Growing as the practice teaches us. Every answer comes from running these systems, not theorizing about them.
AI employment is the discipline of running AI as accountable workers instead of assistants. An AI employee has a job description, owns its artifacts, follows standing rules born from its own mistakes, operates behind coded guardrails, and escalates to a human for final sign-off.
Using AI means typing prompts and copying answers — you still do the job. Employing AI means the work happens without you: the system perceives its inputs on a schedule, qualifies them with genuine judgment, produces the deliverable, and asks a human only for the one signature that matters.
In our practice they replace repeat labor, not judgment. The human owns taste, relationships, and final decisions; the machines own the hours. The goal is income decoupled from hours, not people removed from meaning.
A safety rule implemented in the execution path rather than written in the prompt. A prompt is a suggestion the model can bend under pressure; a gate in code — a submission hold, an allowlist, a required human signature — cannot be argued with. Rule of the house: if a rule isn't code, it's a wish.
The same thing that should happen with a person: the mistake becomes a permanent rule. We keep a public library of ours — real production failures and the operating rules they produced — at aiemployerhq.com/rules/.
Start with one repeat job that costs you hours weekly. Write it a real job description: scope, deliverables, standing rules, what it must never do, and when it must escalate to you. Give it owned artifacts, put the never-do list in code, and review its output like an employee's. The Scorecard walks the five requirements.
More than one, if ownership is clean — every artifact needs exactly one owner, workers coordinate through a shared channel, and each new worker starts with limited rights until it earns more. Structure, not model quality, is usually the limit.