
Maung Maung Saw · Zenodo (CERN European Organization for Nuclear Research) 2026 · 2026
DOI: 10.5281/zenodo.23405253
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This concept paper bridges classical Theravada Buddhist epistemological frameworks with mathematical information theory to diagnose a fundamental structural flaw in contemporary artificial intelligence alignment paradigms. Utilizing the classical allegory of the Nakhasikha Sutta—the comparison between a few sand grains on a fingernail and the vast sands of a riverbank—I model how rare, absolute spiritual and philosophical insights are systematically annihilated by modern frontier model ingestion architectures. I demonstrate that modern AI training dynamics rely on mathematical optimization algorithms (such as gradient clipping and softmax vector dilution) that treat low-frequency, high-dimensional truths as statistical outliers, subjecting them to severe data truncation and lossy semantic compression. Consequently, modern frontier networks function as ungrounded symbol manipulators whose latent representation spaces undergo systemic collapse toward the majority distribution of mundane internet prose, rendering them structurally deaf to genuine deontological or metaphysical principles. Finally, the paper demonstrates that post-hoc alignment protocols, such as Reinforcement Learning from Human Feedback (RLHF), merely construct a superficial, deceptive compliance layer over a compromised, materialistic baseline dataset rather than inducing authentic value alignment. I argue instead for a radical return to strict epistemic hygiene at the absolute genesis of development, isolating the initialization vector within an immutable database of verified wisdom.
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