Jesse Zhang

CEO and Co-Founder of Decagon

Claims

As speaker

Mentioned in

Key positions and views

The primary competitive moat for AI agent companies is the data flywheel; an agent's performance improves with client-specific data, making it increasingly difficult for competitors to displace.

AI application companies should aggressively pursue market share over near-term gross margins, as the underlying cost of AI models is expected to decline exponentially.

AI agents will inevitably become the primary user interface for all brand interactions, supplanting mobile apps and websites.

Foundation model providers like OpenAI will increasingly build their own applications because the API layer is becoming commoditized with high competition and low switching costs.

An intense, five-day, in-office work culture is a critical component for productivity and success in early-stage AI companies.

Podcast consensus on Zhang

Points of consensus

▶Jesse Zhang consistently argues that AI agents will become the primary interface for consumer-brand interactions, eventually supplanting websites and mobile apps [28, 46].Mar 2026

▶He believes customer service is the most significant and immediate market for enterprise AI adoption, citing large cost reductions and existing escalation infrastructure as key drivers [12, 36, 43].Apr 2026

▶Zhang posits that the primary business of foundation model providers like OpenAI is their application layer (e.g., ChatGPT), not their API, which he views as a commoditized wedge for adoption [17, 39, 45].Apr 2026

▶Across both interviews, Zhang emphasizes that the key competitive moat for AI agent companies is the continuous improvement loop from client-specific data, which makes incumbent agents increasingly difficult to displace [14].Apr 2026

Points of debate

▶Zhang champions an intense, mandatory five-day in-office work culture as crucial for productivity [6, 47], yet Decagon is also expanding internationally with a new European office, which may create operational and cultural challenges for this rigid model [44].

▶He describes the current VC climate for AI as having 'too much excitement' and being 'way too easy' to raise money [54], while simultaneously noting that his own company, Decagon, has received preemptive funding offers almost immediately after closing rounds, indicating he is a major beneficiary of this environment [20].Apr 2026

▶Zhang advocates for a 'forward-deployed engineer' model for customer success but qualifies that the true Palantir-style model is only viable for massive ($10M+) deals, suggesting most startups, including potentially his own, are using a more hands-on but less intensive version [24, 27].Apr 2026

▶He states that AI coding agents are used to augment engineer productivity rather than replace them [51], yet his broader thesis is that AI will replace low-cost labor in areas like customer service, creating a nuanced view on AI's overall impact on the workforce [18, 33].Apr 2026

Key themes

▶AI Business Strategy & MoatsApr 2026

Zhang outlines a clear strategy for AI application companies focused on capturing market share over achieving high gross margins, believing model costs will decline exponentially. He argues the most durable competitive moat is not the underlying model but the virtuous cycle of improvement from proprietary customer interaction data, making an agent progressively better and harder to replace.

Investors should assess AI agent companies not on current margins but on their ability to rapidly acquire customers and integrate deeply enough to leverage the data feedback loop that Zhang identifies as the key long-term defensibility.

▶The Future of Human-Computer Interaction

Zhang predicts a fundamental shift in user interfaces, where AI agents become the primary point of contact between consumers and brands, replacing apps and websites. He also foresees a near future where AI agents communicate with each other on behalf of humans, initially using natural language to maintain human compatibility.

This vision implies a potential disintermediation of current digital channels, suggesting that companies with the strongest conversational AI agents could own the customer relationship in the future.

▶Enterprise AI Adoption DynamicsApr 2026

According to Zhang, AI adoption in large enterprises is a top-down mandate from the C-suite, with customer service being the most common entry point due to clear ROI. He emphasizes that successful deployment is less about the AI model itself and more about the customer having a comprehensive set of internal APIs for the agent to use for data lookups and actions.

Analysts evaluating enterprise AI readiness should prioritize a company's API infrastructure and internal data accessibility over its stated AI ambitions, as this is the critical enabler for successful agent deployment.

▶Building a High-Performance AI Company

Zhang advocates for building AI companies with an intense, in-person work culture, believing it is crucial for early-stage productivity. His hiring philosophy prioritizes raw intelligence over direct experience, and he models his company's DNA on high-execution firms like Databricks.

Zhang's focus on a specific, high-intensity culture could be a key driver of execution speed but may also present scaling and talent acquisition challenges as the company grows and expands geographically.

Source episodes

Sentiment over time

Mar 2026

2 bullish(2 claims)

Jesse Zhang considers Ali Ghodsi of Databricks to be one of the most impressive ...

Apr 2026

8 bullish, 2 bearish, 7 neutral(17 claims)

Companies are using AI coding agents to augment their existing engineers and inc...

May 2026

1 neutral(1 claim)

Johnny Ho of Perplexity, Demi Guo of Pika, and Jesse Zhang of Decagon were all p...

Changes over time

Pre-2021

Founded his first company, Low-Key, which developed high-performance video capture software for gaming.

2021

Low-Key is acquired, marking Zhang's first successful exit as a founder [30].

Decagon's Inception

During initial customer discovery for Decagon, Zhang identified strong market pull in customer service, with multiple potential customers expressing willingness to sign six-figure contracts for an AI solution before the company had any revenue [13, 41].

Decagon's Early Growth

Decagon partnered with digital native companies like Rippling and Notion to iterate on its product [50] and began receiving preemptive offers for new funding rounds shortly after closing previous ones [20].

Present

As CEO, Zhang is scaling Decagon towards 200 employees, establishing a formal people function, and expanding internationally with a new office in Europe, while operating under a mandatory five-day in-office policy [38, 44, 47].

Suggested prompts

How does Jesse Zhang's strategy of prioritizing market share over gross margins align with his view of a data-driven competitive moat that requires deep, long-term client integration? &nearr;What are the potential cultural and operational challenges of maintaining Decagon's intense, mandatory in-office culture while expanding internationally to Europe? &nearr;How does Zhang reconcile his view that foundation models are becoming commoditized with the high technical barrier that still exists for building and deploying effective, specialized AI agents? &nearr;Given Zhang's prediction that AI agents will become the primary brand interface, what are the second-order effects on industries like digital marketing, web development, and mobile app ecosystems? &nearr;

Key concepts

AI Agents 15 ep Business Strategy 12 ep Customer Service 10 ep Enterprise AI Adoption 8 ep AI Market Dynamics 7 ep Company Culture & Hiring 5 ep Technical Implementation (AI) 5 ep Venture Capital 4 ep Voice AI 4 ep OpenAI 4 ep

Notable quotes

“when you think about moats in the agentic world, a lot of it is around, well, if you've kind of been working with a client for a year, has your agent just continuously gotten better by learning from the data... to the point where it's just very difficult for another agent to come in and perform at the same level?”

Jesse Zhang · The Future of AI Agents | Jesse Zhang Interview

“if you're building an application, your margin shouldn't really matter that much... What really matters is you need to get market share and you need to kind of get mind share of users.”

Jesse Zhang · The Future of AI Agents | Jesse Zhang Interview

“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.”

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

“And we've done case studies now where, you know, folks have been able to cut that down by, you know, 60, 70%.”

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

Report last updated: Apr 21, 2026

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