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 tool. It performs interpretive work: it reconstructs information, assigns relevance, generates context, and influences coordinated behavior across institutions. These behaviors place AI directly inside the proportional architecture defined in Meaning System Science. AI interacts with truth fidelity, signal alignment, structural coherence, drift, and affective regulation at velocities that exceed the stabilizing capacity of human systems.

The central risk of AI is not autonomy or intelligence, it is variation. Synthetic interpretive change now outpaces the structures designed to stabilize meaning at human scale. This proportional imbalance produces inconsistent meaning across environments and increases volatility in discourse, workflows, cultural norms, and institutional decision-making.

This monograph reframes AI in its correct scientific category: a meaning system whose outputs shape interpretation. It examines the structural consequences of machine-driven drift, including authenticity anxiety, accusations of “fake art,” AI shame culture, polarized narratives, and the search for coherence in unstable environments. These reactions are not cultural anomalies but indicators of rising drift.

The fear that “AI will take our jobs” is typically interpreted as economic speculation. Within MSS, it reflects a structural insight. Work is one of the few meaning environments with stable role boundaries and predictable coordination pathways. When synthetic variation enters these stabilizers, people experience proportional instability long before tasks change. Job-loss fear is an early, intuitive detection of rising drift.

Meaning-System Governance is introduced as the discipline required to stabilize interpretation at machine scale. This monograph establishes the scientific foundation for governing meaning in the century ahead.

1. Introduction

AI is often described as a predictive engine or productivity technology, but these descriptions are insufficient. Modern AI generates meaning. It produces interpretations that influence how individuals understand information, how teams coordinate, and how institutions validate decisions. Once these interpretations circulate through environments, AI becomes a meaning-producing participant inside human systems.

Meaning System Science defines meaning as a proportional product of five variables. Interpretation destabilizes whenever these variables lose proportion. AI interacts with all five simultaneously at synthetic velocity. The result is variation that exceeds the stabilizing capacity of human structures.

This imbalance produces more than technical uncertainty. It produces structural instability. The rise of authenticity policing, accusations of “fake art,” stylistic purity norms, and emerging shame about AI use reflect attempts to compensate for weakened interpretive invariants. These are not aesthetic debates so much as system-level adaptations when conditions no longer stabilize at the pace required.

This monograph examines these reactions as structural events. It treats machine-generated interpretation and human response as a single proportional phenomenon. It then introduces Meaning-System Governance, the framework required when human and synthetic meaning-systems must coexist inside the same interpretive environments.

2. Meaning as a Proportional System

MSS establishes that meaning is not produced by intention, cognition, or context alone. Interpretation behaves structurally. Reliability requires proportional relationships among five variables:

Truth Fidelity (T)
Accuracy, verification, and correspondence conditions.

Signal Alignment (P)
Authority weighting, signal consistency, and the relationship between messages and reality.

Structural Coherence (C)
The architecture that conducts information, decisions, and correction.

Drift (D)
The misalignment rate: the speed at which inconsistencies accumulate when stabilizing variables lose proportion.

Affective Regulation (A)
The regulatory capacity required to interpret complexity.

A4 formalizes these relationships through the First Law of Moral Proportion:

L = (T × P × C) ÷ D

Legitimacy (L) is the stability of interpretation in a system. When inconsistencies accumulate faster than stabilizing variables can compensate, meaning destabilizes regardless of intention or expertise.

Before synthetic meaning-systems, variables changed at human-scale speeds. Institutions stabilized interpretation through habit, shared baselines, and interpersonal correction. Today variation increases continuously. Drift rises as inconsistencies propagate faster than correction cycles. Meaning no longer stabilizes on its own.

3. Artificial Intelligence as a Meaning System

AI qualifies as a meaning system because it performs the functional activities that define interpretation in MSS. It does not require consciousness or intent, it only needs to influence how meaning is produced, modified, or carried forward. At scale, AI performs these functions continuously.

3.1 Interpretation

Models reconstruct information, apply context, and generate representations that users treat as meaningful. They assign relevance, reframe situations, and transform ambiguous signals into coherent output. Accuracy is important but secondary. Interpretation occurs the moment a user updates understanding on the basis of synthetic output.

