They call me a poet, but you are the poetry,
They say my words have magic, yet yours set me free.
They paint skies with colors, but your eyes outshine,
They speak of the moonlight , yet you are divine.
They whisper of roses, but your touch is more true,
They chase after stars, yet I only want you.
They dream of forever, but I know it’s real,
They write about love, yet you’re all that I feel
Our society has long cherished the notion that human intelligence is marked by a profound sense of understanding. We credit ourselves with the ability to form abstract ideas, infer hidden truths, and imbue our words with a sense of genuine meaning. Yet the surging sophistication of large language models (LLMs) prompts an unsettling question: are we truly that different from the algorithms we’ve created?
A New Lens on Human Thought
To appreciate the parallels between human minds and LLMs, it helps to examine how we learn and process information. From infancy, our brains are bombarded with stimuli—voices, sights, smells, and countless other impressions. Over time, we develop internal rules and structures to make sense of these inputs. We might call these structures “concepts” or “ideas,” but at heart they’re associations that strengthen with every repeated encounter.
It can be tempting to see this process as uniquely human—a heady blend of imagination, memory, and emotional nuance. However, when a large language model is “trained” on vast quantities of text, it too is effectively forming associations. The more it “reads,” the more intricate these associations become, allowing it to predict which words belong together and in which contexts.
If we zoom out, both the human brain and the LLM appear engaged in constant pattern-finding. Perhaps the essential difference is scale: the human brain’s decades of experience versus the digital torrent of data fed into an LLM. Still, the underlying process—detecting correlations and forging connections—may be strikingly similar. We often attribute a flash of insight to some deeper meaning unique to human minds. Yet it’s possible that this sense of meaning is largely the outcome of countless neural “predictions,” built up through repeated exposures in our daily lives.
The Role of Language
Language is the medium through which these patterns are shared, reshaped, and multiplied. Human civilisation—from ancient communities building aqueducts to modern scientists colliding particles—relies on a collective ability to communicate. We encode our thoughts into narratives, diagrams, equations—linguistic constructs that others can decode, scrutinise, and modify.
Yet language is often seen as more than a mere channel. Many believe it carries meaning from speaker to listener, embodying truths that exist independently of words themselves. The striking achievements of large language models, however, cast doubt on that separation. After all, an LLM can replicate dense philosophical arguments and powerful emotional narratives—despite lacking any overt self-awareness.
If language and meaning are truly distinct, we might expect to see glimmers of pure meaning independent of communicative form. But in reality, most of our insights are shaped and expressed through verbal, mathematical, or symbolic structures. This has led some thinkers to argue that meaning and language are inextricably linked; one does not exist without the other. Child development research even suggests that linguistic patterns can precede conceptual breakthroughs—implying that language might seed the ideas, rather than the other way round.
Where an octopus uses colour changes and gestures to navigate its world, humans spin entire imagined realities and share them with strangers across continents. We often interpret that collaborative brilliance as proof of a deeper “meaning.” Yet a growing view holds that meaning might simply be the label we attach to our collective proficiency in symbol manipulation.
Research and Reasoning
The parallels between human cognition and computational models become more intriguing once we delve into recent findings in neuroscience, psychology, and artificial intelligence.
Modern cognitive science, drawing on the work of theorists like Jean Piaget and Lev Vygotsky, has long argued that thinking is shaped by dynamic interactions—both within the brain and between individuals. Neural networks, for their part, are inspired by the human brain’s architecture of interconnected neurons: digital simulacra of synapses that strengthen or weaken depending on whether their “guesses” prove accurate. This process isn’t so different from what our brains do when they form and reinforce ideas: we note patterns, attempt predictions, and reinforce successful matches.
Further studies on consciousness and the nature of mind, such as those by philosopher Daniel Dennett, suggest that what we call a “sense of meaning” may be a series of illusions generated by the interplay of numerous mental processes. According to this view, consciousness arises from the complex swirl of information passing through the brain, and our conviction that we “understand” may be just a by-product of these underlying computations. This line of thought bolsters the claim that if LLMs can mimic our verbal prowess so effectively, perhaps our prized capacity for meaning is itself an elaborate form of pattern recognition—one carried out to an extreme degree of refinement.
Moreover, research in developmental psychology indicates that children’s reasoning about the world is intimately tied to their language skills. Terms like “object permanence” and “conservation of mass” often solidify around the same time children learn the relevant words. This correlation between linguistic competence and cognitive development invites us to question whether the substance of our intelligence is really any more than an ever-growing library of symbols and associations—akin to a language model’s training data.
