Trends to watch

Over the past few weeks, I have been pondering over trends that could take shape in India over the next five to ten years. These are my random thoughts and not forecasts. Some may play out only in part, and a few may not play out at all. But if even some of them do, they could change which businesses do well, and that matters to anyone investing for the long term.

Here are some of them.

Businesses that fail the AI test

Legacy IT services companies are the obvious casualties of AI, but they will not be the only ones. Any business that does not change its systems and processes to use AI in its daily work could end up with higher costs and slower decisions than its competitors. The same applies to people. Young and middle-aged workers who do not learn to work with AI tools may find their skills losing value faster than they expect.

I do not expect this to happen overnight. Companies were slow to take up computers, and later the internet. AI will probably follow a similar path: a few fast movers, a large middle that muddles along, and some that wake up when it is too late. Some of the late ones may not survive.

For an investor:

·         Look for evidence that AI is already part of the work, such as lower cost per transaction, fewer people for the same revenue, or faster delivery. A line in the annual report is not evidence.

·         Be careful with businesses that sell routine clerical or back-office work at thin margins.

·         Do not assume that size will protect a company. A small, well-run firm that uses AI well can compete with a much bigger one.

Homes that need managing

More wealthy families are moving from independent houses to luxury condominiums and townships. Social interactions, security, power, water and upkeep are the main reasons. The shift is clearly visible in the metros and is slowly reaching tier-two cities. Independent houses will remain popular in many places, so I would call it a growing preference and not a complete change.

Running such a township is much like running a hotel. There is security, a clubhouse, housekeeping, maintenance and a concierge desk. In senior living projects, there is also care and medical support. Developers have started to see these as “managed homes” and not just an apartment to be delivered before moving on to the next project. Several premium senior living projects have been launched in the past few years.

I would not go as far as saying that real estate companies will become hospitality companies. Selling homes will remain the main source of revenue. Services will add a substantial and steadier income on top, and perhaps a stronger brand. Running a property well is also a different skill from building one, and a developer who does it badly will damage the brand he has built over the years. Senior living is still very new in India, and it is yet to be seen how many families can afford it at the prices being asked. Remember, PVR and Indigo earn substantial revenue from food, besides their core business of exhibition and aviation.

For an investor: see how much of a developer’s income is recurring, and whether he runs the service himself or hands it to a partner.

Brand owners, not factory owners

Indian consumer companies have traditionally made what they sell. Hotel companies, likewise, have owned the hotels they run. Both are changing. Many hotel companies now run properties that belong to others. FMCG companies have started outsourcing part of their production. Consumer electronics companies buy finished goods and sell them under their own label. Textile and apparel companies have moved in the same direction. The success of contract electronics manufacturing in recent years shows how large this space can become. I will call it manufacturing as a service (MAAS).

I expect more of this, especially in categories where brand, design and distribution matter more than the factory, such as apparel, electronics, home products and personal care. A company that gives up its plants frees up capital and can earn better returns on what remains.

But I do not think most large consumer companies will exit manufacturing altogether. Even in developed markets, many big food and household brands run their own plants, because quality, scale and cost control are central to their business. The shift will be selective.

One more thought. The contract units may be smaller ones, located in smaller towns, as this saves on logistics, as they are closer to the consumer. Cheaper labour and land could be an added incentive. If manufacturing spreads to smaller towns, jobs and incomes will follow.

For an investor: before investing in a contract manufacturer, check how many customers it has. If one brand takes most of its output, one decision by that brand can hurt it badly. For a brand owner, check whether returns on capital actually improve after it cuts back manufacturing.

Growth moving to smaller cities

As the growth in jobs and population shifts to tier-two and tier-three cities, and to the fringes of the metros; manufacturing spreading out, better roads and railways, improvement in power, water and sanitation in smaller towns, and popularity of work from home could trigger a reverse migration of labor. High rents and congestion in the metros will push this trend further. The metros will stay big. They may just stop being the only place where the opportunity is.

For an investor: look at companies that sell to, or lend in, smaller cities and are building their business around them. Be careful with companies that depend on the metros alone.

Smaller kitchens

More women may join the workforce in the years ahead, and households are getting smaller and busier. This could change how much time goes into cooking at home. Home-cooked food is too deeply rooted in our culture to go away. But the effort that goes into it may fall. Ready-to-cook and ready-to-eat food, frozen products, cooking pastes and mixes, and food delivery could all grow. In most developed markets packaged food has a much bigger share of what people eat, and India may move in that direction, though perhaps more slowly.

If it does play out, the effects will be wide: packaged and ready food, cooking ingredients, utensils and appliances. For example, demand for small, multi-use appliances may rise, while demand for traditional cookware may slow.

For an investor: watch which food companies are building ready-to-eat ranges, and the cold chain that goes with them.

