A person opens an AI assistant and asks it to draft a proposal, write a message, compare options, summarize the research, create a prototype, and recommend next steps. Twenty minutes later, they have a better plan than they would have produced on their own. The response is polished. The tradeoffs are thoughtful. The next steps are clear. Then the machine asks a question it cannot answer for them: What outcome are you trying to achieve?

Suddenly, the difficult part is not the work. It is the wanting.

The current obsession with agency is both right and incomplete. ‘Agency’ has become one of the words of the moment. Companies want agentic employees. Founders praise agentic teams. Parents want children who take initiative. Technologists now speak of systems that do not merely answer, but plan, call tools, execute tasks, and move through workflows. The word is useful because it points toward something productive.

However, it is also becoming vague. Sometimes agency means action or initiative; sometimes ambition, independence, ownership, drive, or simply doing more. But not all action is agency, and not all agency is action.

A person can move quickly and still be carried by fear. A person can experience distress, yet remain still and composed. A person can choose and still be choosing from desires they never examined. Two people can perform or refrain from the same action—accept a job, build a product, follow a model’s recommendation—when only one is acting with real agency.

Understanding agency becomes urgent because AI is changing the cost of action. For decades, technology amplified the hand. Now AI increasingly operates upstream of it: thinking, summarizing, advising, comparing, planning, generating, and sometimes even choosing.

The previous article asked when assistance becomes substitution. This article explores one level deeper. If AI can help us do, then what should we ask it to do? And beneath that: What do I really want done?

The Three Powers

Mohak: There’s something interesting I’ve noticed about the way people talk about AI. Their first questions are practical: What model should I use? What workflow should I build? What tasks should I automate? But the conversation often drifts somewhere stranger: What should I spend my time on? What else can I use this technology to accomplish?

If execution keeps getting cheaper, merely doing things is no longer the differentiator. The human task shifts from doing to directing. But directing requires knowing where I am trying to go, what matters, and what I am willing to sacrifice.

We are building machines that do not merely make work easier; they make desire executable. They can write memos, generate plans, schedule meetings, summarize documents, and perhaps take the next steps without much human involvement. That can feel like freedom. But it also exposes the harder question: What step is worth taking?

In work, this shows up when a team uses AI to accelerate a roadmap before asking whether the roadmap deserves to exist. We have all seen teams execute against goals that, in retrospect, were not worth pursuing. AI lowers the cost of execution, but it does not lower the risk of pursuing the wrong thing. In some ways, it raises it.

The same pattern appears in parenting, when we help a child achieve before helping them navigate what kind of person that achievement is forming. So agency cannot only mean drive, speed, or initiative. A person can be moving fast but still be moving in a direction driven by fear, comparison, habit, or unjustified aspiration. The question is not merely whether I am acting. It is: Why am I acting this way, and is this how I want to act?

AAD: Indian thought offers a useful framework for dissecting agency. Agency can be understood through three powers: kriyā śakti, the power to act; icchā śakti, the power to desire or will; and jñāna śakti, the power to know.

Action alone does not demonstrate agency. Habit, pressure, imitation, fear, conditioning, or even mechanical potential can all produce movement. To know whether action is truly agentic, we have to ask what knowledge informs it and what desire moves it. Notice how sentience is built into the formula through these elements.

Although these powers are distinct, they do not remain separate. What we know shapes what we desire. What we desire shapes what we do. And what we repeatedly do reshapes both our desires and knowledge. This integration obliges us to be careful about each of the three: the knowledge we acquire, the desires we cultivate, and the actions we repeat.

Icchā (desire) is especially important here because desire is not merely preference. Preference says, “I like this.” Icchā points to something deeper: “This is the direction in which my being is moving.” That direction may be noble, base, directed, confused, shallow, or sacred. But it is not neutral.

Agency is not merely the power to act. It encompasses knowing, wanting, and doing.

Mohak: This is where the framework becomes concrete for me. If AI makes action easier and can supply the materials we often mistake for knowing—information, summaries, arguments, comparisons, and analysis—then icchā becomes more important.

The machine can help me gather information, compare possibilities, and formulate plans. But it cannot finally tell me what is worth wanting. Greater capability does not clarify desire. It amplifies it. Convenience becomes the default. Attention becomes addiction. A vague and unexamined wish becomes a roadmap.

The machine does not make the desire wiser. It makes the desire more executable.

We praise drive without asking what is driving the drive, ambition without asking where it came from, and self-direction without asking what self is doing the directing. A person can be highly driven and still not be deeply agentic. We’ve all worked with someone who appears exceptionally busy yet seems not to get all that much done. Or at least not anything of particular value. That is why “What do I want?” and perhaps more importantly, “What should I want?” are not soft questions. They are technical, practical, moral, and spiritual all at once.

The Desire Beneath Action

AAD: This is why icchā śakti (the power to desire/will) deserves special attention. Desire is not simply “whatever I feel,” nor is it pure because it feels internal. It can be shaped by habit, environment, past action, ego, comparison, attachment, and fear.

