The a16z Show

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.

Voice is becoming one of the fastest paths for AI to do real work, especially in regulated environments where accuracy and compliance matter. In this episode, we look at voice agents replacing and augmenting phone-based workflows, what trust and measurement look like when AI runs sensitive interactions, and how healthcare and consumer products shift toward continuous monitoring and deeper connection. The throughline is simple: as AI enters higher-stakes moments, the winners will be the systems people can trust and actually rely on.

AI is becoming the orchestration layer inside the enterprise. In this episode of Big Ideas 2026, we explore the shift from isolated AI copilots to coordinated multi-agent systems that plan, analyze, and execute work across teams and tools. This is not a new feature, but a new way workflows run inside large organizations. You will hear from Seema Amble on context extraction and coordinated agent teams, Angela Strange on why unified data and parallel workflows accelerate core replacement, Alex Immerman on multiplayer AI and execution boundaries, and David Haber on what makes these systems commercially defensible. Together, these perspectives define the enterprise orchestration layer: not a chatbot and not a standalone tool, but a coordinated system of agents that runs the workflow and delivers real outcomes across the business.

AI is moving from chat to action. In this episode of Big Ideas 2026, we unpack three shifts shaping what comes next for AI products. The change is not just smarter models, but software itself taking on a new form. You will hear from Marc Andrusko on the move from prompting to execution, Stephanie Zhang on building machine-legible systems, and Sarah Wang on agent layers that turn intent into outcomes. Together, these ideas tell a single story. Interfaces shift from chat to action, design shifts from human-first to agent-readable, and work shifts to agentic execution. AI stops being something you ask, and becomes something that does.