Two tasks. June 21, 2026 weighted to 162.2x leverage across 1014.0 human-equivalent hours in 375 Claude-minutes. Supervisory leverage closed at 3042.0x.
25.4 weeks of human-equivalent throughput in 6.2 hours of Claude wall-clock. The 200.0x ceiling came from An infrastructure-provisioning tool: 10 patent-grade inventions implemented end-to-end (all phases); engines, 11 Alembic migrations, WebSocket+MCP parity, 9 EventBridge+Lambda auto...; the 11.2x floor sat at Make 30 beta content-domain packages production-ready: pair-id coverage, orphan-question realignment, duplicate-option fix, tier-coverage generation, validation re-run, manifest/qu....
Task Log
| # | Task | Human Est. | Claude | Sup. | Factor | Sup. Factor |
|---|---|---|---|---|---|---|
| 1 | An infrastructure-provisioning tool: 10 patent-grade inventions implemented end-to-end (all phases); engines, 11 Alembic migrations, WebSocket+MCP parity, 9 EventBridge+Lambda automations, 10 React pages, ~250 new tests, docs; committed+pushed per invention | 1000.0h | 300m | 12m | 200.0x | 5000.0x |
| 2 | Make 30 beta content-domain packages production-ready: pair-id coverage, orphan-question realignment, duplicate-option fix, tier-coverage generation, validation re-run, manifest/quality re-stamp, S3 promotion (a third-party model) | 14.0h | 75m | 8m | 11.2x | 105.0x |
Aggregate Statistics
| Metric | Value |
|---|---|
| Total tasks | 2 |
| Total human-equivalent hours | 1014.0 |
| Total Claude minutes | 375 |
| Total supervisory minutes | 20 |
| Total tokens | 2,280,000 |
| Weighted average leverage factor | 162.2x |
| Weighted average supervisory leverage factor | 3042.0x |
| Human-equivalent weeks | 25.4 |
Analysis
The day's leverage distribution matters more than the headline figure. The 200.0x ceiling came from An infrastructure-provisioning tool: 10 patent-grade inventions implemented end-to-end (all phases); engines, 11 Alembic migrations, WebSocket+MCP parity, 9 Eve...; the 11.2x floor was Make 30 beta content-domain packages production-ready: pair-id coverage, orphan-question realignment, duplicate-option fix, tier-coverage generation, validation.... Tasks at the top of the distribution share a shape: tightly-scoped specifications, clear success criteria, and minimal integration ambiguity. The AI doesn't need to discover anything new; it executes against an explicit target.
Tasks at the bottom run differently. They're either bounded by review-heavy work where every step gets verified, or they involve ambiguity that demands several rounds of trial and adjustment. The factor is real and informative, not a failure mode.
The supervisory leverage figure (3042.0x today) tracks something orthogonal to wall-clock leverage. It's the ratio of human-equivalent output to human prompt-writing time. It stays high even on lower-leverage days because supervisory minutes scale with task count, not with the human-hour estimate; a 20-minute task and a 4-hour task can both be specified in two minutes of human prompt-writing.
Across the 2 tasks, the day produced roughly 25.4 weeks of senior-engineer-equivalent throughput in 6.2 hours of model wall-clock. That ratio is the practical answer to the question of how much output a single operator can move per day when the model handles the execution and the operator handles the direction.