How Meaning Emerges from the Physics of Persistence

This essay addresses a fundamental question: What’s “significance” and how does it arise in systems?

Through the integration of three powerful frameworks—autopoiesis (biological self-organization), predictive processing (how systems model their world), and semiotics (meaning structures)—it is demonstrated that significance isn’t an abstract philosophical concept, but rather the apex of a three-level functional hierarchy that emerges necessarily from any system that must persist over time.

The argument proceeds in three stages:

• WHY meaning exists: Systems that maintain boundaries must interpret their environment
• HOW meaning-making works: Systems minimize surprise by building predictive models
• WHAT the structure of meaning is: A universal three-level hierarchy culminating in significance

The conclusion is that meaning isn’t optional, it’s the biophysical solution to the problem of staying alive. And that significance, the highest tier of meaning, can’t be extracted from raw data. It can only arise within a system that has something at stake.

Part I: The Problem of Existence

To understand how significance arises, we must first understand why any system would completely need meaning. The answer lies in a fundamental paradox at the heart of existence itself.

Every Organism Inhabits Its Own World

In 1909, the biologist Jakob von Uexküll introduced a concept that’d quietly revolutionize how we think about perception and meaning: the Umwelt, or “surrounding world.” Uexküll’s key insight was deceptively simple: every organism inhabits a subjective universe constituted by what it can perceive and what it can act upon.

A tick, waiting on a branch for a passing mammal, lives in a world containing exactly three features: the smell of butyric acid (signaling a mammal below), warmth (signaling skin), and a certain texture (signaling where to burrow). Everything else simply doesn’t exist for the tick. This isn’t an impoverished perception of a richer objective world. The tick’s Umwelt is complete. It contains everything the tick needs to persist as a tick.

This insight, that the environment isn’t an objective, pre-existing reality but is constituted by the organism’s own capacities, anticipates everything that follows. Each living system draws a boundary between itself and the world, and in doing so, brings its own world into being.

Staying Whole in a World That Dissolves

The concept of autopoiesis, introduced by biologists Humberto Maturana and Francisco Varela, defines what makes a living system living. Derived from the Greek for “self-production,” the term describes a system as a network of processes that continuously regenerate both their own components and the network itself.

The biological cell is the paradigm case. Metabolic processes produce enzymes that catalyze further metabolism, while also producing the membrane that encloses the whole operation. The system makes itself, continuously, from within. This creates what Maturana and Varela call “organizational closure”—the defining characteristic of life.

But here lies the fundamental problem: to maintain organizational closure, the system must be thermodynamically open. A living system must constantly exchange energy and matter with its surroundings. Without this flow, it slides toward equilibrium, which, for a living system, means death. The system must simultaneously:

• Interact with its environment to acquire resources
• Defend its boundary and identity against environmental perturbations

Interpretation becomes necessary because a system that must selectively interact with its environment, absorbing sustenance while rejecting threats, must distinguish between the two. It must transform raw sensory data into actionable information, by definition, creating meaning.

Part II: The Mechanism of Interpretation

Having established why systems need meaning, we now turn to how this process actually works.

Difference That Makes a Difference

Gregory Bateson, working at the intersection of cybernetics, anthropology, and ecology, offered a definition of information that cuts to the heart of the matter: “a difference that makes a difference.”

This deceptively simple phrase carries enormous weight. Not every difference matters. The world is full of variations, fluctuations, and changes that wash over a system without a consequence. Information, as in meaningful difference, is only that subset of differences that matter to a particular system given what that system is trying to maintain.

The concept connects directly to what neuroscientist Karl Friston would later formalize as the Free Energy Principle. The core insight: to exist and maintain its identity, a system must minimize surprise.

What does “surprise” mean for a living system? Every living organism exists within a narrow range of states that allow it to survive.  If it moves too far from these expected states, the system breaks down.  Survival requires staying within these boundaries, which means predicting what’ll happen and taking action to ensure those predictions are met.

The Boundary as Translation Zone

This gives us a precise understanding of the boundary that defines a living system. It’s not just a wall, it’s an active interface where translation happens. The boundary has two aspects:

• Sensing: the system receives signals from outside
• Acting: the system influences (by way of change or signals) what’s outside

The crucial point: the system never directly experiences “the world as it really is.” It only experiences its own sensory states. Everything it “knows” about the outside world must be inferred from patterns in what it senses.

