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It was called the Quipu, or Khipu.

A quipu consisted of a main cord from which multiple coloured strings hung. Information was encoded through the position of knots, type of knot, spacing, colour and arrangement of the cords. The system was sophisticated enough to represent numbers using a decimal structure.

An ancient data infrastructure

The Inca governed an enormous empire (~14000 sq Kms) stretching across much of western South America. Managing it meant keeping track of populations, resources, taxation, agricultural production and obligations across distant settlements.

Quipus helped administrators record and communicate this information. Specialist record keepers could interpret the cords, effectively turning bundles of fibre into portable structured datasets.

In modern terms, you could think of a quipu as having something resembling a schema:

• Main cord → Dataset

• Hanging cord → Data field

• Cord colour → Attribute / category

• Knot type → Data type

• Knot position → Numerical value

• Group of cords → Record

• Entire Quipu → Database

The numerical part of the system is relatively well understood. What remains fascinating is that researchers believe quipus may have stored more than numbers. Historical evidence suggests they could also have represented events, contracts and possibly stories or other narrative information. Scholars have still not fully deciphered this non-numerical layer.

Data without writing

That makes the quipu interesting.

Most civilizations developed record keeping around marks on a surface, clay tablets, inscriptions or paper. The Andes developed a very different information architecture: information encoded physically in fibre.

The medium itself became the database.

The larger lesson

Technology changes. The fundamental problem doesn’t.

Every era needs a way to convert the messy physical world into structured information that can be stored, transmitted and interpreted.

The Incas solved that problem with knots.

Today we solve it with tables, ID Graphs, data lakes and vectors.

Five hundred years from now, our databases may look just as unusual as a bundle of knotted strings looks to us today.

Quipu is a reminder that data technology did not begin with computers. It began the moment humans learned to encode reality into a system someone else could decode.

To conclude that little series, over the weekend I went for a long solo drive.

And somewhere on the expressways, between the trucks, SUVs and the toll plazas I passed by, I started thinking: what does India’s toll data actually tell us about how India moves?

So, I pulled up the PAN-India toll and FASTag data for July 2026 which covers the NHAI data, the state highway/expressway toll data is excluded.

Here are some interesting things the data tells us about cars, trucks, freight movement, cities and India’s highway economy.

12.7 crore FASTags, 33.29 crore vehicle passages and ₹5,731.2 crore in collections in July 2026,

India’s toll network recorded ~33.29 crore vehicle passages and ₹5,731.2 crore in collections in July 2026, based on an analysis of 1,235 toll-plaza records.

That means Indian toll plazas processed, on average, more than 1 crore vehicle passages daily in July.

The average toll collected worked out to ~₹172 per vehicle passage.

The difference between the average and median per plaza is important.

An average toll plaza processed about 2.7 lakh vehicle passages during July, while the median plaza processed only 1.67 lakh. The same gap appears in collections: average revenue was ₹4.64 crore, against a median of ₹2.46 crore.

A small number of large highway corridors therefore pull the national average upwards.

Of every 100 vehicles at a toll plaza, ~63 are cars

Cars and jeeps are the largest category by count.

~20.83 crore car and jeep passages were recorded during July. That is 62.5% of all vehicle movements through toll plazas.

The next largest category was four-to-six-axle vehicles, at 5.36 crore passages, followed by buses and trucks at 4.17 crore.

This gives a clearer picture of India’s road economy. For every 100 vehicle passages, there are approx:

• 63 are cars or jeeps

• 16 are four-to-six-axle vehicles

• 12-13 are buses or trucks

• 5 are LCVs

• 4 are three-axle vehicles

Yet those 63 cars generate only about ₹20 of every ₹100 collected. The 16 four-to-six-axle vehicles generate about ₹46.

One heavy truck is worth roughly nine cars in toll collections

The difference is clear when collections are divided by vehicle count. The average car or jeep passage generates ~₹53 in toll collections. For a four-to-six-axle vehicle, the corresponding figure is approx ₹494.

That is more than nine times the revenue per passage.

Three-axle vehicles average about ₹385, while the bus/truck category averages around ₹292. So while passenger vehicles determine traffic volume, commercial vehicles determine much of the economics of the toll system.

Heavy commercial categories, bus/truck, three-axle, four-to-six-axle and Over sized vehicles (OSV) together represent ~10.94 crore vehicle passages, or roughly 33% of traffic. But they generate ₹4,408 crore, close to 77% of all toll collections.

Include Light Commercial Vehicles(LCVs), and all non-car categories together account for around 12.47 crore passages, 37.4% of total traffic but 80.6% of collections.

Jaipur makes the most money. Bangalore handles the most vehicles.

Regional-office (RO) data shows why vehicle counts and collections need to be looked at together.

Together these five regions account for approximately 13.55 crore vehicle passages, or about 41% of all toll traffic in the dataset.

Jaipur was the country’s largest RO by collections, generating ₹542.2 crore from 2.58 crore vehicle passages. But Bangalore carried considerably more traffic. The Bangalore RO handled 3.46 crore vehicle passages, about 88 lakh more than Jaipur, yet generated ₹132 crore less revenue.

The reason is visible in the average collection per passage: ₹119 in Bangalore against ₹211 in Jaipur and ₹240 in Gandhinagar.

The five busiest ROs by vehicle count were:

Together these five regions account for approximately 13.55 crore vehicle passages, or about 41% of all toll traffic in the dataset.

The revenue ranking looks different. Gandhinagar, for example, is seventh by vehicle count but second by collections. Traffic volume alone therefore does not identify India’s most valuable highway corridors.

Bangalore is the busiest Project Implementation Unit(PIU), Surat is one of the most valuable.

The same divergence appears when the data is aggregated at the PIU/city level.

Here, Surat stands out. Surat saw only 34.4 lakh vehicle passages, less than half Jaipur’s count and little more than one-quarter of Bangalore’s. Yet it produced ₹125.8 crore in toll collections, making it the second-largest PIU by revenue.

Bangalore recorded roughly 1.27 crore vehicle passages, almost 3.7 times Surat’s traffic, but collected less money.

The source data cannot tell us why each individual vehicle travelled or what freight it carried, but the category mix provides a strong explanation for why some corridors monetise traffic very differently.

India’s busiest toll plaza handled 27 lakh vehicle passages in one month

Individual toll plazas show the same pattern.

Devanahalli Toll Plaza in Bangalore processed approximately 27.3 lakh vehicle passages during July, the largest count in the dataset. It generated about ₹28.3 crore.

That is an average of roughly 88,000 toll transactions per day. But Devanahalli was not the country’s highest-revenue plaza. That position went to Jaladhulagori toll plaza in Kolkata, which generated ₹37.6 crore from 14 lakh vehicle passages.

Shahjahanpur is particularly interesting. It generated ₹36.1 crore from just 7.6 lakh vehicle passages. Devanahalli processed about 3.6 times as many vehicles as Shahjahanpur, but collected less money.

Again, traffic and economic value are not the same thing.

What 33.29 crore vehicle movements tell us

July’s toll numbers effectively describe two highway economies.

The first is India’s passenger-mobility economy. It is large in volume: more than 20 crore car and jeep passages in a single month.

The second is the commercial-road economy. It is smaller by vehicle count but much larger in monetary contribution. Around 10.8 crore heavy-commercial vehicle passages generated approx 75% of all toll revenue.

This makes toll data potentially useful beyond simply measuring congestion or road utilization.

A city recording more car movements indicates mobility, economic growth and lack of public transport as well.

A state/corridor recording sustained growth in four-to-six-axle truck movements could indicate higher movement of goods, manufacturing activity, construction demand, port traffic or supply-chain flows or just higher tolls also.

P.S

The toll plaza maintained by the state road authorities are excluded from the analysis.

The Mumbai-Pune Toll Naka has a collection of ~₹120 Crores monthly with over 1.5 Lakh daily vehicles but its part of state corporation, hence excluded.

All Images/graphs generated using Claude.

That picture is changing fast.

India is now creating wealth across technology, financial services, pharmaceuticals, consumer businesses and the startup ecosystem. More importantly, wealth is spreading beyond of Mumbai and Delhi into Bengaluru, Hyderabad, Ahmedabad, Pune, Chennai, Gurugram and even cities such as Surat.

The one question which as a data intelligence company we always get is “How many rich people does India have?” Who are India’s HNIs and UHNIs, where do they live, how is their wealth being created, and how do they behave as consumers?

Let me try to answer a few of those

First, who exactly is an HNI?

Actually, there is no universally accepted definition of HNI of UHNI from India perspective.

The Mercedes-Benz Hurun India Wealth Report 2025 defines an HNI household as one with wealth above ₹10 crore, while an UHNI household has wealth exceeding ₹100 crore. Hurun additionally defines an “International HNI” at wealth above ₹200 crore.

Source: Mercedes-Benz Hurun India Wealth Report 2025.

The base of this pyramid is expanding particularly fast. India had 458,000 millionaire households in 2021, compared with 871,700 in 2025, an increase of about 90% in four years. These households now account for approximately 0.31% of all Indian households.

That might look like a tiny percentage of India. But economically, it represents an extremely significant consumer group.

Why UHNI estimates often appear contradictory

Another widely followed wealth report, Knight Frank’s Wealth Report 2026, defines ultra-high-net-worth individuals differently: individuals with net wealth of US$30 million or more.

On that basis, India has nearly 20,000 UHNW individuals in 2026, compared with just over 12,000 in 2021 growth of approximately 63% in five years.

So is India’s UHNI population 66,800 or 20,000?

Both numbers can be right.

For marketers, banks, luxury brands, defining the wealth threshold before sizing the audience is therefore critical.

Mumbai remains India’s wealth capital

India’s wealth is growing geographically, but it remains concentrated.

Mumbai alone has approximately 142,000 millionaire households, followed by New Delhi with 68,200 and Bengaluru with 31,600, according to the Mercedes-Benz Hurun India Wealth Report 2025.

At the state level, Maharashtra dominates with 178,600 millionaire households. Delhi follows with 79,800, Tamil Nadu 72,600, Karnataka 68,800 and Gujarat 68,300. The top ten states collectively account for more than 79% of India’s millionaire households.

But the more interesting story may be what happens outside Mumbai.

Ahmedabad, Bengaluru, Hyderabad and Pune represent different engines of new wealth: entrepreneurship and manufacturing in Gujarat, startups in Bengaluru, technology in Hyderabad, and technology, manufacturing around Pune.

Henley & Partners’ World’s Wealthiest Cities Report provides another lens. Unlike Hurun’s household measure, Henley defines HNWIs as individuals with US$1 million or more of liquid investable wealth. Its 2025 work identifies Bengaluru as one of the world’s fastest-growing wealth hubs.

At the very top end, Henley estimates Mumbai has 205 centi-millionaires people with investable wealth above US$100 million. Delhi has 125 and Bengaluru 43; Henley forecasts the latter two cities could see their centi-millionaire populations more than double by 2035.

The new wealthy Indian may also be younger

Wealth is no longer purely synonymous with inheritance.

The Mercedes-Benz Hurun India Luxury Consumer Survey found that 36% of respondents identified investments as a source of their wealth, 26% wages and bonuses, and 20% inheritance.

This suggests that India’s wealthy consumer universe increasingly contains investors, executives, professionals and first-generation wealth creators alongside traditional business families.

We have relatively good estimates of how many HNIs exist and where they live, but much weaker public data describing their age, profession, household structure and day-to-day behaviour.

That represents both a research gap and an opportunity.

Affluent does not necessarily mean extravagant

Intent and Ability are 2 different things

One of the more interesting findings from Hurun is that wealth does not automatically translate into conspicuous consumption.

In its affluent consumer survey, 60% reported annual household consumption below ₹1 crore. Tourism accounted for the largest stated spending category at 32%, followed by education at 27% and entertainment at 22%.

Travel was also the most popular hobby, selected by 45% of respondents.

Cars remain an obvious marker of affluence, but here too the behaviour is nuanced: 55% of Hurun respondents owned more than one car, while roughly 40% said they typically upgraded their cars every three to six years.

And India’s wealthy are highly digital.

In the Hurun survey, 35% selected UPI apps as their preferred payment method, while 36% named social media as their primary source of news, ahead of television at 21% and newspapers at 15%.

For marketers, this is significant. HNI targeting should not automatically mean premium magazines, airport advertising and luxury events. The same consumer may be managing a substantial investment portfolio, watching YouTube, using UPI, travelling internationally and consuming mainstream digital platforms every day.

Wealth is becoming a data problem

This is perhaps the biggest implication for marketers.

Traditional affluence targeting relied heavily on relatively simple proxies: income, premium pincodes, expensive cars, luxury purchases or high-value credit cards.

