Something massive is coming, and the silence around it is deafening.
We are possibly in the final years before a full-blown intelligence explosion — a moment when AI systems dramatically outpace human cognitive output across nearly every domain. Yet if you walk through any tier-2 or tier-3 Indian city today and speak to parents, students, local politicians, or journalists, you will find almost no awareness of what this actually means.
Not because people are unintelligent. But because no one is telling them the truth.
The IT Dream That Became a Trap
Over the last three decades, the IT boom reshaped the aspirations of an entire generation of Indian families. For parents who had spent their lives doing physically demanding work — on shop floors, in warehouses, behind counters — watching their child sit in an air-conditioned office in formal wear, earning a salary they could never have imagined, felt like the ultimate victory.
That dream hardened into dogma: engineering college, software job, FAANG salary. This became the socially accepted definition of success, especially in middle-class households. IIT became the holy grail, and where that wasn’t accessible, a thousand coaching institutes, backdoor consultancies, and referral networks filled the gap.
It worked, for a while. Global Capability Centres opened. Multinational companies set up local branches. State governments competed with one another, offering land, tax holidays, and long-term leases in special economic zones. Real estate boomed around tech parks. Infrastructure followed. Politicians claimed credit. People believed them.
And for at least two decades, it made sense to believe them.
Then Came ChatGPT
The launch of ChatGPT in late 2022 was a watershed moment — not just technologically, but psychologically. For the first time, a broad swath of people witnessed a machine performing knowledge work: drafting, reasoning, coding, summarising, translating — tasks that until recently required years of education and experience.
And the pace hasn’t slowed. Every quarter, every major AI lab releases something more capable than the last. The progress is not linear; it is compounding.
What once took a human days or weeks can now, in many cases, be done in seconds. That isn’t an exaggeration — it’s a product demo anyone can run themselves.
This isn’t just a hunch. In November 2025, the McKinsey Global Institute released one of its most striking findings yet: technologies that already exist today, not ones still in development, could technically automate around 57 percent of all work hours in the US economy, roughly split between software agents handling non-physical tasks and robots handling physical ones.¹ That figure nearly doubled McKinsey’s own 2023 estimate within two years, and the researchers frame it as a ceiling on technical potential, not a prediction that this many jobs vanish overnight. But the direction is unambiguous, and the pace of revision — upward, every year — tells its own story. No industry is truly exempt: not IT, not finance, not law, not medicine, and certainly not government administration.
What Indian Governments Are Actually Saying
Here’s where things get frustrating.
Instead of an honest conversation about displacement, reskilling, and economic restructuring, what we get from most state governments is chest-thumping: “We signed an MOU with Company X.” “Our state will have an AI city.” “Data centres will bring employment.”
This isn’t a hypothetical script. In 2026 alone, Uttar Pradesh approved a Data Centre Policy targeting roughly ₹2 lakh crore in investment and up to 2 gigawatts of new capacity, with the state government’s own estimate putting the resulting indirect employment at around 50,000 jobs.² Gujarat signed a ₹25,000 crore MoU with L&T for a 250 MW AI-ready data centre campus in Dholera.³ Odisha, Telangana, Andhra Pradesh, and Haryana have all signed similar headline-grabbing agreements over the past year, each one covered as a jobs win.⁴
Here’s what rarely makes the headline: internationally, the research on data centre employment is now fairly consistent, and it isn’t flattering. A 2026 Brookings Institution study that tracked counties in the US which received their first large data centre found only a modest 4–5 percent rise in overall private employment over five to six years — and that’s the positive finding.⁵ Separate industry workforce benchmarks put permanent staffing at the most automated hyperscale campuses at roughly 20–40 people per 100 megawatts once construction wraps up.⁶ Construction itself creates real, if temporary, work — often in the hundreds or low thousands of jobs for 18 to 36 months — but the facility that remains afterward typically runs with a skeleton crew of specialised technicians, security, and site managers, not the mass local employment implied by ribbon-cutting ceremonies.
This isn’t a secret. It’s documented in report after report. Yet no journalist of consequence, no editorial board, and no policy think tank in mainstream Indian media is pushing back on this narrative with the urgency it deserves.
Because it doesn’t affect them yet. And in India, problems only become real once they land at your own door.
