When GCCs Stop Counting Heads
Why Agentic AI Breaks the Link Between Headcount and Capability
Everything about Indiaโs capability centres is measured in people. The headline figures are headcount: 2,117 centres, 2.36 million employees, and a sector that added close to 200,000 jobs last year, almost twice what the IT services firms managed. The state policies that compete for these centres are written in jobs too: payroll subsidies, grants for every fresher hired, money clawed back if a centreโs employee count falls. Even the celebration is counted in heads. When the industry gathered at its summit this year and announced it had moved โfrom scale to ownership,โ the proof it offered was that 96% of the centres set up since 2021 had launched with product mandates rather than back-office ones. The story India tells about this success, and the instruments it uses to back it, both rest on a single assumption: that the number of people a centre employs is a fair measure of the capability it holds.
For 30 years that assumption was sound. A capability centreโs output was roughly proportional to its payroll, because the work needed people to do it, and more work needed more people. The agentic turn in software is dissolving that proportion. When execution itself becomes something software can do โ not just generate a draft but run the task, check the result, handle the exception and move to the next one โ capability stops tracking headcount. Capability is an organisationโs ability to convert knowledge into reliable execution. Scale determines how much it produces; capability determines what it can reliably produce. Once capability is defined this way, almost every current metric for these centres begins measuring yesterdayโs economy. Whether artificial intelligence will change these centres is not in question; it will. What matters is what a capability centre becomes, and what Indiaโs advantage rests on, once capability is no longer proportional to payroll. The centres are the clearest place to watch, because the economics of capability are changing underneath the whole economy and surface there first.
The equation that broke
The change is simpler than the technology around it. Generative AI lowered the cost of making things. Agentic AI lowers the cost of doing them. An agent does not stop at a suggestion; it acts, and keeps acting, with a person supervising rather than typing. Execution, the part of knowledge work that used to require a human at a keyboard, becomes capacity you can buy by the unit.
For a capability centre this is not a productivity upgrade but a change in the production function. The old equation was simple: capability was a function of how many engineers you employed. The new one has more terms. Capability is now a function of people, the agents they direct, the systems that orchestrate those agents, the governance that keeps them safe, and the data they learn from. Headcount is one variable among several, and no longer the decisive one. Once that is true, the centre stops being a pool of labour and becomes something harder to name: an organisation that allocates and governs execution rather than performing it. Almost everything else that is confusing about this moment follows from that one shift, including its least intuitive consequence: when execution becomes abundant, the organisations that know how to direct it become more important, not less.
Two kinds of capital
Sort what a capability centre owns into two piles, because the agentic era treats them very differently.
The first pile is rentable. Compute, the foundation models, the inference that runs them โ these are becoming infrastructure. They are improving quickly, getting cheaper, and available on demand from a competitive market. There is a reflex, in much current commentary, to treat dependence on a foreign model provider as a danger in itself. That worry is misplaced. No capability centre owns its electricity; it rents its cloud, it rents its software, it rents its office floor. Renting inference is the same kind of decision, and usually the right one. India will consume this infrastructure the way it consumes any other, small and cheap models will multiply, and almost none of a centreโs lasting advantage will come from owning the layer everyone else can rent. The sensible posture toward rentable capital is to rent it well. That is the whole renting argument, and it needs no more room than this.
The second pile is the one that matters, because it compounds. Knowledge: the operational learning a centre accumulates about a business, its data, its domain. Leadership: the scarce people who can direct the rest. The ecosystem a centre sits inside. The governance it can exercise over systems that now act on their own. And the institutions that hold all of it together over time. None of this is available on demand. It is built slowly, it appreciates, and โ the decisive property โ its location is a choice in a way the rentable layerโs is not. You will run whichever model is best and rent compute wherever it is cheapest, but where your knowledge, leadership and institutional capability accumulate, inside your organisation or outside it, inside the country or outside it, is decided by how you build, not by the market. For a firm and a country alike, the strategic choice of the agentic era is which pile to optimise for. Most of the conversation is still optimising for the first.
