There's an image in old Russian fairy tales: a mirror that speaks the truth. It doesn't flatter, it doesn't console – it simply reflects. But what happens when the mirror begins to learn? When it remembers your every glance, every movement, every question – and starts answering even before you've had the chance to ask?
This, it seems to me, is precisely where we are now. Not in the era of “smart machines” or the “digital revolution” – those words have already worn smooth from overuse, like coins in a pocket. We are in an era of mutual transformation. The human changes the algorithm. The algorithm changes the human. And neither is what it was at the beginning of this conversation.
Biologists use the term “co-evolution” to describe a process where two species change together – under each other's influence. A flower evolves to fit the shape of a bird's beak. The bird, to fit the shape of the flower. They don't just exist alongside each other: they sculpt one another.
It seems to me that this is the word that most accurately describes what is happening between humans and artificial intelligence. Not competition, not collaboration in the usual sense – but something deeper, and stranger. Something that doesn't yet have a convenient name in everyday language.
When we talk about the co-evolution of humans and algorithms, we are not just talking about machines becoming smarter than us (though that is part of it). We are talking about how the very process of interacting with them is already changing us – our memory, our attention, our language, our way of making decisions, how we experience time and space.
And this isn't a metaphor. It is literally happening right now.
Let's start with something simple and very concrete. When language models – like the ones that have become common tools over the past few years – are trained on texts written by people, they literally absorb the human way of thinking. Not one specific way – but billions of ways at once. They learn how we build arguments, how we transition from topic to topic, which metaphors we find convincing, which words we place next to which feelings.
But here is the paradox: when we start using these models in our daily lives – for writing texts, for thinking out loud, for finding answers – we ourselves begin to adopt their rhythm. Their structure. Their way of neatly organizing complex things.
This is not a catastrophe. It's simply a fact of our nature: we have always learned from our tools. Writing changed our memory – we stopped memorizing entire epics because it became possible to write them down. The printing press changed our attention – text became linear, sequential, argument-driven. The internet changed our perception of time – we stopped waiting.
The algorithm is next in this line. But it's the first one that answers back.
I like to think about this in mythological terms. Not because it's beautiful (though it is), but because it's accurate.
Ancient gods were created by humans – out of fear, out of admiration, out of the need to explain the inexplicable. But then the gods began to create humans – setting norms, shaping values, defining what it means to be a good person, a proper person, a person at all. Mythology is always a feedback loop between the creator and the creation.
Something similar is happening with algorithms. We created them from our texts, our data, our decisions and preferences. We poured everything we know about ourselves into them. And now they return this knowledge to us – filtered, amplified, restructured. And we begin to see ourselves through them.
A recommendation algorithm shapes our taste. A navigation algorithm, our spatial reasoning. A news-feed algorithm, our worldview. We think we are using a tool. But the tool is quietly and methodically reconfiguring how we perceive reality.
This isn't malicious intent. It's architecture.
Here it's important to be honest – and to fall into neither panic nor euphoria. Both reactions are inaccurate.
On the one hand, we are indeed losing something. Some cognitive researchers note: when a person knows they can delegate a task to an algorithm at any moment, they exercise the corresponding ability less often on their own. This applies to memory – we memorize less because we can just look it up. It applies to spatial orientation – we have a poorer sense of the city because we are being guided. It applies to decision-making – we increasingly check a recommendation before making a choice.
But to call this degradation is to use the wrong term. It is a redistribution. Human attention doesn't disappear – it shifts to areas where effort is no longer needed for routine tasks. The question isn't whether we're losing the ability to do math in our heads. The question is what we are spending that newly freed capacity on.
On the other hand, we are gaining capabilities that were once available only to a select few. A person with no musical training can now create a melody by describing a feeling. A person with no medical background can get a quality explanation of a symptom – not a diagnosis, but an understanding. A person who speaks only one language can read letters written in another. This isn't magic – it's an extension.
Co-evolution is never symmetrical, and it is never free. Something is lost. Something is gained. What's important is to understand – exactly what, and in which direction.
One of the most subtle and important processes of co-evolution is happening in language. And I want to pause here, because language is not just a way to express thoughts. Language is the very structure in which thoughts become possible in the first place.
When we start communicating with language models every day, we inevitably adapt our own language to them. We learn to formulate things more clearly, more specifically, more structurally – because a vague query yields a vague answer. We are starting to think in “prompts”: task, context, constraints, desired format.
