got AI anxiety? Do this RIGHT NOW!

AI anxiety about the job market can be met with a practical counter-move: build a “Telos file,” a single markdown document that captures who someone is—problems they care about, missions they’re pursuing, the story that connects the two, and measurable goals—so AI (and trusted people) can give sharper feedback and reveal blind spots. The pitch is that this kind of self-context becomes a portable “elevator pitch” for an AI-driven world where job identity is shifting, and where being able to clearly articulate one’s purpose matters more than matching a static job title.

The core mechanism is straightforward. Telos (from Greek, “purpose”) is framed as a text-based infrastructure for humans: one document that stores goals, missions, challenges, insecurities, and a running journal. The structure is designed to be both brutally honest and usable by AI. The process starts with four main sections—Problems, Missions (including narratives), and Goals—then expands into optional sections like History, Challenges, Insecurities, Finances/Strategies, and a bottom “journal/logs” section that records daily notes with timestamps. The document is treated as a living artifact, edited over time, so it accumulates patterns rather than staying a one-time self-help exercise.

Problems are defined as what someone sees as wrong in the outside world that they want to solve, not internal complaints. Missions are the actions someone will take to address those problems, often unified by a “most important sentence” formula such as “one of the biggest problems in the world is this, which is why I am…” Narratives are the “elevator pitch” version of what someone is about—short enough for eight seconds, but expandable into longer stories for interviews or conversations. Goals then translate missions into SMART-style commitments: specific, measurable, achievable, relevant, and time-bound. The emphasis is on chaining these pieces together so projects make sense, and so the person can track whether they’re actually moving.

A major claim is that AI can “red team” a person using the Telos file. By feeding the document into an AI system, the user can ask for pattern detection and critique—what’s missing, what contradictions show up, and what blind spots persist. The transcript includes an example of using Fabric (a command-line tool) with Telos prompts to generate critiques such as procrastination disguised as busyness, measuring success through metrics while claiming to surrender outcomes, and treating interruptions as failures rather than meaningful moments. The point isn’t just self-insight; it’s using AI as a sounding board to adjust behavior and refine narratives.

The transcript also addresses privacy and practicality. Sharing with AI is optional; local or private AI is suggested, and even creating the document alone is framed as valuable. Because Telos can grow large, the guidance is to split sections (like journals) or summarize older entries to fit context limits. The overall takeaway is that while AI is changing work, proactive clarity—knowing what problems to solve and why—can make someone more resilient, regardless of how job markets evolve.