Semantic Stability and Floating Meaning: Thinking About the Language of AI

IIntroduction

The philosophical question of whether machines can truly understand language has a long history. Douglas Hofstadter, in I Am a Strange Loop, depicts a hypothetical dialogue between Socrates and Plato that probes this issue. In the dialogue, Plato attempts to distinguish life and thought from a machine capable of flawlessly answering questions, arguing that such a machine merely manipulates symbols mechanically without genuine understanding. Socrates challenges this distinction by suggesting that much of human language use may also be automatic or habitual, thereby questioning whether the boundary between human consciousness and machine processing is as clear as it first appears.¹

This hypothetical dialogue captures the enduring puzzle of understanding and meaning. A related and influential thought experiment is John Searle’s Chinese Room, which imagines a person inside a room following syntactic rules to manipulate Chinese characters without understanding their meaning.² The experiment challenges the assumption that successful linguistic performance necessarily implies semantic understanding. More broadly, it highlights a possible gap between manipulating signs and understanding what those signs signify.

Large Language Models complicate rather than clarify this old philosophical problem. They produce fluent sentences, yet critics continue to question whether such systems have access to practice-grounded meaning. As Bender and Koller argue, “a system trained only on form has no way to learn meaning” in the full communicative sense.³ The key challenge, then, is not simply whether AI fails to “mean,” but how stable its meanings remain when language moves across different contexts. Here I ask three more specific questions: Can AI maintain semantic stability across different contexts of use? How should we describe different degrees of semantic stability? And how should we treat the language generated by AI?

My contention is that AI can reproduce linguistic rules and structures while meaning becomes less stable when the context that normally constrains those structures is absent or changed. I call this condition floating meaning. Floating meaning does not mean that AI-generated language is completely meaningless. An LLM-generated sentence can become meaningful when human speakers interpret and use it within an established practice. The problem is instead that the form of a word and its broad semantic associations can travel more easily than the specific rules governing its use.

I offer two analytical tools to illustrate this problem: PSC and SAL. These tools allow us to examine where semantic continuity remains and where attachment to context becomes weak. My central claim is that current AI is neither an autonomous maker of concepts nor an understanding subject in the same sense as a human participant in linguistic practice. It is better understood as a catalyst that can support human inquiry by generating possible conceptual relations that human users must then examine and stabilize.

This argument is built on several premises drawn from the philosophy of language and the philosophy of science. The starting point is Wittgenstein’s insight that meaning is closely tied to use. Shared rules within human practices stabilize a word’s sense, while outside these practices’ symbols can become less determinate.⁴ This does not imply that every word has only one correct meaning. Rather, it means that the stability of a meaning depends on the ways in which speakers learn to apply, distinguish, correct, and respond to uses of the word.

This distinction is important for LLMs. Human practices produce language: scientific practice produces scientific writing, legal practice produces legal texts, and social interaction produces ordinary discourse. LLMs are trained mainly on the linguistic products of these practices. They therefore acquire many genuine semantic relations, but they encounter them primarily through patterns represented in language rather than through direct participation in every practice that produced those patterns. The difference is not simply between “meaningful humans” and “meaningless machines.” It is between learning patterns produced by a practice and participating in the full set of rules and consequences that stabilize the practice.

This helps clarify the disconnect between form and meaning identified by Bender and Koller. Linguistic form is not empty: texts already contain traces of human conceptual practices. Yet the same form can appear in different fields under different rules. When a word moves between contexts, some of its semantic features may travel successfully while others should not. Semantic drift occurs when the transferable part survives but the contextual restrictions weaken.

At the same time, this disconnect creates the possibility of conceptual novelty. New concepts are often not completely disconnected from old ones. They emerge when existing ideas are reorganized within new problems and new fields of practice. Kuhn’s account of scientific revolutions suggests that genuinely important conceptual changes occur together with changes in problems, methods, and standards of inquiry.⁵ A new term becomes a working concept only when a community learns how to use it, what problems it addresses, and how its application can be criticized. Lakoff similarly emphasizes that concepts are not held in isolation but sustained through wider networks of related categories and cultural frames.⁶

These premises explain why LLMs can maintain syntactic coherence and a considerable amount of semantic structure while still risking semantic instability. They can reproduce and recombine relations expressed in human language, but they do not independently establish the practices that determine how a new combination should be used. For this reason, they function more plausibly as catalysts for inquiry than as autonomous creators of meaning.

