Fluent Algorithmic Language and the Language Game: Challenges to Wittgenstein’s Framework

Abstract

This essay analyzes several challenges posed to Wittgenstein’s ordinary language philosophy by fluent algorithmic entities. Recent developments in LLM technology introduce strains in Wittgenstein’s core argument. We summarize the crux of Wittgenstein’s criticism against Russell in understanding language, and extract core tenets and argumentative directions of his branch of philosophy. Modern frontier LLMs display near-perfect language use to the extent that they are indistinguishable from humans in textual interaction. However, these algorithms lack what is traditionally associated with participation in the language game – meaningful social presence, physical responsibility, and the ability to receive pragmatic consequences. We argue that this either renders traditional later Wittgenstein ideas contradictory, hollow, or less relevant, depending on the various paths of revision the philosophy may attempt.

Keywords:

• Algorithmically generated language • Context • Ordinary language philosophy • Generative A.I. • Large language model • Algorithmic utterer • Language game

Introduction

The analysis of language use arises from the need to comprehend, evaluate, and act upon verbal or written interaction. Therefore, language in practical use is dependent on the context of the interaction in which it is used, unlike self-contained propositions of formal logic. A single lexical component may yield a wide spectrum of different interpretations, based on the element of context. For the purpose of this essay, define context as the set of all extralinguistic components of an interaction – that is, information that cannot be determined from the spoken or written language alone.

Context is an abstract umbrella terminology. What types of information, then, may be part of context? One obvious example is the set of entities participating in the linguistic exchange, which most often cannot be derived from literal analysis of a sentence. Few would directly codify this information – as in “I, Alice, demand that you, Bob, hand me that brick” rather than saying “Hand me the brick!” This information is usually left extralinguistic, and thus constitutes context. Yet more examples include the physical location of the exchange, the intention and sanity of the utterer, and applicable social norms. Note that this information may or may not be considered context, as any utterer can choose to embed what would otherwise be considered context into her words.

In grossly simplified terms, Bertrand Russell attempts to develop infallible methods of encoding context in each logical statement. Russell encodes contextual information into linguistic statements by asserting that every reference must concern an existing, uniquely identifiable object, and that predicate statements regarding the object must all hold true. For example, Russell will encode additional information in statements such as “the king of France is bald,” such that “the king of France is an existing, uniquely identifiable object that is bald.”

Wittgenstein’s school of thought, known as ordinary language philosophy, believes that such attempts are limited to informative use of language. Their sharpest criticism of Russell is that informative use constitutes only a small fraction of ordinary language; and as Russell’s logical interpretation is inapplicable outside a very specific subset of language, the logical analysis of language is hollow. Wittgenstein proposes a non-exhaustive inventory of language use, ranging from informative, prescriptive, instructional use to nonsense and exclamation. For instance, how can we possibly embed premises of existence, uniqueness, and presupposition in “My gosh!” or “Thou shalt not steal”? J. L. Austin, rather than building a long list of language use, instead argues that language carries three components – locutionary, illocutionary, and perlocutionary – yet still shows that logical analysis is valid in very limited circumstances.

Criticism aside, Wittgenstein also proposes a new model of everyday language. He places less emphasis on the individuals’ intention and thought process when uttering language. Language carries meaning because entities who hear it react in a certain way, and also form language themselves to communicate. It is a mysterious series of role-playing, according to Wittgenstein, who calls this the “language game.” And instead of wielding a logical framework in uncovering the rules of the game, philosophers must observe patterns of reaction and responses as if they are excavating an ancient cityscape.

A Brief History of Language-emitting Algorithms

The widespread adoption of large language models (LLMs) since 2022 makes generated language feel ubiquitous. However, generated language need not be a lifelike replica of human interaction. While LLMs dwarf the capabilities of A.I. algorithms developed decades earlier, several of these simple algorithms also count as generated language. Let us consider a few such examples.

