By Srinath Sridharan

As a silent but unpleasant parting gift to India, the Biden administration’s “Framework for Artificial Intelligence Diffusion”, spelt that the US wants to be the Big Brother for the emerging technologies of the 21st century. By instituting a three-tiered structure that restricts the export of advanced AI chips and model weights, the US has weaponised technology as a tool of geopolitical power.

This framework severely hampers India’s access to critical resources and tools necessary for building advanced AI capabilities. It restricts the availability of high-performance AI chips, such as graphics processing units (GPUs) and specialised accelerators produced by companies like Nvidia, which are essential for training and deploying sophisticated AI models. These chips are the backbone of large-scale machine learning and high-end computational tasks, and without them India’s ability to develop cutting-edge AI systems is significantly diminished.

Additionally, access to large pre-trained AI models and their weights, crucial for applications ranging from natural language processing to predictive analytics, is also curtailed. This forces India to either rely on outdated models or expend considerable resources building its own from scratch, widening the gap between it and the global leaders in AI innovation. But then, it may not be such a bad idea, provided we wake up now.

The restrictions further extend to cloud computing resources and proprietary AI software developed and hosted in tier-I countries. Scalable computational platforms like AWS, Google Cloud, or Microsoft Azure, along with critical frameworks such as TensorFlow and PyTorch, are foundational for AI research and development (R&D). The risk of limited access to these resources makes it harder for Indian institutions to compete with global players.

The framework also undermines India’s capacity to develop AI for strategic applications like national defence, critical infrastructure, and security. Licensing and export controls compound the challenge by increasing costs and delaying access to essential technologies. This compromises India’s ability to achieve technological self-reliance and leaves it vulnerable to geopolitical pressures and supply chain disruptions.

In the 1990s, India embarked on a bold and urgent journey to build its nuclear capabilities, recognising that global power dynamics and national security could no longer be determined by external forces. Faced with international sanctions and pressure, India pushed ahead with its nuclear tests in 1998, sending a strong signal of self-reliance and strategic autonomy. The government galvanised the nation’s scientific community, prioritised resources, and channelled efforts into indigenous R&D. The result was a nuclear deterrent that not only fortified India’s security but also elevated its global standing.

The key lesson from this mission is the need for urgency, national focus, and a clear, strategic vision when pursuing technological self-reliance. Much like the nuclear programme, India’s AI mission must be driven by a similar sense of purpose and immediacy, ensuring that investment in talent, infrastructure, and R&D is prioritised.

AI is poised to define economic power, societal advancement, and military superiority for decades. The nation that leads in AI innovation will dictate global standards, control critical supply chains, and hold sway over international alliances. Yet, our preparedness for this race is dismal.

The National Programme on Artificial Intelligence, initiated in 2018, has made scant progress. The IndiaAI Mission, backed by a budget of $1.2 billion, is embarrassingly inadequate when compared to global benchmarks. The US has committed $280 billion under the CHIPS and Science Act. China has invested over $104 billion in private AI ventures, while its government invested over $208 billion in AI start-ups globally. India’s spending is a fraction of this.

India’s STEM (science, technology, engineering, and mathematics) ecosystem, while prolific in output, suffers from a quality deficit. India produces lakhs of engineers annually, but few are equipped with the skills required for advanced AI research or development. Much of our academia is outdated, and industry operate in silos without investing much in R&D, and the research collaboration between them is limited. Without systemic reforms to enhance quality, India risks becoming merely a consumer of AI technologies developed abroad, as happened with the Web 1.0 and Web 2.0 revolutions. Such dependency will not only stifle innovation but also leave us exposed to geopolitical pressures and supply chain vulnerabilities.

While India might raise this issue of unfair access with President Trump, it is naïve to expect concessions without a heavy price. Given his transactional style, the question is what will we be forced to trade in return. This is the risk of dependency.
This could include easing market access for US products in India, welcoming companies like Starlink and Tesla without resistance from entrenched Indian conglomerates, and providing greater flexibility to e-commerce giants like Amazon and Walmart — not just in retail, but in expanding their licensing access for financial services. The US might also push for a strengthening of defence ties, including high-value purchases. The question for India is whether the concessions Trump would demand come at too steep a cost.

Without indigenous capabilities, India will find itself at a disadvantage, unable to bargain effectively in global negotiations. The assumption that India’s market size alone can secure favourable terms is both simplistic and flawed. AI innovation thrives on intellectual property, talent, and infra — not just consumer demand.

India’s approach to AI must shift from complacency to mission urgency. Jugaad innovation, while celebrated, will not suffice for the complexity of AI development. This requires sustained investment in R&D, robust computing infrastructure, and a talent pipeline capable of competing on a global scale. Public-private partnerships, cross-border collaborations, and policy coherence are critical.

(The author is a Corporate advisor & independent director on corporate boards)

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