3.2 Coordination

AI outputs shape how people act, how teams distribute work, and how institutions design pathways. A classification can redirect a workflow; a summary can reframe a meeting; a recommendation can change a decision chain. Once outputs influence coordination, they function as embedded interpretive structures.

3.3 Propagation

Synthetic interpretations do not remain local. They circulate through documents, platforms, conversations, and generative chains. They produce second-order meaning—clarifications, disputes, corrections, updates—and these propagate into new environments. Meaning circulates even without intent.

In MSS, meaning is defined by system function, not by consciousness. A system participates in meaning formation when its outputs influence interpretation for others. Synthetic systems do this reliably. The challenge is not that AI interprets, but that it introduces variation at a velocity human structures cannot stabilize.

4. The Crisis of Synthetic Interpretation

Human systems rely on interpretive invariants: stable conditions that allow institutions, professions, and cultural practices to function. Synthetic variation destabilizes these invariants by altering T, P, C, D, and A faster than human architectures can accommodate.

4.1 Drift Acceleration

Drift rises when interpretations diverge across conditions. In human systems, this divergence accumulates gradually. In synthetic systems, drift increases through:

• version changes

• distribution shifts

• context-dependent reconstruction

• architecture-level variability

• lack of stable interpretive baselines

Each operates as a drift catalyst: a factor that increases the rate at which inconsistencies accumulate relative to stabilizing forces.

In human environments drift can appear episodic because inconsistencies accumulate slowly. In synthetic environments variation is continuous because inconsistencies propagate at machine speed. Systems accumulate variation faster than human correction cycles can resolve it. The issue is proportional velocity.

4.2 Loss of Interpretive Invariants

Institutions depend on structural conditions that remain stable regardless of who interacts with them. Interpretive invariants are these stable conditions.

AI weakens invariants in several ways:

• Identical inputs no longer guarantee identical outputs.

• Context windows alter interpretation dynamically.

• Prompt style becomes a hidden variable.

• Update cycles modify interpretation without visible notice.

• Platform diversity introduces fragmented meaning.

Meaning becomes environment-dependent instead of system-dependent. The result is erosion of coordinated action: teams cannot predict outputs, institutions cannot anticipate interpretation, and platforms cannot guarantee consistency.

Synthetic variation pushes instability past the threshold where inherited structures can stabilize it.

4.3 Human Compensation Behaviors Under Rising Drift

Synthetic variation triggers immediate compensatory behaviors. These are not cultural quirks but structural adaptations that appear when stabilizing variables weaken faster than people can recalibrate.

Authenticity Policing

People draw sharp boundaries between “real” and “AI-generated” work. This is a search for coherence when interpretive boundaries fail.

Stylistic and Linguistic Gatekeeping

Communities enforce purity norms or treat subtle stylistic cues as indicators of credibility. These norms function as improvised signal regulators when structural signals lose reliability.

AI Shame

Individuals conceal or downplay AI use. Stylistic markers become socially risky because readers use surface cues to judge authorship. Shame functions as a protective adaptation when interpretive filters grow unstable.

Moralization of Creativity

Accusations of “fake art” emerge when authorship becomes a stabilizer of meaning. People defend origin because it preserves coherent lineage under structural instability.

Identity and Status Responses

Group membership becomes an interpretive anchor when other stabilizers weaken. Identity operates as a fallback interpretive structure when proportional balance declines.

These behaviors all reflect the same structural condition: inconsistencies accumulate faster than stabilizing variables can compensate, and people adopt substitutes to restore temporary interpretive footing.

4.4 Environmental and Institutional Multipliers of Drift

Synthetic variation enters environments already under structural strain. Long before AI, several conditions elevated baseline drift. AI amplifies these pressures not by introducing new problems, but by accelerating inconsistency accumulation across systems.

Information Volume

Digital environments produce more information than institutions can verify or contextualize. Truth fidelity weakens because throughput exceeds the system’s ability to stabilize meaning.

