About this episode

Parag Agrawal discusses the future of web search with AI agents and the economic implications of this shift.

Parag Agrawal is making a bet that goes against two decades of web search: agents will query the web a thousand times more than humans ever have, and the infrastructure built around human clicks is wrong for them. The former Twitter CEO, now founder and CEO of Parallel Web Systems, explains why Parallel treats human click data as a bug and trains on agent feedback instead. He unpacks the counterintuitive choice to ship a search agent before a search engine, building an index incrementally, and how the new Turbo product cut agentic search to 200 milliseconds. But the problem Parag keeps returning to is economic: the ad-supported internet collapses when agents show up instead of people. His fix draws on Shapley values to pay content owners for the value their pages provide agents, with real dollars reaching publishers, he predicts, within 12 to 24 months. Hosted by Sonya Huang and Andrew Reed, Sequoia Capital 00:00 Introduction 03:25 What Is Web Search 05:17 Why Start a New Index 07:52 Search Agents First 10:17 Not a Neolab 13:14 Agents vs Google Search 19:38 Inside the Search Stack 28:59 Search Multipliers With Agents 30:21 Meeting Prep Agent Workflows 31:46 Quality Cost Latency…

People in this episode

Hosts: Sonya Huang, Andrew Reed

Guest: Parag Agrawal

Topics covered

• AI agents
• web search
• search infrastructure
• economic models
• content monetization
• Shapley values

Keywords

• AI agents
• web search
• search infrastructure
• content monetization
• Shapley values
• Turbo product
• ad-supported internet

Mentioned in this episode

Organizations: Parallel Web Systems, Twitter, Google, Sequoia Capital

Books & works: Turbo product

Places: United States

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