High-agency professionals constantly wrestle with the same question: “Do I need another person, a better tool, or just better discipline?” Most answers are reactive—triggered by burnout, dropped balls, or a flashy new AI product. This episode introduces “Executive Scaling Thresholds”: a simple way to predefine the conditions under which you hire, automate, or redesign, so scaling becomes a deliberate move, not an emotional one. You’ll learn a three-move model—Count, Cap, Convert. First, Count: quantify recurring workloads and decision volume in plain units (requests per week, hours per cycle) instead of vague “busy-ness.” Second, Cap: establish explicit thresholds where the current setup breaks—quality slips, latency spikes, or you violate your own time floor. Third, Convert: decide in advance which lever you’ll pull at each threshold—new hire, AI workflow, or system change—and document the minimal version of that move. By the end, you’ll have a one-page playbook that tells you exactly when and how to scale capacity without over-hiring, tool sprawl, or chronic overload. Clarity is leverage; this is how you apply it to scaling decisions.