The Inevitable Failure of LLMs - Predictions.

Summary:

Current approaches to artificial intelligence (AI), particularly stochastic large language models (LLMs), are fundamentally limited. These limits are not merely technical but structural, philosophical, and economic. Based on the Procedural Coherence Framework, the following predictions are made:

Prediction 1: Hallucinations Are Inherent

Large Language Models cannot systematically eliminate hallucinations without continuous external correction.
Reason: They lack true procedural grounding of meaning; they only optimize for statistical plausibility.

Prediction 2: No Autonomous Self-Correction

LLMs cannot continuously and autonomously self-correct through iterative self-monitoring.
Reason: Stochastic models lack the internal procedural dynamics necessary for genuine epistemic feedback.

Prediction 3: No Reliable Expression of Uncertainty

LLMs cannot reliably recognize or communicate their own uncertainty without engineered intervention.
Reason: Uncertainty requires internal models of epistemic confidence, which stochastic architectures do not possess.

Prediction 4: Diminishing Returns on Scaling

Scaling LLMs leads to diminishing epistemic returns.
Reason: More parameters amplify memorization and surface correlation without proportionate procedural or semantic gains.

Prediction 5: No Procedural Determination of Truth or Coherence

LLMs cannot internally determine the truth, coherence, or validity of a proposition.
Reason: Truth and coherence require dynamic process validation, not just probabilistic association.

Prediction 6: Economic Collapse of the LLM Paradigm

LLMs cannot scale economically in the long run.
Reason: Their marginal costs (training, error correction, maintenance) grow faster than their marginal cognitive utility.

Ultimate Conclusion

The current AI paradigm, dominated by stochastic scaling, is cognitively ineffective, philosophically incoherent, and economically unsustainable.

If we seek genuine artificial cognition, we must transition to systems that optimize for procedural coherence, not statistical correlation — building intelligent processes, not merely probabilistic outputs.

Postscript

This framework is rooted not in rejecting mathematics, probability, or statistical techniques, but in reframing them:

  • Mathematics is a symbolic system, not the only one.

  • Statistical methods describe patterns, but cannot generate or validate meaning on their own.

  • True cognition requires coherent processes capable of self-monitoring, adaptation, and internally sustaining symbolic interaction.

In short:

Without process coherence, there is no cognition. Without cognition, there is no intelligence. Without intelligence, there is no sustainable AI.

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