The Counter-Argument — And Why It Isn’t Enough
To be fair, the optimistic case deserves a hearing. Every major wave of automation — mechanised farming, computers, the internet — triggered predictions of mass unemployment that didn’t fully materialise. New job categories emerged that no one could have predicted in advance. McKinsey itself is careful to say its 57 percent figure measures technical potential, not an inevitable wave of layoffs, and argues the more likely outcome is humans, software agents, and robots working in partnership rather than humans simply being replaced.⁷
The trouble with leaning on this comfort is threefold. First, previous automation wave, however painful, typically unfolded over multiple decades — giving families, institutions, and labour markets time to adjust. The current pace, by McKinsey’s own account, is compressing timelines that used to run to 2060 into a window centred on the 2030s.⁸ Second, past automation mostly displaced physical and routine labour; this wave targets cognitive and knowledge work first — precisely the category India spent thirty years training an entire generation to enter. Third, “new jobs will emerge” is true in the aggregate but says nothing about where and for whom. A software engineer in Vizag whose role is automated is not automatically re-employed by whatever new category emerges in San Francisco or Bengaluru’s AI labs.
Historical precedent is a reason for hope. It is not a plan. And right now, India doesn’t have one of those either.
The Economic Chain Reaction No One Is Modelling
Let’s trace the logical chain that almost no one in public discourse is willing to follow through.
If private-sector IT employment stagnates or contracts — and given the automation trajectory above, that is a real risk — consumer spending contracts with it. The restaurants, malls, real estate, ride-sharing, retail, and hospitality sectors built on the back of tech-sector salaries begin to slow. Tax revenue falls. State governments with bloated payrolls and committed infrastructure spending face a fiscal squeeze.
A government that cannot pay its employees on time is not a stable government, regardless of how permanent those jobs appear on paper.
The money stops rotating. And once that happens, the consequences for daily-wage workers — the very people politicians claim AI cities will help — are severe and immediate.
This is not a slow, manageable transition we’re discussing. It’s a structural shock for which India has no public preparation plan.
Universal Basic Income(UBI) Is Not the Answer
UBI tends to be the first policy reflex in these conversations. It shouldn’t be.
Distributing income without addressing the structural collapse of productive activity is just deferred chaos. The more useful question governments should be asking is: what happens to food security, water access, and basic human welfare when large-scale unemployment arrives? How do we ensure a worst-case scenario doesn’t trigger civil unrest?
What Should Actually Happen
If chest-thumping MOUs aren’t preparation, what is? A few concrete starting points:
Mandate independent, published job-impact audits for every state-level AI or data centre MOU — permanent jobs versus construction jobs, stated separately and verified, not bundled into one impressive-sounding number.
Redirect a portion of the tax incentives currently given to data centres toward vocational reskilling programs aimed specifically at IT and BPO workers whose roles are closest to automation.
Invest seriously in agricultural R&D, water infrastructure, and decentralised food systems — sectors that are far more labour-intensive and far less exposed to near-term automation than the ones currently receiving the political spotlight.
Build a genuine public safety net designed around welfare outcomes (food, health, housing) rather than a single cash-transfer number that sounds good in a headline but doesn’t address the underlying collapse in productive employment.
Create a public, state-wise dashboard tracking AI-linked job displacement and creation, so the debate is grounded in real numbers instead of competing press releases.
None of this requires exotic new policy instruments. It requires treating this as the structural issue it is, rather than a talking point to be managed until after the next election cycle.
Why This Silence Suits Everyone in Power
Politicians benefit from delayed public awareness. The longer people stay distracted — by caste politics, celebrity news, regional rivalries, cricket — the less they demand preparedness.
This isn’t accidental. The art of Indian political communication has, for decades, been to keep voters focused on identity and grievance rather than on structural economic futures. It works, until it doesn’t.
The reckoning will come. It always does. The only question is whether it arrives as a manageable transition or as a full-blown crisis.
People will start asking the hard questions the moment their own livelihood feels the pressure — and by then, the window to prepare will have already closed. We should be asking these questions now, not waiting for a crisis to make them feel urgent.
The storm is coming. Whether we’re ready for it is entirely up to us.
🧠 Human driven, AI assisted. The perspectives shared in this newsletter are my own, refined and structured with the help of Large Language Models.