Argues that hardware literacy is essential for understanding AI systems' interactions with the physical world, implying new educational requirements.
The claim that hardware literacy has been rendered obsolete by AI-operated infrastructure rests on a category error: it assumes that the layers an automated system operates over are the layers that define truth. This paper argues the opposite. Only a thin, physics-bounded slice of hardware — fused keys, mask ROM, the transistors themselves — is genuinely immutable; everything above it, firmware included, is software with a different update path and an active attack surface. An automated system's limit at this layer is therefore not that it cannot act on hardware but that it cannot see hardware except through mediated, spoofable representations. Hardware literacy is consequently revalued as an epistemic function: the human capacity to detect when an automated system's model of the physical layer has diverged from the physical layer itself. Because hardware change is bounded by physics while model and policy drift are not, the physical layer also serves as the only stable reference frame against which unbounded drift can be measured. The paper closes by distinguishing the physical substrate from the governance substrate and proposing that the asymmetry between them be treated as load-bearing rather than elided.
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Narnaiezzsshaa Truong (2026) studied this question.
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