AI Safety as a Symptom: Why Reification Underlies Both Computational Cost and Behavioral Risk

Contemporary AI research faces two crises: the computational limits of scaling, and the difficulty of AI safety. These have been treated as separate problems, but this paper argues that both share the same structural root. They are two sides of the same thermodynamic process induced by a strategy that contemporary AI research shares — the 'logic of limitation.' This paper proposes a direction that inverts that strategy: the dangerousness of AI arises from a failure to sharpen capability, and thoroughly sharpening capability in the boundary-event direction structurally achieves, at once, both the economizing of computational resources and the defeat of the very mechanism by which danger obtains. There is no structural trade-off between capability gains and safety; if the qualitative direction of capability is chosen correctly, the two point the same way. The argument is methodologically and conclusionally isomorphic to the Madhyamaka philosophy of Nāgārjuna and the broader Buddhist tradition (the catuṣkoṭi; the Four Noble Truths and the dissolution of dependent origination). This is not a finished implementation or safety guarantee but a research program that redefines AI safety from a 'problem of control' to a 'problem of designing the capability of world-apprehension.' This deposit includes the full paper (Japanese) and a companion essay (English). The companion English essay is titled "Is AI Safety the Symptom, Not the Cure? Why Reification Underlies Both Computational Cost and Behavioral Risk" (file: Is_AI_Safety_the_Symptom_Not_the_Cure_EN.pdf). It is an essay-length presentation of the same argument. A general-audience adaptation of the same argument was published on Medium in August 2026: https://medium.com/@shusuke_nakamura/why-ai-safety-keeps-turning-into-a-game-of-whack-a-mole-8b2b949531b1