Tuesday, June 30, 2026

 Choose your economic model

In the post-World War II era, two forces have driven economic development more than any other: real estate and exports. Together, they have transported most economies from underdeveloped to middle-income status over eight decades. Technology and productivity gains have played a critical but largely supporting role — improving export competitiveness and raising household affordability for property ownership.

Improvements in social outcomes — inclusiveness, sustainability, equity, and quality of life — have typically followed economic development with a lag, sometimes by decades. It is therefore reasonable to assess any country’s economic model against these two pillars: how well it has built exports, and how well it has developed real estate.

India’s experience on both counts is instructive — and incomplete.

Exports: A story that stalled

As India opened up its economy in 1991, exports began to rise noticeably. Growth then accelerated sharply from around 2004. Much of that momentum can be traced to the structural reforms of the NDA government under Atal Bihari Vajpayee (1998–2004) — aggressive privatization of core sectors, rapid build-out of industrial and trade infrastructure, and the development of engineering and technology capabilities, partly driven by the necessity of surviving international sanctions after the 1998 nuclear tests. Those compulsions produced competencies that later translated into genuine export strength in engineering, technology, and pharmaceuticals.


But after the Global Financial Crisis of 2008–09, the story changed. Exports stagnated. More telling, India’s exports as a share of GDP peaked around 2013–14 at roughly 25% and have been declining since. A sharp post-Covid recovery briefly arrested that trend, but the past three years suggest a resumption of the downward drift.

 



(These figures include merchandise exports, services exports, royalties, and technical fees.)


The structural issues are well known: weak manufacturing competitiveness, logistics gaps, a modest share of global value chains, and a services export base that is deep but narrow. None of these are easy to fix. But the data makes clear that post-GFC India has not been able to sustain the export-led momentum that most successful Asian economies relied upon.

Real Estate: Underdeveloped and under pressure


Granular data on real estate’s direct contribution to India’s GDP is not readily available. The broader category of “Financial Services, Real Estate and Professional Services” under the services classification shows that this group’s share of GDP rose from around 15% in the early 1990s to approximately 22–24% today — a significant structural shift.

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Much of that gain likely reflects financial services rather than real estate itself. A May 2026 KPMG report estimated real estate’s direct contribution to India’s GDP at approximately 7.3%. By comparison, most Asian economies see real estate contributing 13–15% of GDP — a gap that reflects how far India’s urbanization and housing development still have to go.

The enabling conditions for a strong real estate cycle have been present to varying degrees. Since 1991, average lending rates have fallen sharply — from nearly 19% at their 1992 peak to around 9% today — driven by sustained fiscal consolidation, better inflation management, and structural improvements in the current account. Lower rates, combined with rising employment, improving real wages, better connectivity, and expanded urban access, supported a strong real estate growth cycle through the 2000s and early 2010s.

That cycle has since lost momentum. Inflation has proved sticky, the current account remains structurally vulnerable, and real wage growth in the formal economy has slowed. Interest rates, while well below their historical highs, have stopped falling. Without a resumption of the structural improvements that drove the last cycle, the tailwinds for real estate are weaker than they were.​



 The central question

This brings us to the harder question. If both pillars of the conventional post-war growth model — exports and real estate — are underperforming, what does India do?

One option is to double down: fix the structural constraints on exports, accelerate urbanization, and build the financial depth needed to sustain a larger real estate cycle. This is the well-tested Asian playbook.

The other option is harder to define but not trivial to dismiss. As I argued in a 2014 post on this blog (see Utopia: The Economic Solution), the Gandhian model — grounded in decentralization, labor-intensive production, self-reliance, and the primacy of the individual over capital — has attracted serious academic attention and is not without practical merit. The question is whether India has already travelled too far down the urbanization and integration path to reverse course, or whether elements of that framework can be selectively incorporated into a hybrid model.

Borrowing blindly from western models will not work in the Indian context. India’s economic model needs to reckon with a class structure, an employment challenge, and a rural civilizational inheritance that standard industrialization narratives do not address well. At the same time, a pure retreat to pre-industrial self-sufficiency is not a realistic option for a country of 1.4 billion people with legitimate aspirations.

The answer is likely somewhere in the middle — a model that aggressively rebuilds export competitiveness and develops real estate as a genuine engine of growth, while orienting its social architecture around decentralization, broad-based employment, and sustainability rather than pure GDP maximization.

The policy choice ahead

The data from the three charts above does not make a comfortable reading. Exports as a share of GDP have been falling for over a decade. Real estate is significantly undersized relative to India’s peers. Lending rates, while structurally lower than in the past, are not falling anymore. These are not cyclical problems. They are structural.

Policymakers cannot afford to wait for the globally tested model to reassert itself on its own. A deliberate choice needs to be made — either recommit to the conventional model with genuine policy urgency, or develop a credible alternative suited to India’s specific conditions. Muddling through, which has been the default, is not a strategy.

What that alternative looks like is a conversation India has not yet had at the level of seriousness it deserves.

Also readUtopia: The economic Solution

 


Wednesday, June 24, 2026

After the ceasefire: Why the uncertainty does not end here

Tuesday, June 23, 2026

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.)

 



Thursday, June 18, 2026

Modi @ 12: The unfinished agenda of India’s development

Wednesday, June 17, 2026

FCNR(B) – What does this domino effect mean for banks

(Continuing from yesterday…see here)

Tuesday, June 16, 2026

How the Indian financial sector is navigating a perfect storm

In the financial sector, structural problems have a way of announcing themselves quietly, through incremental data, technical jargon, and central bank circulars buried in the weekend briefing. Then, one day, the accumulated weight of those problems demands a policy response that is anything but quiet. India’s banking sector appears to be at exactly such an inflection point.