Those remain useful, but the modern HNI is harder to identify through a single signal.

Consider two people with ₹15 crore of wealth.

1. One could be a 60-year-old second-generation business owner in South Mumbai with substantial property holdings.

2. The other could be a 38-year-old technology founder in Bengaluru whose wealth consists primarily of equity, mutual funds and startup holdings.

Both qualify as HNIs. Their consumption patterns, media behaviour, financial requirements and purchase intent could be entirely different.

The next generation of affluent segmentation therefore needs to combine multiple signals: location, property, investments, financial products, travel, premium consumption, occupation, business ownership, digital behaviour and household characteristics.

And even then, wealth should generally be treated as a probability or affluence index, rather than assuming that any single behaviour conclusively identifies someone’s net worth.

India’s real opportunity: the HNI factory

Perhaps the most important story is not India’s existing HNIs.

It is the pipeline underneath

India’s millionaire household population increased from 458,000 in 2021 to 871,700 in 2025, according to Hurun. Meanwhile, globally, personal wealth grew 10.8% in US-dollar terms during 2025, according to UBS’s Global Wealth Report 2026.

For brands, banks, wealth managers, automobile companies, real-estate developers, travel companies and luxury businesses, the opportunity therefore extends beyond today’s HNIs/UHNIs.

The more interesting audience may be the emerging HNI someone moving from ₹2–5 crore towards ₹10 crore and potentially towards ₹100 crore over the next decade.

Understanding that journey could become far more valuable than simply compiling another list of India’s richest people.

A Shopper in India today seeks

1. Convenience

2. Assortment

3. Price

4. Trust

5. Urgency.

No single format optimises all five and hence no format is disappearing anytime soon.

So, In this week’s DeepMode let’s have a look at where the shopper and channels are headed thru the lense of data

India’s formal retail output grew 30.8% year-on-year in April 2026, according to the country’s first trial Index of Services Production. The number is large enough to suggest a retail boom. It is a evidence that Indian consumers have not migrated en masse from kiranas to apps.

The new index measures formal retail enterprises using GST data. It does not cover the vast informal market where neighbourhood stores and small traders operate. A second government survey (ASUSE-2025) shows that India’s unincorporated trade sector which includes small retailers and neighbourhood stores is also expanding.

The Indian shopper is not choosing one winning channel. Consumers are assembling a personal retail stack: the kirana for familiarity and immediate needs, supermarkets for planned baskets, e-commerce for selection, quick commerce for urgency, and social or messaging platforms for discovery.

1. Formal retail has accelerated

MoSPI’s experimental retail-services index rose from 105.1 in April 2025 to 137.5 in April 2026. It is intended to measure changes in service-output volume rather than sales inflated only by higher prices.

The result is directional, but it needs to be watched in the months to come. This is a trial series which has been launched with a 2024–25 base year. It currently covers formal enterprises, uses GST records and will be tested for stability and data quality in the next surveys. It says formal retail activity expanded rapidly thou it does not identify which channel produced the growth.

During 2025–26, the retail index moved from 105.1 in April to 153.1 in October, fell to 122.3 in November and reached 155.4 in March. Seasonality, festive demand and reporting patterns show up in this, but still fluctuating.

2. The small retailer is changing too

MoSPI’s Annual Survey of Unincorporated Sector Enterprises offers the other half of the picture. Between its 2023–24 and 2025 survey periods, the estimated number of unincorporated trade establishments increased 6.18%. Their nominal gross value added rose 16.77%.

These measures are not directly comparable with the formal retail index, but they reject the conclusion that organised and digital retail growth requires the informal base to contract.

Digital adoption is also reaching smaller enterprises. Across unincorporated manufacturing, trade and services, the share of establishments using the internet increased from 26.7% to 39.4%.

Internet use does not automatically turn a kirana into an e-commerce company. It means digital payments, supplier ordering, customer messaging, bookkeeping or online selling. The distinction matters: digitisation can strengthen a physical store without replacing it.

3. Online remains influential but relatively small

McKinsey estimates that e-commerce currently accounts for about 6% of Indian retail and could reach as much as 11% by 2030. Even if that projection is realised, nearly nine-tenths of retail would remain outside conventional e-commerce.

Online retail nevertheless influences more purchases than its transaction share suggests. A Meta–Retailers Association of India study reports that more than half of consumers research online before buying in a store, while a similar proportion inspect products offline before purchasing online.

BCG, drawing on more than 12,000 urban and rural shoppers, describes the same behaviour as “connected commerce”. It expects participation to broaden among women, rural consumers and shoppers in smaller cities. The consumer journey may begin on WhatsApp, continue in a store and end on a marketplace or travel in the opposite direction.

4. Quick commerce is big online, but metro-shaped

Q-commerce looks dominant or small depending on the denominator. Redseer estimates that it represented about 17% of India’s online retail gross merchandise value in FY2026 and roughly 70% of online grocery. Yet online grocery itself was only about 2% of total grocery retail.

Its impact is more concentrated in large cities. Redseer describes the typical user as Gen Z or millennial, from a household earning more than ₹12 lakh a year, in a metro or Tier-1 city.

The larger point is visible without a winner-takes-all forecast. Convenience, assortment, price, trust and immediacy are different consumer needs. No single format optimises all five.

India’s changing retail market is therefore less about the death of the kirana than the collapse of rigid channel boundaries.

The metrics worth watching are not only online vs offline sales. Formal retail output, small-business internet use, the geographic spread of digital shoppers and the share of purchases researched in one channel but completed in another will reveal how Indian consumers are actually shopping.

This duality strength without sovereignty captures India’s place in the global technology order. It is a country that has demonstrated great ability to deploy technology at population scale, but has yet to fully control the underlying systems on which that scale depends.

Nowhere is India’s achievement more visible than in its digital public infrastructure (DPI). Aadhaar has given 1.4bn digital identities to Indians; UPI has turned the country into one of the world’s most advanced real-time payments markets processing 20 Billion transactions per month; DigiLocker with 950 cr issued documents has quietly made paperless transactions possible at a scale. And the list of such capabilities is long.

Taken together, these systems often described as the India Stack represent something unique and truly “Made in India” : a state-led technological architecture that is both inclusive and scalable. In a country as big and complex as India, the ability to transfer money instantly, verify identity and deliver services digitally is no small feat. It is, in many ways, a model that other emerging and even developed economies are now studying and replicating.

India’s second great strength lies in its role as the world’s technology workshop especially for IT Services. The country’s IT/Tech industry was roughly around $250–280 bn in FY25, with exports accounting for about $219-$224bn of that total. Over three decades, India has made itself indispensable to the functioning of the global software business.

This success has created a generation of software engineers who are deeply integrated in the world’s most sophisticated technological systems be it banks, airlines or the largest retailers globally. Indian-origin executives now lead some of the most valuable companies in the world, a testament to the depth, adaptability and managerial quality of Indian talent. Yet this success story carries within it a quiet constraint. The services model rewards execution over innovation. It builds capability, but not always control. India has become exceptionally good at running the world’s technology not yet at owning it.

That distinction matters more in the age of AI than it did in the era of outsourcing.

For all its digital progress, India’s technological ecosystem still rests heavily on foundations built elsewhere, especially the USA. Its smartphones overwhelmingly run on Android, India Searches on google and finds answers on ChatGPT. Its digital communication flows through WhatsApp and the social life is literally in the hands of Facebook & Instagram. Its startups are built on top of cloud infrastructure controlled by AWS, Microsoft and Google. Even as India digitises at remarkable speed, the underlying stack,operating systems, cloud layers, frontier AI models, app ecosystems remains largely imported. The degree of dependencies on Big tech is extremely high, we can stock LPG or Petrol, but one day downtime of big tech can put India in a Virtual Lockdown

If oil defined dependence in the 20th century, data, compute and platforms may define it in the 21st. By that measure, India is deeply integrated but not totally independent.

The contrast with China is stark. Over the past two decades, China has pursued a more explicit strategy of technological self-reliance, building domestic champions across nearly every layer of the digital stack. HarmonyOS from Huawei is now on Billion plus devices and is in direct competition with Android and Apple having crossed the survival line of 27 Million users with its latest version. China’s AI ecosystem includes major foundation-model efforts like DeepSeek which compete with the best in the business. Baidu, China’s home grown Search Engine has close to 70% market share. In semiconductors, China’s SMIC has pushed into advanced-node manufacturing despite export controls. And on the consumer internet, platforms such as WeChat and Alibaba operate at an immense domestic scale, each integral part of everyday digital life in China.

The dichotomy becomes sharper when one turns from infrastructure to institutions. India has scale in higher education, India still does not place a single university in the global top 100 as per the QS World University Ranking 2026, while the US has 26 and China has 5 excluding Hongkong. IIT Delhi India’s highest-ranked institution is ranked 123 globally and 27th in Asia. Though India has 54 universities in the QS World University Rankings 2026, making it the fourth most represented country, this is again a classical case of Quantity over Quality.

That matters because universities do not merely produce graduates; they produce research cultures, founder networks and technological ambition. American universities, in particular, have become engines of entrepreneurial capitalism. The pipeline from lab to startup to venture funding to platform company is not an accident of geography. It is an institutional design. Stanford did not just educate engineers for Silicon Valley; it helped create Silicon Valley.

India’s universities, by contrast, have historically been optimised for exams, placements and professional mobility rather than frontier research or entrepreneurial risk-taking. This helps explain one of the country’s most striking asymmetries: Indian leaders are trusted to run global technology companies, but the companies themselves are rarely built in India.

The talent paradox sits at the centre of this story. India produces enormous numbers of engineers and graduates, has maximum contribution on platforms like github but quality remains uneven. Mercer Mettl’s 2025 Graduate Skill Index found that only 42.6 per cent of Indian graduates were overall employable. While India has depth of numbers, but not enough depth of excellence.

This is where the comparison with the US becomes uncomfortable. America’s advantage is not simply capital or markets. It is its ability to turn top universities into entrepreneurial factories. The US still dominates global venture capital: American VC investment reached $275bn in 2025,. India’s latest widely cited annual total, by contrast, was ~$13.5bn in 2025, according to Bain and IVCA. China remains ahead of India in capital mobilisation as well: at roughly $26bn. China does have large State Backed Funds, which according to various estimates is around $160bn.

Capital is not everything, but it does matter, especially for early stage startups. A founder in the US is building inside a capital market that is an order of magnitude larger than India’s. A founder in China, even amid a weaker private funding cycle, still operates in an ecosystem with far more domestic industrial depth, state backing and hardware ambition. An Indian founder often begins with less patient capital, thinner research infrastructure and a market that still culturally favours certainty over experimentation.

That cultural point matters more than we often admit. India is an entrepreneurial country in the informal sense millions hustle, trade and build livelihoods but it remains more cautious in the formal, venture-backed sense. One big reason often cited across surveys is “Fear of Failure”. In India, failure is still too often treated as social proof of poor judgment rather than evidence of ambition. In America, failed founders frequently re-enter the system with better networks, sharper instincts and, often, fresh funding. In India, failure is still more likely to invite family pressure, reputational damage and the suggestion that one should have taken the safer job. The seeds of this are sown right in the school days where lower grades even in primary school are frowned upon.

That difference in attitude shapes outcomes. America celebrates the repeat founder. China backs strategic sectors with national intent. India still often celebrates the rank-holder, the expatriate executive and the secure career path. It produces strivers in abundance, but not yet enough category creators.

None of this should obscure India’s real strengths. The country has built world-class public digital infrastructure. It has a vast domestic market. It has managerial talent respected across the world. It has a 350Mn GenZ population, well established consumer Internet companies and a growing startup ambition to build India First AI. And AI, by reshuffling the hierarchy of global technology, offers latecomers an opening that may not come again soon.

But time is not infinite. The next decade will probably determine whether India becomes a producer of foundational technologies or remains a sophisticated consumer of them.

For that shift to occur, three transitions matter. Hyderabad or Pune must move from being an outsourcing and GCC hub to being an AI first product capital. India must build sovereign capabilities in AI not just applications, but data, compute and models. And its higher education system must become less of a placement machine and more of a research-and-entrepreneurship engine.

If Bangalore aspires to become a true analogue to San Francisco, it will need more than spanky IT parks and engineering talent. It will need universities that create founders, founders who take risks, capital that tolerates failure, and institutions that reward original research as much as execution. Silicon Valley was not built only by talent. It was built by a strong alliance between academia, risk capital, bold founders and cultural permission to fail.

India has already shown that it can build for a billion people. The harder question is whether it can build what the billion people depend on.

As India approaches the next decade, the ambition of technological self-reliance is neither romantic nor unrealistic. It is strategic. India does not need to copy China’s playbook or America’s style. But it does need to decide whether scale alone is enough.

