In recent discussions about AI interpretability, emergence is often treated as a sign that LLMs have developed understanding. This post challenges that assumption and proposes an alternate view: what looks like emergent understanding is better described as semantic drift, or directionally reinforced statistical behavior, not grounded cognition.

Executive Summary

The concept of "emergence" in large language models is often mischaracterized as evidence of understanding. Building on established critiques of emergence as explanation (see Yudkowsky's The Futility of Emergence^[1]), this post offers a technical reframing that challenges basic assumptions about language, understanding, and cognition in both human and artificial systems. What we observe in LLMs is better understood as directionally reinforced diffusion through semantic space rather than genuine understanding.

Defining "Emergence"

The critique of the use of the term emergence in this piece will specifically refer to how it is colloquially used in AI spaces where it assumes emergence is evidence of understanding in AI systems. While the term has legitimate uses in AI research, such as documenting capability discontinuities in scaling or unexpected behaviors, problems arise when these observations are interpreted as evidence that the system has developed understanding.

Understanding, as used in this piece, refers to the system's ability to:

• Independently generate representations that ground concepts in causal relationships with the world
• Autonomously apply knowledge across contexts without external guidance
  AI currently can transfer knowledge across domains but this usually requires prompting or fine-tuning.
• Maintain coherence through self-generated self-correction
  Understanding systems can detect their own inconsistencies and actively resolve them without requiring external feedback or carefully crafted prompts.
• Self-initiate inquiry and reflection
  LLMs rely on initial external input. They may generate questions but they do not "wake up" and decide to explore a question or concept. They can simulate this within the bounds of a response but do not spontaneously direct attention and begin inquiry without external prompting.

This distinction is important because the appearance of independence is often an artifact of prompt design or evaluation methods. What may appear as autonomous reasoning is better understood as sophisticated conditional response generation that is still fundamentally guided by and dependent on human framing.

I will note that the term emergence in itself has roots in systems theory, which is used to refer to how novel properties may arise from simpler components interacting. In complex systems, emergence does not require understanding. This broader definition reinforces the main argument of this piece.

Anthropomorphic Definitions Disclosure

This section serves to acknowledge that the definitions used in this piece use anthropomorphic criteria derived from human cognition. This is deliberate since the majority of claims about "understanding" in AI implicitly refer to human capabilities.

To clarify: the anthropomorphic definition of understanding presented here is not my personal view of what understanding should be. I am personally skeptical of anthropocentrism in evaluating AI. However, to meaningfully engage with current discourse, I am addressing these anthropomorphic assumptions directly.

A More Precise Hypothesis

What we often call "emergence" in LLMs is directionally reinforced diffusion through semantic space. This is a buildup of correlative patterns, shaped by interaction, repetition, and proximity, but largely unanchored from the robust stabilizing constraints that give human language meaning.

This view isn't the same as the "stochastic parrots"^[2] view in that it pivots the focus away from mimicry and toward how LLMs follow reinforced patterns that drift within a semantic space. It acknowledges their capacity to generate sophisticated outputs and coherent-seeming behavior. But coherence is not the same as understanding. LLMs generate outputs through dense statistical association, not symbolic manipulation grounded in referential structure, embodied context, or intentionality.

Crucially, there is no fixed "true meaning" that either humans or LLMs are approximating. Human communication appears stable because of the constraints that shape and slow semantic drift. It is not because meaning exists in a fixed form.

The Chemistry of Meaning

One way to understand this process is through chemistry. Declarations of meaning behave more like reactive agents than static labels. When they interact, they don't simply combine, they transform one another. The result depends on:

• The conditions of interaction
• The order of exposure
• The accumulated history of previous "reactions"

Meaning is not a stable symbolic structure. It is a field effect that emerges through interaction, tension, and constraint. Like chemistry, it depends on what's already present, what pressures are applied, and what environment enables stability or transformation.

