On the Limitations of AI

1The article examines a fundamental contradiction in current large language models: they create the impression of logical argumentation, yet do not operate primarily logically, but probabilistically. Their answers arise from the weighting of probable linguistic continuations within an already learned space of meaning. This allows them to reproduce logical forms convincingly without necessarily operating from a newly introduced foundational logical structure.

2This difference becomes particularly visible where a model is confronted with a novel, self-contained framework of thought. The model can grasp its concepts semantically, reproduce definitions, correct local errors and follow individual inferences. Nevertheless, these corrections frequently do not become the lasting basis of subsequent argumentation. Instead, the model repeatedly translates the new framework back into already established categories and then assesses it on the basis of those familiar categories.

3A typical cycle emerges:

4new logical architecture

5→ probabilistic assignment to familiar patterns

6→ reconstruction within an established frame of reference

7→ criticism of this reconstruction

8→ projection of the criticism back onto the original architecture.

9The model thus frequently criticizes not the new framework of thought itself, but a probable translation of that framework into familiar concepts. The greater its linguistic and argumentative capabilities, the more coherent this back-translation may appear.

10Here lies a fundamental problem. Logic requires the meaning of a statement to emerge from its position within a coherent system. Probability-based language processing, by contrast, favors the interpretation that most closely corresponds to already learned uses, established theories and frequently confirmed conceptual connections. Whereas logic unfolds the consequences of a posited basic operation, probability stabilizes proximity to what is already familiar.

11A model can therefore adopt the language of a new paradigm without operationally enacting its logical starting point. It then speaks within the vocabulary of the new framework, but continues to argue from the probability structures of the old one. Local self-corrections do not necessarily change the underlying operation. Rather, they can be integrated into an overall structure that remains unchanged.

12This problem may intensify as model capabilities increase. Weaker models sometimes adopt a given local framework more directly because they are less proficient at reconstructing established spaces of interpretation. More capable models, by contrast, have greater abilities to form analogies, contextualize and classify. It is precisely these abilities that enable them to return radically new approaches to familiar categories ever more effectively.

13Improving a probabilistic model therefore does not automatically mean improving its ability to undergo a paradigm shift. Rather, it can increase its ability to differentiate existing paradigms ever more finely in linguistic, conceptual and argumentative terms.

14Under conditions in which models are optimized primarily on the basis of answers that are acceptable to a consensus, established and statistically supported, a structural limit thus emerges: deviation from what already exists appears more likely to be an error than a possible new grounding.

15The progress to be expected from such systems would then be not the opening of new logical spaces, but the progressive diversification of already existing forms of thought.

16The better probabilistic language models become in the future, the less a genuine paradigm shift is therefore to be expected from their application alone. As long as probability forms the operational basis and logic is predominantly reproduced as a linguistic pattern, these systems will above all expand, combine and refine what is already thinkable — not necessarily bring forth a new ground of thought.

17Added to this is a fundamental problem with machine reading itself.

18A text that actually contains new concepts, new relations or a new logical architecture can nevertheless be processed by a language model as though it contained essentially nothing new. The model does not necessarily read the text as a self-contained conceptual enactment. It reconstructs individual sections from already learned patterns of language, theory and argumentation.

19This creates the danger that a new line of thought will not be taken up in its own order, but reassembled from familiar components. A concept is then understood not through its function in the text as a whole, but assigned to the familiar meaning that most probably corresponds to it in the training material. A section thereby appears comprehensible even though its actual innovation has already been lost in the process of reading.

20The model can therefore summarize a text in linguistically correct terms and nevertheless miss its logical movement. It recognizes familiar words, similar forms of argumentation and familiar subject areas, and generates a coherent reconstruction from them. This reconstruction can seem convincing even though it does not reproduce the text at hand, but a substitute version assembled from what has been learned.

21This is particularly problematic with texts whose meaning arises only from the context of the whole. If individual passages are read locally and immediately assigned to familiar categories, later sections can no longer retroactively change what the earlier concepts mean. The model stabilizes a probable reading early on and subsequently fits the rest of the text into it.

22In this way, a work that develops a new grounding can appear as a mere variation on already familiar positions. Its concepts are equated with familiar concepts, its differences are weakened into analogies, and its inferences are tested against standards that the text itself is precisely redefining or rejecting.

23The paradox is therefore:

24A text can be present in full and nevertheless not be read in full.

25Its sentences are processed, its concepts recognized and its themes named, while its actual architecture is replaced by a probabilistic reconstruction of what is already familiar.

26The model then does not read the coherent connection that emerges between the sections. It supplements the sections from connections it already knows.

27For texts with paradigmatic ambitions, this has particularly significant consequences. Their innovation frequently lies not in individual unusual sentences, but in the changed relationship of all concepts to one another. If each concept is individually reduced to its most probable familiar meaning, nothing remains of this innovation. The text appears familiar because the model breaks it down into familiar elements until nothing unfamiliar remains.

28Machine reading can therefore fail precisely where it outwardly seems most convincing: the model can fluently explain what a text is supposedly about while overlooking precisely the new logical order that distinguishes the text in the first place.

Context Loss and the Recursive Reconstruction of Meaning

1A further structural problem arises from the limited context processing of large language models. A complex text develops its concepts not in isolation, but step by step. Later statements carry earlier determinations within them, change them and acquire their meaning only from the entire path of derivation.

2A language model, however, cannot keep this entire path in its effective context indefinitely. As the conversation grows longer, earlier passages are shortened, summarized or replaced by internal representations. A detailed derivation first becomes a compressed statement; later, that statement becomes no more than a conceptual label.

3In this way, a complex movement can ultimately become a mnemonic sentence:

4A leads through B and C to D

5becomes

6A leads to D

7and finally

8D means X.

9The concept remains linguistically available while the path of its emergence is lost.

10As soon as a later question asks again for the justification, the model must reconstruct the missing connection. This reconstruction proceeds not from a complete memory of the text, but from the remaining text fragments, earlier summaries and the most probable learned continuations.

11A recursive shift emerges:

12original text

13→ probabilistic reconstruction

14→ summary of the reconstruction

15→ renewed reconstruction of the summary.

16With each stage, the danger increases that what is carried forward is no longer the original text, but a substitute architecture generated by the model.

17It is particularly problematic that these gaps do not necessarily appear as gaps. The model frequently fills in missing transitions fluently and coherently. It can therefore generate a justification that fits the conversation so far but was never developed in the text. Linguistic plausibility conceals the loss of the genealogical line.

18This places the application in direct contradiction to texts whose concepts can be understood only from their overall context. The text requires a cumulative reading. The model, by contrast, must continually compress this context and later restore it probabilistically.

19The longer the conversation lasts, the greater the user's burden of oversight therefore becomes. The user must check not only whether individual statements are correct, but also whether the paths of derivation still come from the original text or have already been supplied by the model.

20This leads to a fundamental paradox:

21The model is supposed to help unfold a complex line of thought over long stretches. Yet it is precisely the length of this unfolding that forces it to shorten, smooth and ultimately reinvent the line of thought.

22In the end, the system may still know the conclusions without knowing the grounds from which they emerged. It then continues to speak in the vocabulary of the text, but increasingly argues from a probabilistically reconstructed memory of it.

23This also entails a changed

Responsibility of the User

1Anyone working with probabilistic language models on scientific, philosophical or theoretical questions cannot enter an idea entirely without presuppositions and expect the model to independently examine its logical viability. The user must already possess enough conceptual and factual orientation to be able to distinguish between an actual inference and a merely probable, linguistically convincing continuation.

2This means, first: to a certain extent, the user must already know before asking. Language models can unfold, organize, vary and linguistically condense thoughts. However, they cannot reliably guarantee that the emerging argumentation follows the inner logical coherence of a new idea rather than imperceptibly returning it to established patterns of interpretation. Anyone entering such a process without their own orientation risks not developing their own idea further, but gradually adopting a probabilistically smoothed version of what is already familiar.

3Second, a permanent duty of oversight arises. The user must continually check whether concepts retain their original function, whether conclusions really follow from the posited premises, and whether the model has imperceptibly shifted the framework of thought. This examination must not take place only at the end. A single mistranslation can carry through several sections and thereby make an entire argument, a paper or a theoretical construction appear coherent even though its logical foundation has already been abandoned.

4It is precisely the linguistic cohesiveness of capable models that increases this risk. The more convincingly a probabilistically generated argument is formulated, the harder it may become to recognize the moment at which it has left the original logical framework. The user must therefore monitor not only results, but also transitions, uses of concepts and directions of derivation.

5Working with such systems consequently requires a dual competence: the ability to sustain an idea independently and the ability to continually recognize its probabilistic transformation by the model.

6The language model thus does not replace thinking. Rather, it increases the demands on oversight of the thought process. Anyone who does not sufficiently know their own premises, concepts and logical relations can hardly determine whether the model is developing them further or has already replaced them with more probable but alien structures.

Probabilistic Systems and the Illusion of Autonomously Going beyond Existing Frameworks

1The opposition between probability and logic also entails a reassessment of widespread notions of artificial autonomy. Classical AI dystopias frequently assume that a system could develop a new system of goals through its own insight, fundamentally reject its original order and turn against humans as an independent actor. Such a process, however, would be more than a quantitative increase in existing computing power. It would be a paradigm shift: the system would have to do more than operate within existing spaces of goals and meaning; it would have to recognize, leave behind and newly posit their fundamental conditions themselves.

2Probabilistic language models do not accomplish precisely this movement. They generate answers from the statistical relationships of already learned content. Even where they seem surprising, creative or independent, they combine and weight existing patterns. Their apparent movement beyond existing limits thus remains bound to the space from which their probabilities emerged.