India has earned the right to ask those questions. In the next decade By 2036, it will need to build for those questions.

The Numbers Game: More Colleges, More Seats, More Students

After nearly a decade of contraction, engineering education in India is expanding again. The All India Council for Technical Education (AICTE) has approved 5,875 engineering institutions for 2025-26, up from 5,845 the previous year the first increase in six years, though still below the 6,062 colleges that existed in 2020-21.

The seat numbers tell a interesting story. AICTE approved 14.90 lakh B.Tech seats for 2024-25, of which 12.53 lakh were filled the highest enrolment in eight years and a 67 per cent jump over 2017-18. The vacancy rate, which had hovered between 30 and 44 per cent for years, collapsed to 16.36 per cent, an all-time low. For 2025-26, AICTE has sanctioned 15.98 lakh seats, a further 7 per cent rise.

This is some turnaround. Approved intake had shrunk from around 17 lakh seats in 2014-15 to a decade low of about 12.54 lakh in 2021-22, as hundreds of poorly performing private colleges shut down or surrendered seats. The revival owes much to policy: in 2023, AICTE lifted its five-year moratorium on new engineering colleges, scrapped the long-standing cap on intake per institution, and introduced supernumerary seats for working professionals.

At the elite end of the pyramid, the 23 IITs offered 18,160 seats in 2025 and the IIITs another 9,940. The Joint Seat Allocation Authority (JoSAA) offered 62,853 seats across IITs, NITs, IIITs and centrally funded institutions in 2025 nearly 3,000 more than the year before. These seats see almost no vacancies; the admission to these remains as competetive as ever.

The Great Branch Divide: Everyone Wants to Code

Scratch the surface of the enrolment numbers, however, and a one sided picture emerges. The surge is a computer science story.

In 2024-25, Computer Science and Engineering topped the charts with 3,90,245 enrolments. Mechanical engineering followed at 2,36,909, civil engineering at 1,72,936, electronics and communication at 1,60,450, and electrical engineering at 1,25,902. Add the expanding ranks of CS cousins Artificial Intelligence, Data Science, Machine Learning, Cybersecurity and the tilt toward comp science becomes heavy.

The core branches, meanwhile, are struggling. In Bengaluru, India’s IT capital half the civil engineering seats at some government institutions have gone vacant for two or three consecutive years. Colleges across the country have been quietly converting mechanical and civil seats into computer science ones, prompting pushback from state governments. The irony is India is more in midst of Infrastructure boom than a AI wave.

AICTE has tried to bridge the divide by embedding AI, data science and other new-age modules into the curricula of core branches. Some colleges report that the strategy works institutions that have modernised their syllabi and industry linkages are filling core-branch seats too. But nationally, the imbalance persists, and some academics warn of a reckoning: if AI automates entry-level coding work, the very jobs drawing lakhs of students into computer science may thin out just as this expanded cohort graduates.

Where India’s Engineers Come From: The Southern Tilt

India’s engineering map is quite uneven. Roughly half of all engineering seats in the country are concentrated in just five southern states Tamil Nadu, Andhra Pradesh, Telangana, Karnataka and Kerala with the rest spread thinly across the remaining states.

Tamil Nadu is the undisputed capital of Indian engineering education, with close to 900 AICTE-approved engineering colleges the highest of any state and the largest annual enrolment in the country. Maharashtra comes second with around 700 colleges, followed by Uttar Pradesh (about 600), Karnataka (over 500) and Andhra Pradesh (roughly 500). At the other extreme, most northeastern states have fewer than 15 engineering institutions each, and Lakshadweep has none.

The reasons for the southern dominance are historical as much as economic: early institution-building (IIT Madras was established in 1959), decades of proactive state policy encouraging private self-financing colleges, subsidised fees in government institutions, a deep entrance-coaching culture, and thriving IT and manufacturing hubs in Chennai, Bengaluru, Hyderabad and Pune that soak up graduates.

But volume does not equal quality. In 2024-25, it was Maharashtra, Karnataka and West Bengal that recorded the highest seat-fill rates, with Odisha and Maharashtra filling over 90 per cent of their engineering seats while Tamil Nadu, despite (or perhaps because of) its enormous capacity, continues to report substantial vacancies in its long tail of private colleges. Employability studies have repeatedly found an inverse relationship between the number of colleges in a state and the average quality of its graduates: states that expanded fastest often diluted standards the most.

The Quiet Exodus: Dropouts

Enrolment is only half the story; retention is the other. Even at the most coveted institutions, thousands of students leave without completing their degrees. Data tabled in Parliament showed that 7,248 students dropped out of the IITs over a five-year period, with over 2,400 exiting in just two years the older campuses of Delhi and Kharagpur accounting for the largest shares. More recent figures suggest annual dropout rates of roughly 7 per cent at the IITs (about 1,750 students in 2022) and around 5 per cent at the NITs.

The reasons are layered. At the postgraduate level, the biggest driver is opportunity, not failure: M.Tech students routinely abandon their programmes mid-way when job offers arrive some IIT faculty have reported batches where a majority of an M.Tech cohort walked out for employment. At the undergraduate level, academic pressure, misaligned branch choices and language and preparation gaps play a bigger role.

In the vast private-college sector, dropout data is patchier, but the pattern inverts: students leave not for better jobs but because of financial strain, poor teaching quality and the dawning realisation that the degree may not deliver the promised placement.

The Employability Puzzle: Qualified on Paper, Unhired in Practice

If the supply side looks buoyant, the demand side is where India’s engineering story turns a bit sober.

Depending on which report you read, the employability of Indian engineering graduates is either encouraging or alarming. The India Skills Report 2025 pegged it at 71.5 per cent, based on standardised skills tests. Mercer-Mettl’s India Graduate Skill Index, drawing on assessments across 2,700 campuses and over a million students, put overall graduate employability at a low of 42.6 per cent. And TeamLease estimated that only about 10 per cent of the 1.5 million engineers graduating in a year would actually land jobs aligned with their training.

Strikingly, the deal-breaker is often not technical skill at all. More than half of graduates reportedly stumble in interviews because of weak spoken English and presentation skills rather than engineering incompetence.

Placements: A Tale of Three Indias

Placement outcomes in Indian engineering are less a single market than three parallel ones.

The IITs. At the summit, packages remain eye-watering. IIT Bombay reported an average of roughly ₹23.5 lakh per annum in 2024-25, with median salaries across most IITs in the ₹15–25 LPA band. Top domestic offers in 2025 touched ₹85–90 lakh, and international offers crossed ₹1.3 crore. Yet even the IITs have felt the chill of the tech slowdown: IIT Bombay’s placement rate slid to about 83 per cent and IIT Delhi’s to around 73 per cent between 2022 and 2024. In a sign of the times, several IITs stopped publicising their “highest package” figures from 2025, releasing only averages and medians to temper the crore-chasing frenzy.

The NITs and top state institutions. The second tier delivers solid, if less spectacular, outcomes: average packages of ₹6–12 lakh depending on branch, with placement rates typically between 80 and 90 per cent.

Everyone else. For the vast majority of India’s engineers graduates of the thousands of private colleges that make up the system’s long tail the first job usually comes from IT services majors like TCS, Infosys, Wipro or Capgemini, at salaries of ₹3.2–5 lakh a year. Glossy brochures advertising “average packages” of ₹8 lakh often conceal medians closer to ₹4 lakh. These salaries have barely moved in a decade, even as engineering fees have climbed.

A Shifting Job Market

The ground beneath these placements is moving. The IT services industry, historically the great absorber of Indian engineering talent, slashed fresher hiring from about 6 lakh in 2022 to under 1.5 lakh in 2024. Filling part of the void are Global Capability Centres the India-based technology arms of multinational firms which plan to expand fresher hiring by around 40 per cent, and a manufacturing sector energised by production-linked incentives, where a large majority of firms report intent to hire freshers.

The skills in demand are shifting just as fast. NASSCOM projects that India will need over a million engineers with advanced AI and allied skills within the next two to three years, while the demand-supply gap for digital talent is expected to widen from about 25 per cent today to nearly 30 per cent by 2028. The opportunity is real: semiconductors, electric vehicles, renewables and AI are all hungry for engineers. The question is whether India’s colleges can produce graduates those industries can actually use.

The Road Ahead

India’s engineering machine is running at full throttle again more colleges, more seats, more students than at any point in nearly a decade. But quantity was never India’s problem. The challenge for the years ahead is threefold: rebalancing enrolment so the country doesn’t stop producing the civil and mechanical engineers its infrastructure push demands; closing the gap between classroom curricula and workplace reality through internships, apprenticeships and industry-designed courses; and being honest about outcomes, measuring colleges by jobs secured rather than test scores earned.

Until then, India’s 1.5 million engineers a year will remain what they are today: an extraordinary asset, only partially unlocked.

Sources: AICTE approval and enrolment data (2024-25, 2025-26); India Skills Report 2025 (Wheebox); Mercer-Mettl India Graduate Skill Index 2025; Unstop Talent Report 2025; TeamLease Degree Apprenticeship; NASSCOM projections; institutional placement reports of IITs and NITs; Ministry of Education / HRD dropout data tabled in Parliament; state-wise AICTE institutional data and Aspiring Minds (SHL) employability studies.

Quick quiz. Which Indian city has the highest share of adults working? Not Bengaluru. Not Mumbai. Not Delhi, Delhi in fact ranks a shocking 44th out of 46.

It’s Surat.

Now, we can actually answer questions like this. In June 2026, for the 1st time, the National Statistics Office released city-level jobs data from the PLFS 2025 survey, 46 million-plus cities, each measured on participation, unemployment, salaries, and more. Until now, all of this was buried inside one “urban India” average.

Let’s see how cities in India fair in the Job Market

Round 1: Who actually works?

Surat is a work machine. Two out of three adults are in the labour force, 87.9% of men (highest in India) and 40.6% of women (second highest). Diamonds and textiles don’t polish themselves.

Labour force participation rate (share of adults 15+ working or seeking work):

Prayagraj is the mirror image: barely one in three adults participates, and among women it’s at a low of 9.3% the lowest in the country. And yes, Delhi the national capital, with all its metros, malls and money, has a lower share of working adults than Dhanbad. Only 15.5% of Delhi’s women are in the labour force.

Round 2: Where the jobs actually exist

Unemployment rate and the spread here is a 20X gap:

Unemployment in Patna is twenty times higher than in Surat. In Gujarat’s cities, joblessness has been statistically abolished, Surat 1.0%, Ahmedabad 1.3%, Vadodara ~2%, Rajkot under 1%. In Patna, one in five job-seekers can’t find work. Among Patna’s women, it’s 36.2% one in three.

The quiet shocker here is Jaipur: at 12.8%, the Pink City has the worst unemployment of any big metro, worse than Delhi (5.3%), Hyderabad (6.8%), Mumbai (3.6%), Bengaluru (2.8%). And Kota, India’s coaching capital, posts a 28.6% unemployment rate among women. The city that preps everyone for jobs struggles to provide them.

Round 3: Show me the money

Surprisingly Not Bangalore or Gurgaon or Mumbai but Navi Mumbai is India’s paycheck capital — its salaried workers earn 3.4x Ludhiana’s and comfortably beat even Bengaluru (₹34,323). The planned-city bet of the 1970s is paying literal dividends.

Two reputations take a beating here:

• Kolkata is a bottom-five salary city. The grand old metro pays its salaried workers less than Meerut. Chennai pays ₹28,955, Hyderabad ₹31,153, Kolkata, ₹19,093. It’s also bottom-five on self-employment income (₹17,989/month, ahead of only Dhanbad). Whatever you do for a living, Kolkata pays the least of any mega-metro.

• Ludhiana is the great paradox. Second-highest participation in India (59%) and the lowest salaries (₹15,205). Punjab’s industrial hub employs everybody, cheaply.

And notice Prayagraj sneaking into the top five on pay despite being dead last on participation. When almost nobody works, the few who do are government officers and professors the average looks great precisely because the job market is tiny. Prayagraj’s salaried women average ₹42,254 a month, the highest female figure in all 46 cities.

Self-employment tells the same story, louder. Being your own boss earns ₹66,613/month in Navi Mumbai and ₹53,773 in Mumbai versus ₹16,844 in Dhanbad. Even at the bottom of the ladder, geography rules: a casual labourer makes ₹904 a day in Vasai-Virar and ₹802 in Chennai, but ₹415 in Dhanbad.

Round 4: The women’s map, a 4.5x divide

If one indicator splits India’s cities into two worlds, it’s this one.

