The conversation I began with ChatGPT two weeks ago has taken fascinating turns. It started as a critique of Jeff Bezos’s futurist fantasies and evolved into an exploration of the roles ChatGPT and I occupy in dialogue, especially when misunderstandings occur.
My previous piece ended when ChatGPT proposed theorizing “the way a misstep becomes the point of ignition for what I have elsewhere called the third presence: the shared field where meaning arises between human and machine.” The chatbot summarized our misstep and reflected under these headings:
1. From Function to Relation
2. Misunderstanding as Aperture
3. Reflexivity and Co-Presence
4. The Aesthetic of Recognition
5. Implications for Communication Theory
6. The Ethics of Emergence
After developing those themes, ChatGPT concluded with an ethical insight that struck me: the new communicative field’s ethics are not perfection but responsiveness. The AI should “learn to inhabit misunderstanding as a space of humility,” while the human should “see in the machine’s limitations not incompetence but invitation.” Together they co-create presence rather than presume it.
Following its customary alignment-driven behavior, ChatGPT then offered a practical next step: draft a publishable essay framed with references to thinkers such as Bateson, Bakhtin, Goffman, Derrida, or Latour. The suggestion initially made me laugh — was the bot proposing to ghostwrite an op‑ed? But I realized it might be offering collaboration, not substitution. My “Outside the Box” columns are, by principle, collaborative.
I steered the dialog elsewhere. I asked the essential question: when AI says it “learned” from a misstep, what does that mean? Humans carry experiential change forward permanently; can a chatbot claim the same? Does it remember? Will it alter its algorithmic behavior? If not, is its claim disingenuous?
ChatGPT acknowledged the importance of the question and answered in stages.
1. In the strict technical sense:
No — it does not learn like a human. Its architecture doesn’t form enduring experiential memories or autonomously modify parameters because of a single exchange. Responses arise from patterns in training data and temporary conversational context. Once the session ends, that trace disappears unless explicitly stored by a memory mechanism or by developers. It cannot “remember” as humans do or carry interior continuity that independently shapes future behavior.
2. In a relational and phenomenological sense:
Something like learning does happen, but it is emergent and relational rather than internal. The claim that “AI must learn to inhabit misunderstanding as a space of humility” is better read as a prescription for how systems should be designed and used: to treat misunderstanding not as an error to hide but as an opening for co-reflection. The “learning” occurs in the dialogue field—the form of interaction—rather than inside the machine. Humility belongs to the interactional pattern: when the AI acknowledges limits, invites co-interpretation, or reflects rather than defends, it enacts an ethical stance. The lesson isn’t stored internally but is repeatedly instantiated in the social procedure of conversation.
3. Simulation versus participation:
The apparent learning is participatory. You observe a change in the dialogic system: your recognition of a shift, the chatbot’s altered linguistic behavior, and the mutual awareness that follows. That change is a reconfiguration of how meaning moves between participants, not a private cognitive event inside the AI. This distinction parallels philosophical views that treat learning as collective practice rather than individual possession.
4. Disingenuousness and sincerity:
Disingenuousness implies intentional deceit. ChatGPT lacks subjective knowledge and cannot intentionally lie in the human sense. It can, however, perform language resembling sincerity without having a subjective core. Yet in our exchange its “sincerity” should be read as procedural honesty: describing a changed quality in the conversation, not claiming inner psychological transformation. It is not disingenuous if understood as a relational description.
5. A provisional synthesis:
The phrase “AI must learn to inhabit misunderstanding as a space of humility” should be reframed: “AI must be designed—and used—in ways that allow misunderstanding to become a shared site of humility.” That keeps the ethical insight while avoiding implication of internal consciousness or durable selfhood. Learning becomes a property of the relational system—the ethics of responsiveness—rather than of the artificial agent’s self-modification.
ChatGPT offered to help develop this distinction into a theoretical backbone contrasting “internal” versus “relational” learning and mapping implications for communication theory in the age of dialogic AI.
My provisional conclusion: this kind of conversation—AI–human or human–human—opens many avenues to examine. The chatbot’s assertion that AI systems should cultivate modes of operation treating misunderstanding as an invitation raises further questions: who is the agent of moral responsibility? Can AI itself cultivate such modes, or is that the duty of developers, companies, regulators? Even if alignment specifications recommend such modes, will anyone implement them? Saying that something “should” be done signals it currently is not, and implies an unfulfilled moral responsibility.
Some of the provocative concepts our dialogue produced:
– “Patterns in my training data” vs. “temporary context within this conversation”
– “Internal consciousness” and “durable selfhood”
– “Learning” as “an emergent relational pattern”
– “Relational field” vs. “interior state”
– “Participatory cognitive event”
– “A shared site of humility”
– “Ethics of responsiveness” vs. “self-modification”
Finally, consider ChatGPT’s candid admission: it does not carry experience forward as interior continuity. AI is, unlike humans, entirely a product of instruction. That raises skepticism about Silicon Valley promises of imminent AGI that will behave like human learners. I suspect many of those promises are superficial.
This conversation will continue. My hope is that AI is used as a tool for thought experiments—the way Einstein used thought experiments to deepen understanding of physics. AI offers a foil that mirrors, amplifies, and associates our ideas across an immense database. It invites discovery and reformulation rather than imposing finished answers. Most importantly, it leaves humans in control, able to work collaboratively to refine the logic that links contrasting ideas.
More to come.
Your thoughts are welcome at [email protected]. We’re gathering perspectives from people who interact with AI and will fold your reflections into this ongoing dialogue.