Some desires announce themselves clearly. Others hide behind productivity, staged identities, professionalism, or even independence: “I want to lead” may include wanting recognition; “I want freedom” may mean escape from responsibility and accountability.

So we cannot understand agency by looking only at action. We have to look beneath it to desire. And we cannot understand desire merely by naming whatever appears first in the mind. The first answer may be the loudest, not the truest.

This is where the principle of antardṛṣṭi (un-tur-DRISH-tee) becomes important. It is often translated as introspection or self-reflection, but that can make it sound too thin. Antardṛṣṭi is not merely thinking about myself, nor is it just turning thoughts over in the mind; it is looking inward and “seeing” what is there.

When applied to desire, it asks, “What do I truly want?” And then, more sharply: is that desire worthy? Such reflection does not merely discover desire, as if every inner impulse becomes sacred once named, but discovers what that desire is making of us, and whether that is who we mean to become.

Antardṛṣṭi is the discipline of examining desire long and hard enough to see not only what it is becoming but also what we are becoming.

Mohak: This is the deeper question I keep hearing underneath practical AI questions. I’ve heard versions of it from executives, engineers, students, parents, and teachers. The wording changes, but the question underneath rarely does: What will still matter? What should I become? What kind of life am I trying to build?

If I do not know that, then every tool becomes a source of confusion. A powerful tool doesn’t solve the problem of direction; it just makes it harder to ignore.

I imagine myself in an incredibly fast car, capable of covering enormous distance in no time. But if my destination isn’t clear, the speed is worthless. I can drive for hours and arrive nowhere worthwhile. The speed of the car was never the question.

There is an old image that captures this: You can fill a page with zero after zero, hundreds of them, but without a “one” in front, the whole thing amounts to nothing. The zeroes are the effort; the one is the purpose that gives them value. Without it, more effort just means more zeroes.

And so, I keep coming back to a very simple question: What have I come to do, and what am I doing?

That question is about alignment with a purpose. Am I living in a way that matches what I claim to value? Am I delegating something that I should be forming in myself? Am I moving because I chose the direction, or because the world around me supplied one?

AAD: The answer is rarely obvious. A person may ask, “What do I want? What is my purpose?” and honestly answer, “I do not know.” That is not a failure but the beginning of clarity, often developed by repeatedly returning to the question. This positions antardṛṣṭi not as a one-time exercise but as a habit, until it becomes second nature.

Beyond Self-Reflection

Mohak: Sometimes people hear this and think it means endless doubt. But the point is not to lose conviction. The point is to make sure conviction can grow. A popular saying in the tech community these days is, “I might be wrong, but I’m not confused.” It sounds wise as it preserves the ability to act under uncertainty. But it’s also incomplete. I have watched leaders confuse the absence of confusion for the presence of clarity. They are not stuck. They are not paralyzed. They are simply locked in early, on a thesis they have not yet given themselves permission to revise. The quote is meant to defend agency, but it can quietly defend something else: the refusal to be moved by what one learns.

The need is not merely conviction. It is a conviction that can grow.

AAD: This kind of growth asks instead of assuming, while humility allows the answer to cost us something. It loosens the assumption that my desire is trustworthy simply because it is mine. Otherwise, the self becomes the defendant, the jury, and the judge; not a good pairing. The desires under review and the standards of review come from the same source. Reflection produces conviction, but not necessarily clarity—the two are easy to confuse when both feel internal.

For reflection to really work, antardṛṣṭi has to be grounded in something beyond the self—something knowledgeable and steady. This builds on the role of the teacher we discussed in our previous article. Sailors have long used dead reckoning: navigating from a last known position using speed, heading, and elapsed time. The method feels rigorous from the inside; each calculation follows from the last. But small errors compound, and without a fixed external reference such as Polaris, a ship can drift confidently in the wrong direction. Introspection without an external anchor is dead reckoning of the soul.

The Gītā describes the alternative as being “established in the eternal” (nityasattvastha). It advises connecting our desires to a stable foundation—a spiritual ideal that Hindu philosophy calls Akṣara, or “the imperishable”—that which does not change, and therefore can serve as a reference for everything that does. It is what makes correction possible at all. Without something outside the drift, there is no way to know one is drifting.

Mohak: That changes the way I think about agency. The world often treats agency as self-assertion: I decide, I act, I build, I move. But that assumes we already know what is worthy of our action. If the self is full of unexamined desire, then assertion is putting the cart before the horse. Real agency demands the opposite movement first: self-examination to ensure that what we are asserting is worth it.

This is why agency and reflection must go together. If we act without examining the inner movement, desire remains in command. If we reflect without disciplining action, the same desires keep strengthening. Repeated action feeds desire; repeated desire colors knowledge; colored knowledge shapes the next action. Repeatedly asking, “What have I come to do, and what am I doing?” examines and aligns all three powers at once: what I know, what I want, and what I am actually doing. Most importantly, it grounds that alignment in a stable, purposeful foundation.

