Just ask ChatGPT, and the vanishing mind

We are building machines that can answer almost any question while quietly forgetting how to ask one.

The convenience is real. So is the loss.

Human beings were not shaped by answers alone. We became capable by struggling with questions, making mistakes, remembering where we failed, trying again and staying with a problem long enough for an understanding to emerge. The difficult part was never merely reaching the answer. The difficult part was becoming the kind of person who could reach it.

That is the part technology can quietly take away.

I understood this years ago, on a winter evening in Hazaribagh, when Mukesh Chacha refused to let me get off a bicycle despite my deep desire to give up. I would fall, scrape my knees, lose my balance and ask him to hold the bicycle while I tried again. He refused. He would pick it up, put me back on the seat and make me ride.

At the time, I thought he was being unnecessarily cruel. I wanted someone to show me how to balance so that I could simply learn it. What I did not understand was that there was no explanation he could give me that would do the work my body had to do for itself. I had to fall often enough, try often enough and correct myself often enough for balance to become instinctive.

Decades later, I can still ride a bicycle.

The knowledge survived because I had not just received it. I had acquired it through effort.

Much of what we call learning works in exactly the same way. A mathematical proof stays with us when we understand how a step leads to another rather than simply memorising its conclusion. A difficult book changes us when we struggle through its argument instead of reading a summary of it. An architect begins to understand a building after drawing it, measuring it, walking through it and observing how light, weather and people interact with its spaces. A writer becomes better by writing, failing, rewriting and discovering slowly what he actually wants to say.

The answer is only the visible part of learning. The harder and more important part is what happens to the person while searching for it.

For most of human history, there was no way to escape that process. If you wanted to become a mathematician, you had to do mathematics. If you wanted to become an architect, you had to draw, measure and observe. If you wanted to write well, you had to write badly for a long time before you learned how to write well. Knowledge could be preserved in books and passed from one generation to another, but the ability to use that knowledge still had to be built inside the individual.

Technology changed many of these conditions, and much of that change was liberating. While the telescope extended the eye, the microscope revealed worlds too small to see, the calculator removed tedious arithmetic and the computer transformed the speed and scale at which we could work. Yet these tools did not necessarily remove the need to understand the work itself.

Artificial Intelligence (AI) is different in one important respect. It can increasingly give us the finished intellectual product before we have gone through the intellectual struggle that once produced it.

That difference deserves far more attention than the usual arguments about whether AI will take our jobs or make us more productive.

What happens when a student can obtain an explanation without struggling with the problem? What happens when a writer can produce a polished essay without wrestling with an argument? What happens when an architect can generate hundreds of images without first learning to understand the site? What happens when a lawyer can summarise a two-hundred-page agreement without reading it closely enough to understand why its clauses exist?

The immediate result is convenience.

The deeper question is what happens to the mind that no longer performs the work.

That is where the real danger lies. The human mind was not shaped merely by possessing information. It was shaped by the effort required to acquire, test, remember and use that information. Our civilisations did not simply accumulate knowledge; they developed habits of attention, memory, reasoning, patience, judgement and perseverance in the process of acquiring it.

We may now be entering an age in which machines can increasingly perform the process for us.

And if we are not careful, we may discover that while we were busy making knowledge easier to obtain, we were also making ourselves less capable of obtaining it.

AI, internet-based image libraries and the enormous quantity of information available today are beginning to disturb that arrangement. For the first time, the finished product can appear before the person has gone through the process that would have taught them how to make it.

That distinction matters because almost everything we admire in the human story was born the slow way.

Mathematicians Baudhayana, Brahmagupta and Euclid did not inherit geometry as neatly packaged principles. They wrestled with problems. Paini did not compose the Astadhyayi by asking for a concise explanation of Sanskrit grammar. The architects of Brihadiavara and the sculptors of Kailasa at the Ellora Caves did not begin with an infinite library of reference images.

They observed, tried, failed and corrected their work. Years disappeared before certainty arrived.

When we stand before Brihadiavara today, we see granite rising against the sky. What we do not see are the years of measurement, experiment, argument and correction buried inside the finished structure.

Architecture makes this particularly clear because it has always been learned through attention.

Long before computers, an architect learned with the feet before learning with the hand. The education began by travelling, walking, measuring and sitting quietly inside buildings long enough to watch them change with the hours. A return in summer revealed one building; the same building in the monsoon revealed another. Light entered a courtyard at one hour and disappeared at another. Wind found its way through an opening. Streets filled and emptied according to rhythms that no drawing could fully capture.

Slowly, the sketchbook became a record of these discoveries. Why did one courtyard remain cool when the afternoon became unbearable? Why did the first light enter one sanctum but leave another in darkness? Why did one staircase seem effortless while another made the body conscious of every step? The architect learned to look beyond the shape of a building and listen to the forces that gave it life.

Those answers were not handed over. They were earned.

To sketch a column or pillar was to begin understanding how it carried its weight. To measure a street was to understand why generations had continued to use it. The sketchbook was, therefore, a perfect record of attention.

Today, that experience is increasingly replaced by the image library.

Pinterest may be the largest architectural archive ever assembled. An architect can now see more buildings in an afternoon than earlier generations encountered in years. That access is extraordinary.

Many of those buildings are extraordinary, but the problem is the manner in which they are encountered.

On a screen, a courtyard appears without the climate that shaped it. A facade appears without the civilisation, craft and history behind its form. A roof appears without the monsoon it was designed to withstand. A street appears without the people, rituals and everyday life that gave it meaning. The image preserves the appearance of architecture while quietly removing the conditions that made that architecture necessary.

