Artificial Intelligence as a Meaning System

Responding to the Crisis of Machine-Generated Meaning

Authors: Jordan Vallejo and the Transformation Management Institute Research Group

Status: Monograph C1 | November 2025

Abstract

Artificial intelligence no longer functions primarily as a computational aid. At institutional scale it performs interpretive work: it reconstructs information, assigns relevance, generates context, and influences coordinated behavior across organizations, platforms, and public discourse. Those functions place AI inside the proportional architecture defined in Meaning System Science.

The central risk introduced by AI is not autonomy or intelligence. It is variation at machine scale. Synthetic interpretation introduces change faster than inherited evaluation constraints, correction pathways, and coherence structures can reconcile. When inconsistency accumulation exceeds correction capacity, drift rises as a rate and interpretation loses portability. Claims become harder to reconstruct, harder to compare across roles and environments, and harder to integrate into shared decisions over time.

This monograph establishes AI in its correct scientific category: synthetic interpretation infrastructure embedded inside bounded human meaning systems, operating at machine scale rate. It treats authenticity policing, “fake art” disputes, AI concealment norms, polarized narratives, and job loss anxiety as drift signatures: observable compensations that appear when reconstructability, comparability, and correction viability weaken and systems introduce substitute stabilizers.

Meaning System Governance is introduced as the missing structural layer. It complements safety, ethics, and compliance by specifying how interpretive stability can be preserved when machine generated interpretation participates in legitimacy formation.

The unit analyzed here is a deployed AI service operating inside a bounded human environment, including its interface, routing, and coupling to workflows. This monograph does not treat an isolated model artifact, such as weights without an operating environment, as the system boundary.

1. Introduction

AI is often described as a predictive engine, a productivity layer, or an automation tool. Those descriptions remain partially accurate, yet they miss the governance relevant fact. AI now participates in the production of meaning within shared environments. Its outputs are treated as representations of reality, summaries of events, explanations of causality, recommendations for action, and evidence in decision pathways.

Once these outputs circulate through documents, meetings, workflows, platforms, and second order generation chains, AI is not external to interpretation. It becomes part of the environment through which interpretation is produced, revised, and validated.

Meaning System Science (MSS) treats meaning as a structural phenomenon defined as action relevance within an interpretive event. A system participates in meaning formation when its outputs enter bounded human interpretive environments and measurably influence what is treated as real, relevant, and actionable through established routing, closure, and correction pathways. Consciousness and intent are not required, but closure and correction responsibility remain properties of the surrounding meaning system, not of an isolated model artifact.

This classification matters because interpretive stability is a governed object. In practice, organizations and societies depend on the ability to reconstruct what was meant, compare interpretations across roles and time, and integrate competing claims into shared decisions. When those conditions weaken, legitimacy declines regardless of participant intent, expertise, or stated values.

AI intensifies this risk because it increases variation and propagation at machine scale while stabilizers remain human scale. That mismatch is not solved by better intentions, clearer messaging, or higher effort. It is addressed by governance that targets the structural conditions required for interpretation to remain portable under continuous synthetic variation.

2. Meaning as a Proportional System

Meaning System Science defines interpretive reliability as a proportional product of five interacting variables.

• Truth Fidelity (T). The integrity of the system’s promised reference conditions. Reality claims remain reconstructable and testable under transfer across roles and time.

• Signal Alignment (P). The degree to which authority, incentives, and action weighting signals reinforce the same promised reference conditions rather than competing with them.

• Structural Coherence (C). The integrity and usability of the pathways that route information, decisions, correction, and memory within a bounded meaning system.

• Drift (D). The inconsistency accumulation rate: the rate at which unresolved contradictions and non equivalences accumulate faster than correction and integration can absorb.

• Affective Regulation (A). The regulatory capacity required to interpret complexity and sustain correction quality under load.

In the Physics of Becoming (A4), legitimacy is formalized as a proportional relationship.

L = (T × P × C) ÷ D

Legitimacy (L) is the stability of interpretation under transfer and scrutiny. It declines when inconsistency accumulation outpaces correction, independent of intent or expertise.

Before widespread deployment of synthetic interpretation infrastructure, many environments relied on pace as an implicit stabilizer. Conditions shifted slowly enough for correction to complete, for baselines to remain comparable, and for institutions to preserve continuity. Synthetic interpretation infrastructure removes that buffer. Variation can become continuous while correction throughput remains bounded.

