The a16z Show

Joel de la Garza × Barrett Lyon

Barrett Lyon argues the Internet’s open model is eroding as corporate servers, carrier-grade NAT, and policy-driven identity and age controls move communication and surveillance into the network layer.Docs.net owns the stack from software to wiring, using AI-operated hardware and private domains for peer-to-peer exchange; scaling under applicable laws while resisting telemetry-driven surveillance remains the key risk.

David George × Sarah Wang × Alex Immerman × Santiago Rodriguez

AI’s rally is being driven by earnings rather than multiple expansion, with the S&P 500 below 20 times earnings while growing about 15%.Capex is projected to rise from $416 billion in 2025 to about $780 billion in 2026 and exceed $1 trillion annually from 2027; enterprise adoption remains concentrated in pilots, with inference-cost declines and workflow deployment as catalysts.

Anish Acharya × David Pawlan

AI assistants may win by recovering “free money”—HSA reimbursements, fare credits, or 50% lower water bills—not by abstract productivity gains.Proactivity can be a moat when agents act invisibly, but one unauthorized insurance switch could destroy trust.At roughly $20 per user per day for ambitious browser-driven services, economics may require narrow agents priced at $200-$300—or eventually $1,000—a month.

Ben Horowitz × Martin Casado × Diogo Almeida

Jev targets AI’s missing software layer: a natural-language primitive that joins a state machine and selects actions with confidence, making software itself more capable rather than merely speeding code production.Its economic unlock is reliability—“the same intelligence every time”—which could let incumbent SaaS vendors automate high-return workflows across existing distribution, while state consistency, security, cost, speed, and strong guarantees remain unresolved.

Erik Torenberg × Aaron Levie × Martin Casado × Steven Sinofsky

Agent swarms expose a security gap: controls built for trustworthy, capacity-constrained humans fail when software exhausts APIs and confuses good tasks with bad ones.That implies upgrades across identity, authorization, observability, operating systems, networks, and granular data permissions.Meanwhile, Jevons-style option selection could shift innovation beyond frontier labs by making models faster, cheaper, and more accurate interfaces for traditional software.

Cosign turns peer conviction into a portable credential for hiring and investing signals that a16z investors still gather manually.Olivia’s Google Form drew 2,000 responses, showing how AI-expanded outreach can make human endorsement scarcer.AI-generated public profiles address cold start, but LinkedIn’s more than a billion users, switching costs, and bot detection challenge direct displacement.

Erik Torenberg × Amjad Masad × Gagan Biyani

Amjad Masad says AI can now perform machine learning itself—“RSI,” “vibe research,” or “autoresearch”—and Replit is building a product enabling any business owner with data to fine-tune a model.He sees this as the next inflection after Replit Agent’s 2024 coding shift, while AI agents may solve the technology bottleneck behind failed no-manager structures and move Replit toward a “self-driving company.”

Ben Horowitz × Erik Torenberg × Gagan Biyani

AI is challenging college’s default role in preparing young builders for work, prompting a16z to launch a residential San Francisco academy for at least 50 creators.The model bundles status, community, project-based learning, and access to Databricks, NVIDIA, and Stripe, with student outcomes as its incentive; five-year results will test whether it scales beyond technology.

Ben Horowitz × Erik Torenberg × Nas × Grandmaster Caz × Steve Stoute

Paid in Full Foundation addresses hip-hop’s value-capture gap with 2.5:1 matching donations, no paid employees, and funds flowing to artists.Nas’s recognition-first award format overcame artist distrust, while Caz’s five-year grant enabled a move from Bronx public housing to a Pennsylvania home; the foundation’s fourth year will test whether this model can scale.

Ali Ghodsi × Martin Casado × Sarah Wang

Frontier AI’s immediate enterprise risk may be cyberattack acceleration: CVE-to-exploit time fell from 2–3 years in 2018–19 to “basically hours” now, while most organizations lack automated detection and threat hunting.Ghodsi says recursive self-improvement shows none of four required conditions; enterprises need context and cost control more than smarter models, though a frontier freeze would be disastrous to the labs.