From User to Creator

IN 2022, WHEN ChatGPT burst onto the world stage, it did more than capture the public imagination—it signalled the dawn of a new industrial revolution. For the first time, artificial intelligence (AI) had crossed from research labs into everyday economic activity. However, American and Chinese public sector AI investments had already been underway for decades. India’s arrival is late, and its ambition superficial. While generative AI captured headlines, foundational investment decisions were made years earlier in Washington and Beijing—building compute infrastructure, investing in NLP models, national labs, specialised chips, and cross-sector AI pipelines. By the time the IndiaAI Mission was announced in 2024 with a ₹10,300 crore ($1.2 billion) commitment, the frontier had already moved. Two years behind in decision-making is a lifetime in technology cycles. Without indigenous compute, data sovereignty, or flagship public labs, India is at risk of becoming a permanent downstream consumer, not a global innovator. To make matters worse, mounting evidence suggests that even massive AI hype is converting into limited business impact. A recent MIT study found that 95 per cent of generative AI pilot projects fail to deliver measurable returns—only 5 per cent achieve meaningful revenue acceleration. This is a sobering warning: hype is outpacing real outcomes, and the AI bubble may already be showing cracks. India’s challenge is thus twofold: catch up in foundational capacity, and avoid replicating global mistakes of deploying AI without rigor, domain integration, or systemic feedback loops. UNDERSTANDING THE AI STACK AI leadership can be conceptualised as a three-tier stack: ◗ Level 1: Foundation Models & Compute Infrastructure: Large language models (LLMs), generative AI on the way to AGI, high-performance GPUs, data pipelines, model training platforms. ◗ Level 2: Enterprise / Vertical AI: Sector-specific models and systems tailored for finance, health, logistics, defence, agriculture. ◗ Level 3: Horizontal AI Applications: General-purpose AI tools (copilots, automation agents) deployed across workflows and sectors. India’s strategic bet must be clear: the country has not invested anything to be able to compete at Level 1. It can compete at Levels 2 and 3, but only with immediate headway on Level 1. Without that, Indian startups will forever depend on imported models and compute, weakening both competitiveness and sovereignty. LEVEL 1 The Race for LLMs and Compute The global AI arms race is defined by scale and infrastructure. The US marshals over $25 billion per year across DARPA, NSF, NIH, and defence labs. China channels $15-20 billion annually through its national AI plans and chip development programmes. In contrast, India’s cumulative public AI investment since 2018 remains below $1.5 billion—peanuts in a trillion-dollar race. The IndiaAI Mission pledges GPU development and AI labs, but has yet to articulate procurement, open-access compute policies, or data lake architectures. As a result, Indian researchers and startups today rely almost entirely on leased GPUs, cloud credits, and foreign models. Such dependence means every major AI project is anchored to external platforms and licensing, undermining autonomy. Worse, bureaucratic cycles drag every decision out over multiple budgets. By the time new compute infrastructure is approved, the global frontier may have leapt ahead twice over. In this era, delay is defeat. LEVEL 2 Enterprise & Vertical AI—India’s Opportunity India’s massive IT services sector—a $250 billion export engine—combined with a thriving startup ecosystem (140,000+ firms, with over 7,000 AI startups in Bengaluru alone) gives India a unique edge at Level 2. This base of domain knowledge, access to enterprise clients globally, and systems integration experience is rare among global middle-income economies. Indian firms such as Sarvam AI, BharathGen, Yotta, and the service arms of TCS, Infosys, and Wipro are already building domain-specific agents for Indian languages, health systems, and logistics. This base can be scaled if the government acts as a catalytic buyer: anchor contracts in defence, health, rural infrastructure, and state procurement. By injecting demand certainty, public-sector adoption can de-risk private investment and stimulate rapid scale. Without state-led commitment, the private sector will continue to work at modest margins, serving only incremental AI features rather than building world-class models. LEVEL 3 The Automation Dividend— Public and Private AI’s real economic payoff lies in automation. In the public sector, this means automated citizen services, predictive health diagnostics, tax systems, infrastructure monitoring, and grievance redress. To date, India has only deployed AI at pilot scale in a few pockets. The leap to systemic, national deployment is missing. In the private sector, the gains are even larger. According to McKinsey, AI may displace or transform 20-30 per cent of repetitive work in sectors like finance, logistics, retail, and BPO by 2030. The boost to productivity