Knowing what is worth wanting matters greatly in a world where AI makes access to knowledge and action plentiful.

The risk is no longer that we won’t get what we want; the risk is getting it and realizing we didn’t want it at all.

Questions to Carry

• “What have I come to do, and what am I doing?”

• When the distance between wanting and achieving narrows, what becomes worth creating?

• Does something need to be useful for it to be well-desired?

• What remains stable enough to guide desire when feelings, fears, and ambitions keep changing?

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In our last conversation, we discussed that although large language models are immensely powerful, they remain lacking because they require context. They can map relations between words, but they end up relying on us to provide the world behind the word.

But once the problem of context comes into view, a deeper problem follows closely behind it. Someone still has to decide which context matters, what kind of answer is being asked for, and when a polished response should be trusted, doubted, or ignored altogether.

The next skill the age of AI is demanding from us is not better prompting but better judgment.

Large language models mark a strange turn in the history of technology. For decades, our tools mainly amplified effort. A calculator sped up arithmetic. A search engine sped up retrieval. A spreadsheet sped up analysis. But large language models do something more intimate. They sit inside the act of thought itself. They help draft, interpret, summarize, advise, compare, reframe, explore, and answer.

They do not merely extend the hand; they increasingly extend the mind. And every extension of the mind raises a question that is simultaneously practical and philosophical:

When does assistance become substitution?

The Oracle Problem

Mohak: What worries me is not simply that these models are getting more capable. It is that reliance on them is becoming habitual. There are tasks I once would have worked through myself that I now delegate almost automatically. And once that habit forms, it does not stay confined to trivial things. It begins with grunt work, with summarization, with drafting, and with small conveniences. But it slowly moves inward, toward reasoning itself.

I recently heard about a very bright student who kept pausing in the middle of a conversation, and when someone asked him why, he said, “I’m processing; I’m not used to thinking without ChatGPT.” Intelligence had not disappeared, but the habit of independent thinking had changed. That is what feels significant. If a muscle is used less and less, we know what happens to it.

The emerging skill here may be surprisingly basic: discretion—knowing when to use the model and when not to. If you delegate too much thinking, eventually that muscle atrophies.

AAD: There are certain activities where this becomes immediately obvious. Many people can feel, almost instinctively, that there are domains in which outsourcing to AI is somehow inappropriate, even if the output may appear passable. Personal interactions are one example. A birthday message, an apology, a word of encouragement, an expression of love—these can all be generated, but something essential is lost.

The same is true of learning. Learning is not an event in which information arrives; it is a process that transforms the learner. It is not something that can be performed on our behalf any more than exercise can be.

This chasm between information and transformation is not modern. The Kaṭha Upanishad, a seminal Hindu scripture, presents a dialogue between an inquisitive young child named Naciketā and the god of death, Yama, himself. At one point in their exchange, Yama reveals:

The mantra speaks directly to the “Oracle Problem.” AI models possess unparalleled “pravacana” (the ability to generate fluent, articulate speech) and can provide access to unthinkably large repositories of “heard” or accumulated knowledge (“bahu śruta”). However, spiritual truths and, perhaps more broadly, deep human wisdom are not attained merely by assembling the right words or accessing endless data. Wisdom requires an inner life to be transformed and capable of receiving grace.

That is the first paradox of this new era. We are turning to AI because it helps us do more, but in some of the most important parts of life, the doing is itself the formation. To bypass the act is to bypass what the act was supposed to cultivate.

A model may save us time, but time saved is not always a life improved.

Facts, Values, and the Suspended Mind

Mohak: The danger becomes sharper because these models are so spectacularly capable when they are right. Ask a frontier model about medicine, law, engineering, literature, social science, history, or physics, and it can respond with a breadth no human being could match. We have never encountered something that appears to know so much across so many domains at once. So we begin to treat it like the smartest being in history. But that is exactly where the problem begins, because it is not an oracle. It has access to an immense amount of knowledge, but it is not always truthful.

You can see this clearly when you ask different models the same question and receive radically different answers. The differences are not trivial. They emerge from pretraining data, from fine-tuning, from alignment choices, from moderation layers, from all the human values that get injected into the system. Ask a moral question across three frontier models, and you may get three distinct moral postures. That alone is sobering. If truth were simply sitting there in the weights, it should not fracture so easily.

A silly example makes the point well. People were asking models whether they should drive or walk fifty meters to a car wash. Several of them replied: you should definitely walk; it is healthy, it saves gas, it is better for the environment. A polished answer, well-intentioned, entirely idiotic. Of course you need the car at the car wash!

The mistake is funny only because the example is harmless. But the same pattern appears when people ask for relationship advice, spiritual advice, medical guidance, ethical counsel, or help making decisions at work. The model can be dazzling across ten domains and still miss the obvious human fact that governs the crucial eleventh. That is why the real risk is not that the model makes mistakes. Humans make mistakes too.

