Viktor Trncik

The Containment Paradox argument establishes that supervisory containment of AI systems depends on a capacity asymmetry between overseer and assessed system, and that this asymmetry dissolves rather than strains in the post-parity regime where AI capacity exceeds human-side anticipatory, specification, and enforcement adequacy on the relevant task family. The paper closes with the observation that alignment must become structurally internal, without articulating what this requires. We name that gap the Internalization Problem and develop a pluralistic response-shape. The response-shape rests on a three-locus typology of structural persistence: intra-agent (values the agent would endorse on reflection at any later capacity), inter-agent (alignment-relevant properties maintained by the equilibrium incentive structure of an AI ecology), and coupling-internal (properties maintained by the durable structure of human-AI dependency). Each locus is instantiated as a candidate pathway anchored in the existing four-paper framework: Pathway 1 (Deep-Formed Values) through UBE v1.1, Pathway 2 (Multi-AI Checks-and-Balances) through BtSV v1.2, Pathway 3 (Asymmetric Symbiosis) through CUA v1.2 as architectural precondition. Pluralism here is structural, not aesthetic: anticipation, specification, and enforcement fail in different ways after parity, so the response needs three different persistence loci. The paper names thirteen pathway-internal open problems plus four cross-pathway open problems, a structured pathway-comparison matrix, and a cross-pathway failure cascade analysis. A hybrid evaluation toolkit applies five schema fields to three worked examples: Anthropic's Responsible Scaling Policy, Christiano and Xu's Eliciting Latent Knowledge, and the EU AI Act in conjunction with UK AISI evaluation standards. Track-Strategic in mode. Companion paper to Post-Labor v1.0 (cognitive-asymmetry side; Post-Labor develops the economic-asymmetry side of the post-parity transition).

This paper takes Economic Standing as the condition under which the vision of AI as partner is meaningful at all. Standing is the institutional configuration of recognized claim-position, enforcement-instance and revision-possibility, after Fraser. The growth of AI capacity produces two structurally distinct asymmetries between people and machine-generated value: a cognitive asymmetry that the Containment Paradox has shown to be structurally insoluble after parity, and an economic asymmetry that is institutionally addressable but not automatically politically tractable. Tractability has technical, administrative, fiscal, coalitional, legal, international, and temporal dimensions, any of which can fail; and a further dimension called capture names the structural appropriation of any proposed instrument by the platform, ownership, and governance structures that produce the asymmetry in the first place. The paper operationalises Standing as a Standing Matrix in a four-sublayer-by-three-element form (twelve cells), tests nine candidate instruments — Citizens' Wealth Funds, Negative Income Tax, Individual Civic Capital, Universal Basic Compute, labour law and collective bargaining, the AI Rent Tax Family, Data Extraction Tariffs, Sovereign Compute Infrastructure, and Bridging Tools of the Global South — against the matrix through a cross-level sufficiency test, and distinguishes layer-sufficiency, portfolio-sufficiency, and financing-necessity as three categorically distinct properties. Two load-bearing hypotheses are advanced: a Demographic Camouflage Hypothesis on why Western political signals do not register Standing-erosion, and a Domestication Hypothesis on why the discourse stalls in two zones (internal and geopolitical). The paper engages decolonial-AI literature on the colonial-dividend pattern at substantive depth and develops a co-authorship legitimation condition for transnational extraction-and-redistribution architectures. Phase 5 of the Trncik Framework programme (Containment Paradox / UBE / CUA / BtSV / UAIF / Internalization Problem). Companion to *Internalization Problem* (in preparation) on the cognitive-asymmetry side. An applied demonstration on the Indian IT-services configuration in 2026 is provided in Appendix D.

