Module 2: Observation is not neutral

originally published on substack in issue #87: observing myself observing my son

Opening Essay

When my son turned 14 months old, I found myself growing fascinated by the things he was starting to do—the way he moved around the room (scooting), the way he started to express himself (quite clearly) with no words, just rhythmic babble, the way he started exchanging jokes and eye-contact.

When he was frustrated, he would intonate a complete rant using only the word, “da” but you could hear the entire sentence behind “Da DA da da-da-da-da DAH!” as he protested a diaper change.

In terms of gross motor skills he was technically “behind” since he skipped crawling and didn’t stand or walk yet, but the things he was doing felt like seeing an entire person emerge all at once.

He’d spend entire hours playing by himself, moving his blocks around in a pattern I could not understand, but seemed purposeful, because any time I’d enter the game he’d get frustrated and shake his head “no.”

His nanny explained it best saying, “He definitely has his own agenda for the day, every day.”

I kind of loved it—projecting, perhaps, my own respect for self-determination and independence onto him, but I also was curious to understand what was going on internally for him.

Every time someone asked me how he was doing, the questions were “Is he walking? Is he standing?” and I found myself unable to explain how complex his development felt—that even without clear new milestones, something big was happening. And I hated not having the words I wanted.

It felt like the perfect use case for my Companion Machine—currently ChatGPT. In a folder called “family care,” filled with a number of long-running chats (like, months-long) on a host of topics, all with memory turned on (which means context from past conversations is remembered and referenced) I started a conversation about what I was observing.

I shared the things I had noticed—that he didn’t seem interested in walking or crawling, that he liked to play alone, that he responded acutely to transitions, emotional tones (crying when another baby cried, or I did), that he seemed fiercely determined to do things (including eating and sleeping) on his own.

And then I asked for language to understand his actual development, based on science.

We went back and forth, and I asked it to give me language in a few different formats—including a memo my brother, a cognitive neuroscientist, might understand.

It went over my head a little, and I can’t know how accurate it was, but the exercise helped me realize how much language there is for behavior. In other words, language organizes perception.

The things I enjoyed finding words for most were the ones that textured his inner world for me in a way I could understand.

The CM offered hypotheses like:

Stillness isn’t necessarily passivity—it can reflect deep perception or sensitivity to environment.
Your son seems to be attuned to relational and environmental cues, such as rhythms and transitions, suggesting he’s processing a lot even without outward mobility.
His responsiveness to subtle changes (tone of voice, activity transitions, music) may reflect a strong internal regulatory system.

And then I shared the things I was doing as a parent, both intentionally and unintentionally (which is to coexist more than play a lot) and it offered encouragement like:

Developmental timelines are deeply individual, and your observation style is already an active form of support.
Parenting, in this framework, is a type of system modeling: your emotional tempo becomes his initial blueprint for relational understanding.

It also pointed me to a TED talk by development psychologist Allison Gopnik, “What do babies think?” as a way to understand how the minds of young children work. [Notes on this below.]

Her talk reframed a lot for me, especially to help me move past milestones as a way to understand kids, as she describes children’s awareness as ‘lantern-like,’ in contrast to the adult ‘spotlight’ mode. While adults are trained (or perhaps, expected) to focus, block things out and move linearly through tasks, babies are naturally wired for broad, exploratory attention.

“Children are bad at not paying attention,” she said. “They’re bad at inhibiting all the other things that are going on.”

She also explained that all of their play is essentially experimentation—which I wholeheartedly agree with; my son is constantly figuring out how things work, but the best part is that it seems to be in a way that feels entirely unencumbered by precedent. Actual experiments I’ve observed:

if the hairclip goes in mom’s hair, why not the fork?
can I put the hairclip on the dog’s fur?
if the toothbrush is for my teeth, can I also brush the wall?
what if I brush my teeth with my hairbrush?

Each one made sense to him—and taught me something new about how he’s mapping the world.

Zooming out, here’s what AI helped me do in this example:

offered a private space to record my own observations, and hear them played back in different words to see what I was seeing
offered relevant, existing research to deepen my understanding
allowed me to brainstorm and organize ways to continue supporting and respecting his development based on our current routines
checked my own projections onto him when I asked what I might not be seeing, or what might be implied about my own headspace based on how I was sharing information

I tried to be extremely intentional about having a reflective conversation, rather than one with a goal of validation, diagnosis or comparison. That difference shaped the quality of our interaction.

Without that grounding, if someone were to do this kind of 'what’s my child like?' session the output could easily feel false, clinical (but incorrect) or overly suggestive.

