The ‘Picks and Shovels’ Strategy
For years, the Indian startup ecosystem was defined by consumer-facing applications and software-as-a-service (SaaS) platforms. Venture capital chased the next big app for food delivery, e-commerce, or fintech. The AI boom initially followed a similar
pattern, with a flurry of startups creating 'wrappers'—thin layers of application built on top of powerful foreign models like GPT-4. However, investors have matured, realizing that true, long-term value lies not in the applications themselves, but in the foundational building blocks. This strategic pivot is akin to a gold rush; instead of funding every prospector, investors are now backing the companies that sell the picks, shovels, and Levi's. In AI terms, this means funding the core infrastructure: foundational models, specialized cloud computing services, data platforms, and even the silicon chips that power it all. This shift is underscored by a dramatic influx of capital. By the third quarter of 2026, India's AI sector had attracted over $12 billion in venture funding, with a significant portion now earmarked for these deep-tech ventures.
Why Infrastructure is the New North Star
The rationale behind this investment thesis is multi-layered. Firstly, AI infrastructure companies have the potential for much higher defensibility. While an AI-powered photo editing app can be easily replicated, a company that builds a large language model (LLM) trained on 22 Indian languages, or one that provides cost-effective GPU-based cloud services, creates a deep competitive moat that is difficult for others to cross. Investors like Peak XV Partners and Lightspeed are increasingly focusing on these foundational plays. Secondly, there is a strong push for 'sovereign AI'—the idea that India must build and control its own AI capabilities to reduce dependence on foreign technology and cater to its unique linguistic and cultural landscape. The Indian government has reinforced this through its IndiaAI Mission, which provides funding and, crucially, access to high-end computing resources for select startups. This policy support reduces risk and signals a clear, long-term national priority, giving investors the confidence to make substantial, patient bets.
The Builders of India’s AI Future
This trend is not just theoretical; a new class of startups is already emerging. Companies like Sarvam AI and Krutrim are at the forefront, developing full-stack AI platforms and foundational models from scratch, trained on vast amounts of India-specific data. Sarvam, for instance, secured a major government contract to build a sovereign LLM, while Krutrim, founded by Ola's Bhavish Aggarwal, became India's first AI unicorn. On the hardware and cloud front, startups like Neysa, backed by a massive investment from Blackstone, are building out the essential GPU cloud infrastructure needed for training and deploying these complex models. Others, like Agrani Labs, are taking on the audacious goal of designing AI chips domestically. These companies represent a fundamental departure from the app-first model, requiring deep technical expertise and significant capital but promising a much greater strategic and economic payoff if successful.
The Road Ahead is Not Without Hurdles
Despite the optimism and surging investment, the path forward is challenging. Building foundational AI is incredibly expensive and requires access to vast amounts of computing power, which largely relies on high-end GPUs from foreign companies like Nvidia. While government initiatives are helping, access to this hardware remains a significant bottleneck. Furthermore, these Indian startups must compete with global technology giants—Google, Microsoft, and Amazon—who are also investing billions in their own infrastructure within India. Another critical challenge is the availability of high-quality, clean, and labelled data specific to the Indian context, without which even the most powerful models cannot be effectively trained. Finally, there's the perennial issue of talent; while India has a vast pool of software engineers, there is a shortage of the specialized researchers and scientists needed for cutting-edge AI development.
