The a16z Show

America’s defense, energy, manufacturing, and robotics booms are increasingly becoming one story: the rebuilding of the country’s industrial base. This year’s AD50 highlights the companies building across that stack, from critical materials and factories to the systems they ultimately power. a16z’s Erin Price-Wright and Connor Love join Elena Burger to unpack what has changed. They discuss how defense demand can help finance manufacturing capacity that ultimately serves much larger commercial markets, why AI is changing the economics of American factories, and how a new generation of founders is applying lessons from SpaceX, Tesla, Anduril, and Palantir to mining, energy, manufacturing, and defense. They also make the case that these companies are no longer simply important to America. With private capital increasingly willing to fund physical infrastructure and production before government demand is guaranteed, they can become some of the most valuable companies in the world.

Mark Cuban thinks AI could transform healthcare, but it won’t fix opaque prices and broken incentives on its own. His starting point for employers: run every benefits contract through AI and ask where you’re getting ripped off. Mark joins a16z General Partner Julie Yoo and Erik Torenberg to discuss what he’s learned building Cost Plus Drugs, why trust is its core product, and how greater transparency could put pressure on healthcare costs. They also explore where AI can help, what personalized medicine could look like, how the role of doctors may change, and why Mark believes founders should use AI to learn, not as an excuse to stop thinking.

What should school look like if AI can help students learn dramatically faster? Alpha School founder Joe Liemandt argues the answer isn’t to fill the extra time with more academics, but to redesign school around the skills traditional classrooms struggle to teach: agency, teamwork, entrepreneurship, communication, and doing hard things. Joe joins Erik Torenberg and Horowitz and Andreessen Academy co-founder Gagan Biyani to discuss Alpha’s model, where students spend two hours a day on core academics and the rest developing real-world skills through projects. They explore what that approach can teach higher education, why motivation may matter more than curriculum, and how HAA is applying similar principles to college-age students by putting building, relationships, and real-world experience at the center of the learning experience. They also discuss what parents need to believe before choosing a new educational model, why Joe expects AI to drive an explosion in entrepreneurship, and why the most important question for students may not be which field is safest from AI, but what they care enough about to build.

AI agents are approaching a point where they can use computers faster than expert humans, turning the user interface itself into a universal way for AI to interact with software. OpenAI’s Ari Weinstein and James Sun argue that computer use is now entering its scaling moment, as better models, faster execution, and stronger safety controls make agents practical for more consumer and enterprise work. Ari and James join a16z’s Seema Amble and Fabrizio Serafini to explain how computer-using agents are already navigating legacy ERPs, reconciling financial data, handling procurement, and operating software without APIs or custom integrations. They also discuss why the harness around the model can matter as much as model intelligence, how enterprises can set deterministic guardrails around agent behavior, and why computer use could simultaneously extend the life of legacy software and accelerate the arrival of new AI-native applications. Looking ahead, they make the case for a more proactive and fluid relationship with computers, where agents work in the background, collaborate with users in real time, and increasingly adapt to us rather than requiring us to learn how to use them.

Can a 10-person startup outperform a company with hundreds of employees? Box CEO Aaron Levie argues AI agents are giving small teams a level of leverage that simply wasn't economically possible before, creating a window for startups to outrun incumbents. In this episode of Speedrun Office Hours, Aaron explains why founders are shifting from doing the work themselves to orchestrating agents, and why that puts a new premium on strategy, taste, and knowing exactly what to build. He makes the case that nearly every industry and job function could support specialized AI agents, potentially creating hundreds of new billion-dollar companies. They also discuss why enterprise AI may capture much of the next wave of model improvements, how dramatically cheaper AI labor could unlock demand that doesn't exist today, and one thing Aaron thinks AI won't change: distribution. If you build it, they still won't necessarily come.