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she leaves out a third category: ai/ml/ds, which also has huge cultural differences with traditional product and infra/platform teams. check out hilary mason's recent interview in the twiml podcast. she confirms something i've suspected for a very long time: agile is a bad fit for ml enhanced product/project development.

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The biggest issue I've had with bridging the DS<>Product gap is that our (DS) work is not linear and often not incremental. Sometime we go backwards and sometime we have to build for a really long time only to discover that this way doesn't work and we'll have to do something else and that there is usually no stopgap. ML-based solutions often don't work at all until they work.

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