Who will teach the next generation?

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

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

The Jevons Paradox is not the point

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

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

The training problem nobody wants to own

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

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

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

A few firms will win big — Later

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

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

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

 So, what should a young person do?

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

Chapter 4, verse 34:

तद्विद्धि प्रणिपातेन परिप्रश्नेन सेवया |

उपदेक्ष्यन्ति ते ज्ञानं ज्ञानिनस्तत्वदर्शिन: ||

 

Seek this knowledge through humble surrender, sincere inquiry,

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

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

The three-part method

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

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

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

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

Jnani versus Tattva-Darshi

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

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

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

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

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

The practical implication

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

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

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

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

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

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

 

Indian economy – at an inflection point

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

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

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

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

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

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

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

Why Did India's Growth Falter?

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

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

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

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

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

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

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

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

Rent seeking over innovation

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

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

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

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

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

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

Growing disparities

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

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

Conclusion

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

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

Where does AI fit in the business paradigm

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

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

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

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

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

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

The framework of business

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

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

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

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

Scarcity – a governance failure or arithmetic

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

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

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

What actually makes businesses succeed

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

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

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

And so: where does AI go?

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

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

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

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

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

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

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

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

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

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

Lessons from market cycles – Chapter 5

The years after the 2008 global financial crisis – from 2011 to now in 2026 – have been packed with big changes for financial markets worldwide.

The 2010s started on shaky ground:

·         The world was still recovering from the GFC. Globalization faced pushback. Europe's debt crisis worsened in countries like Greece (with “Grexit” talk), and the UK moved toward Brexit. Ultra-low interest rates and massive money printing (quantitative easing) in rich countries sparked fears of new asset bubbles and soaring commodity prices.

·         Gaps between rich and poor nations grew as aid dried up. The Arab Spring, Gaddafi's death, and Bin Laden's killing reshaped the Middle East. Immigration surged from poorer to richer countries. Protectionism and nationalism – forces that had faded after World War II – came roaring back. (Around 2011)

·         IBM's Watson won Jeopardy! in 2011, signaling the start of the AI revolution.

As the decade rolled on:

·         China overtook Japan as the world's second-largest economy in 2012 and helped launch the BRICS-backed Asian Infrastructure Investment Bank (AIIB) in 2013. Russia annexed Crimea in 2014. The UK voted for Brexit in 2016.

·         AI made huge leaps with deep learning and big neural networks (2013–14). AlphaGo beat a top human Go player in 2016.

·         Donald Trump became US President in 2016–17, sparking a US-China trade war from 2018 that slowed global growth.

·         Trust in traditional money wobbled a bit; cryptocurrencies caught on with everyday investors (2017–18).

·         The 2015 Paris Agreement kicked off serious climate action, boosting renewables fast.

Then came the end-of-decade shock:

·         COVID-19 hit in 2020, crashing economies and markets. Supply chains broke. Governments and central banks poured in record stimulus to avoid depression.

The post-COVID world looks different:

·         Inequality widened. Geopolitical fights grew fiercer and longer. Protectionism and nationalism shape policies more than ever.

·         Asset prices bounced back hard; stocks hit records. But central banks reversed course – hiking rates and tightening money.

·         Trust between countries eroded further. Russia invaded Ukraine in 2022, spiking energy and food prices. The Israel-Palestine conflict escalated in 2023. In 2025, India and Pakistan fought a short four-day conflict (May 7–10) after a terrorist attack in Kashmir triggered India's Operation Sindoor missile strikes. Then in early 2026 (starting February 28), the US and Israel launched major strikes on Iran (Operation Epic Fury / Roaring Lion), killing Supreme Leader Khamenei and others in a push for regime change, with Iran retaliating across the region – creating huge uncertainty in the Middle East.

·         AI large language models like GPT-3 went mainstream in 2022. Massive spending on AI data centers followed. Doubts grew about traditional IT services' future, and job losses sped up.

All these events reshaped markets, capital flows, policies, industries, and global power.

For Indian investors, this period brought its own ups and downs:

·         India handled the 2008 crisis fairly well thanks to earlier growth. But in 2013, a “taper tantrum” (US Fed signaling less QE) triggered capital outflows, plus high oil/gold imports and a weak rupee pushed the current account deficit to a record 6.7% of GDP. India was labeled a “fragile” economy – but RBI and government steps fixed it fast.