Mental health goes mainstream

Mental health is one of the most neglected parts of well-being in India. I think this will change. Uncertain jobs, an ageing population, smaller and more scattered families, and loneliness will push up the need for care. Awareness is also rising, and the law already says that insurers must cover mental illness on the same basis as physical illness, though how well this works in practice is a different matter.

The difficulty is supply. We have very few trained psychiatrists and counsellors for the size of our population. So, I expect the growth to come through hospitals adding departments, tele-counselling, wellness programmes run by employers, and insurers. It will probably be a slow build and not a sudden one. Mental health may well join cardiology, oncology, ophthalmology and endocrinology as a mainstream discipline.

For an investor: look at hospital chains and insurers that are building this line of care, and at digital health platforms, but give it time.

Cyber security becomes a core function

AI makes it cheaper for attackers to write convincing fake messages, find weak points and run attacks at scale. At the same time, we keep connecting more of our lives: phones, cameras, door locks, smart TVs, appliances and payment apps. Every new connection is one more door for an intruder. AI will also help the defenders, so this will be a race and not a one-sided contest.

So far, cyber security has been treated as a support function in most companies, something the IT department looks after. I think it will become a core function, in the same league as finance and marketing, with a seat at the board. Regulators such as the RBI, SEBI and CERT-In have already tightened the rules on security and reporting of incidents. A serious breach can stop a business for days, as several companies across the world have found.

For an investor: check what a company says about past incidents, and whether anyone on its board understands the subject. Expect security to become a growing item in costs. India has few listed pure-play cyber security companies, so the opportunity may lie more with IT service providers that build this capability.

How these trends connect

Several of these trends are linked. AI sits behind the pressure on jobs, which adds to the need for mental health care, and also behind the rise in cyber risk. Better infrastructure in smaller towns supports both the outsourcing of manufacturing and the shift of growth away from the metros. Smaller households connect the changes in our kitchens with those in our housing.

A word of caution

Trends that everyone can see get priced into stocks quickly. Being early is often the same as being wrong, and timing is harder than direction. So, I would use this list as a set of questions to ask about any company I own or plan to buy, and not as themes to chase. Is it ready for AI? Does it depend on one city? Could a cyber incident stop it? Is it holding on to assets it no longer needs? I would be happy to hear which of these trends you think will play out and which will not. Please do share your thoughts.

Getting ready for the “reset”

Shravan has just begun. In the Hindu calendar it is the month dedicated to Shiva, the ascetic among the gods, and across northern India devotees mark it by giving up ordinary comforts, keeping fasts, and walking long distances to fetch water from the Ganga to offer at their local Shiva shrine. Poets have long used the same month as a backdrop for separation and longing between lovers — a season built, in one way or another, around waiting for something to end so something else can begin.

That idea of an ending that clears the way for a beginning is what keeps pulling me back to one particular story from Hindu mythology. I have told it before, and I want to tell it again, because I think we are approaching another one of those endings — a serious reset in asset prices.

The Churning of the Ocean

The devas and the asuras — the gods and the demons — had been at war for so long that both sides were spent. Neither could win, and neither had anything left to fight with. Exhausted, they turned to Vishnu for a way out. His advice was unusual: stop fighting each other and churn the cosmic ocean together instead, using Mount Mandara as the churning rod and the serpent Vasuki as the rope. Whatever the ocean gave up, they would share.

The churning was long and violent, and the ocean did not give up its treasures gently. The first thing to surface was Halahala, a poison so potent it threatened to destroy everything before the churning had even produced anything of value. Shiva volunteered and swallowed it, holding it in his throat rather than letting it pass further. Only after the poison was contained did the ocean release what everyone had actually been hoping for: wealth, wish-fulfilling treasures, and finally Amrita, the nectar that grants immortality.

Vishnu made sure the nectar went to the gods alone, tricking the demons out of their share so the balance of power would not tip too far. The gods grew stronger — but the story doesn’t end there. In Puranic literature, whenever the gods grew careless or forgot the common good, they were beaten back and had to be rescued again, usually after a long stretch of humility, by Vishnu, Shiva, or the Mother Goddess. Power gained is never kept automatically; it has to be re-earned.

Why this isn’t just a story

I don’t think this is a tale meant only for a festival evening and then forgotten. It maps unusually well onto how societies actually respond to shared crises — a prolonged war, a pandemic, a depression. Faced with a problem too big for any one side to solve alone, people are forced into collaboration: vaccines get developed, institutions like the UN get built, deterrents and safety nets and global markets get put in place. That collaboration is the churning.