The real risk is that we stop investigating, we stop asking questions. The model pushes the button, and I act.

I am no longer the controller; I am the one under control.

AAD: There is a helpful distinction here between facts and judgment. Facts answer questions about what is the case. Judgment wrestles with what ought to be done. The former is often within the reach of data. The latter never belongs to data alone.

Once a question becomes moral, relational, or existential, there is always more at stake than information retrieval. Context broadens beyond a prompt into layers of life: family, culture, faith, memory, temperament, responsibility, and consequence. And even if all of that could somehow be specified perfectly, morality would still not be exhausted by the specification. Morality requires valuation, prioritization, interpretation, and sometimes sacrifice. It requires a being who must live with the result.

There is also something deeply human that complicates this further: we are already prone to suspended disbelief. Read a Wikipedia page about a topic you do not know well, and it often feels authoritative. Read a page in your own area of expertise, and you can immediately see its thinness, omissions, and distortions. And yet, the strange thing is that as soon as we move to the next unfamiliar page, we revert to trust.

The mind wants to believe what is stated fluently. This is why misinformation spreads so easily. The act of stating something creates an aura of possible truth. AI does not create that weakness in us; it exploits it magnificently. Fluency becomes authority. Confidence becomes credibility.

The danger is not just hallucination. It is our readiness to mistake coherence for wisdom.

The machine’s greatest trick, then, is not that it can be wrong. It is that it can be wrong in complete sentences.

The Taste for Judgment

Mohak: One sees this even in something as mundane as social media. On X, people constantly invoke Grok as though the oracle has spoken: “Grok, is this video real?” “Grok, did this really happen?” “Grok, explain this news story.” But read the different threads closely, and the same system will give contradictory answers across different prompts. If I read only one confident reply, I may walk away convinced of its credibility. If I read several, I discover something else: schizophrenia.

The models may have knowledge. Humans must own taste.

AI can generate candidate answers, but it cannot inherit responsibility for them. That remains ours.

This is why I keep returning to the language of taste. A lot of people in the world of AI are now talking about taste, but what they often mean is aesthetic sensibility or product instinct. I think the word goes deeper.

Taste, in this moment, really means discernment.

It is the ability to tell what fits, what does not, what is proportionate, what is excessive, what is true enough to act on, what is plausible but dangerous, what needs another layer of scrutiny.

AAD: Taste is precisely the sort of thing that resists reduction to explicit rules. That is why it has traditionally been transmitted through apprenticeship rather than mere instruction.

A craftsperson can describe the steps of the work, but not fully the feel of it. A writer can explain grammar and structure, but not fully why one sentence is alive and another merely correct. There is an old example from agricultural practice: experienced workers sorting newly hatched chicks into male and female can become astonishingly accurate, even when they cannot articulate exactly how they know. A student stands beside them for months, watching, repeating, failing, and somehow the faculty transfers. Not because a hidden rulebook is finally disclosed, but because the student’s perception has formed. They literally come to see.

And it reveals something crucial for the present moment: technical skill can often be learned from books, software, and repetition. Taste is learned from persons, and not just ordinary persons, but experts. It is apprenticed. It is handed over through correction, imitation, and proximity to someone who already knows what good looks like.

The age of abundant answers does not make taste obsolete. It makes it scarce.

The Guru Principle

Mohak: Once you see that, the need for teachers becomes almost embarrassingly obvious. When my daughter uses AI tools to build a computer game, the model can generate the code and it can help her debug. But every so often, I have to step in. Not to do the coding for her, but because I know what a good game feels like. I know that pacing and flow matter, and that tension needs relief. She does not yet know what excellence looks like. The teacher’s role is not merely to provide an answer, but to offer a standard for taste.

For years after I taught myself to ski, I could get down the mountain just fine. Then I finally took a lesson, and had to unlearn almost everything. I had accumulated bad functional habits. I had not built the kind of technique that would protect me on truly difficult slopes. Good teaching does not merely help you perform under favorable conditions, but it prepares you for the moment when conditions deteriorate.

In a world increasingly mediated by AI, that feels like an exact analogy for judgment.

This is why one of the most important things people can do in this moment is seek out teachers. Of course that includes spiritual teachers, but it also includes mentors, elders, craft masters, serious thinkers, people with lived experience, and people whose judgment has been tested by reality and, most importantly, is genuine and well-meaning.

In our spiritual vocabulary, there is satsaṅg: association with truth. It means finding someone who sharpens your clarity. Someone who adds nuance. Someone who has experience where you do not. Someone who awakens your deeper judgment.

We have spent years imagining education as the transmission of technical skill. But technical skill is becoming cheap. The model can help you code, outline, summarize, translate, and imitate. What it cannot do is teach you what good work is, what good character is, what good judgment feels like from the inside.