could generate $450-500 billion in incremental value annually—if India builds the underlying compute and data environment domestically. For India’s IT services firms, automation is existential: ‘services as software’ will replace much of the human-driven work they perform today. If Indian firms don’t lead in deploying and operating AI systems, they risk being disintermediated by foreign AI. Again, government support is crucial for India’s IT sector to dominate in the age of AI. TOKENISM TO TRANSFORMATION India’s future in AI depends on whether the government can effectively perform three essential functions: strategic investor and early customer, policy stabiliser, and ecosystem orchestrator. I State as Strategic Investor and Early Customer The government must move beyond announcing missions and actually invest in transformative scale. India’s deep tech ecosystem will not mature without the state underwriting both early-stage research and predictable demand. That means: ◗ Expanding the volume and tenure of public R&D grants and procurement budgets. ◗ Creating transparent and time-bound channels for pilots to transition into full-scale deployments. ◗ Developing military and civil AI test ranges with academia and startups, so new systems can be stress-tested locally. ◗ Shifting from a PSU-dominated model to one that co-creates innovation with private firms. Without this kind of market-making intervention, no private investor frontier technologies.II Providing Policy Continuity and Investor ConfidenceDeep tech innovation thrives where investors can see across decades. India must therefore replace ad-hoc policymaking with clear, long-term direction. Steps include: ◗ Guaranteeing regulatory predictability and protection against retrospective changes.◗ Creating sustained tax incentives and co-investment vehicles for deep tech and AI R&D. ◗ Publishing long-horizon national roadmaps—15 years or more—for AI, quantum, and semiconductors, complete with export and IP frameworks. Such predictability can unlock long-term domestic capital, allowing institutional investors and family offices to back deep tech with confidence.III Orchestrating Integrated Ecosystems between PSUs, Academia, and IndustryTo compete globally, India must nurture an ecosystem where universities, startups, and PSUs work together rather than in silos. The government can: ◗ Reorient PSUs from being slow procurement endpoints into fast-moving innovation partners. ◗ Set up collaborative testbeds and integration labs connecting startups, universities, and public missions. ◗ Support modular, dual-use architectures that serve both sovereign and civilian applications. ◗ Build NPCI-style public–private frameworks for deep tech domains like AI, quantum, and semiconductors.These three roles—investor, stabiliser, and orchestrator—must converge into a national AI strategy that institutionalises innovation capacity. A ₹50,000 CRORE SOVEREIGN-SCALE DEEP TECH & COMPUTE FUND As a start, India must immediately launch a 5₹0,000 crore ($6 billion) sovereign-scale Deep Tech & Compute Fund, deployed over five years. The key features are: ◗ Professional governance: Operated as a fund-of-funds with private-sector co-investment and outcome-driven mandates. ◗ Four strategic pillars:1. Compute Infrastructure: an open India GPU cloud accessible to startups, academia, and enterprises.2. Foundation Models: co-funding open-source, multilingual LLMs trained on Indian datasets and languages.3. AI Talent & Research Hubs: 50 mission-focused Centres of Excellence at Indian institutes and research labs.4. Public Procurement as Demand Engine: government as anchor customer for Indian AI systems in health, education, defence, urban services. ◗Mission-mode operations: Adopt DARPA-style grant mechanisms—fast, milestone-linked disbursements, flexible experimentation, limited bureaucratic constraint. ◗Signalling effect: At <0.2 per cent of GDP, the fund would signal serious commitment and could attract 5–10 times private capital. ACT NOW OR MISS THE CENTURY India missed the semiconductor revolution. It cannot afford to miss AI. The global foundation model arms race is consolidating power. The AI ‘stack’ is being hardened, and compute and model systems are becoming de facto national infrastructure. Without sovereignty over that foundation, India will be confined to peripheral roles in the economy of this century. India has talent, scale, and ambition— but lacks the necessary infrastructure and strategic capital to convert these into a global AI capability. If the government acts decisively—establishing a `50,000 crore Deep Tech & Compute Fund, building sovereign GPU platforms, anchoring public procurement, and enabling mission-mode execution—India can still compete in enterprise and horizontal AI. It can capture the automation dividend and position itself to participate meaningfully at Level 1 by 2035. Failing that, India will remain the world’s largest AI user—not an AI creator—and yet another generation of technological leadership will slip away.

From User to Creator

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