The Playbook For Building An AI Native Company

**Diana** (0:09)
Hi, I'm Diana, and I'm a partner at YC. Over the past few months, it's become clear to me that AI is not just going to change how quickly software gets built or what workflows get automated. It's going to fundamentally change the way startups should be run, from what roles will exist to what products are possible to build. In this episode, I'm going to discuss how founders should think about building an AI native company, what roles their team should have, and what concrete internal practices they can adopt right now to move much faster. Currently, most people talk about AI in terms of productivity. They'll talk at length about how it can make engineers more productive, or say we need to add co-pilots to existing workflows and ship more features. This framing misses the shift we're currently seeing, which is less about productivity boosts than entirely a new capabilities. The right person with AI tools can now build features that used to require an entire team or were just impossible. Thinking about AI in terms of new capabilities has several implications for how founders should run their companies. At a high level, the way to think about AI is that it should not be a tool your company just uses. It should be the operating system your company runs on. Every workflow, every decision, and every process should flow through an intelligent layer that is constantly learning and improving. What this means concretely is every important process in your company should be captured by an intelligent closed loop. A closed loop captures information, feeds it back into an intelligent system, and improves the process over time. If you've ever studied controlled systems, you'll be familiar with the difference between an open loop and a closed loop system. Open loops are controlled systems without feedback loops. In the old world, companies basically ran as open loops. You made a decision, executed it and didn't always systematically measure the outcome and adjust the process.
Open loops are inherently lossy. A closed loop, on the other hand, is self-regulating. It continuously monitors its output and adjusts its process to better meet the stated goal. Closed loops are extremely powerful for correctness and stability. With self-improving agents, your company should run as a closed loop. To build these closed loops, you will need to make your entire company queryable. In other words, the whole organization should be legible to AI.
Every important action should produce an artifact that the intelligence at the center of the company can learn from and use to self-improve.
This means recording your meetings with an AI note taker, minimizing DMs and emails, and embedding agents throughout communication of all channels. It also means building custom dashboards with everything in the company. Revenue, sales, engineering, hiring, ops, everything. Here's a concrete example of how it could work. Take engineering, management, and sprint planning. If you have an agent that has access to your linear tickets, all your Slack engineering channels, all customer feedback from emails or tools like Pylon and GitHub, high-level plans in a Notion or Google Doc, sales calls and recordings from daily standups, then the agent can analyze what was actually shipped in your previous sprint and how well they met customer's needs for real. From there, you can go a step further. With full visibility into what shipped, what worked, and what didn't, agents can start looking ahead. They can propose sprint plans for engineers that are way more predictable and accurate and on track. The days of eng manager status roll-ups that are super lossy are gone. Having managed engineering teams myself and now seeing this across multiple YC companies, this is a game changer. What used to require constant coordination becomes legible and queryable by default. I've seen teams that do this cut their engineering sprint time in half and get close to 10x more than in that time. The overarching principle here is that to get their full capabilities, you need to provide models with as much context as you would provide an employee. When you do this, your company stops operating as an open loop, where information is fragmented and manually interpreted. It becomes instead a closed loop system. Status, decisions and outcomes are continuously captured and fed back into this intelligence layer. The result is a system that always has an up-to-date view of what's actually happening. There's also a new paradigm emerging for how the highest velocity companies build product.
AI software factories. If you're familiar with the test-driven development or TDD, this is the next evolution of that. With software factories, humans write a spec and a set of tests that define success. And then AI agents generate the implementation and code and iterate until the tests pass.