What it feels like to work with Mythos

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• Core argument: Claude 5 Fable (“Mythos-class”) marks a shift from “steering” AI to “commissioning” it — users act as patrons who lose visibility into the model’s decisions but gain access to dramatically greater capability.
• Findings: Fable outperforms all prior public models across diverse tasks (academic papers, interactive games, math-rendered mapping apps) and autonomously spins up sub-agents to research, verify, and test — e.g., pulling 2,200+ flight/rail schedules for an isochrone map, or running 9.5 hours straight on a data-calibration tool.
• Tradeoffs: token consumption roughly doubles versus prior models; output quality exceeds spec but still needs expert review for errors; safety guardrails are oversensitive and default to weaker models; the shift favors well-capitalized entities over independent developers.
• Practical takeaway: human agency is moving from active steering to outcome validation — “the details of the AI’s decision making are not shown to me.”