Quote · SOLVED with Mark Manson
Solved, Answers: Finding Purpose, Failing Better, and the AI Future
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Purpose AI App: Architecture & Philosophy
At 36:40 · chapter starts 36:15
The question about how Purpose was trained opens a surprisingly candid window into the product development process. Mark's first finding was humbling: he built a detailed spreadsheet of all the psychological frameworks he thought the AI needed to know, then tested the major models and found it was all already in there — including his own books, as evidenced by the class-action settlement checks arriving from OpenAI and Anthropic [1] — Mark Manson "The knowledge is already in every AI — OpenAI and Anthropic are literally sending Mark settlement checks because his books are in the train…" 36:15 . The pivot came fast: knowledge isn't the bottleneck; optimisation is. ChatGPT is tuned for engagement and quick action items, not for guiding someone through an existential crisis. Claude is the most philosophical model. Getting different models to play to their strengths and then check each other's work became the core engineering challenge. The result is a three-layer system: a primary conversational AI, an evaluative AI that critiques its outputs in real time, and a memory-and-summary system that feeds context back into the primary model. The system prompts alone took 2–3 months to write — an intensity, Mark says, comparable to writing a book.
The knowledge is already in every AI — OpenAI and Anthropic are literally sending Mark settlement checks because his books are in the training data. The real challenge is optimization: a primary conversational AI, an evaluative AI that critiques it, and a memory system that feeds context back in. The system prompts alone took 2–3 months to write.
Mark Manson spent 2–3 months working on the system prompts for the Purpose AI app, comparing the intensity to writing a book.
