Garry Tan: The traditional business rule that 'revenue growth must be accompanied by large-scale hiring' is failing

Y Combinator President and CEO Garry Tan stated that AI startups Emergent and Retell are achieving rapid recurring revenue growth with very few employees, indicating that the traditional business rule of 'revenue growth must be accompanied by large-scale hiring' is failing.

Reportedly, Emergent achieved a nine-figure annualized revenue in about 8 months with a team of around 15 people; based on a lower limit of $100 million annualized revenue, its annualized revenue per employee is at least about $6.67 million. This figure has not been corroborated by Emergent's official financial reports or audit documents and should be viewed as operational data cited by Garry Tan rather than verified revenue disclosures.

Retell is described as having an annualized recurring revenue of about $60 million with a team of around 40 people, corresponding to an annualized revenue per employee of about $1.5 million. Public information shows that Retell previously disclosed about $40 million in annualized revenue and a team of about 25 people, with subsequent market estimates placing its annualized revenue at about $60 million; differences in figures can arise from varying time points, statistical criteria, and employee scopes.

Retell's product positioning is as a real-time AI voice agent infrastructure for enterprises, which customers can use for call centers, sales, customer service, and phone automation. Its core business logic is to replace some high-frequency, standardized phone tasks traditionally handled by agents with models, speech recognition, speech synthesis, workflow orchestration, and real-time call infrastructure.

Garry Tan proposed the organizational view that 'one Markdown file equals one employee': companies write repeatable processes, knowledge bases, task specifications, and failure correction rules into callable documents, which are then executed by AI agents. After each manual correction of agent errors, the process can be rewritten as new rules, allowing organizational knowledge to expand without increasing management layers, training cycles, and repetitive human resources.

In market mechanisms, buyers are enterprise clients looking to reduce costs in customer service, sales, R&D, operations, and outsourcing; sellers are AI companies providing model invocation, voice agents, code generation, and automation orchestration capabilities. Funding will shift from traditional BPO, low-end SaaS implementations, repetitive operational roles, and substantial middle management coordination costs to GPU cloud resources, foundational model suppliers, agent platforms, data governance, and high-value sales deployment teams; beneficiaries will be AI vendors that master customer workflows and low marginal cost delivery capabilities, while service companies relying on 'headcount expansion' to maintain revenue will face pressure.

Source: Public information