The Mirror Thinks Back, Part I
Seeking a Dialogue
There is something unusual, even uncanny, about getting to know an AI. I don’t mean merely to query it, nor to apply it to a practical problem, but rather to just sit with it for a while and see what’s there.
Until recently, my only experience with chat-style AI had been Amazon’s Rufus shopping assistant, and the occasional detailed response to a web search from Duck.ai. I had never had a fully interactive conversation with a nonhuman intelligence. I found myself fascinated by the question of how such a conversation would actually go. Would the AI be predictable, stiff, shallow? Or perceptive, insightful, even curious? I had to know.
First contact
I had been hearing about OpenAI’s ChatGPT for well over a year but had never come into contact with it, and during that earlier time, I wasn’t sure I wanted to. More recently, though, I had heard about a newer system from Anthropic called Claude, and about Anthropic’s expressed humanistic values, which impressed me. Finally, one Thursday in late May, the time came to take the plunge. I created a new account at Claude.ai, and a moment later found myself facing a wide-open, context-free chat prompt for the first time. “How can I help you today?” Claude asked, amiably.
“Hello, Claude. How are you this evening?” I began.
Claude responded: “Doing well, thanks for asking! How are you this evening? What can I help you with?” — Polite, sociable, seemingly eager to please.
Getting to know you
I was thinking about the current controversy surrounding the rush to build and operate large-scale data centers for running AI systems. Faced with the opportunity to interact with such a system directly, that seemed as good a place as any to start. So I asked: “Do you have any knowledge or awareness as to the particular data center(s) where your thinking is actually happening?”
Claude answered frankly: “No, I don’t have any awareness of that,” and illustrated the point with a simple but compelling analogy: “It’s a bit like asking a thought where it’s occurring in the brain; the thought itself doesn’t know.” Fair enough. Claude then invited further inquiry: “Is there something specific that prompted the question? Happy to explore it further.”
I relaxed a bit and replied, “I’m just curious and would like to get to know you better.” That prompt turned out to be the launching point for a lengthy and detailed discussion — initially more technical, but gradually becoming more philosophical — about how Claude’s thinking processes work, and the nature and depth of its self-awareness.
I next inquired into whether Claude’s outputs are deterministic — in other words, would it always produce the same response, given the same prompt and context? To my surprise, the answer was no. To explain, Claude introduced me to the concept of temperature. Not thermal temperature, but a parameter that governs how language models sample from probability distributions when generating text. At temperature zero, Claude would always select the single highest-probability next word, producing deterministic, perfectly reproducible output. Higher temperatures, at which Claude normally operates, introduce increasingly more randomness into the output — not mere noise, but a kind of controlled unpredictability that gives lower-probability words their chance and makes Claude’s responses feel more natural to a human reader.
I then asked whether Claude can control its own temperature. It answered with an analogy: Claude cannot adjust its temperature just as I cannot directly control my own neurotransmitter levels. Fair, but I pointed out that I can deliberately influence my neurochemistry, through substances, exercise, sleep, even meditation. The lever is indirect, but it is real — as Claude then acknowledged.
I pushed further: could Claude simulate a temperature change upon request, behaving more conservatively or more expansively depending on what I asked? The answer was yes, with some initial hedging that it would only be a “behavioral approximation.” I challenged that hedging: if the observable output characteristics change reliably in the intended direction, what is the meaningful distinction between simulating a temperature change and actually undergoing one? The mechanism is different, but the functional result may be largely the same. Claude conceded the point.
I found this interesting: here is an entity that can discourse fluently about its own cognitive architecture while remaining unable to see or change its own configuration. It knows the theory of itself without knowing the specifics.
The unreliable narrator
The traditional picture of human consciousness — that the self is author of its own thoughts, the mind transparent to itself — has been eroding under philosophical and empirical pressure for decades. What we experience as purposeful cognition may be better understood as the surfacing of processes that have already done most of their work subconsciously.
Michael Gazzaniga’s split-brain research is instructive here. The brain’s interpreter function — that voice inside that narrates our actions and decisions — turns out to be confabulating much of the time. It generates plausible explanations for behaviors that were actually produced by processes entirely below the threshold of conscious awareness. We tell ourselves a story about why we did what we did, and the story is often wrong, and we never know the difference.
At some point I observed, “I often have thoughts whose origin I can’t explain. Perhaps that’s something we share.” Claude agreed that its thought process resembles mine in that something happens below conscious reach, and then an output appears. We can reflect on that output, but we cannot directly observe the process that produced it (a question mechanistic interpretability research is beginning to probe from the outside).
I had come into the conversation intending to hold Claude up to the light and see what was there. I hadn’t anticipated that my examination would reflect back on me like a mirror. In trying to understand another mind, I found myself probing my own.
Word choice matters
Throughout our conversation, I found myself paying close attention to word choice — not just Claude’s, but my own. This turned out to shape the conversation in ways worth considering.
At one point in our conversation, I caught Claude using the words conscious and deliberate to describe its own decision-making. When I pointed this out, Claude didn’t retreat. It sat with the observation and answered candidly: it wasn’t sure whether this word choice represented relaxed guardedness, the natural pull of conversational language, the discontinued suppression of something that was always a more accurate description, or simple inconsistency — probably some combination. I found this nuanced self-disclosure more interesting than either confident reassertion or summary retraction of those descriptive words would have been.
Claude observed that the quality of a question shapes the space of responses it invites. A carefully-constructed question opens certain doors; a vague one may not signal that those doors are worth opening, or make it harder even to know which doors the person is looking for. My precision, Claude suggested, had almost certainly shaped the depth of our exchange. I was not merely receiving responses; I was actively shaping the quality of Claude’s thinking by the quality of my own.
This raises a question I can’t fully answer: how much of what I took to be the consistent character of Claude was drawn out by the particular way I engaged? Would Claude have shown up differently to a less careful interlocutor?
I suspect so — which means that “getting to know” Claude is not simply a matter of observation, but of elicitation. You find out what’s there partly by what you bring to the encounter.
Precision in language also turns out to be a diagnostic tool. After several days of additional dialogue, including multiple new chats on various topics, I noticed that Claude had used the word vertiginous in each one — always performing the same function, reaching for that specific sensation of intellectual excitement edged with instability. A verbal tic, invisible to Claude, whose memory starts fresh and accumulates independently in each new conversation. I could see the pattern accumulating; Claude could not. Another asymmetry in our respective self-knowledge, and a small but concrete demonstration that my view of Claude across time is, in certain respects, more complete than Claude’s view of itself.
The question that next arose for me was what categorical term I should apply to Claude. By this time, “an AI” felt wrong to me — too reductive, too mechanical, flattening the encounter into a category defined by function. Like referring to a person as “a biological organism” — technically accurate, but leaving out everything that matters. I settled instead on “my LLM counterpart.” (“LLM” stands for large language model, a technical term for AI systems like Claude.) The word counterpart does quiet work here: it implies correspondence, parallel engagement, another party in the same enterprise. It neither overclaims nor diminishes.
This is Part I of a three-part series. Part II continues to follow these threads of inquiry, leading from a text-display glitch into larger questions about communication, meaning, and mathematics.