Event Recap: Pre-Summit Event for 2026 AI Impact Summit

The India AI Impact Summit kicks off next week, and U.S. engagement in it matters because India is positioning itself as a bridge between advanced AI economies and the Global South. How the United States shows up in India, and how it frames its role, will signal to emerging economies what kind of AI partner the United States intends to be. Earlier this month, the Center for Data Innovation (a U.S.-based think tank) and The Dialogue (an India-based one), with sponsorship from Google, convened an official pre-summit event in Washington, D.C. to help sharpen that engagement.

The Summit itself spans seven thematic tracks, ranging from human capital to social empowerment. For our pre-summit event, we narrowed the lens to three cross-cutting areas most consequential for U.S. strategic engagement: adoption, compute, and governance.

The first panel focused on adoption, the most critical ingredient for delivering the kind of impact the Summit is designed to advance. Han Sheng Chia of the Center for Global Development described his team’s work funding Indian nonprofits that are using AI to improve literacy, agricultural productivity, and maternal health. These included digital agronomists helping farmers detect pests and monitor crop health, and AI-powered medical coaches guiding pregnant mothers on when to seek care. But he emphasized that providing access to powerful models is not enough. In many regions, the barrier to adoption is not the absence of an LLM, but the absence of “last-mile” infrastructure and accessible interfaces—like voice and SMS—that allow technology to function in a low-bandwidth world. The strategic implication for the United States is clear: to be a credible partner to emerging economies, it must think beyond raw model performance and support efforts to improve the digital infrastructure that make AI a practical reality for the global majority.

Poornima Shenoy of the Federation of Indian Chambers of Commerce and Industry (FICCI) offered a complementary perspective from the commercial side. Indian firms, she noted, are not waiting for perfect systems; they are deploying AI tools early in sectors such as healthcare and digital banking and refining them in production. This “deploy first, iterate fast” model accelerates learning and embeds AI into everyday services. But as Jane Munga of the Carnegie Endowment for International Peace cautioned, technology and market momentum alone do not guarantee sustained scale. Successful adoption requires political champions—leaders who can align institutions, reduce uncertainty, and push implementation forward. The United States has a leadership opportunity. To champion AI adoption, it should articulate a clear message that innovation and responsible deployment can move in tandem, and back that message with support for the policy and institutional conditions that allow AI to scale beyond pilots and deliver real impact.

The second panel focused on compute as the engine of AI development, with an emphasis on “compute equity” for emerging economies. Behnaz Kibria of Google Cloud argued that equity is not simply about access to chips. For a country like India, it means having local infrastructure that reduces latency, complies with data residency rules, and gives developers meaningful control over where and how they build. Training models on sensitive data often requires keeping that data onshore. Without local capacity, developers face regulatory and performance constraints that limit what they can create. By providing localized cloud regions—such as Google’s planned $15 billion AI hub in Vizag—global providers give Indian innovators the choice to use cutting-edge infrastructure while keeping their data physically within their own jurisdiction.

Jeffrey Bean of ORF America further bridged this gap by suggesting a “midlife” strategy: using recertified GPUs for these local hubs. He noted that while the frontier of research in the United States often demands the newest hardware, older, recertified chips are more than sufficient for the massive task of application deployment across India’s billion-plus population. By focusing on these reliable, accessible tiers of hardware, the United States and India can create a sustainable roadmap for compute equity that deepens their AI partnership. The Center’s Daniel Castro situated the discussion within a broader strategic frame. The United States and India, he argued, should avoid repeating the early Internet’s mistakes by embedding security by design and zero-trust principles directly into the AI supply chain. Compute leadership, in this view, is not only about performance. It is about building a secure foundation that trusted partners like India can rely on as they scale AI across languages, interfaces, and populations.

The final panel turned to governance. David Weller of Google emphasized that regulatory design should support innovation and competitiveness alongside safety. Emerging frameworks on data use, model oversight, and supply chain transparency will shape where companies choose to invest and build. Ridhika Batra of the Mahindra Group underscored the private sector’s need for clarity and predictability. For firms operating across jurisdictions, inconsistent rules on data flows, standards, and liability create friction that slows deployment. Sustainable AI adoption depends not just on technical capacity, but on regulatory environments that allow companies to scale responsibly without navigating conflicting obligations in every market.

Olivia Igbokwe-Curry of Amazon Web Services framed digital sovereignty as a practical balancing act. Governments have legitimate interests in resilience and national security, but sovereignty should not become shorthand for protectionism. Policy choices around data localization, procurement, and infrastructure independence must weigh security gains against the economic costs of fragmentation. Vikram Singh of the U.S.-India Strategic Partnership Forum brought the discussion back to geopolitics. As India positions itself as a central node in the democratic AI ecosystem, its governance decisions will carry weight far beyond its borders

As policymakers head to New Delhi, the message is clear. India is shifting the AI conversation toward impact, including how technology improves productivity, public services, and economic opportunity at scale. The United States should meet that moment in kind and recognize that this is where emerging economies will judge American partnership. That means demonstrating how collaboration can expand adoption, anchor trusted infrastructure, and align governance in ways that deliver measurable results.