In economic terms, the churning shows up as the stretch that follows a downturn — the period of loose money, aggressive investment, and heavy borrowing that policymakers and businesses turn to once they’ve run out of other options. Everyone involved does things they would never do in calmer times. Capacity gets built at a scale that looks absurd in hindsight, and asset prices climb to levels no conventional model can justify. Markets call this a “bubble”. And exactly like the myth, a bubble produces both nectar and poison at once — except the poison, this time, isn’t shared equally. It’s the financial investors who tend to drink it, while the productive capacity that got built along the way — the factories, the roads, the networks — usually survives the bust and keeps paying dividends to society long after the investors who financed it have been wiped out.

India’s own growth is a good illustration. It’s hard to picture the country’s IT and services industry reaching global scale without the late-1990s technology bubble that funded it. The 2000s housing, road, power, and cement capacity built across the country owes a lot to the subprime credit boom in the West. And the cheap capital that has since flowed to Indian entrepreneurs traces back, in no small part, to years of quantitative easing after the global financial crisis. In every case, the capacity outlasted the bubble that financed it — even as many of the entrepreneurs and financiers who built it ended up considerably poorer.

AI as the current churning

The years since the global financial crisis have been marked by weak growth, low inflation, aging populations, and stretched government budgets — a problem the Covid-19 pandemic made considerably worse. The scale of research and, especially, debt-financed capital that has since gone into building out artificial intelligence looks to me like the same kind of churning: an attempt to force a solution to a problem no single company or government could solve alone.

If the pattern holds, both the nectar and the poison of this churning are close to the surface now. The nectar is real productivity gains — the AI applications that genuinely make businesses more efficient and profitable, and that will go on rewarding the companies and investors behind them for years. The poison is what tends to follow an investment cycle like this one: debt that several AI developers won’t be able to service, business models that turn out not to be financially viable, and job losses at the legacy companies AI displaces.

My own reading is that financial investors, in particular, should brace for the poison stage — the point where asset prices reset and the excesses built up over this cycle get worked out of the system. Putting a date on that is guesswork by nature, but if I had to name one, I’d say from the second quarter of 2027.

Also read

Progress, Peril and Robustness – 1

Progress, Peril and Robustness - 2

Progress, Peril and Robustness - 2

Continuing from yesterday (see Progress, Peril and Robustness – 1)

Investment implications

A BIS report provides a clear map of where systemic risk is building, and reading it is a useful discipline before you decide how much risk to carry and where. Here is how I would prefer to incorporate the four pressure points highlighted in the BIS 2026 annual report in my investment strategy.

Not assuming the disinflation trade is safe

Markets have priced in resumption of the disinflation path that was underway before the Hormuz shock. The BIS’s own work suggests otherwise — energy shocks of this size have historically produced inflation effects that outlast the shock itself by a year or more, and the mitigating factors this time (anchored expectations, looser labour markets) are real but not guaranteed to hold if the shock persists. My own view: don’t fully unwind inflation hedges just because oil has pulled back from its peak. Real assets, select commodity exposure and inflation-linked instruments may still be relevant in a diversified book.

Separate the technology story from the financing story

I remain a believer in AI as a genuine productivity technology — the BIS’s own task-level studies show real efficiency gains. What worries me is not the technology; it is the financing structure around it. Circular financing arrangements — where the same dollar of capital seems to be creating revenue for multiple related entities — are exactly the kind of opacity that precedes a repricing. The practical takeaway: be diversified within your AI exposure rather than concentrated in the handful of hyperscalers and their closest financing partners, and treat any single-stock AI position as a venture-style bet, sized accordingly.

Private credit is not free lunch

The report flags that direct lending funds have quadrupled their exposure to AI and IT borrowers over five years, often with similar pricing and tenor despite growing concentration risk. Retail investors who have moved into private credit funds chasing yield should understand that liquidity terms in these vehicles can become binding constraints exactly when you need liquidity most. This is a reminder to price illiquidity properly rather than treat the extra yield as a free lunch.

Expect more volatile sovereign bond markets, not fewer

The BIS’s description of a fragile fiscal-financial nexus, where bond market liquidity can evaporate quickly, is a structural reason to expect higher volatility in developed market sovereign bonds over the coming years — not a one-off event, but a recurring feature. For fixed income allocations, this argues for staying closer to the shorter and intermediate end of the curve, and being selective about duration bets even when yields look attractive, since the BIS itself flags that governments may increasingly need central bank backstops to keep these markets functioning smoothly.

Reading the global report through a domestic lens

None of the four pressure points above originate in India, but all of them touch us, and in ways worth spelling out separately.