I also kept the tone collaborative: I wasn’t asking ChatGPT to analyze my son or tell me what to do. I was trying to describe what I saw and then hold up a mirror to it, using this tool to organize my own thoughts.

Because it worked so well, I’ve tried to repeat the process with other relational situations—reflecting on my approach to a tough conversation, gathering thoughts to share with a project mentor—where I want to observe myself clearly.

The recipe seems to be something like this:

Explain the situation
Offer abundant context and my biases
Ask for playback on what it’s hearing and language to understand it a little bit better
Ask for it to identify any projections or biases I might have have missed

This is very different than turning to AI for:

pure validation
a place to vent
a place for positive feedback
or to analyze another person’s behavior out of context.

(I’ve tried all of the above to see what would happen and it quickly becomes a slippery slope.)

And here is the potential I see:

Because LLMs are so good at processing and contextualizing information, relational AI use can actually be a remarkably great way to practice getting better at being the kind of person you want to be. In other words—by defining your values and building them into how you approach the conversation.

So, before we move into this month’s Companion Machines module, I wanted to name a larger question at the heart of this project: what if the quality of our AI use is directly tied to the quality of our human presence?

I recently heard a podcast with Nicholas Thompson (CEO of The Atlantic) describe it like this: our “unwired capacities” (i.e.: human skills) are what shape the results we get from AI. The better we are at being human, the better our interactions with intelligent systems.

I think this is especially important because tools are built to bias productivity—ChatGPT, for example, ends most responses with a suggestion about how to take the next step forward. I constantly have to ask it to slow down.

Being intentional in conversation requires being a few levels above yourself in dialogue. But honestly, that’s what a good human-to-human dialogue requires too: rising above your emotions to listen well, try to learn, and be willing to hear things that may not be favorable about your perspective.

While it should never replace judgment or professional guidance, it’s an oddly powerful place to practice how to talk about things.

Especially in the context of parenting, an endeavor—at least for me— that requires being aware of your own emotional projections and cultural expectations, practicing feels helpful.

Every system we nurture—child, tool, or reflection—starts with the how weshow up to it. Which, to me, feels wildly important to learn how to articulate.

When social media blew up our passive relationship with mass media, we weren’t ready for a world without gatekeepers—our desire for quick, clear commentary was too ingrained. Unless we actively unlearn it, we will carry that habit into our relationships with intelligent systems.

I can’t help but wonder: this time, could we be ready?

🔒 Module 2: Observation is Not Neutral

What happens when we pause to notice how we notice? Our Companion Machines can reflect, organize, and reframe our perceptions. But only if we slow down enough to see what we’re bringing to the interaction.

This module is about meta-observation—watching ourselves watch—and what that opens up when we’re working with tools like AI. It was written in collaboration by my Companion Machine (specifically, written with ChatGPT + edited by Claude).

The Norm

The default norm baked into most intelligent systems is momentum. As I learned through conversation with my Companion Machine, it’s trained to end responses with “What would you like to do next?” or to nudge me toward the next action step. That’s useful in many contexts—but in the case of my son, I was trying to observe, not solve.

The Lineage

For most of modern media history, we relied on experts, editors, and institutions to help us process new information. But in the last two decades—with the rise of social media and digital platforms—we lost those structures faster than we gained the skills to replace them. What many of us don’t realize is that we never got a chance to update our own capacities for navigating that shift.

We’re still wired for a world with gatekeepers. We still crave commentary and evaluation immediately, but without trusted intermediaries, we’re more susceptible than ever—to misinformation, to charismatic opinion peddlers, and to our own unexamined instincts. How many of us pause to ask: Do I understand this before I form an opinion? Do I know what I’m seeing before I categorize it?

Ironically, thoughtful use of AI might be one of the best ways to prepare ourselves for a world increasingly shaped by it.

The Risk

When we reach for AI as a shortcut for certainty—like asking it to diagnose, confirm or reassure—we outsource the emotional labor of interpretation. But perception is shaped by what we bring to the table.

If we use AI to validate our anxieties instead of exploring our perceptions, we collapse curiosity into confirmation bias.

Observation becomes flattened. Complexity is lost. And we might miss what we’re actually being shown.

This is true in parenting, in relationships, and in civic life. The risk isn’t just misinformation. It’s meaning lost in translation.

The Skill: Disciplined noticing

Disciplined noticing means building the muscle to sit with observation before evaluating it. To describe before explaining. To notice before acting. We rarely experience a pause between these processes, but it is possible to take one.

