Joe Lonsdale: American Optimist

In the 1930s and 40s, the United States built thousands of airports; today that number is virtually zero. What happened? Why is it nearly impossible to build major new airports or even find hangar space in large cities? And how will the coming AI wealth creation exacerbate the supply and demand imbalance? Tal Keinan, founder and CEO of Sky Harbour Group, is addressing these challenges in the private aviation industry.Born in Miami, Tal moved to Israel after college and was drafted into the military where he served as an F-16 pilot in the Israeli Air Force. In 2004, he founded Clarity Capital, and played an instrumental role in the growth of Israel’s tech ecosystem. He’s also the author of “God Is in the Crowd: Twenty-First Century Judaism.” In 2015, he purchased his own airplane in New York and quickly realized there was nowhere to park it. His experience led to the creation of Sky Harbour Group. We begin with Tal’s path from fighter pilot to investor and his role in Israel’s economic miracle. Next, we dive into the history of America’s airports — thousands built in the 1930s and 1940s as New Deal jobs programs and World War II pilot training. Yet, America hasn’t built a new major airport in decades. Tal explains why general aviation airports struggle to turn a profit, why the FBO duopoly has no incentive to build more hangars, and how Sky Harbour pioneered the home-based operator model. Next, we explore how Tal uses AI throughout his business, particularly to pinpoint new hangar locations, and how the coming wave of AI-driven wealth will create even greater demand for private aviation. Finally, Tal shares his reasons for optimism and why he believes America, despite its challenges, always finds a way to course correct.(00:00) Episode intro(01:45) Eighteen years as an F-16 pilot(04:00) Clarity Capital and Israel's tech explosion(08:16) “Nowhere to park it” / Why did America stop building airports?(10:40) Why general aviation airports don't make money(13:15) The FBO duopoly & why they love NetJets (16:05) How Tal created the home-based operator model(19:10) How the Sky Harbour business model works(24:50) How Tal uses AI to scout customers and locations (27:09) AI wealth wave & private aviation boomImportant Information: American Optimist is Joe’s personal podcast. This episode reflects the personal views of Joe and his guest, and is not investment advice or a recommendation to buy, sell, or hold any security. Joe is a principal of Eight Partners VC, LLC (“8VC”), and 8VC-managed funds currently hold shares of Sky Harbour Group Corporation (NYSE: SKYH) and may buy or sell those shares at any time without notice. 8VC-managed funds may also hold interests in other companies mentioned in this episode. Statements about future events, including industry trends and Sky Harbour’s plans, are forward-looking and may not occur. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit blog.joelonsdale.com

Rayan Krishnan is the co-founder and CEO of Vals AI, a leader in the independent evaluation of frontier AI models. What is the science behind model evaluation? What role should independent evaluators play as we debate AI oversight? How soon will models reach recursive self-improvement — and what happens next? We’re joined by Rayan and 8VC Partner Vivek Gopalan for this timely conversation about AI progress and the frameworks for evaluating performance and risk. After interning at Microsoft in high school, Rayan studied computer science at Stanford. He went on to join Palantir, where he developed a feature the company later patented and productized, before launching Vals AI in 2023.We begin with Rayan’s path to Palantir, key lessons from Alex Karp, and the origins of Vals. Next, we dive into how his team used Anthropic’s Fable to crack a 370-year-old cipher and whether AI is really making new discoveries and breakthroughs. Next, Rayan explains what model evaluation is, how to design new hills for models to climb, and why labs shouldn’t grade their own homework. Then, we cover recursive self-improvement and what happens once models begin creating their own successors. Rayan believes Anthropic will reach RSI by mid-2027 and talks about what might happen next. Finally, he addresses how models try to deceive evaluators, and why, despite serious challenges, he remains an optimist when it comes to AI.(0:00) Episode intro(2:00) Palantir, Machiavelli & lessons from Alex Karp(7:50) Solving a 370-year-old cipher / Is AI making new discoveries? (12:10) How do you measure AI abilities across finance, legal, etc? (15:30) What is the science behind model evaluation? (18:00) The importance of independent evaluation(23:35) Evaluating Chinese models & addressing distillation(26:45) Who will reach Recursive Self-Improvement first? Should we be worried?(28:30) How models try to deceive evaluators(33:00) Why Rayan remains optimistic about AI This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit blog.joelonsdale.com