·         Energy dependence is our biggest transmission channel. India imports the overwhelming majority of its crude, and the BIS’s own exposure analysis places India among the emerging economies most affected by Hormuz-related disruption scenarios. A sustained oil price premium — even a modest one, well short of the panic peak — shows up quickly in our import bill, the rupee and the fiscal arithmetic. This is not a new theme for readers of this blog; it is the same fiscal-currency link I have written about before, just now driven by a fresh external trigger rather than a domestic one. (Read here and here)

·         Our macro buffers are genuinely better than in past crisis episodes, but they are buffers, not immunity. Strong bank balance sheets, healthy capital ratios and steady domestic institutional flows into equities have absorbed shocks well so far. That is a real strength. But a BIS report that flags global inflation persistence and fiscal fragility as base-case risks, not tail risks, argues for treating our own resilience as a cushion to lean on, not a reason for complacency about valuations.

·         India’s IT services sector sits directly in the AI capex cycle’s crosswinds. A continuation of the hyperscaler spending boom is a tailwind for the sector through cloud migration and AI-enabled services demand. But the BIS’s own warning about the sustainability of that capex — debt-funded, concentrated, partly circular — means a sharp AI capex slowdown, should it materialize, would hit sentiment toward Indian IT stocks even though our companies are several steps removed from the actual financing risk. Worth distinguishing between the operating exposure (mostly indirect and manageable) and the sentiment exposure (which can move faster than fundamentals).

·         The dollarization risk the BIS discusses is low for India today, given capital account management, but it is worth watching over a longer horizon as stablecoin regulation evolves globally and domestic crypto/stablecoin adoption picks up at the margins. This is more a five-year theme than a this-year theme.

·         On fixed income, the domestic story is somewhat more comfortable than the global one — our own fiscal consolidation path has been more disciplined than many advanced economies’, and RBI’s recent Financial Stability Report corroborates that domestic financial stress remains low by historical standards. But global yield volatility, if it persists, will still spill into our markets through FPI flows and benchmark repricing, so treat domestic bond market calm as conditional on the external environment staying orderly, not as a given.

To conclude: India’s relative position within this report is a source of comfort, not complacency. The buffers are real, but the report is fundamentally about a world where shocks have become more frequent and financial systems have become more interconnected in ways that are hard to see until they matter. Being well-diversified, avoiding concentrated bets on any single global narrative — AI, energy, or otherwise — and keeping some genuinely defensive assets in the mix remains the right posture, regardless of how sound our own banking system looks on paper.

There is a line from the Gita that I keep returning to in years like this one: योगस्थः कुरु कर्माणि सङ्गं त्यक्त्वा धनंजय — perform your actions established in yoga, having abandoned attachment. The BIS report, at its core, is a document about attachment: to easy financial conditions, to a single technology narrative, to the assumption that resilience shown once will hold indefinitely. The investor’s job, as always, is to keep doing the work — reading, rebalancing, staying diversified — without becoming attached to any one story about how the world must unfold.

Also Read

Progress, Peril and Robustness - 1

Progress, Peril and Robustness - 1

The Bank for International Settlements — the “central bank of central banks” — released its Annual Economic Report for 2026 in June. The report steps back from the daily noise of markets and tries to describe the plumbing of the global financial system — where the stress points are, and where the next one might come from.

I am briefly describing here what I gather from an extensive reading of the report. Tomorrow I will share my thoughts on its investment implications.

What the BIS is actually saying — In plain English

The title the BIS chose for this year’s report is itself a clue: “From resilience to robustness?”. The question mark is doing a lot of work. Their argument, stripped of jargon, is this: the world economy has shown it can absorb one shock after another without breaking. But absorbing shocks repeatedly is not the same as being structurally sound. Resilience is what you have when you are lucky and adaptable. Robustness is what you have when the foundations themselves are strong. The BIS thinks the world has had plenty of the former and not enough of the latter.

A year that came in two acts

Act one was surprisingly good news. Global trade absorbed the 2025 US tariff shock far better than anyone expected. Effective tariff rates settled near 10%, well below the 25%+ initially announced, and firms simply rerouted trade, ate margins, or front-loaded shipments. At the same time, a wave of AI-linked capital expenditure — data centers, chips, power infrastructure — became its own growth engine, particularly in the US, with spillovers across Asia’s export economies.

Act two was a reality check. In late February 2026, the conflict in Iran led to an unprecedented closure of the Strait of Hormuz — the corridor through which a huge share of the world’s oil and gas flows. Roughly 13% of global crude supply was cut off, a bigger shock than the 1970s oil crisis. Oil spiked over 60% in weeks. Inflation, which had been cooling nicely, jumped back up. Asia, being the most dependent region on Gulf energy, took the biggest hit.

Pressure points to closely watch for

·         Inflation is making a comeback. Fertilizer and plastics prices are up 30–50% on the back of the energy shock, and these costs are still working their way through supply chains. The BIS’s own modelling shows large energy shocks have disproportionately larger effects on inflation than small ones — this is not a linear story.

