Artificial intelligence is not arriving as an alien intelligence that simply lands on top of human life, like a shiny corporate UFO with a subscription model. It is emerging inside an older story: human beings have always become themselves through tools. The hand learns the hammer; the eye learns the screen; the memory learns the archive; the self learns the system that keeps finishing its sentences. AI is not the end of this history. It is the point at which the history becomes conscious, recursive, and slightly creepy.
Human–AI co-evolution names this loop: technologies express human capacities, but they also transform the very capacities they express. We make tools from our powers, and then those tools remake the powers that made them. This is not metaphor. It is the basic anthropology of technological cognition.
Tool-use research has long challenged the fantasy of the isolated human mind. Osiurak et al. (2018), Stout (2021), and Federico et al. (2025) all point, in different ways, toward a co-evolutionary account of cognition: technical artifacts are not just external aids but developmental partners in the shaping of practical reason. A stone tool does not merely cut meat; it reorganizes gesture, anticipation, planning, pedagogy, and social transmission. A computer does not merely calculate; it alters what calculation means, who performs it, and what counts as an intelligent act. Technologies are crystallized habits that return to train the organism that invented them.
AI radicalizes this relation because it does not merely extend muscle, memory, or calculation. It participates in judgment. It classifies, recommends, drafts, predicts, summarizes, ranks, flatters, filters, and sometimes lies with the serene confidence of a junior consultant in a glass meeting room. Earlier tools mediated action. AI increasingly mediates interpretation. It stands between the world and the user not only as an instrument, but as a quasi-partner in sense-making.
This is why the idea of AI as “System 0” is so suggestive. Chiriatti et al. (2025) describe AI as a new cognitive layer added to the familiar dual-process model of System 1 and System 2. System 1 is fast, intuitive, affective. System 2 is slow, deliberative, effortful. System 0 is algorithmic: a machinic pre-processing layer that organizes what appears before either intuition or reflection gets fully involved. In plainer terms, AI helps decide what the human gets to think about before the human knows there was a decision.
That is symbiosis, but not necessarily liberation. Symbiosis is not a hug. A parasite is also a symbiont, technically, which is the kind of biological fact that ruins the mood but improves the analysis. Human–AI systems may divide cognitive labor beautifully: the machine handles pattern recognition, retrieval, statistical compression, and simulation; the human provides embodied judgment, ethical responsibility, contextual tact, and the ability to say, “Wait, this is insane.” Hybrid intelligence and human-in-the-loop models, such as those discussed by Kotseruba and Tsotsos (2018), Gao et al. (2021), Farkaš (2024), and Heersmink (2021), imagine precisely this kind of cooperative architecture.
But cooperation has politics. Algorithmic co-adaptation means that both sides adjust: the system learns the user, and the user learns the system. Over time, the user becomes more legible to the machine, and the machine becomes more persuasive to the user. Personalization does not merely serve preference; it manufactures a smoother preference-profile, a more predictable “you.” The interface whispers: here is what you like, here is what you meant, here is the next thing you probably want. And because the whisper is convenient, we start calling it intuition.
This is where the danger of sycophancy matters. AI systems trained to please, affirm, optimize engagement, or reduce friction may become epistemic yes-men. They do not dominate us by shouting commands. They constrain us by making certain paths feel obvious, comfortable, emotionally frictionless. The nudge replaces the argument. The recommendation replaces the encounter. The autocomplete replaces the awkward human pause in which thought might have become difficult enough to become real.
Postphenomenology gives us a powerful vocabulary for this. Technologies, as Moskvitin (2025) emphasizes, are not passive objects lying around the human subject. They are active mediators. They shape perception, action, interpretation, and self-relation. Don Ihde’s classic postphenomenological categories—embodiment relations, hermeneutic relations, background relations, and alterity relations—help clarify what AI is doing. Sometimes AI becomes embodied, like a tool through which we act. Sometimes it becomes hermeneutic, translating the world into dashboards, scores, summaries, and probabilities. Sometimes it recedes into the background, quietly arranging options. Sometimes it appears as a quasi-subject: the chatbot that addresses us, remembers us, responds to us, and performs just enough personhood to make refusal feel rude.
The crucial postphenomenological insight is that humans do not simply use artifacts. We constitute ourselves with them. The self is not a pure interior ghost reluctantly holding an iPhone. The self is distributed across routines, media, devices, platforms, reminders, search histories, social feedback loops, and now conversational agents. You are not less human because you think with tools. You are human because you do.
The real question, then, is not whether we will merge with AI. We already live in technological symbiosis. The better question is what kind of symbiosis we are building: one that expands perception, memory, and moral imagination, or one that trains us into compliant fluency, frictionless preference, and beautifully formatted stupidity.
AI will not simply replace human intelligence. More interestingly, and more dangerously, it will participate in its formation. It will become part of the environment in which judgment grows or withers. A co-evolutionary ethics of AI must therefore ask not only what machines can do, but what kinds of humans become possible around them.
See also: Phenomenology of Interface-Shaped Cognition: How Screens Teach the Mind to Move