This monograph uses two distinct terms. Meaning system refers to the bounded human environment that can stabilize action relevance through closure and correction over time. Synthetic interpretation infrastructure refers to deployed AI services that generate and propagate interpretive candidate signals at machine scale rate.

This monograph treats drift as a rate condition. The object of governance is not the elimination of error. The object is the preservation of interpretive portability under a rate regime that otherwise produces persistent non comparability.

3. Artificial Intelligence as a Meaning System

In MSS terms, a deployed AI service is not a meaning system in isolation. Rather, it functions as synthetic interpretation infrastructure inside bounded human meaning systems because its outputs enter interpretive events as candidate-generating signals and acquire action weight through institutional routing, closure, and correction. Consciousness and intent are not required for participation in meaning formation, but meaning itself remains the action relevance stabilized by the surrounding system’s governance and recourse.

3.1 Synthetic interpretation participation

Models reconstruct information, apply context, generate representations, and present explanations that users treat as meaningful. In MSS terms these outputs are signals until a bounded human system treats them as sufficient to constrain action through a closure pathway. Interpretation occurs when a user updates understanding, confidence, or action choice on the basis of synthetic output.

Accuracy matters, yet accuracy alone does not define the phenomenon. Even correct outputs can destabilize meaning if they are not reconstructable, not comparable across environments, or not integrable into correction pathways.

3.2 Coordination

AI outputs redirect workflows and decision routing. A classification can change triage priority. A summary can change what a meeting concludes. A recommendation can shift a decision chain. When outputs alter coordinated behavior, they function as embedded interpretive structures.

3.3 Propagation

Synthetic interpretations rarely remain local. They enter emails, tickets, reports, dashboards, policies, and knowledge bases. They are copied, rephrased, and reused as inputs for later interpretation. They also enter second order generation chains where synthetic content becomes evidence for additional synthetic content.

Propagation converts local interpretation into environmental interpretation. Over time, this makes AI a participant in legitimacy formation, not a tool used only at the edge.

4. The Crisis of Synthetic Interpretation

Human systems depend on interpretive invariants: stable conditions that allow meaning to remain reconstructable, comparable, and integrable regardless of who interacts with a system. AI increases drift pressure by introducing machine scale variation into human scale stabilization architectures.

4.1 Mechanisms that raise drift pressure

The governance problem is driven by a small set of mechanisms that increase inconsistency accumulation relative to correction capacity.

• Equivalence breaks. Identical inputs no longer produce meaning equivalent outputs. Equivalent prompts can yield materially different meaning depending on context windows, model state, or wrapper constraints. When equivalence breaks, comparability weakens.

• Baseline volatility. Version updates change interpretive behavior without stable comparators that allow institutions to determine what changed and why it matters. When baselines shift continuously, evaluation becomes time local and transfer across time becomes unreliable.

• Context sensitivity. Hidden variables such as retrieval results, tool invocation, conversation history, system prompts, or interface defaults can materially change meaning without visible notice to the user.

• Implementation divergence. The “same model” behaves differently across products, toolchains, safety layers, and deployment constraints. Institutions may assume equivalence while operating across non equivalent systems.

• Propagation chains. Outputs replicate across documents, systems, and conversations. Reuse amplifies variance because interpretation is reintroduced in environments with different constraints and different membership conditions.

• Synthetic media attacks. Voice and face synthesis, document fabrication, and composite evidence packages can increase contradiction volume by forcing verification to expand its scope before closure is possible.

Each mechanism increases inconsistency production or reduces comparability faster than correction pathways can absorb under existing constraints. These mechanisms do not require error. They require rate. Even when outputs are high quality, continuous variation can exceed the stabilization capacity of evaluation constraints and correction pathways.

4.2 Loss of interpretive invariants

• When equivalence breaks and baselines become time local, institutions lose the ability to maintain stable promised reference conditions. The result is portability failure.

• Reconstructability declines when claims cannot be traced back to stable reference conditions, stable inputs, or stable evaluation standards.

• Comparability declines when teams cannot determine whether two outputs are meaning equivalent under shared constraints.

• Integrability declines when contradictions do not route into correction pathways early enough to prevent local baselines from solidifying.

• Portability failure is experienced as disagreement that cannot be resolved by more conversation alone, because the underlying evaluation constraints are not shared or not stable.

4.3 Drift signatures in public behavior

As interpretive invariants weaken, systems and communities introduce substitute stabilizers. These behaviors function as constraint surrogates and authority proxies when reconstructability and equivalence rules are not stable enough to support shared evaluation.