Female labour force participation:

A woman in Coimbatore is 4.5 times more likely to be working than a woman in Prayagraj. The bottom of this table is basically largely North India cities and the top isn’t the tech capitals you’d guess. Textile-and-factory towns like Coimbatore and Madurai beat Bengaluru (33.2%), Hyderabad (32.9%) and Mumbai (26.9%).

Why don’t women participate? The survey asked. 68.7% of women outside the labour force cite child care and home-making. For men, that figure is 1%. And in cities overall, the NEET rate (not in employment, education or training) for women aged 30–59 is 67% versus 4.3% for men. There’s a cliff at age 30, and only women fall off it.

The twist: women who do work in big cities hold better jobs than men 65.1% are in salaried employment versus 56.4% of men. The escalator works. Three-quarters of women never get on it.

The final scoreboard: three Indias

Stack every round together and the 46 cities sort themselves into three tiers:

The Work Machines, West & South. Surat, Ahmedabad, Pune, the Mumbai constellation, Chennai, Hyderabad, Bengaluru, Coimbatore. High participation, near-zero unemployment, top-tier pay, and the best cities in India for working women. Surat wins on hustle; Navi Mumbai wins on money.

The Grinders. Ludhiana, Agra, Madurai, Raipur. Everyone works; nobody earns. Maximum employment, minimum reward.

The Stress Belt, East & North. Patna, Prayagraj, Dhanbad, Kanpur, Srinagar, Jaipur. Low participation and high unemployment and low pay. Patna achieves the triple crown of trouble: 41% participation, 21% unemployment, and women’s joblessness at 36%.

One last stat to sit with. The all-India averages say big cities and small towns look identical, participation around 52%, unemployment around 5% everywhere. That averages are misleading, Between Surat and Patna lies a 20x unemployment gap. Between Navi Mumbai and Ludhiana, a 3.4x salary gap. Between Coimbatore and Prayagraj, a 4.5x gap in women’s work.

India doesn’t have an urban job market. It has 46 of them and they’re playing entirely different games.

Source: “Labour Market Dynamics in Million-plus Cities,” National Statistics Office / MoSPI, June 2026 — based on PLFS Jan–Dec 2025, persons aged 15+, Census-2011 city definitions. Figures marked * carry high sampling error; some cities had small survey samples, so treat exact ranks as indicative.

A passport always fells like the strongest document. It has the national emblem. It needs police verification. It is accepted at airports and immigration counters globally. For many of us getting a passport felt like a life milestone. Passport always had the “Nationalism”.

So, when the MEA clarified that a passport is a travel document and not conclusive proof of citizenship, the reaction was, if even a passport is not proof of citizenship, then what is?

The answer is interesting: India has solved identity at massive scale, but citizenship proof is still not simple, its a combination of identities.

Let’s look at India’s set of IDs.

Aadhaar is mass identity. PAN is financial identity. Voter ID is electoral identity. Driving licence is mobility permission. Passport is international travel permission. DigiLocker is just a digital wallet for all documents. None of these, by themselves, is the same as a clean, universal citizenship certificate.

Aadhaar has crossed 144 crore generated numbers. PAN has crossed 80 crore issued numbers.

India had 96.88 crore registered voters for the 2024 election. Driving licences are at about 23.1 crore issued records. DigiLocker already has 67 crore users.

Interesting is the document that feels the most “sovereign” the passport is actually one of India’s least distributed identity documents. Valid passports are only 9.26 crore as of 2024.

What each document really solves

Aadhaar answers: “Are you the same resident?”
It is designed for uniqueness and authentication. UIDAI itself says Aadhaar is proof of identity, but does not confer citizenship or domicile.

PAN answers: “Are you traceable in the tax and financial system?”
It is an identifier issued by the Income Tax Department. It is useful for banking, tax, investments and high-value transactions. But PAN can be issued to persons and entities; it is not a citizenship document.

Voter ID answers: “Are you on the electoral roll for this constituency?”
It is a very important civic document because voting is tied to citizenship. But even here, the real power is not only the card; it is your name being on the electoral roll. ECI has even allowed alternative photo IDs for voters whose names are on the roll but who cannot produce EPIC.

Driving Licence answers: “Are you authorised to drive?”
It is useful as a photo ID, but legally its core job is road-use permission, not identity, residence or citizenship.

Passport answers: “Can you travel internationally?”
The Passports Act is framed around issuing passports and travel documents to regulate departure from India by citizens and other persons. That is why the government can say, technically, that a passport is not the final legal certificate of citizenship.

DigiLocker answers: “Can you access and share verified documents digitally?”

DigiLocker reduces the pain of carrying documents, but it does not reduce the confusion of what each document proves. A digital PAN is still PAN. A digital driving licence is still a driving licence. A digital Aadhaar is still Aadhaar. The container became digital; the meaning did not change.

The growth story

India’s identity stack has grown in different directions.

Aadhaar grew from 61.01 crore generated numbers by March 2014 to 136.65 crore by March 2023, and now over 144 crore generated numbers on the UIDAI dashboard.

PAN grew from 20.24 crore holders in February 2014 to 74.67 crore by March 2024 and over 80 crore by March 2025.

Registered voters grew from about 83.41 crore electors in the 2014 general election to close to 91.2 crore in 2019 and 96.88 crore in 2024.

But growth has also created overlap. The same person has Aadhaar, PAN, voter ID, DL, passport. Each document increases convenience, but also increases confusion when we don’t know what each one proves.

The Real Issue

The passport controversy is not just about passport.

Its a larger UX problem: Citizens experience documents as one big pile, but the state rightly treats them as separate legal instruments.

For the citizen, all IDs feel like “proof of me.”

For the state, each ID has a boundary.

That gap creates anxiety.

What the US and China show us

The US and China solve identity very differently. The US does not have one universal national ID; daily identity often runs through the driving licence. That is why the scale is huge: the US had about 239.8 million licensed drivers in 2024, while valid US passports in circulation were about 169.9 million in 2024. China is closer to the opposite model: the Resident Identity Card is the core domestic identity layer for citizens, while mobility permissions sit separately. China had about 559 million licensed motor-vehicle drivers by the end of 2025.

India sits somewhere in between. Like China, it has a massive foundational identity rail in Aadhaar. Like the US, it also has functional IDs for tax, voting, driving and travel. But unlike both, the citizen experience is still not cleanly explained: every document looks like “ID”, but each document proves a different thing.

The Solution

India does not need one more card. It needs a Safe Sovereign Identity Graph.

Identity is not one question. It is a graph of many questions.

Who are you?
Where do you live?
Can you pay tax?
Can you vote?
Can you drive?
Can you travel?
Are you a citizen?

Aadhaar and PAN already show the direction. For new PAN applicants, Aadhaar-PAN linking happens automatically, and existing PAN holders are required to link PAN with Aadhaar.

The same thinking can be extended carefully to other documents: voter ID, passport, driving licence, birth certificate. Immutable DigiLocker ID on a blockchain can be that master ID.

India has already built identity at population scale; the next leap is to connect these identities with clear legal meaning and in citizen safe way.

The Indian capital market has undergone massive change(s) in recent years, shifting from an institutional playground into consumer-driven movement. The “13-crore investor base milestone” as per NSE Data is a signal that financial participation is reaching a larger population. As we analyze the market pulse of May 2026, we are witnessing the definitive “domesticisation of equity market”

Since April 2020, cumulative wealth creation for Indian households has reached approximately Rs 44 lakh crore. This scale has moved investing from a niche financial circle into a mainstream consumer activity, comparable in its digital adoption to e-commerce.

2. The 130-Million Milestone: Scaling the Individual Investor Base

The volume of unique Permanent Account Numbers (PANs) on the National Stock Exchange (NSE) serves as the ultimate “trust metric” for India’s financial infrastructure. Reaching 130 million unique investors indicates that the market has transitioned from a specialized service into a mass-market.

*FY27TD represents data for the single month of April 2026 only.

This growth is accompanied by high-velocity participation. Individual investors now command a 33.8% share of the Cash Market (CM). Even more significantly, they hold a 38.6% share in Equity Options (FO), suggesting a level of risk, sophistication and engagement that far exceeds traditional passive investing.

However, the real story of this revolution lies in the shifting identity of the people behind these numbers.

3. The Youth Pivot: Demographics and the Median Investor

The “youth skew” is an important indicator for the Indian market. As the investor base expands, it is getting younger, which extends the country’s collective investment horizon and risk appetite.

• The Age Factor:

  • The median age of the Indian investor has dropped to 33, from 38 years in March 2020,

  • Investors below the age of 30 now account for 38.3% of the registered investor base, up from 23.5% in March 2020

  • The share of investors aged 50 and above has declined from 25.6% to 15.3% over the same six-year period

  • Younger participants continue to lead market expansion, with those below the age of 40 accounting for nearly 79% of all new registrations in the last fiscal year

• Mobile Dominance: Critically, “Mobile Trading Share” is rising across all segments. It is no longer restricted to simple cash market trades; tech-savvy 33-year-olds are increasingly using mobile interfaces for complex Equity Options, reinforcing the profile of a digitally fluent investor base.

This demographic shift ensures that traditional investment norms are being replaced by a lifestyle-integrated approach to wealth management.

4. The Geography of Growth: Beyond the Tier-1 Cities

The democratization of wealth is no longer a theoretical goal; it is a geographical reality. The 2026 data reveals a specific “growth engine” currently dominated by the North and West. In April 2026 alone, the Top Five States (Uttar Pradesh, Maharashtra, West Bengal, Bihar, and Tamil Nadu) accounted for 47.1% of all new registrations.

A closer look at the regional distribution for FY27TD (April 2026) shows a significant disparity:

• North India: 60 lakh new registrations.

• West India: 30.7 lakh new registrations.

• South & East India: 10.7 lakh new registrations each.

While the revolution is currently a Northern and Western phenomenon with North India seeing nearly 6x the registrations of the South or East. This migration of capital from physical assets to digital financial instruments in states like Bihar and West Bengal represents a rebalancing of the Indian heartland this decade. The District level activity however shows the usual suspects

5. Breaking the Glass Ceiling: The Rising Influence of Female Investors

A key growth lever for the 2026 economy is the evolving gender split. Female participation stands at 25% of all registered individual investors in India. This reflects a steady structural rise from 22.5% in FY23 and 24.9% at the end of FY26.

The broadening of the ecosystem into regions like West Bengal and Bihar is bringing the “Family CFO” into the fold. As the market penetrates these new territories, it is capturing women who were previously restricted to traditional physical assets.

The increasing participation of women acts as a financial stabilizer. Female-led investment typically moves from physical gold traditionally a “dead” asset in a locker to digital formats and diversified equities. This behavioral shift leads to more stable, goal-oriented, and long-term capital flows compared to the speculative trading patterns often seen in male-dominated cohorts.

6. Diversifying the Portfolio: From Equities to Digital Gold

Indian consumers have a deep-seated affinity for tangible assets. The market has responded with the “consumerization of hedging” turning complex financial tools into lifestyle-aligned products. The strategic launch of Electronic Gold Receipts (EGRs) is the primary example of this, allowing for the mobilization of existing domestic gold into a regulated, digital format. This “smarter way” to hold gold allows individuals to maintain the security of digital ownership while retaining the option for physical delivery.

This trend toward “investment carts” filled with diverse products is further evidenced by the rise of Monthly Electricity Futures and smaller-denomination gold contracts, providing individuals with professional-grade risk management tools tailored for the retail consumer.

7. The Evolutionary Path: Summary of Trends

The individual investor of 2026 is younger, geographically widespread, and increasingly sophisticated. They have moved from the sidelines of the economy to its very foundation.

Future Pulse: Key Trends

1. The Domesticisation Shift: In a historic reversal, FPI ownership has hit a 17-year low (15.8%), while Individuals and Mutual Funds combined now account for 18.7% of the market.

2. Digital-Asset Maturity: The transition to mobile-first ownership of digital-native assets like EGRs has become the new standard for wealth preservation.

The Indian capital market is now a consumer-data ecosystem that reflects the digital maturity and rising aspirations.

A third had simply... left. Welcome to the Indian Census the largest administrative exercise on Earth.

India has been conducting census since 1881 and the population has grown from 253.9 million to 1210.9 Millon in 2011 census, the last census India had, before the 2027 one began a few months back.

A few days back the enumerator(s) were at my place and the question(s) they asked lead me to read on Indian Census and how it has changed over the last 150 years. As someone who spends most days looking at audience and demographic data, I couldn’t resist digging deeper.

So, let's get into the “DeepMode” and traverse through the census journey.

Chapter 1: The Colonial Era (1872–1941)

“A Count of Flies”: The First Attempt

India’s first census wasn’t really a census at all at least not by modern standards.