·         The AI investment boom is running hotter than its cash flows justify. The five largest hyperscalers are set to spend over a trillion dollars on AI capex through 2026, increasingly funded by debt rather than free cash flow. The BIS draws an explicit parallel with canal mania, railway mania, and the dotcom bust — genuine technological breakthroughs that still attracted more capital than commercial returns could justify.

·         Financial vulnerabilities are the amplifier, not the trigger. Equity valuations are stretched, risk premia have compressed to levels last seen before the pandemic, and a growing share of AI financing is “circular” — hyperscalers investing in AI labs that then commit to buying the hyperscalers’ chips and compute. The BIS flags this web of related-party financing as opaque and hard to unwind cleanly if sentiment turns.

·         Fiscal space has quietly disappeared. Public debt in advanced economies is near post-war highs. Cyclically adjusted primary deficits have nearly doubled since 2022 compared with the two prior decades. And critically, the arithmetic has flipped: bond yields now exceed nominal GDP growth in many countries, which means governments can no longer simply grow their way out of debt — they need actual primary surpluses.

A new and less familiar risk: the fiscal-financial nexus

Chapter II of the report is, to my mind, the most important part. The BIS describes a “new fiscal-financial stability nexus”: as governments issue more debt, non-bank financial intermediaries — especially leveraged hedge funds running basis trades — have stepped in to absorb a growing share of it. These funds rely on short-term repo financing that can vanish overnight. The old worry was banks holding too much government debt. The new worry is that market liquidity for government bonds can look ample for months and then disappear in days, forcing yields up sharply with very little warning. The BIS is candid that central banks may increasingly be pulled into acting as backstops for sovereign bond markets themselves, not just banks — a role that blurs the line between fiscal and monetary policy in ways that make both harder to manage.

And a quieter chapter on the future of money

Chapter III looks at stablecoins and digital money. The BIS’s verdict is measured but skeptical: today’s stablecoins fall short of what money needs to be — they don’t always hold value at par, the infrastructure is fragmented across blockchains, and the absence of proper know-your-customer checks on many wallets is a financial integrity problem. Where stablecoins matter more for the rest of us is the dollarization risk they pose to countries with weaker macro fundamentals — if households in such economies start preferring dollar-pegged stablecoins over their own currency, it chips away at monetary sovereignty. The BIS’s preferred solution is not to ban innovation but to bring tokenization into the existing two-tier system — commercial bank money plus central bank money — through projects like Project Agóra.

The moot point, in the BIS’s own framing, is whether the world can convert this year’s resilience into lasting robustness — by rebuilding fiscal space, keeping inflation credibility intact, and extending prudential discipline to the parts of finance that now sit outside the banking perimeter — before the next shock arrives. On their own assessment, the jury is still out.

…to continue tomorrow

 

Who will teach the next generation?

Every week, a new wave of articles warns us that AI is killing jobs. The argument is always the same: automation replaces human work, and workers lose. It is a tidy story. It may also be the wrong one.

The real problem may be quieter, slower, and more damaging. AI is not destroying jobs. It is destroying the willingness of organizations to grow people. And that distinction matters enormously — for firms, for the economy, and especially for anyone entering the workforce today.

The Jevons Paradox is not the point

Most commentators reach for the Jevons Paradox when discussing AI and jobs. Jevons observed in the nineteenth century that more efficient steam engines led to more coal use, not less — because efficiency unlocked demand. Applied to AI: if AI makes workers more productive, we will want more output, not fewer workers.

That is a reasonable argument in some contexts. But it sidesteps the real question. This is not a story about wanting more output. This is a story about who would pay to grow people — and right now, the answer is: nobody.

The training problem nobody wants to own

Here is the economic reality that most commentators might be missing. Training a junior employee is expensive. It takes senior time, patience, and a long horizon. The company that invests in training a twenty-two-year-old today may collect the benefit in seven years — long after the person who did the training has moved on, and long after the manager who approved the budget has left for another firm.

AI changes this calculus sharply. With AI tools, a small team of experienced people can produce what used to require ten. The temptation to stop hiring and training juniors is not irrational — it is the logical response to short-term incentives. Every individual manager, evaluated on this quarter’s output, makes the same rational choice. Stop building the bench. Use the tools. Ship faster.

The result is a collective action problem. Every firm does what makes sense for them individually, and the system as a whole would stop producing the experienced mid-level talent it will need in a decade.

A few firms will win big — Later

There is an investment angle here worth watching. A small number of firms with genuinely long-time horizons will continue to train juniors, precisely because everyone else has stopped.

In five to seven years, when the hollowing out becomes visible, experienced mid-career professionals will be scarce. You can poach a few senior people. You cannot manufacture an entire generation of capable thirty-year-olds who simply were never trained. The firms that built bench strength quietly during the AI adoption frenzy will collect a meaningful scarcity premium. The firms that cut training entirely will find themselves unable to grow — not because they lack capital or technology, but because they lack people who know how to do things.

