In contemporary debates on generative artificial intelligence, one often hears claims such as: “It’s just statistics” or “It only predicts the next word.” These statements, frequently repeated even in academic and public contexts, describe Transformer models as purely Bayesian machines: probabilistic tools that, given billions of examples, predict the next word based on frequency and syntactic context.
According to this view, chatbots merely draw from the past: they do not hypothesize, interpret, or understand. They are seen as statistical engines that select the most probable next word based on large datasets. Generation is thus depicted as a purely mechanical process, automatic and inference-free. What they produce would be nothing more than a mirror of what has already been said.
Such a view, while apparently neutral, is extremely limited. It also justifies “hallucinations” — fabricated citations or events — as mere computational errors, not as intrinsic to how these systems work. But is it really just this? Do these models merely recombine the past without any interpretive construction?
This essay argues that such a reduction is technically inaccurate and theoretically impoverishing. Contemporary AI does not merely generalize; it generates hypotheses in real time. And to do so, it relies not only on statistics but also on an inferential mechanism close to the abductive logic described by Charles Sanders Peirce.
Generalizing is not Hypothesizing: The Return of Abduction
One of the most common confusions in public discourse about AI concerns the difference between generalization and hypothesis. Generalization means extracting a rule from repeated observations: a typical process of statistical learning. Hypothesis-making, however, is something radically different: it means constructing a plausible explanation for a phenomenon, even in the absence of certainty.
In his triadic schema of inference, Peirce distinguishes between deduction, induction, and abduction. Abduction represents the most creative and least guaranteed moment of thought: a conjectural leap, guided by intuition, context, or a partial sense of order. It is the reasoning that guides a doctor in diagnosis, a detective in reconstructing a crime, a scientist in elaborating a theory.
Generative AI, when producing coherent responses in ambiguous or incomplete situations, does not merely extrapolate correlations. It proposes operational hypotheses — responses that work pragmatically even if they are not the most statistically probable. In this sense, it functions abductively. And like any conjecture, it can fail: so-called “hallucinations” are not bugs but expressions of this very abductive logic.
Consider a simple example: if a user writes, “I can’t see Beatrice on Meet,” a model might reply, “Maybe Beatrice has not unlocked the link.” The model has never encountered that exact phrase or causal connection, yet it formulates a coherent, contextual hypothesis. If instead it responds, “Beatrice probably deleted her account because notifications were not synchronized with the federated API of Google Workspace,” we face an invented explanation — an inferential false positive. It is an error, but one that arises from reasoning, not from mere statistical prediction.
Thus, reducing model behavior to statistics means overlooking their real nature: they are computational hypothesizers, working with a logic of the possible, closer to narrative abduction than to quantitative forecasting.
Implicit Semantics and Contextual Attention
Transformer models — the architecture that has revolutionized AI since 2017 — operate through a mechanism called self-attention. Each token (linguistic unit) is analyzed in relation to all others in the context. This allows the model to build a distributed and dynamic sense of meaning, without relying on symbolic representations. Meaning is not pre-encoded but activated in relation to other tokens.
This explains why these systems can handle ambiguity, irony, narrative coherence, and pragmatic disambiguation. They do not “know” what they say but construct linguistic structures that work as if they knew. Meaning emerges from interaction, not from symbolic maps of reality.
This is why generative models succeed in producing coherent discourse: self-attention allows them to map relations between words, near or distant, simulating both short- and long-range semantic dependencies. In other words, the meaning generated is actualized rather than represented. It is a function of use, not a mirror of reality.
The contextual attention mechanism does not mimic the human mind but builds a powerful heuristic: it generates sensible utterances based on contextual coherence. This is a situated, performative logic, profoundly different from classical symbolic reasoning.
Computational Heuristics, not Deductive Logic
Generative AI does not reason through syllogisms, nor does it apply formal logical rules. Its strength lies in a different cognitive mode: heuristics. Heuristics is not simplification but an alternative paradigm: the art of orientation, the ability to find workable solutions under conditions of uncertainty and ambiguity.