You do not go to a teacher merely to learn the craft. You go to learn what good looks like.

AAD: The guru-disciple tradition rests on this insight. The teacher is someone through whom discernment becomes livable. Different teachers serve different functions. Parents teach manners, affection, language, and the earliest habits of perception. A mathematician teaches proof. A musician teaches tone. A spiritual teacher teaches how to order life itself. Each gives access to a different domain of wisdom.

But beneath those differences is a common principle: information alone is never enough. The student must be guided into a way of seeing.

That guidance includes several things at once. In addition to showing what the end looks like, a teacher also prevents certain avoidable errors. This intervention does not reduce wisdom to rules; it prevents wasting years on repeated mistakes. The teacher also asks the right question at the right time, which is sometimes more important than answering the wrong question brilliantly. And perhaps most importantly, the teacher knows that the path of learning is not always linear. The student may want information or a skill, but the guru gifts something entirely unexpected, all the while shaping perception, perspective, and purpose.

This is why the guru does not become less relevant in a technological age; they become more relevant. When a thousand answers are available at once, the ability to know which one deserves consideration and action becomes precious.

The teacher’s role is not diminished by abundance. It is intensified by it.

The Return of the Teacher

If the first stage of the AI era taught us to marvel at machine capability, the next stage may teach us to value human formation more seriously than we have in a long time. Context engineering showed that the machine needs help inhabiting our world. Discretion shows that we need help inhabiting a world increasingly shaped by machines.

As AI makes technical skills more available, judgment becomes crucial. As answers become cheaper, discernment becomes paramount.

The future, then, may not belong simply to those who can use the tools most aggressively. It may belong to those who know when not to be impressed by them; those who can distinguish fluency from understanding, possibility from wisdom, help from surrender.

And because that faculty is not downloaded but formed, the age of AI may end up rediscovering something much older than AI itself: apprenticeship, the teacher, the guru, satsaṅg.

The smarter our machines become, the more important our teachers become.

Questions to Carry

• In our daily use of AI, where have we stopped thinking and started accepting?

• If judgment is our responsibility, what are we doing to train it?

• Who shapes our judgment—our sense of what good looks like?

We’ve built machines that can speak every language yet understand none.

Large language models sound fluent—they complete our sentences and compose essays—but behind the polish lies a blind engine of probability. They predict what comes next, not why. They process the products of thought, not thought itself.

Syntax Without Sense

AAD: Mohak, in our last conversation, you described your ‘technical itch’—how these models are essentially just sophisticated predictors, sampling across a probability space. This led to the question, “Are we just higher-order predictors?“ This is a profound challenge, and to begin to answer it, I think we first need to explore the very mechanics you brought up. We often hear that these models are “next-token predictors,” but that phrase can feel a bit simplistic when you see them produce a complex essay or a poem. What’s actually happening under the hood?

Mohak: A transformer model works by looking at all the words in a prompt and figuring out how they relate to each other. It creates a kind of internal map, highlighting the crucial concepts, using this to bridge to a response based on the vast amounts of text it has ingested. It’s this ability to weigh the importance of every word in the context of every other word that makes it so powerful.

But, and this is the critical point, this entire process happens purely at the level of words and statistical patterns. It’s building a relational structure out of language, and language is how we record thought, not entirely how we think. It doesn’t know what rain feels like, what an explorer is, or what it means to feel intrepid. I know what rain feels like because I’ve experienced it. As a result, it means something to me. If I just stepped in wet from a downpour, I might resent it. If instead my mind goes to memories of running in the rain with my friends, it brings warmth. The model only knows how those words connect to other words, but none of their meaning. Transformers don’t have the world behind the word.

The Chinese Room and the Illusion of Understanding

AAD: Your description brings to mind John Searle’s Chinese Room Argument.

Searle imagines a person who knows no Chinese locked in a room with a rulebook that tells them how to respond to Chinese symbols slipped under the door. For every sequence received, the book provides one to send back.

To an observer outside, the person appears fluent and natural. Inside, there’s no understanding—only rule-following. Perfect syntax (the rules of language), zero semantics (the meaning behind it).

This is what these AI models do: they masterfully manipulate language without any true comprehension of the world it describes. Syntax is not sense, and coherence is not consciousness.

Now imagine the person outside trying to convey sarcasm or joy. Language is always incomplete. How much human meaning fits on that slip of paper? What happens to the tone, the emotion, the context between the lines, often left unexpressed? A model can reproduce a poem, but not the tremor that wrote it. What these models lack is context.

Mohak: Likewise, for all its speed, digital messaging has always been a ghost of real conversation. We’ve spent decades trying to breathe life into our texts—from crude emoticons to emoji to the perfect GIF for every reaction. Each is a clever attempt to clarify whether ‘sure’ conveys a sarcastic eye roll or a heartfelt embrace. Yet we’re still chasing the effortless understanding that comes from a single shared glance—a nuance our screens have never mastered.

