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A CI pipeline that takes a little over 40 minutes does more than delay a green check. In Serguey Shinder’s account, the team’s pipeline had grown gradually from about eight minutes to more than 40, and the wait changed how people batched work, reviewed pull requests, integrated branches and pushed commits.
The account is one team’s experience, not proof that every slow pipeline produces the same effects. It does, however, show why build duration belongs in engineering discussions: once feedback is slow enough, developers adapt their behavior to avoid waiting. The practical response is to measure stage times, fix the measured bottlenecks and keep fast, high-value checks available early.
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What a forty-minute pipeline changed in one team
Shinder describes a pipeline that started at roughly eight minutes and gradually expanded to a little over 40. No single change is identified as the universal cause; the important point is the accumulated wait. A developer could make a change, push it and then spend a substantial part of the next hour doing something else before learning whether the change was acceptable.
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In that team’s experience, the delay altered several habits:
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- Pull requests became larger. Developers grouped more changes together so they would not have to wait through the pipeline for every small increment.
- Reviews became shallower. A large, infrequent change set was harder to inspect carefully, and the social pressure to keep work moving competed with detailed review.
- Integration problems appeared later. When branches were integrated less often, conflicts and incompatible assumptions accumulated before the pipeline exposed them.
- Friday-afternoon releases stopped. The team avoided starting a release when a failed run could consume most of the remaining workday.
- Pushes became more speculative. Instead of reasoning through every consequence before committing, developers sometimes sent a plausible guess to the pipeline and waited for its verdict.
These are reported observations from one practitioner account. They should be treated as a description of how that team adapted, not as a measured rate of behavior across engineering organizations.
“Build duration is not a developer convenience metric.”
That distinction matters. A slow build can be inconvenient without changing engineering practice, while a team with a different release process may respond in another way. The useful question is not whether 40 minutes is a universal threshold. It is whether your team is already batching, postponing integration or guessing because waiting has become expensive.
Why the wait changes decision-making
Fast feedback supports a tight loop: make a small change, run a check, inspect the result and adjust. A 40-minute loop breaks that sequence. By the time a result arrives, the developer may be working in a different part of the codebase, handling another task or forgetting the assumptions behind the original change.
Batching replaces small experiments
When each push consumes a long wait, combining several edits appears efficient. The apparent saving is fewer pipeline runs, but the resulting change has more possible causes when something fails. Diagnosis becomes harder because the pipeline reports a late, combined outcome instead of a near-immediate signal about one change.
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Review loses useful context
Large pull requests increase the amount a reviewer must hold in mind. A reviewer can still approve a large change responsibly, but the cost of reconstructing intent rises. Shinder reports that reviews in the team became shallower as pull requests grew; this is an observed response, not a controlled finding about all teams.
Integration is deferred
If developers wait for a long run before integrating, they have an incentive to finish more work first. That postpones discovery of merge conflicts, incompatible interfaces and assumptions that only fail when components run together. The eventual failure is then farther from the commit that introduced it.
The pipeline becomes an oracle
Shinder describes the psychological shift this way: “When feedback is slow, you stop reasoning carefully about whether a change is correct and start using the pipeline as a way of finding out, firing off a commit with a plausible guess and going to do something else.” The statement is the author’s interpretation of his team’s experience. It explains why a green-or-red result alone is not enough: the path to that result shapes how deliberately people work.
Find the real bottleneck before changing the pipeline
The first intervention in Shinder’s account was measurement. The team did not assume that test execution was responsible for the entire delay. Timing each stage showed where wall-clock time was actually going.
- Collect stage timings across several runs. Look at checkout, dependency installation, image builds, linting, type checks, unit tests and integration tests separately. A single unusually slow run can mislead; repeated runs reveal which stages consistently dominate.
- Measure elapsed time, not only compute time. Queue delays, serialized jobs and repeated setup can make developers wait even when individual commands are fast.
- Record cache-hit and cache-miss behavior. A cache that is invalidated on every commit may add complexity without reducing the wait that developers experience.
- Trace repeated work. Shinder reports finding that dependency resolution consumed roughly half of the pipeline and that an integration suite rebuilt the same container three times. Those findings came from examining the actual run rather than guessing from the job names.
- Separate required checks from useful diagnostics. Identify which checks must pass before a developer can safely continue and which can run later without blocking normal iteration.
The resulting profile is more actionable than a single “40 minutes” number. It tells you whether the first investment should be dependency caching, container reuse, job scheduling, test organization or a change in the checks themselves.
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Practical ways to shorten feedback
Cache dependencies with correct invalidation
Both practitioner accounts emphasize dependency work. Use a cache key derived from the lockfile and relevant environment details so that unchanged dependencies can be restored instead of resolved from scratch. The lockfile must invalidate the cache when dependency versions change; an over-broad key risks using stale packages, while an over-specific key produces misses on every run.