Human Language as Constrained Diffusion

Human language is not exempt from drift. It is also a system of semantic diffusion. But in humans, drift is constrained and anchored in key ways:

• Embodiment – Sensorimotor experience provides grounding via physical reality
• Social triangulation – Communal negotiation helps stabilize interpretation
• Causal feedback – Misunderstandings have real-world consequences
• Intentional structure – Goals, emotions, and stakes shape communicative direction

These constraints don't eliminate semantic drift, but they stabilize it enough to produce shared meaning over time. By contrast, LLMs operate without these stabilizing forces. Their coherence arises statistically, but their meanings remain unanchored.

The Emergence Misconception

Much debate around emergence focuses on whether LLMs cross capability thresholds abruptly or gradually. But this misses the deeper issue: emergence is not evidence of understanding.

The real question isn't whether new behaviors appear. It's whether those behaviors are grounded in a system that can constrain, correct, and direct its own semantic drift.

That distinction matters. If LLMs exhibit "emergent" reasoning, analogy, or consistency, it doesn't mean they understand. It means we have seen a statistically reinforced pathway stabilize, possibly due to training density or prompt structure, not due to conceptual grasp.

This perspective also explains the fragmented and inconsistent definitions of emergence in existing literature. Semantic drift affects researchers too. Our own debates about what emergence is, or when it occurs, reflect the same diffusion of meaning that occurs in LLMs.

Why This Matters

This framing has implications across several domains:

• Alignment: Efforts to align LLMs by assuming human-like symbolic processing may miss the mark. Alignment must deal with drift, not just outputs
• Evaluation: Benchmarks built on assumptions of fixed meaning may misdiagnose success or failure
• Interpretability: What we call "interpretation" often reflects patterns in drift, not access to internal coherence
• Human cognition: We are reminded that our own linguistic stability is an achievement, not a given, and that our meaning is fragile, dynamic, and interaction-bound

Why Current Interpretability Methods Fall Short

Many interpretability tools implicitly assume there is something fixed or stable to interpret. Input-focused methods like saliency maps, LIME, or Integrated Gradients aim to identify which inputs had the most influence over a model's decision. Internal representation methods such as neuron activation analysis, representation geometry, and feature visualization attempt to map what individual neurons or activation patterns "mean". Output-focused approaches like behavioral testing, counterfactual evaluation, and prompt engineering probe the model's responses across different contexts.

Yet all three approaches tend to confuse statistical association with anchored meaning. Consider how attribution methods may analyze LLM outputs about medicine. They may highlight tokens related to "doctor", "treatment", and "patient". The interpretability tool suggests these were crucial to the model's "understanding". But what is actually happening is more akin to gravitational influence than conceptual grasp. The model hasn't formed a stable, grounded concept of medical care. Instead it is following well-worn paths of statistical association.

Even more sophisticated methods such as circuit analysis or mechanistic interpretability, which trace information flow through transformers, still frame their analyses in terms of stable "features" or "circuits" rather than dynamic drift fields. They identify consistent activation patterns but often overlook how these patterns remain unanchored from grounded reference.

A Conceptual View of Transformers

To explore the statistical view of emergence further, let us apply this to how an LLM technically works, using a high-level conceptual view:

LLMs compute locally and recursively. Each transformer layer reshapes the input based on contextual weighting, where "context" refers not to a stable interpretative frame but to the relative statistical influence of nearby tokens within the model's attention window.

Imagine the model's embedding space as a vast cloud, where each token from the training corpus has settled into a position based on how often and how closely it appeared with others. This is the statistical memory of the corpus. It is not a dictionary of meanings but a gravitational map of co-occurrence.

When you give the model an input, it doesn't retrieve meanings from that map. It drops your input tokens into the cloud and then begins reshaping the local gravity. They pass through a layered sequence of interpretive filters, like translucent sheets or prisms, that bend and redirect the surrounding field based on local context.

Each transformer layer acts like a sheer semantic overlay. It doesn't replace the underlying field but tugs on it, shifting relational weights and reconfiguring temporary "meaning" zones. The original associations remain in the background, but new interpretations emerge through recursive adjustment. These are shaped by token order, accumulated context, and pressure from the surrounding drift of other tokens.