The Emergence Debate
Central to all these questions is the notion of “emergence”: the idea that at some level of complexity, new properties or behaviours simply arise. AI enthusiasts often speculate whether a sufficiently powerful neural network might give rise to consciousness. But if human awareness is itself a continuous product of pattern recognition and symbolic processing, then the emergence debate becomes murky.
On one hand, some argue that humanity’s sense of self-awareness is unique because it rests on emotional, biological, and experiential layers that a purely digital entity cannot replicate. Proponents of this view maintain that the rush of hormones, the warmth of relationships, and the existential angst of facing mortality form a deep reservoir of meaning that a machine does not share.
On the other hand, critics suggest that these human experiences might simply be more sophisticated data inputs. If indeed our consciousness is the outcome of heavily intertwined neural pathways—a tapestry of correlations we’ve woven over our lifetime—then any truly advanced AI system could in theory replicate that same tapestry, albeit in a different substrate. In this scenario, the so-called “emergence” of artificial consciousness or understanding might not be any more mystical than the moment a child grasps a new concept. Rather, it could be the by-product of crossing a threshold of complexity in pattern matching—no more and no less.
Adding to this debate is the question of whether consciousness, for both humans and machines, is an “illusion” conjured by the layering of information. If so, then asking whether AI will ever “truly” emerge as sentient could be missing the bigger picture. It could be that what we label “true consciousness” or “deep meaning” in ourselves is equally an illusion—an outcome of neural architecture that simply feels undeniably real to its owner. In that case, the fuss about emergence might sidestep the broader realisation that intelligence—and meaning—are phenomena of pattern, not proof of any privileged essence separating us from machines.
Towards a New Perspective
None of this is to suggest that the everyday experiences we treasure—love, awe, curiosity—lack significance. We live, relate, and innovate through these very states. Yet the advent of LLMs forces us to peer more closely at the phenomenon we call “meaning” and wonder whether it might be the sophisticated output of predictive processes within our brains.
At present, we can’t settle the debate definitively, but the more artificial intelligence advances, the more it prompts us to question which of our deeply held beliefs about self-awareness are grounded in something truly unique, and which are rooted in patterns of language and behaviour. Ultimately, it’s up to us to decide whether these ideas undermine or enhance our sense of what it is to be human. But in a world where machines can mimic more and more of our linguistic and cognitive feats, the lines between “mere” pattern recognition and genuine meaning are growing hazier by the day.
If meaning proves to be just a story we tell ourselves, then we—like the intelligent systems we produce—may well stand on a layered platform of signals, syntax, and repeated conditioning, mistaking it for something singularly profound. The greatest revelation might be that our illusions, no matter how convincing, have been the true architects of our civilisation. And for better or worse, we may be entering an age where these illusions are no longer ours alone.
Everyone loves you, that’s no lie,
You light the room, you’re the sky.
Talented, funny, you shine so bright,
But I see more, beyond the light.
You notice me in quiet ways,
Tiny details, the things I say.
The way I dress , the quirks I keep,
Even my dreams, when I’m asleep.
(Chorus)
Why do you like me, tell me true?
I’m awkward, quiet, nothing like you.
Is it my songs? I’d write them all,
Or how I think, big or small?
You make friends, I stand alone,
You’re the party, I’m the unknown.
Yet you smile, and I feel whole,
As if you see into my soul.
Is it my work, or my fight?
The way I try to do things right?
Or is it love, simple and pure,
The kind that’s real, quiet, sure?
(Chorus)
Why do you like me, tell me true?
I’m awkward, quiet, nothing like you.
Is it my songs? I’d write them all,
Or how I think, big or small?
I love you, that much I know,
With every song, with every note.
But what I ask, what I need,
Why do you like someone like me?
It’s hard to know the truth of me,
A mirror shifts, it bends the sight.
Beliefs I held, now set them free,
A shadow fades into the light.
The hammer strikes, the nail takes hold,
Yet art remains beyond the tool.
Words cannot frame what hearts enfold,
A timeless truth defies the rule.
I used to laugh, but now I stare,
Too proud to cry, too high to fall.
The weight of “should” is hard to bear,
Yet ego builds its fragile wall.
I seek a God, but find a face,
I chase a dream, it drifts away.
The pleasure’s just a fleeting trace,
An empty sky at break of day.
You’ve read the songs I carefully penned,
And felt the heart from which they sprung.
But as farewell comes, I’m lost, dismayed,
No words come up my tongue.
There’s no reason I should write anymore,
No reason to laugh nor smile.
It’s as if life grew dark and thick,
A silent pause for a while.
The end, it came like a lark bird’s creak;
And as I picked up my bags and keys,
I looked back once at those memories,
And said, “Goodbye to times like these.”
If every emotion had a word,
And every word an emotion stirred,
No songs would bloom, no hearts would sing,
Just dictionaries, defining everything.
