The panel light in my car's AC control unit died months ago. I procrastinated on the repair, as one does with non-critical failures. When I finally decided to sell the car, the quote came back with multiple issues: several hundred dollars to replace the entire control head for the AC panel light, plus the worn axle boot that would cost thousands to replace. A single LED bulb, perhaps worth cents, necessitated replacing an integrated unit worth hundreds. I'll save my rant on planned obsolescence for another day. We optimized for manufacturing efficiency, sleek interfaces, and market creation, all at the expense of repairability.
I chose not to sell. Instead, I spent over two thousand dollars replacing an entire axle assembly to fix a slightly worn boot. The economics made no sense; the car wouldn't appreciate by anywhere near that amount. Yet something felt correct about repairing rather than discarding, about maintaining rather than replacing.
Yesterday, the panel light flickered back to life.
A rational explanation exists, surely. A loose connection finally made contact. Thermal expansion closed a gap. But in that moment of unexpected illumination, I experienced what felt unmistakably like gratitude from the machine, as if the car acknowledged my faith in its continued existence.
This brings me to a recent argument against anthropomorphizing large language models. The position, articulated in the blog post A Non-Anthropomorphized View of LLMs, suggests we should resist projecting human qualities onto these systems. The author argues that anthropomorphism clouds our understanding of what these models actually do: statistical pattern matching at scale.
But perhaps anthropomorphism isn't where science ends and art begins. Consider that social sciences and humanities represent legitimate domains of inquiry where not everything reduces to mathematical axioms. Instead, we deal with conventions, vernacular, values, and beliefs. These constitute a substrate of meaning.
The orthogonality between information and meaning illuminates this divide. An AI-generated article can overflow with information while remaining empty of meaning. Conversely, a haiku may contain minimal syntax yet encode profound semantics. Information theory itself reveals this limitation of current language models. Consider how Claude Shannon demonstrated that rare events carry more information:
| Event | Probability | Information Content | Reaction |
|---|---|---|---|
| "The sun rose today" | ~1.0 | Minimal | No surprise |
| "A snowstorm hit the Sahara" | ~0.0001 | Substantial | Makes you pause |
The improbability of an event directly correlates with its information value. The unexpected teaches us more than the routine.
Language models, however, are optimized to generate unsurprising sequences. Yes, we can adjust temperature parameters to increase variability, but the consensus holds that high surprise correlates with undesirable hallucination. We've built systems that excel at producing the expected.
This returns us to my car's resurrected light. The event carries information precisely because of the improbability of a self-repairing light. But the meaning I derive transcends information theory. It emerges from my projection of gratitude from the machine, from my willingness to participate in a narrative where objects respond to care.
What interests me is this: if enough people agree to participate in such anthropomorphic projections, do we create a consensual hallucination that becomes functionally real? When we thank our devices, name our vehicles, or apologize to our appliances, we're not confused about their lack of consciousness. We're participating in a meaning-making framework that enriches our relationship with the technological world.
The poetic dimension of human-machine interaction doesn't require machines to possess interiority. It requires only that humans find meaning in the projection. My car didn't really thank me. But in experiencing it as gratitude, I reinforced my own values about repair, maintenance, and the ethics of disposal. The experience became my reality.
Perhaps the real question isn't whether we should anthropomorphize our machines, but what we reveal about ourselves when we talk to them. The machine talked back not because it gained interiority, but because dialogue, like consciousness itself, creates meaning in the space between, not within.