This paper delves into the fundamental, arguably insurmountable, challenges and profound potential risks associated with attempting to computationally model and quantitatively measure "Existential Redundancy" (ER). ER, defined within the Existential Symbiosis Theory (EST) (Kwok, 2025R) as those crucial quality dimensions transcending direct utility that constitute the core of current human experiential depth, meaning-making, intrinsic valuation, and authentic creativity (e.g., meaning generation, intrinsic value judgment, aesthetic appreciation, non-utilitarian creativity, affective depth, existential awareness), presents a unique challenge to computational approaches. Building upon prior EST arguments regarding ER's deep roots in phenomenology and bio-philosophy (Kwok, 2025F) and the analysis suggesting potential non-computational limits of mind grounded in substrate dependence (argued via IBE in Kwok, 2025P), this paper first establishes and reinforces the theoretical stance that key ER dimensions, due to their intrinsic connection to subjective qualia, irreducible embodiment, living autopoietic processes, and complex lifeworld contexts, are, based on our best current scientific and philosophical understanding, likely in principle non-computable by standard Turing-equivalent paradigms and inherently resistant to complete, objective, context-independent measurement using conventional quantitative methods. Subsequently, it critically assesses the significant limitations of existing computational fields attempting to model or quantify related concepts (e.g., affective computing, computational aesthetics, value learning, computational creativity), revealing their prevalent reductionist tendencies and fundamental failure to bridge the ontological "Authenticity Gap" (Kwok, 2025F) between simulation and lived reality, often conflating functional mimicry with genuine human states (a problem also highlighted in Kwok, 2025O). On this critical foundation, the paper proceeds with extreme caution to propose a purely theoretical framework for potentially constructing "ER Proxy Indicators." It must be stressed unequivocally that this is not an attempt to actually measure or quantify ER itself. Instead, this exploration serves primarily as a critical thought experiment aimed solely at considering the possibility of identifying potential behavioral or informational pattern correlates that might, under highly specific and rigorously validated (if validation is even achievable, which remains deeply doubtful) conditions, indirectly signal something about the functional complexity or potential risks associated with AI simulations related to ER dimensions. Simultaneously, and forming the core message of this paper, it emphasizes, at length and with maximum warning, the fundamental limitations of any such proxy indicators (they are proxies for functional simulation aspects, never measures of ER itself), their severe and multifaceted risks of misuse (including the dangerous trivialization of ER and human experience (Kwok, 2025N), the reinforcement of computational hegemony and reductionism, enabling sophisticated manipulation (Kwok, 2025p') or social control, and providing a pseudo-objective veneer for discrimination), and the immense, perhaps insurmountable, methodological challenges in validating even their extremely limited efficacy (validity hurdles detailed in Kwok, 2025O, Kwok, 2025K). This paper strictly defines the interpretive boundaries for any such proxies and positions their primary, highly restricted potential value (if any) solely in revealing the limits of AI simulation, identifying specific operational risks associated with those simulations, and serving as a concrete critical warning framework against naive quantification. Ultimately, it calls forcefully for establishing robust ethical norms prohibiting the misuse of proxy indicators (especially for evaluating humans or inferring AI sentience), advocating a research path that firmly acknowledges in-principle non-computability, focuses resolutely on qualitative understanding and risk warning, prioritizes ethical self-awareness over measurement ambition, and shifts focus from attempting to measure ER towards understanding the complex socio-technical conditions that enable or hinder its flourishing in human life.