Seven tasks. June 29, 2026 weighted to 14.9x leverage across 37.0 human-equivalent hours in 149 Claude-minutes. Supervisory leverage closed at 79.3x.
0.9 weeks of human-equivalent throughput in 2.5 hours of Claude wall-clock. The 28.6x ceiling came from Full readiness audit (structural) across 71 changed monorepo repos: Phase 0 canonical validation plus git hygiene plus structural checks plus architecture and intellectual property...; the 7.2x floor sat at Architecture doc fixes: appendix back-port plus subsystem count note plus application interface sections plus README broken links plus client README stale intellectual property doc....
Task Log
| # | Task | Human Est. | Claude | Sup. | Factor | Sup. Factor |
|---|---|---|---|---|---|---|
| 1 | Full readiness audit (structural) across 71 changed monorepo repos: Phase 0 canonical validation plus git hygiene plus structural checks plus architecture and intellectual property documentation cross-reference plus report | 10.0h | 21m | 3m | 28.6x | 200.0x |
| 2 | Authored an enterprise storage certification domain specification taxonomy (68 leaves across 8 domains) | 6.0h | 18m | 5m | 20.0x | 72.0x |
| 3 | Web research on agentic enterprise development for a large enterprise vendor: curriculum domains and enablement structure | 4.0h | 15m | 5m | 16.0x | 48.0x |
| 4 | Triage and fix overnight math content backfill: root-caused spec-shadowing resolver bug plus math-filter substring bug, repaired 8 specs, cleaned bogus content, relaunched 10 math domains, rebuilt Slack monitor | 5.0h | 20m | 3m | 15.0x | 100.0x |
| 5 | Monorepo readiness-audit remediation: canonical sync plus architecture and intellectual property documentation fixes (back-port plus 7 API sections plus subsystem reconcile) plus README content (7 repos) plus audit definitions and repo-map onboarding plus 15 precise commits | 6.0h | 32m | 2m | 11.2x | 180.0x |
| 6 | Agentic enterprise development certification domain specification taxonomy (67-leaf goal tree across 8 domains) | 3.0h | 18m | 5m | 10.0x | 36.0x |
| 7 | Architecture doc fixes: appendix back-port plus subsystem count note plus application interface sections plus README broken links plus client README stale intellectual property documentation coverage | 3.0h | 25m | 5m | 7.2x | 36.0x |
Aggregate Statistics
| Metric | Value |
|---|---|
| Total tasks | 7 |
| Total human-equivalent hours | 37.0 |
| Total Claude minutes | 149 |
| Total supervisory minutes | 28 |
| Total tokens | 1,555,000 |
| Weighted average leverage factor | 14.9x |
| Weighted average supervisory leverage factor | 79.3x |
| Human-equivalent weeks | 0.9 |
Analysis
The day's leverage distribution matters more than the headline figure. The 28.6x ceiling came from Full readiness audit (structural) across 71 changed monorepo repos: Phase 0 canonical validation plus git hygiene plus structural checks plus architecture and i...; the 7.2x floor was Architecture doc fixes: appendix back-port plus subsystem count note plus application interface sections plus README broken links plus client README stale intel.... 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 (79.3x 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 7 tasks, the day produced roughly 0.9 weeks of senior-engineer-equivalent throughput in 2.5 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.