeah. That'll be fantastic.Corey: Thank you so much for being so generous with your time. I appreciate it.Martin: This was great. Likewise, Corey. You're always a class act and I really appreciate that about you.Corey: I do my best. Martin Casado, general partner at Andreessen Horowitz. I'm Cloud Economist Corey Quinn, and this is Screaming in the Cloud. If you've enjoyed this podcast, please leave a five-star review on your podcast platform of choice, whereas if you've hated this podcast, please leave a five-star review on your podcast platform of choice along with an angry comment telling me that I got it completely wrong and what check you wrote makes you the most interesting.Announcer: The content here is for informational purposes only and 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. For more details, please see a16z.com/disclosures.Corey: If your AWS bill keeps rising and your blood pressure is doing the same, then you need The Duckbill Group. We help companies fix their AWS bill by making it smaller and less horrifying. The Duckbill Group works for you, not AWS. We tailor recommendations to your business and we get to the point. Visit duckbillgroup.com to get started.Announcer: This has been a HumblePod production. Stay humble.

In this episode from December 2018, Hennessy, currently the chairman of Alphabet as well as Turing Award-winning computer scientist, joins a16z co-founder Marc Andreessen, a16z general partner Martin Casado, and host Sonal Choksi for a wide-ranging conversation about moving from academia to startups, the history of Silicon Valley, the “Stanford model”, how to build enduring organizations, and more.Hennessy also co-founded startups, including one based on pioneering microprocessor architecture used in 99% of devices today (for which he and his collaborator won the prestigious Turing Award)... so what did it take to go from research/idea to industry/implementation? And  how has the overall relationship and "divide" between academia and industry shifted, especially as the tech industry itself has changed? Finally, in his book, Leading Matters, Hennessy shares some of the leadership principles he's learned, offering nuanced takes on topics like humility (needs ambition), empathy (without contravening fairness and reason), and others. What does it take to build not just tech, but a successful organization?

My guest today is Martin Casado. Martin is a general partner at Andreessen Horowitz where he focuses on digital infrastructure. Before joining a16z, Martin pioneered software-defined networking and co-founded Nicira, which was bought by VMware for $1.3 billion in 2012. Martin has studied, built, and invested in digital infrastructure his whole career and is the perfect person to discuss the most interesting aspects of the industry. Please enjoy this great conversation with Martin Casado.   For the full show notes, transcript, and links to mentioned content, check out the episode page here.   -----   This episode is brought to you by Tegus. Tegus streamlines the investment research process so you can get up to speed and find answers to critical questions on companies faster and more efficiently. The Tegus platform surfaces the hard-to-get qualitative insights, gives instant access to critical public financial data through BamSEC, and helps you set up customized expert calls. It's all done on a single, modern SaaS platform that offers 360-degree insight into any public or private company. As a listener, you can take Tegus for a free test drive by visiting tegus.co/patrick. And until 2023 every Tegus license comes with complimentary access to BamSec by Tegus.   -----   Today's episode is brought to you by Brex, the integrated financial platform trusted by the world's most innovative entrepreneurs and fastest-growing companies. With Brex, you can move money fast for instant impact with high-limit corporate cards, payments, venture debt, and spend management software all in one place. Ready to accelerate your business? Learn more at brex.com/best.   -----   Invest Like the Best is a property of Colossus, LLC. For more episodes of Invest Like the Best, visit joincolossus.com/episodes.    Past guests include Tobi Lutke, Kevin Systrom, Mike Krieger, John Collison, Kat Cole, Marc Andreessen, Matthew Ball, Bill Gurley, Anu Hariharan, Ben Thompson, and many more.   Stay up to date on all our podcasts by signing up to Colossus Weekly, our quick dive every Sunday highlighting the top business and investing concepts from our podcasts and the best of what we read that week. Sign up here.   Follow us on Twitter: @patrick_oshag | @JoinColossus   Show Notes [00:02:43] - [First question] - The state of the digital infrastructure industry today  [00:04:02] - The major stages and eras of cloud technology  [00:06:30] - Overview of Dropbox's story and the two major trends at the time of its emergence [00:10:12] - Lost margin and lost market cap from big users of the public cloud  [00:12:14] - Whether or not there is a headwind coming for public cloud providers [00:14:07] - The base level primitives of the digital world and innovation within those areas [00:17:33] - How entrepreneurs might go after the biggest public cloud providers [00:19:37] - His view on API first companies and granular monetizable units in growing markets [00:23:20] - Developer facing tools and what works well when going to market  [00:25:34] - Lessons learned about successfully building relationships between a company and developers as a buying class  [00:27:12] - The difference between a front-end and back-end developer and what is changing in their responsibilities  [00:28:45] - What he looks for as an investor when he's processing a new API first company [00:30:31] - Common redflags and disqualifying observations for an API first company  [00:31:59] - Pricing usage and building a revenue model around one of these businesses [00:33:49] - Reasons why this proliferation is happening and important parts of the data stack [00:36:35] - Frank Slootman Episode; Snowflake's offering for their users, their explosive growth, and primitives in their sector [00:39:06] - The history of digital security and potential opportunities as an investor [00:40:19] - How digital infrastructure intersects with the real world and hardware world [00:42:14] - What he's most excited about that digital infrastructure might unlock in the future [00:43:33] - How to screen out people for their potential to deliver transformative technology [00:45:38] - What he'd like to know about the future that he isn't sure of yet  [00:47:45] - Things he's most intrigued about by cryptocurrencies as an infrastructure person [00:51:36] - Where he's most bullish and bearish relative to his peers in digital infrastructure [00:52:49] - The kindest thing anyone has ever done for him

Gross margins–which are essentially a company's revenue from products and services minus the costs to deliver those products and services to customers–are one of the most important financial metrics for any startup and growing business. And yet, figuring out what goes into the “cost” for delivering products and services is not as simple as it may sound, particularly for high-growth software businesses that might use emerging business models or be leveraging new technology. In this episode from June 2020, a16z general partners Martin Casado, David George, and Sarah Wang talk all things gross margins, from early to late stage. Why do gross margins matter? When do they matter during a company's growth? And how do you use them to plan for the future? The conversation ranges from the nuances of and strategy for calculating margins with things like cloud costs, freemium users, or implementation costs, to the impact margins can have on valuations.

Over a decade after the idea of “big data'' was first born, data has become the central nervous system for decision-making in organizations of all sizes. But the modern data stack is evolving and which infrastructure trends and technologies will ultimately win out remains to be decided.In this podcast, originally recorded as part of Fivetran's Modern Data Stack conference, five leaders in data infrastructure debate that question: a16z general partner and pioneer of software defined networking Martin Casado, former CEO of Snowflake Bob Muglia; Michelle Ufford, founder and CEO of Noteable; Tristan Handy, founder of Fishtown Analytics and leader of the open source project dbt; and Fivetran founder George Fraser.The conversation covers the future of data lakes, the new use cases for the modern data stack, data mesh and whether decentralization of teams and tools is the future, and how low we actually need to go with latency. And while the topic of debate is the modern data stack, the themes and differing perspectives strike at the heart of an even bigger: how does technology evolve in complex enterprise environments? We're re-running this episode as part of a special report on Future.com, the Data50: the World's Top Data Startups, which covers the bellwether private companies across the most exciting categories in data, from AI/ML to observability and more.