By: Rohan Sharma
U.S.-India cooperation on artificial intelligence is entering a more consequential phase. This shift is already visible in recent diplomatic and policy initiatives. Leaders have begun to elevate AI infrastructure and responsible deployment in joint statements, framing them as pillars of a broader economic and technology partnership. Initiatives such as the U.S.-India Initiative on Critical and Emerging Technology (iCET) and the 2025 Joint Leaders' Statement have explicitly prioritized secure AI deployments and resilient supply chains. The next phase of cooperation will hinge on whether Washington and New Delhi can align not only on shared objectives for AI, but also on the technical and institutional foundations that make those objectives credible.
Currently, India is leveraging its digital public infrastructure (DPI), including Aadhaar, UPI, DigiLocker, and related platforms as a template for how large-scale systems can deliver inclusion and resilience. Meanwhile, the United States is focusing on export controls, industrial strategy, and standards-setting in multilateral venues. If these trajectories remain uncoordinated, both sides risk a future in which their AI ecosystems are substantial but only partially interoperable.
The challenge is no longer defining shared ambitions but operationalizing them. Furthermore, because implementation heavily relies on the private sector, both governments must translate this political signaling into predictable incentives and procurement signals that draw in cloud providers, semiconductor manufacturers, and DPI-layer innovators for cross-border development and deployment.
On the standards front, the two countries approach AI governance from different starting points but converging interests. AI standards are the technical rules and benchmarks that dictate how systems are built, tested, and documented. They are critical because, without shared baselines, AI models cannot operate safely, be evaluated consistently, or interoperate across different international networks. The United States tends to emphasize multi-stakeholder processes, voluntary frameworks, and industry-led technical standards, reinforced by targeted regulatory tools such as export controls on advanced AI chips and model weights. India, by contrast, has treated technology standards and governance as extensions of state capacity, embedding them in national platforms that now serve over a billion people and increasingly exploring how AI can be layered onto this infrastructure. These models are not inherently at odds; together, they offer a hybrid approach that combines open technical standards, strong public institutions, and policy experimentation oriented towards inclusion.
Institutional proposals are beginning to reflect this opportunity. The U.S.-India AI and Emerging Technology Compact, released in February 2026 by the Special Competitive Studies Project in partnership with ORF America, for example, calls for a standing AI Standards Council to align standards, enable mutual recognition of certifications, and develop joint frameworks for data quality aligned with international baselines such as ISO/IEC 42001, and for intellectual property in co-developed systems. The Compact was developed to inform bilateral engagements, Quad coordination, and the India AI Impact Summit. It is now being used as a reference point in U.S.-India AI working groups and Track 1.5 dialogues. These mechanisms would give governments and firms clearer pathways for certifying models, sharing data securely, and complying with export and privacy requirements across jurisdictions. They would also position both countries to shape global AI norms in areas such as digital infrastructure, telecommunications, and sustainable development.
Infrastructure is the second pillar where alignment will be decisive. AI capability is now tightly linked to access to advanced semiconductors, scalable cloud resources, robust data centers, and reliable connectivity. The United States is investing in reshoring and diversifying chip manufacturing and has signaled interest in partnering with trusted countries to extend these capabilities abroad, while new bilateral initiatives envision U.S.-origin AI infrastructure in India supported by streamlined investment and regulatory pathways. India, for its part, is seeking to expand compute and energy capacity, modernize data infrastructure, and extend its digital public infrastructure model into emerging domains such as health, agriculture, and education, often with AI as a core enabler.
These trajectories support a more deliberate cooperative agenda. A practical next step is the development of joint AI infrastructure corridors that bundle data centers, connectivity, and cloud platforms with shared safeguards and procurement standards, enabling cross-border research and deployment while managing security and privacy risks. Another priority is co-developed testing and evaluation facilities that apply jointly agreed benchmarks to high-risk systems, from financial algorithms to models deployed in public services. These projects would translate commitments into operational capacity, creating reference implementations that regulators and partners can examine, adapt, and scale. The difficulty is that U.S. efforts to export an integrated "full stack" of chips, cloud, and models sit alongside India’s pursuit of sovereign, indigenous infrastructure, so any joint corridor or test facility must reconcile dependence on U.S.-origin technology with New Delhi’s desire to reduce that dependence over time.
The broader lesson for policymakers is that dialogue and declarations remain necessary but are no longer sufficient. In recent years, both governments have invested in high-level engagements and summit processes that place AI alongside other critical technologies. These efforts have clarified where interests overlap and where sensitivities remain, including export controls, data governance, and defense applications. Yet the mechanisms that will determine whether AI systems are usable, safe, and trustworthy across both markets are now being shaped in standards bodies, regulatory design, procurement rules, and infrastructure investment programs.
For the next phase, U.S.-India AI cooperation will need to treat standards and infrastructure as shared strategic assets rather than technical afterthoughts. Priorities could include a jointly staffed AI Standards Council drawing on agencies such as the National Institute of Standards and Technology (NIST) in the United States and the Ministry of Electronics and Information Technology (MeitY) and the Bureau of Indian Standards (BIS) in India, linked to international standardization work, a unified portal for compliance information and licensing related to AI hardware and software trade, and streamlined national procedures for AI-related infrastructure that recognize trusted partners and common security baselines. Given India’s role as a reference point for digital public infrastructure and the United States’ influence over key AI technologies and norms, alignment at this level would extend beyond the bilateral relationship.
If Washington and New Delhi can move beyond dialogue to joint implementation on these foundations, they will not only make their own ecosystems more resilient and interoperable. They will also offer a practical template to other countries, especially in the Indo-Pacific and the broader Global South, seeking pathways to adopt AI that are consistent with openness, inclusion, and security. Ultimately, the effectiveness of U.S.-India AI cooperation will be measured not by the number of joint statements issued, but by whether both countries can build systems that are trusted, interoperable, and deployable across borders.
Rohan Sharma is a U.S. Delegate to the ISO/IEC JTC 1/SC 42 committee on AI standards, an Aspen Institute Civic AI Leader, and a Fulbright Specialist with the U.S. Department of State.