AI Engineering Trends

Jellyfish measures Access Percentage as the fraction of engineers at a company who have a license to an AI coding tool. As a baseline adoption metric, Access Percentage establishes the foundation for deeper analysis, including how frequently and deeply AI tools are integrated into developer workflows.

Jellyfish measures Weekly Active Users (WAU) percentage as the fraction of engineers at a company who actively use an AI coding tool in a given week. This metric captures the frequency of adoption, showing whether engineers are actually integrating AI tools into their regular workflows.

Jellyfish defines a “frequent AI user” as an engineer who is using AI coding tools three or more days a week. Frequent AI Users Percentage measures the fraction of engineers that have achieved this level of adoption as a separate metric beyond basic WAU.

Jellyfish measures AI Code Percentage as the fraction of a company’s shipped that is AI-assisted. It is calculated as the fraction of merged code additions that were AI-assisted, relative to all code additions in merged pull requests for each company.

This metric moves beyond tool usage frequency to capture the actual depth of AI’s impact on codebases and coding work.

Jellyfish measures Autonomous Agent Activity as the percentage of pull requests that are autonomously created or generated by AI agents. A PR qualifies as agent-generated if it was opening user id is an agent or if it contains commits where the committing user id is an agent. This metric tracks the emerging frontier of AI adoption, where AI isn’t just assisting engineers, but independently producing shippable work.

Jellyfish measures Token Consumption as the total number of tokens consumed by a developer while using Claude Code. It is calculated by summing all tokens – input, output, cacheRead, cacheCreation – from all Claude Code sessions for each developer each week. This metric tracks Claude Code usage and the potential budgetary implications of increased AI maturity.

Jellyfish measures productivity impact in terms of PR throughput, specifically through differences in the average PRs per engineer merged (by company). The data below represent weekly PRs per engineer averaged over the last three months. Companies are grouped by adoption level, defined by the percentage of “frequent” AI users (i.e. engineers using AI coding tools 3+ days per week).

The average PR throughput increases with the adoption level tier, with approximately 2x the throughput at the highest tier relative to the lowest.

One measure of quality is the fraction of “reverted” PRs – code that was deployed but needed to be rolled back. The data below represent the percent of reverted PRs relative to total PRs for each company, averaged over the last three months. Companies are grouped by adoption level, defined by the percentage of “frequent” AI users (i.e. engineers using AI coding tools 3+ days per week). 

The average revert percentage increases with higher adoption level tiers, representing a 16-19% increase over the baseline (+0.09–0.11 percentage points). Note that the recent relative increase is due to a decline in the metric for the baseline (as opposed to an increase in metric for the higher tiers).