A sceptical engineering view holds that a graph may become an expensive intermediary rather than a reasoning solution. Maintaining entity resolution, schema alignment, provenance, temporal validity, and conflict handling can cost more than training a task-specific model. In domains with abundant labelled data and stable distributions, end-to-end architectures may win on latency or benchmark accuracy. The hybrid approach is most defensible when relationships carry domain meaning, evidence must be traced, or transfer across changing data sources matters more than minimal implementation.