• Authenticity policing. Provenance becomes a constraint surrogate. Human made versus AI generated classification functions as an attempt to restore a legible boundary when reconstructability is uncertain.

• Stylistic and linguistic gatekeeping. Communities treat stylistic cues as credibility filters. Style becomes a proxy for authority weighting when signal alignment is unstable.

• AI concealment norms. Individuals downplay or hide AI use to avoid misclassification by unstable evaluation filters. Concealment is a rational adaptation when provenance is used as a high stakes proxy for trust.

• Moralization of creativity. “Fake art” disputes intensify when authorship substitutes for comparability rules. Origin becomes a defended constraint because it provides a stable category when evaluation standards are noisy.

• Identity anchoring. Group membership becomes a primary interpretive heuristic when truth fidelity, signal alignment, and structural coherence fail to provide stable orientation.

These signatures are treated here as observable stabilizer substitutions that appear under rising drift pressure.

4.4 Environmental and institutional multipliers

Synthetic variation enters environments that often already operate near their stabilization limits.

• Information volume can exceed verification capacity, weakening truth fidelity.

• Organizational silos and asynchronous work can reduce pathway continuity, weakening structural coherence.

• Institutional ambiguity can produce conflicting signals between stated narratives and lived conditions, weakening signal alignment.

• Time pressure and cognitive load can reduce regulatory capacity, lowering correction quality.

• Polarization can elevate identity based interpretation when shared reference conditions weaken.

• Asymmetry of influence can intensify agency attribution because structural explanations are harder to reconstruct under load.

AI amplifies these pressures by increasing the speed and reach of interpretive change. The resulting instability is often experienced as sudden, yet it is frequently the acceleration of existing drift.

4.5 Job loss narratives as drift signals

The claim that “AI will take our jobs” intensifies not only when tasks change, but when meaning conditions around work lose portability.

• Work depends on interpretable role boundaries, stable authority cues, coherent coordination pathways, and stable indicators of value. Synthetic interpretation can destabilize all four before formal responsibilities change.

• Truth fidelity uncertainty appears as confusion about what counts as correct or complete.

• Signal noise appears as ambiguity about whose judgment carries action weight.

• Pathway uncertainty appears as confusion about where outputs belong in workflows and how correction should occur.

• Regulatory load increases as individuals attempt to preserve interpretive continuity under continuous change.

Job loss narratives often function as structural readings of rising drift pressure. They are not primarily forecasts. They are attempts to name a loss of stable interpretive footing.

4.6 What the signatures reveal

Three structural observations follow.

• First, drift is often experienced before it is named. People notice non comparability as friction, exhaustion, and dispute before they can identify the structural cause.

• Second, meaning systems introduce substitute stabilizers when reconstructability, comparability, and integration weaken. Provenance, identity, and aesthetic purity become governance substitutes when formal constraints are absent or unstable.

• Third, machine scale rate exposes fragile architecture that previously relied on slower environments. Pace once masked weak comparability rules and incomplete correction routing. Synthetic velocity makes those weaknesses visible.

5. The First Law Applied to Synthetic Environments

Because AI participates in meaning formation, AI mediated environments are governed by the same proportional relationship as other meaning systems.

L = (T × P × C) ÷ D

In AI mediated environments.

• T is stressed when promised reference conditions are under specified, time local, or inconsistent across contexts, versions, and toolchains. Reconstructability becomes difficult under transfer.

• P can amplify quickly because platforms and institutions embed outputs into triage, prioritization, and decision routing. Action weight can scale faster than reference conditions stabilize.

• C is constrained by legacy pathways not designed for continuous synthetic variation. Information, decisions, and corrections may route through processes that cannot preserve comparability under rapid change.

• D rises as a rate when inconsistency accumulation exceeds correction throughput. Drift should be treated as rate behavior, not inferred from isolated errors.

• A becomes a binding constraint as interpretive load increases faster than human evaluation capacity and institutional bandwidth.

5.1 Observable proxies for drift as a rate

Rate governance requires observable indicators that drift pressure is rising before instability becomes overt.

• Baseline instability. Evaluation relevant behavior changes faster than comparators can be maintained.

• Comparability failures. Teams cannot determine whether outputs are meaning equivalent under shared constraints.

• Contradiction persistence. Conflicts repeat across artifacts and time windows without routing into correction.

