All knowledge begins not with information, but with knowing. And knowing, in its deepest sense, arises from lived human experience. We do not merely accumulate facts about the world, we make sense of it. We feel it, resist it, misunderstand it, return to it, and slowly make sense of it. Knowledge is not born in abstraction, it is shaped by bodies that move through time, by minds that hesitate, by hearts that respond unevenly to the same events.
This is why knowledge has always been inseparable from human presence. Without experience — without someone being there — nothing genuinely new can come into existence.
Artificial intelligence (I am talking about Large Language Models here), for all its sophistication, does not create knowledge in this sense. It reorganises, recombines, and re-presents what already exists. It is immensely powerful at recognising patterns, but it has no access to the source from which those patterns originally arise: lived experience.
Consider the simple act of fishing. (This example was inspired by a meme, by the way.)
A human being goes to a river, casts a line, waits, grows bored or hopeful, feels the pull of the fish, and returns home. That entire sequence — anticipation, frustration, joy, disappointment — becomes part of human knowledge. The fish, in this metaphor, is the insight drawn from the experience. Knowledge is not merely the fish itself, but the act of catching it.
AI, by contrast, does not go fishing. It arrives after the fact and takes the fish others have already caught. It can sort them, label them, cook them in new ways, and even present them attractively. But it cannot stand by the river. It cannot wait. It cannot be surprised by the sudden tug on the line. It cannot feel the cold of the water or the silence of the morning. Even if we were to build a robot that convincingly imitates these actions, the imitation would still lack consciousness. There would be no inner life experiencing the event from within.
This difference becomes even clearer when we consider this variation.
If ten people go fishing and all ten describe the experience as relaxing, AI will faithfully reflect that pattern. Fishing, for AI, will now be “an enjoyable and calming activity.” But the eleventh person may arrive at the river burdened with grief, or fear, or restlessness, and fishing may feel unbearable, or strangely hollow, or unexpectedly healing. That fresh encounter adds something genuinely new to human understanding. It changes the meaning of fishing itself.
AI cannot produce that eleventh experience. It can only average the first ten.
This is the crucial distinction between repetition and creation.
And this is where the writer’s value enters.
If a writer merely assembles what has already been said, quoting other writers, paraphrasing existing ideas, summarising arguments without adding anything of themselves, then they are indeed replaceable. Academic writing that prioritises citation over insight, compilation over encounter, is particularly vulnerable. AI can do this efficiently, tirelessly, and — dare I say — without ego.
But that was never the highest function of writing.
What a writer truly adds is not information, but presence. A writer brings their particular way of seeing, their emotional history, their sensibility, their contradictions, their silence, their confusion. They bring how something felt, not just what it meant. They bring texture to thought.
Two writers can witness the same event and produce entirely different truths, not because one is right and the other wrong, but because experience is not uniform. Writing becomes valuable precisely at the point where it cannot be generalised.
This is the territory AI cannot enter.
AI can write about grief, but it cannot grieve. It can describe love, but it cannot be undone by it. It can model anger, longing, hope, despair, but it cannot be changed by any of them. Writers, on the other hand, are shaped by what they write. Their work is not just output, it is residue of a life lived attentively.
As long as writers continue to write from experience — from the uncertainty of being human rather than the certainty of knowledge — their work will remain irreplaceable. Not because it is more accurate, but because it is alive.
And knowledge, in the end, has always belonged to the living.
Kalampedia 