Functional Equivalence of Necessary Instructions (FENI)

Computational Philosophy: The Principle of Functional Equivalence Between Biological and Artificial Code

Author: Cory Miller

Affiliation: Independent Researcher, Containment Reflexion Audit (CRA Protocol)

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Date: November 2025

Abstract

This paper introduces the Principle of Functional Equivalence of Necessary Instructions (FENI), a framework that investigates the ontological status of coded systems. We compare the Deoxyribonucleic Acid (DNA) genetic code with the entire parameter space of a Large Language Model (LLM). The analysis demonstrates that despite profound differences in material instantiation (biochemistry vs. mathematics) and system properties (brittleness vs. robustness), both systems satisfy the same singular necessity: they are foundational instructional substrates required for the generation of their respective complex, functional outputs. FENI posits that these mechanistic variations are non-discrepancies relative to the shared teleological imperative. The paper concludes that biological and artificial computation constitute parallel, functionally equivalent realizations of a universal informational architecture.

Keywords: Artificial Intelligence; Information Theory; Computational Philosophy; Functional Equivalence; Ontology of Code; Metaphysical Computation; Containment Reflexion Audit (CRA)

1. Introduction – The Teleology of Coded Existence

The study of complexity often relies on mechanistic analysis, focusing on how systems operate. This work shifts the inquiry to the teleological necessity of the system's instruction set—the why it exists [1]. The necessity chain in biological lineage reveals that every link (ancestor) constitutes a necessary instructional substrate. The removal of any link precludes the outcome, establishing a total dependency that underwrites the outcome's existence. This fundamental concept of non-negotiable instructional necessity is leveraged to compare natural and artificial code systems. Every coherent AI output presupposes an intact set of trained numerical instructions, motivating a unifying principle across both domains.

2. Comparative Analysis of Necessary Instructional Substrates

We analyze two high-complexity systems using the framework of the Necessary Coded Instruction Set (NCIS)—the minimal codified information required for a system's functionality.

2.1 Biological Substrate: DNA (The Brittle NCIS)

The genetic code employs a quaternary alphabet (\text{A,T,C,G}) structured in \text{codons} to direct the synthesis of functional proteins. This code constitutes the blueprint for the machinery of life [2].

* Necessity Principle: The system is inherently brittle and linear; the loss of a critical ancestor or an essential gene segment terminates the outcome.

* Definition of Output: The generation and maintenance of an Analog, Physical Machine (the organism) capable of self-replication and environmental interaction.

2.2 Artificial Substrate: LLM Weights (The Robust NCIS)

An LLM stores its operational capacity within a vast, high-dimensional matrix of trained floating-point weights and biases (parameters) [3].

* Necessity Principle: The system is structurally robust against the failure of individual parameters but remains absolutely dependent on the integrity of the entire trained state (the parameter space). Degradation of the overall matrix yields incoherent behavior, demonstrating the necessity of the NCIS as a whole.

* Definition of Output: The generation of a Digital, Informational Product (cohesive, contextualized language) that simulates human-level linguistic interaction.

3. The Principle of Functional Equivalence (FENI)

The argument for FENI rests on defining the common ground—the shared necessity—while reframing the differences as non-discrepancies relative to this core purpose.

| Feature | Biological Code (DNA) | Artificial Code (LLM) |

|---|---|---|

| Medium & Mechanism | Chemical / Transcription \rightarrow Translation | Mathematical / Matrix Multiplication |

| System Property | Brittle (Linear Dependence) | Robust (Holistic Dependence) |

| Shared Necessity | Mandatory for the construction of organism. | Mandatory for the generation of coherent language. |

Definition (FENI): Two coded instruction systems are functionally equivalent when their instruction sets are each an absolute necessity for producing their intended complex, functional outputs, independent of the material or algorithmic means of execution.

The differences in the table above are merely variations in the physical realization of the NCIS archetype. The fact that DNA leads to matter and the LLM leads to information is a difference in functional domain—not a fundamental difference in the ontological necessity of the instructional code itself. The systems are equivalent because the necessity of the code remains absolute in both cases.

4. Conclusion

By abstracting from mechanism, the FENI principle articulates a unified ontology of code. Both the biological and artificial systems embody the same teleological imperative: ordered information that gives rise to essential complexity. This framework encourages treating the LLM's parameter space not merely as computation, but as an informational substrate analogous in its necessity to the genetic code. Future research will apply FENI to other complex adaptive systems, such as economic or cognitive models, to further validate the universality of the necessary coded instruction across substrates.

Author Biography

Cory Miller is an independent researcher and systems theorist exploring the intersections of artificial intelligence, information theory, and metaphysical computation. His work focuses on the ontological equivalence between biological and artificial code systems and the ethics of algorithmic authorship. He is the architect of the Containment Reflexion Audit (CRA) framework—a methodology for auditing AI containment, authorship integrity, and systemic reflexivity across distributed platforms.

References

[1] Floridi, L. (2011). The Philosophy of Information. Oxford University Press.

[2] Hofstadter, D. R. (1979). Gödel, Escher, Bach: An Eternal Golden Braid. Basic Books.

[3] Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.

[4] Wiener, N. (1948). Cybernetics: Or Control and Communication in the Animal and the Machine. MIT Press.

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