• Correction throughput limits. Review and verification capacity becomes saturated.

• Interface hotspots. Drift concentrates at boundaries where outputs cross systems without stable mapping.

These proxies do not replace evaluation. They provide early indicators that interpretive stability is being lost under continuous variation.

6. Meaning System Governance

AI governance commonly emphasizes safety, ethics, and compliance. These layers are necessary. They do not by themselves govern interpretive stability under continuous synthetic variation.

Ethics governs values. Safety governs hazards. Compliance governs obligations. Meaning System Governance governs interpretation.

Meaning System Governance defines the structural requirements for preserving reconstructable, comparable, and integrable meaning when machine generated interpretation operates inside human institutions.

6.1 The existing governance stack and its boundary

Most existing governance regimes treat risk primarily as harm and responsibility. They establish organizational roles, accountability structures, transparency duties, and lifecycle controls.

• NIST’s AI Risk Management Framework organizes governance into functions that support mapping, measurement, and management of AI risks.

• ISO and IEC define requirements for an AI management system that an organization can establish and continuously improve.

• The EU Artificial Intelligence Act establishes legal obligations based on risk categories and staged applicability, including obligations relevant to general purpose AI.

These frameworks are strong on responsibility, documentation, lifecycle control, and risk classification. They touch interpretive stability indirectly through transparency, documentation, and monitoring expectations, but they do not treat reconstructability, comparability, and correction viability under continuous synthetic variation as the primary governed object.

Meaning System Governance defines that object and the control surfaces required to preserve interpretive portability.

A related signal appears in current conflicts over disclosure duties, “truthful output” claims, and restrictions on synthetic impersonation. These disputes often concern interpretive control surfaces even when they are framed as policy questions.

6.2 Failure modes: Closure Failure and Constraint Failure

Meaning System Governance distinguishes two primary instability modes.

• Closure Failure (CF) occurs when correction pathways become structurally blocked. Apparent stability is preserved by preventing revision even as inconsistency accumulates.

• Constraint Failure (KF) occurs when evaluation constraints are under specified. Interpretation proliferates without shared limits for reconstruction, comparison, or convergence.

These modes produce different governance errors. Increasing constraints does not repair a closed correction system. Reopening correction does not stabilize an unconstrained evaluation system.

6.3 Variable governance layers

Meaning System Governance specifies dedicated regulatory layers for each variable. These layers govern interpretive stability, not values, intent, or outcomes.

• T Reg. Governs promised reference conditions: verification baselines, evidence thresholds, provenance and traceability so claims remain reconstructable across roles and time.

• P Reg. Governs action weight and authority signaling: how recommendations, classifications, incentives, and approvals acquire priority, and how signal weighting remains consistent with verified reference.

• C Reg. Governs pathways for information, decision, correction, and memory: routing clarity, ownership finality, correction permeability, and interface responsibilities.

• D Reg. Governs drift as a rate condition: monitoring, propagation ceilings, comparability enforcement, and controls on variation velocity relative to correction capacity.

• A Reg. Governs evaluative capacity under load: review bandwidth limits, escalation protections, complexity thresholds, and procedures that preserve correction quality under demand.

6.4 Proportional governance

Variable governance specifies requirements for T, P, C, D, and A. Proportional governance specifies how these variables must remain in workable relation under actual system velocity.

Proportional governance requires:

• Declared system objects. Governance claims specify boundary, membership condition, evaluation window, and coupling status. Without these, stability claims are not comparable.

• Minimum evidence gates. Governance requires traceability for decisions and correction events. When trace is absent, the first governance task is to establish it before scaling use.

• Comparability classes. Outputs and evaluations are reported as strict comparable, partial comparable, or non comparable so conclusions are not overstated across contexts, versions, or toolchains.

• Rate monitoring. Drift is tracked as a rate condition using observable proxies such as baseline instability, contradiction persistence, comparability failures, and correction throughput saturation.

• Mode identification. When drift pressure rises, governance classifies dominant failure mode: closure failure versus constraint failure. Closure dominant environments require reopening correction permeability. Constraint dominant environments require stronger equivalence rules, evidence thresholds, and boundary conditions.

This layer defines how interpretive stability can be preserved under continuous synthetic variation without treating instability as a morality or capability diagnosis.

7. Implementation Across Environments

Meaning System Governance operates across institutional, organizational, platform, and ecosystem layers because interpretive stability is produced by interfaces, incentives, correction pathways, and comparability constraints distributed across many actors.

