The New Economics of AI | Martin Casado & Steven Sinofsky

This week, Meta agreed to pay up to $17.1 billion and make major changes to Facebook and Instagram over claims it endangered children with addictive social media platforms. We discuss why it capitulated and what it means for the entire social media industry. Then, Princeton computer science professor Arvind Narayanan returns to offer his thoughts on why banning data centers won’t slow down A.I. progress. And finally, it’s all hats on deck for the final HatGPT. Guests: Arvind Narayanan, professor of computer science at Princeton, and co-author of the “A.I. as Normal Technology” newsletter. Additional Reading: Meta to Pay Up to $17.1 Billion in Landmark Settlement Over Social Media Addiction Claims Mark Zuckerberg Wants to Make Sure His Competitors Share His Pain Arvind Narayanan’s Post About A.I. Data Centers Apple Vision Pro in Surgery: A New Paper Rockstar Finally Breaks Its Silence Over The GTA 6 Leaks, Calls Them ‘Heartbreaking’ Amazon Delivery Drone Dumps Texas Woman’s Parcel Strai

Ben Horowitz, Martin Casado, Raghu Raghuram, and Erik Torenberg discuss the launch of a16z's new Machine Age Fund and the infrastructure buildout behind AI, from chips, memory, and networking to power, cooling, and data centers. Why a dedicated fund now? The group argues that the bottleneck in AI is increasingly shifting from the models themselves to everything beneath them. Hyperscaler CapEx is surging, critical components are booked years in advance, and each new generation of reasoning and agents requires dramatically more compute. They unpack why this cycle looks different from previous infrastructure booms and how AI is turning problems once constrained by engineering into problems that can increasingly be attacked with capital and compute. They also explore where the next generation of infrastructure companies could emerge, why founders are returning to hard technical problems across hardware and systems, and what it will take to rebuild the computing stack for the Machine Age. R

AI is making cyberattacks faster, cheaper, and more accessible by allowing attackers to automate tasks such as reconnaissance, vulnerability discovery, infrastructure mapping, and attack planning. In a recent Lexicon interview, Redsift CEO Rahul Pavar explains how AI is shifting cybersecurity from human speed toward machine speed, why critical infrastructure such as water utilities could become softer targets, and why organizations need to focus on fundamentals like MFA, continuous asset discovery, attack surface management, and faster threat detection. He also expects the next 6 to 18 months to bring a surge in vulnerabilities as AI uncovers weaknesses that may have existed for years but were previously too difficult to find. Also, don’t forget to subscribe to IE+ for premium insights and exclusive content!

a16z General Partners Martin Casado, Sarah Wang, and Matt Bornstein unpack the story of Cursor: how a small, product-obsessed team entered one of the most competitive markets in technology, took on incumbents with seemingly unbeatable advantages, and repeatedly made decisions that ran against conventional startup wisdom. They revisit the early bet that the interface between humans and AI would matter more than building a coding-specific foundation model, why Cursor built its own product rather than a VS Code plugin, and how the founders' ability to say "no" became one of the company's defining strengths. They also discuss Cursor's rapid evolution from IDE to agent and model platform, and why the team was willing to cannibalize its own products as AI capabilities improved. The conversation gets into what founders can learn from Cursor's approach to competition, hiring, enterprise sales, M&A, and company culture, including why the team remained unfazed by competitors from Microsoft to An