India has long been seen as a hub for IT services, and it is now taking confident, accelerated steps in AI as well. Over the past decade, the country has built unicorns at scale, created world-class digital infrastructure, and continues to produce some of the strongest engineering talent globally. UPI alone now processes over 250 billion transactions annually, and with more than 950 million internet users, it feels like all the pieces are finally in place.
But when you step away from this build-up and look at the reality, the contrast becomes clear. Google still controls over 96% of the search market in India, and no credible challenger has emerged from within the country.
This gap keeps bringing the same question back, almost in the same words each time, that India should have its own Google by now. Over time, it begins to feel less like a delay and more like something deeper, a question of design, because the way a system is built ultimately shapes the outcomes it produces.
Seen this way, what the system ultimately requires is patient capital that is willing to stay with long-term, research-led work, even when outcomes are uncertain and commercial value is not immediately visible, because most foundational breakthroughs do not begin as obvious businesses but gradually evolve into systems the ecosystem comes to rely on.
Which leads to a more useful question, not why India has not built its own Google yet, but what needs to change in the system for it to become possible. And to answer that, we first need to understand what Google actually was in its earliest form.
Google Was Never Just a Search Engine
To understand the gap, the first step is to reframe what Google actually was.
Google is typically understood as a search engine, a product that indexes web pages and returns relevant links. But that is what Google became, not what it was when it started.
It did not begin as a startup trying to capture market share, but rather as a research problem. Larry Page and Sergey Brin were trying to figure out how to rank information on the internet so that it reflected relevance. PageRank was not built to attract users, but to solve how to rank information meaningfully.
For a long time, Google functioned more like a research lab than a business. There was no immediate pressure to monetise. No expectation to define a clear go-to-market strategy in the early years. The work was driven by curiosity and technical depth, not immediate commercial validation. Only later did it become a business, once the system’s utility became undeniable.
This distinction matters because building Google was never about creating a better product, but about backing long-term, uncertain research until it compounds into something indispensable.
And this immediately shows that India needs more systems that allow important work to exist even before it becomes commercially obvious.
India’s Superpower and Its Ceiling
India, in contrast, has built a different kind of strength. It has become exceptionally good at building on top of existing infrastructure. Whether it is global platforms like Android and AWS or domestic systems like Aadhaar, UPI, and ONDC, Indian companies have consistently demonstrated world-class execution at the application layer.
This is not convenience, but a rational choice, where, as a billion users come online, the fastest way to create value lies in building services that solve immediate problems across payments, commerce, logistics, mobility, and healthcare. The focus naturally shifts toward usability, distribution, and scale, a playbook that India has executed with remarkable efficiency.
However, this very strength creates a ceiling, as companies like Google are not built on infrastructure. They become the infrastructure and define the layer on which everything else operates.
When an ecosystem is optimised to build on top of rails, very few participants are incentivised to build the rails themselves. The result is predictable. India becomes the best in the world at applications. But foundational layers continue to be imported.
Therefore, India must move from excelling at building on existing infrastructure to seriously investing in building infrastructure itself. However, early efforts are emerging, with companies like Sarvam AI working on foundational language models and others exploring semiconductor design and AI infrastructure. But these are still exceptions, not the default.
The Capital Problem India Has Started to Solve
Most funds operate on 8 to 10-year cycles. Limited partners expect returns within that period. This creates a natural bias toward companies that can demonstrate product-market fit, revenue growth, and scalability within a few years. In such companies, metrics become the language of validation, but foundational technology companies do not function like this.
In their initial years, they often look uncertain as there is no clear market, no immediate revenue, and no defined go-to-market strategy. Progress is measured in research milestones, not growth charts. From a traditional venture lens, they can appear as high-risk, low-visibility bets, even though they are often building the very foundations of future industries.
As a result, companies building the next generation of AI, new semiconductors, or core protocols often fall outside the typical funding lens at the very moment they need patient capital the most. This is not a flaw in the individuals making investment decisions. It is a structural outcome of how venture capital is designed.
In ecosystems like the United States, this gap is partially bridged by long-term institutional capital, government research funding, and corporate R&D labs that can absorb ambiguity. In India, that bridge is still underdeveloped, but it is steadily taking shape, so capital has historically flowed toward what is visible, predictable, and scalable, while new pathways are beginning to emerge for what is uncertain, foundational, and long-term.
As a result, India needs a different kind of capital, one that is comfortable with long research cycles, low visibility, and delayed outcomes. Encouragingly, early signs of this shift are already visible through initiatives like the Government of India’s Fund of Funds for Startups 2.0 (FFS), under which a ₹10,000 crore corpus was approved in February 2026 to mobilise venture capital for India’s startup ecosystem. The aim is to support innovation-led, technology-driven entrepreneurship, reduce early-stage funding gaps, and strengthen deep-tech sectors such as AI, robotics, biotech, clean-tech, and advanced manufacturing. This is further complemented by the ₹1 lakh crore RDI Fund launched in 2025 to accelerate private sector R&D and innovation in strategic and sunrise sectors.
This shift is already underway, with Indian investors increasingly recognising that deep tech is built not on short-term enthusiasm. However, in the long run, they will require continued support to stay the course with conviction, patience, and a longer view of value creation.
