Today’s AI can explain a great many things it has never actually experienced.
It can tell you how to raise a child, design a building, lead a team, or make a film. It can analyze a conflict, propose a strategy, or explain why a relationship went wrong. It can move across enormous domains of knowledge and, within seconds, produce an answer that is structured, coherent, and sometimes genuinely insightful. More importantly, this capacity is becoming an ordinary part of how we think. In the past, when we encountered an unfamiliar problem, we might search for information, read a few articles, ask other people, and slowly form an understanding of our own. Now, increasingly, the moment a question appears, we begin thinking through it with AI.
But lately I have found myself returning to a more basic question: Is what AI knows about the world the same kind of knowing that comes from actually living in it?
Most of what today’s AI encounters has already passed through several layers of translation. Something happens in reality. A person experiences it, notices only part of it, interprets it through prior experience, then tries to speak, write, photograph, measure, or record what happened. Some fraction of that becomes text, images, video, documents, or data.
Roughly, the chain looks like this:
reality → experience → perception → interpretation → language → data
At every step, something is compressed.
What Representation Leaves Behind
This compression is not a defect of representation. Civilization depends on it. We do not have to experience a war ourselves to understand something about war through history; we do not need to repeat thousands of experiments to inherit what others have discovered. Language, images, and systems of knowledge allow experience to travel across people, time, and place.
But representation has never been the same thing as experience. A space can be described with extraordinary precision—its dimensions, materials, lighting, acoustics—and still feel different once you walk into it, when your body encounters scale, temperature, sound, and atmosphere all at once. A meeting may leave behind a complete transcript, but the people who were actually in the room may know that the direction of the conversation changed not because of any particular sentence, but because someone hesitated, someone else did not respond, or the relationship among several people subtly shifted in a particular moment.
This loss already exists between people. Someone lives through an event, turns it into an interpretation, and expresses it through language. Another person hears the account and reconstructs its meaning through a different set of experiences. We often imagine that we are exchanging experiences when, more precisely, we are exchanging representations produced after experience has already been compressed.
AI magnifies this process to an unprecedented scale. It is extraordinarily good at working with what has already been expressed, recorded, categorized, and preserved. It is becoming equally good at finding structure among those representations, completing incomplete logic, and drawing connections across them.
Yet some capacities do not emerge simply from exposure to more information. A child does not learn to walk because he understands that falling results from a loss of balance. He falls, repeatedly, until his body gradually learns how to correct itself. Someone entering a profession does not acquire mature judgment by reading every available best-practice guide. She has to make mistakes, see how other people react, and discover why a plan that made perfect sense on paper can fail once it reaches the world. Many things we later call experience, intuition, judgment, or taste grow this way.
There is nothing necessarily mystical about them. Often, they are simply what remains after years of feedback from reality have been compressed into judgment. An experienced director may not be able to explain exactly why a performance feels wrong. An architect may look at a detail and immediately sense that it will cause trouble later. A creative director presented with ten perfectly defensible directions may quickly know that eight of them are not worth pursuing. They do not necessarily possess some secret rule. More often, they have seen, made, broken, revised, and discarded enough things that judgment no longer needs to summon every rule explicitly.
I have started thinking of this kind of intelligence—the kind formed through sustained contact with reality as grounded intelligence.
It is not a mystical faculty reserved for humans, nor is it a way of drawing a permanent boundary around AI. It is simply a reminder that intelligence does not form only when information is understood and processed. It also forms through repeated collision between a person, or a system, and the world. Reality provides feedback, but it also imposes constraints. Some forms of understanding emerge only after consequences have actually been borne.
When Coherence Arrives Too Early
This matters more in the age of AI because AI is not merely good at processing representations. It is especially good at making things coherent.
Give it fragments, and it can quickly organize them into a structure. Give it a vague idea, and it can build a complete explanation around it. Give it a contradiction, and it will often find a plausible reason that makes the inconsistency disappear. This is one of AI’s most powerful qualities, and one of the reasons we increasingly rely on it.
Reality, however, is not always so orderly. Sometimes a user behaves in a way that does not fit the persona we designed. A child learns differently from the way we expected. A material produces an effect that never appeared in simulation. A cultural phenomenon emerges before we have a useful category for it. At first, these things are rarely elegant. They show up as anomalies, deviations, or simply as a vague sense that something is off.
Our instinct is often to explain that discomfort away. AI happens to be exceptionally good at helping us do so.
Sometimes, though, an explanation arrives too early. The fact that something does not yet make sense does not mean that it is meaningless. It may simply mean that we have not developed the language required to understand it. Many discoveries begin not as better answers but as moments when someone notices: Why is this different from what we expected?
AI is very good at making the world legible. Yet many of the world’s most interesting things exist precisely where our existing explanations stop working. One important function of grounding is to keep those things in view before they have been fully absorbed into logic—to allow reality not only to verify our hypotheses, but to continue generating new questions.
