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No Priors: Artificial Intelligence | Technology | Startups
How AI Agents Are Transforming Customer Support, with Decagon’s Jesse Zhang
No Priors: Artificial Intelligence | Technology | Startups

How AI Agents Are Transforming Customer Support, with Decagon’s Jesse Zhang

Conviction 30m 15 months ago
At this moment of inflection in technology, co-hosts Elad Gil and Sarah Guo talk to the world's leading AI engineers, researchers and founders about the biggest questions: How far away is AGI? What markets are at risk for disruption? How will commerce, culture, and society change? What’s happening in state-of-the-art in research? “No Priors” is your guide to the AI revolution. Email feedback to show@no-priors.com. Sarah Guo is a startup investor and the founder of Conviction, an investment firm purpose-built to serve intelligent software, or "Software 3.0" companies. She spent nearly a decade incubating and investing at venture firm Greylock Partners. Elad Gil is a serial entrepreneur and a startup investor. He was co-founder of Color Health, Mixer Labs (which was acquired by Twitter). He has invested in over 40 companies now worth $1B or more each, and is also author of the High Growth Handbook.

Show Notes

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Today on No Priors, co-founder and CEO of Decagon, Jesse Zhang, joins Elad to discuss the future of agentic customer support. Decagon provides AI-powered customer interactions for companies like Rippling, Notion, Duolingo, Classpass, Substack, Vanta, Eventbrite, and more. Jesse shares the thesis behind starting Decagon, why he sees customer support as the ideal entry point for agentic technology, and what areas of AI excite him most. They also discuss voice-based interfaces, issues with latency in current capabilities, and the connection between young math olympiad communities and today’s AI startups.
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Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @TheJesseZhang
Show Notes: 
Introduction 
Starting Decagon 
Business impact of adopting agents for customer support and customer ops
AI infrastructure and models for customer success agents
Voice-based capabilities and text-to-speech engines 
Combatting latency 
Crossover of math and AI communities
Exciting areas of AI 
Strengths and weaknesses of agents

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