Organizational Fragmentation

Distributed teams, parallel platforms, and asynchronous workflows create structural pathways that are loosely coupled. Even well-designed processes produce interpretive divergence in these environments.

Institutional Ambiguity

Public narratives often diverge from lived conditions. When signals conflict with observable reality, trust decreases and drift rises.

Time and Cognitive Pressure

Workload compression reduces regulatory capacity. Low bandwidth prevents individuals and teams from absorbing inconsistency, accelerating drift.

Polarization

Group identity becomes a dominant interpretive heuristic when truth fidelity and signal alignment weaken. Interpretive reliability decreases as group membership substitutes for stable reference conditions.

Inequality and Asymmetry of Influence

Concerns about concentrated power become interpretive placeholders when institutional structures feel unclear. People ascribe agency to visible actors because structural explanations are harder to access.

AI interacts with each of these conditions by increasing the speed and volume of interpretive change. These factors operate as environmental drift catalysts, raising baseline drift before synthetic systems enter the loop. Machine-scale variation then amplifies instability that human systems were already struggling to regulate.

4.5 Identity-Based Compensation in Low-L Environments

As drift increases and stabilizers weaken, individuals turn to identity-based cues for interpretive grounding. Identity becomes a surrogate stabilizer when truth fidelity, signals, and structure no longer provide reliable orientation.

People tell symbolic stories about AI that stand in for structural analysis: narratives about generational attitudes, political intentions, moral decline, or technological purity. These stories feel explanatory because identity remains legible when structural cues do not.

Identity-based compensation appears whenever legitimacy declines. It is not a moral failure but a structural adaptation. Drift increases faster than stabilizing variables can rebalance, and identity fills the interpretive gap.

4.6 “AI Will Take Our Jobs” as a Drift Signal

The narrative that “AI will take our jobs” intensifies not when tasks change, but when meaning conditions around work destabilize. Work depends on four stabilizers:

• coherent role boundaries

• predictable authority cues

• consistent coordination pathways

• stable indicators of value

AI disrupts all four by introducing synthetic variation into workflows before responsibilities actually shift.

Truth fidelity becomes uncertain (“What is correct now?”).
Signal alignment becomes noisy (“Whose judgment counts?”).
Structural coherence weakens (“Where does this fit?”).
Affective capacity drops (“What does this mean for me?”).

Job fear becomes a structural reading of rising drift, not an economic forecast. It is the system’s intuitive detection that work’s meaning architecture is becoming less coherent.

4.7 What These Reactions Reveal About Meaning-System Behavior

The human reactions surrounding AI reveal three structural truths:

1. Drift Is Felt Before It Is Recognized

People experience instability emotionally before they can articulate it. Synthetic variation increases this tension by operating faster than human meaning-systems can stabilize.

2. Meaning-Systems Substitute Missing Stabilizers

When truth, signals, and structure weaken, people rely on authenticity, authorship, identity, or aesthetic cues to re-establish footing. These substitutions are predictable structural compensations.

3. Machine-Driven Drift Exposes Fragile Architecture

AI reveals how dependent institutions were on slow-changing environments. What looked coherent was often stabilized by pace, not structure.

These dynamics demonstrate that AI is not simply an emerging technology. It is a structural event that changes the proportional conditions under which meaning is produced and maintained.

5. The First Law Applied to Synthetic Cognition

Because AI participates in meaning formation, it is governed by the same proportional law as human interpretation:

L = (T × P × C) ÷ D

In AI-mediated environments:

T varies by version, context window, and data regime
P amplifies rapidly through distribution networks
C is constrained by legacy structures not built for synthetic variation
D rises as inconsistencies accumulate across models and interactions
A decreases as individuals and institutions struggle to metabolize complexity

Interpretive stability depends on maintaining proportionality among these variables. At present, drift accelerates faster than stabilizing conditions can adjust. This defines the governance problem.

6. Meaning-System Governance

Ethics governs values.
Safety governs hazards.
Meaning-System Governance governs interpretation.

AI requires proportional regulation because it is a meaning system operating at a velocity that destabilizes human meaning environments.