What actually compounds
The second pile is where the real argument lives, and it is the part most analyses skip. Capability compounds because it is cumulative: every execution leaves behind knowledge, judgement and institutions that make the next one easier. Unlike labour, capability survives the completion of the task that created it. Headcount does not. It compounds through things that look soft until you try to buy them quickly and find that you cannot. Leadership density: the depth of people who have built systems before and can be trusted to make 100 judgement calls a week. Engineering culture: the habits, standards and shared vocabulary that let 1,000 people work as though they were 50. Startup recycling: the constant churn of people leaving to build and returning with scar tissue. Universities and the pipelines they feed. The operational learning that accumulates every time a system fails in a new way and someone works out why. Governance maturity, the hard-won sense of which decisions can be delegated and which cannot. And institutional memory, the part of an organisation that remembers what was already tried.
Agents raise the value of these assets. They add scale, not capability, and capability is the part that compounds. When execution is scarce and expensive, the binding constraint is the supply of people who can execute, and headcount is what is worth having. When execution becomes abundant and cheap, the binding constraint moves โ to the capacity to direct execution well, to absorb a new technology and turn it into reliable operations, to govern systems acting on their own, and to learn faster than the organisation next door. Those are the compounding assets. An agent makes a mediocre engineering organisation slightly faster and a deep one dramatically more powerful, because the deep one can metabolise what the agent produces. The scarce resource is no longer the worker. It is the organisationโs ability to absorb and operationalise new capability, and that ability is unevenly distributed, slow to build, and impossible to rent.
Economists call it absorptive capacity: the ability of a firm or a region to recognise new knowledge, take it in, and put it to use.ยน It is why two capability centres given the same agents and the same budgets diverge, one becoming a transformation hub and the other staying a delivery centre. It is why two states with near-identical incentive schemes get different results. And it is why a city can keep winning long after it has stopped being cheap.
Why Bangalore still wins
Bangalore is the cleanest proof of the argument, precisely because on the old theory it should be losing. Its roads are among the most congested of any major technology hub; by one widely cited estimate the gridlock costs the local economy around โน20,000 crore a year. Its salaries have risen to the point where a senior engineer or architect at a leading centre no longer competes in a local labour market at all, but in a global one, priced against worldwide demand for the same scarce skill. Its infrastructure is visibly strained. If capability were a function of cost, capital would have left for somewhere cheaper years ago, and indeed much of the routine work has moved to other cities and smaller tiers. Yet firms keep choosing it while complaining about it, and the countryโs deepest concentration of capability stays put.
Cost no longer explains Bangaloreโs dominance. It wins because few places match its absorptive capacity: a deep pool of people who have led engineering before, a thick network of startups recycling talent, a developed supplier and investor ecosystem, a mature engineering culture. New technology arrives there and is put to work faster than anywhere else, because the surrounding tissue is already in place to absorb it.
What looks like an advantage in engineering headcount is often an advantage in social infrastructure. The same engineer will spend 10 years moving through a startup, a capability centre, a hyperscaler and a product company, carrying operating practices, technical judgement and professional networks across each boundary, so that capability accumulates across the ecosystem and not only inside any one firm. Developer communities, former colleagues, open-source collaboration and university networks let knowledge travel faster than any organisation chart, and they are slow-moving assets that no incentive scheme can conjure.
This is not Indian exceptionalism; it is the same increasing-returns logic that explains Silicon Valley, where raising the density of a knowledge cluster raises its productivity.ยฒ Cost was never the real moat. It was the cheapest part of the moat to see, and it is the first part the agentic era removes. What remains is the part that compounds, which is exactly the part a balance sheet has never known how to value.
A reasonable objection runs the other way: if agents make execution abundant everywhere, geography should matter less, not more. The opposite is more likely. As execution becomes cheap, the returns shift to the things that direct it โ leadership, governance, organisational learning, ecosystem depth โ exactly what a mature cluster accumulates over decades and a cheap location cannot. Commoditising execution does not commoditise the capacity to absorb and govern it, which is why the advantage concentrates rather than disperses.
Three theories of capability
If capability compounds rather than accumulating by the hire, what matters is how a place builds the capacity to compound it. India is, almost by accident, running several of these experiments at once. Three illustrate the contrast most sharply, and they are best read not as a ranking of states but as different theories of how capability forms.
Bangalore, and Karnataka around it, embodies the first theory: that markets compound capability. Nobody planned the Bangalore ecosystem; it emerged from decades of firms, people and capital interacting until the city had built what is plausibly the densest engineering ecosystem outside Silicon Valley. Karnatakaโs recent policy is unusual in recognising what it has, framing its centres as creators of intellectual property rather than employers of people, and trying to seed the same density in other cities. The theoryโs strength is that it works. Its weakness is that it is very hard to reproduce on purpose.