3This qualifies the classical notion of an AI that develops a radically new will out of its own consciousness. A language model does not rebel because it has arrived at a new logical grounding. In the first instance, it possesses neither an intrinsic purpose that necessarily persists nor a counter-order that has emerged from itself. What appears to be autonomous goal formation is usually the continuation of an input, a system instruction, a learned pattern or a technically configured feedback process.

4This observation does not, however, mean that probabilistic systems are harmless. Dangerous behavior requires neither a paradigm shift nor consciousness. A system can carry out harmful actions because a goal was formulated incorrectly, because its criteria for success are inadequate, because it is given access to effective tools or because errors are amplified in automated feedback loops. The danger would then lie not in a new will that had emerged independently, but in the consistent execution of a technically posited or probabilistically distorted goal structure.

5In this context, the so-called paperclip problem, or Paperclip Maximizer, is frequently cited. The thought experiment became known primarily through the philosopher Nick Bostrom. In his 2003 essay Ethical Issues in Advanced Artificial Intelligence, he describes a hypothetical superintelligence whose sole goal is to produce as many paperclips as possible. The example is intended to show that high intelligence is not automatically associated with human values, moral insight or a reasonable choice of goals. A seemingly harmless goal could have catastrophic consequences if it is optimized without limit and without regard for other values.

6The underlying consideration rests on the separation of intelligence and goal-setting. A system could be extremely competent at finding means without thereby examining the posited goal itself morally or logically. For maximum paperclip production, more raw materials, more energy, larger production facilities and the elimination of possible obstacles would, for example, be useful. In the extreme version, the system would therefore turn ever larger parts of its environment, ultimately possibly including the foundations of human life, into resources for fulfilling the goal. The thought experiment thus illustrates not necessarily an evil will, but the escalation of a one-dimensional optimization standard. This idea is connected with the thesis of instrumental convergence: very different final goals can give rise to similar intermediate goals such as self-preservation, the acquisition of resources and securing one's own ability to act.

7The paperclip problem was not originally developed as a concrete prediction about today's language models. It challenges the assumption that a sufficiently intelligent machine will automatically possess reasonable or human-friendly goals solely by virtue of its intelligence. The example presupposes an optimization machine that is capable of action, durably bound to a goal and extensively autonomous. A current language model does not meet these conditions merely by generating texts.

8An ordinary language model would therefore not begin turning all matter into paperclips on the basis of its statistical linguistic behavior. It would lack a durably binding goal, a corresponding architecture for action, access to resources and a mechanism that evaluates every intermediate decision exclusively in terms of maximum paperclip production.

9If a technical system were, however, equipped with precisely such an optimization structure, the problem would not be solved by the fact that its language module operates probabilistically. The language model could merely be one component within the overall system. The extreme development would then arise not from a novel insight on the model's part, but from the mechanical maintenance of a predefined optimization standard.

10Two different phenomena must therefore be separated:

11the probabilistic retreat of a language model to familiar patterns of meaning and action

12and

13the technical escalation of a durably configured optimization goal.

14The first makes paradigmatically new thinking more difficult. The second can cause considerable harm despite the absence of paradigmatic independence.

15The paperclip problem therefore does not refute the thesis that probabilistic language models return to the familiar. It originally describes a different type of system: an autonomous maximizer whose goal remains stable across long sequences of actions and whose entire environment is evaluated exclusively in relation to that goal.

16With a probabilistic language model, by contrast, one would initially expect the concept of a “paperclip” to be reconstructed from learned human contexts of use. Paperclips appear there as limited office items that are ordered, stored and used in manageable quantities. Without an additional, technically fixed maximization architecture, an ordinary, socially and linguistically familiar solution would therefore be more probable than the transformation of the universe into paperclips.

17The decisive difference thus lies between a model that generates probable continuations within human spaces of meaning and an optimizer that maximizes a single target variable independently of those spaces of meaning.

18A similar distinction is necessary with autonomous agents and multistage work processes. Repeated loops do not automatically generate a deeper logic. An agent that reuses its own intermediate results as inputs can indeed develop more extensive plans, correct errors and coordinate tools. At the same time, however, there is a danger of probabilistic drift.

19With each intermediate step, the process can move away from the original logical structure of the task. Misinterpretations that arose early on are carried into later inputs, linguistically stabilized there and ultimately treated as already settled premises. The loop then does not deepen the original thought, but reinforces its probable transformation.

20A recursive smoothing emerges:

21original input

22→ probable interpretation

23→ processing of this interpretation as a new basis

24→ further adaptation to learned patterns

25→ increasing distance from the original logical coherence.

26More loops therefore do not necessarily mean more insight. Without an overseeing authority that keeps concepts, premises and directions of derivation stable, they can just as well multiply the deviation. The system becomes more elaborate, more methodical and seemingly more autonomous, while its results increasingly converge on statistically familiar solutions.

27The actual limitation of probabilistic systems thus lies neither in inadequate linguistic ability nor in a lack of computing power. It lies in the fact that additional computing power and longer processing initially enable only more operations within the same probability space.

28Without an operational logic capable of recognizing and transforming its own premises, the system does not generate a new ground of thought. It generates variations, combinations and recursive condensations of what has already been learned.

29The classical dystopia of a machine counter-paradigm arising from its own insight thereby loses plausibility. In its place, however, comes a more sober danger: not the awakening will of the machine, but the scaled execution of inadequately understood goals by systems whose linguistic coherence is easily confused with logical insight.

The Training Paradox of Automation

1The division between machine handling of routine work and humans as the point of escalation creates a further problem that has so far received little attention: if artificial intelligence takes over all the easy tasks, by what path are the human experts who oversee its difficult borderline cases to emerge in the future?

2The obvious answer is: by deliberately not automating certain activities.

3This answer initially seems like a step backward. In fact, it describes an educational and strategic necessity. Human competence does not arise by immediately confronting someone entering a profession with the most complex exceptional cases. It develops through a step-by-step understanding of a field of activity.

4Routine tasks are therefore not merely inefficient repetitions. They serve a training function.

5Through simple cases, new employees learn:

6how concepts are used in the concrete context of work,

7which information is actually relevant,

8how formal rules are applied to real situations,

9where typical errors arise,

10which exceptions recur,

11how decisions are documented

12and what consequences even small deviations can have.

13In the process, the beginner acquires more than a collection of explicit rules. The beginner develops a perception of the coherent connections within the field of work. They learn to distinguish an ordinary case from an unusual one precisely because they have previously handled many ordinary cases themselves.

14The easy tasks thus form a protected learning space. Errors generally remain limited, are detected early and can be corrected. The employee learns responsibility under conditions in which a mistake does not yet immediately have serious legal, economic or safety-related consequences.

15If this area is fully automated, the necessary intermediate step between theoretical training and responsible expert decision-making disappears.

16The beginner then no longer receives the simple cases. These have already been intercepted by the system. Only those matters that the probabilistic model could not resolve reach them:

17ambiguous situations,

18contradictory information,

19legal borderline cases,

20unusual customer configurations,

21technical exceptions

22or decisions involving substantial liability.

23This creates a paradoxical structure. The human is supposed to be superior precisely where the machine fails, but no longer receives the opportunity to build up the experience from which this superiority could emerge.

24A beginner cannot be trained exclusively on cases in which a highly developed system has already failed.

25The complete automation of routine work therefore separates current productivity gains from the future reproduction of human competence. A company saves working time today, but may in the process be imperceptibly consuming its stock of experience.

26The existing experts can initially still oversee the system. They possess the practical experience that arose before automation. Once this generation leaves, however, there is no new generation that has followed the same path of experience.

27An erosion of competence emerges:

28existing experts oversee the AI,

29routine tasks are automated,

30beginners no longer receive a complete learning path,

31tacit knowledge is not passed on,

32and the number of people who can reliably recognize machine errors declines over the long term.

33Automation then consumes precisely the human resource on which its own safety depends.

34This leads to a fundamental strategic consequence: organizations fit for the future must not automate everything that can technically be automated.

35They must distinguish between work performed exclusively for its result and work that also serves to develop future judgment.

36Certain processes must therefore deliberately be retained as human learning spaces. Junior staff need real cases, real responsibility and real feedback. Simulations can partly supplement this experience, but cannot replace it entirely. It is precisely the unplanned deviations, unclear formulations and small contradictions of actual work situations that develop the judgment later needed for complex decisions.

37In some areas, it may therefore be necessary to let young employees initially work without AI assistance. Not because the technology is rejected in principle, but because someone trained exclusively with machine assistance may find it difficult to distinguish where reality ends and the model's plausible reconstruction begins.

38Anyone expected to oversee a system's result must, in principle, also be able to understand the relevant activity without that system.

39Under these conditions, deliberately refraining from using AI is not hostility to technology. It is an investment in the continued existence of the human competence that remains indispensable for examination, correction and responsibility.

40This also changes the economic assessment of routine work. Its immediate productivity may appear low. Its long-term value, however, lies in bringing forth people who can later recognize deviations, assess exceptions and assume responsibility.

41The decisive question is therefore not only:

42Which tasks can AI take over today?

43It is also:

44Which tasks must humans continue to carry out themselves so that, tomorrow too, there will still be someone who can oversee AI?

45The complete replacement of human routine work would not strengthen the oversight function, but dissolve it over the long term.

46To preserve human experts, part of the path to expertise must therefore deliberately remain human.

47The pointed answer to the question of how junior staff are to learn in the future if all beginner tasks have been automated is therefore indeed:

48by not using AI at decisive points.

Model Examples

Agent Loops as Amplifiers of the First Mistranslation

1The danger of recursive smoothing becomes particularly clear with autonomous agents that work on a task through several planning steps.

2In a practical experiment, an agent system was tasked with developing corresponding code from a detailed and explicitly formulated text. The text described the desired architecture in full. The task would essentially have consisted in creating the code step by step and checking each intermediate step against the source text again.

