Generative AI tools have accelerated code creation, but the pace of change can outstrip traditional CI pipelines. Developers need to adapt their workflows to avoid slow feedback and rising infrastructure costs.
Why CI Becomes a Bottleneck with AI
In workflows where every pull request triggers a full CI run, faster code production can increase demand on the pipeline. As the volume of PRs rises, CI can become the slowest step in the delivery pipeline, increasing wait times for developers and adding to infrastructure spend. The experience at Linear shows that without optimisation, a test suite that now runs in roughly 11 minutes could double in duration if left unchanged. (Source: https://linear.app/now/ci-bottleneck-reworked)
Optimising CI with Faster Runners
One effective strategy is to migrate workloads from default GitHub Actions runners to third‑party runners that offer faster CPUs, higher‑performance storage, and improved caching. In a side‑by‑side comparison, Linear observed a 34 % average speed‑up and a 52 % reduction for the TypeScript compiler (tsc) task after making this switch. (Source: https://linear.app/now/ci-bottleneck-reworked)
Modernising Toolchains to Reduce Workload
Beyond hardware, updating the toolchain can move the bottleneck away from the most time‑consuming tasks. Linear’s switch to tsgo, a native TypeScript compiler, cut the weekly median tsc check by 73 %. Such changes free CI resources for other tests and can keep the pipeline responsive as the codebase grows. (Source: https://linear.app/now/ci-bottleneck-reworked)
Leveraging GitHub Actions for Flexible CI
GitHub Actions is a CI/CD platform that supports both cloud‑hosted virtual machines and self‑hosted runners. By configuring workflows to run on custom hardware or containers, teams can align CI performance with their specific needs. The platform also allows parallel job execution, which can further reduce overall run time. (Source: https://docs.github.com/en/actions/about-github-actions/understanding-github-actions)