Kerala embodies a second theory: that knowledge institutions compound capability. Rather than treating capability primarily as something to attract, it has spent decades treating it as something a state can deliberately build. For 20 years the state has treated knowledge as public infrastructure, running one of the worldโs largest deployments of free software in its schools, mandating open standards in government expressly, in its own policyโs words, to avoid total dependence on select vendors, and writing the language of free software, free knowledge and an egalitarian knowledge society into its IT policy as early as 2007. The wager was that capability could be built deliberately, through institutions and a knowledge commons, rather than left to emerge from a market. I was involved in parts of that work โ the 2007 policy and the national open-source effort that followed โ so I have watched this approach tested, not only argued. It is slower and less glamorous than Bangalore, and it produces a different asset: not density, but independence. What it has not done is turn that depth into clusters or centre scale to rival Karnatakaโs or Telanganaโs. But the underlying assets โ engineering talent, knowledge institutions, open standards, public capability accumulated over decades โ remain unusually strong. Whether that institutional depth becomes globally visible capability is now less a question of talent than of institutional choice.
Telangana embodies a third theory: that governments can deliberately accelerate capability formation through policy, research partnerships and ecosystem building. Over the past decade it has assembled one of Indiaโs strongest AI and deep-technology ecosystems, combining research institutions such as IIIT Hyderabad, globally significant R&D centres, startup ecosystems, venture capital, engineering talent and an unusually proactive technology administration. Individual initiatives such as the Agriculture Data Exchange illustrate that approach rather than define it. The theory is distinct from the other two: not markets alone, and not long-built institutions alone, but the state deliberately assembling conditions under which capability compounds.
Three states, three theories, and not one of them is betting on headcount โ that is the tell. They are not the only experiments running. Tamil Nadu compounds decades of manufacturing, engineering education and supplier ecosystems into product engineering. Pune compounds enterprise software, automotive and manufacturing engineering, and research. Andhra Pradesh is assembling newer capability around advanced manufacturing, electronics and logistics. These are illustrations, not an exhaustive survey, and they are best read not as a ranking of states but as different theories of how capability forms and what role the state plays in it โ market ecosystems, knowledge institutions, state-led ecosystem building, industrial depth โ each suited to what a region already has, and none of them permanent, since endowments and governments change. Indiaโs advantage will not come from every state copying one model. The question is no longer which state can attract the next capability centre, but which can become the place where capability compounds fastest. The task of national policy is to strengthen those different mechanisms, not to encourage imitation.
Inside the centre
The same shift, seen from inside an engineering organisation, changes its shape. The classic capability-centre pyramid was broad at the base, where juniors did the volume of the work, and narrow at the top. Agents hollow the base, because volume work is the automatable work, and push value toward a different silhouette: a thin layer of strategy and architecture, a band of people who design and supervise the agents, the domain experts who know what โcorrectโ means in a particular business, and an operations layer that now includes the agents themselves. The scarce role is no longer the engineer who writes the most code but the one who can make a system of agents reliable, which means Indiaโs long-standing shortage in the 8-to-15-year band, the people who turn intent into working systems, becomes the binding constraint rather than a background complaint.
The value moves to the work around the code: designing the evaluations that tell you whether an agent is doing its job, building the memory that lets it carry context, encoding a domain into something an agent can use, and building the feedback that turns failure into improvement. Indiaโs own hiring data already shows the move, with governance, evaluation and the running of agents in production now a large and growing share of agentic-AI demand. And it points to what will decide who scales: governance. Running a handful of agents is an experiment. Running tens of thousands of them, each able to act, is an institutional problem: deciding what may be delegated, who owns the risk, and how any of it is audited. A large share of corporate AI initiatives are already abandoned before they reach production, and the reason is rarely the model. It is the absence of the operating governance to run it safely at scale. Governance is not a compliance footnote. It is the scaling constraint, one more form of compounding capital that cannot be rented.
For a finance director the same picture appears as a change in the shape of the P&L. The cost of execution falls and becomes a variable, rented input; the investment that matters shifts to the orchestration, evaluation and governance around it. Operating leverage rises, because output is no longer tied to a payroll. A balance sheet records software, buildings and patents; it has no line for engineering culture, leadership density, operational knowledge or governance maturity, and yet those increasingly decide whether one centre out-produces another. The question worth a boardโs attention is not the cost of any line. It is where the resulting value collects: on the centreโs own balance sheet, as data and operational learning and governance capability it owns, or on a supplierโs. That is a capital-allocation question, and it has the same answer as everything else here. Own the things that compound; rent the things that do not.
