Parallel’s Parag Agrawal: Building a New Web for AI Agents

ℹ️ EPISODE INFO

Released

August 25, 2026

Duration

55m

Channel

Sequoia Capital

Watch on YouTube

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📝 EPISODE DESCRIPTION

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 And Turbo 32:42 Are Agents Overtaking Humans 34:28 Ads Model Meets Agent Web 37:20 New Incentives For Content 40:48 Shapley Values Attribution 47:46 Parallel Web And Future Vision

🎤 SPEAKERS

• Parag Agrawal

  guest

  Founder of Parallel and former Twitter CEO focused on building agent-first web search and infrastructure.

• Sonya Huang

  host

  Sequoia Capital host of the Training Data podcast.

• Andrew Reed

  host

  Sequoia Capital co-host on Training Data who joins the conversation with questions and prompts.

🧾 EPISODE SUMMARY

In this episode of Sequoia Capital, featuring Parag Agrawal and Sonya Huang, Parallel’s Parag Agrawal: Building a New Web for AI Agents explores Parallel builds agent-first web search and new incentives for content Parallel is building search infrastructure and products designed specifically for AI agents to search and use the web at far higher volumes than humans.

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