In 1872, Viceroy Lord Mayo ordered a count of British India’s population. But different provinces counted at different times, using different methods, with different questions. Bengal counted its people before Bombay even began. Historians refer to it, as a “non-synchronous” census.

The real thing came nine years later.

On the night of February 17, 1881, across the vast expanse of British India from the passes of the Northwest Frontier (current day Pakistan) to the fishing villages of TamilNadu hundreds of thousands of local enumerators started out simultaneously to knock on doors and count.

It was called the first synchronous census of India. Every person counted on the same reference point. Same moment in time.

The man behind it: William Chichele Plowden, Census Commissioner. His office, in Bengal, was housed and this is in his own official report in the elephant shed.

The logistical challenges were humongous. Enumerators worked across hundreds of dialects, in a country with no phones, no railways connecting every village, and deep suspicion. Women refused to be seen by male strangers. Enumerators in some regions were turned away and had to make educated guesses from the outside. The 1881 report is full of such small disasters.

And yet it worked. India had its first proper headcount: 253 million people.

The Year India population stagnated: 1921

For decades after 1881, India’s population grew with every census. The trend seemed inevitable.

Then came 1921.

That year, census workers went out across the subcontinent and returned with a number which was bit shocking, India’s population had remained almost stagnant and grew marginally at 0.8%. For the only time in recorded history, almost same number of people lived in India at the end of a decade than at the beginning.

The cause was a convergence of catastrophes. The Spanish flu pandemic of 1918–19 had swept through India, killing an estimated 12 to 17 million people a death toll that dwarfed the WWI happening in Europe. Famines had ravaged parts of the country. Plague lingered in urban centres.

Demographers today call 1921 the “Year of the Great Divide.” After it, India’s population never fell again.

What the British Were Actually Asking

Beyond the population count, the colonial census was remarkably intrusive by the standards of its time. It asked about birthplace, religion, age, sex, marital status, literacy, language, occupation and in a question that tells everything about who was doing the counting whether the respondent knew any foreign languages and “The Caste question” also.

The census also tracked language in detail, recording not just what language people spoke at home but whether they knew others. This produced India’s first real map of its linguistic diversity that would eventually feed the state reorganization along linguistic lines in the 1950s.

Chapter 2: The Republic Counts Its Own (1951–1991)

The First Census of Free India

When India conducted its first post-independence census in 1951, the country was still bleeding from Partition. Twelve million people had been displaced. Borders had shifted overnight. Cities like Lahore, which had appeared in every previous Indian census, were now in a foreign nation.

The scale of dislocation made counting genuinely difficult. Where was a refugee counted at their place of origin or their camp?

Independent India made a firm administrative decision: the Census Act of 1948 standardized the entire exercise. No more states adding their own questions, no more regional variations in methodology. The Registrar General of India would run a uniform operation, from Ladakh to Kanyakumari. For the first time, the census form looked the same everywhere.

India was trying to see itself whole. Interestingly India had more than 19,500 mother tongues recorded in 1961.

1971: The Year India Started Tracking Where Its People Were Going

By 1971, something new had caught the attention of planners: internal migration. Cities were swelling. Rural workers were flooding into Bombay, Delhi, Calcutta. The countryside was shifting.

For the first time, the census asked people about their last place of residence essentially, where were you before you came here?

It was a small question with big implications. The data would eventually reveal the human movement through India’s development story, the farmer from Bihar who became a construction worker in Mumbai, the woman from a village in Andhra who became a garment worker in Tirupur.

The 1971 census schedule contained 35 questions one of the most comprehensive exercise India had attempted.

1981 and 1991: The Quiet Revolutions

Two changes in these decades are often overlooked, but they shaped policy in ways that lasted decades.

In 1981, for the first time, the census asked married women about fertility how many children they’d had, how many had survived. This data became the empirical foundation of India’s family planning programmes, maternal health policy, and the slow, uneven story of demographic transition.

In 1991, the definition of literacy changed. Previously, children as young as 4 were counted as literate if they could read a few words. The new threshold: age 7 and above. It was a tighter, more honest standard and it produced a more uncomfortable picture of the country’s educational reality.

Chapter 3: The Digital Age (2001–2027)

2001: Teaching Machines to Read Indian Handwriting

For most of India’s census history, data meant paper. Handwritten forms from 600,000 villages, filled out by 2 million enumerators, physically shipped to regional offices, manually tallied by clerks. The data processing took years.

The 2001 census changed this. For the first time, completed schedules were run through high-speed scanners, and the handwritten responses in Hindi, Tamil, Bengali, Gujarati, and a dozen other scripts were converted into digital data through Intelligent Character Recognition (ICR).

An Interesting question which made way in 2001 was the Distance between workplace and home: By this time travelling had started consuming 1-2 hrs of a person life in urban clustered, the question of time spend on road crept, the 1000Kms of today’s metro network can be traced back to this data point.

The 2001 census was the first one where a computer, not a clerk, did the first pass at the data.

2011: Counting the Invisible

The 2011 Census was arguably the most rights-conscious enumeration India had conducted. It added questions and categories that acknowledged the full complexity of Indian lives.

Disability got serious. For the first time, the census distinguished between mental retardation (inborn) and mental illness (acquired). It asked specifically about visual, auditory, speech and multiple disabilities. Policy could now be built on evidence, not estimates.

Date of birth became mandatory. Hundreds of millions of Indians particularly older rural women — genuinely did not know their birth dates. Official documents often listed “1 January” as a birthday because no one had thought to record the actual date. Enumerators were trained to help: Think of a festival close to when you were born. A flood? A harvest? An election? The country was trying, belatedly to anchor its people in time.

2021: The Census That Never Was

The 2021 Census never happened.

COVID-19 arrived in early 2020, and door-to-door enumeration became impossible. What followed, even as the pandemic receded, the census didn’t restart.

The consequences were real and quiet. India’s welfare schemes ration cards, housing programmes, health insurance, employment guarantees were all calibrated to 2011 population figures. The country had grown by roughly 180 million people in the intervening decade.

India was governing 1.4 billion people using a map drawn for 1.2 billion.

2027: The Census of the Smartphone and AI Era

The 16th Census of India is currently underway. Phase 1 the housing census began on April 1, 2026. Phase 2, the population enumeration, happens in February 2027.

It is India’s first fully digital census. Data is collected via mobile app. Citizens can self-enumerate through an online portal meaning, for the first time, we don’t have to wait for an enumerator to knock on your door. Real-time dashboards monitor progress across 640,000 villages and 7,000 towns.

New questions reflect a changing country. Internet access, smartphone ownership are now census attributes the digital divide will be mapped, village by village, for the first time.

Live-in relationships are being counted. Couples in stable live-in unions will be recorded if they describe their relationship, that way a quiet official acknowledgment of the new social reality.

The “Caste question” is also back after 75 years.

What a Census Really Is

We tend to think of a census as a neutral act a counting of heads, a tabulation of facts. India’s census history suggests otherwise.

It is also a story about what a society chooses to see. Every question added to the census reflects a decision: fertility, disability, migration, gender identity, internet access and caste matters enough to measure.

Every Indian has appeared in a census without ever realizing it. The roads we drive on, the schools we build, the constituencies we vote in, and the welfare programs we fund all begin with that simple act of counting.

The census is not merely a database. It is the country’s most ambitious attempt to answer a deceptively simple question:

“Who are we, right now and where are we headed?”

If this made you think differently about data, share it with someone who finds data & statistics boring. They’re not. They never were.

Full List of All Census Questions : Census_Quest | Government of India

In the early months of 2000, India’s footprint on the global investment map was at a modest USD 4,029 million in annual foreign capital. Fast forward to March 2026, and the FDI Inflow has taken a major leap. India has entered the trillion-dollar club, recording a cumulative Foreign Direct Investment (FDI) inflow of USD 1,166,462 million since the turn of the millennium. While India has emerged as an important destination for FDI, the underlying data reveals a story of concentration both in where the money originates and where it eventually lands.

2. The $1.16 Trillion Milestone: A Quarter-Century of Growth

The 26-year trajectory of Indian FDI represents a story of strong capital migration in Indias Financial history. Recent data for the 2025-26 financial year shows annual inflows reaching USD 94,527 million, a contrast to the humble beginnings of the late 90s.

CUMULATIVE AMOUNT OF FDI INFLOW (April 2000 to March 2026): USD 1,166,462 Million.

Crucially, this total is not just comprised of fresh “hot money.” While Equity Capital accounts for the lion’s share at USD 790,607 million, Re-invested Earnings have reached a substantial USD 277,770 million. This figure representing approx 23.8% of the cumulative total is a vital indicator of institutional confidence, as it shows that multinational corporations already operating within the country are choosing to double down on their Indian bets rather than repatriating profits.

3. The Power Duo: Singapore and Mauritius as Primary Conduits

Despite India’s growing diplomatic ties with the G7, the actual flow of equity remains dominated by two specific destinations. Singapore and Mauritius act as the primary conduits for nearly half of all equity investment entering the country, far outstripping the United States.

• Singapore: Maintains its position as the preeminent source, contributing 24.72% of cumulative FDI equity at USD 194,688 million.

• Mauritius: Follows closely with a 23.71% share, totaling cumulatively to USD 186,767 million.

By comparison, the U.S.A. represents 10.39% of cumulative FDI (USD 81,821 million).

However, The USA nearly doubled its FDI into India in a single year from $4,998M in FY24 to $11,171M in FY26, a 124% jump. It’s currently the #2 source country, driven by tech, semiconductors, and the China+1 supply chain pivot.

For last 25 years, the dominance of the Singapore-Mauritius corridor combined for 48.43% highlights the continued importance of these financial hubs in navigating the Indian landscape, even as the source of the ultimate beneficial ownership continues to diversify.

The Mauritius story a 25-year era is slowly unwinding Once India’s dominant FDI gateway (peak share: ~40%+), Mauritius has quietly slipped down 17% in just two years (FY24–FY26)

Interesting is Cayman Island has grown from USD 342 million to USD 2,084 Million USD in last 3 years. China barely registers $2.5 billion in 25 years Despite being India's largest trading partner, China accounts for just 0.32% ($2.5 billion) of cumulative FDI equity into India since 2000

4. The Digital & Service Engine: Sectoral Duopolies

India’s economic identity as a global hub for services is reflected in its sectoral FDI distribution. Two sectors have formed an effective duopoly, each commanding 16% of the total equity inflow:

• Services Sector: Encompassing the high-value pillars of financial services, banking, insurance, and R&D, this sector has absorbed USD 128,853 million cumulatively.

• Computer Software & Hardware: The bedrock of India’s tech reputation, this sector follows at USD 124,644 million cumulatively.

This concentration suggests that the “Software and Services” engine remains the primary driver of international interest, while India is aggressively courting manufacturing, dwarfing traditional sectors like Automobile Industry (5%) and Telecommunications (5%).

5. The “Super-State” Nexus: Geography of Capital

When examining where this capital actually settles, the data maintained from October 2019 to March 2026 reveals a “Super-State” concentration. There is a direct relation between sectoral dominance and geographic destiny:

• Maharashtra (The Financial Gateway): Attracting 31.35% of FDI (USD 107,094 million), Maharashtra’s success is inextricably linked to Mumbai’s status as the epicenter for the Financial Services Sector.

• Karnataka (The Tech Hub): Securing 20.66% of inflows (USD 70,589 million), Karnataka home to Bengaluru serves as the natural landing zone for the Computer Software & Hardware sector.

Together, these two states account for over half of all FDI equity in the last six years. This geographic clustering raises questions about regional inequality, as global capital remains tethered to established urban hubs.

6. The 2026 Surge: Momentum Amidst Volatility

The 2025-26 financial year demonstrated continued momentum, with FDI equity inflow growing by 18% rising from USD 50,018 million in 2024-25 to USD 58,846 million in 2025-26. However, the year was characterized by significant inflow volatility, suggesting a sensitivity to global interest rate cycles and geopolitical shifts.

• The July Peak: A massive monthly surge of USD 8,683 million in July 2025.

• The January Dip: A sharp contraction to USD 2,349 million in January 2026.

This month-on-month fluctuation serves as a reminder that while the long-term cumulative story is one of growth, the short-term environment remains susceptible to external shocks and shifting investor sentiment.

7. Diversifying the Destination

India’s achievement of the USD 1.16 trillion milestone marks its graduation into the upper echelon of global investment destinations. However, the data reveals a theme of concentration. The heavy reliance on the Singapore-Mauritius corridor and the clustering of capital in the Service-Tech corridor of the West and South define the current era.

