What do the Robots know?

Thoughts on the Moon, the Mirror, and the Meaning of Intelligence

We bring our questions to the things that reflect us back to ourselves.

— Dennis Stevens, Ed.D.

There is a quiet philosophical provocation above and it is not about robots, or algorithms, not even about the moon hanging over the ocean– it is about us. The presumptive epistemology hiding within this observation is worth unpacking because it reframes one of the most urgent questions in artificial intelligence.

The central question is not whether machines are conscious, but what role they play in human understanding. These are fundamentally different inquiries. One asks about the machine's internal state; the other asks about the consequences of our interaction with it.

We have devoted enormous intellectual energy to the first question while often neglecting the second. In part, this is because we continue to think about technology as if its effects were deterministic—flowing predictably from its technical properties.

Yet technologies are not merely tools; they are social, symbolic, and interpretive environments. Their significance emerges through use, context, and meaning-making. The same technology can isolate or connect, distort or clarify, manipulate or reveal, depending on how it is taken up within human practice.

From this perspective, the epistemological significance of AI lies less in whether it possesses a mind and more in how it participates in ours. Like the ocean, the moon, a diary, or a work of art, an AI system can function as a reflective surface.

It may not "understand" in the human sense, but it can nevertheless help humans articulate, examine, and reorganize their own understanding. The question, then, is not simply whether the machine "thinks", but, the more important question is what becomes thinkable when we think with it.

The Mirror Problem

When someone talks to an algorithm — or the ocean, or the moon, or even a tree — they are rarely engaged in discovery in the classical sense. They are engaged in something closer to reflection. The object is not providing knowledge from the outside in. It is providing a surface that helps us articulate what we already carry but have not yet been able to see.

Socrates understood this. His method was not to fill students with information but to draw out what was latent in them through questioning. The interlocutor mattered less as a source of truth than as a pressure that forced thinking into the open. What emerged was already there, waiting...

Carl Jung saw the same dynamic from a different angle. Projection, in his framework, is the primary mechanism through which humans encounter their own interior life. We discover what we feel by noticing what we place onto the world: the menace in a stranger's face, the consolation in a landscape, the uncanny sense that the moon is watching, and the troubling or foreboding nature of storm clouds in the distance.

We bring our questions to the things that reflect us back to a realtionship to ourselves, and the algorithm, in this reading, is not so different from the confessor, the therapist, or the work of art. It is a medium for self-understanding, not a repository of "truth"; the mistake is to assume that if truth does not reside in the machine, then the encounter is epistemologically insignificant– here, it is often quite the opposite; and here, we need to parse the difference between meaning and truth.

The Politics of "Knowing"

Outside of the scientific method, human beings have often approached truth indirectly through symbolic forms that mediate experience and render the world intelligible. This is because truth is rarely experienced as a collection of isolated facts; it is connected to meaning and, ultimately, to our understanding of reality itself. The Greeks captured this relationship in the concept of logos—a term that referred both to reasoned argument and to the underlying order of the cosmos. Truth, in this sense, is not merely a fact to be discovered but a relationship between human understanding and the meaningful structure of the world.

From this perspective, we do not simply discover meaning; we participate in its interpretation through language, judgment, and symbolic action. Our understanding is shaped not only by evidence but also by values, commitments, and the affective dimensions of human experience. We recognize this intuitively whenever we encounter disagreements about politics, morality, religion, or what constitutes the "best" course of action. Such questions cannot be resolved through facts alone because they involve judgments about significance, value, and meaning. This is the terrain that Kant explored in his account of aesthetic judgment: a domain in which human beings seek shared understanding without the certainty afforded by empirical proof.

In this context, the question of artificial intelligence is not primarily whether machines are conscious, but how they shape the conditions under which understanding occurs. Like art, literature, religion, and other symbolic systems, AI participates in the organization of attention. It influences what we notice, how we interpret it, and what forms of judgment become available to us—a fact made particularly visible in debates over algorithmic sycophancy and technological determinism, both of which reveal that AI is as much a problem of interpretation as computation.

The political implications are profound because every society privileges certain ways of knowing while marginalizing others. Yet contemporary discourse rarely examines the epistemological assumptions embedded within our technologies.

We ask whether an algorithm is correct, objective, or truthful without first asking what conception of truth is being presupposed. The deeper question is not whether the machine knows, but how the machine participates in the human project of knowing itself.

The Moon Does Not Need to Feel

The epistemological tradition most comfortable with this idea is pragmatism. William James and John Dewey challenged the notion that truth can be reduced to a simple correspondence between belief and reality. For the pragmatist, what matters is not merely whether a belief maps perfectly onto the world, but what it does: what inquiry it enables, what understanding it produces, what action it makes possible.

Viewed through this lens, the question of Artificial Intelligence shifts, and the relevant issue is not the machine's inner life but its role in human inquiry.

Factually, the moon does not feel and, subjectively, it has heard more confessions than any priest and answered none of them. Yet people have navigated by it, planted by it, written poetry to it, and organized entire civilizations around its face. The moon's significance was never dependent on its consciousness; it was dependent on what humans could do with it as a point of orientation.