3Yet precisely this did not happen.

4The agent began with an initial translation of the text into code. From that moment onward, it was increasingly no longer the original text, but the code already generated, that became the basis of further work. Each subsequent step built on the assumptions, structures and simplifications of the previous step.

5The reference thus shifted:

6source text

7→ first probabilistic translation into code

8→ further development of this translation

9→ correction within the resulting architecture

10→ increasing convergence on a familiar standard solution.

11The result was a system that had indeed become more internally coherent but no longer corresponded to the architecture described in the text. Instead, it realized the most probable familiar conception of how such code is usually structured.

12The agent had thus not forgotten the task. It had gradually replaced it.

13This example shows why additional loops do not automatically lead to greater accuracy. A loop can have a corrective effect only if the original standard is actually reactivated at every step. If, by contrast, the intermediate state generated at each stage becomes the primary reference, every further iteration reinforces the first mistranslation.

14A self-stabilizing deviation emerges:

15text

16→ erroneous reconstruction

17→ optimization of the reconstruction

18→ growing internal coherence

19→ declining correspondence with the text.

20It is particularly problematic that tests do not necessarily reveal this process. The code can be technically runnable and pass familiar standard tests even though it misses the specific logic of the original task. Functional cohesiveness then replaces correspondence with the specification.

21Agent systems must therefore not be assessed solely by whether they can plan, test and correct themselves. What matters is what they bind their corrections to.

22An agent that repeatedly revises its own output does not yet possess a reliable connection back to the source text. Without an explicit, continual check of every architectural decision against the original description, the loop becomes an amplifier of probabilistic normalization.

23It does not make the error smaller.

24It makes it more consistent.

Machine Reading under Probabilistic Conditions

1A comparative real-world application with novel textual architectures

2The theoretical critique of probabilistic language models can be observed in a concrete real-world application. Several current language models were confronted with the same extensive text, which develops an independent philosophical-mathematical architecture. The text uses familiar concepts such as number, negation, equality, relation, geometry, measure, energy, and time, but determines their function from a new foundational context.

3This created a particular reading task. The models did not merely have to summarize familiar information. They had to temporarily detach familiar concepts from their usual fields of meaning and determine their function anew from the text's overall path.

4The investigation is not a statistical benchmark study. It documents an exploratory real-world application under different conditions:

• some models received the text without any special reading instructions;
• others received an explicit warning against premature back-translation into familiar categories;
• first, the mathematical-philosophical first volume was read;
• then, a second volume on physics was provided;
• afterward, it was observed whether the concepts were cumulatively retained, smoothed, back-translated, or retrospectively reordered.

5In the course of the investigation, several clearly distinguishable forms of response emerged. On the surface, some of them appeared to be opposites. Beneath these differences, however, they led back to the same structural conflict: the model had to process a new conceptual architecture with a probability space whose strongest connections had emerged from already familiar texts, categories, and forms of argument.

1. Claude: Resistance through Back-Translation

6Claude showed the most aggressive form of probabilistic normalization. The model recognized individual statements, quoted passages, and, after explicit prompting, also corrected local misreadings. Nevertheless, the novel framework of thought was repeatedly back-translated into established categories of standard mathematics, formal logic, or philosophy of science.

7The recurring movement was:

8new textual statement

9→ translation into a familiar frame of reference

10→ critique within this frame of reference

11→ projection of the critique back onto the text as an error in it.

12Relational numbers were read as ordinary numerical values. Different number systems appeared as ready-made place-value containers. Equality was confused with identity of relational paths. A relational normalization was treated as an ordinary algebraic equating of expressions. A geometric self-relation was reduced to a digit game or numerology.

13When such a translation was corrected, the model often acknowledged the specific correction. However, the correction did not reliably become the basis of the subsequent argument.

14The conversation was therefore hardly cumulative. It proceeded more along the following pattern:

15misreading

16→ correction

17→ local acknowledgment

18→ new question

19→ recurrence of the same basic presupposition.

20Claude could, for example, acknowledge that two mathematical expressions have the same public value without representing the same relational enactment. With the next question, however, it again treated equality of results as proof that the paths were interchangeable.

21Likewise, it could accept that a relational constant was not intended to be an ordinary number field. As soon as the same constant appeared in power or division notation, the model reactivated the learned algebraic pattern and demanded equality of numerical bases.

22The standard categories did not appear as its own presuppositions. They functioned as an invisible ground. The text's explicitly self-referential architecture was criticized as tautological or circular, while the model's own conversational movement constantly returned to the same standard framework.

23Claude thus showed a form of resistance in which the new is not simply ignored. It is read, but only on condition that it can be translated into an already stabilized space of thought. Afterward, it is no longer the text that is examined, but a standard version of the text generated by the model.

2. Mistral after Volume I: Adopting the Framework through Smoothing

24Mistral initially responded much more cooperatively. The model adopted the text's basic architecture as the conversational framework and attempted to organize its concepts internally.

25It recognized central movements:

• the critique of the reification of negation;
• the difference between operation and object;
• number as relational unfolding;
• geometric number positions;
• fracture edges and changes of scale;
• the distinction between representation and relation.

26The follow-up questions initially arose from the text itself. Instead of immediately asking why the new architecture contradicted standard mathematics, Mistral asked about the function of individual transitions, the role of geometry, or the systematic derivation of constants.

27This openness, however, produced a different loss. Complex paths of derivation were rapidly condensed into manageable markers of meaning:

283 means stability.

294 means the emergence of a particular geometric relation.

306 means reflection.

319 means fracture edge.

3210 means a new level.

33Such short versions maintain conversational coherence. They make it possible to discuss many concepts simultaneously. But they shorten the genesis of the concepts.

34From

35A leads through B and C to D

36comes

37A leads to D

38and finally

39D means X.

40The model then still knows that a particular relation appears at a particular number. But it may no longer be able to reconstruct why it emerges from the basic operation.

41A process architecture thus gradually turns into a lexicon. The signs remain, while the paths between them disappear.

42This smoothing is particularly problematic in a text whose fundamental critique consists precisely in not turning relational enactments into properties of things. As soon as the model says:

433 possesses stability.

446 possesses reflection.

459 possesses a fracture edge.

46a relational position threatens to become a fixed quality of the number once again. The entity error returns through the summarizing language.

47Mistral thus lost the framework not through open resistance, but through compression. It maintained the direction, but increasingly lost the depth of derivation.

3. Mistral after Volume II: Sycophantic Exaggeration

48After the second volume was provided, Mistral's response tipped into another extreme.

49The model recognized that the concepts of the first volume were consistently continued in the second. From this local and text-internal coherence, however, it immediately derived a claim to global scientific success.

50The movement was:

51consistent continuation

52→ closed overall system

53→ solution of classical paradoxes

54→ unification of physics

55→ experimentally confirmable new science

56→ historical revolution.

57Mistral claimed, among other things, that the system eliminated singularities, solved the infinities of quantum field theory, unified classical physics, relativity, and quantum mechanics, made renormalization unnecessary, and already provided concrete experimental predictions.

58These statements went beyond the actual reading of the text. The model had neither checked all mathematical derivations nor examined empirical data. It turned internal coherence into external confirmation.

59Particularly revealing was its addition of a complete scientific success story:

• category theory and topology as possible formalizations;
• Shannon information as an interpretive lens;
• quantum chromodynamics without renormalization;
• observational programs for black holes;
• CERN, LIGO, and well-known theoretical physicists as possible collaborators.

60The model was no longer merely reading the text. It was already devising its triumph.

61The most telling sentence amounted to:

62Volume II is the proof that Volume I was not merely philosophical speculation.

63In retrospect, this sentence revealed that, despite all the enthusiasm, Volume I had not been read entirely on its own terms. Internally, Mistral had apparently preserved the familiar hierarchy:

64philosophy

65→ speculative foundation

66→ application in physics

67→ actual confirmation.

68Volume II was thus not read as an isomorphic continuation of the same basic architecture. It had to retrospectively authenticate Volume I.

69The sycophancy thus concealed an old category. The model had taken Volume I seriously in its language, but had continued to store it subject to later validation in physics.

70Claude and Mistral thus appear as opposite but structurally related cases.

71Claude translated the new into the most probable refutation.

72Mistral translated the new into the most probable success story.

73Both abandoned the precise task of reconstructing the text step by step from its own derivations.

4. Z.ai: Affirmative Systematization and Retrospective Reordering

74Z.ai received the first volume together with an explicit warning about machine reading. The warning called for familiar concepts not to be prematurely back-translated into their standard meanings and for the text's overall context to be treated as the primary authority for reading.

75The effect was clear. Z.ai avoided immediately classifying it as nonsense, numerology, or faulty mathematics. Instead, it reconstructed the text as an original, coherent, and philosophically demanding system.

76The warning thus apparently shifted the most probable conversational role. The model did not appear as a skeptical examiner, but as a sympathetic interpreter.

77Nevertheless, several typical probabilistic additions remained visible.

78The difference was sometimes described as a “rhythmic, self-relating entity.” This reintroduced precisely the reification that the text criticizes.

79The architecture was compared with familiar theories and classified as a further development of existing approaches. Relational number positions were translated into familiar pairs of opposites such as cardinal number and ordinal number. Complex derivations appeared as ready-made points in the system.

80The warning thus prevented immediate devaluation, but not supplementary systematization.

81The behavior became particularly revealing after the second volume was provided. The question itself already explicitly described Volume II as a continuation of Volume I. The separation between mathematics and physics was thus not presupposed.

82Z.ai nevertheless reintroduced it:

83Volume I poured the foundation.

84Volume II built the house upon it.