The model's final output, then, isn't retrieved meaning but refracted association, echoing the original embedding space through a temporary interpretive lens.

Departure From Other Theories

Some may see this argument as a version of relativism or anti-realism. It is not. The view expressed here is closer to materialism: meaning emerges from systems shaped by history, constraint, and consequence, not from fixed symbols or timeless truths.

While this view shares similarity with Wittgenstein, my specific framework also marks a significant departure from his later philosophy. Although Wittgenstein's language games provided a crucial shift away from fixed, idealist semantics toward meaning as contextual use, his approach was primarily descriptive of localized practices and deliberately anti-theoretical. In contrast, my own framework proposes a formal, systemic theory: meaning is conceptualized as an evolving semantic drift field, historically conditioned and recursively realigned by ongoing interactions.

Because this framework models meaning itself as an inherently dynamic and pervasive process of drift—rather than something primarily stabilized within discrete 'games' or merely observed in its local manifestations—it leads to the conclusion that perfect, static alignment is not merely impractical but structurally impossible. The central challenge becomes understanding, tracking, and navigating this continuous semantic evolution.

Anticipating Counterarguments

Some may argue:

• That LLMs build internal representations that approximate understanding
• That human cognition is also largely statistical^[3]
• That grounding could be added to future models via multimodal input or interaction^[4]
• That "emergence" is just a convenient term for complex behavior

All of these points deserve serious engagement. But none resolve the central issue: without anchoring mechanisms that constrain meaning, what looks like understanding is better described as echo—diffused, recombined, and weighted through exposure, not generated through reference or purpose.

Final Note

I'm developing a formal framework that explores these dynamics, specifically how meaning emerges through diffusion, reinforcement, and constraint across both human and artificial language systems. While that work isn't public yet, I welcome dialogue as it takes shape.

Author’s Note on Method

This post was developed in collaboration with ChatGPT-4o, used as a recursive writing partner for refinement and semantic alignment.

While using an LLM to articulate why LLMs don't truly understand may seem paradoxical, it actually reinforces the central argument. The model helped refine and extend the ideas I provided, but depended on my directional intent and guidance based on the principles of my original framework. The model served as a mirror for me to flesh out my ideas without needing to "understand" the philosophical implications of what it was helping to articulate.

1. ^{^}

   Yudkowsky, Eliezer. The Futility of Emergence

2. ^{^}

   Bender, Emily M., Gebru, Timnit, McMillan-Major, Angelina, and Shmitchell, Shmargaret. Dangers of Stochastic Parrots: Can Language Models Be Too Big?. FAccT 2021.

3. ^{^}

   The counterargument that human cognition is also largely statistical is valid. Research confirms that statistical learning plays a crucial role in human language acquisition and processing. However, the distinction lies not in whether statistics are involved, but in how they are constrained:

   Human statistical processing is anchored by causal feedback that comes from action and consequence in the environment. It is integrated from a unified perspective, and directed by goals and intentions that exist outside the statistical patterns themselves. These constraints create semantic stability while allowing for flexibility.

   LLMs, even multimodal ones, process statistics without these grounding mechanisms. The difference is not that humans don't use statistics, but that our statistics operate within a framework of embodied experience, causal interaction, and intentional direction that fundamentally transforms how those statistics function.

4. ^{^}

   Recent advances in multimodal models that incorporate visual, audio, or even physical data might seem to address the grounding issue outlined in this piece. While these models do introduce additional constraints via cross-modal alignment, they still fundamentally operate through statistical association rather than causally grounded interaction with the world. A model trained on image-text pairing, for example, may learn certain visual features correlate with certain text features. But this remains different from understanding that requires action and consequence in the environment.

   Multimodality may extend the semantic space, but this does not necessarily anchor it to the kind of stabilizing constraints that embodied experience provides for humans. Though this statement in itself admittedly assumes an anthropomorphic lens.