A ledger, simply put, is a record of things that exist. Things that accumulate meaning over time where the value lies not in any single entry but in the pattern that emerges when enough entries are read together. Every accountant knows this. Every historian knows it too. The chronicle of a single day tells you almost nothing. The chronicle of a decade tells you who a person, an institution, or an economy actually is.
Second HQ is being built as a ledger in this sense. The goal is not to index every GCC in India (though eventually it will hold profiles of most of them). The goal is to document them in a way that reveals the patterns underneath: what kinds of organizations India is attracting, what work is actually being done inside them, how the ecosystem is maturing, and what all of this says about India's evolving role in the global economy.
The number 2,100 is the horizon. Getting there requires logic. This article is about that logic.
Why Batches of 30
The decision to document GCCs in batches of thirty is not arbitrary. It reflects a specific philosophy about how knowledge compounds.
When you try to understand a complex ecosystem by documenting everything at once, you produce an encyclopedia. Encyclopedias are useful for lookup, but they are terrible for thinking. They give you facts without revealing relationships, data without argument, coverage without comprehension. The result is an archive that nobody actually reads, because there is no narrative thread connecting one entry to the next.
Batches solve this problem by forcing curation. Each batch of thirty must answer an implicit question: what does this particular set of organizations, when read together, reveal about the ecosystem (that reading them individually would not)? The batch is a lens. It forces you to choose which organizations illuminate each other; which stories, placed in proximity, begin to rhyme in instructive ways.
There is also an narrative argument for small batches. When you are documenting complex institutions, you learn while you document. The first thirty profiles teach you things about GCCs that you did not know before you started. The thirty after that, you approach differently, with better questions, sharper instincts, a more refined vocabulary. The batch structure is a built-in feedback loop. It forces reflection before expansion.
Nasscom and Zinnov report that India currently has over 2100 GCCs. Seventy batches of thirty, roughly.
The logic described here is what makes it feasible: each batch is a discrete, coherent unit with its own argument, rather than a random slice of a database.
THE ARCHITECTURE OF THE LEDGER: BATCH BY BATCH
Batch 1-30: The Archetypes
The first thirty GCCs in Second HQ are chosen to establish the full range of what a GCC can be. Not the biggest names, necessarily, though several will appear. The criterion is legibility: these thirty organizations should collectively demonstrate that the GCC ecosystem is not one thing but many things, and that understanding it requires holding multiple models simultaneously.
Within this first batch, three archetypes anchor the structure.
The first is the scale-first center: organizations where size is the story, where headcount runs into the thousands, where the sheer volume of work being done in India is the defining characteristic. These centers often have the longest India histories; some started in the early 2000s and have grown into genuinely parallel headquarters. They are the foundations of the ecosystem narrative. Some are as young as an year old.
The second archetype is the product-first center: organizations where India owns a specific product or product family end-to-end, from design through engineering through deployment. These are the centers that most clearly embody the shift from execution to ownership. The work being done is not in support of something else but the thing itself. These centers tend to attract the most ambitious engineering talent primarily because the ownership is real.
The third archetype is the research-and-development center: organizations whose India presence is organized around scientific and technical research (rather than product delivery). These include the engineering labs of industrial conglomerates, the AI research arms of technology companies, and the analytics centers of financial services firms. They are the quiet end of the spectrum; sometimes poorly understood even within their own parent organizations. But they represent some of the most consequential work in the ecosystem.
Reading these three archetypes together in the first batch gives the ledger a conceptual skeleton. Everything that follows is a variation on, or complication of, these founding forms.
Batch 31-60: Filling the Matrix
The second batch of thirty expands the frame. If the first batch established archetypes (and outlined the broad contours), the second fills in the dimensions.
Industry becomes the primary lens here. The first batch will inevitably over-represent technology companies, because technology companies were the earliest and most aggressive adopters of the GCC model. The second batch corrects for this by deliberately including the sectors that came later to the model but are now growing fastest: BFSI, healthcare and life sciences, industrial and manufacturing, consumer goods, logistics and supply chain. Each of these sectors has a distinct GCC character. BFSI centers, for example, tend to be unusually deep in risk analytics and regulatory technology; they operate under compliance constraints that shape their culture and their hiring patterns in ways that are quite different from a software product company. Industrial GCCs tend to have large engineering research and development footprints; they are often less legible to the Indian talent market because their parent companies are not brands that young engineers grow up aspiring to join, but the work inside can be genuinely sophisticated.
Geography becomes important in this batch as well. The first thirty will be skewed toward Bengaluru and Hyderabad, because those cities dominate the ecosystem. The second thirty begins to introduce the rest of the map: Pune, which has grown from around 210 GCCs in 2019 to over 360 in 2025, and which is particularly strong in automotive engineering, embedded systems, and enterprise software; Chennai, which has a long history in engineering and is building strength in healthcare and renewable energy; NCR, which is home to large-scale BFSI and professional services centers; Mumbai, which is India's financial capital and the natural home of organizations with deep regulatory exposure.
Maturity levels also become visible across this second batch. Some organizations in their fourth or fifth year of India operations are still in what might be called the establishment phase: building teams, stabilizing processes, trying to figure out what India can actually do for them beyond what they originally planned. Others, older or more intentional, have reached a genuine steady state, where the India charter is stable, the leadership pipeline is local, and the center functions as a genuine peer to other global units rather than as a dependent subsidiary. Understanding where an organization sits on this maturity curve is important information for talent and observers alike.
Batch 61-150: Surfacing the Patterns
The third and fourth tranches (building toward the 150 mark) are where the ledger begins to do something qualitatively different. At sixty profiles, you have data. At one hundred and fifty, you have discernible patterns.