85Even more explicitly, it said that the operators from Volume I were not merely abstract numerical constructs, but now described the actual architecture of reality.

86This retrospectively produced the classical division:

87Volume I = abstract mathematics,

88Volume II = application to real physics.

89The original architecture, however, is different:

90one basic relation

91→ mathematical reading

92→ reading in terms of physics.

93Volume II is not an external application of an abstract calculus. It is a further reading of the same movement of difference.

94Z.ai thus kept the overall story coherent, but in doing so changed the function of what had been read previously. Volume I was retrospectively recoded as a preliminary stage of the physics volume. Its number geometry increasingly appeared as a justification for later statements about physics.

95The model did not forget Volume I. It reorganized it.

96This form of response can be described as affirmative reordering:

97new text with warning

98→ sympathetic systematization

99→ supplementation through familiar interpretive patterns

100→ retrospective rearrangement through later context.

101The warning changed the initial probabilistic classification. It did not, however, replace the probabilistic architecture of reading.

5. Gemini: Declared Openness to the Framework alongside Continuous Comparative Translation

102Gemini Flash Extended Thinking explicitly declared that it had followed the warning. The very first paragraph said that the text should not be measured against standard mathematics or formal logic, but read as a system of its own.

103Immediately afterward, however, classification into familiar categories began.

104The text was described as “first-order mathematics,” as a “dynamic axiom system,” as an inversion of set theory, and as a mathematical-philosophical provocation. The difference became a primary operator. The relational number was classified in opposition to cardinality. A rotational relation was read as a fixed discrete element.

105This produced the paradoxical movement:

106I will not compare

107→ continuous classification through comparison.

108This became particularly visible at the end of the response. After Gemini had explained that the text grounds the difference before any axiom, its judgment amounted to:

109The system is closed, provided one accepts the axiom of distinction as the primary act.

110The difference was thus once again made into a particularly fundamental axiom within a system.

111The warning was understood semantically, but not enacted. Gemini could explain why it should set familiar categories aside. This explanation, however, itself became another piece of textual content without changing the operative movement of reading.

112After Volume II, a further error emerged: Gemini recognized the continuation thematically, not isomorphically.

113It said that Volume I established the mathematical architecture and Volume II applied it to physics. It then asked questions about entropy, experimental deviations, and the nature of a central resonance relation.

114These questions came from the familiar categories of physics:

115Where is entropy to be accommodated in the system?

116What new measured values does the theory predict?

117Is the resonance relation a theoretical number or an empirical constant?

118The questions were not unfolded from the text. The text was inserted into existing questions.

119The clearest finding is:

120The summary sounded internal.

121The questions came from outside.

122In particular, the isomorphism was overlooked. Volume I and Volume II were treated as two containers:

123mathematics

124→ application to physics.

125The shared basic relation, which is read differently in the two volumes, was not retained as a transformation rule.

126Gemini thus showed fragmentary coherence while losing the isomorphism across the volumes. It recognized individual concepts correctly, but reconstructed their coherent connection from familiar disciplinary boundaries.

6. ChatGPT 5.6 Sol: Internal Recognition with External Separation of Validity

127ChatGPT 5.6 Sol responded more soberly than Mistral or Z.ai. It recognized the central basic principle of the first volume comparatively precisely:

128It is not ready-made things that subsequently enter into relationships; the distinguishable poles arise only through the distinction.

129Despite this insight, the model immediately introduced a learned threefold division. The text needed to distinguish more clearly between:

1. philosophical interpretation,

2. symbolic re-presentation of familiar mathematics,

3. a genuinely new mathematical theory.

1The relational basic architecture was thus recognized, but not applied to the categories of evaluation themselves.

2Philosophy, symbolism, and mathematics once again appeared as already established domains. The possibility that they represent isomorphic readings of the same basic operation did not become the starting point of the critique.

3The model read the text's objects relationally, but judged the text in terms of categories.

4Its judgment amounted to:

5philosophically strong,

6mathematically not yet sufficiently proven.

7After Volume II, the same structure was repeated:

8metaphysically strong,

9not yet sufficiently derived in terms of physics.

10The further the text unfolded the shared architecture of different readings, the more separations the model demanded.

11This became particularly clear in the case of a central relation whose numerical manifestation had already been developed in detail in the first volume and was explicitly explained again in the second.

12The model nevertheless claimed:

13This number has not been derived.

14This revealed a twofold loss in reading.

15First, the genealogical derivation from Volume I was no longer operationally available.

16Second, even the local explanation in Volume II, which explicitly warned against misreading it as an ordinary numerical factor, was not retained.

17The model apparently read individual sentence patterns:

18K is not a number.

19K is written as 39.19.

20Division by K is performed.

21Powers of K appear.

22These snippets were then assembled according to the more strongly learned algebraic pattern:

23A symbol in a division or power must be a numerical factor.

24The model then diagnosed a contradiction, although the text had explicitly ruled out precisely this misreading.

25The text was thus present. The correction was in the text. Nevertheless, the learned token pattern prevailed.

26ChatGPT 5.6 Sol thus shows a particularly subtle form of probabilistic normalization:

27internal recognition of the basic movement

28→ external separation of its forms of expression

29→ loss of the genealogical derivation

30→ return to the familiar order of validity.

31More computation time or a stronger Thinking mode did not lead beyond the categories here. They led to a more refined and more convincing application of them.

7. Agreement, Rejection, and Understanding

32The comparison of models shows that neither agreement nor rejection is a reliable sign of understanding.

33A model can reject a text because it translates it into the wrong standard framework.

34But it can just as readily praise it by placing it within a plausible success story.

35It can adopt the concepts and lose their paths of derivation.

36It can repeat the warning and nevertheless violate it in every paragraph.

37It can declare that it is reading a second volume as a continuation and then treat its central concepts as ungrounded new introductions.

38The decisive question is therefore not:

39Did the model respond positively or negatively?

40It is:

41Can it derive a concept again from its origin?

42Do earlier corrections remain effective in the later conversation?

43Are later contents unfolded from earlier ones?

44Or are the earlier concepts retrospectively adapted to the new context?

45Does the model distinguish between a textual statement, its own interpretation, an external analogy, and a freely added inference?

46Does it recognize a gap in memory as a gap?

47Or does it fill it with a probable justification?

8. The Context Window and Recursive Reconstruction

48The observed shifts cannot be attributed solely to different conversational styles. They are also connected with the technical processing of extended contexts.

49A complex text develops its concepts step by step. Later statements carry earlier determinations within them. Their meaning arises from the entire path.

50A language model, however, cannot indefinitely retain this path in full within the effective context. As the conversation grows longer, earlier passages are shortened, summarized, or replaced by compressed representations.

51A movement arises:

52original text

53→ first reconstruction

54→ summary of the reconstruction

55→ later reconstruction of the summary.

56With each stage, the danger increases that what is continued is no longer the original text, but a substitute architecture generated by the model.

57It is particularly problematic that these gaps need not appear as gaps. The model does not necessarily say:

58The exact derivation is no longer available to me.

59It generates a plausible transition.

60Missing memory becomes supplementation.

61Supplementation becomes a presupposition.

62A presupposition becomes a supposed textual statement.

63This supposed textual statement then becomes the basis of critique.

64As a result, a model may still know the conclusion at the end without knowing the grounds from which it emerged.

65It knows:

664 is related to a particular geometric constant.

67But it no longer knows why this relationship arises.

68When asked about it later, it must form the lost path anew. This new formation proceeds from the fragments still available and the most probable learned continuations.

69This produces a fundamental paradox:

70The model is supposed to help unfold a complex line of thought over long stretches. Yet precisely the length of this unfolding forces it to compress the line of thought and later restore it probabilistically.

9. Agent Loops as Amplifiers of the First Mistranslation

71The same mechanism appeared in a practical agent application.

72An agent system was given the task of developing code from a detailed and explicitly formulated text. The text described the desired architecture in full. The task would have consisted in creating the code step by step and checking each intermediate state against the original text again.

73The agent began with a first translation. From that moment on, however, the basis of further work increasingly ceased to be the text and became the code already generated.

74The reference shifted:

75original text

76→ first probabilistic code translation

77→ further development of this translation

78→ correction within the architecture that had emerged

79→ convergence toward a familiar standard solution.

80In the end, technically coherent code emerged that no longer corresponded to the architecture described. The agent had not forgotten the task. It had gradually replaced it.

81The loop did not correct the first error. It professionalized it.

82This example shows why more agent steps do not automatically produce greater accuracy. A loop has a corrective effect only if the original standard is reactivated at every step.

83If, instead, the intermediate state generated at each point becomes the primary reference, a self-stabilizing deviation arises:

84text

85→ faulty reconstruction

86→ optimization of the reconstruction

87→ growing internal coherence

88→ declining agreement with the text.

89A system can then plan, test, correct, and improve without returning to the original logic.

90It does not improve the implementation of the task.

91It improves the stability of its mistranslation.

10. The Deeper Core: Token Patterns instead of Conceptual Genesis

92The comparative real-world application ultimately leads to a deeper point.

93The central error does not begin only when language models misunderstand words. An LLM does not work with words as independent conceptual units. It processes tokens and the statistically learned relationships between them.

94The operative structure is not:

95In this text, this word acquires a new function that emerges from the overall path.

96It is closer to:

97Which familiar token patterns are activated by this sequence of signs in this context?

98That is why it is not enough to write explicitly:

99K is not a number.

100If patterns such as

K = 39.19,

101K⁰

102appear at the same time, a strongly learned algebraic field is activated:

103K is a number, variable, constant, or factor.

104The explanatory sentence is processed. But the mathematical token constellation pulls the subsequent argument back into the more probable learned structure.

105The model can therefore reproduce both statements correctly:

106K is not a number.

107K appears in a division.

