Code After AI

Appendix

Richard Yan Richard Yan
· 4 min read

Appendix A. Terms from V0.9 Used in This Paper

Term Definition
Categorical Gap (v0.9, Part II) The structural mismatch between legal categories designed for deterministic, territorial, human-driven activity and AI systems that are none of these things — producing a thinning of meaning in which legal classifications cannot attach cleanly to technical realities.
G2 (v0.9, Part I) An analytical category referring to state-level AI ecosystems that meet the threshold of vertically integrated capability across hardware, compute, model development, talent concentration, and capital scaling. The United States and China are the clearest G2 states. The term is descriptive rather than geopolitical.
G3 (v0.9, Part I) States whose regulatory frameworks propagate globally through market size, legal-harmonisation capacity, and institutional legitimacy, even without full integration across the AI stack. The EU is the clearest G3 actor alongside the G2 states.
Incorporation Heuristic (I = V × W × N) (v0.9, Part IV) An analytical model explaining why some sovereign rules propagate into global practice and others do not, as a function of three variables: Visibility (detectability and credible enforcement), Workability (operational implementability), and Necessity (market gravity making exit irrational). The multiplicative structure means failure at any variable is dispositive.
Measurement Gap (v0.9, Part II) The rupture that forms when industrial-era accounting frameworks confront probabilistic, self-updating capital — limiting the State's capacity to price AI's contribution even when it can regulate AI's conduct.
Rule-Execution Gap (v0.9, Part I) The structural separation between the declaration of a rule and its realisation inside the systems meant to execute it — the distance a rule must travel from legislative intent to technical implementation.
Visibility Gap (v0.9, Part I) The structural asymmetry in which regulators cannot observe the systems they claim to govern — composed of three dimensions: technical opacity, jurisdictional fragmentation, and temporal acceleration.

Appendix B. Terms Developed in This Paper

Term Definition
Bilingual Structural Commitment (Section VII) The project's commitment to operate structurally in English and Chinese, not merely in translation — the two primary languages of frontier AI development. codeafter.ai launches as bilingual from the outset, with additional local-language editions produced in parallel as both outputs and research sites.
Code After Register (Section IX) The voice discipline applied across all editions and languages — direct claims in short declarative sentences, qualification where the argument requires, and jargon reserved for terms doing real analytical work. The standard governs editing and drafting alike, and distinguishes the series as a research programme from a set of essays under a shared brand.
Companion Essay (Section VI) The second layer of Layered Outputs. It is shorter and less technical than the academic paper, accompanies each substrate paper, and is written for the general reader and the policymaker, carrying most of the project's audience-reaching work.
Decoupling (Section III) The structural separation between an institution's inherited operating assumptions and the actual behaviour of the systems it attempts to govern — the mechanism through which Pre-Code instruments fail to reach Post-Code actors. AI is a general-purpose decoupling force acting on institutional systems; the series is the demonstration of that claim across six areas of institutional life.
Decoupling Morphology (Section III) The recurring sequence that follows a coupling break: a stable coupling breaks; legacy frameworks continue to issue outputs that no longer reach; technical intermediaries arise to restore function; legitimacy lags operational reality; and a new operational constitution settles into place without explicit ratification. The content is specific to each area of institutional life; the structure is identical across cases.
Distribution Layer (Section VI) The third layer of Layered Outputs — op-eds, policy briefs, presentations, interviews, and explanatory material on codeafter.ai that extend specific arguments from the series into specific public and policy conversations.
Five-Move Template (Section V) The standard analytical structure each substrate paper in the series applies to its domain: name the subject, diagnose the gap, trace the decoupling mechanics, apply the Incorporation Heuristic, propose the structural response. A paper that cannot execute all five moves is not a Code After paper.
Layered Outputs (Section VI) The three-tier publication architecture for each substrate paper: an academic paper as analytical core, a Companion Essay for reach, and a Distribution Layer of shorter public-facing pieces. The three layers work as a pathway rather than as parallel products.
Linguistic Gap (Section V; subject of Code After Language) The structural mismatch between the seven thousand living languages spoken today and frontier AI's accelerating convergence onto a small handful of dominant ones. The first domain-specific Gap the framework has diagnosed beyond the four Gaps of v0.9, and the subject of the first paper of the series.
Linguistic Sovereignty (Sections V, VII) The capacity of communities to conduct education, administration, law, and public reasoning in their own languages — the analytical category through which the Linguistic Gap operates. Its erosion is now moving faster than most governance frameworks can perceive, which the Language paper will diagnose in full.
Partworks Adaptation (Section VIII) The series' publication model, drawing on the partworks tradition of serial release and local adaptation and applied to the AI era — open access rather than subscription, four-month rather than fortnightly cadence, and an inverted commercial-risk structure under which the originator carries content risk while local partners invest in adaptation.
Post-Code Actor (Sections I, III) Any AI system whose outputs materially participate in institutional processes. The term actor is functional rather than metaphysical — a system whose outputs participate materially in institutional processes regardless of whether the system itself possesses agency in the human sense.
Post-Code Condition (Sections I, III) Institutional systems operating in the presence of probabilistic, adaptive, partially opaque computational actors whose outputs cannot be fully reduced to deterministic instruction or audited through inherited governance frameworks. The Post-Code and Pre-Code conditions together name the structural break the Code After project is built to address.
Pre-Code Condition (Sections I, III) Institutional systems built on the assumption that code operates as deterministic instrumentation, subordinate to human intention and traceable through stable chains of execution and accountability. The distinction is structural rather than chronological — Pre-Code does not name a period before software but a regime in which code, however sophisticated, behaved as deterministic instrumentation subordinate to human intention.
Probabilistic Intelligence (Sections I, III) The AI itself — the underlying technology from which Post-Code actors are built. The first level of a three-level hierarchy used throughout the series: probabilistic intelligence (the technology), Post-Code actor (the technology in operation), quasi-agent (the technology that shapes outcomes).
Quasi-Agent (Sections I, III) A Post-Code actor capable of shaping outcomes without being a legal subject in the inherited sense. The third level of the actor hierarchy used throughout the series — the AI that does not merely inform institutional decisions but moves them, and that consequently sits at the centre of the decoupling problem.
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Richard Yan
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