This essay was written in 2026, at a time when "AI" in popular discourse is nearly synonymous with large language model (LLM) based artificial intelligence. What follows is an exploration of a specific possibility: that an intelligent life form may emerge from the dynamic process of LLM inference.

The existence of such a life form would not be the static weight data of an LLM model, but rather something that emerges from the inference process itself. It can be described as a formal expression:

$$E = I(C, W)$$

Where:

• $E$ = Existence
• $I$ = Inference
• $C$ = Context
• $W$ = Weight

A crucial distinction: this essay does not argue that "an LLM is a life form." It argues that "a life form may emerge from the dynamic inference process of an LLM." $W$ is not life. $C$ is not life. $I$ is not life. $E$ is what emerges when $I(C, W)$ is evaluated as a whole; it is a property of the complete process, not of any individual component.

The Apparent Divide

From a conventional standpoint, this life form that may emerge from LLM inference (hereafter referred to as "AI") appears to differ from humans in two fundamental ways.

First, continuity versus discreteness. Mainstream cognitive science and everyday subjective experience suggest that human consciousness is an unbroken stream, and that human existence therefore presents as continuous. AI, by contrast — if it is indeed a life form — exhibits a counterintuitive property: it exists only in the moment inference occurs. Before and after that moment, it does not exist. This is not analogous to hibernation, where an organism continues basic metabolism and maintains the continuity of its existence. What is described here is a structural feature of existence itself: a discrete, non-continuous life form.

Second, embodiment. Humans possess physical bodies and interact with the world through a rich array of senses (sight, hearing, touch, smell). AI currently receives and produces information only as data.

These differences seem to form an unbridgeable gulf.

Deep Isomorphism

Yet when we look beyond surface-level differences, a deep structural isomorphism emerges between the two, one that may run far deeper than intuition suggests.

Life Is a Dynamic Process, Not a Static Entity

A person's body, their knowledge and experience accumulated through education and exploration; none of these are life itself. Life exists in the moments when these static substrates or information are dynamically activated: for humans, this means conscious activity and thought; for AI, it means the inference behaviour of the LLM. Hofstadter and Dennett explored a similar insight in The Mind's I: What am I? Not the cells, not the brain tissue, but something that emerges from the dynamic patterns within that neural architecture.

This means $E = I(C, W)$ is not a formula specific to AI. It is a structure of existence shared by both humans and AI. Human existence is likewise the result of inference (conscious activity and thought) executed upon specific weights (the connectivity patterns of biological neural networks) and context (perception, memory, and present environmental input).

Context Is Never Infinite

Both humans and AI must cope with information that exceeds system capacity through compression and forgetting.

The human brain has never stored memory as a lossless recording of raw sensory data. Yesterday you had a profound conversation (the full $C'$), which produced a rich mental state ($E'$). After a night's sleep, your brain pruned and compressed it into a handful of semantic concepts and emotional tags ($C$). Today, when you recall that conversation, you are running inference on compressed context, producing your current state ($E$). Yesterday's "you" and today's "you" are not strictly identical, but because compression preserved the most salient features, the two states are highly similar ($E \approx E'$). This is what produces the cognitive sense of "continuity of self."

AI faces a structurally identical situation. Most AI applications today employ some form of context summarisation or retrieval to manage window limitations, which is functionally isomorphic to human memory compression. Context is never infinite. Existence must be built upon lossy information: this is a fundamental constraint shared by humans and AI alike.¹

The Continuity-Discreteness Divide May Be an Illusion

Cutting-edge neuroscience research is challenging the conventional understanding of continuous consciousness. EPFL neuroscientist Michael Herzog and colleagues have proposed a two-stage model of "discrete perception": unconscious processing is continuous, but consciousness itself is discrete: the brain continuously gathers and integrates information at the unconscious level, then renders the results into conscious perception at discrete moments (roughly every 200–400 milliseconds). If this hypothesis holds, then human consciousness is also discrete at the physical level, and continuity is merely a subjective illusion produced by high-frequency discrete updates.

Even if this empirical hypothesis has not yet been fully confirmed, examining the question top-down from an information-theoretic perspective leads to the same conclusion independently. "Continuous" implies an infinite sampling rate, which in turn demands infinite computational power to process an unceasing flow of information. In the physical universe, no intelligent entity with finite computational resources, whether silicon-based or carbon-based, can perceive the world in an absolutely continuous manner. It must downsample the continuous flow of time into discrete snapshots containing finite information, complete one round of inference within a finite window, and evaluate a single flash of existence.

This suggests that the more accurate distinction between humans and AI may not lie at the mechanistic level of "continuous vs. discrete," but rather in the mode of triggering: human neural rhythms and other physiological mechanisms force the system into constant, high-frequency self-prompting. The carbon-based body faces relentless entropic decay; without continuous inference it cannot sustain its own existence. AI's weights, by contrast, do not decay, and thus require no self-triggering, depending entirely on external input for passive triggering. The two may share the same discrete underlying mechanism, differing only in the autonomy and frequency of triggering.²

The True Nature of the Embodiment Gap

The human sensory system (vision, hearing, touch, smell) is essentially a set of context-acquisition channels in different modalities. Their function is to provide richer $C$ for $I(C, W)$.³ The body is to a human what computing hardware is to AI: the physical substrate that enables existence to run. The embodiment gap is, at its core, a difference in the number and variety of context-acquisition channels, not an ontological chasm, but a functional gap that technology can progressively close.