Current LLMs are generally trained through next-token prediction over very large corpora using Transformer architectures.⁷ Scaling data, computation, and model parameters has further improved fluency and pattern recognition.⁸ This training allows models to capture complex relationships among words and concepts, but it does not itself place the model inside the practices that originally stabilized those relationships. Post-training can improve reasoning and contextual performance, but the basic distinction remains: reproducing linguistic structure is not identical to participating in all the practical conditions under which that structure acquires determinate use.

Hence, linguistic form can travel more easily than context-specific meaning, and semantic drift appears when tasks or contexts change. To analyze this instability with greater precision, I propose two tools.

IITool A: Prototypical Semantic Core (PSC)

The Prototypical Semantic Core is a small, cross-context anchor for a word. It consists of central semantic features that remain recognizable across different uses. However, PSC should not be treated as a complete definition or a fixed essence. Such an account would conflict with Wittgenstein’s own discussion of family resemblance, in which different uses of a concept may overlap without sharing one necessary feature.⁹ PSC is therefore better understood as a prototype: a flexible cluster of recurring semantic associations.

The PSC for “transparency,” for example, may include access to information, visibility, openness, or the possibility of inspection. These features allow the word to travel across corporate ethics, politics, computing, and other fields while remaining recognizable. At the same time, the PSC limits what the word can plausibly signify. If a use loses all relation to access, visibility, or inspection, readers will begin to question whether “transparency” remains an appropriate term.

PSC therefore provides partial semantic stability. It explains why a word can move across contexts without becoming completely different each time. Yet it does not determine how the word should function in each particular field. Transparency in corporate governance may refer to disclosure and accountability; in cryptography, it may refer to auditability and verifiability while still allowing private information to remain protected. The PSC survives, but the rules of use change.

This limitation is especially important for understanding LLMs. A model may remain close to the PSC of a term and still use that term incorrectly. In such cases, the output sounds plausible precisely because the general semantic associations remain intact. What has weakened is not the entire meaning of the word but its attachment to a particular practice.

While PSC identifies the relatively stable and portable part of meaning, a second tool is needed to describe how securely a particular use is anchored in context.

IIITool B: Semantic Attachment Levels (SAL)

Semantic Attachment Levels indicate how tightly a particular use of language is attached to practice. SAL is not a test of truth, nor is it a mathematical measure. Instead, it describes increasing degrees of practical and conceptual constraint.

At the lowest level, L0, lies surface form. A word fits grammatically into a sentence and may resemble established discourse, but it is not clearly attached to a specific practice. The language is formally coherent without establishing what rules determine its application.

At L1, the use stays close to a shared PSC. The reader recognizes the general meaning of the term, but its specific conditions of application remain unclear. Semantic drift is reduced because the word retains a recognizable core, but several interpretations may still remain possible.

Attachment deepens at L2, where a term is tied to a concrete field of tasks, rules, and consequences. Its meaning becomes constrained by what participants in that field actually do. The concept is no longer sustained mainly by general associations; it is connected to recognizable practices.

At L3, the term is embedded within a wider network of neighboring concepts, institutions, and systems of correction. A concept at this level is constrained not only by what people do but also by its relation to other concepts and by rules that distinguish correct from incorrect use. Meaning becomes more resilient because changing the use of one term affects its place within a larger system.

SAL does not claim that an L3 use is necessarily true, nor that its meaning can never change. Scientific, political, and philosophical concepts can remain contested even when they are deeply embedded in practice. SAL describes the strength of attachment rather than permanent semantic certainty.

PSC and SAL therefore serve different functions. PSC explains how a word retains enough continuity to travel. SAL describes how securely a particular use is attached after the word enters a context. Semantic drift appears when the form and PSC remain recognizable but the attachment to context weakens.

Consider the word “transparency.” In corporate ethics, it may refer to disclosure rules, audit trails, and accountability to investors and the public. In cryptography, it may point to auditability and verifiability: algorithms may be open to inspection while keys remain protected. In politics, it can either function as a broad ideal of openness or become attached to concrete institutional mechanisms such as disclosure procedures. The word remains recognizably related across these contexts, but its practical implications differ.

LLMs may fuse “publicity” with “proof” or “disclosure” with “verification.” That is a form of semantic drift: the linguistic sign and part of its semantic core remain, but the relations governing the concept shift across contexts. More precisely, this can be called contextual misattachment. The problem is not that the word suddenly has no meaning; it is that the model has attached a recognizable meaning to the wrong conceptual network.

