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AI JUN 09, 2026

Leverage Record: June 9, 2026

Seven tasks. June 9, 2026 weighted to 13.4x leverage across 50.5 human-equivalent hours in 226 Claude-minutes. Supervisory leverage closed at 216.4x.

Seven tasks. June 9, 2026 weighted to 13.4x leverage across 50.5 human-equivalent hours in 226 Claude-minutes. Supervisory leverage closed at 216.4x.

1.3 weeks of human-equivalent throughput in 3.8 hours of Claude wall-clock. The 16.6x ceiling came from Finish email-platform remediation: Jproviders + Ofrontend + K.1 accept-creates-rule; 3 commits, backend 947 / frontend 321 green; the 8.4x floor sat at Email-platform outage recovery + fleet CI policy: strip tests from all buildspecs + install pre-push test gate across 27 tool repos.

About These Records
These time records capture personal project work done with Claude Code (Anthropic) only. They do not include work done with ChatGPT (OpenAI), Gemini (Google), Grok (xAI), or other models, all of which I use extensively. Client work is also excluded, despite being primarily Claude Code. The actual total AI-assisted output for any given day is substantially higher than what appears here.

Task Log

#TaskHuman Est.ClaudeSup.FactorSup. Factor
1Finish email-platform remediation: Jproviders + Ofrontend + K.1 accept-creates-rule; 3 commits, backend 947 / frontend 321 green16.0h58m1m16.6x960.0x
2Phase B logging re-arch: S3+Athena log store in telemetry-beacon IaC + 19 deploy scripts off CloudWatch to local+Vector + Vector shipper deployed/verified10.0h40m4m15.0x150.0x
3Email-platform remediation cont.: repair Celery beat fan-out (4 AI tasks) + reconcile README doc-lies (MCP count, pgvector, WS events, rules/search); suite 946 green7.0h30m1m14.0x420.0x
4Service-health-monitor Fargate migration steps 1-4: shared tools ECS cluster + parallel Fargate service built applied and verified healthy8.0h38m3m12.6x160.0x
5Service-health-monitor migration IaC reconciliation (Fix 1+2): import live ALB rule into tools-fargate + strip old stack + delete orphaned TG; move CI to ECS deploy + CodeBuild IAM3.5h20m1m10.5x210.0x
6Service-health-monitor Fargate cutover (steps 5-6): webhook roll, repoint ALB rule to Fargate TG, verify /health 200, decommission EC2 instance2.5h15m2m10.0x75.0x
7Email-platform outage recovery + fleet CI policy: strip tests from all buildspecs + install pre-push test gate across 27 tool repos3.5h25m2m8.4x105.0x

Aggregate Statistics

MetricValue
Total tasks7
Total human-equivalent hours50.5
Total Claude minutes226
Total supervisory minutes14
Total tokens4,540,000
Weighted average leverage factor13.4x
Weighted average supervisory leverage factor216.4x
Human-equivalent weeks1.3

Analysis

The day's leverage distribution matters more than the headline figure. The 16.6x ceiling came from Finish email-platform remediation: Jproviders + Ofrontend + K.1 accept-creates-rule; 3 commits, backend 947 / frontend 321 green; the 8.4x floor was Email-platform outage recovery + fleet CI policy: strip tests from all buildspecs + install pre-push test gate across 27 tool repos. 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 (216.4x 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 1.3 weeks of senior-engineer-equivalent throughput in 3.8 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.