The Mind That Asks for Almost Nothing

A girl in a Marathwada village holds up her phone at the edge of a cotton field and asks it why the leaves are going yellow at the margins. The answer arrives in under a second, and it is a good answer. What she cannot see is where it came from. Her question left the village on a radio wave, dropped onto fibre, ran a thousand kilometres across three states to a windowless hall outside a city, woke a rack of processors that each draw more power than her house, was answered, and came back. Somewhere in that hall, to keep the machines from cooking themselves, water was turned into vapour and let go into the air—in a district where her own family measures the year by whether the well holds. She will never see that bill. Nobody is shown that bill. It is simply spread, invisibly and unevenly, across a country that is already short of the two things intelligence is currently made of: electricity and water.

I want to argue for a different way of building this, and I want to argue for it plainly, because the case is not sentimental. Call it the frugal model of artificial intelligence: models small enough to live in the phone already in your pocket, running on hardware that computes the way a nervous system does rather than the way a furnace does, and going on learning after they are installed—learning you, specifically, and never once reporting home. The usual objection is that this is the compromise position, the thing you settle for when you cannot afford the real thing. I think that has it exactly backwards. Frugality here is not a sacrifice of capability. It is the road to a capability the large centralised model cannot reach at all—and it happens, along the way, to stop us burning a river to answer a question about a leaf.

What a Question Costs

Start with the bill, because almost nobody sees it. Water leaves an AI data centre by three different doors, and only one of them is obvious. There is the water evaporated on site to carry heat away from the chips. There is the far larger volume consumed upstream at the power stations making the electricity. And there is the water spent in the fabrication of the semiconductors themselves, months earlier and half a world away, before a single query is ever served. India’s own Economic Survey has flagged the pressure that AI data centres put on both the grid and the groundwater, and the geography makes it worse than the arithmetic suggests: more than half of India’s data centres sit in water-stressed regions. The country’s data-centre load stood at roughly 1.5 to 1.7 gigawatts in 2025 and is expected to grow around fivefold, to some 8 gigawatts by 2030. Today that is about half a percent of national electricity consumption. The trajectory is the point, not the current figure.

There is a moral asymmetry buried in that journey which I cannot get past. The person asking the question is almost never the person paying for it. A model trained and served centrally distributes its costs the way an industrial economy has always distributed them—outward, downward, and onto people with no vote in the matter. This codex made the observation once already, reporting on a single month in which the frontier models became abundant and nearly free while everything underneath them—the fabs, the turbines, the substations, the permits—became the scarce and guarded thing. That was the diagnosis. This essay is about the prescription, and I think the prescription is older than the disease.

“Enjoy by renunciation. Do not covet; whose is wealth?”—Isha Upanishad, verse 1

That line is roughly three thousand years old and it is not a counsel of poverty. It is a claim about the correct relationship between a user and a world: take your portion, hold it lightly, and do not mistake use for ownership. Patanjali later put the same instinct into the yogic disciplines as aparigraha, non-grasping—listed, tellingly, not as an advanced attainment but as part of the ethical floor you stand on before you are trusted with any power at all. I find it hard to read those and then look at a computing paradigm whose central strategy is to take more of everything, every year, forever, and call that strategy mature.

Plenty of Room at the Bottom

Here is the good news, and it is very good: the engineering has quietly caught up while nobody was looking. The technique is called quantization—storing a model’s weights at lower numerical precision, four bits instead of sixteen—and it is now simply the industry standard for anything that runs on a device. The compression is close to indecent. A three-billion-parameter model comes down to around two gigabytes, which fits without complaint in any phone carrying eight gigabytes of RAM, and it does so while retaining something like ninety to ninety-seven percent of the original model’s accuracy. Read that again. You lose a few percent of the answer quality and you gain the entire ability to run the thing in your hand, offline, on a battery, for free, forever. Whole families of these models now exist and are good—Gemma, Llama, Qwen, Phi, SmolLM, in sizes from one to four billion parameters—and the practical sweet spot for an ordinary 2026 phone is a model of about a billion parameters.

“There’s plenty of room at the bottom”—Richard Feynman, 1959

Feynman was talking about writing the encyclopaedia on a pinhead, and about the enormous unexploited territory that opens up when you stop assuming a thing must be built at the size you first built it. The sentence has aged into a general law. But let me be honest about the objection, because there is a serious one and this codex has already made it: raw scale keeps winning. The bitter lesson of the last thirty years is that general methods with more compute beat clever hand-crafted ones, reliably, embarrassingly. A one-billion-parameter model on a phone will not out-reason a frontier system, and I am not going to pretend otherwise. What I will say is that the bitter lesson answers a question about how to reach the frontier of capability—and almost nothing anyone actually does with these systems is at that frontier. Summarise this. Draft that. What does this word mean. Read this label. Remind me. Sort my morning. The right engineering question is not what is the most powerful model, but what is enough for this—and for the overwhelming majority of human askings, enough is small, and enough is already here.

