Generated, not typed
Tables, layer pipelines, jobs and metric definitions all generate from the one model a person approved: Databricks-native, in source format, deterministic from what was agreed.
One to two quarters per data product, because meaning is re-derived by hand at every hand-off and typed out again in a different notation.
One artifact reviewed, six artifact classes generated, and the build reduced to the part that was never the hard bit.
Most of a quarter goes into agreeing what the number means. The rest is typing. Once the agreement exists as a machine-readable model, the typing is not work worth paying a person to do, and worse, every keystroke of it is a chance to introduce a difference between what was agreed and what runs.
So generation is deterministic from what was approved, which is the property that makes AI safe here. The model does not write your pipeline from a prompt at three in the morning. It drafts a model, a person rules on it, and the pipeline follows from the ruling. The blast radius of the AI stops at an artifact somebody reviewed.
The output is your platform's own idiom, not a runtime you now depend on us for: notebooks in source format, jobs you can read, tables in your catalogue. If you turned PYX off tomorrow, the data product keeps running.
Change the meaning in one place
A definition changes in the model and regenerates everywhere it landed, instead of being chased through six repositories and a semantic layer.
Bronze, silver, gold, with the reasons
The layer gradient is argued rather than assumed: thin at bronze, heaviest at silver, routed at gold, and every unplaced line comes back with a reason rather than disappearing.
Partial builds are honest
A build that placed nine of twenty lines says so, names the gap, and does not pretend it shipped. What it could not compile is waived explicitly, with a reason attached.