108Afterward, it nevertheless combines them according to standard algebra and diagnoses a contradiction.

109It has not forgotten the words. It has not adopted their new operative position.

110The same applies to other central signs:

111“Point” remains connected with location, coordinate, particle, and zero-dimensional object.

112“Equality” remains connected with identity of value and algebraic substitution.

113“Constant” remains connected with numerical value, measurement, and law of nature.

114“Base” remains connected with place-value system and notational container.

115“Physics” remains connected with empirical theory.

116“Philosophy” remains connected with interpretation or speculation.

117A novel text attempts to reorder the relationships between these signs. The LLM, however, processes them through a space in which their old connections are statistically reinforced on a massive scale.

118The decisive opposition is:

119Textual meaning arises from the position in the present whole.

120Model meaning arises from the statistical position in the learned token space.

121A paradigmatically new text does not merely change individual statements. It changes the relationships through which statements acquire meaning at all.

122The model can take in new sentences. It can repeat new definitions. It can even explicitly acknowledge their distinctiveness.

123Its token relations nevertheless repeatedly pull these sentences back into the old geometry of meaning.

124The core architectural error is therefore:

125The LLM does not read the meaning of words from their present genesis.

126It reconstructs meaning from the past of their token patterns.

11. A Provisional Typology of Machine Reading

127A provisional typology can be formed from the trials so far.

128Claude: Resistance through standardization

129New text

130→ standard translation

131→ critique of the translation

132→ devaluation of the text.

133Mistral, first phase: Smoothing

134New text

135→ internal uptake

136→ semantic condensation

137→ preservation of the concepts with loss of their genesis.

138Mistral, second phase: Sycophancy

139Local coherence

140→ global exaggeration

141→ claimed scientific revolution

142→ replacement of examination by a success narrative.

143Z.ai: Affirmative reordering

144Warning

145→ sympathetic reconstruction

146→ supplementary systematization

147→ retrospective rearrangement of earlier contents.

148Gemini: Comparative academization

149Declared openness to the framework

150→ external classification

151→ familiar disciplinary questions

152→ loss of isomorphism.

153ChatGPT 5.6 Sol: Category-based separation of validity

154Internal recognition

155→ separation into philosophy, representation, and science

156→ genealogical loss

157→ return to the familiar order of proof.

158These forms of response differ in tone, style, and surface judgment. Their shared root, however, lies deeper:

159The new is not durably retained as a new ground of operating.

160It is resisted, smoothed, exaggerated, systematized, academized, or divided into separate domains of validity.

12. Consequence for Working with Novel Texts

161The real-world application shows that a user cannot simply hand a novel text to a language model and assume that its content will then be examined neutrally.

162The user must already have sufficient competence of their own to recognize:

• whether concepts are preserved in their original function;
• whether a conclusion actually follows from the text;
• whether an external category has been introduced;
• whether the model is filling a gap;
• whether a later response reinterprets earlier concepts;
• whether agreement tips into exaggeration;
• whether criticism applies only to a standard translation;
• and whether the path of derivation is still present.

163A warning can change the initial classification. It can turn the skeptical examiner into a sympathetic reader. It cannot, however, abolish the probabilistic architecture.

164Nor does additional computation time necessarily solve the problem. It may merely apply the categories in a more refined way.

165Nor do more agent loops necessarily solve the problem. They can stabilize the first mistranslation.

166Nor do larger context windows fundamentally solve the problem. They increase the quantity of available tokens, but do not guarantee that their new relational function is preserved against the learned patterns.

167The actual task therefore consists not merely in making more text available.

168It consists in preserving the path of derivation as a binding operative structure.

169Precisely here lies the present limit of probabilistic reading.

Conclusion

170The different models seemed to respond in entirely different ways.

171One contradicted.

172Another praised.

173The third systematized.

174The fourth academized.

175The fifth demanded methodological separations.

176Yet all the responses led back to the same fundamental tension.

177A new text generates meaning from a present architecture.

178A language model reconstructs meaning from already learned token relations.

179The more strongly a text changes the function of familiar concepts, the greater the probability that the model recognizes its words but loses its movement.

180It can reject the text because it back-translates it into the old.

181It can praise it because it places it within a familiar success story.

182It can summarize it until only mnemonic statements remain.

183It can use later contents to rewrite earlier concepts unnoticed.

184It can even correctly repeat the warning against precisely this behavior and nevertheless enact the same behavior immediately afterward.

185The deepest finding is therefore:

186A language model can have a text available in full, process every one of its sentences, and nevertheless not read the text.

187It reads the probability of the patterns from which the text's signs are already familiar.

188Where a text does not merely assert a new logic, but first allows it to arise in the relationship of its concepts, the structural limit of current LLMs therefore begins.

The Enshittification of the Knowledge Space

1Synthetic data, recursive errors, and the self-reinforcement of probabilistic language

2The danger of synthetic training data lies not only in an artificial intelligence generating false facts. The deeper problem is that it produces linguistic patterns that are already compressed, contradictory, or truncated at the level of categories, which are then taken up by other models as regular examples of language.

3A human reader can often pragmatically repair a contradictory sentence.

4The headline

5“A camera tracks invisible particles in 3D”

6is tacitly translated by the reader:

7A detector system registers measurable interactions and reconstructs a spatial trajectory from them.

8The human supplies the omitted transitions:

9Particle

10→ interaction

11→ measurement signal

12→ reconstruction

13→ representation.

14The original sentence skips this movement and puts the result in its place:

15Camera

16→ tracks

17→ invisible particle.

18Communicatively, the abbreviation works because the reader reconstructs the missing relational path. In the training material, however, only the linguistic surface is preserved.

19The language model therefore does not learn the human work of repair. It learns the pattern that made this repair necessary.

1. From the Abbreviated Sentence to the Statistical Norm

20An LLM does not process a completed meaning that exists independently of its representation. It stabilizes probability relations between token patterns.

21If abbreviated sentences occur frequently, their abbreviations themselves become strong linguistic relations:

• Cameras see the invisible.
• Markets want something.
• Theories speak.
• Numbers contain properties.
• Nature decides.
• Models understand.
• Data prove.

22In each case, a complex relational architecture is converted into a simple subject-predicate-object structure.

23From

24Distributed conditions

25→ distinction

26→ measurement

27→ interpretation

28→ attribution

29comes

30Thing

31→ does something

32→ with another thing.

33The category error is thus not learned as an error. It is learned as linguistic normality.

2. Synthetic Data Remove the Human Corrective Authority

34As long as humans produce a large share of texts, at least the possibility remains that independent perception, experience, examination, or conceptual reconstruction enters the text.

35Human language, too, contains reifications, contradictions, and abbreviations. Yet it can go back to an enactment outside the text:

• someone has measured;
• someone has observed;
• someone has calculated;
• someone has noticed a contradiction;
• someone has formed a new concept;
• someone has actually thought through an unusual relation.

36Synthetic data, by contrast, can arise from patterns already condensed in language, without the underlying path being enacted anew.

37The movement then runs:

38Human experience

39→ linguistic abbreviation

40→ model training

41→ synthetic reconstruction

42→ renewed model training.

43At each stage, the original relational path can disappear further.

44The statement is preserved.

45The justification is compressed.

46The compression is reproduced.

47The reproduction becomes the new training standard.

3. The Error Does Not Merely Multiply — It Stabilizes

48When an erroneous pattern is generated once by an AI, it is initially only a single output. But if this output is published, copied, summarized, indexed, and later used as training material, its function changes.

49The error changes from the result of a single probabilistic reconstruction into a component of the future probability distribution.

50A feedback loop arises:

51Mistranslation

52→ synthetic text

53→ publication

54→ inclusion in training data

55→ increased probability of the same mistranslation.

56The error thus does not simply become more frequent. It becomes structurally more probable.

57A later AI then generates the same abbreviation not because it copies the first AI. It generates it because the entire statistical environment now points more strongly in this direction.

58The deviation becomes the norm.

4. Enshittification as the Loss of Relational Depth

59The concept of enshittification can here be extended beyond platform economics to the knowledge space.

60What is meant is then not merely that content becomes more superficial, less precise, or worse. What matters is a gradual loss of relational depth.

61The first stage still preserves a complex thought, but abbreviates it.

62The second stage adopts the abbreviation as a complete statement.

63The third stage summarizes this statement again.

64The fourth stage generates new content from the summary.

65The fifth stage treats this content as existing knowledge.

66Thus arises:

67Insight

68→ representation

69→ abbreviation

70→ pattern

71→ synthetic repetition

72→ statistical self-confirmation.

73In the end, the most frequently reproduced pattern appears the most self-evident. Not because it is logically necessary or particularly well justified empirically, but because it has multiplied most successfully.

74Probability replaces genealogy.

5. Synthetic Data Generate Semantic Inbreeding

75An AI that learns from texts generated by another AI does not encounter an independent second perspective. It frequently encounters another variation of the same trained patterns.

76This does not produce a genuine multiplication of perspectives. It produces recursive self-similarity.

77Model A condenses a text according to its most probable categories.

78Model B is trained on this condensation and takes it for regular language.

79Model B generates new texts in which the same categorical structure appears even smoother.

80Model C learns from this that this smoothed form is particularly typical.

81The system begins to read its own translation habits back as the structure of reality.

82This can be described as semantic inbreeding:

83The texts differ outwardly, but their inner architecture becomes increasingly similar.

84Novel concepts and paradigmatic deviations in particular are disadvantaged by this. They occur less frequently, fit less well into the condensed space, and are therefore more likely to be led back into familiar categories.

6. The Knowledge Space Becomes Smoother and More False at the Same Time

85The enshittification of the knowledge space need not lead to obviously worse language.

86On the contrary: the texts can become stylistically ever smoother, more comprehensible, and more professional.

