incident.io’s AI spending details matter because they reveal how a pre-ChatGPT startup is strategically allocating resources to integrate AI into its software reliability platform.
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Hi, this is Gergely with a bonus, free issue of the Pragmatic Engineer Newsletter. In every issue, I cover Big Tech and startups through the lens of senior engineers and engineering leaders. Today, we cover one out of five topics from the past issue of The Pulse. Full subscribers received the article below two weeks ago. If you’ve been forwarded this email, you can subscribe here.incident.io (note: I’m an investor) is a software reliability platform founded in 2021, a year before ChatGPT, that’s obsessed with measuring everything. In that spirit, the company has shared details about AI spending, changes to PR frequency, and the ROI at the company from using AI. Thank you to Lawrence Jones and the incident.io team for sharing the data!They have come up with the concept of the “effort point” as a way to analyse AI spend. One effort point is a medium-sized pull request written by a human or generated by an agent. The measurement is far from perfect, but it is one step more granular than looking at lines of code, and lets them compare the complexity of AI-generated PRs with those written by human devs in the past. Here’s what has changed in the last two years:What changed in two years for various metrics at incident.ioPull request statsAt a glance:Changes in numbers of PRs over two yearsAI agents started to generate production code in June 2025, which was around the same time as the first agent-generated got pushed at incident.io. The company has used AI-assisted IDEs since the beginning, such as GitHub Copilot and Cursor.Agent usage exploded in June, when Claude Agents launchedIn June this year, a few things happened in quick succession, the incident.io team told me:Claude Agents launched, making it easier to coordinate several agents running at onceWorktree tooling improved a lot, making it easier for agents to do parallel work on the same codebase with worktreesAlso around this time, more engineers realized that they should be running multiple agent sessions using di
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