What remains is something easy to consume and difficult to understand. The eye sees the building, but not the forces that produced it. The image travels effortlessly; the knowledge behind it does not.

Architecture was never learned by looking at more and more pictures. It was learned by paying attention to fewer things for longer.

AI takes this habit of instant consumption further. An architect can now generate 20 persuasive concepts before asking the first serious question about the land on which they may stand. The software can produce possibilities almost instantly, but it cannot decide which possibility deserves to become a building and which 19 should be discarded.

That judgement belongs to the architect.

The same habit has entered other professions. Consultancy agreements hundreds of pages long are uploaded into AI systems and returned as summaries. Development regulations become bullet points. Building bye-laws once studied clause by clause becomes conversational answers.

People build financial models whose assumptions they have never examined. They generate software they cannot explain, prepare presentations they cannot defend and submit research they have never fully read.

While the work exists, the understanding may not.

The attraction is obvious. Nobody enjoys reading hundreds of pages of statutory language. But a contract is not simply information waiting to be compressed. Every clause exists for a reason. A definition on Page 20 may change the meaning of a provision 200 pages later. An exception buried in another section may alter what initially appeared certain.

A summary may tell you what a document says. It cannot necessarily teach you why it says it.

That difference is where expertise lives.

Perhaps no phrase captures the spirit of the moment better than "Just ask ChatGPT."

There is nothing inherently wrong with asking a machine a question. The problem begins when asking becomes a substitute for learning.

For thousands of years, difficult questions demanded effort. They required reading, drawing, calculating, arguing and sometimes beginning again. Increasingly, the first response to difficulty is to hand the difficulty over.

Each individual decision seems harmless, but habits are built from individual decisions.

People now talk endlessly about writing better prompts. But good prompts rarely come from clever wording. They come from knowing what matters. An architect asks a good question because years of looking at buildings have taught him what deserves attention. A lawyer frames a precise question because statutes have taught her where ambiguity hides. An experienced researcher knows which source needs to be checked because years of reading have taught him what can and cannot be trusted.

Expertise is not simply the ability to produce a good answer. It is the ability to recognise the right question.

If that ability weakens, even the world's most powerful AI becomes a strange kind of amplifier: it can give a polished answer to a poorly understood question.

There is another problem. AI can make people appear more capable than they are.

A person can produce an articulate essay without wrestling with its argument. A sophisticated presentation can be generated without understanding its assumptions. Code can be written without knowing why it works. An architectural concept can look convincing without any serious engagement with the site.

The output may be excellent while the person producing it has learned almost nothing.

For centuries, difficulty itself acted as a filter. A person who wanted to become a mathematician had to do mathematics. An architect had to draw. A writer had to write badly before learning to write well. A student had to struggle with a difficult book long enough for its argument to become clear.

That struggle was not wasted time. It was the education.

A child who wrestles with a difficult mathematical problem learns more than the answer. The child learns that confusion can be endured, frustration does not mean failure and an answer can emerge through persistence. The same is true of reading. A difficult book teaches not only its subject but how to remain attentive when understanding does not arrive immediately.

If every difficult moment is immediately outsourced, that particular education disappears.

This is why the argument that "AI is killing humans" deserves to be taken seriously, even if not in the science-fiction sense.

The concern is not that machines will suddenly attack us. It is what happens when human beings stop practising the abilities they have handed over to machines.

A student reads the summary of a book instead of wrestling with the author's argument. An architecture student saves a hundred reference images instead of drawing the building in front of him. A lawyer asks for a summary of a statute instead of tracing a provision through its definitions, exceptions and cross-references. An engineer accepts the number produced by software without working through the calculation once himself. A researcher takes the generated answer instead of following the footnotes back to the original source.

None of these shortcuts looks consequential on its own. The loss becomes visible only after years of taking them.

The mind, like the body, responds to use. We understand this instinctively when it comes to physical fitness. A muscle that is never exercised loses strength. Intellectual decline is harder to see because convenience can look like competence.

That may be the central paradox of AI. It can raise the ceiling of what humanity can produce while lowering the amount of knowledge an individual needs to produce.

That sounds like progress until we ask what happens to the person.

If future generations inherit every answer but lose the patience to discover even a few answers themselves, they may become the most informed people in history without becoming the most understanding.

The finished work has never been the whole achievement. The student who solves the problem changes. The architect who fills the sketchbook changes. The writer who wrestles with a difficult paragraph changes. The researcher who spends years chasing an answer changes.

The knowledge remains, but so does the person who acquired it.

That is what shortcuts can conceal. They give us the product while removing part of the process that produced the person capable of creating the next one.

On those winter evenings in Hazaribagh, Mukesh Chacha was never simply teaching me to ride a bicycle. He was teaching me something I understood only much later: some knowledge cannot be handed over. You have to fall for it, fail at it and try again until the body, and then the mind, finally understands.

Every civilisation that endured did more than build monuments, books and machines. It cultivated people capable of building them.

Our age has created tools more powerful than any generation before us possessed. The question is whether we will use them to extend human capability or to escape the effort through which capability is created.

If AI helps us think better, investigate further and reach places the unaided mind could not reach, it becomes an extraordinary extension of human capability. If it becomes an excuse never to think for ourselves, technology will not be the only thing getting smarter. The machine will be doing more of the thinking, while we do less.

Evolution rewarded our ancestors with larger brains because survival demanded thought.

The tragedy of our age may not be that machines learned to think, but that humans slowly forgot why they ever had to.- Ends(Aabhas Maldahiyar is the author of Babur: The Quest for Hindustan. Views expressed in the piece are those of the author)Published By: Sushim MukulPublished On: Sep 24, 2026 14:12 IST