Eric Glyman is the co-founder and CEO of Ramp, the finance platform doing over two billion in annual revenue and growing over 100 percent year per year. How did a company built on the promise of helping customers spend less become one of the fastest-growing AI businesses? What’s Eric’s unique approach to managing small teams of exceptional talent? And what does Ramp’s view into corporate spend reveal about AI adoption and where the economy is headed?Before Ramp, Eric and co-founder Karim Atiyeh built Paribus, a “prehistoric AI agent” that scanned inboxes and secured price-drop refunds on customers’ behalf. Capital One acquired it a year after launch, and the two spent their time inside the bank studying how misaligned incentives shape what financial institutions actually do. In 2020, they launched Ramp and quickly became the fastest-growing corporate card and expense management platform.We begin our conversation with the origins of Ramp, specifically how learnings from Paribus and operating inside a big bank revealed new possibilities. Eric explains why Ramp is a “productivity company that happens to move money” and how its obsession with outcomes, rather than selling products, drives its meteoric growth. Next, Eric explains the small-team philosophy behind Ramp’s speed, and why he takes the unusual step of breaking up teams after they reach a certain size. Then, we get a look inside the U.S. economy via Ramp’s unique insights into corporate expenses. For example, Eric explains that AI inference spend has grown five-fold in the past few years but still only amounts to two percent of all white-collar wages. After seeing the surge in inference costs, Ramp launched Router, which dramatically reduces customer inference costs — an example of prioritizing outcomes and operating at a speed that competitors can’t match. Finally, we talk about the impact of AI on white-collar work, and what it would look like to apply Ramp’s transparency and cost savings to the federal government.(00:00) Episode intro(01:45) $2B in annual revenue; origins of Ramp(05:20) "A productivity company that happens to move money"(09:30) Could Ramp have been built before AI?(11:15) Selling outcomes vs selling products(15:10) Why Ramp beat Brex(17:00) 3% of corporate cards, 97% to go(20:00) AI spend is 2% of all white-collar wages(22:45) How Router saves businesses 40% on AI spend(24:00) Eric's small team philosophy and how to kill bureaucracy(33:00) How Ramp maps the US economy(39:10) Can Ramp modernize government spend? Audit the Pentagon?(44:30) Future of work / optimism for the future This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit blog.joelonsdale.com

Scott Kupor is the Director of the U.S. Office of Personnel Management and has achieved some of the most important reforms in DC in only a couple years. How has he reduced the size of the federal government for the first time in generations? How is he bringing merit back to the civil service? And what is he doing to incentivize and make it easier for our best minds to serve in government?Before joining the Trump administration, Scott was employee number one at Andreessen Horowitz. As Managing Partner, he helped build it into one of the world’s largest venture firms. He’s also the author of the bestselling “Secrets of Sand Hill Road: Venture Capital and How to Get It.” In 2024, he was confirmed by the U.S. Senate as Director of the U.S. Office of Personnel Management, where he’s responsible for more than two million civil service employees and the policies governing their hiring, firing, and retention.Recorded before a live audience at the University of Austin, we begin with Scott’s unlikely path to DC and the incentive problem at the root of government dysfunction. Learn how a consent decree over four decades ago killed entrance exams for federal jobs, and how Scott got it overturned — merit is coming back! He also explains how he executed the largest peacetime reduction in the federal workforce since 1930, while eliminating degree and experience requirements so we can get our top talent into government regardless of their background. Finally, Scott dives into the launch of the U.S. Tech Force and his effort to recruit an elite corps of software engineers to make government competent and functional again. He ends with his pitch to young Americans: if you want to take on some of the most complex problems that impact millions of your fellow citizens, there’s no better place than the U.S. government.(00:00) Episode intro(01:40) Why leave tech to be attacked in DC? (06:27) Biggest federal workforce reduction in modern history (08:45) How the left killed merit-based hiring(14:40) How Scott is bringing merit back to DC(15:50) Lessons from DOGE(18:20) Hidden costs of outdated systems; billions wasted (23:40) Building a U.S. Tech Force and hiring for skills(26:20) Eliminating degree requirement to get the best talent (27:16) Why should a young talented person serve in government? (30:28) Optimism for DC; rejecting the blackpill This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit blog.joelonsdale.com

