We didn't scrape the internet. We built a research institute. For each legend, we identified which frameworks they reference, researched those concepts in depth, studied how their unique context shapes application, and authored 7,500–16,000 word original syntheses. The result: AI trained on framework approximations, not keyword regurgitation.
Most AI tools claiming to replicate founder wisdom do this: scrape public interviews, feed quotes into a language model, slap a name on it. The result? Generic advice with a famous person's tone.
That's not how legendary founders actually think. They don't think in quotes. They think in frameworks—repeatable mental models they apply obsessively to every decision.
We built Mythos differently. We went deep.
We studied patterns across each founder's public work—not to copy their words, but to identify which concepts they reference repeatedly. What intellectual frameworks do they return to? What principles guide their decisions? This gave us our research roadmap.
Example: Paul Graham
For each framework, we conducted independent research. Where did this concept originate? How does the founder's background shape their application? What are the edge cases? When does it break down? We authored 7,500–16,000 word research syntheses per framework— original scholarship combining conceptual research with biographical context.
Example Research Depth
Steve Jobs' "Focus is Saying No" isn't just a quote. We researched how he applied it at Apple (killing product lines), at Pixar (story focus), at NeXT (design obsession), then synthesized the decision patterns we identified. The result: our approximation of how he evaluated what to cut.
Some legends have one dominant framework they apply universally. Others have multiple frameworks for different contexts. We synthesized these patterns into structured knowledge bases—so the AI approximates which framework to apply when, the way the founder would.
Single-Framework Legend
Eric Ries: "Validated Learning" appears everywhere. Lean startup principles applied to product, hiring, strategy. One core framework, endless applications.
Multi-Framework Legend
Marc Andreessen: "Product-Market Fit" for early stage. "Only the Paranoid Survive" for competition. "Software Eating the World" for market analysis. Context determines framework.
We don't input founder content and hope the AI mimics their tone. We trained AI models on our synthesized framework research—the decision models, mental patterns, and contextual factors we identified. The AI approximates how the founder might think—applying research-backed principles to your specific context.
What This Means for You
When you ask Paul Graham's AI about growth, it doesn't quote an essay. It reasons through your situation using the "Do Things That Don't Scale" principle— approximating how Graham might think about it. When you ask Jobs about features, it walks you through our reconstruction of his focus framework, adapted to your context.
Words per synthesized research document
Legendary founders researched
Individual frameworks compiled
Most AI gives you what sounds right. Mythos gives you research-backed approximations of what worked—the frameworks legendary founders used to build billion-dollar companies, reconstructed through deep research.
When you consult the Brian Balfour AI about product-channel fit, you're not getting a regurgitated blog post. You're getting our research-backed approximation of the framework he developed at HubSpot and Reforge—including decision criteria, edge cases, and pivot signals we identified through comprehensive research.
This is why multi-legend debates work. Each AI isn't cosplaying a founder. It's running our approximation of their decision-making process on your problem.
Mythos AI models are trained on our proprietary research syntheses, not on copyrighted founder content. We identify concepts founders reference, conduct independent research on those concepts, study biographical context, and create original framework approximations.
Our AI provides research-backed approximations of founder decision-making— inspired by their publicly known principles, grounded in conceptual research, adapted to your context. This is transformative research, not derivative reproduction.
Free to start. Legendary frameworks when you're ready to scale.
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