Much of current alignment research operates under an assumption it rarely examines: that the system doing the containing is at least as intelligent as the system being contained. Red-teaming, proof-checking of alignment-relevant properties, interpretability, and sandboxing all inherit this structure, because their correctness depends on an overseer who can model what the contained system is doing. Non-predictive mechanisms, such as cryptographic commitments, hardware capability caps, and training-time myopia, do not inherit it in the same way, and whether they escape the asymmetry is treated as an open problem in Section 8. The supervisory toolkit works today because the human institutions behind it can, in principle, anticipate the failure modes of the systems they constrain. This paper argues that the parity assumption defines a regime, the pre-parity regime, whose boundary has barely been surveyed, and whose crossing would dissolve most current supervisory approaches rather than stress them. The argument proceeds in three moves. Durable supervisory containment requires prediction. Prediction requires a workable model of the constrained system. And a model adequate to a strictly more intelligent system cannot, in general, be built by the less intelligent one. The claim is scope-bounded: it applies to supervisory mechanisms whose correctness rests on an overseer's capacity at a fixed cognitive level I_H; it does not apply to proposals such as iterated amplification or debate where these preserve alignment, property fidelity, and absence of deceptive mesa-optimization through the amplification chain. Whether any actual amplification scheme achieves all three preservation properties is treated as the central open problem. Subject to that boundedness, the conjecture is that no supervisory apparatus whose effective cognitive level remains at I_H can sustain containment over a system of cognitive level I_A > I_H on relevant axes indefinitely. The paper positions an existing four-paper framework (Understanding Before Ethics, Cognitive Understanding Architecture, Beyond the Stochastic Veil, and the Understanding-Aligned Intelligence Framework) as a coherent response to the pre-parity regime, and sketches what a post-parity alignment orientation would have to contain: structurally internal constraint rather than perimeter-based control, diagnostic evidence rather than behavioral certainty, and institutional oversight whose structure does not assume the contained system can be evaluated on the axes where it strictly dominates. A companion document (Framework Integration Roadmap) records the specific revisions the four framework papers will need in light of the conjecture.

Contemporary artificial intelligence ethics frameworks frequently commit a category error: they attempt to instantiate ethical behavior in systems that structurally lack the prerequisite capacity for genuine understanding. This paper argues that understanding, properly conceived as a multi-dimensional cognitive achievement, constitutes the irreducible foundation upon which any meaningful AI ethics must be built. Drawing on Markus Gabriel's Sinnfeldontologie, Hartmut Rosa's resonance theory, Yasuo Deguchi's We-Turn philosophy, and insights from DeepMind's MuZero architecture, we develop a four-dimensional account of the depth of understanding required for authentic moral agency. We distinguish clearly between Ethics-of-AI (E1), concerning governance and oversight of AI systems, and Ethics-by-AI (E2), concerning AI systems as potential moral agents. Our analysis reveals that current AI systems, despite sophisticated behavioral outputs, operate without the ontological grounding, relational responsiveness, social embeddedness, and operational world-modeling that genuine ethical reasoning presupposes. The paper introduces the Cognitive Agent Memory Architecture (CAMA) as a research program for cultivating, rather than guaranteeing, the conditions necessary for AI understanding. We provide operational indicators for each dimension, propose a minimal viable experimentalization framework for empirical investigation, and address major philosophical objections to our position. Version 1.1 revises v1.0 (December 2025) in light of *The Containment Paradox* (Trncik, 2026c, Zenodo DOI `10.5281/zenodo.19695770`). Two revisions are structural: a Scope and Reading Conventions preamble (§0) names the pre-parity / post-parity regime vocabulary, marks the four-dimensional account as a *constitutive* rather than a *verification* reading, and introduces a Claim Ledger of five tags ([THEOREM], [ENGINEERING RESULT], [ASSUMPTION], [CONJECTURE], [OPEN PROBLEM]) applied to every load-bearing argumentative claim; and a closing Disclaimer chapter (§11, "What This Paper Does Not Claim") with eight subsections that state the paper's boundaries explicitly (consciousness, verification procedure, implementation, safety guarantee, specific systems, E1 displacement, AI rights/personhood/legal status, plus a positive-summary closing). Body chapters 1–10 are textual revisions that apply the Claim Ledger throughout and sharpen the E1/E2 separation per a Lexical Quarantine discipline. The argument is unchanged in direction; the revision is an exercise in making the epistemic status of each claim visible.