A Markov chain is a mathematical state machine. A Markov chain switches between a series of different states with probability dependent solely on the previous state. For example, a Markov chain may have states {sunny, rainy} where each sunny state has ⅓ chance of transitioning to a rainy state and each rainy state has ½ chance of moving to a sunny state. Claude Shannon, the mathematician who single-handedly invented information theory, proposed a probability game conducted as follows. Open a random book on your bookshelf, and choose two random consecutive letters. Take yet another random book and search for the two consecutive letters we have found – and write down the letter that follows the two words. Repeat indefinitely by finding the ‘next most probable letter’ as chosen from random books. This Markov chain may emit something like “ON IE ANTSOUTINYS ARE T INCTORE ST BE S DEAMY ACHIN D ILONASIVE TU COOWE AT TEASONARE FUSO TIZIN ANDY TOBE SEACE CTISBE” (taken directly from Shannon’s foundational paper A Mathematical Theory of Communication, 1948). One might attempt the same random walk using word chunks instead of letters, and obtain “THE HEAD AND IN FRONTAL ATTACK ON AN ENGLISH WRITER THAT THE CHARACTER OF THIS POINT IS THEREFORE ANOTHER METHOD FOR THE LETTERS THAT THE TIME OF WHO EVER TOLD THE PROBLEM FOR AN UNEXPECTED.” This is far from a valid use of English. But it is an imitation that feels probable, resulting from an incredibly rudimentary process.

Yet another well-known algorithm showcases generated language. The psychoanalyst program ELIZA, developed by J. Weizenbaum in 1966, is considered the archetype of early lifelike natural language processing programs. The psychoanalyst is programmed to respond with either a static choice of questions, such as “What does that mean to you?” and “Tell me more,” or rudimentary dynamic responses that programmatically quote the user’s previous response. In certain scenarios, ELIZA demonstrated the ability of extremely simple algorithms to mimic human interaction.

Natural language processing remained somewhat stagnant until relatively recently, masked by achievements in image recognition and other fields of A.I. research. The algorithm met a new phase with the introduction of transformer algorithms in 2017. LLMs utilizing the transformer architecture are, as is Shannon’s Markov chain, text prediction machines. These raw models simply predict the most likely chain of answers in a particular language; yet, they can also be tuned to ‘respond’ to human instruction. The abilities of LLMs, adopted for widespread public use after 2022, have pushed the frontiers of lifelike human interaction with A.I. But it is important to note that language-emitting algorithms need not be multi-billion-parameter engines; even a simple set of indeterministic rules gives rise to simulated imitation of language, some of which are surprisingly effective.

Language-emitting Algorithms as Philosophical Points of Investigation

Ordinary language philosophy argues that language is relevant solely in the setting of a language game. Only when its entities abide by the implicit rules of the game, and react in predictable ways upon hearing an utterance, does language carry meaning. With the introduction of algorithmic players in the game, this framework is met with two important questions. First, how does the language game and the role of context shift with the introduction of algorithmic entities? Second, does the case study of algorithmic language strengthen or weaken Wittgenstein’s traditional ideas of ordinary language philosophy? 

Both questions raise an increasingly intriguing – and for many today, growingly concerning – point of investigation: are recent developments in LLM technology any different from previous means of natural language processing from a philosophical standpoint?

Implication of Algorithmic Entities in the Language Game

To analyze language-emitting algorithms’ implications on the Wittgensteinian language game, we must consider what happens when an algorithm partakes in it, in both technical and philosophical aspects. A coherent theme in language-emitting algorithms is the generation of realistic patterns. In Wittgenstein’s terms, these algorithms seek to comply with the rules of a certain language game utilizing patterns observed in the natural human speaker. 

Note that the algorithms’ goal ideally lies beyond the generation of realistic text; elements of specific social context, a particular society’s culture, social stigma, the speakers’ intent, and numerous extralinguistic factors influence a language exchange. These must be accounted for by algorithms, at least to the extent commonly observed by human speakers. Why is this so, under Wittgensteinian analysis of language? Wittgenstein’s comprehensive rebuttal against Russell’s analysis rests on the fact that language cannot possibly be understood deterministically (and, by extension, logically) due to the aforementioned variability of context. In other words, the speakers’ observing the numerous extralogical factors is precisely what disproves logical interpretation of language use. Thus, algorithms should account for the ever-complex list of extralinguistic factors to qualify as a “true language user,” at least for Wittgenstein’s characterization of the notable speaker.

From this point, this essay will primarily focus on large language models (LLMs) as the algorithmic language user of interest. This is because LLMs are the only notable entities capable of comprehending practical context across different language users, as of early-to-mid 2026. At present, no other algorithm is comparatively potent at a similar level in a meaningful sense. A further analysis of comparing LLMs with previous algorithms is deferred to the conclusion section.