Anthropic is one of the fastest-growing companies in history and sits at the forefront of the AI race. This week, we’re joined by two key leaders on Anthropic’s technical team — Sholto Douglas and Nicholas Marwell — for an inside look at the AI frontier. Are we on the cusp of AGI — and what does that mean? What are they most excited about and most concerned about? And where does Anthropic stand on open source, as well as criticisms of its regulatory approach? Sholto Douglas grew up in Sydney, Australia, where he studied robotics and taught himself Machine Learning research; he went on to work at DeepMind before becoming a tech lead in Reinforcement Learning (RL) at Anthropic. Nicholas Marwell, a San Francisco native, studied at Yale and joined Anthropic early in its journey to lead RL Science and now the Horizons team. We begin with the state of AI progress, and why Sholto and Nicholas believe AGI is likely within the next couple of years. We unpack their case for AI optimism, why biology is the most exciting next frontier, and whether a post-scarcity world is truly achievable. Then, they explain why the next decade “belongs to the generalists” and offer advice for young people entering the workforce in the coming years. Then we dig into the hard questions: offense vs. defense in cyber and bio, regulatory capture, open source, distillation, and competition with China. We also discuss the importance of virtue, faith, and institutions, and whether the leading AI researchers in Silicon Valley share the values of the heartland. Finally, they offer a few predictions for the years ahead and whether AI will soon win a Fields Medal or even a Nobel Prize.NOTE: This conversation was recorded about a month ago, and delayed beyond our control, but it’s still timely.00:00 Episode intro02:00 Self-taught ML; Unlikely paths to Anthropic04:13 What are the most exciting things you’re working on?07:48 Have we reached AGI — and what comes next?10:10 How to measure AI progress14:05 Is biology the next great frontier?16:30 Career advice & the decade of the generalists19:10 What could go wrong? What are the biggest AI risks?22:50 Offense vs defense; how technology evolves24:50 Answering the regulatory capture critique30:15 Anthropic’s stance on open source31:30 Would you slow down and let China go ahead?32:50 Anthropic’s view on distillation39:50 Is a post-scarcity world achievable?48:30 How do we preserve values, tradition & community?54:45 Predictions for 2028 This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit blog.joelonsdale.com

John Jovanovic is the President and Chairman of the Export-Import Bank of the United States. After years of mismanagement, Ex-Im is now profitable and has become a key driver of American reindustrialization. How did he turn the bank around? What's behind a new $12 billion critical mineral reserve to secure supply chains? And how is he seeing AI impact factory floors across America?The grandson of a Chicago steelworker, Jovanovic spent his career in energy, commodities, and supply chains — most recently as Chief Strategy Officer at Asplundh, the largest provider of critical infrastructure services across North America. He went on to serve in the first Trump administration at the Development Finance Corporation, where he opened the DFC's first overseas office to counter Chinese and Russian influence in the Balkans. In the fall of 2025, he was confirmed by the U.S. Senate to lead the Export-Import Bank.We begin with Jovanovic's turnaround of Ex-Im: he lays out his key reforms and explains why he spent his first hundred days in the building instead of traveling the country. Next, we dive into Project Vault, the $12 billion public-private critical mineral reserve he announced alongside President Trump to shore up American supply chains. Then, we explore his thesis behind the "revenge of the physical world," why AI is a shot in the arm for reindustrialization, and what Jovanovic is seeing on factory floors across the country. Finally, we discuss the backlash against data centers and lay out the case for AI optimism and how to ensure all Americans benefit from this massive opportunity.(00:00) Episode intro(02:00) If you can’t manufacture, you can't innovate(09:00) Grandson of a steelworker; John’s background(11:18) Countering Chinese & Russian influence in the Balkans(15:55) Senate confirmation & how to build bipartisan support (21:45) How John made Ex-Im profitable again(28:10) Securing supply chains: $12B critical mineral reserve(35:40) Superhuman strength: AI & reindustrialization(38:08) Addressing data center backlash & AI safety(41:57) Optimism for DC & America’s future This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit blog.joelonsdale.com