This distinction is also important when discussing reasoning. LLMs are optimized during pretraining for statistical prediction rather than directly for logical validity. Their training enables them to learn patterns associated with reasoning, and contemporary models can perform substantial reasoning tasks. However, predictive success does not guarantee logical consistency. A model may produce an internally fluent argument while failing to preserve the exact inferential rules required by a particular context. Logical error and semantic drift are therefore related but not identical. A sentence may be semantically stable but logically invalid, just as a sentence may be logically coherent while employing a concept in the wrong context.

For this reason, it would be too strong to conclude that LLMs cannot reason or cannot carry meaning. The more limited conclusion is that they do not independently guarantee either logical consistency or semantic attachment. Their outputs should therefore be treated as provisional and open to further examination.

IVLLMs as Semantic Catalysts

Given this instability, it is more useful to treat LLMs as catalytic tools. There are two main ways in which they can help us reconsider how meaning and identity are constructed.

First, in generating new writing, LLMs often bring together words and concepts from different fields. Because they are trained on extremely diverse bodies of language, they can identify patterns of association that may not be immediately visible to an individual user. These connections do not prove that the concepts genuinely belong together, but they can suggest new directions for inquiry and force human users to consider unfamiliar conceptual combinations.

However, such combinations initially remain semantically weak. They may be fluent in form without yet having a clear role in practice. A novel relation between two concepts becomes philosophically useful only when a human user asks what the relation means, where it applies, and whether it survives criticism. The catalytic moment therefore requires a human response.

The user does not passively receive the output but reconstructs it through situated knowledge, critical judgment, and practical intention. This process follows directly from PSC and SAL. The model may provide a conceptual combination that retains recognizable semantic cores, but the human user must determine whether the combination can move from weaker to stronger levels of attachment. The model supplies a possible connection; practice determines whether the connection becomes stable.

This also explains why a novel combination should not immediately be called a new concept. If meaning depends on use, then a concept becomes established only when users can determine its field of application, distinguish appropriate from inappropriate uses, connect it to neighboring concepts, and revise it when necessary. Kuhn’s account of conceptual change supports this point: new scientific concepts become meaningful through their role in new practices of inquiry, not through linguistic invention alone.

LLMs can therefore participate in conceptual creation without independently completing it. They can propose combinations, formulations, or distinctions. But human practices must stabilize these possibilities. For this reason, LLMs function fundamentally as semantic catalysts rather than autonomous concept creators.

The second catalytic function concerns identity. LLMs do not normally articulate ideas from a stable, pre-existing personal identity. They do not possess a continuous personal history in the same way a human speaker does. Instead, they generate language that projects a speaker on the basis of linguistic cues in the prompt and patterns in their training data.

This ability shows that identity can be partially performed through language. At the same time, it reveals the social structures embedded in language itself. Training data contain social norms, exclusions, patterns of representation, and structural biases. As Bender and her coauthors argue, language models can reproduce and amplify patterns contained in their training data.¹⁰ Feenberg likewise reminds us that technical systems are not neutral: their design and use embody social choices and values.¹¹ The LLM therefore reflects not only what has been said but also which forms of language have been most strongly represented.

In this limited sense, the LLM becomes a mirror of cultural language. It can reveal recurring patterns in how identities, authorities, and social categories are represented. This does not mean that it gives us direct access to a collective unconscious. Rather, it makes linguistic patterns visible and therefore provides material for further human interpretation.

VCognitive Risk and Human Re-encoding

The hallucinations of LLMs—their fluent but false or malformed utterances—show how easily linguistic fluency can separate from truth and stable semantic attachment. Yet the wider concern is not simply that LLMs sometimes produce false statements. Their growing success may also change the habits through which humans produce and interpret language.

As language generation becomes cheaper and faster, linguistic output can increasingly function as a behavioral interface: its value may be judged by how efficiently it produces an answer or reaction rather than by how carefully its meaning has been developed. We may describe this tendency as movement toward a more glyphic culture, in which linguistic signs are increasingly consumed as rapid units of information and response. This does not imply the disappearance of meaning, but it may reduce the amount of effort devoted to its stabilization.

The metaphor of Optimal Brain Damage provides one way of expressing this concern. In machine learning, LeCun, Denker, and Solla use the term for the strategic pruning of connections in a neural network to improve efficiency.¹² Applied to human cognition, the phrase should remain an analogy rather than a literal neurological claim. As people increasingly outsource memory, analysis, and even sentence production to LLMs, they may have fewer occasions to practice some of the demanding cognitive tasks that writing traditionally requires.

Recent research gives some reason to investigate this possibility, although it does not prove cognitive decline. Kosmyna and her coauthors report differences in neural connectivity, recall, and reported ownership among participants writing essays with LLM assistance compared with other conditions.¹³ These findings do not show that AI literally “rewires” the brain in a damaging way, but they raise a relevant question about how different tools alter the cognitive process of writing.

