How to Build a Self-Improving Company with AI

Last Updated on June 3, 2026 by Editorial Team Author(s): Naresh Idiga Originally published on Towards AI. How to Build a Self-Improving Company with AI Most founders are using AI wrong. They’re adding it on top of existing company structures. That’s the old model. This image was created using an AI image creation program. This article was written with the assistance of an AI writing program, based on the author’s notes and analysis of Tom Blomfield’s YC batch talk. Tom Blomfield co-founded Monzo — took it from zero to 5M+ customers and $900M raised. Now he’s a General Partner at Y Combinator. In a recent batch talk, he made one core argument, built partly on ideas he borrowed from Jack Dorsey’s tweets and a talk by fellow YC partner Diana: Most founders are using AI wrong. They’re adding it on top of existing company structures. That’s the old model. The new model: your company is a set of recursive, self-improving AI loops. Here’s the framework — and what it means for every developer, builder, and startup founder right now. TL;DR Most companies bolt AI onto old hierarchies. Wrong model. The right model: recursive, self-improving AI loops that get better while you sleep. Key shifts: record everything → make your company legible to AI → burn tokens not headcount → cut middle management → keep humans at the edge. The constraint is shifting from headcount to token spend. The companies that win will maximize what AI can do, not how many people they employ. Table of Contents The Roman Legion Problem The Self-Improving AI Loop — All 5 Layers The Holy Shit Moment at YC Real Examples: Product, Support, and Ops Loops Four Things to Do Right Now Where Humans Still Matter What This Means for Developers and Builders 1. The Roman Legion Problem Blomfield opened with a sharp analogy — one he credits to Jack Dorsey’s recent tweets. The Roman legions were built to project power from Rome to the far edges of the empire through nested hierarchies: named individuals with fixed spans of control, passing orders down and sending information back up. Most companies today are still organized like Roman legions. Humans are the conduit for information flowing up and down. That’s not a productivity problem. It’s a structural assumption baked into how we think about organizations. And AI doesn’t just improve this structure — it breaks the assumption underneath it. The old mental model: add AI co-pilots to your existing workflows. Make engineers 20% more productive. Ship more code. Blomfield’s critique: that’s like putting a more powerful engine onto a horse-drawn cart. You’re taking the old way of working and adding a more powerful engine onto it. The new mental model: redesign what a company is. This image was created using an AI image creation program. 2. The Self-Improving AI Loop — All 5 Layers Instead of thinking about AI as a tool bolted onto your org, think about your company as a set of recursive, self-improving loops. Each loop has 5 layers. If every single step runs with minimal human intervention, your system gets better and better while you’re sleeping. Here are all five layers: Layer 1 — Sensor Layer This is where real-world data comes in. Emails from customers, support tickets, code changes, people cancelling their subscription, product telemetry. Think of it as the sensory layer that picks up signals from the outside world. Layer 2 — Decision Layer (Policy Layer) This is where rules live. What can the system act on autonomously? What does it have to log? What must it ask a human permission for? This is the policy and decision layer — guardrails for what the AI can and cannot do on its own. Layer 3 — Tool Layer This is where code executes. Deterministic APIs — query a database, look at a calendar, send an email, update a record. These are the tools the AI can call. Blomfield describes this as the “skills and code” layer — the actual mechanisms of execution. Layer 4 — Quality Gate Before any action is committed, it passes through a quality gate. This might be eval checks, safety filters, or human review for high-risk actions. It’s the checkpoint between “the AI decided to do something” and “the thing actually happens.” Layer 5 — Learning Mechanism This is what makes the whole thing self-improving. The system interacts with the real world, picks up where it didn’t work, and loops back to the top again. Failures become training signal. The loop tightens over time. This image was created using an AI image creation program. The key insight: if you can run every single step of that loop without human intervention — or with minimal human supervision — your system gets better and better overnight. Automatically. 3. The Holy Shit Moment at YC Blomfield described exactly when this clicked for him at YC. They built a simple internal agent — a tool their partners could ask questions like “when did I last have a meeting with this founder?” It worked well. Partners got answers faster. Useful, but nothing groundbreaking. Think of it as a smarter search bar for their internal database. Then they put a monitoring agent on top of it. This agent watched every query every YC employee made. It tracked when queries succeeded and when they failed. When they failed, it asked: Why? What would have made this work? Do we need different deterministic tools? A new database view? A different index? Then — overnight — it wrote the fix, opened a pull request to the YC codebase, had another agent review it, and merged and deployed it. By the time a human came in the next morning to ask the same query, it worked. No human involved. This image was created using an AI image creation program. “For me, that was like the holy shit moment. That’s not just AI making you 20 or 30% more valuable. It is the AI going through this loop […]