Garry Tan, president and CEO of Y Combinator, told founders at the startup incubator's Startup School 2026 event that AI agents have fundamentally rewritten the rules of entrepreneurship. He urged the audience to fully embrace these autonomous software tools, citing startups that reached nine-figure revenue in eight months. Tan also introduced a new concept he calls "personal AGI," a portable collection of AI agents that can follow a person across ventures and job roles. The message was blunt: the era of waiting for funding, headcount, or permission is over.
The New Math of Startup Growth
Tan opened with a startling claim about market entry timelines. He said AI-driven ventures can achieve growth and profitability within eight months, a pace he described as "breaking the old math." He pointed to Emergent, an AI startup from the YC summer 2024 batch, as a prime example. Tan said the company "went from public launch to nine figures of revenue in eight months," a trajectory that would have been unthinkable in previous decades. He added, "When they crossed $15 million in annualized revenue, they were 15 people."
Retell, from the YC winter 2024 batch, told a similar story. Tan reported that the company hit $60 million in annualized revenue with about 40 people. He emphasized that such revenue per person was historically impossible. "Not in software, not in oil, not in railroads. And these aren't freaks of nature. They're the first companies built natively on the new physics, and every one of them started as one or two people. Founders are doing what used to be a person's entire year of work."
The trend is broad, not anecdotal. Tan said the majority of YC companies in recent cycles use AI agent models. At least one in four YC ventures have codebases that are 95% AI-generated. He said the latest YC batch "is on track to becoming one of the fastest growing, most profitable batches in the history of YC," a statement that carries weight from the leader of an institution that has nurtured thousands of startups.
Managing a Workforce Made of Markdown
Tan's core argument is that AI agents are not just tools but employees. He described a workflow where founders manage these digital workers using markdown, a simplified text-based formatting language. "When you sit down with an agent, you're managing a workforce made of markdown," he said. This framing shifts the founder's role from coder to manager, from builder to orchestrator.
He offered a concrete vision for how this changes the startup process itself. "Before you ever incorporate anything, before you have a co-founder or a logo or a deck, you can already be running an organization, an organization of one plus your agents," Tan said. The implication is that the traditional barriers to entry, the incorporation papers, the pitch deck, the founding team, are no longer prerequisites for building something real.
Tan also reported a dramatic personal productivity gain. He said he is 400 times more productive than 13 years ago when building his own startup. That productivity extends beyond coding to design, product management, and growth. He described a world where bespoke tools are trivial to create. "You can build exactly the tool you need for the audience of one in a weekend," he said.
Building Your Personal AGI
Tan laid out a practical, five-step system for building what he calls a personal AGI. The steps are: pick a harness, start a library, write your first skill file, wire the task to a recurring job, and never do one-off work. He said his own setup uses specific tools. "I use OpenClaw and Hermes Agent with GBrain," he noted, referring to agent harnesses and a component for memory or orchestration. "The intelligence is on tap, and there are many paths."
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The library, he explained, is a single folder of markdown files. He advised exporting notes and email, then writing one page per project and per person. The skill file is the next step. "It might be expense reports, meeting notes, weekly status updates, competitor research. That page is now an employee. Run it," Tan said. The recurring job is where the magic happens. "The first time you wake up to work that finished while you slept, something shifts in your head permanently," he said. "That's the day that the day stops being the unit of work for you."
The final step, never doing one-off work, is about compounding knowledge. "Most people run one operation with one agent and then throw the context away," Tan warned. "They close the window. At the end of every task, ask the agent to 'skillify' what it did. Turn it into a markdown file you can use and reuse forever. The person who captures what they learn gets smarter every single day."
The Ownership Trap
Tan's enthusiasm came with a sharp warning about ownership. He drew a historical analogy, noting that craftsmen once owned their tools. The factory broke that relationship, and the loom belonged to the mill. Knowledge workers assumed their tools, the skills in their heads, were safe. That assumption, he said, is now false.
"Those files may live in the company's repo under the company's IT policy," Tan said, describing the risk for employees who build skill files at work. "You may leave with nothing. The company keeps running your judgment without you. Forty files executing forever and her name isn't even in the commit history." The image is stark: a worker's expertise, extracted and automated, continues to generate value for the company long after the worker is gone.
Tan's solution is to own your agentic workforce. He described the ideal as "an agent that runs on your infrastructure, reads from a memory you own, executes procedures you wrote, and compounds. Your personal AGI gets better every single day you use it because every day it knows more of your life." This personal AGI, he argued, can help managers in established companies build internal ventures or start their own companies, carrying their digital workforce with them.
A Democratizing Force
Tan framed AI agents as a leveling force in an economy that has long favored the connected and the funded. "For most of history, almost all of that striving never got an audience. It died waiting for funding, waiting for headcount, waiting for permission, waiting for someone else to believe first," he said. The tragedy, in his view, was not a lack of ambition but a lack of access.
AI agents change that calculus. Tan called them "the first technology I've ever seen that lets the striving go straight to work." The tools are inexpensive or free, the infrastructure is personal, and the knowledge compounds daily. The result is that a single person with a markdown folder and a harness can now operate like a small company.
The implications extend beyond startups. Tan said personal AGI can help professionals in established companies build internal ventures or start their own. The same agents that manage a founder's tasks can serve a mid-career manager looking to launch a side project. The barrier is no longer capital or headcount, but the willingness to capture your own cognition and put it to work.