A recent survey has highlighted a significant structural gap in the global artificial intelligence (AI) ecosystem, particularly between India and the United States. According to the findings, only 2% of Indian startups are actively working on AI training data, compared to nearly 40% of startups in the US focusing on this critical layer of AI development. This disparity raises serious concerns about India’s ability to build competitive, scalable, and globally relevant AI systems in the long run.

AI training data forms the foundation of all modern artificial intelligence models, including machine learning and generative AI systems. High-quality, diverse, and well-labelled datasets are essential for building accurate and unbiased AI solutions. While India has emerged as a global hub for software services and application-level AI solutions, the survey suggests that the country is lagging in creating core AI infrastructure, particularly in data creation, curation, and management.

Experts believe that Indian startups largely focus on AI applications and services, such as chatbots, automation tools, fintech solutions, and customer analytics, rather than the deeper layers of AI such as foundational models and training datasets. In contrast, startups in the US are investing heavily in building proprietary datasets, synthetic data platforms, and data pipelines that give them long-term technological advantages.

One of the key reasons behind this gap is the high cost and complexity associated with AI training data. Creating large-scale datasets requires significant capital, skilled talent, domain expertise, and strong data governance frameworks. Many Indian startups, especially early-stage ones, often lack access to such resources. Additionally, regulatory uncertainty around data privacy and usage further discourages experimentation in this space.

The survey also points to a policy and ecosystem challenge. While India has launched multiple initiatives to promote AI adoption, there is limited targeted support for startups working on core AI data infrastructure. Industry leaders argue that without strategic investments and policy incentives, India risks becoming primarily a consumer of AI technologies developed elsewhere rather than a global AI innovator.

Despite these challenges, experts remain cautiously optimistic. India’s vast population, multilingual diversity, and growing digital footprint offer a unique opportunity to create rich and diverse datasets that can power next-generation AI systems. With the right mix of government support, private investment, and academic collaboration, India could rapidly bridge this gap.

The survey ultimately serves as a wake-up call for policymakers, investors, and entrepreneurs. Strengthening AI training data capabilities is not just a technical necessity but a strategic imperative if India aims to compete with global leaders like the US in the rapidly evolving AI landscape.