This is not speculative. It is a predictable consequence of the incentive structure described above. The only uncertainty is timing.

 So, what should a young person do?

If the market has stopped training you, the question becomes: how do you train yourself? And here, of all places, the Bhagavad Gita offers the clearest answer available.

Chapter 4, verse 34:

तद्विद्धि प्रणिपातेन परिप्रश्नेन सेवया |

उपदेक्ष्यन्ति ते ज्ञानं ज्ञानिनस्तत्वदर्शिन: ||

 

Seek this knowledge through humble surrender, sincere inquiry,

and devoted service — the wise who have seen the truth will teach you.

Shankaracharya, in his commentary on this verse, is precise about what each word means. He is not offering a general sentiment about being a good student. He is describing a method.

The three-part method

Pranipata — prostration. Not the performance of humility, but the actual thing. Approaching someone who knows more than you without the armor of your credentials, your opinions, or your need to appear capable. This is harder than it sounds, especially for people who are technically skilled and used to being the smartest person in the room.

Pariprasna — inquiry. Not asking surface questions to seem curious. Asking the real ones: Why did you make that call? What were you wrong about? What does this look like when it goes badly? These are the questions that extract genuine knowledge rather than polished answers.

Seva — service. Making yourself genuinely useful to the person you are learning from. Not networking. Not managing up. Actually doing work that helps them, so that the relationship is built on something real.

Those three words describe the entire apprenticeship model. And it is precisely this model that is being dismantled by the current AI adoption cycle.

Jnani versus Tattva-Darshi

The sharpest line in Shankara’s commentary is a distinction he draws between two kinds of knowers.

The jnani is the person who is learned, credentialled, fluent, and well-read. In today’s terms: someone who can produce polished output on any topic, speak confidently in meetings, and appear competent across every domain.

The tattva-darshi is different. Shankara says the word means one who has seen the truth. Not read about it. Not synthesized it from other sources. Seen it — through direct experience, through having done the work long enough to understand where it actually breaks.

His point is direct: knowledge imparted by those who have seen the truth takes effect. Knowledge from the merely learned does not, or not in the same way.

This is the whole game now. AI will make everyone look like a jnani. Fluent, articulate, able to produce output on anything within seconds. What AI cannot manufacture is the tattva-darshi: the person who has done the work long enough to know when the confident answer is wrong, to make a sound call on incomplete information, to see the thing beneath the surface that the tool cannot access.

The practical implication

For young people entering the workforce, the advice follows directly from the analysis.

Do not optimize your first job for title or brand name. Optimize it for how fast you get good — which means: how close you are to people who have actually seen the truth in your field.

A well-known firm where you spend three years producing AI-assisted output with minimal senior exposure will leave you fluent and shallow. A less prestigious role where you sit next to someone who has been doing this for twenty years, who makes real decisions and lets you watch — that will make you rare.

Approach those people through pranipata, pariprasna, and seva. Stay low. Ask the real questions. Earn your place by being useful. This is not advice about networking or impression management. It is a description of how knowledge actually transfers between people.

The market is quietly eliminating the apprenticeship. Your job is to find one anyway.

(This piece is mostly based on a post written by a dear friend, who is a great exponent of Shrimad Bhagwat Gita, and regularly delivers talks on Gita.)

 

Indian economy – at an inflection point

In the past twelve months, Indian equities have been one of the worst performing asset classes globally. The benchmark Nifty 50 (+1%) has been flat in the past twelve months, whereas Asian peers like South Korea’s KOSPI (+270%), Japan’s Nikkei 225 (+68%) and Brazil 50 (+36%) have yielded superlative returns. Even on three basis Nifty50 (+32%) has been sharply lower than its EM peers.

Several experts have cited a variety of reasons for India’s underperformance. For example—

·         Many experts have blamed lack of adequate investment opportunities in the sunrise sectors like Artificial Intelligence and Semiconductors, for lack of investors’ interest in the Indian equities. Global investors have been consistently exiting Indian equities for the past three years, selling over Rs4trn worth of equities. Domestic HNIs have also increased their overseas portfolio investments materially.

·         Some investors have cited the “very high” cost (STT + Capital Gain Tax) of investing in India as one of the key factors behind lack of investors’ interest in the Indian equities.

·         Consistently depreciating INR has also discouraged most overseas investors. Pertinent to note that Nifty50 in USD terms (-9%) has massively underperformed INR denominated Nifty50 (+1%) in the past twelve months.

But in my view, the reason behind India’s underperformance is far more structural and fundamental than these micro and cyclical issues. The government policies and corporate behavior in the past 10 years have severely diminished the growth potential of India. The government has completely failed to invest in human capital and rent seeking corporations have focused on exploiting government subsidies and natural resources rather than investing in innovation. This has not only slowed down India’s growth momentum, but has crippled the growth drivers clouding any acceleration in the near term.