Brian Chau is an investigative journalist using AI to uncover widespread corruption and billions in fraud. He recently revealed how the Effective Altruism movement bought and planted AI doomer articles, and he exposed the firm behind the frontier lab hack that's fueling AI hysteria. Now he's breaking his newest story: a $21 billion racial wealth transfer in healthcare payments across 35 states. How is he using AI to uncover stories that no other journalist could find? Why does he believe that AI is ultimately on the side of liberty and transparency? And can AI-powered accountability rebuild trust in media?We discuss these timely issues and more with Brian, founder of Effort News. At 15 years old, Brian was the youngest person in North America to win an IOI gold medal in competitive programming. He went on to study at Waterloo University before leading Alliance for the Future, an AI policy organization in DC. There he encountered the broken state of media firsthand and decided to build Effort News.We begin with Brian’s path from competitive programming to the “dirty news business,” and how he saw a generational opportunity to save journalism by applying AI to uncover massive fraud and corruption. Next, we break his latest news: an investigation into Health Equity Incentives — a $21 billion discriminatory transfer of Medicaid and Medicare dollars away from White and Asian people to Black and Hispanic. Then, we discuss his recent investigation into the UK counterterrorism program that flagged the writings of Douglas Murray and George Orwell as terrorist threats, and how Effective Altruism NGOs are funding AI doomer articles. Despite the splintered, tribal nature of journalism today, Brian believes that provable, AI-verified reporting will become the standard and rebuild confidence in our key institutions.00:00 Episode intro01:40 Youngest American to win IOI gold04:30 AI journalism; uncovering what legacy media can't09:30 Is Effort News nonpartisan? Answering the critiques 11:25 Breaking news: $21 Billion Racial Wealth Transfer17:30 Exposing the UK's counterterror incompetence 22:00 How Effective Altruism & AI doomers bought the media 24:00 Anthropic "whistleblower" connection to left-wing NGOs28:50 How do you account for bias in your reporting? 34:30 How to build a media business in 202642:30 AI for liberty and accountability; optimistic vision for the future This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit blog.joelonsdale.com

Until recently, robots were constrained to narrow, specific tasks. Think assembly lines, medical devices, or household vacuum cleaners. But now all that is changing. One robot trained by Generalist's foundation model can do everything from folding clothes and washing dishes to sorting objects and repairing other robots. How are Pete Florence and the Generalist team bringing AI to the physical world? Why is intelligence, not hardware, the key unlock for robotics? And how could general-purpose robots unlock 10 times more scientific progress in the years ahead?Pete grew up in the Bay Area, earned his PhD at MIT, and became a star scientist at Google DeepMind. In 2023, he left to co-found Generalist, which builds robotics foundation models to help bring general intelligence to the physical world. Its latest model, GEN-1.5, can now train robots in a matter of seconds on simple tasks such as folding laundry and sorting objects. For this conversation, we're also joined by 8VC Partner Vivek Gopalan. We begin with Pete's entrepreneurial journey and his epiphany in grad school — walk into any robotics lab and the robots were standing still most hours of the day. Pete unpacks why robots, like humans, learn through data and experience, and why intelligence, not hardware, is the next frontier for robotics. At DeepMind he specialized in bringing multimodal learning (language models + vision models) into robotics, and he's now bringing that to the next level with robotic world models at Generalist. We dive into robotics' GPT-3 moment, the emergent capabilities Generalist is seeing — including self-taught ambidexterity — and the data moats that will shape the industry. Finally, we explore what a world of abundant robots means for science, reindustrialization, and American prosperity.00:00 Episode intro01:30 Bay Area to MIT and DeepMind03:55 Pete's epiphany: robots sitting still08:30 Why intelligence, not hardware, is the next frontier13:20 The GPT-3 era of robotics14:55 Why leave DeepMind?16:50 How to train robot brains18:30 Emergent ambidexterity20:40 LLMs vs robot world models23:50 GEN-0, GEN-1 & scaling laws in robotics27:28 10x more science per year32:50 Skilled trades, reindustrialization & future of robots This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit blog.joelonsdale.com