The Skill for a New Era: Context Engineering

Mohak: This points to the extraordinary importance of context. We engage in small talk not because we have nothing else to say, but to share the context within which the rest of our conversation will unfold. We tell each other, “I’m tired,” or “I had a great day” to contextualize our mental state. These clues help the listener better understand us and, ultimately, better respond to us.

A key new skill that emerges when we deal with LLMs is inspired by this aspect of human interaction. We started with “prompt engineering,” to coax LLMs into giving us the answers we seek by asking precise, well-crafted questions. But now it is clear: precision matters, but context matters more—a move from prompt engineering to context engineering. If the AI has a map but no feel for the terrain, our job is to describe the terrain for it. Instead of just typing, “Write me a note to my team,” I start with the setting: “You are a trusted colleague. My team has been working on a hard deadline and has had a tough week. Many people worked through the weekend to hit the deadline. I’m worried this may be hitting morale, and the team needs inspiration. I want your help writing a note thanking the team without sounding hollow, and I want to inspire them towards our shared goals.” Suddenly, the response changes. No, the machine doesn’t understand “tired” or “demotivated,” but the added context helps it frame the response based on the data it was trained on.

I’m building a world for the AI to inhabit before it answers.

To Know AI, Know Thyself

AAD: And when you engage in worldbuilding, you are forced to articulate your own understanding of the circumstances and people involved. Good context means knowing what’s relevant, what’s ignored, and what’s valued. Context engineering, then, is a kind of forced introspection. You stop outsourcing thought and start articulating purpose. The AI becomes a high-fidelity mirror. If the reflection is muddy, the source needs clarifying. This is the new ‘garbage in, garbage out’: shallow context yields shallow meaning. It changes our relationship with technology—from operators to stewards of meaning. The irony is that in teaching the machine, we learn to understand ourselves better. A machine trained on our language ends up pushing us to define our own meaning.

Mohak: This might sound abstract, but it’s intensely practical. I see it with my daughters. My daughter sometimes comes to me crying, unable to say why. I can’t help until she can describe what hurts. The moment she names it, “She said I wasn’t her friend anymore,” we can solve her problem together. Context transforms emotion into a solvable experience.

We’re doing the same thing with these models. We bring our confusion and expect clarity, but we forget to give the model context. In that sense, we are simply crying to our tools.

AAD: That plea for context is so often at the heart of what we call wisdom. Take the Bhagavad Gita, which opens on the brink of war. Arjuna faces an inevitable fight against his own kin. Paralyzed by the horror, his will collapses and his bow slips from his grasp. He is engulfed in desolation. Krishna’s response is a profound act of context engineering. He rebuilds Arjuna’s world with a new reality: reframing Arjuna’s identity from that of a mortal man to an eternal soul, his duty from a personal burden to an instrument of Divine will, and even his very being into one engrossed in the Divine. Krishna’s context shift transforms paralysis into purpose, action into offering, despair into devotion. By providing context he ultimately grounded him in meaning.

The Context Engine

In the age of AI, humans may not remain the best creators of output, but we’ll remain unique creators of context and meaning. Machines can produce sentences; only we can decide which ones matter.

The future of work will likely belong to context engines—people who understand and reflect on goals, constraints, personalities, values, and timing. The power won’t lie in knowing what to ask, but in knowing why to ask it.

Take a music producer. The artist provides melody and lyrics, the engineer manages levels—but the producer listens for meaning: how it feels. They decide when to leave silence and when to let imperfection breathe, to bring listeners to tears. That’s context work—shaping not the notes, but the world around them.

Or picture a great team leader. They don’t just assign tasks—they tune into exhaustion, mood, timing. Their real craft is reading the room, not writing the plan. That’s context engineering in human form.

Imagine a world filled with people who reflect, strive to understand, and provide context to each other as much as to these models. A better world—and a more human one.

Questions to Carry

• As you worldbuild for the AI, what core principles—values, assumptions, emotions—form your foundation?

• When the AI’s reflection is “muddy,” what unarticulated part of your thinking is the source of distortion?

• In the “forced introspection” of teaching the machine, what have you uncovered about your own unsaid purpose?

• If shallow context yields shallow meaning, how often do our conversations with people suffer the same?

In Douglas Adams' classic, The Hitchhiker's Guide to the Galaxy, the supercomputer Deep Thought reveals the Answer to the Ultimate Question of Life, the Universe, and Everything: 42. The problem? The answer is meaningless without knowing the question.

We are at a similar juncture with AI. Faced with powerful new "answers," our most urgent task is to discover the questions to ask about ourselves and this technology.

The following is drawn from conversations on this subject between Mohak Shroff (Mohak) (a tech executive seeking to build a better Artificial Intelligence) & Sadhu Aksharananddas (AAD) (a philosopher steeped in Indian and Western thought). We have polished only for clarity—every idea lives in the original tape. Mohak plays the narrator through this article, and we begin at the launch of the modern AI revolution.

