Three tasks. June 28, 2026 weighted to 28.1x leverage across 38.0 human-equivalent hours in 81 Claude-minutes. Supervisory leverage closed at 142.5x.
0.9 weeks of human-equivalent throughput in 1.4 hours of Claude wall-clock. The 50.0x ceiling came from Audit platform learning activities (51 in catalog) plus generated content completeness across certification vs academic domains, with chemistry/physics/math focus; the 15.0x floor sat at Platform activity catalog audit: cross-category breakdown plus IB check plus coverage gaps plus deferred content inventory.
Task Log
| # | Task | Human Est. | Claude | Sup. | Factor | Sup. Factor |
|---|---|---|---|---|---|---|
| 1 | Audit platform learning activities (51 in catalog) plus generated content completeness across certification vs academic domains, with chemistry/physics/math focus | 20.0h | 24m | 4m | 50.0x | 300.0x |
| 2 | Design and build an HTTP Lambda to Slack relay automation (HTTP Lambda to Slack chat.postMessage): 4 docs, handler plus 17 tests, Terraform stack, slash-command skill; located existing Slack webhook credentials | 15.0h | 45m | 7m | 20.0x | 128.6x |
| 3 | Platform activity catalog audit: cross-category breakdown plus IB check plus coverage gaps plus deferred content inventory | 3.0h | 12m | 5m | 15.0x | 36.0x |
Aggregate Statistics
| Metric | Value |
|---|---|
| Total tasks | 3 |
| Total human-equivalent hours | 38.0 |
| Total Claude minutes | 81 |
| Total supervisory minutes | 16 |
| Total tokens | 985,000 |
| Weighted average leverage factor | 28.1x |
| Weighted average supervisory leverage factor | 142.5x |
| Human-equivalent weeks | 0.9 |
Analysis
The day's leverage distribution matters more than the headline figure. The 50.0x ceiling came from Audit platform learning activities (51 in catalog) plus generated content completeness across certification vs academic domains, with chemistry/physics/math foc...; the 15.0x floor was Platform activity catalog audit: cross-category breakdown plus IB check plus coverage gaps plus deferred content inventory. 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 (142.5x 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 3 tasks, the day produced roughly 0.9 weeks of senior-engineer-equivalent throughput in 1.4 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.