From this perspective, reality is both feedback and a source of novelty. Real users do not move neatly through a predefined journey. An audience is not moved by a concept simply because its logic is airtight. Reality continually exposes the distance between our models and the world, while also feeding the system things we never anticipated.
An intelligence that operates only among existing representations can still produce astonishing combinations. But accidents, contradictions, breakdowns, misunderstandings, and surprises introduce another kind of change. They do not merely extend an existing logic. Sometimes they force the logic itself to change.
The Weight of Reality
Grounding also matters beyond cognition.
AI has made thought and possibility extraordinarily fluid. An argument can instantly be rewritten into another version. One idea can branch into ten directions at once. A future can be simulated before it exists. Even identity can be continuously narrated, revised, and reconfigured. We are increasingly able to enter new possibilities, and increasingly able to leave old ones behind.
That fluidity creates enormous freedom. It can also produce a peculiar sense of suspension. Not a complete loss of contact with reality, but a condition in which many things become very light: infinitely generatable, explainable, revisable, and reconstructable, without an equal amount of weight arriving from the world itself.
Human life is still shaped by things that are much less fluid. Time passes. Bodies become tired. Other people have wills of their own. One decision changes the conditions of the next. A relationship requires continued presence. A failed project leaves consequences behind.
We can discuss love, responsibility, growth, or meaning without limit, but once we actually bear the consequences of a choice, those words begin to mean something different. The weight of reality is not only a constraint. It is also what gives many abstractions their shape.
Grounding, then, is also existential. It allows meaning to exist not only in interpretation, but in what we are willing to carry.
Not a Return to the “Real World”
This is why I do not find it useful to think of grounding as a call to return to the real world, as if AI and reality were two separate territories.
AI will become more deeply embedded in how we think, work, and learn. It will enter physical spaces, devices, education, culture, and relationships. It is unlikely to remain an external tool. Nor are humans and AI likely to develop along two independent tracks. We have already begun changing one another.
AI will change how a person searches for answers, develops opinions, and creates. It may also help make certain previously inarticulate intuitions easier to express. People, meanwhile, will shape their relationship with AI through repeated acts of choosing, rejecting, correcting, and using it over time. New forms of collaboration will emerge from that history.
If this relationship continues to deepen, the question becomes much less useful when phrased as: Which tasks should humans do, and which should we hand over to AI?
Someone who spends years creating with AI will not emerge with the same judgment they would have developed alone. AI will widen the space of possibility, exposing directions that person might never otherwise have considered. It may also force them to become more precise about why one direction fails and another succeeds. At the same time, that person’s experience, preferences, choices, and encounters with the world will continually reshape how they work with AI.
Judgment itself will change inside the relationship.
Education will face a similar problem. A child who grows up with AI may never experience it as a “tool” in the sense that we do today. It will simply become part of the cognitive environment. AI can help someone ask better questions, but it can also allow them to move past uncertainty too quickly. It can help construct hypotheses, but it can also perform the work of observation and interpretation before the person has really encountered the thing being observed.
We will need to understand how this long-term relationship shapes judgment, curiosity, and agency. We will also need to design forms of interaction in which AI does more than continuously offer better and more complete representations—forms that sometimes send us back toward places where the answer has not yet formed.
Physical experience should not become the opposite of AI, either. AI will exist in spaces, public environments, and human interaction as well. The question is what it does once it gets there. Does it simply keep making the world smoother, more predictable, and easier to explain? Or can it help us engage more deeply with what cannot be completely structured in advance: a particular person, a material, a place, a shared event, a consequence that cannot be infinitely undone?
AI and reality are not competing systems. Grounding is closer to a condition that prevents an increasingly intimate human–AI relationship from circulating only within its own representations.
Letting the World Disagree
Humans and AI may become increasingly coherent together. We may also become increasingly suspended together.
A theory can be jointly developed, refined, explained, reconciled with its contradictions, and polished into an extraordinarily persuasive system. And reality may still disagree.
The point of grounding is to keep allowing reality into that process of co-evolution—to introduce feedback, surprise, constraint, and things that have not yet been made logical.
AI will make more and more things easy to explain. It will give us increasingly powerful ways to organize the world, describe it, and simulate it. At the same time, we will need to preserve our ability to remain in contact with what has not yet been explained, structured, or converted into representation.
Those places are not merely noise. They are often where new understanding begins.
The world is richer than its representation.
As humans and AI begin to think, create, and evolve together, grounding is what gives that intelligence weight—and what keeps it capable of being changed by the world.
About the author
Qinqin (Stella) Yang is a creative AI builder and experiential creative director. She builds workflows, systems, and creative direction that operate under real constraints, and designs speculative frameworks that expand what teams can imagine before they start making. Over the past decade she has built brand experiences, product design, and speculative projects for Tencent, IKEA SPACE10, Burberry, and the Palace Museum. She is the founder of Actual Fiction, where she develops AI-native tools that turn imagination into a working method.
This newsletter is where she thinks out loud about creativity, AI, and what it means to build toward a future that doesn’t exist yet.
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