The First Principle is the operating system for building an AI-native company.
My name is Michael Quoc, and I’m the founder and CEO of Demand.io. I’m not a journalist or a commentator. I’m an architect in the arena, building a high-performance organization from first principles and sharing the blueprints in public.
This is not a newsletter about AI trends. It’s the codification of my life’s work.
For over 15 years, I’ve been building Demand.io—a profitable, AI-native company. We’ve reached $20M+ ARR with 20 people, zero VC funding, and a $106M valuation. We’ve made a series of non-consensus bets that have paid off because they were grounded in first principles. Now, I’m publishing the operating system that runs our company, from our AIOS (AI Operating System) to the cultural Codex that guides our team of “10x operators.”
What makes this different
Everything I publish falls into two categories:
Proof-of-Work: The finished systems we’ve shipped and proven at scale. ShopGraph (our Byzantine Fault Tolerant commerce knowledge graph), our BFT Human Compute architecture, our profit-sharing partnership model, and the technical decisions behind $20M+ ARR with 20 people.
Proof-of-Thesis: The in-progress frameworks we’re actively building and pressure-testing. AIOS axioms, Context Engineering protocols, our economic models for agent-native commerce, and the architectural blueprints we’re refining in production.
When I write about finished systems, I share post-mortems, real data, and documented proof. When I write about in-progress theses, I explicitly label them as works-in-progress and invite debate. I’m here to pressure-test ideas with other builders, not to sell certainty.
The core philosophy: Truth Engines, not Relevance Engines
AI is a relevance engine, not a truth oracle. Large language models excel at pattern matching and persuasive generation, but they cannot verify ground truth. This isn’t a limitation to solve—it’s a fundamental property of how they work.
So we built the opposite: ShopGraph, a truth engine that verifies what AI cannot. We don’t compete with AI. We provide what it fundamentally lacks.
This philosophical foundation shapes everything we build and everything I write about.
Who is this for?
This is for the builders, the architects, and the deep generalists who are obsessed with creating leverage and impact. It’s for the leaders who know the future won’t be built by following old playbooks.
Specifically: CTOs, technical founders, systems architects, and 10x operators who build from first principles.
If you’re wrestling with AI evaluation, context engineering, economic models for agentic systems, or organizational design for human-AI collaboration—you’re in the right place.
If you’re looking for AI news summaries or productivity tips, you’ll be disappointed.
What you will get:
You will get the frameworks, the mental models, and the proof of work behind our systems. You’ll get an inside look at how we’re building a new kind of company—one designed for the age of AI.
The average essay is 3,500 words and requires 15-20 minutes to read. This is intentional. Length filters for serious operators. I publish one canonical essay every two weeks and have never missed a deadline.
Topics include:
AI systems architecture and verification frameworks
Context Engineering as the core craft of AI-native operators
Byzantine Fault Tolerant commerce systems
Economic models for agent-native attribution
Organizational design for 10x leverage
Capital-efficient company building without VC
I also engage substantively in the comments. The real pressure-testing happens there.
The invitation
I’m not here to convince you I’m right. I’m here to share what’s working for us and invite you to challenge it.
If you see flaws in our axioms, tell me. If you’ve built competing systems with different tradeoffs, share them. If you think our thesis is fundamentally wrong, make the case.
The goal is better systems, not winning arguments.
If you’re here to build, you’re in the right place.
