The Allocation Review
Research on how AI changes the allocation of capital, labor, technology, and machine intelligence.
Latest Articles
Starter pieces define the territory before the first empirical benchmarks are published.
AI turns intelligence from a scarce human capability into a purchasable input. The allocation question is what changes once that input becomes cheaper.
AI spending is becoming material, but the harder question is whether that spending is changing the production function of the firm.
Token prices are visible, comparable, and incomplete. The research problem is to convert them into the cost of accepted work.
Research & Data
The site stays static for now, but the front page already makes room for maintained indices, trackers, and reports.
A standardized basket for tracking the full cost of acceptable machine-assisted work over time.
Method sketch TrackerA dated price history for models, context windows, caching economics, and capability metadata.
Dataset seed ReportsShort methods, benchmarks, and essays that turn editorial questions into reusable research assets.
ArchiveMeasurement Signals
The publication can begin with static notes while preserving a path to structured benchmarks and downloadable datasets.
Inference, tools, review time, failures, and rework in one economic unit.
Where paying for a stronger model, more context, or more reasoning changes the result.
The supervision cost that determines whether automation is economically real.
Research Reports
Each line of inquiry can start as a page and later become a maintained data product.
A proposed basket of professional tasks for tracking the total cost of acceptable machine-assisted work over time.
A lightweight time series of model pricing, context windows, caching economics, and relevant capability metadata.
A possible benchmark for how organizations allocate AI spending across inference, software, implementation, training, and human review.