Sep 18, 2025 · 31m · no-priors
No Priors Ep. 132 | With Decagon CEO and Co-Founder Jesse Zhang
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gold bands on the timeline = statements, start to end. Hover to read, click to jump. CC turns on captions
In this episode of No Priors, Decagon co-founder and CEO Jesse Zhang joins Elad Gil to discuss building enterprise-grade generative AI customer service agents, scaling a high-intensity startup culture, and the transition toward outcome-based software and agent-to-agent commerce.
How this conversation actually went
Every chapter scored 0–10 on four independent dynamics. Hover any point for the reasoning behind the score. The hosts hold 29.4% of the talking time here. How this is scored →
The hosts as informed peer 4.7 Guest teaching 2.9 Guest disagreement 0.2 The hosts pushing back 0.2
speaking balance: gold is the hosts, purple is the guest (3 minute bins)
Sharpest disagreement ▶ 11:32 Reframing early-career startup selection advice
Jesse politely but firmly dismisses the common belief that engineers should join pre-PMF startups to learn how to be founders, stating they just learn what not to do.
Hardest push from the hosts ▶ 18:18 Pushing back on API business economicsElad challenges Jesse's dismissal of foundation model API businesses by citing AWS as proof that low-margin infrastructure at scale yields massive profitability.
Biggest teaching moment ▶ 13:08 The neural network metaphor for founder learningJesse educates the audience and host on startup talent development using a machine learning metaphor where positive training examples dramatically increase learning rates.
The host holds their own ▶ 16:57 Elad's historical platform forward-integration thesisElad demonstrates deep historical expertise by comparing AI model labs entering applications to Microsoft bundling Office and Google launching vertical search.
the scores for every segment, with the reasoning behind each
| Chapter | Topic | The hosts as informed peer | Guest teaching | Guest disagreement | The hosts pushing back | Why |
|---|---|---|---|---|---|---|
| Defining Decagon as a Brand Concierge | 4 | 2 | 0 | 0 | Elad establishes the premise of Decagon and asks about early enterprise adoption. Jesse explains how starting with digital natives led unexpectedly quickly to upmarket enterprise demand. | |
| Operational Efficiency, Integration, and 24/7 AI Agents | 4 | 3 | 0 | 0 | Elad asks how ROI is measured and how integrations work. Jesse breaks down direct 60-70% contact center cost reduction alongside high CSAT and CRM compatibility. | |
| Hiring Philosophies and In-Office Work Culture | 5 | 2 | 0 | 0 | Elad compares the AI work ethic to elite athletes training constantly. Jesse explains Decagon's five-day in-office culture and hiring for general horsepower over narrow experience. | |
| Scaling Infrastructure and Transitioning to Long-Term Thinking | 4 | 3 | 0 | 0 | Elad probes on counter-intuitive advice for first-time scalers. Jesse discusses the necessity of switching from a greedy short-term sales mindset to long-term architectural and organizational planning. | |
| Startup Selection Strategy for Aspiring Technical Founders | 5 | 4 | 1 | 0 | Jesse challenges the standard advice that aspiring founders should join pre-PMF startups, arguing that joining post-PMF companies like Decagon provides critical positive training examples on commercial execution. | |
| Balancing Immediate Deal Demands with Core Product Investment | 4 | 2 | 0 | 0 | Elad asks how Jesse found customer service as the core application. Jesse shares that seeing immediate six-figure willingness to pay cut through intellectual doubts about the idea being too obvious. | |
| Defensibility Against AI Labs and Enterprise Tooling Depth | 6 | 4 | 1 | 2 | Elad presents a historical framework of platform providers forward-integrating into killer apps, citing Microsoft Office and Google vertical search. Jesse acknowledges lab ambitions but details the deep enterprise software layer (observability, simulation, QA) required to win. | |
| Differentiating from Legacy SaaS by Empowering Business Users | 4 | 3 | 0 | 0 | Jesse explains Decagon's core moat and product differentiation against legacy systems like Salesforce Agentforce by empowering non-technical business users rather than requiring heavy developer overhead. | |
| Outcome-Based Pricing and Expanding Total Addressable Market | 6 | 3 | 0 | 0 | Elad and Jesse discuss how outcome-based per-conversation pricing fundamentally transforms SaaS TAM by unlocking the entire human labor services pool rather than counting software seats. | |
| Unifying Siloed Enterprise Workflows into a Single Concierge | 5 | 3 | 0 | 0 | Jesse explains how enterprise customer touchpoints are currently fragmented across siloed departments and how Decagon unifies them into an overarching brand concierge. | |
| The Emerging Reality of Agent-to-Agent Commerce | 5 | 3 | 0 | 0 | Elad analogizes personal AI agents to Roman baths and elite personal assistants democratized over time. Jesse outlines how agent-to-agent negotiations will function in natural language and expand into proactive purchasing. |
Statements from this episode (25)
Zhang: Decagon is evolving into a conversational UI concierge for brands
“And as we, as we've grown, it's kind of becoming more and more of you gotta think of like a conversational UI for the brand where it's how every user can interact with it. And we often use the term like concierge to describe this, but that's what we do.”
