AI demos typically showcase toy datasets with clean, well-documented tables, making AI tools appear far more capable than they are in practice. Real enterprise data projects involve messy, massive, deeply interconnected systems — the author cites a lakehouse project requiring 3 years to connect 10 source systems with 1,200+ tables from APIs, Kafka, databases, and files, plus years of accumulated business logic. The gap between demo conditions and production reality means AI cannot simply replace the complexity of real data engineering work with a few prompts.
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