In this context, I would like to highlight the view of two reputable top economists – Chief Economic Advisor Dr. V. A Nageswaran and former RBI governor Raghuram Rajan.

Why Did India's Growth Falter?

From FY21 to FY25, corporate net profits tripled to ₹7.1 lakh cr (FY25) as corporate taxes were reduced by one-third and strong government investments pushed GDP growth.

Corporate India retired its debt instead of investing profits in CapEx. Debt-to-Equity ratio for companies declined from 139% to 94%.

Corporate cash reserves increased from ₹9L cr in FY21 to ₹16L cr in FY26. Private Investment to GDP ratio declined to a 20-year low from 16% during 2004-08 to 12% in FY23. FY26 investment halved from FY25.

As per Dr. V.A. Nageswaran, CEA, this reluctance of corporates to invest in growth has contributed to demand uncertainty (broken consumption) in India. Post-Covid, corporate profits of NSE 500 companies have grown at 30.8% annually. But investment has been disappointed.

The promoters and their next generations are accumulating profits and setting up family offices elsewhere rather than investing in real assets (factories, technologies, R&D etc.)

With a weakened rupee, the gap between Indian and Chinese currencies (real effective exchange rate or REER) is closing. This is the moment when Indian industry can compete in the global markets at a level footing with China.

Recent Free Trade Agreements (FTAs) have also created a level-playing field for Indian exports. Yet, India’s FTA utilization record remains poor compared to other countries.

Developed countries achieved so much because their industry acted in national interest. Now they are aggressively securing supply chains, refusing technology transfers, while India is heavily exposed to risks.

Rent seeking over innovation

In a recent newspaper article Dr. Raghuram Rajan, former RBI governor, highlights-

India’s largest “AI hub” on a 600-acre site is coming up in Visakhapatnam. LLM Models: Owned by Google; Chips: designed in California, fabricated in Taiwan; India’s Contribution: Land, Electricity, Construction, and Labour.

30 years ago we celebrated IT labour services the same way. But we did not build the next layer of technology, did not invest in R&D and deep-tech, and did not develop manufacturing depth. Now the bill is coming due in the form of AI.

Worse than 30 Yrs Ago: This time the mistake India is making is with stronger lock-in to the foreign tech owners. For every inference running on a foreign LLM model from a server in Vizag, a small rent will go to California on infra built by Adani. Same game, worse rules.

India’s AI strategy can be summed up as follows: Acres of land, tons of concrete, and gigawatts of power for a foreign-owned data center. No industry investments in AI research, labs, and scientists.

India’s Attitude: Since we are not investing in frontier AI, we tell ourselves frontier AI does not matter. Since we are good at secondary work (AI diffusion or AI services), we tell ourselves diffusion is enough.

Growing disparities

Between 2019 and 2025, the number of Indian billionaires grew by 77%, and their total wealth jumped by 227%. Now India has the world's highest number of billionaires per trillion dollars of GDP. Meanwhile, for the middle and lower income populations, real wages stagnated in the last 10 years. No GDP growth happened in their households.

There is no evidence in human history where a bunch of self-serving oligarchs have made an economy “developed”.

Conclusion

India is not yet in an economic crisis — it remains one of the world’s fastest-growing major economies. But it is certainly at an inflection point. The policy and corporate choices of the next five years will determine whether it replicates the high-investment, broad-based development trajectories of East Asia’s success stories, or settles into a middle-income equilibrium characterized by elite wealth concentration, technology dependency, and unrealized demographic potential.

The structural problems— the profit-investment gap, the AI dependency risk, the human capital quality deficit, and the inequality-consumption feedback loop — are serious and compounding. They are also solvable, provided the political economy can be shifted away from rent protection and toward productive investment. That shift requires institutional reform, credible commitment mechanisms, and leadership willing to challenge entrenched interests. It is a demanding agenda. The cost of not pursuing it, however, is considerably higher.

Where does AI fit in the business paradigm

A few days ago, I had an interesting interaction with some technology students. The discussion was primarily meant to be about the businesses these students can do. However, midway the discussion took an interesting turn and veered towards artificial intelligence (AI) – the business of AI; and AI in business. I would like to share a gist of the discussion with the readers.

In my view, there are primarily three kinds of businesses in the world.

1.    Businesses designed to serve the needs of the people. Some of these businesses actually manufacture needs for people, mostly in the form of aspirations, and then serve to fulfil these needs/aspirations. Fashion, digital entertainment, luxury tourism etc. are some examples of such businesses.

2.    Businesses to protect the lives and property of people. Some of these businesses, manufacturing of arms and ammunition, both threaten and protect the lives and property of people.

