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AI MAY 24, 2026

Leverage Record: May 24, 2026

One task. May 24, 2026 weighted to 14.4x leverage across 6.0 human-equivalent hours in 25 Claude-minutes. Supervisory leverage closed at 120.0x.

One task. May 24, 2026 weighted to 14.4x leverage across 6.0 human-equivalent hours in 25 Claude-minutes. Supervisory leverage closed at 120.0x.

0.1 weeks of human-equivalent throughput in 0.4 hours of Claude wall-clock. The 14.4x ceiling came from Anthropic cache-token surfacing in an LLM client library calllog (cachecreate/cacheread tokens) + an origin service spend-tracking fix flushing calllog from math runners and tr...; the 14.4x floor sat at Anthropic cache-token surfacing in an LLM client library calllog (cachecreate/cacheread tokens) + an origin service spend-tracking fix flushing calllog from math runners and tr....

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
1Anthropic cache-token surfacing in an LLM client library calllog (cachecreate/cacheread tokens) + an origin service spend-tracking fix flushing calllog from math runners and tribunal6.0h25m3m14.4x120.0x

Aggregate Statistics

MetricValue
Total tasks1
Total human-equivalent hours6.0
Total Claude minutes25
Total supervisory minutes3
Total tokens80,000
Weighted average leverage factor14.4x
Weighted average supervisory leverage factor120.0x
Human-equivalent weeks0.1

Analysis

The day's leverage distribution matters more than the headline figure. The 14.4x ceiling came from Anthropic cache-token surfacing in an LLM client library calllog (cachecreate/cacheread tokens) + an origin service spend-tracking fix flushing calllog from...; the 14.4x floor was Anthropic cache-token surfacing in an LLM client library calllog (cachecreate/cacheread tokens) + an origin service spend-tracking fix flushing calllog from.... 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 (120.0x 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 1 task, the day produced roughly 0.1 weeks of senior-engineer-equivalent throughput in 0.4 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.