In this section, we argue that algorithmic entities including LLMs indeed satisfy Wittgensteinian criteria of the language user, that algorithms function in fundamentally different ways to humans, and that the Wittgensteinian language game is forced to accept entities with previously unseen characteristics as a valid language user. We will then discuss further implications and potential criticism of Wittgenstein in the next section.

Modern developments in LLM technology increasingly support the idea that algorithms are valid participants of a language game. Language models evolved rapidly since their commercial release in late 2022. One classic benchmark to attest to this is the Turing Test, a process in which algorithmic entities engage in a typed conversation with a human judge, attempting to pass as a human speaker. As simple as the test is designed, the Turing test serves as a rudimentary indicator of natural language processing capability, one that no practical system had passed prior to modern LLMs. A 2025 study found that frontier LLMs of early 2025 (notably GPT-4.5 by OpenAI) were selected to be human 73% of the time, effectively successful in acting even more humanlike than real human participants. Trained on vast amounts of linguistic context, these LLMs are capable of taking intricate social conventions, cultural norms, and complex human intention into account. The tech world is perhaps past the initial phase of shock, and is moving on to employ these algorithms in programming, science, mathematics, and law. It is almost as if the lifelike quality of A.I. conversations have become the norm, not a surprise. And benchmarks of the present corroborate LLMs’ ability to be a valid Wittgensteinian language user, one in which the tenet “meaning is use” consistently feels valid.

Although these algorithms pass as seemingly natural language users, they are also very interesting philosophical subjects with qualities previously unseen in a linguistic group. While LLMs can be near-indistinguishable from human entities in a language game, they (currently) possess no practical means of taking social responsibility, performing actions, or reacting physically as a result of a language game. An argument by G. E. M. Anscombe (most interestingly, a British analytic philosopher) is very relevant to this point of view. Anscombe argued for the philosophy of action closely tied to language use. For Anscombe, a physical action was not simply detached from Wittgensteinian language use but served a crucial role in the language game in the form of practical consequences. This shows that language is not always factually descriptive nor purely logical – language is embedded in action.  In simpler terms, consider the thought experiment of a grocery list. The vocabulary of natural language, such as “apple,” “potatoes,” and “shampoo” carry real consequences of purchasing and empirically obtaining the objects of discussion. LLMs complicate this facet of ordinary language philosophy, by introducing entities with lifelike linguistic participation yet with questionable ability to produce physical outcomes. 

This is not to say that LLMs are entirely void of physical liability. Language models are assembled with a variety of technical orchestration and frameworks to finish tasks in a pragmatic sense; model context protocol (MCP) and agentic whole-computer harnesses (notably OpenClaw) are noteworthy examples. However, an important layer of transferring language into physical consequences is very different from that of a human entity. A point that has become increasingly easy to overlook is that LLM actions are entirely built on language manipulation alone. Technical details suggest that the status quo of LLMs severely lacks sufficient means of taking social responsibility, performing physical actions, and replicating what human beings do as participants of a language exchange – or anything other than realistic language complying to context, for that matter. This is not a difficult concept to picture, but has not been realized before the rise of LLMs. Algorithmic language has reached a different phase, one that deserves newfound philosophical analysis.

We argue that this is not merely an unimportant tension in a superficial layer of language – the physical implications, at least according to Wittgenstein – but indeed serves to introduce several complications for traditional ordinary language philosophy.

Relevance in Challenging Wittgenstein’s Ideas

To resolve the complications introduced by such algorithms, Wittgenstein’s argument may be modified in one of several ways. First, ordinary language philosophy can accept the fact that language use can be detached from physical implications and the actions that follow. This stance allows the field to dismiss challenges posed by algorithmic entities with no pragmatic responsibility. Another solution is to place more restrictions on valid participants of the language game. This blocks LLMs from being incorporated into language use under Wittgenstein’s framework by confining its scope. Both directions are problematic, and somewhat place strains on the core argument of ordinary language philosophy.

