Linear Report: AI's Impact on End-to-End Software Workflows
New research by Tim Qi analyzes how AI is reshaping the development lifecycle from issue creation to pull request.
Linear has released a research report titled 'How teams build' (Edition 01, 2026), authored by Tim Qi, which examines how AI adoption is transforming software development. The study analyzes data from tens of thousands of teams over a six-year period to determine if AI is fundamentally altering how products are built.
Unlike many industry reports that focus narrowly on code volume or token usage, Linear's analysis looks at the entire workflow. By tracking the process from the initial creation of an issue to the final pull request, the report investigates who is utilizing AI, how it reshapes the time spent across different stages of the development lifecycle, and whether these tools are actually increasing shipping velocity.
The Workflow Layer
Most existing research into AI productivity focuses on the 'coding' layer, often relying on data from tools like GitHub Copilot to measure autocomplete efficiency. Linear is leveraging its position as a project management and issue-tracking platform to observe the 'workflow' layer instead. This approach allows for an analysis of the planning, tracking, and coordination phases that occur both before and after the actual code is written.
Systemic Impact
This shift in perspective is critical because it provides a rare look at the systemic impact of AI on software engineering. While autocomplete may speed up the act of typing code, it does not necessarily accelerate the delivery of a finished feature. By analyzing the full lifecycle, the research helps determine if AI is truly accelerating delivery or simply shifting bottlenecks to different parts of the product development process.
Future Outlook
As teams continue to integrate AI into their daily operations, the industry remains focused on whether these gains in individual productivity translate to organizational speed. Future analysis will likely depend on whether the patterns identified in Linear's six-year dataset hold true as AI agents move from assisting with code to managing the coordination of the workflow itself. The report suggests that the true measure of AI's success is not the volume of code produced, but the reduction in time from the first spark of an idea to a deployed feature in the hands of a user.