Yuri Lotman, a semiotician, made this same point about cultural systems. The boundary of what he called the “semiosphere”, the space of meaning within a culture, is its most active region. The boundary doesn’t just separate inside from outside; it translates the untranslatable. Foreign signals get converted into internal meaning, or they don’t get in at all. This is true at every scale: cell membranes translate chemical gradients into metabolic responses; sensory organs translate physical energies into neural signals; cultures translate foreign concepts into native frameworks. The boundary is where interpretation happens.

The System as Prediction Machine

This leads to a powerful realization: every living system operates as a prediction machine:

• The system builds an internal model of its world
• This model generates expectations: “If X is true, I should sense Y”
• The system constantly compares predictions to actual sensory input
• The system calculates a prediction error being the gap between expected and actual
• The system updates its model to reduce prediction error

You reach for a coffee cup you think is full. Your model predicts weight. If the cup is empty, you experience a surprise —prediction error—and immediately update your model. This process of minimizing prediction error is simultaneously how you learn about the world, how you maintain yourself in expected states, and how you decide what actions to take.

The Unified Picture

Now, we can see the interconnectedness of all these concepts. Autopoiesis defines a living system as a self-sustaining and boundary-defining process. Prediction explains how this process operates by constructing models that minimize unforeseen outcomes. A system maintains its boundary through selective interactions with its environment. To engage in these interactions, it must predict which ones support versus jeopardize its integrity. This predictive process is essentially interpretation—converting raw sensory data into actionable meaning. The “environment” isn’t an objective reality “out there.” Instead, each system creates its own version of it through the lens of its predictions. The world you interact with is your model of that world, built to predict consequences and guide actions that ensure your persistence.

Part III: The Universal Structure of Meaning

We’ve established that systems must interpret their environment and how this works physically. Now we examine how interpretation is structured—and discover something remarkable about time.

If meaning emerges from the necessity of persistence, we’d expect to find common patterns wherever systems face this challenge. And we do so across fields that had no contact with each other.

Framework 1: Victoria Welby’s Significs (Early 1900s)

Victoria Welby, a pioneering figure in semiotics, proposed a triadic model she called “significs.” Her goal was to understand the practical, ethical, and behavioral import of signs. Her three levels:

Framework 2: Mica Endsley’s Situational Awareness (1990s)

Decades later, in cognitive engineering, Mica Endsley developed a model for decision-making in high-stakes, dynamic environments such as aviation, military operations, and emergency response. Her three levels:

The Isomorphism

These frameworks were developed in different centuries, for entirely different purposes, by researchers who had no knowledge of each other’s work. Yet they describe the same three-tiered architecture.

Charles S. Peirce, the founder of modern semiotics, arrived at the same structure through pure logical analysis. His classification of “interpretants” (the effects signs produce in interpreters) maps perfectly onto Welby’s triad:

Why does this structure keep appearing? Because it reflects something fundamental about how interpretation works. Each level operates on a different time horizon:

This concept presents a temporal cone, encompassing the present point, the integrated past, and the projected future. These three levels aren’t merely distinct cognitive processes; they represent distinct relationships to the very fabric of time.

Part IV: Nested Interpretation

So far, we’ve treated the interpreting system as a single entity, a unified organism comprehending its surroundings. However, interpretation is a complex process that occurs at multiple levels, both within and beyond the system itself.

Semiosis Composes Holonically

Consider the layers:

• A receptor protein “interprets” a ligand
• A cell interprets its biochemical milieu
• A tissue interprets signals from constituent cells
• An organ interprets its functional context
• An organism interprets its environment
• A team interprets its operational situation
• An organization interprets market conditions

At each level, the full triadic process runs: perturbation detection, causal modeling, action selection. But here’s the key: the significance computed at one level becomes sense data for the level above.

The cell’s response (its selected action) is precisely what the tissue perceives as a raw signal. The tissue has no access to the cell’s internal model; it only registers what the cell does. Each level re-interprets what lower levels conclude.

