by Herring | 2026-01-22 23:07:31+00:00
I got halfway through then asked the LLM to extract any surprising or particularly interesting results, below. Verified each one manually.

1. Accelerated US Diffusion vs. Global Stagnation

The report estimates that usage parity across US states could be achieved in 2–5 years. This rate of diffusion is approximately 10 times faster than that of major 20th-century technologies (e.g., electricity or the automobile). However, this rapid convergence is not observed globally. International adoption remains strictly correlated with GDP per capita, with no evidence of lower-income countries "catching up." This suggests AI may currently exacerbate rather than narrow the digital divide between nations.

2. The Education Mirror Effect

A high correlation (r > 0.92) exists between the education level required to write a prompt and the education level of Claude's response. This implies that the model's sophisticated capabilities are only unlocked by users who already possess high formal education. Rather than acting as a cognitive equalizer, AI appears to function as a capital multiplier for existing high-skill workers.

3. Productivity "Haircut" via Reliability

While raw "speedup" data suggest significant time savings (e.g., a 12x speedup for college-level tasks), these gains are substantially offset by task failure. Adjusting aggregate productivity growth estimates for task reliability reduces the projected impact from 1.8 percentage points to approximately 1.0 percentage point of annual labor productivity growth. This 44% "haircut" highlights that current model unreliability is a primary bottleneck for macroeconomic impact.

4. Selective Deskilling of White-Collar Work

The report finds that AI disproportionately covers high-education tasks. In a task-displacement model, this leads to a net "deskilling" effect for several professions. For example, Technical Writers may lose their most complex analytical tasks to AI, leaving behind only routine illustrative or observational work. Conversely, occupations like Property Management may experience "upskilling" as AI handles bookkeeping, leaving managers to focus on high-stakes negotiations.

5. Multi-Turn "Task Horizon" Extension

The "task horizon"—the maximum task duration at which AI achieves a 50% success rate—varies wildly by interface. For single-turn API interactions, the horizon is 3.5 hours. For multi-turn Claude.ai conversations, it extends to 19 hours. This suggests that the iterative, human-in-the-loop chat interface is significantly more effective at managing complex, long-duration tasks than programmatic automation.

Critical Uncertainties:

Temporal limitations: The data was collected over a single week in November 2025 and predates the release of Opus 4.5.

Self-selection bias: Success rates reflect only the tasks users choose to bring to AI. If users avoid tasks they expect the AI to fail at, success rates are artificially inflated.

Measurement of "Education Years": The report uses a Ridge regression model to predict education requirements from task embeddings. If the training data (BLS occupation levels) is a lagging indicator of actual skill requirements, the deskilling/upskilling analysis may be skewed.