Tesla's Road Ahead: The Bitter Lesson in Robotics

This episode is a special replay of David George’s conversation with Harry Stebbings on 20VC. David is a General Partner on a16z’s growth team, and in this discussion he breaks down how he thinks about breakout growth investing: why great business models are now table stakes, where real edge comes from non-consensus views on TAM, and how to underwrite upside in a world of higher prices and increasing competition. They also dig into the mechanics behind the scenes: unit economics at growth, “pull vs push” products, winner-take-most market structures, and how David decides when to double or triple down on a company. Along the way, they touch on SPACs, the rise of crossover funds, single-trigger decision making, and how David manages fear, pressure, and performance over the long arc of an investing career.

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.

As 2025 comes to a close, consumer AI is entering a new phase. A small number of products now dominate everyday use, multimodal models have unlocked entirely new creative workflows, and the big labs have pushed aggressively into consumer experiences. At the same time, it is becoming clearer which ideas actually changed user behavior and which ones did not. In this episode, a16z consumer investors Anish Acharya, Olivia Moore, Justine Moore, and Bryan Kim look back at the biggest product and model shifts of 2025 and then look ahead to what 2026 may bring. They discuss why consumer AI appears to be trending toward winner-take-most, how subtle product design choices can matter more than raw model quality, and why templates, multimodality, and distribution are shaping the next wave of consumer products. Where do startups still have room to win? How will the role of the big labs continue to change? And what will it actually take for consumer AI apps to break out at scale in 2026?

New infrastructure primitives are creating entirely new rails for building. In this episode of Big Ideas 2026, we explore three foundational shifts that unlock new markets and workflows, not through incremental upgrades, but through primitives that compound over time. First, programmable money evolves beyond stablecoins into on-chain credit origination and synthetic financial products, offering lower operational costs and greater composability than traditional finance. Second, autonomy begins entering scientific research through collaborative labs, where AI reasoning models work alongside automation and robotics, and interpretability becomes essential for progress. Third, distribution itself becomes a primitive, as AI-native startups win early by selling to other startups at formation, then scale alongside the next generation of companies. You will hear from Guy Willette on the next phase of on-chain finance, Oliver Shu on autonomous labs and AI-assisted discovery, and James da Costa on the greenfield go-to-market strategy. Together, these ideas define what new infrastructure primitives really mean: the rails that enable entirely new systems to emerge, compound, and scale.

AI is moving into the physical economy. In this episode of Big Ideas 2026, we explore what changes when AI leaves the screen and becomes part of factories, construction sites, supply chains, and critical infrastructure. When the product is physical, reliability matters, real-world constraints appear quickly, and the advantage shifts from standalone software to end-to-end systems. You will hear from Erin Price-Wright on factory-first principles, Ryan McEntush on the electro-industrial stack, Zabie Elmgren on physical observability, and Will Bitsky on why data, not compute, determines who wins. Together, these ideas define what physical AI really means: not smarter chat, but deployable systems built for the real world, grounded in new operating models, industrial infrastructure, and defensible data collection.