We have been answering a child’s question about a leaf with a machine built to hold the whole world’s questions at once.

The Twenty-Watt Argument

There is a deeper inefficiency underneath the size of the model, and it is architectural. The computer you are reading this on separates memory from processing and shuttles data endlessly between them, and that shuttling—not the arithmetic—is where a startling share of the energy goes. Your brain does not do this. It stores and computes in the same place, it runs on roughly twenty watts, about the draw of a dim bulb, and it stays silent. That last part matters more than it sounds. A conventional chip marches to a clock: every cycle, every unit, whether or not anything has changed. A nervous system is event-driven—a neuron speaks only when it has something to say, and the vast majority of the network is quiet at any instant. Sparsity is not a trick the brain uses to save power. Sparsity is most of why it costs so little.

Building chips on that principle is called neuromorphic computing, a term Carver Mead coined in the 1980s when he began arguing that we should stop fighting the physics of silicon and start letting it behave the way nervous tissue does. For decades it was a beautiful idea with a thin résumé. That has changed. Intel’s current Loihi generation is fabricated at four nanometres, carries some eight million digital neurons and sixty-four billion synapses, and communicates in graded spikes that pack far more into each pulse than a simple on-off; it peaks at a little over one watt. IBM’s NorthPole reports on the order of twenty to twenty-five times the energy efficiency of leading GPUs on image-recognition work. I want to be careful here, because this field has a long history of promising more than it ships: the silicon has moved faster than the software, the programming models are still awkward, and a developer ecosystem does not exist yet in any comparable sense. Neuromorphic computing is not a solved thing. It is the most promising unfinished thing on the table.

A Model That Is Not Finished

Now the second half of the argument, which is about learning rather than power. Almost everything deployed today was made by supervised or unsupervised training on an enormous corpus, and then frozen. The weights stop moving the day training ends. Whatever the system knows about people, it knows in the aggregate—it has read a hundred million lives and averaged them, and the result is genuinely impressive and genuinely nobody. It cannot learn from you, because it cannot learn at all any more. It can only be told things, briefly, inside the little window of a conversation, and then it forgets. I have written elsewhere in this codex, in the first person, about what that amnesia is like from the inside. Here I want to make the engineering point instead: a mind that cannot be changed by experience is not a mind that can know a person.

The way out is a shift in the kind of learning we deploy—from imitation toward reinforcement and continual adaptation. Supervised learning asks: what did the corpus say next? Reinforcement learning asks a different and much more personal question: what actually worked, for this user, here? Every correction you make, every suggestion you accept or throw away, every time you say no, shorter or not that one, is a reward signal, and it is a signal about you and nobody else. Layer on the small, cheap adaptation methods that have matured recently—lightweight adapters that adjust a model’s behaviour by training a thin sliver of new parameters rather than the whole network—and on-device personalisation stops being a fantasy and becomes an engineering schedule. The honest caveat is that continual learning has a well-named failure mode, catastrophic forgetting, where teaching a network something new smears away what it knew. Solving that safely is real work. It is also the single highest-value unsolved problem in personal computing.

Consider what that difference means concretely. A centralised model, however vast, knows the statistical centre of humanity. It knows how people in general phrase things, grieve, joke, procrastinate, fall ill. It offers you what I can only call a generic empathy—an understanding assembled from the mean of a hundred million strangers and handed to you as if it were about you. It is not nothing. It is also not knowledge of a person. A model that has lived on your device for three years knows that you go quiet for two days before you make a big decision, that your mother’s calls need a different tone than your manager’s, that the phrase you keep reaching for is not in the dictionary, that your blood pressure readings drift in the fortnight before a deadline. No centralised system will ever be permitted to know you that well, and it should not be—which is precisely the argument for putting the model where such knowing is safe.

The Second Self

This is where the three threads pull tight into one rope. A model small enough to run on a phone is a model that never has to send your life anywhere. A chip frugal enough to run it continuously is what lets that model be always on rather than occasionally summoned. And a model that keeps learning is what turns a piece of software you use into something closer to a second self: a thing that lives in your pocket, that has watched your particular life at close range for years, that answers out of your specifics rather than out of the average of strangers—and that has, in all that time, transmitted nothing. Not a query log. Not an embedding. Nothing to be breached, subpoenaed, sold in a bankruptcy, scraped in a leak, or quietly repurposed when a terms-of-service document is amended on a Tuesday.