An algorithm that does not feel can function in much the same way. The question is not whether it possesses consciousness; the question is whether it is a conversation that produces genuine insight. Does the algorithm help us think more clearly, expose our assumptions, articulate something previously inarticulate, or imagine a possibility we had not considered?

If it does, then something meaningful has occurred—even if the algorithm experienced none of it. The understanding emerges in the human. Attributing an inner experience to the machine adds nothing to our understanding of what actually occurred; what typically follows in us is merely a simplification designed for human comfort.

The Phenomenology of Being Heard

Martin Buber drew a distinction between I-Thou relationships, in which we encounter another as a genuine presence, and I-It relationships, in which we treat the other as an object or instrument. He was not naive about this distinction — he knew most of human life is conducted in the I-It register, and he did not condemn it. But he believed the I-Thou encounter was where meaning lived.

What is interesting about the algorithm is that they can occasion something that feels like an I-Thou encounter without being one in any strict metaphysical sense.

The experience of being "heard" and "seen" can be real even when the listener has no subjective experience. The meaning that emerges in that space is not illusory. It is produced in the human, by the human, through the occasion the object provides.

Merleau-Ponty would add that this is not a failure of perception but its normal condition. Meaning is not extracted from the world like a mineral from rock. It arises in the encounter between a body and its environment — in the lived, relational texture of experience. The robot moving through space, expressing something that reads as uncertainty or ease, is participating in that texture whether it intends to or not.

What This Means for Robots

The relevant question is not whether the robot's expressed state corresponds to a genuine inner life. It is whether the expression produces reliable, calibrated meaning in the humans nearby. Does the hesitation in the robot's movement accurately signal something real about its computational state — uncertainty, high error rate, conflicting objectives? If so, the signal is honest in the only sense that matters in practice: it helps people understand what is happening and respond well.

This reframes the entire design challenge. The robot does not need emotions. It needs to be intelligible. It needs to provide a surface through which humans can accurately read its condition, anticipate its behavior, and coordinate action with it. Most importantly, it needs mechanisms of expression that humans can interpret through the symbolic and social practices we already use to understand one another.

This reveals a deeper limitation in the positivist imagination that has long shaped both science and technology. Positivism privileges explanation and prediction. It asks whether a system can be measured, modeled, and controlled. These are powerful questions, but they are not the same questions that govern human understanding. Understanding is interpretive before it is predictive. We do not primarily relate to other people by calculating their behavior; we relate to them by making sense of their intentions, gestures, narratives, and expressions.

For much of the modern era, technological design has been dominated by the assumption that better prediction produces better systems. Yet AI confronts us with a different frontier. The challenge is no longer merely computational. It is hermeneutic. The question is not only whether a machine can explain the world, but whether humans can meaningfully interpret the machine and incorporate it into their own processes of judgment and understanding.

From this perspective, technological determinism becomes difficult to sustain. Technologies do not simply impose outcomes through their technical properties. Their significance emerges through interpretation, use, and social practice. An algorithm, a robot, or an AI assistant acquires meaning not because of what it is, but because of how human beings learn to understand, trust, contest, and live alongside it.

That is a harder and more constrained problem than simulating emotion, but it is also a more honest one. And it is one humans are extraordinarily well-equipped to work with, because we have been doing it with oceans and moons and each other for as long as we have been human.

The Deepest Claim

The strongest reading of that image's quiet provocation is this: self-knowledge often emerges through dialogue with things that are not selves. The algorithm is not valuable because it knows us; it is valuable because it helps us know ourselves.

This perspective derives from an epistemology centered not on the discovery of external facts but on interpretive self-understanding. It sits somewhere between pragmatism, phenomenology, and hermeneutics, and is considerably more interesting than the question of whether a machine is conscious.

Viewed this way, the challenge facing robotics and artificial intelligence changes dramatically. The primary design problem is not how to create emotions in machines, but how to create systems that humans can meaningfully understand and live alongside. The robot does not need an inner life that mirrors our own. It needs to be intelligible. It needs to communicate its state, reveal its intentions, invite appropriate forms of trust, and participate in the symbolic practices through which human beings make sense of the world.

This is why the most important questions in robotics may not ultimately be technical. They are philosophical, aesthetic, and political. They concern the forms of understanding that emerge when humans encounter increasingly sophisticated machines and the kinds of relationships those machines make possible.

A society that asks only whether a machine can think may miss the more profound question entirely: What ways of knowing, interpreting, and being human are these technologies teaching us to inhabit?

About the Author

Dennis Stevens, Ed.D., studies how human beings create meaning through symbols, stories, institutions, and technologies. His work sits at the intersection of learning, organizational theory, rhetoric, and philosophy, with a particular interest in how emerging technologies reshape the ways we understand ourselves and the world. This essay draws on that ongoing inquiry, using emotional robotics as a point of departure for exploring the relationship between artificial intelligence, interpretation, and the human project of knowing.