As we look toward the next decade, the critical question for policymakers is one of diversification. Can India successfully pivot its FDI strategy to attract manufacturing-heavy capital into states like Tamil Nadu (currently 6%) or Gujarat (15%)? To reach the next trillion, India must broaden its base, moving beyond the digital and financial hubs to become a truly national magnet for global industry.

“The hardest thing to understand in the world is the income tax”

But there is one tax which is more complex and abstract that than the income tax, which individuals and organization pay by the hour, and it is not even accounted for, but its burden is daily, so what is that tax?

In this week’s “DeepMode” let’s see that tax one pays daily in his workplace, and apply a mathematical formula to what is called “The Coordination Tax”

What Is the Coordination Tax?

Every organization levies an invisible tax on itself the moment it adds a new person. Not a salary tax, not a benefits tax something more hidden. A coordination tax: the compounding cost of keeping people aligned and informed as headcount grows.

This tax shows up as endless meetings, Slack threads that multiply faster than decisions, sign-off chains for things that once took seconds, and the classic 'meeting to prepare for the meeting.' None of it appears on any spreadsheet. All of it costs.

The coordination is a mathematical inevitability baked into the non-linear structure of human communication itself.

The n(n−1)/2 Problem

To understand why, think of each person in an organization as a node in a graph. Between every pair of nodes, there is a potential communication path, a relationship that must be maintained, a context that must be shared, a potential misalignment that must be corrected. The number of such paths grows not linearly, but quadratically.

This is the handshake formula the same equation that counts the number of unique pairs in any group. And its implications are severe for every hiring decision ever made.

The pattern is brutal. Going from 10 to 20 people does not double your coordination cost it quadruples it. Every single new hire adds n new paths to maintain, where n is the current headcount. The 50th employee doesn't bring one new relationship. They bring forty-nine.

"Adding one person to a team of ten creates as much new coordination overhead as the entire coordination load of a four-person team."

This is why Brook’s Law that adding people to a late software project makes it later has remained true for fifty years despite every advance in project management tooling.

The AI Era Rewrites the Equation

For most of economic history, scaling required headcount, for the last 20 years of the internet era, you needed people to write code, support customers, produce content, analyze data, and handle compliance. The coordination tax was the price of admission to ambition. You paid it or you stayed small.

That bargain has collapsed.

In last 2 years or so, something unprecedented began happening: tiny teams sometimes two or three people started building products that reached millions of users, not because they found a niche, but because AI agents became capable enough to absorb entire job categories.

These are not exceptions. They are the early signal of a structural shift. When AI can handle your customer support queue, generate leads, write your code, synthesize your market research, and draft your legal contracts the number of people required to build a significant business drops by an order of magnitude.

And when the headcount drops, the organization moves at the speed of thought rather than the speed of process.

The Rise of the Individual Contributor (IC)-First Senior

This shift carries a profound implication for experienced professionals one that runs against decades of career orthodoxy.

In the old model, seniority meant moving away from individual contribution and toward management. The title progression from IC to lead to manager to director to VP was not just organizational it was the definition of career success.

That model is being inverted.

This creates a new role: the principal IC. Someone with 10–15 years of hard-won judgment who chooses to remain deeply technical or deeply creative not because they couldn’t manage, but because they understand that in the AI era, their leverage is maximized by doing, not by directing.

The principal IC uses Claude or ChatGPT or Gemini the way a senior surgeon uses a skilled team: not to delegate judgment, but to extend capacity. They review AI-generated work with the eye of someone who has seen a thousand ways it can go wrong. They set the direction, catch the errors, and compress what would take a junior team weeks into days.

What This Means for How We Build

The strategic implication is clear: hire for density, not for coverage. Build the smallest team that can carry the judgment the work requires. Use AI to replace the tasks and not jobs per se.

The companies that will define the next decade are not those who scale headcount fastest. They are those who scale judgment-per-person fastest keeping coordination paths minimal while multiplying what each person can actually produce.

The coordination tax has always been real. For the first time in history, you can afford not to pay it.

P.S : All images are AI generated and this is not a advice to hire or fire

Confusing them might be one of the expensive mistakes a business can make.

A pivot is a change of direction. You stop. You rethink. You rebuild. And you set yourself back often by atleast 3-6 months right back into the "build phase."

An experiment is different. It's a small, contained bet in the same direction. It doesn't slow you down it sharpens you.

Experiments compound. Pivots reset.

Let's see some interesting examples of Pivots Vs Experiments and how both have worked and not worked and how should companies approach this dilemma.

Airbnb, 2009. Listings weren't converting. A pivot would have said, "We're not really a marketplace, let's become a hotel chain." Instead, Brian Chesky and Joe Gebbia flew to New York, rented a camera, and personally photographed 24 listings. NYC revenue doubled within weeks. That single experiment became Airbnb's professional photography program and a foundation of their growth.

Zerodha, since 2010. Same direction the entire journey: low-cost broking for Indian retail investors. No pivots, ever. Kite, Coin, Varsity, Smallcase each one an experiment within the same north star. The result: one of India's most profitable fintech companies, built without a single rupee of external funding.

Google: The tech giant of last 25 years, is a master of experimentation, we know the big success of google - Search, Gemini, Google Maps, Google Docs, but rarely people know of the failed experiments of google, check this link to find the full list of projects which google killed.

Killed By Google

As of 2026, the “Killed by Google” archive tracks around 299 discontinued Google products, services, and hardware experiments since 2006.

Now pivots, well they aren't always wrong. Sometimes they're necessary. But the good ones are earned by data, and sometimes by external conditions.

Instagram, 2010. Kevin Systrom was building Burbn (Burbn was inspired by Systrom’s passion for whiskey), a cluttered location-based check-in app. The data was loud: users only cared about photo sharing with filters. He stripped everything else, rebuilt the app in 12 weeks, and relaunched as Instagram. Sold to Facebook for $1B two years later. That’s a pivot done right. Earned by signal. Executed once, with conviction.

Burbn, 2010 — the cluttered check-in app that became Instagram. Source: Kevin Systrom.

Slack, 2013. Stewart Butterfield's team at Tiny Speck had spent years building Glitch, a quirky online game. It was dying. But the internal chat tool they'd built to coordinate their own work had quietly become indispensable. They killed the game, kept the tool, and shipped it as Slack. Eight years later, Salesforce bought it for $27.7 billion.

Both pivots gone correct. Now let's check another example of pivot which we have all experienced.

WeWork. Started as coworking in Manhattan in 2010. Then WeLive (apartments). Then WeGrow (a private school for children). Then a rebrand to "The We Company." Each new direction was launched as a grand vision, not tested as a hypothesis. The result: a $47B valuation that lost over 80% in three months when the IPO collapsed in 2019. Adam Neumann was forced out. The company eventually filed for bankruptcy.

All same era. Very different philosophies.

The trap cuts both ways.

Over-pivoting: chasing new directions every time something feels hard or shiny. Resetting the clock, again and again.

Under-experimenting: staying the course but never testing your assumptions. Slowly drifting into irrelevance.

So, what should companies do,

The best companies rarely reinvent themselves every year.

They compound insight through relentless experimentation and pivot only when reality leaves no alternative.

The following 2 statement to shareholders from Jeff Bezos kind of sum it up.

We are stubborn on vision. We are flexible on details.

And

Our success at Amazon is a function of how many experiments we do per year, per month, per week, per day.

Pivoting is optional. Experimentation isn’t.

P.S: Image Fetched & Generated using Claude.

I have been trying to find the answer to these question(s) and as an engineer I look at Maths to find answers to many a problem and then I hit upon an interesting law called as “Price’s Law”. The more I have been reading about it, the more I am able to link it to many a thing including productivity, performance, even IPL and Stock Markets.

So, let’s get into the “DeepMode” and find out what is after all the Price’s Law and its general applicability in real-world work

Look around your team. If you have 100 people, likely only 10 of them are producing half the meaningful output. If you have 25, that number drops to 5. If you have 10, just 3 are carrying the bulk of the load.

This isn’t cynicism. It’s math.

It’s called Price’s Law, and once you see it, you can’t unsee it.

What Price’s Law Actually Says

In 1963, the physicist and historian Derek J. de Solla Price was studying scientific publications when he noticed something peculiar. The literature in any given field wasn’t being produced evenly. A small subset of researchers was writing most of the papers, while everyone else contributed sporadically.

He formalized the pattern: the square root of the total number of contributors in a field produces roughly half of all the work.

So, if 100 researchers are publishing in a discipline, 10 of them (√100) account for 50% of the papers. The other 90 split the remaining half between them.

The law sounds extreme. It is extreme. And it shows up almost everywhere we’ve bothered to measure. It’s Not just academic papers. Price was looking at scientific output, but researchers and practitioners have since observed the same pattern across an unsettling range of domains:

Software development: A handful of engineers ship a disproportionate share of features and fixes

Sales: Top performers consistently dwarf the rest of the team in revenue

NSE: 2,867 listed companies, √2,867 ≈ 54, and the Nifty 50 captures 54% of NSE free-float market cap as of March 2026 per NSE Indices' own data. Almost surreal precision.

The billionaires: 185 Indian dollar billionaires (Fortune India–Waterfield 2024), √185 ≈ 14, and the top 13-14 names hold roughly half of the total $1 trillion in Indian billionaire wealth.

IPL: ~900 players, √900 ≈ 30 batters have produced ~38-42% of the run in IPL across 18 seasons. Almost the law holds true.

This is different from the more familiar 80/20 (Pareto) principle, which says 20% of inputs drive 80% of outputs. Price’s Law is harsher. As an organization grows, the productive minority shrinks as a proportion of the whole. Double your headcount and you don’t double your top contributors you only multiply them by 1.41.

In other words: the bigger the group, the more lopsided it gets.

Why This Happens

There’s no single explanation, but a few forces are at work:

Skill is multiplicative, not additive: A great engineer isn’t twice as productive as an average one. they can be 10x more productive. Talent stacks: judgment, experience, taste, and execution speed all reinforce each other.

Network Effects: People who do excellent work get pulled into more excellent work. They get the interesting problems, the high-stakes projects, the mentorship opportunities. Their reputation creates a flywheel.

Selection effects: Most groups don’t actively cultivate broad excellence. Once a few high performers emerge, systems tend to lean on them rather than develop others.

What This Means If You’re Leading a Team

Price’s Law is uncomfortable, but ignoring it doesn’t make it less true. Here’s what I think it should change:

Stop pretending headcount equals output. Adding people to a struggling team often makes things worse, not better. The math literally works against you. A team of 16 doesn’t have twice the productive core of a team of 4, it has only twice as many top contributors (4 instead of 2), spread across a much larger base.

Identify your square root and then protect them. Who are the people generating disproportionate value? Are they being shielded from busywork, or buried under it? Top contributors are usually the easiest to lose because they have the most options.

Don’t confuse “fairness” with “equality.” Treating everyone identically penalizes your highest performers and props up underperformance. Equity in outcomes for unequal contributions is its own kind of unfairness.

The Harder Question

The part of Price’s Law I find most worth sitting with isn’t the description it’s the implication.

If a small minority is producing half the output, what is everyone else doing?

Some of them are early in their careers, on their way up. Some are in roles that don’t measure their contribution well. Some are quietly excellent at things that don’t show up in the data. And some are just coasting.

The job of a leader isn’t to accept this distribution as fixed. It’s to ask, honestly: Who has the potential to move up the curve, and what’s standing in their way? Sometimes the answer is them. Often, it’s us.

A Closing Thought

Price’s Law isn’t a reason to be elitist or to give up on the broader team. It’s a reason to be clear-eyed.

Excellence deserves to be recognized, protected, and replicated where possible.

The square root is small. Treat it accordingly.

Being from the world of software I felt while time and cost are still well understood in S/W services, but quality remains a hard thing to define and harder to measure, especially for services. In today’s digital world, quality has a different meaning and it's kind of measurable: Quality many a times is linked to views, likes, reshare, reposts. virality etc… but is that even a true measure of “Quality Work”, opinions will be divided here.

I decided to study this topic of quality in some depth, so let’s get into the “DeepMode” and check this out by going back to the people who are known for quality “The Japanese and their cars”, after all I am a Mechanical Engineer by qualification and understand why quality is the ultimate competitive advantage for any organization in today’s fast paced AI world.

Quality is not an act, it is a habit. — Aristotle

If you go to buy a car, there’s a thing you invariably do, almost unconsciously, when you slide into a new vehicle for the first time. You close the door. Then open it. Then close it again. Sometimes a third time. You aren’t checking if it works. you are listening.

A cheap door says clack. A decent door says thunk. But if you happen to be buying premium car, the first time you close one, it doesn’t really make a sound at all. It just sort of... seals. Like a refrigerator. Like the engineers who designed it cared deeply, about a thing you would only ever notice for one tenth of a second, twice a day, for the rest of your life.

