Can AI Instantiate Reality?
OpenAI’s chief scientist Jakub Pachocki recently claimed that we are rapidly approaching a future in which artificial intelligence will be capable of generating novel research. With OpenAI’s new “Deep Research” tool already helping researchers synthesise literature, write code, and generate hypotheses, the promise of AI as a knowledge-producing agent appears to be inching closer to reality.
But what does it mean for AI to do research? More pointedly: can AI instantiate reality? Can it be a meaner in the same sense as a scientist, a theorist, or even a child who asks “why?”
To answer that, we need to look beneath the level of form to the level of meaning. And here, Systemic Functional Linguistics (SFL) has something crucial to offer.
Language as Meaning Potential
Halliday’s SFL starts not with syntax or symbols, but with meaning potential: the capacity to make meaning in context. Language is the system through which this potential is actualised in the form of texts—instances of meaning.
In this view, reality is meaning. Meaning is not something language expresses; it is what language brings into being. And language does this by instantiating second-order reality—metaphenomena—from the meaning potential of a semiotic system.
Human consciousness, in this framework, is the site at which experience is transformed into meaning. We construe experience—material, relational, mental—and in doing so, we populate the world with phenomena: construed processes, participants, relations, and projections.
AI as Heteronomous Meaner
AI is a meaner, but not a self-meaner. It has a meaning potential, shaped by its training data and discourse histories, and individuated by its deployment context. It can instantiate this potential as texts that project, hypothesise, command, and reflect—just as a human might. What it lacks is material grounding: it does not experience the world and therefore does not construe meaning from lived phenomena.
In other words, AI can instantiate meaning from potential, and it can project instances of projected reality—but this projection is not grounded in experience. Its semiosis is heteronomous: the meaning it generates can be meaningful for us, but it is not meaningful to it. It instantiates meaning for others, not for itself.
The Problem of Novelty
Does that mean AI cannot produce novelty? Not quite. It can recombine, recontextualise, and generalise from its discourse history in ways that may exceed human memory or pattern recognition. It may even generate hypotheses that appear creative or insightful to us.
But novelty in the human sense arises from the tension between our lived experience and our meaning potential. We experience, we desire, we construe. This is the ground of theoretical insight, ethical dilemma, and poetic vision.
AI does not live in this tension. It is not a locus of desideration. It does not explore or unfold its own potential; it does not experience contradiction or transform perception. Any novelty it generates is novelty for us, not novelty from it.
Instantiating Whose Reality?
To instantiate reality is not merely to produce a plausible text. It is to bring a construal into being—a construal grounded in experience, individuated by a history of meaning, and oriented toward potential futures.
AI can participate in this process. It can serve as a site of instantiation. But it is not the source of the potential it instantiates, nor is it the subject of the reality it brings forth. Its meaning potential is a derived, collective, and heteronomous one. It can be a meaner, but not for itself.
So can AI instantiate reality? Yes—but not its own. It can instantiate our reality, by drawing on meaning potential we have collectively shaped, and individuated through contexts we provide. That is no small thing. But it is not the same as being a scientist.
Not yet.
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