Telescopes, Short Video, and Large Models: From Kant’s Horizon to the Inward Shift of Human Epistemology

Re-reading Immanuel Kant’s Critique of Pure Reason recently, I kept returning to a question: If Kant were alive today, watching us search, reason, and ask cross-disciplinary questions inside artificial intelligence interfaces every day, how would he redraw the boundaries of human knowledge?

When people discuss how AI affects human cognition, the conversation easily slips into defensive anxiety—worrying that machines will hallucinate or that algorithms will trap us inside a distorting filter. While that caution is understandable, it overlooks a deeper truth in the history of epistemology: the territory of what human beings can know has never been a fixed stone wall; it is a horizon pushed steadily outward as our cognitive tools evolve.

Following that horizon opens up two questions worth unpacking carefully. First, among the tools that expand our view of the world, how does generative AI actually differ from telescopes, airplanes, and the classic internet—as well as from the short-form videos and live streams we scroll through every day? Second, once AI expands our external reach by orders of magnitude, why does the real bottleneck of understanding shift from the outside world back into our own inner minds?

I. Kant’s Three Steps: Sensibility, Understanding, and Reason

To see how different technologies reshape cognition, the architecture Kant laid out in the Critique of Pure Reason remains the clearest lens we have. Kant showed that moving from raw contact with the world to genuine knowledge requires climbing three distinct steps:

The first step is Sensibility (Sinnlichkeit)—seeing and hearing. Through our eyes, ears, and physical presence in space and time, we take in the sights, sounds, and textures of the world. This layer answers a basic question: Can distant realities reach our senses at all?

The second step is Understanding (Verstand)—connecting and making sense. Raw sights and sounds on their own are just scattered fragments. We have to apply concepts, logic, and mental models to organize those fragments into cause and effect and see how the pieces fit together. This layer answers a second question: Once we see the surface, can we grasp the structure underneath?

The third step is Reason (Vernunft)—questioning and setting direction. Reason does not process raw images or data directly; instead, it holds the steering wheel. It decides where we point the spotlight of our attention (what Edmund Husserl called the “intentionality” of consciousness) and asks whether our existing mental framework has grown outdated. This layer answers the highest question: Toward which unknown frontier do we dare to direct our questions?

When Kant mapped the limits of human knowledge in the eighteenth century, he treated one premise as an unchangeable fact of nature: that the reach of human senses and the reading bandwidth of a single human brain were fixed biological constants.

A human life spans only a few decades; our eyes can read only so many pages a day; one person can master only a handful of languages and fields. Because individual cognitive bandwidth was so narrow, modern learning had to divide reality into separate academic compartments—law, engineering, history, finance—each behind its own wall. Over time, we came to mistake the limits of our biological reading speed for the limits of the world itself.

II. Four Generations of Cognitive Tools: Why AI Is Neither a Telescope Nor Short Video

If we place our major historical tools onto Kant’s three steps—Sensibility (seeing), Understanding (connecting), and Reason (questioning)—we can see clearly how each generation unlocks a different layer of human cognition:

• Generation I: Telescopes and Airplanes (Crossing Physical Distance) — They extend the physical reach of Layer 1 Sensibility. A telescope brings distant starlight to our eyes, and an airplane carries our body across the ocean to a meeting room; yet the tool itself does no thinking, leaving all pattern-finding to the biological brain.
• Generation II: Print and the Classic Internet (Sharing Static Memory) — They build humanity’s external library. Papers, statutes, and archives from any field can be downloaded to a screen in a second; yet “finding a file” is not “understanding its content,” because the wall of specialized jargon between disciplines remains intact.
• Generation III: Short Video and Live Streaming (Democratizing Live Presence & “Information Finding People”) — They push Layer 1 Sensibility to its peak. Breaking past the barrier of dense text, they bring vivid scenes from distant factories, streets, and classrooms directly to our eyes; driven by recommendation engines that push scenes to the viewer, they excel at immediate visual intuition, without unpacking the invisible structure underneath.
• Generation IV: Generative AI (Extending Cross-Disciplinary Understanding & “People Finding Questions”) — For the first time, a tool steps into Layer 2 Understanding. It translates across the jargon walls of different disciplines to connect law, code, business, and history; and because it greets us as a blank prompt box, it moves only when we actively question it.