That door sound is the entire argument.

We talk about competitive advantage like it’s a noun. Our advantage is our people. Our advantage is our tech. Our advantage is our data. Our advantage is our proprietary algorithm(s).

None of that is true. Or rather, none of that is true the way we say it.

Every competitor has people. Every competitor has tech. Every competitor, in 2026, has data and most sophisticated algorithms. The advantage was never the noun. It was the adjective sitting in front of it.

The advantage is the quality of the people. The quality of the tech. The quality of the data. The quality of the thing you give to your client in exchange for their money.

The trouble with quality is that it’s almost impossible to measure and almost impossible to miss. You can’t put it in a quarterly report. You can put it in a hand. A person picks up your product, uses your service, and within roughly two seconds, some animal part of the brain has already made the call. This was made by someone who cared or this was made by someone who was in a hurry. There is no in-between. The customer just knows.

The Japanese have a word shokunin, which roughly means “craftsman,” but a craftsman in a particular sense one whose obligation is not just to do the work well, but to do it well whether or not anyone is watching, whether or not anyone will ever know.

In 1983, the chairman of Toyota, Eiji Toyoda, called a small group of engineers into a room and asked them a question, can we build a better car than the world’s best?

He didn’t mean a Camry. He meant a car that could beat a Mercedes-Benz S-Class on its own terms. The project was given the internal name F1 “Flagship One.” The chief engineer, Ichiro Suzuki, set targets that his team initially believed were typos. The car had to be quieter at 100 mph than the S-Class was at 60. It had to be more aerodynamic than anything Mercedes had ever built. It had to weigh less. It had to be more fuel efficient.

The team worked on it for six years. They polished individual components, components customers would never see, because they could tell, on a vibration sensor, that the unpolished version transmitted a tenth of a decibel more sound into the cabin. A tenth of a decibel. In a place no human ear would ever consciously register.

When the Lexus LS 400 launched in 1989, Mercedes initially laughed. Then they stopped laughing. Then they redesigned the S-Class.

There is a famous bit of advertising from that launch: a commercial in which a Lexus engineer balances a pyramid of champagne glasses on the hood of an LS 400 and runs the engine up to 145 kmph. Not one glass falls. The point wasn’t the engineering. The point was that someone, somewhere in Japan, had decided that vibration in a luxury sedan was a thing worth six years of obsession.

Check the ad here

Lexus 400

There’s a paradox sitting at the center of the modern professional life and almost nobody says it out loud.

Everyone wants their delivery in 10 minutes. Almost nobody delivers their own work in 10 minutes.

We are, all of us, simultaneously customers and producers, and we run two completely different sets of standards depending on which side of the counter we’re standing on. As customers, we are unforgiving. We notice even the slightly-too-long wait.

And then we go to work and send an email we didn’t proofread.

It’s just a strange feature of being human the standards we apply outward almost never match the ones we apply inward, because outward standards are felt and inward ones have to be chosen.

The thing nobody tells you about quality is that it isn’t built in the moments when quality is the obvious goal. It’s built in the moments when nobody would notice if you skipped it. The polished component nobody sees. The footnote no one will check.

W. Edwards Deming, the American statistician said it as cleanly:

Quality is pride of workmanship.

That’s it. That’s the whole thing. Not a framework. A feeling, on the part of the person doing the work, this design, this code, this call to a customer, this door is going out into the world with their name on it, and it had better not embarrass them.

You can’t mandate that feeling. You can’t KPI it. You can hire for it, model it, slowly grind it out of people by rewarding speed and short term goals over care for long enough that they forget the door used to close like a vault.

Most companies will not do this. Most companies will continue to compete on the noun and ignore the adjective. They will hire more people instead of better ones. They will ship faster instead of righter. And for a while, in spreadsheets and reviews, this will look like it’s working.

And then one day, someone, somewhere, will close the door, and it will go clack. And the customer, without quite knowing why, will go looking for the one that doesn’t.

By 10 AM, the TV is on. By 10:15, they’ve moved to a mobile game. By 10:30, they’re flipping through a book they begged me to buy last week. By 10:45, they’re back on the sofa, sighing : We are getting bored.

Fifteen minutes. That’s the new attention shelf-life of a vacation morning.

I used to read this as a parenting problem. Then I noticed something strange.

The kid who can’t sit through one chapter will play Roblox for four hours straight and forget to eat. A colleague who scrolls Instagram in seven-second bursts watched all four hours of Dhurandhar on a weekend and she said, “That felt like 40 minutes.”

Same brain. Same day. Very different attention.

So what’s actually going on? Let's get into the “DeepMode” and check this out.

The brain doesn’t have an attention span. It has an attention algorithm.

We talk about attention like it’s a battery you have a long one or a short one. But neuroscientists describe attention more like an algorithm. The brain is constantly asking one question, often unconsciously: Is what I’m looking at worth the metabolic cost of staying with it?

Attention is expensive. Sustained focus burns glucose, taxes the prefrontal cortex, and forces the brain to suppress every competing signal the itch on your knee, the noise two rooms away, the thought about dinner. So the brain runs a cost-benefit check several times a second. If the answer is not worth it, attention drifts. We call this “boredom.”

What makes the answer “yes, stay”? Three things, mostly.

Anticipation: A good story, a good game, a good conversation all share one property you don’t quite know what’s coming next, but you have a strong reason to find out. Dopamine, contrary to popular belief, isn’t the “pleasure” chemical. It’s the “predict and pursue” chemical. It fires hardest in the gap between I want this and I have this.

Stakes: The brain pays attention to things that matter to it. A kid in Minecraft has built something they care about. This is why the textbook is boring and the video game isn’t: one has consequences inside your head, the other doesn’t.

Consistency: The brain hates noise. It wants signal, patterns, rhythm, a sense that the next moment is connected to this one. The familiarity bias.

So the kid scrolling for 15 minutes and the kid gaming for 4 hours aren’t using different attention spans. They’re getting different deals from their brain.

And that’s where trust quietly walks in

Here’s the part that surprised me when I started reading about this.

Trust is not a separate emotional thing that lives far from attention. It’s a shortcut the brain uses to lower the cost of attention.

Think about it. When a stranger starts telling you a story at a wedding, your brain is doing real work. Is this going somewhere? Should I be polite? Why is he standing so close? The metabolic tax is high, and your attention is fragile. But when a close friend starts the exact same story, you lean in. The friend has a track record. Your brain has already pre-approved the cognitive expense.

Trust is what attention feels like when it isn’t tired.

This is why we can listen to a favorite teacher, or a podcaster we love for hours, but a thirty-second pre-roll ad feels like a violation. It’s not the length. It’s the trust.

Neurobiologically, trust dampens the brain’s threat-monitoring systems and lets the focus networks take over. Oxytocin often called the “bonding chemical” does part of this work, lowering our defensive arousal and freeing up bandwidth for connection. When we don’t trust a source, the brain stays half-vigilant, which is why salesy content feels exhausting even when it’s brief.

Simply put, we don’t pay attention to people we trust because they’re interesting. They become interesting because we trust them.

This rearranges a lot of things, especially for anyone trying to build something, a brand, a newsletter, a company, a relationship.

The lesson for brands (and anyone trying to be heard)

Most brands today are trying to win attention. But not many are trying hard to earn trust. Which is strange, because attention without trust is the most expensive, lowest-yield asset on the internet. You can buy a million views and convert almost no one.

As Simon Sinek puts it, “Trust is built in drops and lost in buckets.”

Three quiet ways brands actually fill the bucket:

1. Show up the same way, again and again.

Trust is built almost entirely through pattern. The brain decides you are trustworthy not when you say something brilliant once, but when you behave the same way the tenth, twentieth, hundredth time. The brand that posts thoughtfully every Tuesday for two years beats the brand that goes viral once. Consistency is boring to execute and magical in effect.

2. Be honest about what you are not.

A brand that names its limitations this isn’t for everyone, here’s who it’s for, triggers something the marketing world underestimates. Hiding flaws makes us suspicious. Naming them makes us relax. Patagonia’s “Don’t Buy This Jacket” campaign sold more jackets than any standard ad would have, because it traded a sales pitch for a confession.

3. Deliver small promises before asking for big ones.

Trust is a ladder, not an elevator. If a brand can’t get a welcome email right, no one will believe its ESG report. The fastest way to build trust is to consistently nail the small, unglamorous moments the order confirmation, the support reply, the loading speed of the page. These are not details, They are the brand’s promises in their most testable form.

Back to my living room

My kids will be bored again tomorrow. By 10: 45, probably.

But I notice something now. They aren’t actually bored of things. They’re bored of things that haven’t earned their attention yet the half-watched show, the half-read book, the app they downloaded yesterday.

What holds them, when something does, is never the loudest thing in the room. It’s the thing that has, in some small way, kept its promises.

That’s true for an eight-year-old with a tablet. It’s true for a forty-year-old with an inbox.

It is, quietly, the only marketing principle that has ever really worked

Now fast forward 30 years later, my father-in-law asked me to imagine something which I don't know and I was like is this possible?

These 2 events separated by 3 decades lead me to ponder on how our knowledge, skills and even ability to think and imagine has evolved in this period and what role technology has played.

So, let’s get into the “DeepMode” and trace this revolution.

The Last Thirty Years: A Race We Keep Winning and Losing

Every generation believes it is living through the most disruptive period in human history. The last two to three decades, however, may have earned that distinction more honestly than most. What we have witnessed since the early 2000s is not merely a sequence of technological upgrades — it is a dismantling of the idea of human competitive advantage. The moat(s) have shifted, again and again, beneath our feet. And understanding that shift is no longer optional. It is the beginning of wisdom.

The Era of Knowledge: When Knowing Was Everything

In the late 90’s and early 2000s, Information was scarce, uneven, and expensive. If you knew something most people didn’t how a particular market worked, how a legal clause could be interpreted, what a new technology was capable of — you held power. Experts held the keys to knowledge behind paywalls of years-long education, expensive subscriptions, and geography. A consultant in New York knew things a businessman in Bhopal simply could not access.

This was the age when knowledge itself was the moat.

Then the internet happened. Google search made a lawyer’s reference library available to a teenager in a small town. Wikipedia flattened Britanica. Wikipedia itself was then supplemented by YouTube, Stack Overflow, Coursera, and a thousand other platforms that put world-class instruction in the hands of an ordinary person. The moat of knowledge was drained almost overnight. By the early 2010s, information asymmetry as a competitive advantage was largely dead. Anyone who could type could know.

The Era of Skills: When Doing Became Everything

When knowing became cheap, doing became precious. The new edge shifted to skills — the ability to take that knowledge and execute. A programmer who could build a product. A designer who could make something beautiful. A data analyst who could turn numbers into insight. A digital marketer who could grow a business from zero. The decade of the 2010s was, above all, the decade of the skilled individual. Bootcamps flourished. Side hustles became careers. The freelancer economy exploded. Platforms like Upwork, Fiverr, and GitHub became labour markets where the skilled could compete globally, regardless of pedigree or geography.

The message of that era was : learn to code, learn to design, learn to sell, and you will always find your place. Skills were the new currency, and there was enough for everyone willing to work hard enough to acquire them.

But technology had already begun sharpening its next knife.

The Era of Intelligence: When AI Entered the Room

Artificial intelligence did not arrive loudly proclaiming its intentions. It crept in through autocomplete, through spam filters, through Netflix recommendations — and then, suddenly, it was everywhere and it was capable. The release of large language models and ChatGPT after 2022 become a watershed moment. Because what AI crossed was not merely a technical benchmark. It crossed the threshold of skilled labour.

Writing code, translating documents, drafting contracts, analyzing data, designing interfaces, generating content — the very skills that had defined the previous decade’s winners — are now being performed brilliantly, by machines at near-zero marginal cost. Intelligence, in the narrow technical sense — the ability to process rules, recognize patterns, and produce competent outputs — is now available on tap. You do not need to learn to code to build software. You do not need years of legal training to draft an NDA. You do not need a graphic designer on your payroll to visualize a concept.

Sequoia Capital’s Julien Bek captured this transition with a surgical precision in his March 2026 essay, Services: The New Software. Writing code, he argues, is mostly intelligence — translating a spec into code, testing, debugging: the rules are complex but they are rules. Knowing what to build next is judgement — and judgement is different. It requires experience and taste, instinct built on years of practice.

The New Edge: Judgement

Judgement is the word of the moment — and it deserves more than a moment.

Judgement is not intelligence. Intelligence processes. Judgement decides. Intelligence can draft ten versions of a strategic plan. Judgement knows which one to pursue and when to abandon it. Intelligence is learnable by machines. Judgement, at least for now, appears to be stubbornly, gloriously human.

