This is the tale of two cities—Washington and New Delhi—where the issues of sovereignty and artificial intelligence (AI) have recently come to a head.
In Washington, the United States has adopted a laissez-faire regulatory approach under the second Trump administration, aiming to bolster its privately owned firms in the development of the world’s most powerful AI systems. This strategy has seen a surge in private capital and innovation, producing increasingly sophisticated AI solutions. However, this regulatory light-touch has a caveat: the government asserts its need to be the primary user of these AI technologies, intending to leverage them for national security on its own terms. This has proven complex, as many in the AI community advocate for built-in safeguards in products, including those meant for military applications.
The Pentagon has made its stance clear, indicating that a private firm should not be able to restrict the government’s access to potentially critical military technology. Earlier this week, the Pentagon issued an ultimatum to Anthropic, the company behind the AI assistant Claude, demanding unrestricted access to its AI models by 5:01 p.m. that day. Chief Pentagon Spokesman Sean Parnell emphasized the importance of this request, stating, “Allow the Pentagon to use Anthropic’s model for all lawful purposes. This is a simple, common-sense request that will prevent Anthropic from jeopardizing critical military operations and potentially putting our warfighters at risk.” He warned that failure to comply would prompt the Pentagon to invoke the Defense Production Act, allowing the government to effectively commandeer Claude or label Anthropic a supply chain risk—a designation traditionally reserved for adversaries.
In response, Anthropic refused the request. CEO Dario Amodei stated, “These threats do not change our position: we cannot in good conscience accede to their request.” He pointed out that Claude is already deployed extensively across military and intelligence sectors and was the first major large language model to be integrated into classified networks. However, Anthropic objected to two specific use cases sought by the Pentagon: mass domestic surveillance and fully autonomous weapons. Amodei explained that the law has not kept pace with rapidly evolving AI capabilities, making it irresponsible to provision Claude for creating comprehensive life profiles of individuals at scale. Regarding autonomous weapons, he maintained that current systems lack the reliability to remove humans from the decision-making process.
This confrontation marks a significant moment in the ongoing dialogue about the balance of power, responsibility, and safety in AI deployment. The question persists: will private firms limit their role to producing reliable AI tools for government use, or will they assume a more foundational role in determining acceptable applications for their products? This dilemma underscores the AI sovereignty paradox, wherein the U.S. government’s sovereignty is called into question if it cannot access powerful AI models, and citizens’ sovereignty is equally at stake if the government uses these technologies without checks.
Simultaneously, the challenges faced by nations outside the U.S. and China are markedly different. At the India AI Impact Summit 2026 in New Delhi, Prime Minister Narendra Modi focused the discourse on equitable access, climate resilience, and inclusive growth, rather than the existential risks often highlighted in Western discussions. For emerging economies, the pressing concern is not about the potential dangers of AI but ensuring that its benefits are not monopolized by wealthier countries. Modi and other leaders are grappling with questions about whether nations will rely on the U.S. or Chinese AI infrastructure or develop their own alternatives.
The global AI landscape reveals the staggering technological dependencies of many nations. The U.S. holds approximately 75 percent of global AI supercomputer performance, while China accounts for 15 percent and the remainder of the world only 10 percent. Despite Europe’s commitment to invest $47 billion in AI infrastructure, U.S. firms are poised to spend at least $650 billion on AI-related capital expenditures this year alone.
In response, some indigenous AI firms are emerging, with models tailored for local languages and specific use cases. However, the scale and resources possessed by leading U.S. and Chinese firms still create a substantial technological gap. This situation has led to U.S. firms creating quasi-sovereign solutions for foreign markets, such as Amazon’s new European Sovereign Cloud, which aims to provide localized governance and operations.
On the governance front, the U.S. continues to favor a laissez-faire model that prioritizes innovation over regulation, diverging from the global push for cohesive standards on AI safety and ethical use. While leaders in New Delhi discussed pledging for responsible AI deployment, a coherent and enforceable governance framework remains elusive, resulting in a patchwork of regulatory approaches worldwide. This fragmentation could pose significant challenges for U.S. companies seeking to operate globally.
As the dynamics of AI development and deployment continue to evolve, the world watches closely, navigating the intersection of technology, governance, and sovereignty. The decisions made today will shape the future landscape of AI and its implications for nations and citizens alike.
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