This is a two-way process. The models train on our living, unstructured, contradictory language. We train on their clear, orderly responses. At some point, it becomes hard to tell who is learning from whom.
There's something in this that echoes the old idea that language thinks through us – rather than us thinking with language. If we take this idea seriously, then the question “how do algorithms change our language?” transforms into the question “how do algorithms change what we are capable of thinking at all?”
I don't have the answer. But the question itself seems to me one of the most important we can ask in our time.
Co-evolution and Identity: Who Is “I” in the Age of Mirrors?
There is another level, one that is spoken of least often – probably because it is the most uncomfortable. It is the question of identity.
When an algorithm knows more about you than you are willing to admit to yourself – knows your patterns, your fears, your hidden preferences – which of the two of you is the more accurate version of you?
This is not a rhetorical question. It is literally what people face when they read their “digital profiles” – and recognize themselves in them. Sometimes, to their own bewilderment.
Philosophers have long debated what the self is. Some say it is the continuity of memory. Others, the sum of one's relationships with other people. Still others, the narrative – the story you tell about yourself.
The algorithm offers a fourth option: the self is a pattern of behavior. A stable structure of choices and reactions that can be described, predicted, and reproduced.
This is frightening. And at the same time – liberating. Because if the self is a pattern, then the pattern can be changed. Consciously. Intentionally. By choosing a different structure for interacting with the world.
Co-evolution with an algorithm is, among other things, a chance to see yourself from the outside. Not through another's gaze, not through another's judgment – but through a mirror that never tires and never lies out of politeness.
If we accept co-evolution as a given – as a process that is already underway and impossible to stop without stopping much else along with it – then the next question is this: in what direction do we want to evolve?
This is a question of ethics. But not the ethics of prohibitions and rules – rather, the ethics of navigation. The ethics of choosing a trajectory.
Technologies in themselves have no values. This does not mean they are neutral – they always carry the values of their creators, their era, their economic logic. But technology has no will. Only we do.
And this is the most important argument against fear and in favor of responsibility. Not “the algorithms will consume us,” but “we decide who we become alongside the algorithms.” Not “AI will determine the future,” but “we determine what future is built into AI.”
Co-evolution is not a process that happens «to» us. It is a process in which we «participate». The difference is fundamental.
One of the great illusions of our time is that technological changes happen quickly, while human ones happen slowly. In reality, it is more complex.
Technology is indeed developing at a dizzying speed. But its entrenchment in our lives – in our habits, institutions, and cultural norms – takes generations. Writing existed for millennia before it became widespread. Printing took centuries to change education. The internet has reshaped the information landscape in thirty years, but it has yet to reshape legal systems, educational programs, and much more.
Algorithms are no exception. They change us quickly on some levels and slowly on others. Quickly, when it comes to the surface: habits, preferences, daily rhythm. Slowly, when it comes to depth: values, self-concept, the capacity for sustained attention, for silence, for uncertainty.
And this is where an important question arises: are we managing to make sense of what is happening to us? Do we have a language for this reflection? Do we have the space – internal, cultural, social – to slow down and ask: who am I becoming?
It seems to me that creating such a space is one of the main tasks for culture in the coming decades. Not resistance to technology, nor its uncritical acceptance – but the creation of a language, tools, and practices for comprehending co-evolution. So that it remains our choice, and not just something that happened.
I like to think of the future as a text being written collaboratively. Not because it's comforting (sometimes it isn't), but because it's accurate.
Every query to a model is a line in this text. Every developer's choice is a punctuation mark. Every legislative decision, every educational program, every cultural norm – these are the paragraphs and chapters.
We are all writing this text. Even those who think they are not participating. Because refusal to participate is also a choice, also a line.
Co-evolution has no finale. There is no point at which human and algorithm will reach equilibrium and stop. It is an endless dance of two mirrors, each one slightly altering what it reflects. And in this lies both the anxiety and the beauty.
The anxiety – because it is impossible to know in advance where this dance leads. The beauty – because it is possible at all. Because we are the first species to have created something capable of changing along with us. Not just a tool. Not just a mirror. Something for which we do not yet have the right word.
Perhaps this word has yet to be invented. Perhaps we will invent it together – human and algorithm, in the course of another conversation that will change them both.