Today’s judgement will become tomorrow’s intelligence, Bek acknowledges — as AI accumulates proprietary data about what good judgement looks like in a given domain, the frontier will shift again. But we are not there yet. And the window between where we are and where we will be is precisely where human beings must plant their flag.

So, What does good judgement look like?

Judgement is not instinct alone. It is not gut feeling masquerading as wisdom.

The question then becomes urgent and practical: how does one develop it?

Building Judgement: The Three Disciplines

Read Widely and Hungrily

Reading is not merely the consumption of information. It is the practice of inhabiting another mind. When you read history, you develop a sensitivity to how power corrupts and how it collapses. When you read biography, you understand that even extraordinary lives are full of doubt and failure. When you read across disciplines — philosophy alongside business, literature alongside science — you build a group of mental models that allows you to see a new situation through multiple lenses simultaneously.

The habit of reading widely is not a luxury. In the age of AI, it is the foundation of the irreplaceable human mind.

Meet People and Learn From Their Experience

Experience is time spent confronting reality. But you only have one life, and one career, and one set of failures to draw from. Other people’s experience, if you can access it honestly, multiplies your database a thousandfold.

This means cultivating genuine curiosity about how others have navigated difficulty. It means sitting with an older professional and asking not what they achieved but what they learned the hard way. It means building networks not for extraction but for conversation — for the slow, patient accumulation of case studies that no textbook will ever compile.

Meeting people is not networking. It is fieldwork.

Reflect Systematically

Knowledge and experience, unprocessed, remain raw material. Judgement requires the deliberate act of sitting with what you have seen and heard — and forming a view.

This is the discipline that most ambitious people skip. They accumulate, but they do not integrate. They read but do not think about what they’ve read. They meet people but do not sit quietly afterwards and ask: what did that teach me? Where was I wrong? What would I do differently?

Great judgement is built in the margins — in journals, in slow walks, in conversations with trusted friends who are allowed to disagree.

The Unexpected Destination: Imagination

Once you have built genuine judgement — through reading, through experience, through reflection — you arrive at a threshold that AI cannot cross: imagination. Not the cheap, But the deep, associative imagination that asks: what has never existed but should?

This is the imagination of the founder who sees a market no one else has mapped. The scientist who forms a hypothesis that existing data cannot support but intuition insists is true. The architect who designs a building that makes people feel something they cannot name. The teacher who invents a method that reaches the student no prior method could.

And here is the crucial insight, the one that closes the loop: you can only imagine what you know.

Imagination is not born in a vacuum. It is the product of an extraordinarily rich internal library — experiences, ideas, failures, conversations, books, emotions, half-remembered stories — that the mind recombines in novel ways. Einstein did not imagine the theory of relativity from nothing. He imagined it from a deep and obsessive engagement with physics, mathematics, and philosophy, all swirling together until a new pattern emerged that had never existed before.

The person who has read nothing, met no one, and reflected on nothing will produce, when asked to imagine, only the obvious. The average. The already-done.

The person who has lived richly and thought deeply will produce the genuinely new.

And the genuinely new is precisely what neither AI nor any other tool can replicate, because what has never existed cannot be in a training dataset.

The Circle Closes: Back to Knowledge

And so we arrive at the insight that is both humbling and liberating.

The cycle is not linear. It is a loop. Knowledge builds the foundation for Skills. Skills, practiced deeply and honestly, develop into Judgement. Judgement, operating at its highest level, ignites Imagination. And Imagination — truly creative, genuinely novel — demands new Knowledge to sustain itself.

Knowledge → Skills → Judgement → Imagination → Knowledge

The practical implication is this: in an age where AI handles intelligence, and is beginning to encroach on skills, the human competitive advantage runs through Judgement to Imagination — and both of those require going back, always, to the beginning. To the patient, unglamorous, foundational work of learning.

Read more. Read harder. Read outside your field. Meet people who have done things you haven’t. Ask them the questions that matter. Sit with what you’ve learned until it becomes part of how you see the world.

References: Julien Bek, “Services: The New Software,” Sequoia Capital, March 5, 2026. https://sequoiacap.com/article/services-the-new-software/

The Evolution of Movement

Every human life retraces an ancient journey. A baby first lies still, then learns to sit, then crawls across the floor on hands and knees. Soon the child pulls themselves upright, takes wobbling steps, and finally breaks into a run across the living room. In those first years of life, each of us personally re-enacts what our species took millions of years to achieve.

Our ancestors began as tree-dwellers and quadrupeds. Around four million years ago, hominins like Australopithecus began walking upright on two legs, freeing the hands for tools, for carrying, for gesturing. But the truly transformative leap came later, with Homo erectus around two million years ago, when our bodies evolved for something remarkable: not just walking, but running. And not just running, but running far.

The Ultimate Human Skill

Here is a secret hidden in plain sight: humans are the greatest endurance runners on Earth. A cheetah will outsprint us in seconds. A horse will outpace us over a mile. But over a long, hot day, across open ground, almost no animal on the planet can beat a trained human.

Our bodies are built for it. We have long springy tendons, especially the Achilles. We sweat from nearly every inch of our skin, a cooling system so efficient that most mammals overheat and collapse long before we tire. Our nuchal ligament stabilizes the head while running. Our gluteus maximus, the largest muscle in the body, exists largely to power running strides. Anthropologists call it “persistence hunting” — early humans literally chased antelope to exhaustion, tracking them under the midday sun until the animals dropped from heatstroke.

Running is not just something humans can do. Running is, in a deep evolutionary sense, what humans are.

Celebrating the Gift

We have built entire cultures around this endurance. The marathon itself commemorates Pheidippides, the Greek messenger who in 490 BC ran from the battlefield of Marathon to Athens to announce victory over the Persians — and then, legend says, dropped dead. The modern Spartathlon retraces an even longer route, 246 kilometers from Athens to Sparta, to be completed in under 36 hours. The Ironman triathlon demands a 3.8-kilometer swim, a 180-kilometer bike ride, and then, as if to prove a point, a full marathon on top. Ultramarathoners now race 100 miles through mountains; the Barkley Marathons in Tennessee has been finished by fewer people than have walked on the moon.

These events are not merely sports. They are rituals of what our species can do.

Breaking the Unbreakable

And within those rituals, we keep pushing limits once thought to be walls.

For decades, the four-minute mile was considered biologically impossible. Doctors warned that a human heart might burst attempting it. Then, on May 6, 1954, Roger Bannister ran a mile in 3:59.4 on a windy track in Oxford. Within 46 days, his record was broken. Within a decade, high schoolers were doing it. Today the mile record stands at 3:43.13, held by Hicham El Guerrouj since 1999, and running a sub-four mile is a benchmark for serious middle-distance athletes, not a miracle.

The marathon has seen the same pattern. The two-hour marathon was the new impossible wall — a pace of 4:34 per mile, held for 26.2 miles. In 2019, Eliud Kipchoge ran 1:59:40 in Vienna, though under controlled, unofficial conditions. The official record keeps falling, now in the 2:00 range. The sub-two-hour marathon in a real race remains the holy grail, the next wall waiting to be broken.

And Now, The Machines Arrive

Which brings us to present, A humanoid robot, built by the Chinese smartphone maker Honor, completed a half-marathon in the Chinese capital in 50 minutes and 26 secs — faster than the human half-marathon world record holder, Uganda’s Jacob Kiplimo, who ran the same distance in about 57 minutes in Lisbon in March. (The robot did not beat Kiplimo; it beat his time over the full 21 km, which is not quite the same thing, but the symbolism is unmistakable.) Honor’s engineers said the robot was designed with “outstanding human athletes” in mind, with long legs of about 95 cm and a liquid-cooling system inspired by the way our own bodies shed heat.

The race saw runners from other Chinese tech firms too. Robots fell at the start line. One ran into a barrier. A human traffic officer had to direct the machines with arm gestures. It was, in many ways, a comedy of early steps, the robotic equivalent of a toddler learning to walk.

But a year ago, these machines could not have finished at all.

A New Limit Being Crossed?

So here is the thought to sit with. For two million years, long-distance running has been the defining physical signature of our species — the thing we do better than any other creature on Earth. We have built gods, legends, and Olympic traditions around it. We have chipped away at four-minute miles and two-hour marathons because endurance running feels, at some level, like the truest expression of what a human body is for.

And now machines are learning to do it too. Clumsily, for now. Slowly, for now. Beaten by any trained amateur, for now.

But every wall we ever called impossible — the four-minute mile, the two-hour marathon, flight, space — fell faster than anyone expected once the first crack appeared. The robots in Beijing are today’s Roger Bannister moment for machines. They are slow, they fall, they need a human to wave them around obstacles. Give them a decade.

The question is no longer whether a robot can run. The question is whether endurance — the one physical domain where evolution made us champions of the planet — will remain ours at all. We may be watching, in grainy footage from Beijing, the beginning of the end of the human monopoly on the long run.

PS:

All Images created using AI

So the name had to catch up.

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Watch out for the next Dive..

This isn’t just a data point for a central bank spreadsheet; it is a reflection of the urban psyche. Global uncertainty, rising costs and job insecurity, the “story behind the stats” is one of a population bracing for a tougher, leaner 2027.

Crossing the Waterline of Worry

The headline figure of the March survey is the Current Situation Index (CSI), which slid from 98.1 in January to 95.7. In the world of economic sentiment, the 100-point mark is the “waterline of worry.” Anything below 100 indicates that more people feel the economy is contracting than expanding.

The drop was driven by a pervasive sense of a worsening environment. Notably, households’ current perception of the “General Economic Situation” took a staggering 5.1-point hit this round. This isn’t just about a single sector; it’s a broad-based “ebb” in confidence.

As the RBI highlights:

“Consumer confidence for the current period deteriorated in the pessimistic zone with the Current Situation Index (CSI) at 95.7 vis-à-vis 98.1 in the previous round.”

When a society’s current perception sinks further below that 100-point neutral mark, it signals a psychological milestone: the transition from “watching our backs” to “bracing for impact.”

The Employment Outlook: A Sharper Labor Chill

If the general economy is the atmosphere, employment is the oxygen—and the air is getting thin. While the current perception of the job market dropped 2.8 points (reaching a net response of -9.1), the real story lies in the “future fear.”

According to Survey, the expectations for the job market one year ahead plummeted by 7.8 points, falling from a net response of 33.0 to 25.2. This suggests that while the “present pain” of the labor market is manageable for some, urban Indians are increasingly convinced that the real storm is brewing for next year. This erosion of the future “optimism buffer” indicates that the psychological anchor of a steady paycheck is beginning to drag.

The Inflation Paradox: Spending More, Feeling Less

The survey reveals a jarring contradiction that we might call the “inflationary trap”: urban Indians are shelling out more cash than ever, but they feel poorer for it.

The data is stark: 89.1% of respondents feel prices have surged over the last year, and 83.2% expect them to keep climbing. Typically, high inflation leads to a “lockdown” on spending. Yet, data shows that “Total Spending” net responses actually ticked up to 78.4 currently and 81.2 for the future.

A deeper look explains this paradox:

• Essential Items: 86.9% of households report an increase in spending on necessities. This isn’t “happy” spending; it’s the forced cost of survival in an expensive city.

• The Non-Essential Surprise: In a fascinating twist of lifestyle resilience, future expectations for non-essential spending actually rose from 17.4 to 21.1.

Whether this is “revenge spending” or simply a resignation to higher lifestyle costs, it paints a picture of an urban consumer who is refusing to let go of their standard of living, even as they feel the walls closing in.

Future Fading: Why the Optimism Buffer is Thinning

While the Future Expectations Index (FEI) remains in the optimistic zone at 120.2, it is clearly “ebbing,” having dropped 3.2 points this round. This decline is directly linked to a fading outlook on personal wealth. Data shows that “one year ahead” income expectations have softened, dropping from 50.1 to 48.4.

As income growth fails to keep pace with the perceived rate of inflation, the cushion that once kept urbanites feeling secure is starting to deflate.

Conclusion: The Road to 2027

The March 2026 UCCS paints a portrait of an urban India that is resilient but weary. We see a population working harder and spending more, yet feeling less secure about their career trajectories and their purchasing power. The “optimism” that usually characterizes the Indian consumer is becoming more fragile, replaced by a pragmatism born of rising essential costs and a cooling job market.

As we look toward 2027, the question is no longer whether things are getting more expensive—we know they are—but whether the urban consumer’s income can catch up before the “future fear” becomes a self-fulfilling prophecy.

How do these macro-trends align with your own household budget? Are you currently spending more just to stay in the same place?