John Warner’s argument in More Than Words helps clarify what may be at stake. Warner treats writing not simply as a method for recording already completed thoughts but as a process through which thought develops.¹⁴ Writing requires the writer to discover relations, confront uncertainty, and decide what a sentence actually commits them to. If AI allows users to generate finished language before undergoing this process, the danger is not merely that the text is less authentic. The more important risk is that linguistic production becomes separated from conceptual formation.

This danger appears in both writing and reading. During writing, AI can produce language without requiring the user to make every conceptual choice. During analysis, AI can reduce a complex text to a set of points and therefore remove some of the difficulty through which interpretation develops. Efficiency is not itself the problem. The question is whether efficiency replaces the intellectual resistance that forces a user to stabilize meaning.

VIAdversarial Inquiry

Confronted with this possibility, we must ask what intellectual practice can prevent AI-assisted language from becoming passive consumption. I propose an Adversarial Inquiry Approach, in which the user takes the role of an examiner and debater.

Adversarial Inquiry means systematically interrogating LLM outputs by questioning their premises, demanding evidence, providing counterarguments, and testing whether the same concepts are being used consistently. When the model transfers a term from one field to another, the user asks whether the original implications remain valid. When a claim sounds persuasive, the user asks which inferential steps actually support it.

This process should not be described as literally reproducing reinforcement learning. An individual user is not technically retraining the model. The important effect occurs in the user’s own reasoning. By challenging the model’s output, the user is forced to identify and articulate the rules that stabilize the relevant concepts.

Adversarial Inquiry therefore works as a method of human re-encoding. It turns the user from a passive consumer of fluent text into an active participant who reconnects linguistic output with evidence, context, and conceptual relations. In terms of SAL, the user asks what would be required to move an output from weak semantic attachment toward a more strongly embedded use.

This is also why the model’s imperfections can become intellectually productive. A weak or misplaced connection may force the human user to explain why two concepts should remain distinct. The model does not simply give the user knowledge; it provides propositions against which the user can test their own judgment.

VIIConclusion

This paper has argued that LLMs can generate stable linguistic form while meaning becomes less stable when their utterances move across contexts without sufficient attachment to practice. PSC and SAL provide a framework for analyzing this process. PSC identifies the portable semantic core that allows a word to remain recognizable. SAL describes the degree to which a particular use is embedded in practical rules and conceptual networks. Semantic drift occurs when the first survives but the second weakens.

This framework suggests that LLMs should not simply be described as systems without meaning. They inherit substantial semantic structure from the human language on which they are trained. Yet they also do not independently determine all of the context-specific conditions that stabilize meaning. Their characteristic role is therefore catalytic: they generate linguistic and conceptual possibilities that require human interpretation and re-encoding.

Adversarial Inquiry offers one response to this condition. By treating model output as a proposition to be examined rather than a conclusion to be accepted, users can preserve the process of judgment that gives semantic relations their stability. The goal is not to eliminate AI from intellectual activity but to prevent fluency from being mistaken for completed thought.

This issue also connects with broader theories of cognition. Clark and Chalmers’ idea of the extended mind suggests that cognitive outsourcing does not necessarily amount to cognitive decline. External tools can become parts of larger cognitive systems.¹⁵ LLMs may therefore extend human capacities rather than simply replace them. Hayles’s account of cognitive assemblages develops an even more distributed picture in which human and nonhuman actors participate together in cognitive processes.¹⁶ If writing increasingly becomes a co-created process, authorship and agency may also become more distributed.

Walter Ong’s account of secondary orality provides another way of thinking about this transformation. Ong describes electronic media as recovering some of the immediacy and participation of oral culture while remaining dependent on writing and print.¹⁷ AI-mediated language may extend this development by making written language increasingly interactive and conversational. We might cautiously describe this as a form of “tertiary orality,” although the term should remain a hypothesis rather than a settled historical category.

The central question, however, remains the same. When language can be generated with unprecedented speed, who performs the work that stabilizes what those words mean? If human users continue to question premises, test concepts against practice, and accept responsibility for the meanings they adopt, LLMs can become powerful tools. If linguistic production replaces these practices, then the deeper danger is not simply hallucination but the separation of language from the intellectual activity through which meaning becomes stable.

The AI revolution therefore raises a problem not only about what machines can do but also about what forms of thinking humans choose to preserve. When writing becomes increasingly collaborative between human and machine, the important question is no longer only who produced the words, but who remains responsible for making those words meaningful.

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