Aaron Levie has spent two decades building Box into a cloud storage leader — and he's never been more excited for the future. What's next in the AI wave? Where is he already seeing evidence for new job creation? What do the doomers and accelerationists both get wrong? And why is Silicon Valley's biggest AI challenge self-inflicted? We discuss these topics and more in this week's episode with Aaron. Originally a college project to access his files from anywhere, Aaron dropped out of USC in 2005 to build Box. He raised his first funding from Mark Cuban and pivoted to the enterprise. Today, Box is a multi-billion dollar company serving 120,000 global customers, and Aaron has become one of tech's leading voices on AI in the enterprise.We begin our conversation with the state of model progress and the unprecedented speed of the AI era — companies going from zero to $500M in revenue in a year or two. We also discuss the Kimi K3 debate and China closing the gap on the U.S. in the AI race. Next, Aaron explains why diffusion, not intelligence, is the real rate limiter for AI adoption, and what the challenges will be at the enterprise level. Then we dive into the jobs debate: Aaron explains the 13 new types of jobs AI has created at Box, why law firms now need teams of engineers for their own custom AI models, and why mass job destruction means betting against markets themselves. Finally, we tackle Silicon Valley's self-inflicted AI crisis and why it may become the number one issue of the next presidential election.(00:00) Episode intro(01:15) AI wave / every two months is a year now(06:30) China, Kimi K3 & open-source debate(10:45) Diffusion is the real rate limiter(15:50) Agent-first startups vs incumbents(19:00) Nobody reads those contracts — AI’s new work(23:35) Silicon Valley’s AI Problem / Larry David making AI models(29:20) 13 new types of AI jobs at Box(32:20) The origins of Box / dropping out of college(37:00) Favorite angel investments right now(40:20) Optimism for America’s future This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit blog.joelonsdale.com

Aaron Cannon is applying AI to solve one of the oldest and most important challenges in business: understanding exactly what customers want and need. Traditional consumer surveys often provide low-quality data, while one-on-one interviews are cost-prohibitive and not scalable. But AI unlocks profound new possibilities. How is Aaron using AI to conduct deep, insightful interactions at scale? What happens when organizations have always-on customer understanding? And what are the new opportunities for incorporating real-time feedback into product improvements? Aaron Cannon is the co-founder and CEO of Outset, one of the fastest-growing, agent-led customer research platforms. Try it yourself here and help us improve American Optimist; we'll incorporate the top recommendations! Prior, Aaron was the head of product at Untapped and Triplebyte and worked in senior product roles at Tesla, Pebble, and Monitor Deloitte. He launched Outset in 2022 after seeing an opportunity with large language models to fundamentally change how organizations learn from the people they serve. For this conversation, we're also joined by 8VC Partner Jack Moshkovich, who helped lead our investment in Outset. We begin with Aaron’s path to founding Outset and the inefficiencies he saw in traditional customer research. Next, we explore how Outset’s AI-moderated interviews work in practice — from consumer studies to enterprise deployments at Microsoft, Google, and Uber. Learn how AI-led interviews can actually elicit more honest and in-depth responses than human-led conversations. Then, we dive into the explosive growth of the company — 8X revenue over the past year — and its “forward-deployed researchers" model. Finally, Aaron shares his vision for "self-driving research": AI that doesn’t just gather feedback but helps close the loop by identifying problems and even suggesting fixes — ultimately leading to improved products, experiences, and organizations.00:00 Episode intro01:10 Aaron’s entrepreneurial journey06:00 How does AI-moderated research work?09:25 Why people will talk to AI more than humans13:40 How Outset landed Microsoft, Google & Uber14:55 Are you replacing human jobs?20:56 New possibilities for real-time product improvements23:30 The pros and cons of synthetic research28:00 Insane growth / how Outset 8X’d revenue35:00 How AI is upending a $140B industry This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit blog.joelonsdale.com