Governance occurs on two levels:

6.1 Variable Governance (T-Reg, P-Reg, C-Reg, D-Reg, A-Reg)

Each variable receives a dedicated regulatory layer:

T-Reg
Verification baselines, evidence thresholds, variance limits.

P-Reg
Signal consistency standards, authority weighting, alignment requirements.

C-Reg
Structural integration, workflow coherence, decision-path clarity.

D-Reg
Drift monitoring, drift ceilings, proportional constraints on variation.

A-Reg
Framing clarity, complexity thresholds, regulatory load limits.

Variable governance regulates what the meaning system does.

6.2 Proportional Governance

Proportional governance regulates how variables interact. It establishes:

• stability indices (L-scores)

• proportionality thresholds

• drift-to-fidelity ratios

• cross-model baselines

• context-window stability requirements

Existing AI governance focuses on safety, ethics, or compliance, but none address the proportional dynamics that determine interpretive stability. Meaning-System Governance provides this missing layer.

7. Implementation Across Environments

Meaning-System Governance must be adopted across institutional, organizational, platform, and ecosystem levels. Governance focuses not on constraining capability but on stabilizing interpretation.

7.1 Institutional Governance

National bodies incorporate meaning-stability indices into regulatory standards, maintain drift observatories, and enforce cross-platform consistency requirements. Proportional thresholds determine when drift warrants intervention. The goal is to preserve interpretive reliability at societal scale.

7.2 Organizational Governance

Organizations adopt structural-fit assessments to determine whether AI aligns with existing workflows. They define model-use boundaries, maintain interpretive baselines, and establish verification protocols in high-stakes environments. They become responsible for maintaining proportionality within their own meaning systems.

7.3 Platform Governance

Platforms maintain interpretive invariants across versions, interfaces, and integration paths. They monitor drift, stabilize categories, and ensure predictable behavior across contexts. Platforms are stewards of the interpretive conditions used by millions.

7.4 Ecosystem Governance

Multi-model ecosystems require shared proportionality standards to prevent drift compounding across systems. Cross-model baselines and coordination protocols become necessary to maintain stability across interacting synthetic agents.

Governance ensures that the broader AI environment operates as an interpretable system rather than a patchwork of inconsistent meaning-systems.

8. Future Conditions

Three trajectories describe how societies may manage the proportional relationship between human and synthetic meaning-systems:

8.1 Proportional Adoption

Drift is monitored and counterbalanced. Meaning remains reliable. AI becomes a coherent participant in coordinated action.

8.2 Partial Adoption

Interpretive irregularity persists. Some environments maintain proportionality while others experience instability. Systems oscillate between clarity and confusion.

8.3 Non-Adoption

Drift exceeds human and institutional capacity. Meaning reliability declines across institutions, workflows, and discourse. The fragmentation results from ungoverned variation, not machine intelligence.

In all scenarios, the stability of meaning depends more on proportional governance than on model performance itself.

9. Conclusion

AI must be governed as a meaning system. It reconstructs information, shapes signals, alters structures, accelerates drift, and influences the regulatory conditions under which people interpret their world. These are the functional properties of meaning. When they operate at synthetic speed, they exceed human stabilizing capacity.

The reactions surrounding AI—authenticity policing, gatekeeping, shame, symbolic narratives, job-loss fear—are not cultural quirks. so much as indicators of proportional imbalance. People are not reacting to technology; they are reacting to destabilized meaning conditions.

Machine-scale variation did not create instability; it revealed it. Institutions that seemed coherent were stabilized by slow environments, not by structural design. AI removed that buffer. The crisis is architectural.

Meaning-System Governance provides the structure needed to preserve proportionality. By stabilizing truth fidelity, signal alignment, structural coherence, drift rates, and regulatory capacity, governance allows human and synthetic meaning-systems to coexist without overwhelming one another.

If proportionality is maintained, AI becomes a source of clarity. If partially maintained, environments oscillate. If ignored, drift surpasses human interpretive capacity.

AI did not destabilize meaning; it exposed where meaning was dependent on conditions that no longer exist. Governance is how systems restore structural adequacy in a machine-scale world.

This monograph establishes the scientific foundation for that responsibility.

Citation

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