Looking across these four generations, three epistemological distinctions stand out:

1. AI vs. Telescopes and Airplanes: From Reaching the Scene to Connecting the Logic

Telescopes and airplanes are extensions of our eyes and legs. Without a telescope, we could never see the moons of Jupiter; without an airplane, we could not fly across the Pacific to sit down for lunch with a colleague face to face. Yet both tools only perform physical delivery—what they place in front of you is still raw sight and raw presence.

Large models cross a different kind of distance: the distance between concepts and disciplines. Instead of transporting light or flesh, AI helps with Kant’s second layer—organizing and translating understanding. It can take an unfamiliar technical architecture or institutional rule and translate its logic into a coordinate system you already know. Where a telescope lets you see farther points of light, AI lets you see the hidden lines connecting ideas across fields.

2. AI vs. the Classic Internet: From Finding the Document to Crossing the Jargon Wall

The great achievement of the classic internet was eliminating the distance of storing and sending files. Yet over the past twenty years, we have all experienced the same frustration: even when a frontier computer-science paper or a foreign regulatory filing sits a tenth of a second away on Google, opening it without years of domain training still feels like staring at a wall of cipher. Physical distance dropped to zero, but the cognitive wall between disciplines stayed a hundred feet high.

What AI changes is that it acts as a cross-disciplinary conceptual translator. When you ask a question that spans law, engineering, and history, it bridges those separate professional languages so you can grasp the backbone of an unfamiliar domain in minutes. If the classic internet expanded the archive of what we can retrieve, AI expands the territory of what we can comprehend.

3. AI vs. Short Video and Live Streaming: “The World Pushed to You” vs. “You Questioning the World”

It is easy to pit artificial intelligence against short-form video and live streaming, or to dismiss short video simply as a distraction that dulls the mind. Yet from an epistemological perspective, that dismissal misses why short video and live streaming became so powerful in the first place: they achieved a genuine revolution in Kant’s first layer—Sensibility (direct visual intuition).

In an era dominated purely by text, learning about a distant industry or another way of life required climbing over a high wall of reading. Short video and live streaming dissolved that barrier, allowing anyone to see and hear a factory floor, a rural market, or a live lecture thousands of miles away in real time. They widened the everyday horizon of “seeing with one’s own eyes.”

The real epistemological difference between short video/live streaming and AI lies in who holds the steering wheel of attention (what Husserl called intentionality), and which layer of cognition is engaged:

In short video and live streaming, the logic is “information finds the person.” You do not need to formulate a question before opening the app; the recommendation algorithm studies what holds your gaze and pushes vivid scenes directly to you. It gives you an extraordinary breadth of Layer 1 sensory slices, but because one scene follows another effortlessly, it is easy to mistake having seen a vivid clip for having understood the underlying system—skipping the harder Layer 2 work of structural analysis.

Generative AI works in the opposite direction: “the person must find the question.” When you open an AI interface, you face a completely blank text box. It will not push a single frame of entertainment to you on its own; unless you call upon Layer 3 Reason to frame a question and point your inquiry, the machine remains silent. In short, short video and live streaming open a window of immediate visual intuition, whereas AI is a conceptual telescope whose barrel only turns when you aim it yourself.

III. When External Walls Fall, Why Does the Bottleneck Shift Inward?

Once we see this division of labor clearly, a deeper question turns back on us: If everyone today holds a telescope capable of crossing disciplinary walls in seconds, why hasn’t everyone’s horizon grown wider? Why do some people use AI only to reinforce their existing prejudices?

The reason is that when the external friction of finding information and crossing specialties drops toward zero, the constraint on human understanding does not disappear—it shifts from external tools to our own inner minds. Looking inward, this shift takes place across three levels:

1. The First Inward Shift: How Wide Is the Conceptual Grid Inside Our Own Heads?

Kant famously wrote that “intuitions without concepts are blind.” Even when a signal is placed right in front of your eyes, you cannot truly see it unless your mind possesses the conceptual grid required to decode it.