This creates what we might call semiotic impedance boundaries. Information is inherently transformed at each interface. The organization can’t simply aggregate individual knowledge; it can only interpret the behaviors and outputs of its members, constructing its own model from those traces.

This is why you can’t build significance by collecting enough data. Data is the trace left by interpretation, not interpretation itself. Each system interprets based on its own concerns, its own model, its own stake in persistence. Raw data doesn’t know what matters. Only a system with something to lose can determine significance.

Part V: The Feel of Mattering

We’ve established that things matter for persistence. But we haven’t addressed how mattering feels different from mere registration. Why does the tiger trigger terror while the novel pattern triggers curiosity? Both are prediction errors.

Affect as the Inside of Semiosis

One way to understand this: affect isn’t a response to significance—affect IS what significance feels like from inside.

Fear doesn’t interpret threat; fear is what threat-significance feels like to the system experiencing it. Curiosity doesn’t follow novelty detection; curiosity is what interesting-but-non-threatening significance feels like. The triadic process doesn’t produce affect as output; affect is the first-person mode of the process itself.

This position places emotion at the forefront of interpretation, representing the “view from within” of the meaning-making hierarchy. Consequently, it explains why significance can’t be fully externalized or computed from an external perspective. The complete meaning of a sign encompasses the sensory experience of processing it by the system.

External observation can capture what a system does. Its behaviors. Its outputs. But it can’t capture what things mean to that system, because meaning includes the felt sense of mattering that only exists from within. This isn’t mysticism. It’s a principled limit. Significance is perspectival. It exists only relative to a system that has something at stake.

Part VI: The Synthesis

We can now integrate everything:

The Pragmatic Core

This convergence reveals a critical truth: the ultimate purpose of meaning is pragmatic and future-oriented. The highest form of meaning—Significance, Projection, the Final Interpretant—isn’t about accurately representing past or present. It’s about guiding future behavior to achieve a goal. The meaning of a sign, in its fullest sense, is the difference it makes to an organism’s future conduct in its struggle to persist.

The Answer

Significance is the apex of a functional hierarchy built on the foundation of self-preservation. It’s the capacity to make accurate, future-oriented projections that guide intelligent action in service of continued existence. Significance arises necessarily from any system that must:

• Maintain a boundary against dissolution (autopoiesis)
• Do so by building predictive models that minimize surprise
• Which requires transforming sensory data through three levels of interpretation

The question “What’s the significance of this event?” is, for any persistent system, functionally equivalent to: “How does this event bear upon my future, and what must I do about it?”

Because data is the trace of past interpretations, not interpretation itself. Significance requires:

• A bounded system with something at stake
• A model of the world relative to that stake
• A temporal horizon extending into the future
• The capacity to feel what mattering feels like

Raw signals carry no significance. A thermometer reading doesn’t “mean” danger—it’s just a number. Danger exists only for a system that can be harmed by temperature, that models temperature’s effects, and that acts to avoid those effects. Significance isn’t in the data. Significance is in the interpreter.

Part VII: The Design Gap

Everything that came before might appear quite academic and abstract, but it’s not. We’re currently experiencing a fundamental shift in how computational systems interact with meaning, and we’re not prepared for this change.

The Rupture

For many decades, we’ve designed computational systems under a single unexamined assumption: meaning happens outside the system.

The canonical architecture is a pipeline: capture → store → transform → present → human interprets. Sensors collect data. Databases preserve it. Processing enriches it. Dashboards display it. And then, and only then, does meaning enter the picture, when a human operator looks at the screen and decides what it signifies.

This worked because humans were the only actors with something at stake. Software was infrastructure for human semiosis, not a participant in it. The system didn’t need to know what anything meant. It only needed to faithfully preserve and present traces for someone who could interpret.

Every monitoring system, every analytics platform, every business intelligence tool follows this pattern. They’re elaborate machines for serving data to biological interpreters. The interpretation (the transformation of data into significance) was always outsourced to the human at the end of the pipeline.

This assumption is now breaking and breaking fast.

Agentic systems don’t wait for a human to review a dashboard and decide. They must interpret in-flight. They must answer “what’s the significance of this?” and “what must I do next?” in real-time, invariably without a human in the loop.

