India’s Pie in AI Global Bazaar

As Artificial Intelligence (AI) reshapes global power and production, India is seeking a meaningful share of the emerging AI economy. Beyond declarations and investment headlines, the real contest lies in compute, capital and models. The challenge is not participation, but ownership and lasting strategic leverage

By Geeta Singh

SNAPSHOTS

• Amul’s Sarlaben app reflects indian strength: rapid diffusion. AI tools reached millions of dairy farmers within mere weeks
• Model development requires massive capital and energy. Venture ecosystems must provide sustained financing for high-gestation and intensive research
• India’s vast contextual data creates a unique edge in developing AI for low-resource languages and diverse cultural environments
• Though excelling in deployment, high-value segments remain concentrated abroad. India’s presence in these critical layers is currently expanding

In the vast halls of Bharat Mandapam in New Delhi, under the India spoke the language of artificial intelligence sovereignty. The India AI Impact Summit was staged as a declaration that the world’s largest democracy would not merely consume artificial intelligence but shape its norms, markets and moral vocabulary. The optics were confident. The applause lines emphasised inclusion, public good and leadership from the Global South.

But in the global AI bazaar, rhetoric does not determine market share. Infrastructure does. Capital does. Ownership does. If our country seeks a meaningful slice of the expanding AI economy, the question is not whether it can host summits or attract investment announcements. The question is whether it can secure its position across the deeper layers of the value chain: compute, chips, models, platforms and deployment at scale. The new Delhi summit offered a revealing snapshot of where India stands in that hierarchy.

FRAMING AI AS DEVELOPMENTAL INFRASTRUCTURE

For five days in February, policymakers, technology leaders and strategists gathered in New Delhi for what was described as the first global AI summit hosted in the Global South. The language was expansive: AI for Humanity, welfare for all, measurable impact across governance and development.

The organising framework rested on three Sutras: People, Planet and Progress. The emphasis was on human-centric safeguards, environmentally responsible innovation and inclusive economic growth. Artificial intelligence was framed not as a narrow commercial race but as the next layer of public infrastructure.

In diplomatic terms, India was staking a normative claim. AI, it argued, must reflect democratic accountability and developmental priorities rather than being shaped solely by private capital or geopolitical rivalry.

This framing matters. norm setting influences regulation, investment flows and international partnerships. Yet global technology markets are not restructured by moral vocabulary alone. They are reshaped by production capacity, supply chain leverage and technological control.

The summit’s deeper significance lay in how India is attempting to combine developmental ambition with strategic positioning.

STRATEGIC ALIGNMENT

The summit’s defining geopolitical moment came when India signed the Pax Silica Declaration, joining a United States-led coalition designed to secure what policymakers describe as the global silicon stack. The initiative seeks to coordinate trusted supply chains across semiconductors, advanced AI hardware, critical minerals, energy systems and digital infrastructure.

For India, participation reflects recognition that technological leadership depends on resilient ecosystems rather than isolated capability. Semiconductor fabrication requires access to equipment, materials, intellectual property and stable markets. AI model training requires high performance compute, energy reliability and advanced networking infrastructure.

India holds a strong position in talent, services and large-scale deployment. The next frontier lies in foundational models, hyperscale compute and global platform revenues. Moving from integration to ownership will determine the size of its slice in the AI global bazaar

India’s elevation to co-founder status signals confidence in its manufacturing ambitions and engineering base. Multiple semiconductor facilities are under development across the country. Indian engineers already contribute to advanced chip design within global value chains. Alignment with technology partners in East Asia, Europe and North America strengthens India’s integration into a trusted hardware ecosystem.

Strategic coordination also enhances bargaining power. In an era where supply chains can be weaponised and critical minerals can become leverage points, coalition based production networks provide stability. For India, this alignment supports its Semiconductor Mission and reduces vulnerability to external shocks.

Yet integration into a coalition is not the same as commanding the higher reaches of the value chain. Fabrication capacity must scale. Domestic capital must deepen. Indigenous research must translate into proprietary intellectual property. The path from participation to leadership requires sustained institutional capacity and capital discipline.

FROM INNOVATION TO IMPLEMENTATION

If Pax Silica represented the summit’s strategic layer, the domestic agenda unfolded along another axis: diffusion.

Infosys co-founder Nandan Nilekani framed the current AI phase as comparable to the transformative moment that followed the launch of the Unified Payments Interface (UPI) in 2016. His argument was not about model size or parameter counts. It was about deployment at population scale.

