LLMs can sound correct even when they aren’t. That’s the power—and risk—of formal coherence without material verification. For instance, an LLM can produce a business plan that ignores local regulations, a legal argument that misses key exceptions, or a training plan that ignores injury history and recovery capacity. The point isn’t whether LLMs are useful—they are. The point is how we use them.
“On the ‘realm of freedom’
We can think about far, far more things than we are able to do or experience—meaning that our thinking is superficial and content with the surface; it does not even notice that it is a surface. If our intellect developed strictly in proportion to our strength and to our practice of that strength, the chief principle of our thinking would be that we can comprehend only what we can do—if the concept exists at all. A thirsty person lacks water, yet his thoughts continually place water before his eyes, as if nothing were easier to obtain—the intellect—shallow and easily satisfied—cannot grasp a genuine, painful need, and in doing so it feels superior: it is proud that it can do more, that it runs faster, that it is almost at the goal in an instant—and thus the realm of thought, compared with the realm of doing, willing, or experiencing, appears as a realm of freedom; whereas it is, as we said above, only a realm of surface and satisfaction.“
Source: Niče, Fridrih. Zora: Misli o moralnim predrasudama. Beograd: Dereta, 2011. Stranice 87 – 88.
In the passage on the “realm of freedom,” thought is presented as an almost infinite space. The limit is our imagination, reality is not. It feels liberating, but it also highlights a practical problem: not every coherent idea is implementable. Imagination may have endless space to play, but practical applications of generated ideas are limited. Without reality as a constraint, generated ideas may be unrealistic. Simply, imagination plays, projecting possibilities at no cost, without the friction of reality, without consequences.
Nietzsche’s diagnosis becomes especially relevant if we bring in the distinction between formal validity and empirical soundness. Formal logic assesses correctness by the form of reasoning: whether the conclusion follows from the premises, independently of their truth. Material (content-based) logic assesses correctness by the truth and justification of content—whether the premises are true, the concepts adequate, and the relations among claims relevant and verifiable. In the “realm of thought,” it is easy for formal neatness to replace material correctness—an argument can be perfectly coherent and yet empirically unfounded and practically inapplicable.
An LLM is a system that learns regularities from large quantities of text and then generates likely continuations given the context. Its “intelligence” is primarily linguistic: an ability to produce coherent definitions, arguments, and narratives. This naturally situates it in the “realm of thought,” i.e., a domain in which much can be said and combined without immediate confrontation with the real world. Because of how it works, an LLM often produces statements that seem formally “good”—the text flows, premises and conclusions appear connected, analogies persuade, and style remains consistent. In other words, the model is strong in formal coherence and rhetorical fluency. It can simulate “if… then…” structures, build explanations, and build reasons and consequences. Yet without additional verification mechanisms, an LLM offers no guarantee of material correctness. It has no experience, it does not check facts against reality, and it does not feel the resistance of practice. It can therefore produce an argument that is formally convincing but materially wrong. It may also fill gaps in its sources with plausible-sounding fabrications, which can then be used as premises in further reasoning—making the error harder to detect.
The example of the thirsty person in the passage is very good for illustrating this problem. The person lacks water, yet his thoughts continually place water before his eyes “as if nothing were easier to obtain.” However, there is a difference between representation and realization. Formally, the idea “water is available” can be fit into a neat chain of inferences: “If there is a source, then it is possible to get water; there is a source; therefore, it is possible to get water.” But material correctness demands what the formal structure can conceal: where the source is, how far it is, whether there is an obstacle, how much strength is required, whether water is safe, and what alternatives exist. The “realm of thought” can skip these material conditions and remain formally smooth; while the “realm of action” cannot, because it considers real-world feasibility.
While the intellect may feel superior because it can “do more,” “run faster,” and “be almost at the goal” in an instant, in effect, it mistakes a tidy conclusion in language for an outcome in the world. An LLM can, in seconds, lay out a training plan, a legal argument, or a business growth strategy. Formally, everything may look settled. But material correctness does not end with formulations. It requires checks, data, definitions, exceptions, risk assessment, implementation costs, and evidence that the conclusion holds under real-world constraints. And in the end, applying a concept requires practical action, a change in the world produced by implementing those concepts.
And here a productive relationship between LLMs and human rationality opens. Models can expand the space of imagination, accelerate the search for possible solutions, and articulate options that would otherwise remain unspoken. But for this expansion to avoid superficiality, it must be anchored in verification: in experiment, in source-checking, in the discipline of criteria, in repeating procedures, and in confronting error. In other words, an LLM can be an instrument of the “realm of thought,” but it must not become its metaphysical substitute for acting. And acting requires considering real-world constraints. The limit of thinking about practical and feasible solutions to real problems lies not only in what we can imagine, but in what we can confirm, endure, and carry out. Thus the “realm of freedom” ceases to be a deception and becomes a working hypothesis—open yet disciplined by reality.
Treat LLMs as engines for hypothesis generation and structure, not as guarantors of truth. Move fast in the space of possibilities—but verify each candidate against data, constraints, and practice. Formal logic is necessary to ensure valid reasoning, to avoid obvious mistakes such as non-sequiturs or fallacious moves. Material correctness is equally necessary to ensure truth and usability—to verify premises, define concepts clearly, and ensure relevance, ultimately creating a sound, practically implementable plan of action. Ideally, the LLM serves to generate hypotheses, options, and structures; while human judgment, experiments, and institutional checks supply the verification. In this way, the “realm of freedom” of thought is transformed from possible deception into a disciplined methodological advantage. It provides the ability to move quickly through possibilities, but each possibility should be measured against practice—what can actually be done. Translated into practice:
- LLMs generate options; humans verify claims.
- Coherence is not truth; validity is not soundness.
- Speed in ideation must be matched with rigor in checking.
In practice this means: demand sources, test claims on small prototypes, and treat every key premise as a hypothesis until checked.
Thought is powerful when it is formally orderly and materially responsible. However, when it is satisfied with the surface—whether in a human being or in an LLM—it becomes a “realm of surface and satisfaction.” Reality has depth. Like an iceberg, reality has most of its mass below the surface.
A common objection is that this limits creativity. Some may say that historical breakthroughs were achieved when extraordinary persons “imagined the impossible.” Well, not quite. Many breakthroughs looked impossible because the method was unknown, not because the outcome violated reality.
Many things are discovered, but many more are still ahead of us. As Calvin (Bill Waterson, Calvin and Hobbes) would say: “Let’s go exploring!”