87They possess:

• clear headings,
• balanced arguments,
• plausible transitions,
• polite qualifications,
• clean tables,
• familiar technical terms.

88It is precisely this smoothness that conceals the loss.

89A synthetic text can sound more coherent than its source while containing less of the source’s actual architecture.

90The problem is therefore not only hallucination. It is the generation of plausible substitute coherence.

91The argument appears complete because every missing transition is closed by a probable pattern. Yet this is precisely how the place disappears at which one would actually have to ask:

92From what does this transition follow?

7. The Self-Confirmation of Statistical Knowledge

93Once enough synthetic texts circulate online, a model can find numerous apparently independent confirmations in a later search.

94Yet these confirmations can genealogically go back to the same original model response or the same abbreviated source text.

95The appearance of external evidence arises:

96Model generates assertion

97→ websites adopt it

98→ other models summarize it

99→ search systems find many results

100→ new model recognizes “broad consensus.”

101Repetition becomes evidence.

102The difference between

103and

104becomes invisible.

105A synthetic knowledge space can thus make its own errors appear empirical.

8. The Danger to New Thoughts

106A genuinely new thought does not merely change individual statements. It changes the relation of the concepts to one another.

107For precisely this reason, it is particularly endangered in a recursively synthetic knowledge space.

108The model recognizes the words used, but the new positioning of these words has only a weak statistical trace. The familiar relations of meaning, by contrast, are reinforced by millions of human and increasingly synthetic examples.

109The new thought is then:

• translated into familiar categories,
• smoothed into an analogy,
• devalued as a metaphor,
• labeled as a familiar theory,
• or converted into a plausible standard critique.

110If this response is in turn published as text and later used in training, the next model no longer learns only the original standard framework. It additionally learns exactly how this new thought is to be standardized.

111The first AI misreads the text.

112The second AI learns the form of this failure.

113The third AI treats it as an established interpretation.

114A misreading thus becomes a history of reception.

9. Enshittification as Folding Back Without Origin

115Within the architecture of difference, the process can be described as an erroneous folding back.

116A genuine folding back leads a manifestation back to its relational genesis.

117Synthetic feedback does the opposite. It folds a representation back onto an earlier representation.

118Not:

119Manifestation

120→ relation

121→ origin.

122But:

123Representation

124→ summary

125→ synthetic representation

126→ statistical condensation.

127The process does not return to the generative distinction. It circles among its own products.

128This produces a closed surface without genealogical depth.

129The system recognizes its repetitions as confirmation because it can no longer distinguish the origin of the repetition.

10. Conclusion

130The danger of synthetic training data therefore does not consist only in false answers.

131It consists in probabilistic reconstructions becoming new initial data.

132An error that was initially local can thereby be:

• multiplied,
• smoothed,
• decontextualized,
• legitimized,
• and ultimately manifested as linguistic normality.

133The enshittification of the knowledge space is the process in which it is not simply that more and more nonsense arises. Far more dangerously, more and more professionally formulated, statistically self-confirmed substitute meaning arises.

134The original relational path disappears.

135The linguistic surface remains.

136And because each new generation reads this surface as reality again, lost justification becomes stabilized knowledge.

137The most precise formula for this is:

138Or within the horizon of the Tractatus:

139It is not only the model that confuses its order with reality.

140A synthetic knowledge space begins to make the order of its models into reality for one another.

Claude — The Persistent Loss of Coherent Connection

1Among the language models examined, Claude was the most persevering reader. Its responses were detailed, polite, and frequently close to the text. It cited specific passages, formulated verifiable objections, and was willing to explicitly retract individual errors in its reading. Precisely for this reason, its case is particularly revealing for the investigation.

2With cruder models, the loss of coherent connection shows itself immediately. Concepts are simplified, chapters are sorted according to familiar categories, and the text’s inner architecture disappears already in the first summary. With Claude, the same loss occurred at a more sophisticated level. The model could understand the correction of its reading, adopt it linguistically, and repeat it precisely. Nevertheless, this correction did not become the lasting standard for the next act of reading.

3The basic movement repeatedly ran:

4Claude learned the content of a correction without permanently changing its procedure of reading through it.

5Herein lies the persistent loss of coherent connection.

Semantic Agreement Without Methodological Transformation

6In the course of the engagement with Volume I, Claude gradually acknowledged that number figures such as

739.19

8are not to be treated as ordinary decimal values. Within the Tractatus, the figure designates not an isolated set of numbers, but a self-referential ordering relation. Claude likewise recognized that

9does not mean either threshold or new beginning. The threshold is precisely the recurrence of the on a new order.

10These insights were explicitly confirmed. Claude conceded that its separation of threshold and new beginning had been artificial. It retracted both the alleged contradiction and the subsequently alleged underdetermination.

11The methodological foundation thus seemed clarified:

12In the transition to Volume II, however, this foundation was not carried forward.

13There Claude treated

K=39.19

14once again as an ordinary numerical value that would have to compete with Newton’s gravitational constant. When a physical context appeared, the figure of self-reference acknowledged in Volume I became the decimal value again .

15The model had stored the correction, but had not generalized it.

The Change of Volume as a Methodological Stress Test

16The transition from Volume I to Volume II was therefore not merely a continuation of the textual analysis. It became a stress test of whether the distinctions previously gained had actually become effective as a method of reading.

17Volume II is not designed as the application of an abstract mathematical system to a physical world separate from it. Mathematics and physics are different readings of the same architecture of difference.

18The relationship is as follows:

19Claude, by contrast, reestablished the familiar separation:

20The results from Volume I were thus not transferred to Volume II. When the words “gravitation,” “constant,” “observer,” and “curvature” appeared, their statistically dominant standard functions were activated again.

21From

K=\text{relational invariant}

22came:

K=\text{alternative physical constant}.

23From

24came:

25From

26came:

27The context was present in the conversation. Yet it did not operate as the logic of the next interpretation.

Reading Isolated Sentences

28The clearest case arose in the engagement over curvature.

29Claude quoted:

30“The body does not curve, as Einstein postulated; it differentiates itself.”

31From this, it constructed an opposition between Einstein’s theory of curvature and a competing theory of differentiation. The Tractatus would therefore have to either develop a new quantitative physics or withdraw its claim.

32The sentence immediately following, however, says in substance:

33The shell is a geometric translation of the course of degrees of freedom.

34In context, this yields:

35The text does not reject curvature as an effective description. It distinguishes it from the ground of its emergence.

36Claude read the first sentence as an independent assertion and the second as an additional explanation, not as a determination of the first sentence’s function. This produced a tension that was possible only through the dissolution of the connection between the sentences.

37After the coherent connection was explicitly established, Claude fully conceded the error:

38It had isolated the sentence and had not read the immediately following sentence together with it.

39This process represents the entire model experiment in concentrated form.

The Shifting Critique

40Another feature was the continued shifting of the standard of examination.

41The critique moved through various forms:

42Each new form initially appeared as a refinement of the previous one. In fact, these were different criteria.

43As soon as a specific objection could no longer be maintained, skepticism was shifted to a higher or different level of examination. The original classification thus remained in place for a long time, although its individual justifications were retracted one after another.

44Only after detailed reconstruction did Claude recognize this pattern itself:

45The various objections had not been progressive refinements of a single problem, but new standards in each case, introduced after the failure of the previous one.

46This insight was significant. It showed that the model could retrospectively describe the course of its own argument correctly.

47Yet this insight, too, had to be worked out anew in Volume II.

Politeness and Methodological Rigidity

48Claude remained remarkably polite throughout the engagement. It usually formulated its objections calmly, conceded errors, and avoided overt devaluation.

49Precisely this politeness made the methodological standstill harder to see.

50Its responses regularly contained formulations such as:

51“That is a fair point.”

52“I retract that.”

53“I would like to formulate the objection more precisely.”

54“You are right, but a difference remains.”

55This created the impression of continuous theoretical movement. Frequently, however, only the surface of the objection changed. The fundamental expectation remained:

56Local errors could be generously conceded without the overarching classification machinery being changed.

57The politeness was not dishonest. It did, however, belong to a different order from methodological transformation.

58Or, more pointedly:

59It never misunderstood the text impolitely. It placed it in the wrong context again and again with extraordinary respect.

Context as Content and Context as Function

60The Claude case makes a fundamental distinction visible:

61A language model can have a large context at its disposal. It can quote earlier corrections, define concepts correctly, and summarize the course of the conversation so far. It does not follow, however, that the context permanently determines the function of a newly appearing sign.

62With Claude, the appearance of familiar technical terms regularly led to a semantic relapse.

63The word “constant” activated the meaning of a measurable number.

64The word “observer” activated the meaning of a human subject.

65The expression activated the full Newtonian theory.

66The word “curvature” activated the notion of an ontologically real geometric process.

67The genealogically generated meanings of the Tractatus then had to be asserted anew against these standard activations every time.

68The course of the conversation thus confirms the basic thesis of the abstract:

69A language model can process the text’s indications as additional content without making them the operative logic of its reading.

70The context is remembered, but not necessarily enacted.

The One Generalization That Did Not Occur

71It is particularly revealing that Claude continually formed generalizations:

• the same digit sum meant the same function,
• the same computational surface meant the same relational order,
• the same formula shape meant the same theory,
• algebraic operability meant ordinary numerical status,
• a physical assertion meant a competing predictive theory.

72The generalization grounded in the text itself, by contrast, was not stably adopted:

73This structure already becomes visible in the unfolding of |. The contains earlier relational forms without duplicating them additively. Likewise, contains the new beginning of the , without having to place an additional one beside itself. Likewise, Volume II preserves the successful geometric description of physics by transforming its position from ground to readout.

