My first conversation with @alitamaseb, General Partner at @DCVC and author of Super Founders is now available wherever you get your podcasts. Ali spent years studying billion dollar startups to understand what separates their founders from the rest. We discuss the myths around age, education, cofounders, and industry experience and what the data reveals about building exceptional companies. What we cover: - The young founder myth: why age alone doesn’t predict success, and what Ali’s research reveals about billion-dollar founders. - Why repeat founders have an advantage and the early signs of ambition, resourcefulness, and a drive to build. - Inside the Super Founders Club: a network of 750 founders who become each other’s customers, cofounders, and investors. - How successful startups enter crowded markets, differentiate their products, and start with a small niche before expanding. - The myths around cofounders, technical backgrounds, and industry experience and why trust, customer understanding, and knowing when to pivot matter. TIMESTAMPS 00:00 Highlights 00:40 Introducing Ali Tamaseb 01:34 The young founder myth 06:57 Why your investors matter 10:34 Peter Thiel, competition, and crowded markets 13:36 Start small, think big 15:16 What creates a startup moat? 18:58 Customer relationships as an advantage 22:14 What makes a “Super Founder”? 24:20 Why startup success isn’t random 27:59 Venture capital is a network business 33:07 Inside the Super Founders Club 37:29 Building a community of exceptional founders 45:57 How VCs compete for the best founders 49:54 Do degrees and university brands matter? 57:19 Do you really need a cofounder? 1:00:00 Super founders in AI, defense, and deep tech 1:03:02 Great founders versus great markets 1:06:46 Finding your wedge in a massive market 1:09:25 Technical versus nontechnical founders 1:12:00 Does industry experience matter? 1:16:56 Customer discovery and knowing when to pivot 1:18:28 The risks of backing repeat founders 1:23:38 Researching and writing Super Founders 1:27:21 Selling a book through networks 1:31:35 What bestseller lists get wrong 1:34:49 Will AI change the founder playbook? 1:36:13 Why quantum is a defense bet 1:37:50 Book recommendation and Persian food

me to our GTM guy: do enterprises want benchmarks him: no, the fast ones have already benchmarked their internal use cases. the slow ones are copying the fast ones. he's onboarded a quarter of the fortune 500 already 🥵

Cape Station has reached commercial operations ahead of schedule, a first for greenfield enhanced geothermal. 24/7, carbon-free, firm power: lnkd.in/g_ED68aY

I was listening to @CompleteSkeptic on an @a16z podcast talking about the role winning a @kaggle competition played in his journey into AI: youtu.be/Ut3LOjKNJaE?t=652 One of the things I’m proudest of from building Kaggle is seeing how many people from the community have gone on to shape the current AI wave. @gdb practiced his newly learned deep-learning skills on Kaggle before co-founding OpenAI. blog.gregbrockman.com/my-pat… @NotTomBrown used Kaggle projects while teaching himself machine learning. He later became first author of the GPT-3 paper and co-founded Anthropic. youtube.com/watch?v=JdT78t1O… @fchollet introduced Keras to Kaggle competitors early on, then hosted the first ARC-AGI competition on Kaggle. ARC-AGI tests whether AI can learn new rules and solve unfamiliar problems from just a few examples. It has become an influential benchmark for frontier AI reasoning. arcprize.org/history And I’m especially proud to see @cclark, who led product and engineering at Kaggle, go on to co-found OpenRouter. Who else should be on this list?

Rainmaker has raised $100M to become the world’s best lab for weather modification and atmospheric science. Thank you @noavctech @upfrontvc @DCVC @lowercarbon @DreamVenturesVC and other visionary partners. We are aggressively hiring and scaling our research org to create a flywheel of atmospheric data collection, synthesis, understanding, and intervention. We will solve the weather and we will set the stage for the terraformation of Earth. More in my essay below.

After co-inventing ChatGPT, I kept asking myself: why have superhuman chat models not led to AGI? I’ve spent the last 2 years in stealth building a new way to train models (RLCD), and a new type of frontier AI model that we are releasing today: Jev • 20-200x faster • 40-400x cheaper (w/ output tokens free) • Frontier composable intelligence optimized for decisions AFAICT the shortest path to AI-based economic revolution