The a16z Show

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.

a16z is expanding a16z crypto into a16z Frontier, a broader mandate to back founders building at the edges of what's possible. Chris Dixon joins Marc Andreessen, Ben Horowitz, and Erik Torenberg to explain why founders should increasingly ask not “is this too ambitious?” but “is this ambitious enough?” — and how AI could pull decades of technological progress forward across hardware, robotics, entertainment, science, and entirely new forms of media. They also explore the coming internet of AI agents, why open source matters as AI becomes more powerful, and why some of the most important companies of the next decade may combine technologies and business models that previously belonged in separate categories.

AI agents can reason over enormous amounts of information, but much of the world’s actual work is still trapped in PDFs, spreadsheets, scans, and other formats that models struggle to reliably understand. Reducto co-founder and CEO Adit Abraham argues that solving this document layer is essential if agents are going to take on consequential work in healthcare, legal, finance, and insurance. Adit joins a16z General Partner Jennifer Li to explain why traditional OCR breaks down on messy real-world documents, how Reducto combines vision-language models with specialized infrastructure to reach high accuracy without sacrificing cost or latency, and why frontier models can silently drop information on long extraction tasks. Reducto now processes billions of pages each month, including its seven billionth page the morning of the recording. They also discuss Reducto’s R1 model, agents that can review and correct their own document extraction, and the company’s push beyond simply reading files toward letting agents reliably work with and edit them. Underneath it all is a higher-stakes problem: when a misread word can affect a healthcare claim or financial decision, getting the input right matters just as much as making the model smarter.