But we’ve given these systems access to the same data streams we gave humans, as if the data itself carries meaning. It doesn’t. Data carries traces. The signals in a telemetry stream don’t mean anything until interpreted by a system with concerns, context, and a stake in what happens next.

The deeper challenge isn’t autonomous AI but human-AI collaboration. We’re building systems where biological and digital actors must coordinate, where meaning must flow between interpreters with radically different structures.

The Collaboration Problem

The deeper challenge isn’t autonomous AI but human-AI collaboration. We’re building systems where biological and digital actors must coordinate, where meaning must flow between interpreters with radically different structures.

Consider: a human operator and an AI agent are jointly managing a complex process. The AI detects an anomaly and must communicate its significance to the human. But what the AI can represent (statistical deviation, pattern mismatch) isn’t what the human needs to understand (threat level, required action, stakes). The AI has data; the human needs meaning.

Or reverse it: the human recognizes something significant based on experience, intuition, felt sense—the phenomenology of interpretation we discussed earlier. How does this get communicated to the AI in a form it can act on? Natural language helps, but natural language is itself a trace of meaning, not meaning itself.

We lack the infrastructure for this. We have data interchange formats, API protocols, message queues—plumbing for moving signals around. We have nothing for coordinating interpretation across heterogeneous systems. No protocols for communicating not just what was observed but what it means, to whom, for what purpose, with what confidence.

What Semiotic Infrastructure Requires

If significance can’t be extracted from data but must be generated by interpreters with stakes, then our systems need fundamentally different architecture. They need:

Explicit interpretive context encompasses not just data, but the framework within which it gains significance.  It raises questions about the system’s objectives, the criteria for perturbation, and the futures that hold relevance.

Compositional semiosis recognizes that interpretation occurs at multiple nested levels, and the significance computed at one level serves as a raw signal for the subsequent level. Therefore, systems must be designed for holonic meaning flow, rather than merely data flow.

Temporal integration involves three time horizons: registering a present perturbation, integrating it with past context, and projecting it into a future consequence. Systems focused solely on historical reconstruction, such as event sourcing and log aggregation, are limited to Level 2. They lack the ability to project.

Perspectival grounding involves acknowledging that significance is always significant to some interpreter.  Systems must clearly state and communicate whose perspective they represent, rather than pretending to have a “view from nowhere.”

Affective signaling is crucial when affect is the phenomenology of significance.  Systems need to communicate not just conclusions, but also urgency, confidence, and concern.  This involves conveying the felt weight of interpretation, beyond just its content.

This isn’t a feature request. It’s an architectural challenge at the level of how we think about computation itself. We’ve built seventy years of infrastructure on the assumption that meaning is someone else’s problem. That assumption has expired.

The Opportunity

The systems that get this right will have an enormous advantage. They’ll be able to coordinate human and artificial intelligence in ways that current architectures can’t support. They’ll enable genuine collaboration rather than awkward handoffs between incompatible interpreters.

More fundamentally: as artificial agents become more capable, the question of what things mean to them—not just what they compute, but what they’re for, what they’re trying to persist, what futures they serve—becomes unavoidable. Semiotic infrastructure isn’t just an engineering convenience. It’s the foundation for building systems that participate in meaning rather than merely processing signals.

The alternative is a world of increasingly powerful pattern-matchers with no ground truth about significance, coordinating with humans through data pipelines that strip context at every interface. That world will be fragile, opaque, and dangerous in ways we’re only beginning to glimpse.

Conclusion

The thesis presented here is both simple: the solution to the problem of staying alive is meaning. Through the integration of autopoiesis, predictive processing, and semiotic theory, we’ve demonstrated that:

• Meaning isn’t optional—it emerges necessarily from the structure of self-organizing systems
• Meaning has a universal architecture—a three-level hierarchy from sensation to future-oriented significance
• This architecture reflects temporal structure—from specious present through integrated past to projected future
• Significance composes holonically—nested systems interpret each other’s conclusions as raw data
• Affect is the phenomenology of significance—what mattering feels like from inside

And we’ve shown why this matters urgently for the systems we’re building now:

• Systems assume meaning happens elsewhere—in human interpreters at the end of data pipelines
• Agentic AI breaks this assumption—systems that must act need to interpret, not just present
• Human-AI collaboration requires semiotic infrastructure—protocols for coordinating interpretation

This framework argues that meaning isn’t a philosophical luxury but a biophysical necessity, forged in the constant interplay between an organism and the world. And it’s now a design imperative, as we build systems that must participate in semiosis rather than merely facilitate it.

