Dimension 6 of 6
Delegation Calibration
How you judge what to give AI versus keep for yourself — including when NOT to automate — based on an accurate mental model of what AI can reliably handle in your specific domain and context.
The five levels
Absent
Uses AI for everything without differentiation, or hasn't started using AI at all. No mental model yet of what AI handles well vs. poorly. This is a starting point — when AI is new, you don't yet have the experience base to calibrate.
Personal
Has a rough sense of what AI is good at in their domain. Delegates routine tasks, keeps "important" ones. But the boundary is based on feeling rather than evidence. May over-delegate in unfamiliar areas (the verification asymmetry: delegates most in exactly the domains where they can't check the output).
Systematic
Can articulate what AI handles well and poorly in their specific domain. Adjusts delegation based on stakes, domain difficulty, and verifiability. Has a mental model of AI capabilities that updates with experience. Knows when to preserve cognitive friction deliberately.
Expert Exemplar
Precise mental model of AI capabilities — knows exactly which tasks, which domains, which stakes warrant delegation vs. retention. Treats AI like "a good employee on day one" (needs onboarding, needs checking, has strengths and blind spots). Strategic restraint is a deliberate, articulated choice, not avoidance. Can explain their delegation philosophy and why it works for their context.
Compounding
Codifies your delegation philosophy into shareable guidelines, playbooks, or decision trees that help others calibrate without having to learn everything through trial and error. Actively updates these as AI capabilities change — you are the person who notices when a model upgrade makes a previously-unreliable delegation safe, or when a new risk emerges. Mentors others on domain-specific calibration (what works in finance vs. infrastructure vs. design). Designs security-aware delegation patterns and teaches others where AI delegation creates attack surfaces. Others make better delegation decisions because of frameworks you built and maintain.
Key quotes
“”
No clean L1 quote exists in the dataset. Tribe is a technology-forward company — no interviewee represents pure "uses AI for everything without calibration" or "total avoidance." The L1 description captures a starting point that predates most interviewees' current state.
“I just have like two hour conversations talking with it. And then all of a sudden, like, just does stuff.”
“These tools are really more of a thinking partner for me. They're coming with me on this journey, accelerating it immensely, but I can't completely offload it.”
“The model is, like, a really good employee on day one... so much of how I interact with the model or these agent harnesses is, alright, cool. You're first day on the job? Great. Go read this doc and go read that doc.”
Transitions — what distinguishes each level
The shift is from *undifferentiated use* to *intuitive boundaries*. At L2, you have a gut sense of what AI is good and bad at. The gap is between "I use it the same way for everything" and "I have a feel for where it helps and where it doesn't."
The shift is from *intuitive boundaries* to *articulated calibration*. At L3, you can explain what AI handles well in your domain, you adjust based on stakes, and you deliberately preserve friction where it serves you. The gap is between "I have a feel for it" and "I can explain my delegation philosophy."
The shift is from *domain-calibrated delegation* to *precise mental model with principled restraint*. At L4, you treat AI like a new employee you're onboarding — you know its strengths, its blind spots, and exactly how much context it needs. Strategic restraint is articulated as a principled position, not default behavior. The gap is between "I know what works" and "I have a transferable philosophy of human-AI delegation."
The shift is from *personal philosophy* to *organizational calibration infrastructure*. At L5, you've codified your delegation judgment into playbooks and decision trees others use, and you update them as tools evolve. The gap is between "I calibrate well" and "others calibrate well because of frameworks I built."