Meet Michael Crowe
From mushroom grower to AI architect. 18 years of biological systems expertise, distilled into code.
The Origin Story
Michael Crowe started growing mushrooms at age 15 in Phoenix, Arizona. What began as teenage curiosity evolved into a deep obsession with biological systems, contamination patterns, and the art of cultivation.
By age 26, he founded Southwest Mushrooms, building it into a $470K/year commercial operation with distribution across 7 continents. He mastered spawn production, substrate formulation, environmental controls, and contamination management at scale.
But there was a problem: no software understood biological systems the way he did. Generic tools couldn't capture the nuance of mycelial behavior, the complexity of contamination triage, or the uncertainty inherent in living systems.
So he taught himself to code. Not just to build tools—but to encode 18 years of biological intuition into AI models that could reason about life the way he does.
Age 15
Started growing mushrooms in Phoenix, Arizona
Age 26
Founded Southwest Mushrooms - $470K/year, 7 continents
Today
Building AI that understands biological systems like a scientist
The Insight
Most AI platforms are trained on internet data. They know facts, but they don't understand systems.
Crowe Logic is different. Every model is trained on 18 years of real-world biological operations—actual contamination logs, growth cycles, environmental telemetry, and production outcomes. The AI doesn't just generate text. It reasons about biology the way an expert mycologist would.
That's the difference between generic AI and domain intelligence. One gives you answers. The other understands the question.
Why Crowe Logic Exists
Domain Expertise First
18 years of hands-on biological research and commercial production. Not generic internet knowledge.
Production Validated
Every model trained on real operational data from facilities spanning 7 continents.
Built by Scientists
Tools designed by researchers who understand uncertainty, contamination, and biological complexity.
Open Research Ecosystem
Models, datasets, and hardware available to accelerate biological research globally.