AI Summary

The issue lies in how companies approach generative AI projects. When these initiatives fail, technical leaders often point to the model as the problem. However, data engineers suggest that the root cause often lies in the pipeline, not the model itself. This discrepancy highlights a common problem in the development process. By blaming the model, companies may be overlooking the underlying issues in their data pipelines. This could lead to wasted resources and failed projects, as the root cause of the problem remains unaddressed. The implications of this issue are significant for companies investing in generative AI. If the pipeline is the true source of the problem, then addressing it could be key to the success of these projects. However, this requires a shift in focus from blaming the model to examining the underlying infrastructure.

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