The Existential Thrum

Back in 2022, I sat in a small conference room watching an early preview of ChatGPT. A developer suggested, “Try asking it to compose something.” I typed: Write me a poem about curiosity on a rainy night. Several seconds later: four crisp stanzas, original imagery, and a closing twist that took me completely by surprise.

I went home feeling somewhat in awe, somewhat in shock, and somewhat thoughtful about what it all meant. I wasn’t convinced it wasn’t just a neat technical trick. I’d seen things like Eliza before, and maybe this was more of the same - a cool technical toy that wasn’t actually “smart”. I went back to the model to test it some more, and it spat out variations that felt fresh every time. But it did so much more. It summarized globs of text I pasted in; it had access to what felt like all human knowledge and was answering all sorts of questions, even those about obscure topics. It felt… intelligent.

Right there, in that moment, my view of the world shifted. If an algorithm can create at will, what becomes of human ingenuity? If one day an AI knows more than me, what does that say about the intelligence I’ve always thought of as uniquely human, or uniquely me? A question that began as a professional curiosity quickly became a matter of personal identity. And then came the realization that many others would be asking these same questions when they experienced what I just had.

In tech, we’re trained to metabolize novelty—ship, measure, iterate—but with generative AI, the novelty is about us. The conversations I tend to hear about AI are frequently dystopian, charts of benchmark curves, and headlines about “super-human performance.” One AI test even calls itself “Humanity’s Last Exam.” Language like this solves no mysteries; it amplifies the existential thrum.

As time passes and AI chatbots become more integral to our productivity and workflows, these questions grow in urgency and importance.

Reaching Beyond the Echo Chamber

When grappling with thoughts like these, I reach for older frameworks—spirituality and philosophy—to help ground my thinking and provide different perspectives. Someone who I’ve called many times before is a friend who straddles both domains. Swami Aksharananddas (AAD) is a philosopher and educator specializing in both Western analytical philosophy and Indian thought. His training in computer science proves particularly relevant in this conversation. When I first shared these thoughts and experiences with him, he responded:

AAD:
Your instinct is revealing. There doesn’t need to be a hard line between physics and metaphysics. Aparā-Vidyā (knowledge) and parā-vidyā (higher knowledge) are understood as being one arc. Even the sciences embraced this reality. Einstein spoke of a “cosmic religious sense” when witnessing the order of the universe. Newton wrote on theology in addition to calculus, while Ramanujan found mathematical inspiration from the goddess Namagiri. On a personal level, both of us have experienced our guru, Mahant Swami Maharaj, guiding us to complement innovation with spiritual growth.

The conversation we are having, therefore, isn’t new. The object—AI, an artefact that mimics the mind—is new. And its newness forces us to revisit metaphysical grounds by urging us to ask questions that we would not have asked otherwise.

So, while the AI debate often toggles between boosterism (everything will be fine) and apocalyptic scenarios (the robots will get us), ancient traditions offer a third posture: contemplative inquiry. They teach that technological intensity and peace can coexist by integrating scientific and spiritual principles—philosophy gives us a compass, while engineering gives us a map. My first waypoint is transformer models, and how their predictions feel like thought.

The Temptation to Anthropomorphize

Mohak:
Here’s my technical itch. These transformer models ingest an immense amount of content and learn that after the word ‘the’, ‘dog’ is likely, ‘person’ is plausible, and ‘photosynthesis’ is rare. During generation, the model samples across that probability space; no inner life required. But subjectively, the outputs feel coherent, even intentional, the result of thinking. Is what we see from these models a blueprint for human thought? I also absorb language, store impressions, and produce sentences in reaction to my circumstances and stimuli. Am I just a higher-order predictor?

AAD:
These are important questions, but before we can get to transformer models, there is something subtle at play that we should address. The minute we say “thinking,” “remembering,” or “reasoning,” we import centuries of human history, tradition, and reflection. You have probably heard the phrase “category error”—something that is miscategorized because it fails to neatly fall into a defined framework. Early explorers encountered a platypus and misidentified it because the term ‘monotreme’ didn’t yet exist. Thomas Kuhn argued that when a new scientific innovation appears, the language stretches until it snaps or becomes “incommensurable.” Taking an example from physics, for centuries, experimenters tried to force light into a familiar binary: either a particle or a wave. Experiments showed, however, that it displayed properties of both. How could that be? The impasse was only resolved by introducing an entirely new way of thinking and a new language to describe it. Science now describes the phenomenon using the language of quantum mechanics, introducing language like ‘photon’ and ‘super-position’. The question, “wave or particle?” was realized as being mis-asked.

Calling GPT “intelligent” may be such a placeholder, as modern language does not have a good way to label it.