The central insight: Significance can’t be mined from accumulated data, however voluminous. Data is dead—the residue of past events, stripped of the concerns that made those events matter to someone. Significance is alive—the ongoing assessment of what this moment means for what comes next, performed by a system that has something at stake.

For decades, we could ignore this because humans provided all the interpretation our systems needed. That era is ending. The systems we’re building now—agentic, autonomous, collaborative—require meaning as a first-class architectural concern, not an afterthought outsourced to biological interpreters.

To ask “what’s the significance?” is to ask: to whom, for what, toward what future? Without those anchors, the question has no answer—because significance, by its nature, is always significance for a persisting system navigating an uncertain world.

The challenge we face is to construct systems capable of holding those anchors — systems that can represent not only signals but also stakes, not only data but also concerns, not only events but also the significance of those events for the future.

Meaning is the compass. Significance is the bearing. And we must now build ships that can navigate.

Appendix: Semiotic Infrastructure in Practice

How Semiosphere Addresses the Design Gap

The preceding argument demonstrates that significance necessitates a semiotic infrastructure—systems specifically designed for meaning-flow, rather than merely data-flow. This appendix delves into how the Semiosphere platform implements this architecture through a signal model that is based on sign classification.

The Signal as Semiotic Unit

In Semiosphere, the fundamental unit isn’t raw data but the signal—a flow from source to subscriber. Each signal contains two elements:

• Sign: the interpretive token
• Dimension: time, space, identity, etc.

This is already a departure from conventional telemetry. The signal isn’t a naked measurement waiting for external interpretation. It carries its interpretive frame with it.

Three Classes of Signs

Signs in Semiosphere are classified into three types that directly instantiate the triadic hierarchy developed above.

Compositional Semiosis

This classification isn’t merely taxonomic—it reflects how interpretation actually composes:

• Perceptual signs are the raw material
• Judgmental signs integrate perceptual signs into an assessed state
• Situational signs integrate judgmental signs into actionable significance

Each level builds on the previous. The significance computed at the situation level emerges from judgments, which emerge from events. But crucially, the situation isn’t reducible to its constituent events—it represents a qualitatively different interpretive act. This is the holonic composition discussed in Part IV, implemented as signal architecture. The system doesn’t just move data; it moves interpreted meaning at appropriate levels of integration.

Semiotic Collaboration

This architecture directly addresses the human-AI collaboration challenge identified in Part VII. When an AI agent and a human operator must coordinate, they can exchange signals at the appropriate level of interpretation:

• Perceptual: When the receiver needs to interpret for themselves
• Judgmental: When context is shared and the receiver needs current understanding
• Situational: When the receiver needs to act and requires the sender’s full interpretation.

The signal classification makes explicit what conventional data pipelines leave implicit: at what level of interpretation is this information being communicated? This doesn’t solve the problem of heterogeneous interpreters—humans and AIs still have different Umwelten, different stakes, different models. But it provides infrastructure for coordinating across that difference. The protocol carries not just what was observed but how interpreted, at what level, toward what horizon.

From Data Infrastructure to Semiotic Infrastructure

Conventional observability captures events and leaves interpretation to humans reviewing dashboards. This works when humans are the only interpreters that matter. Semiosphere captures signs at three levels of interpretive integration, making the structure of meaning explicit in the signal model itself. This is what semiotic infrastructure means in practice: systems designed to flow meaning, not just data.

The theoretical framework in this essay—autopoiesis, predictive processing, the triadic hierarchy of sense/meaning/significance—isn’t background philosophy. It’s the design rationale for an architecture that takes seriously what significance is and how it arises. Significance emerges from systems with stakes, interpreting signals through models, projecting into futures that matter. Semiosphere is infrastructure for that emergence.