Recalling a conversation with the Prime Minister earlier this year, Nilekani described a simple challenge: can artificial intelligence help farmers detect illness in cattle? Within weeks, an AI assistant integrated into the Amul cooperative network was deployed. Known as Sarlaben, the system provides voice based guidance in regional languages to dairy farmers on cattle health, pregnancy cycles and milk production.

Amul represents one of the largest cooperative ecosystems in the world, linking millions of farmers and managing billions of milk transactions annually. The rapid rollout of Sarlaben demonstrated how AI can be embedded into an existing institutional network rather than remaining confined to laboratories or urban enterprises.

The symbolism is significant. Just as UPI moved digital payments from elite adoption to everyday transactions, AI is being framed as the next layer of digital public infrastructure, extending into agriculture, health and local governance.

This diffusion model reflects a distinctive Indian advantage. Frontier model innovation may be concentrated in a few global hubs, but large scale deployment across heterogeneous populations requires coordination between government, enterprises and community institutions. India has demonstrated this capability through Aadhaar, UPI and other digital public infrastructure systems.

However, diffusion is not automatic. It requires enterprise readiness and institutional adaptation.

THE SCALE CHALLENGE

Global AI investment has expanded dramatically over the past two years, with funding rising sharply and enterprises allocating larger portions of their technology budgets to AI integration. Model capabilities have also scaled rapidly, with parameter counts expanding and new agent frameworks emerging.

Yet enterprise systems lag behind frontier innovation. A significant share of corporate technology budgets continues to be absorbed by maintaining legacy infrastructure. Greenfield deployments show meaningful productivity gains, but large scale adoption within older systems remains limited.

Nilekani characterised this challenge as a deployment gap. Foundation model innovation may advance at remarkable speed, but diffusion requires institutional redesign, data strategy, workforce training and governance guardrails.

He warned that AI could displace tens of millions of jobs globally even as it generates new categories of employment. Roles such as front-end development, quality assurance testing and routine IT support may shrink, while demand rises for AI engineers, data specialists, orchestration architects and AI governance professionals.

For India, this transition carries both opportunity and responsibility. A young workforce and strong engineering base provide advantages. But reskilling at scale, modernising enterprise infrastructure and preventing social backlash will require policy coordination and private sector investment. Diffusion without preparation risks uneven outcomes. Diffusion with institutional support can expand productivity and inclusion.

CAPABILITY & TEST OF AUTHENTICITY

Rapid technological ambition inevitably invites scrutiny. During the summit, a Galgotias University pavilion showcased a quadruped robot presented as an example of indigenous innovation. Technology observers quickly identified the device as a commercially available Chinese model. The organisers intervened and the exhibit was withdrawn. The episode became a brief controversy, but its broader significance lies elsewhere.

In a competitive global AI environment, credibility is a strategic asset. Symbolic displays cannot substitute for demonstrable capability. Institutional discipline, transparency and authenticity determine investor confidence and international partnerships.

India occupies a distinctive position between advanced economies and emerging markets, possesses deep engineering talent, strong digital public infrastructure and expanding startup activity, and has high skill penetration in artificial intelligence

The same standard applies to startups, academic institutions and public agencies. India’s AI ecosystem is expanding rapidly, with generative AI ventures multiplying and sectoral applications emerging in healthcare, agriculture and finance. Sustaining this growth requires rigorous research, responsible communication and measurable performance. Reputational capital compounds alongside technological capital. Both must be protected.

BUILDING INDIGENOUS MODELS

India’s long term share of the global AI bazaar will depend on its ability to move beyond application layer integration toward ownership of foundational capabilities.

In early 2025, the government selected Sarvam AI to develop a large scale indigenous foundation model optimised for Indian languages and regulatory requirements. The model, trained domestically using thousands of high performance GPUs, is designed for population scale deployment.

Subsequently, additional teams including Soket AI Labs, Gan.ai and Gnani.ai were supported to develop specialised language and speech technologies. Backed by a substantial innovation fund and proposals from academia and industry, this multi team strategy reflects ambition to cultivate indigenous intellectual property.

Domestic model development carries strategic advantages. It reduces dependence on foreign proprietary systems, aligns with local data governance norms and enables fine tuning for linguistic diversity. It also strengthens bargaining power in negotiations with global cloud providers and technology firms.

Yet model development is capital intensive. High performance compute clusters require sustained energy supply and financing. Venture capital ecosystems must support long gestation research. Public and private investment must converge around long term capability building.