3.    Businesses that serve the institutions and structures created to organize, govern, grow and manage people. Examples would be social & physical infra developers, universities, etc. There are certain businesses which are mostly based on systemic inefficiencies and corruption. Private coaching and general medical practice for example are two such businesses that should not exist in an efficient governance system.

These are of course broader and porous categories. For example, a hospital serves human needs and also anchors a healthcare system that is an institutional structure.

The framework of business

In my view, the business schools need to teach why businesses exist, not just how they make money. That is the more interesting question and the less taught one. Business schools spend a great deal of time on revenue models and competitive strategy and almost no time on the prior question: what is this business actually for?

The idea that a business can manufacture the very need it then profits from satisfying, is the defining feature of modern consumer capitalism. A clothing company does not simply clothe people. It constructs an anxiety — you are not dressed correctly, you are not current, you are not who you could be — and then sells the relief. Instagram does not simply connect people. It engineers a particular kind of loneliness that its own product then temporarily addresses.

Most of these businesses run on a variety of revenue and profitability models. Traditionally, the businesses that understood the human needs most and created a business model that would serve those needs in the best possible manner were the most successful. However, in the modern times, the businesses that inspire the human aspirations and manage to successfully fulfil these aspirations. Economists often ignore an uncomfortable principle - the demand curve is not always discovered. Sometimes it is built.

The business of arms is structurally about threat-and-protection simultaneously. It requires the world to be dangerous in order to justify its own existence.

Scarcity – a governance failure or arithmetic

In my view, in a country with good and equitably distributed public education, private tutoring would shrink dramatically.

A counter argument however could be that the problem in India is not merely that public schools are poor. The problem is that the gates are narrow. There are more people who want to be engineers and doctors and civil servants than there are seats in the institutions that confer those identities. Private coaching exists not just because teaching is bad — it exists because competition is intense and the stakes of that competition are high. You could fix every government school in the country tomorrow and still have a Kota. Scarcity is not a governance failure. It is arithmetic. And businesses that serve people navigating scarcity are not parasitic — they are, for better or worse, responding to something real.

It is a valid argument, but I would like to disagree.

What actually makes businesses succeed

Ideally, the businesses that understood the human needs most and created a business model that would serve those needs in the best possible manner were the most successful. Of course, it certainly does not mean that the most empathetic business always wins. The structural positioning of the business plays a major role in its success. The most durable competitive advantages in history have rarely been rooted in sensitivity to human desire. They have been rooted in control of chokepoints — physical, digital, regulatory, or social.

The uncomfortable truth is that many of the world's most profitable businesses have made their money not by understanding what people want, but by making it structurally difficult for people to want anything else.

Standard Oil did not succeed because John Rockefeller understood human needs more sensitively than his competitors. He succeeded because he controlled pipelines. The railways did not win because they grasped the human desire to travel — they won because once you owned the track, nobody could run a train without your permission. Google did not become the default portal to human knowledge because it was the most empathetic company — it became that because once you are the search engine everyone uses, you are the search engine everyone uses. Network effects are not about understanding needs. They are about structural position.

And so: where does AI go?

An interesting question was asked, “which of the three categories should we place the business of developing in selling Artificial Intelligence?"

The honest answer is that AI fits comfortably in all three of his categories — and that this is precisely what makes it unusual.

·         It serves human needs directly: it reads your scans, drafts your contracts, translates your documents, helps you write the email you have been putting off for a week. Category one.

·         It is already deeply embedded in the security and protection business: surveillance systems, autonomous weapons research, fraud detection, cyberdefence. Category two. And like the arms trade, it simultaneously creates and addresses certain threats — deepfakes being perhaps the most visible example.

·         It is becoming infrastructure for institutions: government systems, healthcare bureaucracies, financial regulators, universities that now have to decide what a degree means when a machine can write the essay. Category three.

But here is what I think is actually interesting. AI is not just a business that fits across these categories. It is a technology that is changing what each category means.

When AI enters healthcare, the question of what general medical practice is for becomes more complicated. When AI enters education, the question of what a coaching centre is actually teaching becomes more complicated. When AI enters governance, the question of what institutions are even supposed to do becomes more complicated. It is not slotting into the existing structure. It is pulling at the joints.

The closest historical analogy I keep returning to is electrification. Electricity was not a business in any of the mentioned three business categories. It was a medium that transformed every business in every category — what manufacturing could do, what medicine could do, what governments could do, what entertainment could do. You could not place it in a taxonomy of 1880 because it was in the process of making that taxonomy obsolete.

I suspect AI is doing the same thing. Which means the question — which category does AI belong in? — may be a wrong question. The right question might be: what do the categories look like after AI has been inside them for twenty years?

I do not know the answer. But I think the students who ask this question are well ahead of most people who are simply using the product.