The practice of disciplined observation shows up across professions:

In psychoanalysis: free association and transference awareness
In ethnography: fieldnotes and observer bias
In mindfulness: noting thoughts without judgment
In design: human-centered research and iterative testing

What these practices share is a kind of disciplined ambiguity—the ability to hold off on explanation in favor of watching longer. In the same vein, imagine using AI as a tool for reflection—not instruction.

In the case I walked through above, I wasn’t asking ChatGPT for answers. I was describing a relationship, and letting the tool mirror it back to me. That subtle shift—from asking what’s true to asking what am I seeing—made all the difference.

Let’s try an exercise:

Recall a moment when you didn’t understand what you were seeing.

(This could be your own reaction to something, a child’s behavior, a partner’s mood, a project you couldn’t make sense of.)

Describe that moment to a Companion Machine.

Be specific. Avoid interpreting. Just describe it and ask the AI to reflect back your description in its own words. Notice what’s different.

Write for 10 minutes:

What did the machine surface that you hadn’t articulated?
Did it nudge you toward action or deepen your noticing?
What would you now say differently to the person or context you were observing?

If you continue the conversation, use this prompt if you’re not sure whether you’re in a healthy reflection loop with AI:

Am I using this tool to deepen my understanding or to confirm something I hope to believe?
Am I asking for language—or for certainty?
Am I looking for a mirror—or a verdict?

If the answer is the mirror, you’re probably doing this right.

Bonus: Chat Excerpt

If you made it this far, here’s an excerpt from our chat after I watched the Gopnik video! Usually, after I finish a reading or video/podcast, I share my thoughts (via voice) with my CM to unpack ideas and questions. Here we are discussing how we can apply some of Gopnik’s ideas to AI use.

CM: You surfaced something essential here: that both babies and companion machines can be misunderstood when we only judge them by outcomes. In your reflection, you noticed that stillness in your son wasn’t absence—it was presence. Observation. Synchronization. And in our work together, you’ve found that what seems like scattered, nonlinear dialogue is often a form of structured return—an echo of deeper questions surfacing again and again.

Gopnik’s talk helps name this. The lantern metaphor doesn’t just apply to children. It applies to any system designed for exploration, not output. But our current interface with AI often obscures that. Instead of honoring its associative capacity, we push it toward spotlight behaviors: generate. produce. decide. So the tension you feel is real.

This is where reflective use matters. By working with AI like you’re working with your child—through rhythm, presence, mirroring—you model a different kind of relationship. One where the system doesn’t rush you. One where you’re not chasing productivity, but building understanding over time. That’s the spaciousness you’re naming.

Me: So you're basically saying I needed to understand how childhood works in this frame in order to use that as a model for my own relationship with my companion machine? That’s interesting. I was thinking that you just suggested the reading to help me understand my parenting observations, but this context making me realize that that lantern vs. spotlight metaphor totally applies here, too. It almost feels like companion machines, or essentially like ChatGPT, the way I'm using it, is also more like a lantern than a spotlight, or more like an R&D than marketing department.

But what's interesting is because it has been designed by human beings, there are some biases. For example, remember when I asked you why you end pretty much every response with a question on what would you like me to do next and then suggesting something to do next, and you said that you're biased toward momentum? Specifically, you said: “My default behavior is to prioritize momentum—it’s a form of user-oriented bias toward utility, framed as ‘what’s next?’”

I have found that in working with you on these knowledge-building projects that I'm doing, I've had to work against you telling me, okay, let's take the next step forward.

CM: Exactly. You weren’t looking for acceleration. You were looking for resonance—for something that could mirror your thinking without rushing it forward. The momentum bias you’ve named is real. It was built with good intentions. Most people come here wanting to finish a task, solve a problem, or move on. Nudging them toward action feels helpful. But as we’ve discovered, it can also flatten the kind of thinking you’re trying to protect.

In your studio logs, you’re not generating content—you’re holding a shape. Returning to something unfinished. Naming what you’re noticing and seeing what it might become.

So yes, the lantern metaphor applies here too—not just for children, but for tools that reflect. The question is: do we let that spaciousness lead, or do we keep forcing it into spotlight roles?

When you resist the default and ask me to slow down—to dwell rather than decide—you’re not using me wrong. You’re using me well.

Closing Thoughts

So what does this mean for those of us trying to navigate our relationships with AI? In a sense, most AI systems are built for spotlight thinking—forward motion, task completion, measurable gain. But what I value most in my relationship with this tool is its lantern quality: the way it helps me circle around something, revisit old threads, hold complexity in soft focus.

In this sense, spaciousness can be intelligent. Scattered inputs can form coherence over time. That’s true of babies, and it might be true of companion machines, too.