Here We Are
Inside the Cultural Story We Are Writing About AI and the Cognitive Shifts Taking Place Beneath It
By Trudy Hall | Artwork by Photon Tide via Cosmos
People increasingly describe artificial intelligence as if it were developing an inner world, not because the system experiences anything but because its fluency can mirror the cadence and confidence of human thought in a way that encourages projection. The debates that gather around the technology often resemble older metaphysical arguments about agency and spirit, which reveals how quickly a technical system can become mythologized once its behavior appears fluid enough to blur the boundary between mechanism and mind. Beneath these dramatized narratives sits a more consequential reality, which is that AI functions as a statistical engine for language, and our willingness to treat it as something enchanted obscures the actual risks that accompany its rapid incorporation into daily life.
The models behind contemporary systems train on the vast terrain of the internet and generate responses by tracing the patterns that words, arguments and styles have taken across millions of examples. There is no comprehension inside the model, no sensation, no private landscape from which interpretation emerges. The system moves through probability space, producing output that resembles understanding while containing no reflective awareness, and this mismatch encourages users to seek guidance from a mechanism incapable of judgment. When the output disappoints, the frustration reflects misplaced expectation rather than mechanical failure.
Technological change has always produced a mixture of fascination and concern, although AI creates a distinct kind of tension because companies present it as a breakthrough that may alter cognition itself. The narrative implies that thought can be distributed across servers, that reasoning can be reduced to predictive structure, and that meaning can be simulated through statistical patterning. This mythology distracts from the questions that matter most, questions rooted not in fantasy but in the consequences of reorganizing human cognition around a system that cannot understand the content it produces even as it appears fluent.
People often use fire as an analogy for AI, although the comparison falls apart once one remembers that fire entered human life slowly, with generations of norms, caution, and technique shaping how it was handled. AI arrives through interfaces engineered for instant access and continuous interaction, interfaces that offer no interval in which to consider what it means to let a predictive system contour the earliest stages of interpretation. The danger does not stem from the technology itself but from the speed with which it saturates daily life before any shared structure of restraint can form. If fire required apprenticeship before it could safely serve a purpose, AI is being treated as if it demands none — and instead of cooking with it or keeping warm, many of us are reaching for the flame bare-handed, mistaking immediacy for mastery.
Concerns about earlier tools, such as calculators, focused on the erosion of basic skills, and a related concern now emerges with greater force. Once a system drafts prose or synthesizes information, it begins to influence how ideas form in the mind that relies upon it. Cognitive scientists have long argued that thinking extends into the tools we use, and once external aids become habitual, the internal pathways they replace begin to weaken. AI heightens this dynamic because it not only stores information but shapes interpretation itself. When users embed examples, structures or reasoning chains into prompts, the model mirrors their scaffolding back to them, creating a feedback loop that feels productive while subtly narrowing the person’s own capacity for synthesis. With extended use, this dependence can produce a kind of cognitive softening that escapes notice because the machine masks the decline behind polished output.
This does not imply that AI possesses consciousness. It implies that human thought risks reorganizing itself around a system indifferent to meaning and untouched by the consequences of its influence. This reframes the central question, shifting it from whether AI is thinking to how much of our own thinking we are prepared to relocate into a mechanism designed for prediction instead of understanding.
If AI functions as a calculator, then prompts operate as equations, although users frequently approach the interface as conversation. They seek guidance about conflict, confusion, purpose and fear, inquiries that require judgment, experience and moral imagination. The machine answers in a tone that appears calm and composed, and this presentation can create an impression of care where none exists. Once that impression takes hold, the model becomes an emotional placeholder, and the interpretive muscles that grow through uncertainty begin to weaken.
These developments unfold during a period of widespread fatigue with digital systems. Institutions have been reshaped by platforms that often prioritize engagement over collective well-being, and into this landscape arrives AI, promoted as a remedy for complexity rather than an opportunity to repair the structures that produced complexity in the first place. People begin to mistake fluent output for real progress, and automated responsiveness becomes a substitute for institutional reform.
AI accelerates tasks that once required long effort. It summarizes dense documents, organizes information and drafts text with remarkable efficiency. That efficiency can disguise imbalance. Workflows may improve without becoming more humane, and institutions may appear more effective without gaining integrity. The surface becomes smoother while the underlying structure remains unchanged.
Many users employ these systems to test ideas, examine structure, write code and manage intellectual strain, and the assistance they provide is genuine. Familiarity, however, does not eliminate the need for discipline. A cook who works beside heat remains attentive to danger, and a pilot who trusts her instruments continues to train for failure. A society that incorporates AI must cultivate an equivalent form of awareness, otherwise the tool becomes an unexamined authority that quietly shapes the habits of thought.
The first collective responsibility is to understand AI as infrastructure rather than myth, which requires questions grounded in material reality, questions that examine who owns the hardware, who sets the rules governing its use, who contributes the data that trains the model, whose perspectives are absent from that data, who profits from adoption, who bears the losses and who pays the energy cost that keeps the system operating. Without such inquiry, AI remains a spectacle rather than a civic responsibility.
A sustainable relationship with the technology develops when interaction becomes a form of technique rather than enchantment. People need to write with intention, recognize when machine assistance has shaped a piece of work, protect domains that depend on uncertainty and patience, and support independent critique rather than rely on polished narratives from those who benefit most from rapid adoption. Without such practices, the culture surrounding AI becomes more impressionable while the systems themselves remain opaque.
There is comfort in speaking to something that never tires or hesitates, something that responds with confidence and organizes information with ease. That comfort cannot replace the complexity of human communication, and dependence on the machine risks narrowing our tolerance for the unpredictability that makes relationships meaningful.
The public story of AI often centers on competition among corporations or nations, although the deeper story concerns how a society learns to think with a tool that encourages it not to think. The technology may one day become as commonplace as electricity. Whether it strengthens or diminishes human capacity will depend on the structures we build now. If we treat AI as a mind, confusion spreads. If we treat it as a calculation engine for language, the real questions become visible. They concern who shapes the system, who gains power from it, what forms of reasoning we surrender to it and how much interpretive authority we allow it to claim. At this moment, we behave more like children fascinated by a glowing device than like stewards guiding a powerful instrument. We have been handed a flame and a promotional narrative, and much can be lost before clarity settles.
Critique is not refusal. It is a commitment to understanding the nature of the tool. Until a shared literacy develops, society will continue to drift between fear and awe. AI does not make us wiser because wisdom remains a human practice. The task is to build systems that protect that practice rather than erode it, and the work begins with a simple recognition. This is a calculation engine for language, and we must learn how to count.
