No Priors Ep. 132 | With Decagon CEO and Co-Founder Jesse Zhang

The traditional call center may soon be a thing of the past. Jessie Zhang is building AI agents designed to replace monotonous human labor and transform how consumers interact with brands. Elad Gil sits down with Jesse Zhang, co-founder and CEO of Decagon, an AI agent company at the forefront of AI customer service. Jesse talks about how Decagon secured large enterprise clients and the impact of its AI agents, his journey as a second-time founder, and Decagon’s company culture. Plus, they discuss what the future of agentic customer service may look like. Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @thejessezhang | @DecagonAI Chapters: 00:00 – Jesse Zhang Introduction 00:30 – Decagon’s Services 01:11 – Decagon’s Customers and Growth 02:41 – Productivity Gains with Decagon 03:33 – How Decagon Integrates in Customer Workflows 04:25 – Jesse’s Second Time Founder Story 05:41 – Jesse’s Hiring Philosophy 09:13 – Counter-intuitive Advice for Founders 11:19 – How Decagon Thinks About Talent 14:12 – Areas for Longer Term Planning 15:37 – Decagon’s Path to Customer Service 16:57 – Thoughts on Pushing Into the Application Layer 19:40 – What Decagon Does Uniquely 22:05 – Pricing Services in the AI Age 24:46 – How Decagon Sees Customer Service 25:53 – Defining Long-Term Success for Decagon 27:41 – Jesse’s Views on an Agentic Future 31:22 – Conclusion

Elad GilhostJesse ZhangguestSarah Guohost

Sep 17, 202531mWatch on YouTube ↗

WHAT IT’S REALLY ABOUT

Decagon’s AI Concierge Is Quietly Reinventing Enterprise Customer Conversations Globally

Decagon CEO and co-founder Jesse Zhang explains how the company builds AI customer service agents that handle high-volume, complex conversations for major enterprises like banks, airlines, and telcos. The agents integrate into existing systems and replace mundane human labor while improving both efficiency and customer satisfaction, often cutting contact center costs by 60–70%. Zhang discusses how second-time founding, commercial discipline, and an intense in-office culture helped Decagon reach large enterprises quickly and scale past 200 employees. He also explores pricing models, defensibility versus foundation model providers, and a future where consumer and enterprise agents talk directly to each other to get things done.

IDEAS WORTH REMEMBERING

5 ideas

AI agents can dramatically reduce contact center costs while improving satisfaction.

Decagon’s enterprise deployments have shown 60–70% reductions in contact center spend, while maintaining or improving customer satisfaction scores, making the ROI case straightforward for large organizations.

Success in AI enterprise adoption is increasingly top-down and board-driven.

Unlike prior tech waves that started with single teams experimenting, AI adoption is now framed as company-wide “AI transformation,” with C-suites prioritizing customer service as low-hanging fruit.

A productized, non-technical-friendly platform is a key differentiator in enterprise AI.

Decagon explicitly designs its system so business users, not just engineers, can configure, iterate, and analyze AI agents—contrasting with legacy SaaS that required heavy technical implementation and maintenance.

Early-stage AI startups benefit from intense, in-office, execution-focused cultures.

Zhang argues that most leading AI companies are heavily in-office because colocation radically accelerates iteration speed, especially before the company reaches larger scale where remote can be more workable.

Founders must deliberately shift from short-term deals to long-term product and org design.

Once initial product-market fit is found, continuing to optimize only for near-term customer wins leads to future bottlenecks; investing in core product capabilities and thoughtful org structure prevents later breakdowns.

WORDS WORTH SAVING

5 quotes

You can kind of think of us as a conversational UI for the brand.

— Jesse Zhang

We’ve done case studies now where folks have been able to cut [contact center spend] down by 60–70%.

— Jesse Zhang

One of the things that LLMs unlock is that you can really empower the non-technical business users.

— Jesse Zhang

If you join a pre-PMF team and you never actually get to see the commercials in action, you’re not really learning much.

— Jesse Zhang

Eventually you want this to be a unified concierge experience… it becomes the go-to way that [customers] interact.

— Jesse Zhang

Decagon’s AI customer service agents and enterprise use casesGo-to-market motion, early traction, and working with large enterprisesHiring philosophy, culture, and building a high-intensity in-office teamShifting from short-term execution to medium- and long-term planningDefensibility, differentiation, and relationship to foundation model providersNew AI-era pricing models and the size of the “labor as a service” marketFuture of agentic interactions, personal AI assistants, and unified concierge experiences

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