Investor Patrick Feeley commented on Y Combinator President Gary Tan's speech at Startup School, stating that Tan was not arguing for the rise of one-person companies but rather describing operational models that can already generate numbers. Most valuations still treat headcount as a scarce input, while what is truly scarce are tightly written processes that can be executed daily by intelligent agents, and the routing that allocates work to the correct processes.

Feeley provided an example: Emergent, founded by Mukund Jha and Madhav Jha, was noted to have achieved approximately $15 million in annualized revenue with about 15 people, reaching nine-figure revenue about eight months after going public; in July, they raised $130 million in Series C funding, with a post-money valuation of $1.5 billion, annualized revenue of about $120 million, and over 200,000 paying users, with around 200 employees at that time. The voice AI company Retell has an annualized revenue of about $60 million with a team of about 40 people. Revenue per employee exceeds the old software and industrial benchmarks.

Tan mentioned in his speech that the next generation of startup teams will be smaller, as personal-level general intelligent agents can now compound knowledge on their own infrastructure; a skills manual is equivalent to one employee, and in the winter batch, 95% of the code from a quarter of the companies was generated by AI. Feeley added a caveat: untrimmed memory is like a cluttered drawer with search capabilities, where outdated facts can be retrieved with certainty, and bad skill files will forever disrupt processes. Every fact must have a source, new information must be verified against old information, and someone must delete content that no longer belongs to the system.

Software is cheap enough to create dedicated tools for a single user. The old advice was to scratch your own itch before hoping for a market; the new version is that costs are nearly zero, so you create first and turn it into a company if someone shows interest.

In market mechanisms, those paying the bills are rewriting per capita output into growth stocks and venture capital, while those selling are valuing old software based on linear extrapolation of headcount. The events are driven by speeches and commentary, with capital flowing to companies that can write processes into intelligent agent skills; benefiting are high-revenue, low-headcount AI-native teams, while pressured are public company models that still explain growth through headcount.

Source: Public Information