EP 01·14 min·Open episode
Inside YC's AI Playbook
Narration: WOW Mentor
What WOW Mentor takes apart here is the rare record of an organization running its own advice on itself. YC started from a small friction in its finance team and layered a single Postgres of shared context, a registry of more than 350 tools, and skills that reread their own transcripts nightly to grow better than the people who wrote them — making AI the building layer of the company rather than a co-pilot. Underneath sit three choices: a culture of egalitarianism and default trust that keeps context open instead of locked, a willingness to overspend on tokens now to live in the future early, and a philosophy of shifting control from developer to user. The conclusion WOW Mentor draws is that none of this is a rich company's magic trick — it is a door anyone can choose to open in their own organization today.
Show notes
The moment you use AI as the building layer for all work rather than a bolt-on co-pilot, the character of the organization itself changes.
Gathering your important context into a single store is often enough to let an agent answer arbitrary, complex questions about your business.
When the cost of an answer collapses, the number and boldness of questions explode — Jevons paradox at the organizational scale.
A tool registry that grows from 20 to 350 shows that pooling tools in one place lets people and agents both reach for them.
Skills can improve themselves — feed a skill the transcripts of its own use every night and it eventually surpasses its authors.
The precondition for a superintelligent organization is culture, not technology: relative egalitarianism and trust-by-default come first.
An org that invests heavily in tokens now buys a one-time time warp into a way of working that only becomes common years later.
Good AI software is small — lead with the least code needed to let the model shine, and hand control to the user rather than hoarding it.
Transcript80 sentences
Today, WOW Mentor breaks down how Y Combinator — the firm famous for building other people's startups — actually uses AI inside its own walls. The conversation features Garry Tan and Pete Koomen, a partner who once built Optimizely and has since built all of YC's agent infrastructure. Pete explains that for years YC has mostly funded AI companies, and that the advice it kept handing those founders is now the same advice it runs on itself. What most people miss, he says, is that YC has quietly built the very tools it tells startups to build, becoming deeply AI-native itself. WOW Mentor picked this episode because it is rare to see an advisory firm experiment on its own body first, and that lab notebook holds a more honest strategy than any keynote.
The story begins, improbably, in finance. About a year ago Pete and a few engineers started a project to help finance run its workflows — booking journal entries, logging priced rounds. He noticed a strange loop: finance would describe a procedure to the engineers; the engineers would build deterministic, purpose-built software that encapsulated it; then they'd hand it back, and around it went. What WOW Mentor highlights is that his complaint was not merely inefficiency but that control sat in the wrong place: the people who understood the workflow were in finance, yet only the engineers could turn it into software. His goal was to remove the engineers from the loop and let finance encode its own workflows — not in Ruby, but in English, with prompts.
At the same moment, on his own laptop, Pete lived in a different world. Windsurf and Cursor were established, and Claude Code had just arrived; those tools gave him superpowers, and the gap with the old way of building software at YC grew wider. Narrow and cautious at the office, able to do anything at home — that split is what WOW Mentor treats as the root of the whole conversation — every experiment inside YC reduces to bringing the freedom he felt at home into the workplace. As later moments with Open Claw foreshadow, what held the world back was worry about security and privacy; worry a little less, and these things become unbelievably powerful.
The first magic was not agentic coding but SQL. Garry Tan notes that years ago, when LLMs had arrived but agentic coding hadn't, one startup idea each of them had watched fail was precisely "get an LLM to write SQL." This time it worked — non-technical finance people began asking real questions with it. The purpose-built tool was rewritten into a general agent loop with a shared registry of YC-specific tools. Then a partner named Jared built two tools: one to run read-only SQL queries against the database, one to read the model files. Jared confesses he felt like he was breaking the rules; frustrated that narrow tools weren't powerful enough, he gave the agent complete access to the production database and pushed it out late one night. And it worked extremely well.
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