·         2014 brought a stable majority government after 25 years.

·         Demonetization in 2016 (scrapping high-value notes) hit small businesses hard and slowed growth.

·         GST rollout in 2017 added pressure on the unorganized sector.

·         COVID lockdowns in 2020 crushed SMEs and informal jobs again. Organized large firms gained market share. Government ramped up welfare support, straining the budget.

Stock market impacts:

·         These shocks weakened small/micro businesses. Bigger organized players took share. Many family businesses sold out to corporates or PE firms. Jobs got scarcer in some areas. Work-from-home spread. All this pulled millions of households – especially younger people – into regular stock investing.

·         Government boosted capex with big infra projects (roads, railways), plus incentives for manufacturing (chemicals, electronics, renewables) and defense amid global tensions. Theme stocks in these areas soared, often ignoring valuations.

·         New companies with unproven models launched IPOs at high prices.

·         Recently, geopolitical risks, sticky inflation, higher rates, and doubts about the financial system pushed gold and silver prices up sharply. Many investors shifted away from their planned mix to buy more metals.

·         But corporate capex and profits haven't met hopes. Government spending fell short too.

·         Higher US yields, a weakening rupee (hitting 89–92/USD range by early 2026), stretched valuations, and limited direct AI/semiconductor plays drove record foreign outflows (~$18 billion in 2025 alone).

·         After euphoric post-COVID years, markets disappointed newcomers. Many theme/momentum stocks corrected sharply. Gold/silver turned volatile below peaks. Even bonds underperformed.

·         The hardest hit were momentum-driven stocks popular with retail investors – when liquidity dried up, prices plunged with few buyers. This is classic: fast-rising assets on hype and easy money fall hardest when mood shifts. No single big event caused the recent correction – just stretched valuations, crowded trades, and a slow global macro change. When everything's priced for perfection, small letdowns cause big reactions.

My final lesson from all these cycles

Stick to a solid asset allocation plan. It's not about maxing returns every year – it's about matching your risk comfort, cash needs, and long-term goals through ups and downs.

Rebalance regularly and calmly. View equity dips (especially in good companies) as chances to allocate more for the long run, not panic signals. Keep fixed income and gold at planned levels – don't overload on fear.

Markets reward patience and discipline far more than chasing the latest hot theme or reacting to headlines. The best investors stay steady when others chase or flee.

This is the concluding part of the series. I will be happy to receive readers’ comments; especially if someone wants to share his/her experiences and lessons learnt from them.

Also read

Chapter 1

Chapter 2

Chapter 3

Chapter 4

How the paradigm of power is shifting

For much of modern history, power was mostly measured by military strength. Borders shifted through conquest, and influence was enforced through force.

In the past couple of decades, there has been a gradual shift in this paradigm. While military capability still matters, the primary instruments of power today are economic and technology.

In the contemporary world, access to capital, technology, markets, and resources often determines outcomes more effectively than armies. Trade rules can shape behavior. Financial sanctions can immobilize economies. Control over technology standards can define the future of entire industries.

Unlike traditional warfare, economic power operates quietly. There are no declarations, no battlefields, and no formal endings. Yet its effects can be just as lasting. The latest events in Venezuela also need to be looked at from this Lense.

Export controls, tariffs, financial restrictions, and regulatory barriers are now routine tools of statecraft. They are justified as measures of national security or economic protection, but they also create dependencies and asymmetries. Countries that control key nodes—finance, energy, technology, or logistics—gain leverage over others.

This does not resemble old-style colonialism. There is no direct rule or occupation. Instead, influence is exercised through terms of access.

Who can trade? Who can borrow? Who can build?

From an economic perspective, intent matters less than outcomes. When countries or firms are forced to align behavior to retain access, power has been exercised—whether or not it is acknowledged as such.

The replacement of military power with economic power has not made the world more peaceful. It has made conflict less visible, more persistent, and harder to resolve.

Understanding this reality is essential for anyone trying to assess long-term risks in a changing global system.

For markets, this shift has important implications. Economic decisions are no longer evaluated purely on cost and efficiency. Political alignment, regulatory risk, and strategic sensitivity increasingly shape investment outcomes.