The Talent Paradox
India produces a large share of the technical talent required to build companies like Google, with Indian engineers and researchers contributing to some of the most advanced systems globally.
Indian professionals now hold about 10–13% of leadership positions within global capability centres (GCCs), marking India’s growing impact in international corporate frameworks, according to a Moneycontrol report.
But that talent is not building those systems in India. In fact, their pathway is clear: undergraduate education in India, often through institutions like the IITs, followed by higher studies or research at places like Stanford and other leading US universities, eventually leading to integration into well-funded labs or companies. This is a rational decision driven by opportunity, as environments that support long-term research, access to computing, mentorship, and funding are more developed elsewhere.
Within India, what largely remains is execution talent, such as engineers who are exceptional at building, scaling, and optimising products. However, research-oriented talent largely leaves the country, and it is they who invent new systems, models, and paradigms.
But these roles are not interchangeable, as execution talent builds companies, while research talent builds the underlying systems that those companies depend on. A company like Google requires a dense concentration of research-oriented talent operating in an environment that supports long-term exploration. Today, that environment exists more strongly outside India, and as a result, so does the talent.
Hence, India will not get its own Google merely by producing more engineers. Instead, it must become a place where research talent can stay, do ambitious work, access computing and mentorship, and build at the frontier from within the country. That will require stronger research institutions, easier access to computing, deeper industry-academia collaboration, and sustained funding for long-term research.
The Founder Archetype Our Ecosystem Has Yet to Support
The Indian startup ecosystem has a clear mental model of what a successful founder looks like. Someone who can understand a problem clearly, articulate a market opportunity, build a go-to-market strategy early, and show signs of traction within a short period, and scale quickly.
This model serves most companies but fails at foundational ones.
The founder of a deep technology system, or something like Google, often operates differently and may not have a clear market in the early years. They might spend years refining a core algorithm or model without being able to clearly articulate its commercial application. Their progress is non-linear, and outcomes are uncertain.
Often, this approach is mistaken for a lack of clarity, but this is simply the nature of foundational work.
Our ecosystem is not short of such founders. What it lacks is a framework for identifying and supporting them. Without that framework, these founders either reshape themselves to fit expectations or move to environments where their approach is better understood.
Therefore, India will need a different founder lens. Founders of foundational companies often seem unclear or unready for venture investment at the start.
What Is Actually Shifting in India’s Ecosystem
While the structural challenges remain, there are early yet clear shifts in how parts of India’s ecosystem are approaching deep tech and foundational innovation.
One of the most visible changes is the steady expansion of India’s deep tech landscape, where, according to NASSCOM-Zinnov, over 3,600 deep tech startups are working across areas such as artificial intelligence, space technology, and advanced computing, ranking India sixth among the top nine deep tech ecosystems globally.
At the capital level, a new set of early-stage funds is emerging, willing to make longer-term, research-driven bets, though this remains limited compared to traditional venture funding focused on faster returns.
On the talent side, there is a gradual reversal, with some founders and researchers with experience in global AI labs and frontier technology companies returning to build from India, bringing with them exposure to research-led environments and long-horizon thinking.
At the company level, early examples such as Netradyne, which is building AI-led systems for fleet safety, and Sarvam AI, which is working on Indic language models, reflect an emerging interest in creating foundational technologies rather than purely application-layer businesses.
In parallel, India’s digital public infrastructure continues to act as a unique base layer, offering interoperable systems that can support new forms of innovation, even though it has so far been leveraged more for applications than for building core technological primitives.
Taken together, these shifts show that parts of the ecosystem are slowly moving beyond execution-led innovation and beginning to engage with deeper, more foundational problems, although this is still early and far from becoming the default approach.
What Is Still Missing in the Ecosystem
While these shifts are real, they have not yet translated into a bigger structural change in how the ecosystem operates. At a fundamental level, the design remains largely the same, where venture capital continues to prioritise predictable growth and near-term validation, research institutions still operate with relatively limited funding and global integration, and top research talent continues to move to environments that are better suited for long-term, frontier work.
At its core, the system still struggles with ambiguity, even though building foundational companies almost always requires operating in conditions where outcomes are unclear, progress is hard to measure, and failure is a real possibility. This creates a natural mismatch because the kind of work that eventually leads to companies like Google often looks uncertain and unstructured in its early years, making it difficult to support within a system built around clarity and speed.
Until capital, talent, and institutions come together to support that uncertainty, the likelihood of building a Google-like company will remain limited.
To conclude, the path forward is not about waiting for more time, more capital, or another wave of optimism, but about gradually reshaping the ecosystem itself to support a different kind of work, one that is willing to fund unclear problems, retain research talent, recognise non-linear founders, and stay committed to foundational ideas long before they become commercially obvious.
At its core, this requires a shift in the ecosystem, where investors are aligned with patient capital and are willing to stay invested through long research cycles without expecting immediate returns. It will also require strong government support through enabling policies such as the ₹1 lakh crore RDI Fund, initiatives like the ₹10,000 crore Fund of Funds for Startups, and other similar initiatives, which together signal a growing intent to bridge early-stage funding gaps and strengthen India’s deep-tech capabilities over the long term.
India has the talent, scale, and technical capability, and is steadily building alignment around long-horizon and highly uncertain work. As this shift gathers momentum, the question will no longer be whether India can build its own Google, but what else it can build once it learns how.