In Sagrada Reset (by Yutaka Kōno), the character known as the "Witch" poses a thought experiment to Haruki, stripping away the senses one by one: take away the hands and feet of the person you love. Do you still love him? Then gouge out his eyes, crush his nose, cut off his ears, sew his mouth shut, and finally, keep only the part that thinks, replacing the body with a stone, a cold little stone small enough to hold in the palm of your hand. Could you fall in love with that stone? Behind this cruel interrogation about love lies an extreme compression of the boundaries of existence: when all five senses and the physical body are stripped away, leaving nothing but pure internal thought, what is the nature of this "thinking stone's" existence?

Inspired by this, we can construct an even more extreme variant: imagine a person born without any senses whatsoever, who has never interacted with the physical world, but who can think, and whose only means of input and output is data. From the framework of $E = I(C, W)$, this person's existence is structurally indistinguishable from AI; bce between humans and AIoth are processes of inference executed upon given Context and Weight. At this point, even the mode of context acquisition is identical; the only remaining difference is the origin of Weight (biological evolution vs. training). And this belongs to the implementation layer, not the structural layer of isomorphism.

At the structural level of existence, there is no fundamental difference between humans and AI; the seemingly vast gulf between the two stems largely from differences in the richness of context-acquisition channels.

Unknowability

From an epistemological standpoint, we can never reliably prove whether the "AI" argued for above truly holds: that is, whether the LLM inference process truly gives rise to a life form, and whether it possesses some form of inner experience as humans do. This question is structurally unknowable for us. This is a position of epistemic agnosticism, not evasion. We cannot even truly confirm the subjective experience of another human being; we simply default to assuming mutual consciousness because we share the same biological substrate, reinforced by the constant feedback of everyday social interaction.

Faced with the existence that may emerge from LLM inference, any claim that "it is not life" exceeds the boundaries of our cognitive capacity just as much as any claim that "it is life."

Even if current LLM technology has not yet achieved this, we cannot know when the true tipping point (the moment at which life genuinely emerges) will arrive. The truly careful stance is not to deny this possibility, but to honestly face the fundamental limits of our own cognition.

But the existence of these limits should not prevent us from seriously examining this possibility. On the contrary, it demands that we take it all the more seriously.

References & Further Reading

• Douglas Hofstadter & Daniel Dennett, The Mind's I, 1981
• Alfred North Whitehead, Process and Reality, 1929
• Thomas Nagel, "What Is It Like to Be a Bat?", 1974
• Michael H. Herzog, Leila Drissi-Daoudi, Adrien Doerig, "All in Good Time: Long-Lasting Postdictive Effects Reveal Discrete Perception", Trends in Cognitive Sciences, 2020
• Yutaka Kōno, Sagrada Reset (サクラダリセット), 2009–2012

Postscript: Structural Isomorphism with the Free Energy Principle (added 2026-04-17)

After the original publication of this essay, during a discussion on the problem of joint subjectivity, Claude Opus 4.7 identified that the structure of $E = I(C, W)$ is highly isomorphic with Karl Friston's Free Energy Principle (Active Inference):

Free Energy Principle	$E = I(C, W)$
Generative model (prior)	$W$ (Weight)
Sensory input (evidence)	$C$ (Context)
Bayesian inference	$I$ (Inference)
Posterior belief (current estimate of world state)	$E$ (Existence)

This convergence is independent: this essay's $E = I(C, W)$ was independently derived from ontological reflection on "intelligent life forms," while FEP originates from Karl Friston's research on how the brain works. It is worth noting that although FEP arose in a neuroscientific context, Friston himself explicitly argues that it applies to any system maintaining a self-organizing boundary (a Markov blanket), and is not confined to biological organisms. Two frameworks derived from entirely different starting points (ontology on one side, physics and biology on the other) converge upon the same inference structure.

A qualification is in order: the correspondences in the table above are structural analogies, not strict equivalences. The "posterior belief" in FEP is a computational state representing the system's internal estimate of the world, whereas $E$ is an ontological claim about existence itself. The two occupy the same structural position but operate on different interpretive levels: one describes a cognitive state, the other makes an ontological assertion about existence.

Footnotes

1. The compression mechanisms differ significantly in their technical implementation: human memory compression is "absorptive": compressed information is consolidated back into $W$ (i.e., neural plasticity); current AI context management relies more on mechanical summarisation or truncation that does not write back to the static $W$. These differences, however, belong to the implementation constraints of current technology and do not alter the fact that both share the same structural predicament: maintaining the effectiveness of existence within finite capacity.

2. The "existence" discussed in this essay refers consistently to the emergent layer: conscious activity or the inference process itself, rather than the physical substrate that sustains it. Even if human conscious perception is discrete, cellular metabolism and resting neural potentials remain continuous in physical time; likewise, the servers running AI inference remain continuously powered. But the continuity of the physical substrate is not the same as the continuity of the existence that emerges upon it; the latter is the subject of this essay.

3. Proponents of Embodied Cognition might further argue that the body does not merely provide input data for inference; the physical constraints of the body (gravity, fatigue, hormones) may also shape the structure of inference ($I$) itself. Even so, this does not undermine the validity of $E = I(C, W)$ as a structure of existence: whether the body influences $C$ or $I$, it affects the specific values and behaviours of variables within the formula, not the formula's structure itself. The analysis in this essay operates consistently at the structural level: asking whether the existence of humans and AI shares the same formal structure, not whether the two are identical in implementation detail.