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Materials AI is rapidly converging toward single-model regimes in which a handful of dominant architectures, particularly graph neural networks, have become the de facto standard for property prediction, inverse design, and materials discovery. This model monoculture does not merely reflect technical superiority; it actively produces convergent scientific narratives that shape what the community considers valid knowledge, worthwhile problems, and genuine progress in the field. The present critique identifies four interlocking epistemic risks of this convergence: epistemic narrowing, suppression of alternatives, paradigm lock-in, and the illusion of consensus. These risks threaten the long-term robustness of materials science by limiting the diversity of phenomena that can be observed, the range of methods that can be explored, and the kinds of disagreement that can be productively acknowledged. The consequences include missed discoveries in complex materials systems, methodological stagnation, overconfidence in model outputs, and path-dependent research trajectories that will prove difficult to reverse. Alternative approaches grounded in deliberative methodological pluralism, adversarial benchmarking, narrative diversity, paradigm auditing, and deliberate switching-cost reduction are therefore proposed as necessary correctives if the field is to preserve its epistemic openness while retaining the undeniable benefits of data-driven methods.