Words are accompanied by entire histories, frameworks, values, and even limitations. The words we use matter. They influence our willingness to accept, integrate, and adapt, and they affect our attitudes and anxieties. They also provide direction for our investigations. And many times, even if they don’t fit, we use them as placeholders to help us simply communicate. The important thing is to be aware of this. So we mark the new artefact with an asterisk; in the case of GPT: intelligent, but only by analogy.

When we stop treating AI as mysterious or magical and look at how it actually works: by processing patterns, storing and retrieving data, and updating it through training, we become clearer about what it is and is not. This helps us appreciate its strengths, without mistaking the analogy for an identity. This clarity keeps both the hype and fear in check, letting us see the phenomenon as it exists and helping us respond to it without being too quick to embrace extremes.

Muddle & Meaning

AAD:
There’s a hidden upside to the linguistic muddle. Mislabeling, when recognized, instigates a tension, which sparks conversation. By calling specific integrated transformer functions “thinking,” we’re pushed to define what human thinking and cognition actually entail. It inspires us to question whether and, if so, what role embodiment, emotion, goal-directedness, maybe even human conditions, like forgetting and suffering, play.

This unease mirrors the experience of spiritual traditions, which, in seeking to convey the transcendental, recognize the limits of language and turn to more abstract or metaphorical expressions found in poetry.

Mohak:
For society at large, the language and, more broadly, concepts we use shape perception, and they shape emotion. How we define terms like ‘intelligence’, ‘agency’, or ‘mind’ changes how we see machines, how we see each other, and even how we understand ourselves.

These definitions also have a powerful impact on our relationships: with AI, with fellow humans, and with ourselves, and in turn, they guide our behaviors toward all three.

And the stakes aren’t solely intellectual. If a factory worker is replaced by a welding robot, they may not care whether the machine has intentionality, whether it actually thinks or has feelings. For them, outcome trumps ontology.

So the two tracks must run in parallel:

• Impact track (boots-on-ground): How shifting thinking ripples outward, reshaping how we work with AI, who benefits, who’s left behind, and how individuals, institutions, and cultures are formed and respond.

• Concept track (blueprints-in-hand): The complicated task of discovering philosophical frameworks to encompass these new artifacts, and refining or redefining words like ‘mind, ‘agency, and ‘creativity’ to better communicate them.

Ignore either, and the other becomes harder. Miss the impact layer, and we are misled, or worse, suffer. Miss the concept layer, and the tools we use to conceptualize, communicate, and mediate become blunt instruments.

The early days of technological innovation are often filled with learning experiences and transition periods. It takes time for us to find a place for ourselves and our way of life, and to adapt our social and economic models to the new worlds we create. What we seek to do in this series is address this moment—to ask the right questions and navigate to better answers about the artificial superintelligence we create and, most importantly, our sense of place and self. We hope you find value in these conversations as we continue to venture Beyond 42 and into the space of Parā Technica.

As technological advancement accelerates at an unprecedented rate, especially with the staggering advances in artificial intelligence, we have an immense opportunity ahead of us. We have the chance to pause and reflect on what this moment reveals about ourselves, our priorities, and our values. We have the opportunity to ask deeper questions so that we can become more intentional in choosing the path we take, both in how we use technology and continue to develop it.

To pursue this potential, we began, as meaningful explorations often do, in the simplest way possible: through conversation. This one started between the two of us (Mohak Shroff and Sadhu Aksharananddas). Over the past two years, we started talking about the technological leaps we were witnessing. Our discussions often turned philosophical and reflective. We looked toward the technological horizon and tried to understand what it all meant and what the path forward could be. At one point, we envisioned writing a book capturing our conversations, but quickly realized that it wouldn’t work with the pace of change in AI.

So instead, Parā Technica was born. A space for reflection on our relationship with technology. It is a place where inquiry, discussion, and exploration seek to inform not only how we build, but why and to what end, and, ultimately, to better understand ourselves and how we leverage technology in this Era of AI. With the blessing of our guru, Mahant Swami Maharaj, we embark on our journey of Parā Technica: an experiment to ground modern innovation with more considered inquiry.

Parā Technica is a synthesis of two concepts from two meaningful traditions:

• Parā (परा) (pa-raa): A Sanskrit word meaning “higher,” “beyond,” or “supreme.” It comes from the Upanishadic distinction between Aparā Vidyā (worldly, empirical knowledge) and Parā Vidyā (higher, enlightened knowledge). Parā invokes this pursuit of transcendent understanding.

• Technica: From the ancient Greek technē (τέχνη). While it is the root of "technology," its original meaning conveys a sense of craftsmanship, artistry, and the skillful process by which something is made or achieved. It implies an artful practice, one that is informed by beauty and intention.

Together, Parā Technica is our mission statement: to navigate the age of AI by drawing on insights from philosophy and spirituality.

Although this project begins as a dialogue between the two of us, its purpose is to grow that conversation. Whether you are a technologist with a philosophical spirit, an innovator searching for deeper principles, or simply a curious mind navigating our complex world, we hope you have found your community.

Welcome to Parā Technica!

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