A new arXiv paper introduces RAMP (Repository AI Maturity Profile), a four-level cumulative scale for describing how deeply coding agents are integrated into a software repository. The authors argue that prior studies report only average outcomes across adopters, which masks large variation between teams, and that greater agent adoption can come with higher quality costs and technical debt.
The latest Ramp AI Index shows that the heaviest AI-spending US companies reduced their AI costs per employee by almost 10% in August. This drop coincides with a 41% decline in price per million tokens since March 2026, as businesses move workloads from costly frontier models to cheaper alternatives. The shift suggests enterprises are increasingly optimizing for cost rather than defaulting to the most powerful models.
The Ramp AI Index reports that per-employee AI spending at the top 1% of US companies declined almost 10% in August. The cost per million tokens has fallen 41% since March 2026, and businesses are increasingly moving workloads away from pricier frontier models toward cheaper alternatives. The shift points to both falling inference prices and a change in how enterprises choose which models to use.