So much of the conversation about AI and the future of work focuses on what will be taken away, yet to even frame that debate one must start by defining the nature of work itself and whether there is anything to be substituted in the first place. Much of the global discussion assumes a world of structured job descriptions, weekly payrolls, HR departments, and clear lines between functions; this formal construct presumes projects neatly organized into discrete tasks that can eventually be automated out of existence, and under this framework AI looks like a clear and direct threat to human labor.
“AI threatens India only if you assume India is already a Western-style labor market. It isn’t.”
To accept this as India’s fate, one must first ask whether India actually operates within this framework, because for the vast majority of the country the boundaries between formal and informal work are flexible, porous, and constantly in flux. More than 80% of the workforce has no contract, no benefits, and no department to downsize, and people move between gigs and trades based on seasonality and opportunity (OECD average: 20%); rather than work being assigned from the top down, it is often assembled by workers and shaped by circumstance.
This dual system is visible almost everywhere. A FASTag scanner reads a license plate in a few seconds and deducts a toll through the country’s digital rails, yet at the same toll booth a man in a reflective vest sits in a plastic chair and writes license plate numbers by hand in a khata. The digital layer and the human layer operate side by side, and the system does not treat these overlaps as contradictions.
If one accepts this reality, it becomes clear that India’s operating environment is built on adaptability, and this is where things become interesting because AI is not confronting a neatly organized labor market. For most of the country, it is arriving in a landscape of overlapping systems, uncertain boundaries, and fluid roles, although some parts of this landscape are clearly vulnerable. The IT and BPO sectors grew out of a specific form of global cost arbitrage and directly employ over 5 million people, with millions more indirectly, and much of that work involves structured processes that AI can learn to perform. Similar pressures exist in professions that rely on routine analysis and predictable documentation, and teachers, accountants, paralegals, lawyers, and journalists will all face forms of disruption.
But the fundamental question for India is not how many jobs disappear but how work mutates. The country has lived through repeated shocks- from demonetization to GST to abrupt digital transitions and the pandemic- and each time it has reorganized itself rapidly; people found new forms of activity, new ways to earn, and new ways to navigate the altered landscape, and this capacity to reconfigure is deeply embedded in the country’s social and economic structure.
It is instructive to consider how global labor markets respond to technological change. Global labor spending, at roughly $50 trillion dollars, dwarfs global software spending at $300 billion dollars, and AI is entering the economy primarily as a mechanism to reduce labor costs. In countries where labor is expensive, the pressure to automate is immediate; Western economies face this acutely because human capital across the skill ladder is costly, and replacing people with machines becomes a rational choice.
India occupies a different position. Labor is inexpensive across factories, hospitals, services, and professional roles, and the economic incentive to automate is therefore far less forceful. Some work will shift, but the underlying calculation is different. The global cost arbitrage that powered India’s outsourcing boom will weaken in certain categories, especially lower-end tasks. About 3000 global capability centers employ nearly 2 million people, and parts of that activity will contract as AI becomes more capable.
Yet this is only a partial picture. More interesting is the emergence of new categories of “offshorable” work that did not exist previously. Radiology provides a clear example: the United States faces a severe shortage of radiologists and the gap widens each year. AI can now perform a highly accurate first pass on a scan, a technician in India can provide a confirmatory layer, and a licensed radiologist in the United States can then focus on the interpretive layer, performing more reads with greater precision. This is not a story of displacement but one of combining AI with human judgment across borders to produce faster, better, and more affordable care.
This first-pass and second-pass structure will not remain limited to radiology, as many regulated industries (think legal, compliance, accounting, risk underwriting) will adopt similar models; regulators will insist on human oversight even when AI provides the initial analysis, and India is the only country with the scale, expertise, and cost structure to operate this human layer at meaningful volume. What emerges is not the erosion of offshoring but a transformation of it; certain categories will shrink, but others will grow significantly, especially where AI augments rather than replaces human capability.
The long-term implications may be even more significant. A large share of future AI activity will be directed at tasks that were never handled by humans at all, and these tasks represent net-new categories of work that will require people who can supervise, guide, refine, and interpret AI outputs. India’s comfort with unstructured opportunity, its willingness to move across categories of work, and its capacity to recombine skills in real time create a meaningful advantage in this environment.
This intersects with India’s domestic economic story. The country is approaching an inflection point where per-capita income will cross the threshold that unleashes large-scale discretionary consumption; the top 200 million consumers already drive much of the country’s discretionary spending, while the next 500 hundred million are beginning to move from necessities toward healthcare, financial services, travel, housing, and digital goods. As this transition accelerates, it will create enormous demand for small businesses, freelancers, service providers, and new types of ventures.
India registered over 170,000 new companies last year, a dramatic shift in the country’s entrepreneurial landscape, and it now hosts one of the world’s largest communities of digital freelancers on global platforms. AI-enabled tools will become lighter, more accessible, and available in Indian languages, meaning that millions of people who were once peripheral to the formal economy will gain new forms of leverage. As consumption rises, the domestic market will become an even larger driver of employment; smaller teams will deliver high-quality services at scale, and solo entrepreneurs will take on tasks that previously required entire departments. AI will not simply replace work; it will enlarge the space in which work can take place.
Automation inside India will follow its own logic. Images of humanoid robots walking through Indian factories or public spaces make for striking illustrations, but they are unlikely to become common because the economics simply do not support them; a humanoid priced at $30,000 dollars is out of reach for nearly everyone. Where automation makes sense in India, it tends to arrive in simpler and more targeted forms, and this is already visible in manufacturing, where robotic arms operate efficiently at a fraction of the cost of a humanoid. From automobile plants to solar-module factories to large warehouses, automation is expanding in ways that match the realities of Indian labor markets.
The world is moving into a period where tasks will shift rapidly, careers will be episodic, and identities won’t be tied to a single form of work. In such a world, the traditional idea of a job matters less; what matters is the ability to adapt, learn, and shape opportunity as it appears.
India has lived in this reality for a long time.
Its informal intelligence may be its most valuable export in an AI world.
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Tom Hyland is a long-time investor and operator in India, working at the intersection of entrepreneurship, supply chains, and global markets.
https://indianspotlighttime.substack.com/
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Akhil Handa is a global C-suite financial services leader, building the intelligence infrastructure to shape the future of SMB capital.
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