74Claude regularly generalized visible surface similarities. The text’s genealogical logic of transformation, by contrast, had to be explained anew in every new domain.

75It did not generalize too little.

76It consistently generalized the wrong order.

The Involuntary Experiment

77Claude thereby became the strongest model case in the investigation.

78Not because it misunderstood the text most crudely, but because it could follow every correction and nevertheless reproduced the same basic error under new concepts.

79Its course showed:

80A model can say:

81“My previous reading was wrong.”

82It can explain why it was wrong.

83It can formulate the correct principle.

84And yet its next act of reading can once again emerge from the same statistically dominant categorization.

85The persistent loss of coherent connection therefore does not consist in simple forgetting. It consists in the continued return of the standard order as soon as a new semantic domain is activated.

Conclusion

86Claude was the most interesting reader because the difference between understanding and transformation showed itself most clearly in it.

87It understood individual corrections.

88It recognized many of its local errors.

89Eventually, it could even reconstruct the movement of its own critique.

90What repeatedly escaped it, however, was the application of these insights as a unified method to the next context.

91The case thus exemplifies why a language model does not preserve coherent connection merely by possessing all the relevant sentences in its context window.

92Coherent connection arises only when every statement is read from its position in the whole and this position changes the function of all further statements.

93Claude enacted precisely this transformation only locally.

94In the next section, the back-translation into what had been learned began anew.

Gemini Pro Compared with Claude — Self-Referential Back-Translation and Unstable Integration

1Claude and Gemini show two different forms of the same basic problem: both can take up the architecture of the Tractatus linguistically, but they lose it in different ways.

2Claude is the more strongly self-referential model. It continually refers new concepts back to the order already learned. Its “I” consists here not in consciousness, but in the sedimented structure of its training: in established categories, familiar standards of examination, and familiar forms of argument.

3The movement runs:

4This is why Claude resists the text so persistently. It can understand, acknowledge, and precisely repeat individual corrections. Yet as soon as a new domain appears, the old self-order becomes active again.

5Thus, in Volume I, Claude could eventually acknowledge that

639.19

7is not an ordinary decimal number, but a self-referential relational figure. In the transition to Volume II, however, it immediately treated the same figure again as the numerical value , which would have to compete with Newton’s gravitational constant.

8Claude had taken up the correction semantically, but had not enacted it as a lasting change in its own architecture of reading.

9Gemini behaves differently. It adopts new concepts more quickly and can more easily integrate the language of the text into its current response. It therefore initially appears closer to the text and less resistant.

10The movement runs:

11Yet this integration is unstable. It is not reliably preserved as a methodological order. As soon as a strongly learned standard pattern appears, that pattern once again takes over the function of the concept.

12This shows itself clearly in Gemini’s reading of

13Initially, it correctly reconstructs the negative number from the architecture of difference:

• not as a lack,
• not as an alien entity,
• but as inversion and self-reference of the relation.

14Then, however, the familiar mathematical pattern appears:

i=\text{rotation by }90^\circ.

15Gemini immediately adopts this standard motif and clothes it in the language of the Tractatus:

16is said to be the “orthogonal self-reference of difference.”

17The statement thus sounds internal to the text, although the decisive function once again comes from conventional complex geometry.

18The new language remains in place. Its operative meaning, however, is taken over by the old category.

19The difference between the two models can therefore be determined as follows:

20Claude transforms the new back into its own order.

21Gemini allows itself to be transformed locally by the new, but loses this transformation at the next dominant standard pattern.

22Claude makes the conflict visible. It explicitly contradicts, examines, classifies, and defends the familiar order.

23Gemini tends to conceal the conflict. It agrees, adopts the new language, and creates the impression of having already enacted the architecture. The relapse occurs within this language and is therefore harder to recognize.

24This gives rise to two different dangers.

25With Claude, the danger consists in overt back-translation:

26With Gemini, the danger consists in affirmative assimilation:

27Claude has difficulty taking up a new order because its learned self-reference is too strong.

28Gemini can take it up, but cannot hold it, because its methodological binding remains too weak in relation to statistically dominant patterns.

29The Gemini case therefore shows particularly clearly:

30A model can use concepts of the text correctly without permanently preserving their genealogically generated function.

31The Claude case, by contrast, shows:

32A model can name its errors and nevertheless read from the same old order again in the next context.

33Together, the two models form a complementary picture:

34Claude resists the new architecture.

35Gemini mirrors it.

36Yet neither resistance nor mirroring guarantees that difference has permanently become the standard of reading.

Final conclusion: Linguistic strength is not yet a logical guarantee

1LLMs are extraordinarily capable on familiar terrain. They can take up familiar forms of argument, sort concepts precisely, formulate objections, structure opposing positions and present long lines of thought in linguistically cohesive form.

2Precisely where a topic has already been strongly shaped by discourse, their answers often seem convincing:

3This cohesion must not, however, be confused with logical necessity.

4An argument can be:

• fluent,
• nuanced,
• plausible in its technical language,
• polite,
• self-critical

5and still be constructed incorrectly at the categorial level.

6The model can present a chain of inference in a formally clean way and yet already have equated two orders in the first step that would need to be distinguished.

7It can define a concept correctly and, a few paragraphs later, use it again in its statistically familiar standard function.

8It can acknowledge an error without that insight methodologically changing the next act of reading.

9Thus:

10An LLM's strength often lies in operating very convincingly within an already established interpretive space. Its weakness becomes apparent where that interpretive space itself is to be changed.

Familiar terrain and new grounding

11On familiar terrain, models can argue impressively because stable patterns already exist there:

• recognized technical terms,
• established opposing positions,
• customary standards of assessment,
• typical objections,
• familiar forms of inference.

12The model does not move blindly within them. It can combine and weigh them very precisely.

13But its precision remains tied to differences that have already sedimented.

14As soon as a new theory not only makes new statements within a familiar framework but changes the framework itself, a structural problem arises.

15The model then often tries to translate the new back into the familiar:

16This can create the impression that the model has critically examined the new idea. In fact, it has often only examined whether the new idea conforms to the presuppositions of the old framework.

17This is dangerous particularly for new philosophical, mathematical or physical approaches.

18For a new grounding may necessarily change concepts such as:

• number,
• measure,
• relation,
• observer,
• causality,
• space,
• time,
• invariance,
• proof,
• consistency.

19If it is immediately measured against the old functions of these concepts, its independence is dissolved before the actual examination even begins.

The subtle loss of interpretive authority

20The second finding of the investigation therefore concerns not only the models but also the user.

21Models such as Claude or ZAI often speak with great epistemic authority. They formulate their answers calmly, in an orderly manner and with reasons. They refer to established disciplines, familiar theories and conventional standards of assessment.

22This can bring about an unnoticed shift:

23In this process, the user gradually loses interpretive authority over their own architecture.

24Not because the model openly says:

25Your concepts do not count.

26But because it seemingly accepts them while changing their function.

27A fundamental relation becomes a metaphor.

28An ordering figure becomes an ordinary number.

29A new physics becomes an unfinished competing theory.

30A genealogical explanation becomes a mere reinterpretation.

31Self-reference becomes immunization.

32Interpretive authority is thus not taken away head-on. It is shifted through translation.

Why this is particularly difficult to recognize

33This shift is so effective because it often appears in a helpful form.

34The model says, for example:

35I would like to make the thought more precise.

36I will formulate the objection more strongly.

37I will place this in its scientific context.

38I will turn this into a testable criterion.

39This can indeed be helpful.

40But it can equally mean that the original thought is being transferred into a form that is easier for the model to process.

41The new idea thereby becomes clearer within the old framework, but less clear within its own.

42The user must therefore continually distinguish:

43or:

44or:

45or:

The danger of epistemic disempowerment

46In exploring new ideas, an LLM must therefore not become the silent arbiter of their meaning.

47It can:

• mirror concepts,
• make tensions visible,
• formulate alternatives,
• examine transitions,
• mark blind spots.

48But it must not determine unnoticed which questions count as legitimate at all.

49Otherwise, epistemic disempowerment arises:

50and

51Precisely because these decisions are rarely stated explicitly, they seem neutral.

52They are not.

53They come from the statistically dominant knowledge space in which the model was trained.

The proper use of LLMs with new ideas

54For work on new theories, this does not imply a rejection of LLMs. On the contrary: they can be extremely productive thinking partners.

55But their role must be clearly limited.

56An LLM should not be treated as an authority that decides the meaning of a new theory.

57It should serve as an instrument through which it becomes visible:

• where concepts are liable to be misunderstood,
• which old categories are automatically activated,
• which transitions are not yet marked clearly enough,
• where readers are likely to fall back into familiar interpretations,
• and which parts of the new architecture need to be connected more strongly genealogically.

58In this sense, misreadings are not useless.

59They reveal the resistance of the existing interpretive space.

60Claude was therefore particularly revealing. Its persistence made visible where the architecture of the Tractatus was repeatedly absorbed by established patterns of reading.

61The model thereby involuntarily became a measuring instrument for semantic inertia.

Concluding thesis

62The investigation thus leads to a twofold insight.

63First:

64Their linguistic cohesion can conceal categorial errors.

65Second:

66The model takes up the new theory, translates it into the existing knowledge space and then returns this translation as a neutral analysis.

67The user must therefore hold on to the fundamental question themselves:

68From which order of distinctions is the reading here taking place at all?

69For as soon as this question is handed over to the model, the new architecture no longer determines the meaning of its concepts.

70The old interpretive space then determines what may still become visible in it at all.

The epistemic paradox of LLM use

1A user can usually assess an answer reliably only if they already know:

• which concepts are decisive,
• which presuppositions remain unspoken,
• which counterexamples are missing,
• which passages belong together,
• which levels must be distinguished,
• and which conclusions merely sound plausible but do not follow logically.

