Why India’s AI Startups Are Moving to the US—and How Europe Could Change Everything

In recent months, more than 100 Indian AI startup founders have relocated from India to the United States. The primary drivers behind this shift include easier access to capital, availability of specialized talent, and the presence of a mature, large-scale AI market. Even high-profile startups labeled as India’s fastest-growing AI companies are no exception.

 Emergent, which recently secured $70 million from SoftBank at a valuation of $300 million, operates out of San Francisco rather than India. Atomic Work raised over $40 million and subsequently moved its headquarters to San Francisco. Compose followed a similar path after raising $29 million. Startups such as Smallest.AI, B21AI, and Get Krux reflect the same trend.

These companies are Indian only by origin. Their strategic decision-making, customer acquisition, fundraising, and core operations are centered in the US. This pattern points to a deeper structural imbalance rather than isolated relocation choices.

The most visible driver is capital. The funding gap between the US and India in artificial intelligence continues to widen. Between 2013 and 2024, the US invested approximately $471 billion into AI, while India invested only $12.3 billion—a difference of nearly 39 times. The gap becomes even sharper in recent data. In 2025 alone, US-based AI companies raised $121 billion, while Indian AI startups raised just $643 million, a 188-times difference. Rather than narrowing, the divide is accelerating.

However, access to money alone does not explain the exodus. The more damaging constraint is the lack of patient capital for AI infrastructure. Most Indian venture funding favors short-cycle, lower-risk application-layer products such as chatbots and productivity tools. Deep-tech infrastructure projects—requiring five to ten years of sustained investment—struggle to find domestic backers willing to absorb long-term risk. As a result, foundational AI innovation rarely scales within India.

Customer dynamics further intensify the problem. Indian enterprises remain highly cost-sensitive and cautious in AI adoption. Services that are commercially viable in global markets are often perceived as expensive domestically, even when they replace human-intensive workflows. In parallel, many Indian enterprises prefer building AI systems internally rather than purchasing ready-made solutions, slowing down procurement and limiting early revenue for startups. In contrast, US enterprises are structured to buy, deploy, and scale AI products rapidly.

This imbalance creates a geographic mismatch. Even when startups are founded in India, their customers are predominantly overseas. Over time, proximity to customers becomes critical for closing enterprise deals, refining products, and scaling revenue, forcing companies to relocate where demand already exists.

Infrastructure availability remains another major constraint. India currently operates around 38,000 GPUs, a significant improvement in absolute terms. However, the US controls roughly 75% of global GPU infrastructure, amounting to millions of units with continuous expansion. Funding allocation mirrors this imbalance. Nearly 90% of Indian AI investment flows into application-layer products, while only 10% supports infrastructure. Since infrastructure requires the largest capital commitments and longest timelines, startups operating in this space face a dead end—limited investor support and minimal domestic demand.

This skew toward applications is already visible in how AI is deployed across Indian companies. Adoption is concentrated at the interface level—automation layers, conversational systems, and user-facing assistants—rather than at the foundational model or infrastructure layer. Swiggy’s recent integration with large AI assistants through a Model Context Protocol reflects this trend. The platform now enables external AI systems to place orders, manage transactions, and track deliveries using natural language. While this marks a meaningful step toward conversational commerce, it also reinforces a broader pattern: AI capabilities are increasingly consumed rather than owned. The core intelligence, orchestration layers, and foundational models remain external, leaving domestic firms positioned primarily at the deployment edge rather than the value-creation core.

Another reinforcing factor is the validation trap. Indian startups often gain domestic credibility only after achieving success abroad. This dynamic shifts intellectual property filings, strategic decisions, and long-term value capture outside the country. India currently holds 16% of the world’s AI talent and ranks first globally in AI skill penetration. Yet a significant share of this talent builds and scales companies overseas. Over time, this risks positioning India as an execution hub rather than a center of innovation and ownership—replicating the trajectory seen during the IT services boom.

There are signs of course correction. The Indian government has launched the IndiaAI Mission with an allocation of $1.25 billion. GPU capacity has expanded, and compute access has been subsidized to approximately ₹65 per hour, nearly 80–90% below prevailing market rates. The 2025 Union Budget allocated ₹4,349 crore to AI initiatives, the largest allocation to date.

Yet global comparisons place these efforts in perspective. The US has announced a $500 billion AI infrastructure plan. China is investing $47.5 billion in semiconductors alone. France has committed $109 billion, while Saudi Arabia is spending $100 billion. Against this backdrop, India’s current investments represent a beginning rather than a competitive scale-up. Addressing the structural gap will require significantly larger commitments, deeper pools of patient capital, stronger domestic procurement, and easier business execution than competing global hubs.

This is where the India–European Union trade agreement becomes strategically significant. The migration of Indian AI startups to the US is not driven by geography, but by access—access to capital, customers, compute, and predictable regulation. If those constraints are reduced, the incentive to relocate weakens. The EU agreement offers an alternative expansion pathway that does not require abandoning the domestic ecosystem.

Covering nearly 2 billion people and a combined GDP of $24 trillion—around 25% of global output—the agreement extends far beyond tariff reductions. For Indian startups, it opens access to a large, distributed market with strong purchasing power, mature enterprise demand, and clearer regulatory frameworks. Unlike the US, where value concentration often requires physical relocation, Europe allows cross-border scaling without a single geographic center of gravity.

Indian fashion and apparel brands now receive zero-duty access to European markets, compared to earlier duties of 8–12%, unlocking an estimated $100 billion opportunity. Technology startups gain access to 144 opened service sectors, alongside a legal gateway office in India to facilitate professional mobility. Within five years, social security agreements across all 27 EU countries will further ease workforce movement. For pharmaceuticals and health-tech startups, a $572 billion European market opens up, with 97% of Indian chemical exports receiving zero-duty access.

In this context, the EU agreement functions not just as a trade pact, but as a pressure valve for India’s AI and startup brain drain—offering global market access without forcing strategic or geographic exit.

Recent market developments highlight both competitive pressures and shifting dynamics. As of January 2026, Ola Electric’s share in India’s electric two-wheeler market has fallen below 6%, down from nearly 25% a year earlier, driven by service issues and customer dissatisfaction. Competitors such as Ather Energy, TVS Motor, and Bajaj Auto have gained ground, with Ather overtaking Ola in revenue and market capitalization.

Logistics startup Shadowfax listed on the Bombay Stock Exchange at ₹113 per share, around 9% below its IPO price of ₹124. The company raised ₹197 crore through the issue, which saw moderate demand led primarily by institutional investors.

On the funding front, Indian startups raised a total of $83 million this week, down from $216 million the previous week. Bengaluru-based Forbear Care raised ₹90 crore ($9.8 million) in a Series B round for its precision oncology platform. Arani Labs secured $8 million in seed funding to build indigenous high-performance AI GPUs and a full enterprise software stack. 

Chennai-based Antelus Industries raised ₹50 crore ($5.43 million) to scale rare earth metal production. Dropulse Aerospace raised ₹25 crore ($2.7 million) for next-generation propulsion systems, while Bengaluru-based Aqua AX raised ₹12.5 crore ($1.36 million) to develop AI-powered air-and-underwater drones.

Together, these developments underscore a critical inflection point. India possesses the talent, early infrastructure momentum, and emerging global partnerships to compete in AI. Whether it captures long-term value—or continues exporting it—will depend on how decisively these structural gaps are addressed.

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