Zhang: Enterprise AI adoption is top-down, with customer service as lowest-hanging fruit
“Yeah, I mean, another specific dynamic is that at the enterprise, it's becoming a lot more of a top-down motion, so You know, in the past, any of these technologies could have been just, like, one team trying to vet it or decide it, but now it's like a, it's a…”
Zhang: Decagon cuts enterprise contact center costs by 60% to 70%
“And we've done case studies now where, you know, folks have been able to cut that down by, you know, 60, 70%.”
Zhang: Technical Founders Have Untapped Potential When Embracing GTM Problems
“One of my I guess theses is that there is a lot of untapped potential and just like really strong technical folks in making them a bit more commercial because the types of problems on the go to market side there, I would say generally a little bit more hairy. …”
Zhang: Decagon prioritizes raw intelligence over direct experience across all roles
“We're generally just selecting for very smart people. First of all, I think we care more about that than like, you know, direct experience and so on.”
Zhang: Decagon mandates five days in-office, with many working weekends voluntarily
“We're five days and then a lot of folks come in on the weekends, but it's not like a requirement.”
Zhang: All successful AI companies share heavy in-office cultures
“I think pretty much all the AI companies that are doing well have, you know, pretty heavy in office cultures. It's just, you get way more done, especially, especially in the early stage.”
Zhang: Decagon is approaching 200 employees
“Never really needed that, but, you know, we're approaching 200 people.”
Zhang: Post-PMF founders must shift from short-term to long-term thinking
“I think at the beginning you have to short, you have to think short term. You're just optimizing for closing the deal or closing a couple customers, but once you have your legs under you both can think more long-term and also you have obligation to, because if…”
Zhang: Aspiring founders learn more at post-PMF startups than pre-PMF companies
“Generally, when I talk to engineers that want to join startups, for example, and let's say they eventually want to start their own company, which is a very common profile, it's, in my opinion, it's like way more useful to join somewhere where they've already k…”
Gil: Golden growth period for companies is between 50 and 2,000 employees
“I think a lot of the golden periods for many companies is between, say, 50 and a hundred people up to, you know, a thousand, maybe 2000 if the thing keeps going in terms of growth, because that's the era where I think you see the most change”
Zhang: Customer service AI drove six-figure demand at zero ARR
“The real answer is we just saw a lot of folks that were willing to pay us like, you know, six figure contracts, which at the time when you're at zero ARR, it's like,
oh wow, that's huge.
And a lot of folks that were willing to, you know, do the same thing.
And…”
Gil: OpenAI tried to acquire Windsurf to enter the AI coding market
“OpenAI famously tried to buy Windsurf and sort of enter coding more directly.”
Zhang: Most OpenAI revenue and margin comes from ChatGPT, not API
“I think OpenAI, for example, most of their revenue and most of their margin for sure is coming from ChatGPT and the application layer, because you actually own the customer”
Zhang: Foundation model labs will target consumer applications before enterprise software
“I think it makes a lot of sense for them to push into application layer, and I think they will. In terms of what applications, I mean, generally they'll probably start with applications where it's more consumer prosumer-y because there's, it's just more self-c…”
Zhang: AI labs will target coding before customer service
“I think before they tackle our space, there will probably be other spaces they have to tackle first. Yeah. Coding's probably one of them.”
Zhang: Engineering teams want to offload AI agent logic to business users
“What we found is even when the engineering teams are very much involved, they don't necessarily want to be on the hook for every little change, and so in that case, we can work very well with them, and you have them still owning how does the AI agent interact …”
Zhang: Salesforce's configuration-heavy SaaS model is wrong for the AI era
“Again, obviously, we respect like the sales forces of the world that build amazing businesses, but we just don't think that's the right approach for the AI era.”
Zhang: Pricing AI agents per minute incentivizes needlessly long calls
“You also don't want to price per like, you know, minutes of the call either like that. That's just kind of weird. And also incentivizes the agents to just like have really long calls. So you price basically the number of conversations that it can have.”
Zhang: AI agent industry will shift toward output-based pricing
“And we'll probably start seeing that more and more in the AI agent space where you generally price per like the output that that's doing. I think that that works. I think that's just very clearly the right pricing model for our space”
Gil: Outcome-based AI pricing expands software TAM into human labor budgets
“It also really changes how you think about the total addressable markets for some of these things, because if you're charging per seat, you're really limited by the number of people working at the company. If you're charging per conversation or per some aspect…”
Zhang: Consumers Will Interact With Agents Over Apps and Websites
“And eventually, if it's good enough, most consumers will just interact with the agent instead of even logging into the mobile app or the website.”
Zhang: Near-Term Agent-to-Agent Communication Will Remain in Natural Language
“I think in the near term, they'll still communicate in natural language, just because, like, each agent also needs to be compatible with humans, right? So if they talk to a human agent, a human support agent, or if we talk to a human customer, of course it has…”
Zhang: Consumer AI Agents Contacting Enterprise Support Not Yet Happening at Scale
“It's not something we're seeing at scale now where you have agents writing in for you.”
Zhang: Customer Support AI Will Expand into Upselling and Proactive Outreach
“Right now a lot of the conversations are more reactive support. It's like, hey, I have an issue. Can you fix it? But over time, it'll be more and more kind of broader, right, in terms of, like, being able to do purchasing decisions, being able to upsell folks,…”