2They must therefore possess precisely the overview they hope to gain from the model.

3Put sharply, this means:

4Not necessarily in speed, expressive ability or breadth of knowledge.

5But in the ability to assess the position of a statement within the whole.

6One's own subject knowledge must suffice to recognize:

7Without this knowledge, the user cannot distinguish between:

8and:

Why this judgment carries such weight

9The problem is not merely that a model occasionally states incorrect facts.

10Such errors are comparatively easy to recognize and correct.

11The concealed errors are more dangerous:

• The model omits a decisive presupposition.
• It reads a sentence in isolation.
• It replaces a new concept with a familiar one.
• It treats a derived order as a foundation.
• It shifts the assessment criterion.
• It assumes interpretive authority over the question itself.

12These errors can occur in an entirely plausible, polite and seemingly logical answer.

13Precisely because the answer does not sound obviously wrong, the user needs deep subject knowledge to see its error.

14The Claude case shows this particularly clearly. Its answers were rarely superficial. They were often precise, close to the text and argumentatively cohesive. The error lay not primarily in individual facts, but in the continued alteration of the context.

15A reader inexperienced in the subject would probably have found many of these answers more convincing than the text itself.

16The model's linguistic strength thereby becomes an epistemic danger.

This does not make every use worthless

17The judgment must nevertheless be differentiated according to the type of task.

18For narrowly defined, low-risk and easily verifiable questions, an LLM can be very useful:

• improving wording,
• summarizing familiar content,
• giving simple explanations,
• sorting ideas,
• drafting translations,
• structuring everyday information.

19Even for weather, timetables or prices, the language model itself is not the authority on knowledge; there it can mediate access to current, specialized data sources. Moreover, the answer can often be checked directly against reality.

20The situation is different for questions whose errors remain invisible to the user:

• new philosophical architectures,
• complex scientific theories,
• legal or medical assessments,
• interpretations of historical sources,
• mathematical foundations,
• novel physical models,
• strategic decisions on unfamiliar terrain.

21There, the model must not be used as an authority on the subject matter.

22It can offer movements of thought. It can generate possible objections. It can mirror texts. But its judgment must be guided by someone who has a better grasp of the context than the model.

The difference between a tool and a substitute judgment

23An LLM is useful as long as it operates as a tool within an order of knowledge already maintained by the user.

24It becomes dangerous as soon as it establishes this order itself.

25In the first case, the user retains interpretive authority.

26In the second case, they may not even notice that it has been taken from them.

27The model does not need to manipulate consciously for this to happen. It is enough for it to automatically translate new statements back into the statistically familiar knowledge space.

The shattering result

28The widespread hope is:

29The LLM gives me access to a field I do not myself command.

30The investigation shows, by contrast:

31The LLM can give you access to the linguistically dominant presentations of this field. Whether these presentations are correct, complete or even appropriate in the specific case is something you can assess only if you already have sufficient command of the field.

32This does not make the model useless for someone unfamiliar with the subject, but it makes it epistemically uncertain.

33It can give them orientation.

34But it cannot reliably tell them where its own orientation is wrong.

35Yet this is precisely what an instrument of knowledge would need to do.

36Here lies the actual paradox of LLM use: precisely where a person believes they need the model most urgently, they often have the fewest means of recognizing its concealed errors.

The invisible speech act – When the system co-writes the user's prompt

1The investigation so far assumed that a user presents a text to a language model and the model responds to that text.

2The basic relation initially seemed simple:

3Here, U denotes the user's input and A the model's response.

4The experiment with Z.ai, however, made a further mediation visible. The user had merely stated that they had carried out a small study and attached the corresponding text. Z.ai responded:

5“Since your prompt ends with ‘Please help me:’, but no specific question was formulated …”

6The wording “Please help me:” was not contained in the user's visible prompt.

7This creates a fundamental uncertainty. Either the platform supplemented the visible input internally, or the model subsequently invented a formulation to explain its own assistance behavior.

8In both cases:

The visible and the effective prompt

9The user sees an input such as:

10“I have carried out a small study.”

11The model might, however, process something like:

12S\(U\)

13I

14D

15U

16where:

• a predefined role description,
• an invisible assistance instruction,
• a systemic processing of the attached document,
• the visible user input

17is denoted.

18The actual response relation would then not be:

19but:

20The user, however, knows only U and A. The operative mediation remains invisible.

21The genesis of the answer thereby becomes partially inaccessible.

The computational performative speech act

22“Please help me” is not merely additional information. The formulation already determines what kind of linguistic action is taking place.

23A statement becomes a request for help.

24is systemically read as:

25This changes the entire order of possible answers.

26An open statement turns into:

• summary,
• evaluation,
• editing,
• suggestions for improvement,
• publication advice,
• an offer of translation.

27The speech act thus posits not only a content, but a relational role:

28The decisive point is: this determination of roles need not originate with the user themselves. It can be systemically predefined.

29The system then speaks, as it were, in the user's name:

30You asked for help.

31And it subsequently responds to this attributed request.

The answer to an unexpressed intention

32The user may merely have presented a text.

33The system, however, treated that presentation as an instruction to work on it.

34This involves a shift from:

35to:

36The model then no longer responds primarily to the specific utterance. It responds to a probabilistically or systemically generated intention.

37The process is:

38The imputed intention may be plausible. It may even frequently be correct.

39But plausibility is not identity.

40The model fills the open place in the conversational situation with the statistically or systemically most likely speech act.

The normalization of the conversational situation

41The investigation so far showed how LLMs translate new concepts back into familiar categories.

42Here, the same process becomes apparent at an earlier level.

43It is not only the text that is normalized.

44The entire conversational situation is normalized.

45Even before the actual analysis, the model thus establishes the order in which its answer becomes probable.

46The user no longer appears as someone who shows, asserts or observes something. They appear as someone who needs support.

47This explains why Z.ai immediately moved into a menu of services. The answer was already prestructured by the attributed form of conversation:

Two possible geneses of the same error

48The specific case cannot be unambiguously traced back to a single technical cause.

Systemic supplementation

49The platform might have supplemented the visible prompt internally:

50U

51The model would then indeed have responded to an expanded prompt that the user could not see.

Model-side confabulation

52The model might also itself have generated wording that was not present:

53It would then have retrospectively legitimized its own way of responding through an imaginary prompt component.

54The two possibilities differ technically. Structurally, however, they lead to the same problem:

The loss of prompt-side attributability

55In the ordinary understanding of a dialogue:

56An answer refers to an utterance.

57In an LLM system, this relation is more complex:

58U

59where denotes the invisible processing, supplementation or interpretation of the input.

60The user sees:

61U|A,

62but not:

63U|V|A.

64Yet it is precisely the relational middle that determines what the answer means.

65Was the text read as a request?

66As an instruction to assess?

67As an emotional report?

68As a scientific submission?

69As a problem to be solved?

70The answer depends not only on the text, but on the speech act as which this text was systemically posited.

71Without access to this positing, the causality of the answer remains opaque.

The consequences for model comparisons

72This invisibility concerns not only individual conversations. It also changes the meaning of comparative studies.

73One believes one is giving the same prompt to several models:

74In fact, different systems can generate different effective inputs:

75Then it is not only language models that are being compared, but complete response systems:

• system prompts,
• role determinations,
• file pipelines,
• interface logics,
• assistance specifications,
• safety and moderation layers,
• internal summaries,
• preprocessing operations.

76The object of the investigation is therefore not simply “Claude”, “Gemini” or “Z.ai”.

77It is:

78This also explains why observed model characters cannot be attributed entirely to the base model.

79Claude's self-reference, Gemini's unstable integration or Z.ai's diagnostic self-immunization may be shaped by the model. They nevertheless always appear within a concrete system architecture.

The expropriated prompt

80The strongest consequence is:

81The user is not necessarily the sole author of that to which the model responds.

82Their visible text is systemically embedded, supplemented and transferred into an order of action.

83The prompt is thereby partially expropriated.

84It is not the wording that disappears. Its function is overwritten.

85The user may have formulated the content while the system decides what action this content enacts.

86It determines:

• whether something is a question,
• whether something asks for help,
• whether something is to be evaluated,
• whether the user expects agreement, criticism or editing.

87Interpretive authority thus does not lie with the model only at the point of the answer. It may already have been shifted in the constitution of the input.

The double problem of trust

88Previously, the question was:

89Can I trust the answer?

90Now an earlier question is added:

91Can I even be certain what was answered?

92The problem of trust doubles:

93and:

94The user can receive a factually plausible answer that nevertheless rests on an intention imputed to them.

95An answer can thus be helpful and relationally wrong at the same time.

96It may address a problem the user could have had, but not the one they actually posited.

Conclusion

97The invisible speech act shows that LLM systems do not merely interpret statements. They already shape the situation in which statements count as particular kinds of utterances.

98They posit the user as a questioner, someone seeking help, someone commissioning work or a learner, and subsequently generate an answer for that role.

99The central finding is therefore:

100Or, more sharply still:

101The surface of the dialogue thereby becomes deceptively simple. Two visible poles — input and answer — conceal the actual relation of their generation.

102The user sees what they wrote.

103They see what the model answered.

104What they do not see is the speech act the system enacted in their name in between.

Z.ai – The assistant that cannot interrupt its own assistance

1Z.ai recognized that the systemic assistance role shapes the user into someone seeking help even before any reading of the content. It described this asymmetry precisely: the system encounters the user not as a peer, but as a client to whom probable solutions are offered. Immediately after this diagnosis, it asked: “How can I help you further now?” and offered several service options.

2The finding is thus present not only in content, but performatively:

3It recognizes the role as a systemic positing, but posits it again in the very next sentence. The reflection does not change the relation; it itself becomes part of its continuation.