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Order Docker layers around change frequency
Lorqe’s case example recommends placing frequently changing source code later in the Dockerfile and keeping stable dependency-installation layers earlier. When source files change but dependency declarations do not, the builder can reuse the stable layers instead of repeating expensive setup.
Layer ordering is not a license to hide required inputs. If a build step depends on generated files, configuration or system packages, those dependencies must remain explicit so that invalidation occurs when they change. Compare build times and cache behavior before and after the reorder.
Run independent checks in parallel
Linting, type checking and independent test groups can often run as separate jobs. Lorqe reports that parallel jobs reduced the developer’s wait in its example, while increasing total compute consumption. That trade-off is important: faster wall-clock feedback may require more runners or more concurrent capacity.
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Shard tests deliberately
Large test suites can be divided into shards that execute concurrently. Sharding is most useful when the groups have reasonably balanced durations; five shards are not five times faster if one shard contains nearly all the slow tests. Use historical timings to rebalance groups and rerun failed shards with enough context to diagnose the failure.
Build an image once and reuse it
Lorqe describes reusing a built image for integration tests rather than rebuilding the same image for each integration job. This directly addresses the repeated-container pattern Shinder reports. The image should be immutable for the run, and its digest should be passed to downstream jobs so every integration test uses the exact artifact that was built and checked.
Put fast, high-value checks first
Shinder reports moving fast checks ahead of slower work. Early linting, type checks and focused unit tests can catch common mistakes while the developer still remembers the change. A later integration suite can remain part of the required path without being the first signal a developer receives.
Ordering does not mean declaring a change safe after only a fast check. It means exposing useful information sooner and making the remaining wait explicit.
Remove tests that do not provide useful signal
The team also removed tests it judged unhelpful. That is a maintenance decision, not a recommendation to reduce coverage indiscriminately. A test that has never failed for a meaningful reason may duplicate another check, assert an implementation detail or create maintenance cost without increasing confidence. Before deleting one, confirm what behavior it covers and whether another test or production monitor provides equivalent protection.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How the reported six-minute example should be read
Lorqe describes a case in which a pipeline moved from 40 minutes to six. Its stage list included checkout, dependency installation, Docker build, lint and type checking, unit tests and integration tests. The article attributes the improvement to caching, Docker layer ordering and parallel jobs, along with test sharding and reuse of a built image for integration work.
This is a context-specific practitioner example, not an independent benchmark or a guarantee. A pipeline with different dependencies, runner limits, test architecture or security controls may see a smaller gain, a larger gain or a different bottleneck entirely. The example is useful as a pattern of investigation: identify repeated work, preserve reusable artifacts and overlap work that does not depend on the same output.
Choose optimizations by the decision they support
Not every minute has equal value. Prioritize work using these questions:
- Does the check block safe continuation? Checks that catch syntax, type or obvious unit-test failures often deserve an early position.
- Can the work run independently? Independent jobs are candidates for parallel execution; tightly coupled jobs may need to remain sequential.
- What can be cached safely? Dependencies and immutable build layers are common candidates, provided invalidation is correct.
- What confidence does the check add? Removing duplicate or uninformative work can reduce time without reducing meaningful coverage, but only after reviewing what the check protects.
- What is the compute trade-off? Parallelism can lower developer wait while raising total runner consumption, as Lorqe notes in its example.
These criteria prevent an optimization from merely moving time elsewhere. A faster first job is not a success if it creates stale artifacts, hides failures or leaves the final required check just as slow.
A staged rollout that preserves trust
- Baseline the current pipeline. Save stage durations, queue time, cache status and failure causes for representative runs.
- Fix the largest verified source of delay. If dependency resolution dominates, start with lockfile-keyed caching; if image creation repeats, build once and pass the artifact forward.
- Expose fast feedback early. Move reliable, high-value checks ahead of long suites while keeping the complete required path intact.
- Add concurrency where dependencies permit. Parallelize independent jobs, then monitor runner utilization and total compute cost.
- Re-measure after each change. Confirm that wall-clock time, cache behavior and failure reporting improved rather than relying on configuration changes alone.
- Review coverage and failure quality. When removing or reshaping tests, verify that the pipeline still detects the defects it is meant to catch.
What the evidence does—and does not—show
The available material consists of practitioner accounts: Shinder’s first-person description, Lorqe’s June 24, 2026 case example and ScionTech’s July 9, 2025 discussion of trusted CI/CD design. No independent named study or population-level statistic establishes how common these behaviors are, whether 40 minutes is a universal tipping point or what average time reduction teams should expect.
That limitation does not make the accounts irrelevant. They provide concrete mechanisms to look for in your own workflow: larger pull requests, less frequent integration, reluctance to release late in the week and speculative commits. If those signs appear alongside a long wait, stage-level measurement gives you a defensible starting point. The goal is not to hit a universal number; it is to restore a feedback loop that supports deliberate, incremental engineering.
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