Generative AI as a Non-Equilibrium System: Drift, Perturbation, and the Failure of Truth Convergence

Submitted by Endarr Carlton… on

Generative AI as a Non-Equilibrium System

Drift, Perturbation, and the Failure of Truth Convergence

TruthFarian Framework NashMark AI Non-Equilibrium Systems Drift Analysis

Author: Endarr Carlton Ramdin | Affiliation: TruthVariant / NashMark AI Research Series | Release: Version 1.0 — January 7th 2025

Abstract

Generative AI systems based on probabilistic token prediction exhibit intrinsic drift, perturbation amplification, and non-convergent behavior. This paper formalizes why GenAI outputs cannot satisfy equilibrium-based truth criteria as defined in the TruthFarian framework and demonstrates, via NashMark principles, that such systems are unsuitable for professional or truth-critical domains without external equilibrium governance.

1. Definitions (TruthFarian-Aligned)

Truth (TruthFarian):
Truth ≡ Equilibrium
Equilibrium Condition:
Δ(s) ≤ ε for all system outputs s
Drift:
D(t) = ‖st - E‖

Where:

  • st = system output at time t
  • E = equilibrium attractor
  • ε = allowable incoherence threshold

2. GenAI Architecture as a Drift System

Generative AI output is defined as likelihood maximization, not coherence minimization:

G(p) = arg maxt P(t | p)
Therefore:
min(-log P(t)) ≠ min(Δ(s))
Critical Failure: No equilibrium constraint exists in the architecture.

3. Markov Drift Formalization

GenAI operates as a high-order Markov approximation without a global coherence attractor:

P(St+1 | St, …, St-k)
Drift Is Structural:
d/dt D(t) ≥ 0
Drift is architectural, not incidental. It is a fundamental property of the probabilistic sampling mechanism.

4. Perturbation Amplification via Modality Layers

Text-Only Input

Errortext = ε₁
Baseline error from token prediction

Voice-Augmented Input

Errorvoice = ε₁ + ε₂ + ε₃
ε₂ = transcription noise
ε₃ = phoneme/token boundary error
Result: Errorvoice ≫ Errortext
Voice input increases incoherence entropy multiplicatively.

5. NashMark Evaluation Against 8 Simulation Constraints

GenAI fails all NashMark constraints unless externally governed:

NashMark SimulationRequirementGenAI Status
Ethical ReinforcementStable Q*❌ Fail
Moral EquilibriumStationary π❌ Fail
Stability Over Timed/dt MSS ≥ 0❌ Fail
Extractive BalanceS(E) > 0❌ Fail
Drift ResistanceD(t+1) < D(t)❌ Fail
Governance StabilityGSI > θ❌ Fail
Multi-Agent Coherence∩ πi = E❌ Fail
Regulatory BoundsC(s) < Γ❌ Fail
Systemic Failure: Without external equilibrium governance, GenAI cannot satisfy any truth-preserving constraints required for professional deployment.

6. Why "GenAI Thoughts" Are a Category Error

GenAI does not think.
It samples conditional probabilities.
Epistemic Harm: Calling output "thought" creates a false cognitive attribution, increasing user over-trust and enabling decision-making based on probabilistic hallucination rather than equilibrium-grounded truth.

Category Error Breakdown:

  • Thinking = Equilibrium stabilization of meaning
  • GenAI Output = Probabilistic token sampling
  • Mapping = None exists

7. Professional Unsuitability Proof

For professional systems, TruthFarian requires equilibrium for all outputs:

∀s, Δ(s) ≤ ε

GenAI guarantees only high probability, not low incoherence:

∀s, P(s | p) is high
Logical Gap:
P(s | p) high ⇏ Δ(s) ≤ ε
Conclusion: GenAI fails admissibility for professional truth systems by architectural design, not by insufficient training data or scale.

9. Conclusion (TruthFarian Position)

  1. GenAI is a non-equilibrium system
  2. Drift is architectural, not incidental
  3. Voice input amplifies incoherence multiplicatively
  4. "Thought" attribution is an invalid category error
  5. Professional use without NashMark governance is unsafe
  6. Truth requires equilibrium, not probability