Some of those patterns will be expected. Technology and IT-led GCCs will dominate, as they do now, accounting for roughly forty percent of the ecosystem. BFSI will form a strong secondary layer. Engineering research and development will grow its share as organizations move from cost arbitrage to capability arbitrage.
Others will be more surprising. The mid-market story, for instance, is one that the broad narrative has only recently begun to register. Over 480 GCCs in India (~ 25 percent of the total) are now operated by mid-sized enterprises (and not Fortune 500 giants). These are companies that most people in the Indian talent market have never heard of: specialized manufacturers, regional financial services firms, healthcare companies operating in niche verticals, SaaS businesses with a few hundred million dollars in revenue. They are setting up GCCs in India because the model has become legible enough, and the supporting ecosystem (real estate, talent, legal, HR infrastructure) mature enough, that a company without a dedicated global operations team can now execute it.
This batch will also surface the Tier 2 city story in its full complexity. Ahmedabad's GIFT City is developing a distinct GCC character driven by financial services regulation. Coimbatore is attracting manufacturing and industrial centers drawn by its engineering talent and its proximity to Tamil Nadu's industrial belt. Bhubaneswar and Thiruvananthapuram are early in their GCC journeys but have deliberate state-level strategies behind them. These cities are not Bengaluru / Hyderabad, and they will not be; but they represent a real diversification of the ecosystem that matters to anyone trying to understand where Indian GCCs are going.
Batch 151-300: The Long Tail of Consequence
By the time the ledger reaches the mid-hundreds, the profiling effort is no longer primarily about discovery. It is about completeness and about the kinds of insights that only become visible at scale.
The organizations in this range tend to be smaller, newer, or more specialized. Some are mid-market companies in their first or second year of India operations. Some are large companies that have historically kept a low India profile. Some are in sectors that are genuinely new to the GCC model: climate technology, defense technology, space, biotechnology. These centers are individually less significant than the giants profiled in the first batch, but collectively they tell a story about where the ecosystem is going, and where India's capabilities are being extended into domains that would not have been conceivable a decade ago.
The AI story becomes particularly important here. A Nasscom-Zinnov report notes that India now has over 120,000 AI / ML professionals and more than 185 dedicated AI and ML centers of excellence within the GCC ecosystem. By the time the ledger reaches this range, the variation in AI maturity across organizations will have become one of the most legible dimensions of the ecosystem; especially as a spectrum that reveals the different ways organizations are thinking about what AI means for their India operations.
Batch 301-2100: The Steady State
The final two-thirds of the eventual ledger will be, in many ways, the most important. This is where the ecosystem's true diversity lives: the organizations that are not obvious profiles, that require the most reporting, and that collectively constitute the bulk of what India's GCC economy actually is.
Some of these organizations will be companies entering India for the first time between now and 2030. Based on current trajectory, India is adding around 100 - 250 new GCCs per year. Each of those organizations is a new entity in the ledger, with its own charter, its own logic, its own contribution to the ecosystem's evolution.
Others will be existing centers that have been understudied or overlooked by the ecosystem's existing media. The GCC in a smaller city, run by a company most people have never heard of, doing work that is genuinely consequential to a narrow industry.
The path to 2,100 is therefore a long arc of accumulation (and not a sprint), where each batch teaches the ledger something the previous batch did not know.
What Each Batch Asks
Every batch of thirty is, at its heart, an argument. A structured claim about what this particular group of organizations, read together, reveals.
The argument might be about a sector: why financial services GCCs in India have developed a distinct operational culture around risk and compliance that shapes everything from their org structures to their talent philosophies. It might be about a geography: what the rise of Pune as a GCC city says about the relationship between manufacturing legacy, engineering talent, and global enterprise strategy. It might be about a function: what happens when organizations move AI to the center of their India operations.
These arguments are the difference between documentation and understanding.
A Note on What Gets Left Out
Any curation involves exclusion, and intellectual honesty requires naming what this one excludes.
Second HQ profiles centers (and not necessarily companies in the strictest sense). A company with three India offices — a large technology center in Bengaluru, a smaller analytics center in Hyderabad, and an early-stage operation in Pune — will appear in the ledger, but its profile will be organized around understanding those centers as institutions in their own right, not as a consolidated corporate entity. This is a deliberate choice. The Indian GCC is often a more revealing object of study than the global parent, because it is where the parent's real-world commitments are made visible.
The ledger also does not rank. This is worth emphasizing, because ranking is the default grammar of most ecosystem publications, and the absence of it will feel strange. There is no list of the top ten GCCs, no best employer designation, no innovation index. Rankings are seductive but they are also reductive: they impose a single dimension of comparison on organizations that are genuinely different in kind and degree. The ledger offers something more difficult and more useful: a portrait of each organization that tries to capture what it actually is, in all its specificity (instead of trying to probe where it falls on someone else's scale).
The Compounding Logic
Jorge Luis Borges wrote a story about a map so detailed that it was the exact size of the territory it represented. It was, of course, useless; perfect in its fidelity but near-impossible to use. There is a lesson in this: a representation that tries to capture everything captures nothing.
This ledger is not trying to be the territory. It is trying to build a representation that is detailed enough to be useful and structured enough to reveal the patterns that matter. At thirty profiles, it is a starting point. At three hundred, it is a reference. At two thousand, it is something closer to institutional memory (and hopefully: a running record of what India's GCC ecosystem has been, is now, and is becoming).
That is what the path to 2,100 is about. A commitment to showing up, doing the work, and letting the picture emerge over time.
This is the second article in a three-part canon. The first explains why Second HQ exists. The third explains why cities matter as much as the companies that occupy them.