The conventional principles of economics that advocate efficient use of factors of production to maximize economic output are being overlooked for strategic reasons. The developed countries like the US, which outsourced manufacturing function to the more populous countries (lower wage cost) and resource rich countries (lower logistic cost) are aiming for relocating their industrial ecosystem onshore.

In view of this shift, India has two choices to make. One, to focus on fiscal discipline and compromise on capex or increase capex and let the deficit stay high. Two, carve out a space of its own in the emerging multipolar global order, or chose to become a vassal state of one of the major powers. These choices will define the investment opportunities available for the Indian investors.

India’s AI Moment: A ±5 million job swing by 2031

(Photo Credit IET)

AI (Artificial Intelligence) is no longer a future disruptor—it’s already reshaping how the world operates. For India, a country with 10–11 million tech and customer experience (CX) workers, the stakes are unusually high. AI is already reshaping how India codes, tests, designs, supports, and runs digital work. India’s millions of Tech and CX workers are at the threshold of a major transition to a future that is full of historic new opportunities and risks.

The latest report published by NITI Aayog “Roadmap for Job Creation in the AI Economy” (NITI Aayog–BCG–NASSCOM), delivers a clear message- AI can either shrink India’s tech workforce sharply by 2031—or expand it dramatically. The outcome depends entirely on what India does next.

Here are the key points highlighted in the report.

The Stakes: A ±5 Million Job Swing by 2031

India faces two sharply diverging paths:

If India does nothing:

Tech workforce drops from 7.5–8M → 6M

CX workforce drops from 2–2.5M → 1.8M

If India acts decisively:

Tech workforce grows to 10M

CX workforce grows to 3.1M

This is not about technology alone. It's about policy, skilling, and national coordination.

What AI Is Changing (Fast)

Work

AI boosts productivity across the tech value chain:

·         Code generation: +15–25%

·         Testing & documentation: +20–50%

·         Overall SDLC: +10–20%

·         CX automation: Handles majority of L1 queries

·         Routine, scalable tasks get automated first.

Worker

At risk

·         Junior QA engineers

·         L1 IT support

·         Basic CX representatives

Evolving

·         Full-stack developers

·         Data engineers

·         Cloud DevOps

·         Cybersecurity roles

·         New roles created:

·         Prompt engineers

·         AI architects

·         Ethical AI specialists

·         Quantum ML engineers

·         LLM researchers

Workforce

The pyramid compresses:

·         Fewer entry-level roles

·         Faster ramp-up

·         More judgement-led work

·         Leaner teams

·         Higher skill premium

India’s Three Big Vulnerabilities

Job Displacement Risk

·         60% of formal-sector jobs face automation risk.

·         Entry-level roles are most exposed.

Weak AI Talent Pipeline

·         Limited CS in schools

·         AI curriculum lags global benchmarks

·         Falling share of AI patents & citations

AI Talent Shortage

·         India meets only 50% of AI talent demand

·         Net negative migration of top AI researchers

·         Demand growing 25% CAGR

·         India is rich in talent, but not yet in AI-ready talent.

The Playbook: The India AI Talent Mission

A single, unified, all-of-government mission to make India the world’s AI talent capital.

Embed AI from school to university

·         Universal CS education

·         AI + X degrees

·         Scale AI PhDs

·         Faculty-industry exchanges

Make India a global AI talent magnet

·         AI Talent Visa

·         Competitive grants

·         Returnee researcher programme

·         Tier-1 AI Centres of Excellence

Build a national AI reskilling engine

·         AI Masters for working professionals

·         Sector-specific reskilling (IT, CX, BFSI, healthcare)

·         Large-scale AI literacy (PMKVY/NAPS)

Two Critical Enablers (with IndiaAI Mission)

·         Open-Source AI Commons

Public datasets, models, benchmarks

·         National Compute Grid

Affordable GPU access for students, startups, universities

Remember: Without compute + open data, talent simply migrates abroad.

The Bottom Line

AI can make India a global AI workforce hub or a net job loser

The difference rests on speed, scale, and strategic coordination.

India has the people. AI gives them leverage. A national mission gives direction.

The next 4–6 years will decide whether we ride the AI wave—or get swept under it.