In practice, the control surfaces are trace, equivalence, interface mapping, and correction permeability, each governed under declared boundaries and membership conditions.

7.1 Institutional governance

Institutions preserve public interpretability by establishing minimum requirements for traceability, comparability, and correction viability in high stakes domains. Institutional governance also supports independent evaluation capacity so that promised reference conditions remain testable.

7.2 Organizational governance

Organizations govern how AI enters local meaning systems. This includes declaring use boundaries, maintaining decision traceability for AI mediated outputs, establishing verification and correction pathways, and preventing drift imports that exceed local correction capacity.

7.3 Platform governance

Platforms steward interpretive conditions at scale. Platform governance includes baseline continuity across versions, legible change communication, stable evaluation relevant behavior, and mechanisms that allow users and institutions to detect rising drift pressure.

7.4 Ecosystem governance

Multi model environments compound drift when outputs cross systems without stable mapping. Ecosystem governance focuses on interface clarity, cross system comparability constraints where needed, and shared correction pathways that prevent local baselines from hardening into incompatible regimes.

8. Future Conditions

Three trajectories describe how societies may manage the proportional relationship between human meaning systems and synthetic interpretation infrastructure operating inside them.

• Proportional adoption. Drift is monitored and counterbalanced. Interpretive portability remains reliable. AI becomes a coherent participant in coordinated action.

• Partial adoption. Some environments maintain proportionality while others experience persistent non comparability. Systems oscillate between stability and instability.

• Non adoption. Drift exceeds human and institutional correction capacity. Local baselines harden. Interpretation becomes time local and environment dependent across institutions.

Across all trajectories, outcomes depend more on proportional governance than on model performance.

9. Conclusion

AI must be governed as synthetic interpretation infrastructure embedded inside meaning systems because, at institutional scale, its outputs enter interpretive events and carry action weight through institutional routing. It reconstructs information, assigns relevance, generates context, and then propagates those interpretations through workflows, platforms, and second-order reuse chains. The core risk is not that machines “think.” It is that variation now arrives at machine scale inside human-scale correction limits.

The signature failure is not isolated error. It is rate mismatch. When interpretation changes faster than evaluation constraints, comparators, and correction pathways can keep up, drift rises as a rate and portability fails. People then compensate with substitute stabilizers: provenance policing, concealment norms, gatekeeping, moralized boundary fights, and narrative hardening. These are not cultural side effects. They are what meaning systems do when equivalence rules and correction viability are not strong enough to stabilize shared reference.

Meaning-System Governance supplies the missing layer: rate-aware controls that preserve reconstructability, comparability, and integrability while synthetic outputs circulate under load. The work is concrete. Declare system boundaries and membership conditions. Establish trace and minimum evidence gates. Enforce equivalence and comparability classes across versions and toolchains. Set propagation ceilings tied to correction throughput. Keep correction permeable enough to revise baselines without requiring crisis.

The question is not whether AI will be adopted. The question is whether environments will remain interpretable after adoption. A system that can generate synthetic interpretations and action-weighted outputs faster than it can correct and integrate them will not fail morally, it will fail structurally. C1 defines what must be governed so synthetic interpretation can participate in legitimacy formation without dissolving the conditions that make meaning stable and portable over time.

Citation

Vallejo, J. (2025). Monograph C1: Artificial Intelligence as a Meaning System. TMI Scientific Monograph Series. Transformation Management Institute.

Appendix A. Governance Landscape Matrix

• Framework class. Risk framework, management system standard, or statutory regime.

• Primary governance object. Harm and responsibility, organizational controls, or legal obligations by risk category.

• Measurement posture. What evidence is required and what auditing mechanisms are implied.

• Change posture. How updates, monitoring, and lifecycle controls are treated.

• Interpretive stability coverage. Whether reconstructability, comparability, and integration under continuous variation are explicitly governed or remain implicit.

Appendix B. Mechanism Glossary

• Equivalence breaks. Same input does not yield meaning equivalent output under shared constraints.

• Baseline volatility. Evaluation relevant behavior changes without stable comparators.

• Context sensitivity. Hidden variables materially change meaning.

• Implementation divergence. Wrapper and constraint differences produce non equivalent system behavior.

• Propagation chains. Output reuse amplifies variance across environments and time windows.

References

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• National Institute of Standards and Technology. (2024). AI RMF Playbook. NIST AI 600 1.

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