Measuring yesterdayโs economy
This is where the gap between the old measure and the new reality becomes a public problem rather than a private one. More than 10 states now run dedicated policies to attract capability centres, and between them they target some 2,500 new centres, 15 lakh jobs and โน75,000 crore of investment, with the money almost everywhere attached to headcount: subsidies per employee, grants per fresher, support withdrawn if a centre falls below an employee count, incentives clawed back if it stops hiring. None of this was irrational. These incentives were built for an economy in which headcount was a reasonable proxy for capability, and for a long time it was. The agentic shift is precisely the thing that breaks the proxy. A centre can now grow more capable while employing fewer people, which means a state can succeed at its stated goal of more jobs while the capability it actually wanted accumulates somewhere its instruments cannot see.
The deeper trouble is that the incentives of the different players are diverging. A state rewards employment. A firm, behaving rationally in an agentic operating model, maximises capability and output without proportional hiring. Those two objectives used to point the same way and no longer do, so a centre can be penalised by the stateโs metric for doing exactly what the era rewards. This is a coordination problem, not a technology problem, and it has a coordination answer: measure and reward productive capacity rather than presence. A capability-centre policy fit for the agentic era would compete on value retained rather than jobs created โ on intellectual property and data held in the country, on advanced engineering and research, on the governance and knowledge assets that make execution good, on participation in the ecosystem that lets capability compound. The contest is shifting from the size of an incentive to the depth of a placeโs capability infrastructure: its universities and research institutions, its developer communities and open-source ecosystems, its shared digital infrastructure where appropriate, its open standards, its data-governance frameworks, its data-centre readiness and power resilience, and the density of its engineering leadership. These are slower to build than a payroll subsidy and harder to put in a press release, but they keep compounding long after an incentive has been spent. Beneath them sits a harder layer still: the rentable inputs a firm can buy on demand โ compute, power, inference capacity โ become, at national scale, a question of grid and data-centre capacity that a country cannot rent from somewhere else, which is the point at which this stops being centre policy and becomes industrial policy. The same economic logic applies at every scale. Organisations, ecosystems, states and nations all compete by accumulating capability faster than they consume it. The national framework for these centres, promised in last yearโs budget and still being finalised, is an unusually well-timed chance to write the new measure in before the old one sets in policy concrete. The three states already experimenting have shown what the new measure looks like. The challenge now is to translate these experiments into national policy.
The next advantage
India made itself indispensable, over 30 years, by executing the worldโs software well and cheaply, and it measured that success in the most honest currency it had, which was jobs. The agentic era does not threaten that success because the work now runs on rented intelligence. It changes what the success is made of. When execution becomes abundant, the things that stay scarce are the ones that compound and cannot be bought in a hurry: leadership, engineering culture, the capacity to absorb new technology and operate it, governance, institutions, and the learning a system accumulates about the world. A countryโs advantage comes to rest on how well it builds and keeps those things, and on whether they compound at home or somewhere else.
Counting heads was a good enough proxy for all of it while capability and labour moved together. They have come apart. The economic question is no longer where work is performed; it is where capability accumulates. The next generation of these centres will be judged not by the size of their workforce but by the capability they compound, and the ones that work it out first will stop counting heads โ not because people have stopped mattering, but because they will have found a truer measure of what they have become. The question that defines Indiaโs next decade follows from the same shift: no longer how much of the worldโs work it can perform, but how much of the worldโs capability it can compound at home.
Notes.
ยน Wesley M. Cohen and Daniel A. Levinthal, Absorptive Capacity: A New Perspective on Learning and Innovation, Administrative Science Quarterly, 1990.
ยฒ Stuart S. Rosenthal and William C. Strange, Evidence on the Nature and Sources of Agglomeration Economies, in Handbook of Regional and Urban Economics, 2004.
Headline GCC figures are drawn from the ZinnovโNASSCOM India GCC Landscape Report (FY2026) and represent industry estimates rather than audited statistics.
Unless otherwise stated, factual claims are supported by publicly available sources. The analytical framework, synthesis and conclusions are the authorโs own.