Three tasks. May 28, 2026 weighted to 49.9x leverage across 104.0 human-equivalent hours in 125 Claude-minutes. Supervisory leverage closed at 416.0x.
2.6 weeks of human-equivalent throughput in 2.1 hours of Claude wall-clock. The 168.0x ceiling came from Built comprehensive content-domain curriculum: 4 domain specs (Math/RLA/Science/Social Studies) totaling 249 leaf goals plus README with 16 novel adult-learner activities.; the 16.0x floor sat at Resume Phase 0 of a security-scanning service; verify backend pytest plus frontend build plus git init/commit plus reserve ports; then a 48-agent adversarially-verified project rev....
Task Log
| # | Task | Human Est. | Claude | Sup. | Factor | Sup. Factor |
|---|---|---|---|---|---|---|
| 1 | Built comprehensive content-domain curriculum: 4 domain specs (Math/RLA/Science/Social Studies) totaling 249 leaf goals plus README with 16 novel adult-learner activities. | 70.0h | 25m | 3m | 168.0x | 1400.0x |
| 2 | Resume session: re-stamped 584 spec/manifest exam_metadata fields across 142 packages plus restamp tool plus 3 recall backfills (+2555 questions) plus content-audit Phase 27 plus P25.7 plus audit rerun (144 findings) plus 2-agent synthesis/engine field-coverage investigation. | 22.0h | 55m | 8m | 24.0x | 165.0x |
| 3 | Resume Phase 0 of a security-scanning service; verify backend pytest plus frontend build plus git init/commit plus reserve ports; then a 48-agent adversarially-verified project review and fix of every confirmed broken-now/Phase-0 finding (TS2559 dead LoginPage; phantom defect-reporter dep; CSS @import order; vitest passWithNoTests; /me 401; AUTH_DISABLED default; pinned Trivy; ModalProvider; FOUC else-branch; branded favicons; container-registry docker token; backend tests to 98% coverage; docs); verified green and committed locally. | 12.0h | 45m | 4m | 16.0x | 180.0x |
Aggregate Statistics
| Metric | Value |
|---|---|
| Total tasks | 3 |
| Total human-equivalent hours | 104.0 |
| Total Claude minutes | 125 |
| Total supervisory minutes | 15 |
| Total tokens | 3,180,000 |
| Weighted average leverage factor | 49.9x |
| Weighted average supervisory leverage factor | 416.0x |
| Human-equivalent weeks | 2.6 |
Analysis
The day's leverage distribution matters more than the headline figure. The 168.0x ceiling came from Built comprehensive content-domain curriculum: 4 domain specs (Math/RLA/Science/Social Studies) totaling 249 leaf goals plus README with 16 novel adult-learner...; the 16.0x floor was Resume Phase 0 of a security-scanning service; verify backend pytest plus frontend build plus git init/commit plus reserve ports; then a 48-agent adversarially-.... 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 (416.0x 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 3 tasks, the day produced roughly 2.6 weeks of senior-engineer-equivalent throughput in 2.1 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.