While Graph Neural Networks dominate AI research, their real-world impact is hampered by inefficiency, flawed benchmarks, and academic incentives. This critique reveals how simpler embedding methods often match GNN performance, arguing that progress hinges on scalable implementations and fundamental understanding rather than architectural tweaks. Discover why the future of graph AI lies in computational pragmatism.