From Swipe to Scale: How Tinder Became #1

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.

What does it take to build a company when AI is changing the rules this quickly? Ben Horowitz argues that technology may make companies easier to start, but the hardest parts of entrepreneurship, making decisions under uncertainty, building momentum, and managing your own doubts, aren't going away. Speedrun's Andrew Chen sits down with Ben to discuss why AI feels different from previous technology shifts, whether billion-dollar companies can be built with tiny teams, and why Ben thinks the application itself may eventually become less important as agents work directly against data. They also get into defensibility in AI, what founders should look for in an investor, how venture capital is changing, and why the best entrepreneurs aren't defined by a lack of weaknesses, but by having a strength or insight that's genuinely exceptional.

Amazon expects to spend roughly $220 billion in CapEx in 2026, yet CEO Andy Jassy says the company is still leaving meaningful AI demand on the table.** He believes the buildout could ultimately help make AWS a $1 trillion annual run-rate business. Andy joins a16z co-founder Ben Horowitz and Erik Torenberg to discuss why AI demand is growing so quickly, how Amazon decides how much infrastructure to build, and why lower inference costs could unlock broader enterprise adoption. They also discuss how AI is reshaping Amazon itself, from smaller teams building products faster to reimagining Alexa, and why Andy sees cybersecurity, human judgment, and critical thinking as increasingly important in the AI era.