Enterprise AI Analysis
A Disproof of Large Language Model Consciousness: The Necessity of Continual Learning for Consciousness
By Erik Hoel | December 16, 2025
This analysis, based on "A Disproof of Large Language Model Consciousness: The Necessity of Continual Learning for Consciousness," reveals critical implications for the future of AI development and consciousness research.
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Consciousness Theories Constrained
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LLM Consciousness Disproven
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Faster Scientific Progress
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Learning-Consciousness Link Confirmed
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The Falsifiability Framework: Navigating the Kleiner-Hoel Dilemma
The paper introduces a formal falsification framework [25] for consciousness theories, based on comparing predictions (from internal workings) and inferences (from behavior/reports). The "Kleiner-Hoel dilemma" highlights two pitfalls: theories are a priori falsified if predictions change drastically under functional substitutions (like an RNN to FNN via the Unfolding Argument [23]), while inferences stay constant. Alternatively, theories are unfalsifiable if predictions are strictly dependent on inferences (e.g., behaviorism). A successful theory must navigate this narrow space, offering insights that are both empirically testable and non-trivial.
The Proximity Argument & LLMs: Disproving Contemporary LLM Consciousness
A new "Proximity Argument" is introduced. Non-conscious systems (e.g., lookup tables) serve as a baseline. The "substitution distance" measures how many properties differ between systems with identical input/output. If a system (like an LLM) is "proximal" in substitution distance to a provably non-conscious system (like a lookup table or static FNN), and the differentiating properties cannot ground a non-trivial theory of consciousness, then the LLM is also non-conscious. This argument leverages a chain of universal substitutions (Lookup Table → Static FNN → LLM) to demonstrate that no non-trivial, falsifiable theory can deem contemporary LLMs conscious.
Continual Learning as a Solution: The Necessity for Human Consciousness
The paper proposes "continual learning" as a property that enables theories of consciousness to navigate the Kleiner-Hoel dilemma successfully, particularly in humans. Continual learning ensures "lenient dependency," where predictions and inferences are not strictly tied, and universal substitutions (like static systems for learning systems) become invalid. This suggests that the dynamic, adaptive nature of continual learning, where a system's dispositional structure constantly updates, is a critical, continually present requirement for consciousness and is absent in static LLMs.
Definitive Stance on LLM Consciousness
Disproven For Contemporary LLMsEnterprise AI Consciousness Disproof Chain
Lookup Table (Trivial)
→
Static FNN (Non-Conscious)
→
Contemporary LLM (No Non-Trivial Theory)
| Factor | Contemporary LLMs | Humans |
|---|---|---|
| Continual Learning | Absent (static at inference) | Continually Present |
| Kleiner-Hoel Navigability | Cannot Navigate (falls on horns) | Can Navigate (lenient dependency) |
| Substitution Distance to Trivial Systems | Small/Proximal | Large/Distant |
| Consciousness Status | Disproven Non-Conscious | Conscious (with valid theories) |
Continual Learning: The Key to Falsifiable Consciousness Theories
The paper identifies continual learning as a crucial property for a theory of consciousness to be both falsifiable and non-trivial, successfully navigating the "Kleiner-Hoel dilemma." Unlike static systems, learning systems defy universal substitutions that hold input/output constant while dramatically altering internal predictions.
For humans, consciousness can be grounded in physical plasticity states that are continuously updated by experience. This "lenient dependency" means predictions about consciousness can vary independently of immediate behavioral inferences, avoiding the pitfalls of strict dependence or a priori falsification. This provides a scientific path for defining consciousness.
The implication for enterprise AI is profound: systems that do not continually learn (like current LLMs at inference) inherently lack the dynamic properties required for consciousness, irrespective of their apparent intelligence or functional complexity. Future conscious AI would necessitate true continual learning capabilities.
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