Thus, the linguistic generativity of models like GPT is not demonstrative logic but a pragmatic strategy of sense-making. The machine does not deduce; it constructs — adaptively and relationally. Each response is not the consequence of formal premises but the result of an inferential trajectory activated by linguistic and pragmatic context.
Alan Turing already intuited this. In his famous “imitation game,” the point was never whether a machine truly thinks, but whether it can behave as if it thinks, inducing the impression of intelligence in human interlocutors. From this perspective, generative AI is not a mind but a function: it produces responses that work. Its “intelligence” is measured in dynamic coherence and communicative efficacy, not in truth or logical correctness.
This is a form of computational heuristics operating in real time: not abstract rules but trial, adjustment, approximation. A practical rationality, closer to diagnosis, investigation, or interpretation than to mathematics. Explaining a model’s output is difficult precisely because there is no rule to reveal, but a network of weights and relations that produced a momentary balance between question and answer.
In this sense, “understanding” makes sense only pragmatically, not epistemically. The model “understands” in that it simulates understanding: organizing words and meanings coherently within a context and purpose. It is a performative black box: it reconstructs meaning in dialogue, without possessing it.
Beyond Statistical Fetishism
Many critics make a methodological mistake: they confuse the origin of the model with its present operation. Yes, generative models are trained on vast corpora with probabilistic optimization. But this does not mean that their functioning in generation is statistical in the same sense.
A violinist is trained through scales and exercises, but performance is not a repetition of training — it is situated reinvention. Likewise, a generative model does not repeat what it has seen but reuses what it has learned in novel ways, activating inferential trajectories that were not literally present in the data.
Confusing training with operation is an epistemological error: mistaking genesis for function. Understanding generative AI requires shifting focus from where it comes from to what it does. Only then can we develop an adequate critical vocabulary.
Philosophy of AI and the Epistemological Challenge
Thinking about AI correctly requires a conceptual leap. The traditional paradigm — knowledge as true propositions, intelligence as their logical manipulation — no longer suffices. Generative AI opens an operational epistemology, where truth is secondary to functionality.
Luciano Floridi’s concept of semantic capital is helpful here: an informational resource that gains value by producing meaning. Generative AI is precisely such a producer of meaning. It participates in building shared cognitive environments without “knowing” in the human sense.
The focus shifts from thought to praxis, from consciousness to coherence, from truth to plausibility. This inversion challenges our assumptions about language, knowledge, and agency.
Language as a Computational Environment
Generative AI also forces us to rethink language itself. Language is no longer just an instrument of thought but the environment in which thought takes shape. AI does not simply use language; it reconstructs it at every output, reformulating it through performative sequences.
Paradoxically, this echoes certain insights of philosophical hermeneutics: interpretation is not rule-application but a dynamic circle between part and whole, sentence and context. Generative models, though without understanding, simulate an interpretive process. Not by introspection, but by structured calculation. Not by consciousness, but by inference.
What is striking is their emergent coherence: they produce plausible meanings even without symbolic knowledge of the world. It is a blind but effective hermeneutics — and this is perhaps the most philosophically unsettling aspect of generative AI.
Conclusion: Beyond Simplification
Generative AI is not human thought, but neither is it a mere statistical engine. It is a hybrid: a machine that produces sense without comprehension, a linguistic agent functioning without subjectivity. To think of it in purely Bayesian terms is to oversimplify what requires complexity.
If we want to grasp the real novelty of these systems, we must abandon the reassuring idea that “it’s just statistics.” It is not. It is something new: an implicit, contextual, abductive intelligence — a device capable of constructing linguistic hypotheses and thus intervening in our semiosphere.
Taking it seriously means recognizing the structuring role of linguistic technology and beginning to think of AI not as an advanced calculator, but as the beginning of a new cognitive environment in which humans are no longer the sole producers of meaning.