Model ownership without sovereign compute is incomplete. Compute without deep capital is vulnerable. In the AI economy, infrastructure, finance and intellectual property form an indivisible triangle.

INDIA’S POSITION IN GLOBAL HIERARCHY

India occupies a distinctive position between advanced economies and emerging markets. It possesses deep engineering talent, strong digital public infrastructure and expanding startup activity. Skill penetration in artificial intelligence is high relative to global averages. Workplace adoption rates are reported to be higher than global averages. At the same time, global foundational model development and hyperscale cloud infrastructure remain concentrated in a small group of firms. Semiconductor manufacturing is capital intensive and globally competitive. Pricing power in AI platforms is shaped by access to compute and proprietary research.

India’s opportunity lies in combining three strengths: strategic partnerships that secure supply chains, domestic capability that builds foundational infrastructure, and large scale diff usion that embeds AI into everyday economic activity.

The summit illustrated progress across each dimension. Strategic alignment through Pax Silica strengthens supply resilience. Diffusion initiatives such as Sarlaben demonstrate application at scale. Indigenous model development signals intent to own intellectual property.

The remaining task is coordination. Industrial policy, capital formation, energy planning, talent development and regulatory clarity must operate in concert.

WHERE OUR COUNTRY ALREADY HOLDS A SLICE

India’s position in the AI global bazaar is not aspirational alone. It is grounded in tangible strengths across several layers of the ecosystem.

First, talent and services. Our country remains one of the world’s largest suppliers of AI engineering and data expertise. Major IT services firms and a fast-expanding startup ecosystem provide implementation, integration and localisation capabilities that global technology companies rely upon for deployment at scale. In many enterprise transformations, Indian engineers form the operational backbone. Second, language and contextual data advantage. With hundreds of languages and decades of experience in localisation, India possesses unique user-context datasets and linguistic diversity. This creates an edge in developing AI systems suited for low-resource languages and culturally varied environments. For consumer applications and public-interest models, such diversity is not a constraint but a competitive advantage.

Third, public-sector demand. State and central governments are among the largest buyers of digital solutions in the world. Challenge grants, AI innovation tracks and sector-specific pilots showcased at the summit indicate accelerating adoption in agriculture, health and governance. Large-scale institutional demand enables faster diffusion.

Finally, cost-efficient deployment ecosystems. India is increasingly attractive for distributed edge computing and enterprise cloud adoption. Partnerships with global cloud providers and domestic infrastructure expansion support scalable deployments. These strengths provide a foundation. But commanding a larger share of the AI economy requires moving up the value chain.

THE LAYERS STILL TO BE SECURED

While India excels in deployment and services, several high-value segments remain concentrated elsewhere. Foundational model innovation and advanced chip design continue to be dominated by a small group of firms in the United States, East Asia and parts of Europe. Control over architecture, training at scale and semiconductor fabrication captures disproportionate economic returns and strategic leverage. India’s presence in these layers is expanding but still limited.

In a competitive ai landscape, credibility is strategic capital. Symbolism cannot replace capability. Discipline, transparency and authenticity shape investor trust and global partnerships across startups, academia and public institutions

High-end compute infrastructure also presents a constraint. Although cloud partnerships are growing, vertically integrated hyperscale data centres, large GPU clusters and high-throughput domestic training environments remain under development. Without sufficient compute density, training frontier models domestically becomes difficult.

Productization is another frontier. Indian firms have excelled in services and specialised applications, but recurring revenue global platforms, whether SaaS ecosystems, AI APIs or large-scale marketplaces, remain fewer in number. Sustained value capture requires building such platforms rather than integrating those built elsewhere.

Finally, the research-to-commercialization pipeline needs strengthening. Academic and corporate research output is rising, yet converting breakthroughs into defensible intellectual property and scalable startups remains an area for improvement compared to leading AI economies.

The path forward therefore lies in reinforcing strengths while deliberately climbing toward these higher-value layers. Ownership of models, control of compute and depth of capital will ultimately determine how large a slice of the AI bazaar our country commands. India’s pie in the AI global bazaar will ultimately be measured not by summit visibility or startup velocity alone, but by durable control over infrastructure, intellectual property and capital formation.

Our country has demonstrated its ability to scale digital public systems, mobilise talent and coordinate across institutions. The next phase demands deeper compute capacity, sustained investment and platform-level ownership.

Artificial intelligence is reorganising economic power. Nations that build, finance and govern its foundational layers will shape the next era of growth. The summit signalled ambition. The task now is execution. The size of our country’s slice will depend on how decisively it converts capability into control.