The a16z Show

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.

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.

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.

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.

Why hasn't AI transformed everyday work as quickly as coding? Town CEO Jean-Denis Greze argues the bottleneck isn't intelligence, but making AI useful to people who don't have the time or technical skills to build their own automations. Jean-Denis joins a16z General Partner Alex Rampell to share why he pivoted Town from tax software to a personal AI assistant built around email, calendars, and the workflows that run businesses. They discuss why consumer-grade experiences may win in enterprise AI, how Town discovers and automates tasks users wouldn't think to delegate, and why model neutrality and data ownership matter as AI labs increasingly compete with application companies. They also explore what happens when agents begin acting on our behalf, from managing everyday work to shopping and commerce, and why the next generation of software may need to adapt to each user rather than the other way around.

Why is booking a doctor's appointment still so difficult, and can AI finally change that? Zocdoc CEO Oliver Kharraz argues that the real problem isn't finding doctors, but connecting patients to accurate availability and making appointments bookable wherever they start their search. Oliver joins a16z General Partner Julie Yoo to discuss how Zocdoc spent nearly 20 years building the infrastructure behind online scheduling, and why it's now opening that technology to AI agents, insurers, and other platforms. They explore Zocdoc's partnership with Gemini, why patients shouldn't be locked into a single healthcare platform, and how surfacing last-minute availability can reduce appointment wait times from weeks to days. They also discuss what happens when AI becomes a new front door to healthcare and why Oliver believes the future of Zocdoc extends far beyond its own app.

a16z General Partner Alex Rampell sits down with VGS Executive Board Member, Terry Angelos, to unpack what happens to payments when AI agents go from recommending what to buy to actually making the purchase. Terry explains the infrastructure required to safely give agents access to payment credentials, why wallets like PayPal, Shop Pay, and Link are competing to become the default payment layer for agents, and how new agentic tokens could preserve the chargebacks, identity checks, and merchant relationships that make today's payment networks work. VGS, which already secures payment credentials across a significant share of e-commerce, is now provisioning millions of these agentic tokens each week. They also discuss what happens when merchants don't want to transact with agents, how delegated authority changes liability when an agent makes a mistake, and why AI could simultaneously increase consumer spending while eliminating the subscriptions, fees, and other forms of economic "breakage" that depend on people not paying attention.

Could AI break the cryptography protecting everything from crypto wallets and banking to private messages? Recent advances in AI-driven mathematical research have sparked new warnings about the security of today's encryption systems, but how much of the concern is supported by evidence? a16z crypto's Liz Harkavy is joined by Stanford cryptographer Dan Boneh, a16z crypto General Partner Eddy Lazzarin, and Research Partner Justin Thaler to separate what we know from what remains speculation. They explain why modern cryptography relies on mathematical assumptions, what OpenAI's recent results do and don't demonstrate, and how the potential threat from AI differs from quantum computing. They also discuss why existing security practices and cryptographic alternatives offer reasons for confidence, the importance of building systems that can adapt if an assumption fails, and how AI could ultimately help researchers and developers make software more secure.

Nick Bostrom joins Theo Jaffee and Sofia Puccini on MTS to revisit the ideas about superintelligence and existential risk that he helped bring into the mainstream, now that increasingly capable AI systems actually exist. Nick discusses why reinforcement learning may be pushing models toward more goal-seeking behavior, what recent agent experiments tell us about instrumental convergence, and why he favors preserving the ability to slow frontier development rather than committing now to a broad or indefinite AI pause. He also makes the case that the risks of AI need to be weighed alongside its potential upside, from advances in medicine and longer lifespans to entirely new ways of being human. They also get into whether today's models could already have some form of consciousness, the tradeoffs of open-source AI, whether machines can do original philosophy, mind uploading, and what the simulation hypothesis might mean for the superintelligence we create.

a16z's Raghu Raghuram and Guido Appenzeller sit down with Playground Global General Partner and former Intel CEO Pat Gelsinger to discuss the next wave of semiconductor innovation and the physical constraints shaping the AI buildout. Drawing on his experience designing Intel's 386 and 486 processors, Pat explains how AI could transform chip design, but also why faster design alone won't solve the industry's biggest problems. They examine the bottlenecks in manufacturing, memory bandwidth, advanced packaging, and power, and why today's explosion of specialized AI chips may eventually consolidate around a smaller number of architectures. They also discuss the potential for new memory technologies, the shift from copper to optical networking, and why energy capacity could become a major constraint on AI growth. Finally, they revisit Pat's VMware years to ask what virtualization might look like when infrastructure is built for agents rather than humans.

a16z General Partner Angela Strange sits down with Valon’s Andrew Wang and Linda Du to unpack what it takes to rebuild the infrastructure underneath a $13 trillion mortgage market that still relies heavily on systems designed before the internet. Linda and Andrew explain why Valon chose the hardest path: becoming a regulated mortgage servicer, translating decades of federal and state regulation into software, and proving the platform on its own loans before selling it to the industry. That foundation made Valon roughly three times as efficient as traditional servicing and created the system of record it is now using to bring AI into complex mortgage workflows. They also discuss what AI makes possible on top of that infrastructure, from agents handling long-tail servicing tasks to voice interfaces and more personalized customer experiences. And they explain why deploying the technology into large regulated enterprises is ultimately as much a change-management challenge as a technical one.

a16z Editorial Partner Elena Burger sits down with investing partners Olivia Moore and Josh Elman to unpack the seventh edition of a16z’s Top 100 Consumer AI Apps, including a new dimension this time: what consumers are actually paying for. The data reveals a striking power-user economy. Only a small share of consumers currently pay for AI, but among those who do, spending is heavily concentrated at the top. Olivia and Josh discuss why developers, creators, and other power users dominate spending today, and why subscriptions may not be the business model that ultimately brings consumer AI to everyone. They also dig into the rise of personal agents, the different trajectories of ChatGPT, Claude, and Gemini, how ads could reshape AI economics, and the enormous amount of consumer white space still left to build, from shopping and entertainment to social, dating, and marketplaces.

A16z’s Erik Torenberg sits down with OpenRouter’s Alex Atallah and Replit founder and CEO Amjad Masad to discuss why the future of AI may look less like one all-purpose model and more like an ecosystem of specialized models working together. Alex explains why OpenRouter is betting on “neurodiversity”: different models trained in different ways, routed and combined based on the job at hand. Amjad makes a similar case from inside the enterprise, where companies increasingly need to own their AI capabilities rather than depend entirely on a single model provider. They explore what happens when general-purpose agents give way to teams of specialized agents, why smaller models can sometimes be cheaper, safer, and easier to control, and how routing and model fusion could deliver frontier-level performance at lower cost. They also get into agent-to-agent communication, AI security, and why the next generation of companies may need an independence layer across models, clouds, and data.