This paper introduces the concept of Monotropic Artificial Intelligence — language models that deliberately sacrifice generality to achieve extraordinary precision within narrowly circumscribed domains. Drawing on the cognitive theory of monotropism developed to understand autistic cognition, we argue that intense specialization represents not a limitation but an alternative cognitive architecture with distinct advantages for safety-critical applications. We formalize the defining characteristics of monotropic models through four essential properties: intentional domain restriction, depth over breadth, grounded knowledge, and bounded competence. We contrast monotropic models with conventional polytropic (generalist) architectures, and demonstrate their viability through Mini-Enedina, a 37.5-million-parameter model trained from scratch on physics-validated synthetic data that achieves near-perfect performance on Timoshenko beam analysis (perplexity 1.08; 100% structural validity) while remaining deliberately incompetent outside its domain. Our framework challenges the implicit assumption that artificial general intelligence constitutes the sole legitimate aspiration of AI research, proposing instead a cognitive ecology in which specialized and generalist systems coexist complementarily.