AI Engineering Trends

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 its opening user identity is an agent, or if it contains commits where the committing user is an agent. This metric tracks the emerging frontier of AI adoption, where AI isn’t just assisting engineers, but independently shipping work.

Jellyfish measures Model Usage as the share of each tool’s active users who used a given model, reported separately for Claude Code, GitHub Copilot, and Cursor. It is calculated from the models invoked by each developer each week. Users can appear under multiple models.

By analyzing the top three models by share of users each month, we measure how quickly engineering organizations absorb new model generations. As each new release ramps, its predecessor fades, so the top models can change from month to month.

Jellyfish measures open weight model usage as the fraction of engineers who selected an open-weight model (e.g. Kimi, GLM, DeepSeek, Qwen) in a given week, alongside which model families lead among the companies using them. It is calculated from per-event model selections, classified by model family.

As software organizations mature in their AI transformation, their AI budgets are growing correspondingly. Teams are employing a number of strategies to manage that spend, including leveraging open-weight models and routing usage through model routers and gateways. This behavior remains concentrated in the most AI-mature companies, with under 2% of companies employing open-weight models or routers regularly, but that rate is growing rapidly, with the overall share roughly doubling over the last six weeks alone.

Jellyfish measures AI-Only Review as the fraction of a company’s merged pull requests whose only review activity, whether comments or formal reviews, came from AI tools, with no human review input before merge. It is calculated per company over merged PRs opened by humans or AI agents. The data below shows the distribution across companies by percentile.

This metric captures the sharpest edge of AI adoption: work that ships with AI as the only reviewer. While the median company auto-merges just 0.7% of its PRs, lead adopters of this workflow are shipping 3.5%+ of their PRs without human review.

Jellyfish measures AI Spend as the cost of AI coding tool usage per developer per week, reported for Claude Code and Cursor. It is calculated by summing each developer’s reported usage costs each week, shown as a distribution across developers.

This metric turns token consumption into budget terms, and the distribution matters as much as the level: spend is highly concentrated, with the heaviest users consuming an order of magnitude more than the median developer. For leaders, it anchors the ROI question by showing what the organization actually pays per developer for the productivity signals measured alongside it.

Jellyfish measures Token Consumption as the total number of tokens consumed by a developer while using Claude Code, Cursor, and Codex. It is calculated by summing all tokens as reported from Claude Code, Cursor, and Codex for each developer each week. This metric tracks usage of those tools 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).

Jellyfish measures Productivity by Token Consumption by grouping developers into deciles of weekly token consumption, and comparing PR throughput across those groups. It is calculated as weekly PRs per engineer within each consumption decile, linking what organizations spend on AI directly to what they ship. Each data point is a group of developer-weeks binned by token usage percentile.

This metric addresses the core ROI question of whether consumption converts into output. Throughput rises with consumption decile, though not linearly: the increase levels off toward the top deciles, where high consumption reflects heavier per-PR token use. As with all comparisons in this report, the relationship is correlational rather than causal.

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 measures AI Code Percentage as the fraction of a company’s shipped code 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.