Abstract: Previous editorials of this journal have discussed academic lineage, intelligent tools, research criteria, and disciplinary direction. After these discussions, a more fundamental question remains: when tools become increasingly powerful, can human researchers still preserve their ability to ask questions, make judgments, and grow? From the perspective of mechanics, artificial intelligence is not the first powerful tool revolution. The finite element method, computers, and engineering software once liberated mechanicians from tedious mathematical calculations and enabled complex structures, boundaries, and loading conditions to become computationally tractable. Yet they also reshaped the identity, confidence, and institutional space of mechanics. In many universities and engineering schools, independent mechanics, applied mechanics, or engineering mechanics programs were compressed, merged, or marginalized during broader disciplinary reorganizations. The unease and sense of displacement brought by AI today are therefore not entirely new; they echo, and in some ways deepen, the impact once brought by computational tools and finite element software. AI goes beyond numerical solution. It enters literature retrieval, writing, coding, diagram generation, scheme construction, and even the appearance of problem formulation.In the age of powerful tools, what is truly scarce is not the ability to call tools more skillfully, but the ability to see the object, explain the mechanism, formulate the question, and take responsibility. Being able to build a model, generate a mesh, and obtain a contour plot does not mean that one has understood the object, the boundary conditions, the underlying mechanisms, or the responsibility of engineering judgment. Generating answers, texts, or schemes is not the same as posing questions, forming judgment, or creating knowledge. Generating a new appearance is not the same as opening a zero-to-one original entry point; such an entry point can only be recognized, undertaken, and advanced by human researchers through real objects, real contradictions, real boundaries, and real responsibility. In a recent MechanoEngineering Editorial, Gao Huajian emphasized that mechanism must precede performance and that AI may accelerate search, but mechanics must judge what is real. This statement captures a central issue in the age of powerful tools: tools may generate candidate outcomes faster, but they cannot automatically provide physical admissibility; algorithms may expand the search space, but they cannot replace conservation laws, constitutive structures, boundary conditions, stability criteria, failure envelopes, or experimental validation. Speed without mechanism is not progress; it is only faster motion.AI is not a matter of whether it should be used, but of how it is used, by whom, to what extent, and with what responsibility. True education is to help students pass through the fear and illusion created by powerful tools and bring them above tools. After tools, problems remain; after problems, human growth begins.