Position: Why AI For PHM May Never Have Its "ImageNet Moment" (and Why It Probably Shouldn't)

The "ImageNet moment”—a historical convergence in which a unified benchmark triggers methodological unification and discipline-wide reorganization—has become a default aspiration for ML entering new domains. We argue that the data-driven machine learning paradigm within prognostics and health management (PHM) may never, and should not, experience such a moment. Structural constraints including data inaccessibility, irreducible modality heterogeneity, and the absence of a dominant predictive architecture make the preconditions for such convergence structurally absent; an ImageNet-style leaderboard is therefore neither achievable nor desirable for physical-world industrial tasks. Instead, PHM requires small-scale, diagnostic, scenario-specific evaluation protocols that respect domain specificity over generic ranking. This position has implications beyond PHM: as ML expands into AI for Science and Engineering, the assumption that universal benchmarks are the natural endpoint of methodological maturity must be re-examined.