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Why AI Development Is Breaking the Core Promise of CI Pipelines

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Continuous Integration was built on the principle of repeatability — ensuring software behaves the same way across every automated test run, eliminating the 'works on my machine' problem. However, AI-based development is undermining this foundation, as model outputs are non-deterministic and models themselves can change without notice or triggering a new pipeline run. This means a passing test today no longer guarantees the same result tomorrow, turning binary pass/fail outcomes into unreliable, shifting signals. Developer and testing consultant Gil Zilberfeld argues that traditional CI confidence metrics are no longer sufficient when AI features are involved. He recommends shifting from single-run results to trend analysis, running tests more frequently, and rethinking how software quality is measured in AI-driven projects.

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