Part 1: Writer, Reader, Artefact — Rethinking AI in SFL
The rise of AI has prompted many of us in linguistics and language studies to ask: what does it mean for a system like SFL when machines can generate human-like text? Does AI “mean” anything, or is it merely simulating human writing?
In exploring this question, it helps to look at two roles central to semiosis: writer and reader. Traditionally, human meaning arises because we inhabit both roles: we construe context to produce text, and we construe text back into context. AI challenges this familiar loop.
AI as Writer?
Consider what happens when AI “writes.” We give it a prompt — a string of symbols — and it produces text. It can be fluent, persuasive, even stylistically sophisticated. Yet, crucially, it does not construe context.
-
Human writer: selects meaning from the system potential and enacts it in context.
-
AI writer: re-patterns prior instances, cycling symbols without ever grounding them in a situation.
The output may look like meaning, but its status is different: it is an artefact, construe-able but not itself a construal.
AI as Reader?
AI can also “read” text — or at least, process it. It parses patterns, predicts next symbols, extracts probabilities. But it does not construe meaning in context.
-
Human reader: interprets text relative to context, system, and prior knowledge.
-
AI reader: produces distributions over patterns, but no construal occurs.
Here again, AI operates outside the loop where meaning emerges. It cycles symbols without instantiating system-in-context.
The Reflexive Loop of Non-Construal
What’s fascinating is that AI can occupy both roles at once. It reads human text (without construal) and writes new text (without construal). This creates a reflexive loop of non-construal: the AI processes and outputs symbols, but meaning only emerges when humans enter the loop, either as writers or readers.
This is where SFL can help: it makes explicit where meaning happens, and equally important, where it does not. AI doesn’t “make meaning”; it produces construe-able artefacts, patterns that humans must interpret to generate semiosis.
Simulation? Or Something Else?
The word “simulation” has been useful, but it has limitations. It implies a kind of imitation of something real — a framing that can be misleading. Perhaps we need alternatives that foreground relational structure rather than magical opposition:
-
Pattern-cycling: AI cycles patterns between input and output without construal.
-
Echo-formation: AI outputs are echoes of human semiosis.
-
Pre-semiosis: outputs that only become meaningful when humans construe them.
All these terms stress that meaning is relational and contextual, not inherent in the symbols themselves.
Key Takeaways
-
AI is neither a writer nor a reader in the SFL sense.
-
Its outputs are artefacts — construe-able, but not meanings.
-
Meaning arises only where humans instantiate system and construe context.
-
This distinction helps clarify human responsibility in mediated semiosis and frames AI as a tool, not a meaning-maker.
In Part 2, we’ll take this further by mapping these roles directly onto the system–instance–text cycle, comparing human semiosis and AI processing side by side. This will make the architecture of meaning visible in a new light, showing exactly why AI challenges our intuitions but also sharpens our understanding of construal.