The first approach seeks to allow LLMs into the language game, yet deny the importance of pragmatic consequences or responsibility in interpreting ordinary language use. This is closer in nature to the original argument by Wittgenstein. For Wittgenstein, language was not meaningful because it corresponded to an object or a concept. Not because it represented a logical statement. He based his understanding of language in pragmatic use, in which individuals react in certain non-random ways to language utterance. The analysis of patterns in these language games mattered, a process analogous to deciphering an ancient cityscape. His ordinary language philosophy deemphasizes individual intent, internal purpose, and logical provability. However, is it a sound philosophical argument to discard physical context in its entirety? Accepting this resolves challenges posed by the LLMs’ inherent physical limitations with uncanny linguistic fluency.

However, ordinary language philosophy becomes less fallible yet increasingly hollow in its argument. What do we mean by this? Discarding any significance of pragmatic responsibility taints the relevance of the philosophy’s core tenet in two major ways. First, this approach fragments, isolates, and unnaturally disconnects instances of the language game. Previously, language games could be understood as a fluid series of language exchange usually connected by actions and long-term implications that involve some pragmatic component. For example, formal language exchange between a judge and a defendant carries potential long-term implications on its participants, either facing formal sentencing or being acquitted. This crucially impacts future language games involving its participants in the longer term, with an undoubted physical component required for the process. However, discarding the pragmatic requirement fragments the otherwise fluid process of language use – is it still valid to treat the judge-defendant exchange and future conversations with the convicted defendant as separate encounters, with no physical connection possible in a natural sense? Even by somehow codifying additional context to connect fragmented events, philosophers lose a crucial aspect of analyzing the ancient cityscape. Second, the need to incorporate physical context into a detached language game fragment goes against ordinary language philosophy’s primary identity. Ordinary language philosophy is built on the criticism of ad hoc codification of context to interpret language, again summarized by the maxim “meaning is use.” Indeed, such patchwork resulting from denying the importance of practical context aligns more closely with Russell’s argument!

Moreover, the second approach to understand algorithmic language in the language game is not without its risks. The language game may be made to become limited in its scope to avoid contradictions and counterexamples. Language users may be confined to entities that are capable of interacting beyond textual discourse (rather than just being biologically human). That is, this framework rejects LLMs as valid participants of the language game, and claims that impeccable language fluency alone (even with context and cultural sensitivity) is insufficient to be a Wittgensteinian language user.

This revision creates fewer obvious problems for traditional arguments in ordinary language philosophy. However, this also chooses to avoid discussing the algorithmic entities in question rather than to address the issue. Its evaluation of the interesting philosophical entities is disqualification by definition (“LLMs are simply out of scope, we choose not to discuss them”), not logical argumentation against their usefulness in analyzing the nature of language. Evading the grey area of language use when it has practical significance in people’s language is hardly desirable. It is also less compatible with the paramount goal of the philosophy of language in general, or to understand the fundamental characteristics of language and its mechanisms for carrying meaning in human life. By discarding the analysis of prominent, problematic, and unprecedented entities that have become integral to modern language use, ordinary language philosophy risks losing relevance.

Conclusion

The implementation of large language models presents a unique challenge to the Wittgensteinian model of language analysis. LLMs exhibit major differences to earlier forms of algorithmic language, let alone performance: the exact mechanisms of LLMs are not fully understood, and these models acquire humanlike tendencies of evasion and lying without explicit instruction. Essentially functioning as black boxes, LLMs are not wholly understood beyond their general design pattern; in this respect, they resemble human brains more than imperative computer algorithms. Also, vast amounts of language data give rise to evasive or lying tendencies, in which a raw model seemingly “realizes” that it is under interrogation and responds untruthfully (documented recently during a process known as alignment). Both illustrate the extent to which algorithmic language has evolved to mimic human behavior, beyond humanlike textual response.

It is interesting to note that modern capabilities of LLMs are derived almost entirely from language patterns. As these algorithms’ potential applications in science, mathematics, medicine, programming, other academic disciplines and industry increasingly receive attention (and unprecedented levels of investment), their rise provides an opportunity to reconsider the value of human language in a modern context. And today, earlier philosophies of language are met with a previously unseen entity – one possessing near-perfect language use but with questionable physical and social presence. In an era when the study of language receives newfound attention with LLMs on the scene, Wittgenstein’s ordinary language philosophy serves as a valuable asset and useful precedent framework. But it is one that deserves careful reexamination and further discussion, bringing fluent algorithmic language into the equation.