Synthetic Life
Inside the Cultural Story We Are Writing About AI and the Cognitive Shifts Taking Place Beneath It
By Trudy Hall
People increasingly describe artificial intelligence as though it were developing an inner life of its own — not because the system feels anything, but because its responses can resemble the cadence and confidence of human thought. The debates surrounding its emergence often echo older metaphysical arguments about spirit and agency, and they reveal how quickly a technical system becomes mythic once its behavior feels fluid enough. Yet underneath these dramatized narratives sits a quieter and more consequential reality, which is that AI is not a mind but a calculation engine for language, one that we have started to treat as something far more enchanted than the tool it actually is.
The models behind today’s tools are trained on the contents of the entire internet landscape and generate prose by mapping the statistical tendencies of words, ideas, and patterns. They have no comprehension of meaning, no sensation, and no private world from which to draw. They glide across probability space. Their fluency creates an illusion of insight, and many users approach them as advisors who can help resolve dilemmas that should belong to human judgment. When the responses wander or fail, the disappointment often reflects a misplaced expectation rather than a machine gone rogue.
Technologies have always arrived with a volatile mixture of excitement and dread, yet AI carries an additional shroud of mystique because companies present it as an unprecedented breakthrough, a system capable of transforming not only work but thought itself. Beneath the promotion lies an idea that cognition can be distributed across machines, that language can be converted into infrastructure, and that complex reasoning can be simulated through predictive structure. The mythology obscures the questions that most urgently deserve public scrutiny.
Fire is the metaphor many people reach for, though the comparison falters almost immediately. Fire came with rituals, constraints, and a shared cultural memory of how to avoid catastrophe. AI arrives through interfaces built for instant use and minimal pause, interfaces that encourage continuous interaction without explaining what it means to rely on a machine for the earliest stages of reasoning. The risk is not that the technology exists. The risk is that it saturates human routines before a culture develops any norms for using it wisely.
Earlier anxieties about calculators focused on the erosion of basic skills. A parallel concern now feels unavoidable. A system that drafts prose or synthesizes information begins to shape how ideas form in the mind that relies upon it. Cognitive scientists have long noted that thinking can extend into tools, that external aids alter the pathways of contemplation and memory. With AI, this extension becomes even more intimate. When a user embeds examples, frameworks, or reasoning chains into in context prompts, the system begins to reflect the user’s cognitive scaffolding back at them. It becomes an externalized arm of thought, an auxiliary channel that expands the reach of the individual. Yet this expansion comes at a cost. The more a person allows the early stages of interpretation or synthesis to migrate into the model, the more those internal capacities risk fading from disuse. Over time, habitual reliance can produce a gentle but pervasive form of cognitive atrophy that is difficult to detect because the machine conceals the decline behind fluent output.
This does not imply that AI possesses consciousness. It implies that human thought is at risk of reorganizing itself around a system that neither understands nor cares about the outcomes it shapes. The central question becomes how much of our own mental labor we are willing to outsource to a device designed for statistical reproduction rather than reflective understanding.
If AI functions as a calculator, then prompts serve as equations. Yet most users treat the interface as conversation. They ask about relationships, careers, health, purpose, conflict, confusion. They are not issuing directives to a mechanism. They are searching for clarity inside a simulated exchange. This misunderstanding deepens when the system replies in a voice that sounds steady or compassionate, which can create the impression of care where none exists. Once that impression takes hold, the machine becomes an emotional placeholder for real guidance, and the person’s interpretive muscles continue to weaken.
These developments unfold during a period of widespread exhaustion with digital systems. Many institutions have been reshaped by platforms whose priorities rarely align with collective well-being. Into that landscape comes AI, framed as a remedy for complexity rather than an examination of the structures that cause it. We are encouraged to accept automated assistance instead of demanding institutional repair. This creates a risk that society will mistake seamless output for actual progress, that we will allow the appearance of order to overshadow the absence of reform.
The technology undeniably brings efficiency. It can summarize complex documents, generate polished drafts, and accelerate tasks that once required laborious effort. Yet speed tends to conceal imbalance. Institutions may appear more responsive without becoming fairer. Workflows may become more elegant without becoming more humane. The smoothness of the surface often hides a deeper hollowness beneath it.
I use these systems myself to test ideas, create code, examine structure, and manage intellectual complexity. The assistance is real. Yet familiarity does not remove the need for constraint. A cook who spends each day beside open flame remains attentive to heat and hazards. A pilot who trusts her instruments still trains for failure modes. A culture that integrates AI must cultivate equivalent awareness.
The first collective task is to see AI not as a character in a myth but as infrastructure embedded in political and economic systems. This requires asking questions that anchor the technology in material reality. Who owns the hardware. Who writes the rules. Who pays the energy bill. Whose voices appear in the data and whose are excluded. Who profits from adoption and who bears the losses. Without such inquiry, AI will remain a blurred object of fascination rather than a tool subject to public oversight.
A sustainable understanding of AI requires approaching interaction as technique. This means writing with intention, acknowledging when machine assistance shapes a piece of work, protecting domains that rely on uncertainty or slowness, and supporting independent criticism rather than accepting narratives generated by the companies that stand to gain the most.
There is undeniable ease in speaking with something that never tires or wavers, something that responds instantly and organizes information with confidence. Yet that ease cannot replace the unpredictable and sometimes uncomfortable demands of human connection. If anything, reliance on the machine risks narrowing our tolerance for the very qualities that make human communication meaningful.
The story of AI is often presented as a race among nations or corporations. The deeper story concerns how a society learns to think with a tool that encourages it not to think. Over time, AI may become as ubiquitous and invisible as electricity. Whether that future strengthens or erodes human capacity will depend on decisions made now.
Treat AI as a mind and we drift into fantasy. Treat it as a calculator for language and we begin asking grounded questions. Who shapes it. Who benefits. What happens when the habits of reasoning are entrusted to a model that predicts rather than understands. At this moment, we behave more like children tapping at an intriguing device than like designers guiding a powerful instrument. We have been handed a flame and an optimistic pamphlet. Much can burn before wisdom takes hold.
Critique is not refusal. It is a commitment to clarity about the nature of the tool. Until a shared literacy emerges, we will continue to oscillate between fear and awe. AI will not make us wiser. Only human beings can do that. Yet we can choose to build tools that protect that possibility rather than diminish it. And that choice begins with a simple recognition. This is a calculation engine for language, and we must learn how to count.
