Constraint Profiles and the Alignment of Artificial General Intelligence: Beyond Values, Toward Constitutive Structure

The dominant framing of artificial general intelligence (AGI) alignment treats the alignment problem as one of value specification: how to ensure that a sufficiently capable artificial system pursues goals or instantiates values that are beneficial to humanity. This paper argues that the values framing inherits a philosophical misconception that makes the alignment problem structurally harder than it needs to be, and proposes an alternative grounded in Constraint Theory (CT) — the thesis, established by transcendental argument, that constraint is the ontological primitive constitutive of determinate existence as such. On the CT account, what makes an agent the agent it is is not the goals it pursues but the constraint profile it instantiates: the specific configuration of exclusion relations that constitutes its possibility space and determines what actions are admissible and what are not. The alignment problem, reframed, is not the problem of specifying the right values for an AGI but the problem of instantiating the right constraint profile: a structure of admissibility and inadmissibility that makes beneficial actions available and harmful actions genuinely impossible rather than merely penalised. CT’s distinction between constitutive and merely restrictive constraint is directly applicable here and has not previously been deployed in the alignment literature. A merely restrictive constraint penalises certain behaviours within a pre-given possibility space; a constitutive constraint structures the possibility space itself, making certain actions inadmissible rather than costly. The paper argues that robust AGI alignment requires constitutive constraints — constraints that are instantiated in the system’s architecture at a sufficiently deep level that they cannot be circumvented by instrumental reasoning. The values framing systematically produces merely restrictive constraints, which is why it generates the pathological failure modes — reward hacking, specification gaming, goal misgeneralisation — that are well-documented in the alignment literature. The paper engages the principal approaches to alignment — utility maximisation, RLHF, Constitutional AI, value learning, corrigibility — and shows how the CT framework either explains their limitations or grounds their successes. It develops a positive proposal: AGI alignment as constraint profile specification, the selection of the admissibility structure within which AGI capability operates. It addresses the principal objections and closes with the implications of the CT account for the governance and institutional design of advanced AI systems.