ROI

The Jellyfish AI Engineering Trends study of 7,548 engineers found a stark pattern: the heaviest AI token users produced twice the PR throughput but consumed ten times the token budget. More tokens do not equal more productivity — they equal a steeper cost curve that most engineering leaders aren’t measuring. What Is the Jellyfish AI Engineering Benchmark — and Why Should You Care? The Jellyfish AI Engineering Benchmark is the largest continuous dataset of real-world AI coding behavior ever assembled: as of early 2026 it covers 1,000+ companies, 200,000 engineers, and 37 million pull requests analyzed over rolling quarters. Unlike survey-based studies that capture developer sentiment, Jellyfish pulls instrumented telemetry — actual PRs merged, code churn rates, token consumption logs, and review cycles — making it a ground-truth view of what AI coding tools actually produce rather than what developers believe they produce. The benchmark is updated quarterly and published at jellyfish.co/ai-engineering-trends. ...

85% of developers now use at least one AI tool in their daily workflow, and 22% of all merged code across a 135,000-developer dataset is AI-authored. Those numbers sound like a productivity revolution. The reality is messier. Some controlled experiments show developers completing tasks 19% slower with AI assistance, even while believing they are 24% faster. Meanwhile, enterprises running disciplined AI programs report 4:1 returns — $150 in developer time saved for every $37.50 spent on AI tooling per incremental pull request. The gap between those outcomes is not about which tool you picked. It is about how you measure, deploy, and constrain the tool. This guide works through the actual data — the good numbers, the uncomfortable numbers, and the calculation framework your team can run today to find out which bucket you are in. ...

Enterprise AI coding ROI must be measured for the work and costs actually affected. A tool can accelerate part of development without producing an equivalent financial return across the organization. This guide separates research findings, planning assumptions and realized value. Correction — October 7, 2026: The earlier article attributed a 376% ROI figure to GitHub Copilot alone, asserted universal enterprise ROI ranges and treated shorter PR cycle time as directly billable labor savings. Those statements were not supported as written. The research scope and calculation method below replace them. ...