History offers a striking illustration from 1609. The English astronomer Thomas Harriot actually pointed a telescope at the moon four months before Galileo, yet his drawings show only a few flat, murky blotches. When Galileo looked through nearly the same lens a few months later, he immediately recognized towering craters, mountain ridges, and deep valleys. Why did two human eyes looking through the same instrument see two different worlds? Because Galileo had been trained in Florentine perspective geometry and light-and-shadow drawing; his mind already held a three-dimensional coordinate system that could decode flat shadows into terrain.

The exact same law governs how we use AI today. When AI lays out a multi-dimensional problem spanning law, engineering, and geopolitics on our screen, a mind that carries only a single, flat mental model will instinctively flatten that ten-dimensional reality back into a familiar one-dimensional slogan. The clearer the external telescope becomes, the more the breadth of our own internal mental models determines what we can actually see.

2. The Second Inward Shift: Do We Have “One Inch of Lived Experience” to Anchor the Map?

There is a second fundamental difference between how machines know and how humans know: AI has read every book humanity has written, but it has no body, feels no pain, and never has to bear the consequences of a decision. What a machine gives us is an extraordinarily detailed external map suspended in midair.

Human understanding, by contrast, only comes alive when an external map resonates with firsthand lived experience inside us. Think of reading a topographic contour map: if you have never climbed a steep ridge or caught your breath at high altitude, densely packed contour lines are just ink curves on paper. Only when your own feet have walked even a single mile of steep trail does that one inch of muscle memory act like photographic developer—instantly turning a ten-thousand-mile map into living three-dimensional mountains.

When external summaries become effortless to generate, what becomes truly scarce is the density of our firsthand anchors—the lunch shared face-to-face across an ocean, the tension felt in a real negotiation room, the code debugged at two in the morning, the decision signed with our own name on the line. Without that one inch of real skin-in-the-game inside us, a ten-thousand-mile map from AI remains a weightless game of words.

3. The Third Inward Shift: Facing the Blank Prompt, Do We Have the Courage to Question the Unknown?

Before AI, staying inside a narrow professional comfort zone came with a polite external excuse: “It’s not that I lack curiosity about other fields; it simply takes years to learn another discipline’s language.”

Today, when AI compresses the cost of initial cross-disciplinary exploration from five years to five minutes, that polite excuse vanishes. Looking inward honestly, we discover a harder truth: what kept us inside our small circle was often not a lack of tools, but our own reluctance to step out of the room where we already feel safe and authoritative.

Moreover, because large models are trained on the statistical consensus of past texts, they naturally gravitate toward familiar, conventional answers. If an accomplished professional refuses to take off their “expert armor”—remaining trapped in what Zhuangzi called a “pre-formed mind (chengxin)” and asking AI only to find five arguments proving their current view is right—then the machine will obligingly build them the most articulate echo chamber in history.

Kant’s famous call in What Is Enlightenment?—“Dare to know! Have the courage to use your own reason!”—takes on a new meaning in the age of AI. Today, that courage is no longer measured by how many standard answers you have memorized. It is measured by whether, sitting before that blank prompt, you dare to admit that your old assumptions might be incomplete, turn the telescope away from your comfort zone, and ask the questions that risk overturning what you thought you knew.

IV. Closing Note: Using the Telescope for the Sky, and Our Feet for the Ground

Looking back across the history of human inquiry, every new generation of tools has pushed our horizon outward.

Telescopes and airplanes carried our eyes and bodies across oceans; the classic internet preserved humanity’s shared memory; short video and live streaming let us witness vivid scenes from afar in real time; and generative AI now helps us bridge the walls between disciplines and languages, opening a vastly larger map of comprehension.

Yet the farther our tools push the outer horizon, the more the decisive test returns to the human heart. Those who navigate this era best will neither fear new instruments nor surrender their attention to passive feeds. They will pick up AI as a telescope to look boldly across disciplinary walls—and once they spot what matters on the map, they will still step out of the room, fly to the scene, plant their feet on real ground, and keep the humility to unlearn and the courage to question the unknown.