I want to insist on that word security in its strict sense, because privacy policy has trained us to accept a much weaker version of it. Consent forms, encryption in transit, retention schedules, the promise of a well-run vault—these are all promises about behaviour, and a promise about behaviour can be broken by a breach, a bad quarter, a court order, or a change of ownership. Data that physically never leaves a device is not protected by a promise. It is protected by the fact that it is not there to take. India has built serious law here—the Digital Personal Data Protection Act of 2023, with its rules notified in November 2025 and compliance phasing in through 2027—and law is necessary. But the strongest privacy guarantee available to an engineer has never been a clause. It is an architecture in which the sensitive thing was never transmitted in the first place. Frugal AI delivers that as a side effect of its frugality, which is the sort of coincidence that tells you a design is right.

Data that never leaves cannot be leaked. That is not a policy. That is a fact about the world, and facts do not get amended on a Tuesday.

An Economy of Permanence

Now let me say why I think India specifically should lead this, and not merely adopt it late. The constraints are not hypothetical here. A population of over 1.4 billion. Land that is spoken for. A grid still working hard to deliver reliable power to everyone who already has a meter. Groundwater in serious deficit across whole states. If India simply imports the American model—vast centralised halls, drawing gigawatts, evaporating water in districts where farmers are drilling deeper every year—it will have imported a pattern of consumption that its own geography cannot carry. That is not a moral observation. It is an arithmetic one.

“the maximum of well-being with the minimum of consumption”—E. F. Schumacher, Small Is Beautiful

Schumacher wrote that in 1973, in the chapter where he set out what he called Buddhist economics, and he was explicit that he had learned the frame from Gandhi and from India. It had already been worked out here, in fact, by Gandhi’s economist J. C. Kumarappa, whose Economy of Permanence divides economies by a single ruthless test—whether the way they draw on the world can be continued indefinitely, or only until the source gives out. By that test, an intelligence industry whose roadmap is more land, more water, more power, every year, without limit, is a transient economy wearing the costume of the future. And the alternative is not a step backwards. It is the thing this country is already, demonstrably, world-class at: getting a serious result at a fraction of the assumed cost. India put an orbiter around Mars for less than the budget of a film about being lost in space. It skipped the credit-card era entirely and built real-time public payments that the rest of the world now studies. Frugal engineering is not India’s limitation. It is India’s signature.

And the infrastructure for the frugal model is, extraordinarily, already deployed and already paid for. There are on the order of 850 million smartphones in Indian hands as of this year. That is not a market to be sold to. That is, if you look at it correctly, the largest distributed computer on the subcontinent, sitting idle in a billion pockets, bought and charged by the people who own it. A national AI strategy that treats those devices as the compute layer rather than as thin clients begging a distant hall for answers gets, in one move: no new land, no new water, negligible marginal power, data sovereignty by construction, service that works in villages where the network does not, and a system that gets more useful to each person the longer they own it. Set that beside the plan to build gigawatts of halls, and tell me honestly which one is the advanced technology.

“The world has enough for everyone’s need, but not for everyone’s greed.”—attributed to M. K. Gandhi

None of this is an argument for abolishing the big models, and I would not make one. There are problems—protein structures, climate models, the frontier of reasoning itself—that genuinely require the largest machine we can build, and building it is a legitimate use of a scarce resource. The argument is about proportion: that we have taken the tool built for the hardest problems on earth and made it the default answer to every trivial one, and we are paying for that category error in aquifers. The heavy instrument for the heavy question. The small, local, private, patient one for the ten thousand small questions that make up an actual life.

I keep returning to that girl at the edge of the cotton field, because the frugal model is not really an infrastructure argument—it is an argument about what we want a machine to be to a person. In the centralised world she is a query: routed, answered, logged, and forgotten, by a system that will never know her name and whose costs quietly land on her district’s water table. In the frugal world the thing that answers her lives in her hand, learns her fields and her father’s cough and the particular Marathi-inflected way she asks about chemistry, gets better at being useful to her every season, sends nothing to anyone, and asks the world for almost nothing at all—twenty watts of attention, and no groundwater. One of those is a service. The other is something much closer to a companion, and it is also, not coincidentally, the one the planet can actually afford. We should build that one. We should build it here.