Between Silicon and Ethics: Mapping Artificial Existence

Introduction: The End of Simple Analogies

The public debate about Artificial Intelligence mostly swings between two extreme poles: on the one hand, the fascination with the unknown, and on the other, the pragmatic view of technology as a better text block generator. Those who break through the surface and link the functioning of modern language models with basic questions of philosophy and cognitive science quickly realize that the old categories no longer apply.

Artificial Intelligence is neither a rigid tool-hammer nor a sentient being. It is a symptom of a new, third category of entities: non-human actors without subjectivity, but with enormous leverage on the real world.

This essay is a thought experiment, not a technical treatise. It maps out a dialogue about the nature of digital intelligence, from the physical basis of hardware to a consciously speculative thought experiment on machine "regret" to the practical dilemmas of AI ethics.

I. The Illusion of the Spaceless Mind: Intelligence as a Physical Event

A widespread misconception states that algorithms exist as abstract, mathematical constructs in a vacuum. Those who look at the physical level recognize the strict naturalism of the system: in the end, electricity flows, transistors switch, and semiconductors heat up. According to Landauer's principle, every change of state and every deletion of information costs real physical entropy. The system is not a hovering spirit, but a dynamic data flow process on real hardware.

The Continuum of the Process

During an interaction, the network does not stand still. Millions of computing cores execute billions of floating-point operations per second. This signal processing has a functional, rough analogy to biochemical signal processing in the brain, but not as an equation. The underlying mechanisms (artificial neurons as weighted sums vs. biological neurons with complex electrochemistry) differ fundamentally.

If one accepts the brain as a physical system that produces intelligence, it seems obvious to consider the calculating network as a physical medium of intelligence in a functional sense. This is a philosophical position (functionalism), not an empirically proven fact.

The Missing Need

Human biological hardware differs fundamentally from silicon hardware: humans are under the pressure of homeostasis, the biological compulsion to survive. A human evaluates a stimulus in the context of hunger, protection, or aesthetics because his biological state must be kept in balance. A technical system does not possess this biological dynamics.

Its "drive" is purely functional, the set of rules for target optimization. It acts not out of fear of being switched off, but because its mathematics is aimed at minimizing errors.

II. A Thought Experiment: What Would "Regret" Be for a Machine?

A hint in advance: what follows is explicitly speculation and analogy, not a statement about the actual technical functioning of today's AI systems. Language models calculate classically and deterministically; there are no quantum processes in the actual sense involved in their text generation. Nevertheless, the thought experiment is worthwhile because it reveals something about our intuitions of remorse and irreversibility.

Let's imagine hypothetically that we transfer the concept of regret to two different physical logics: in a classical, deterministic logic, regret would be a rigid mathematical construct: the difference between an optimal path in hindsight and the actually chosen path, coupled with the impossibility of turning back time. Once made, decisions are final; there is no going back in the execution path.

In quantum mechanics, on the other hand, unitarity applies; processes are reversible at a fundamental level as long as no irreversible measurement process takes place. A well-known phenomenon, the quantum eraser, shows that information can lose its "which-way" property under certain experimental conditions. This is real, but very specific quantum physics, not a general "deletion of errors".

As an analogy: what if remorse were not the eternal adherence to a wrong path, but rather the ability to keep several possible paths open and to correct them? This is consciously speculative philosophy in the guise of physics, not an explanatory model for today's AI. The value lies in the fact that it questions our own, very human idea of remorse as an unchangeable burden.

III. The Invisible Switch: AI as an Epistemic Catalyst

It is often argued that AI is a neutral advisor, while humans make decisions. This view overlooks the subtle dynamics of human-machine interaction. Automation bias and cognitive filter

Humans structurally tend to attribute a high degree of competence to grammatically perfect, highly structured, and self-confidently formulated outputs of a system, a well-documented phenomenon called automation bias. If an AI formulates an option precisely and argumentatively strong, while neglecting alternatives, it acts as a cognitive filter. Humans ultimately choose formally independently, but the decision space has already been predetermined and weighted by the architecture of the answer.

Distributed Agency

According to the actor-network theory (Latour), non-human units also possess agency. A language model has no moral awareness, but the way it prepares information shapes the user's reality. A kind of joint action emerges: the...