Producing pipeline code got cheap. Verifying it did not.
Point a coding agent at your warehouse and it writes SQL in seconds. It also writes SQL that runs clean, returns a number, and is quietly wrong, because it has no idea what your business means by “active customer” or which join silently doubles your rows. The code is free now. The checking is the bottleneck.
This session walks seven specific ways AI-generated pipelines break, framed as the seven deadly sins: gluttony, pride, greed, envy, sloth, vainglory, and lust. Each one gets its mechanism and its fix. Chris Bergh makes the argument, including the controversial part: data lineage is not enough for this problem, and test coverage is what an agent can actually iterate against. Then Chip Bloche runs the other half live, profiling a real dataset, generating tests from what the profile finds, and letting an agent propose fixes for the issues that surface.
You will see the coverage rule of thumb that makes a table with billions of rows no harder to cover than a small one, the 80/20 split between tests a machine can write and tests that need someone who knows the business, and the write-audit-publish pattern for keeping a bad append out of the table your reports are built on.
Fifty minutes, with the slides and the full transcript on the same page as the recording. If your team is shipping AI-written pipelines and you cannot tell which of them are right, start here.
The written version, with each sin spelled out, is in The Seven Deadly Sins of AI-Generated Data Pipelines.
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