In his speech before the United Nations General Assembly on 8 December 1953, President Dwight Eisenhower proposed – in paraphrased terms- that the atom bomb be given to those who can “strip its military casing and adapt it to the arts of peace.” Commonly referred to as the ‘Atoms for Peace’ speech, Eisenhower’s words launched an International Atomic Energy Agency and a generation of research into nuclear energy. Since the Cold War’s end, America’s relationship with nuclear power has attracted less attention, but the artificial intelligence (AI) revolution is forcing the United States to take a “new look” at its power grid.

Throughout 2025, senators, think tanks, and federal commissions likened the pursuit of better AI to the Manhattan Project that built the bomb. The vast sums of energy required to fuel such a task, however, may need its own project. Although President Donald Trump issued an executive order to reinvigorate the nuclear industrial base last May, these energy demands have been overshadowed by mounting fascination with the need to win a technology race with China. Considering U.S. public opinion toward atomic energy reached a near record high last year, there is no better time to expand the atom’s role in support of a coherent AI strategy.

The Dawn of a Nuclear Renaissance

During the early Cold War, nuclear technology drove a revolution in energy generation, powering everything from American cities to aircraft carriers. The skyrocketing number of AI data facilities in the United States, on the other hand, represents a potential crisis in energy consumption. When asked if the country can support the growing demands of its data centers, former President of Energy at Microsoft Brian Janous responded: “No. Utilities have not experienced a period of load growth in almost two decades and are not prepared for—or even capable of matching—the speed at which AI technology is developing.” The White House is exploring nuclear options to meet this challenge, yet its AI strategy released last July only mentions nuclear power briefly on page sixteen. This point deserves more attention.

America’s 94 reactors currently supply twenty percent of its energy with 97 gigawatts (GW), and the largest of them—located in Georgia—has a generating capacity of 4.5 GW. A recent Goldman Sachs report projected that the United States needs 47 GW of additional energy to power its AI centers through 2030—the equivalent of half the country’s nuclear capacity. Meta CEO Mark Zuckerberg has taken notice. In January, he secured a series of nuclear energy deals to power his 6.6 GW AI compound under development in Ohio. Companies that did not exist twenty years ago, such as Meta and OpenAI, could soon demand more than ten percent of the nation’s power grid, and the needs are only increasing.

Professor Joohyun Moon of Dankook University suggested recently that small modular reactors (SMRs)—automobile-sized nuclear batteries—could offer energy solutions for national security purposes in forward areas, such as the Indo-Pacific. Although the United States approved its first SMR design in 2022, it will not be operational until 2029, and only three SMRs are currently active in Japan, China, and Russia. Some studies cast doubt on the affordability of SMRs and question whether they would increase the risk of proliferation given the enriched uranium they need to operate. Moreover, these reactors only generate up to 300 megawatts, so while they could be useful in certain military contingencies, their output pales in comparison to the forecasted energy demands of AI.

Microsoft alone plans to build at least six data centers in Texas, each of which might consume enough energy to power more than 100,000 homes. Once Meta completes its Ohio facilities, it will have at its disposal energy reserves capable of powering roughly five million homes. Data centers in the United States could therefore devour nearly one quarter of the energy used by all American households before 2030. Without tighter integration between a national AI strategy and America’s nuclear sector, these numbers appear unsustainable.

Reversing the Ship

Going all in on nuclear energy also requires sustainable solutions to disposing of spent nuclear fuel and investing in high-capacity pressurized water reactors, but such solutions have not been forthcoming. President Barack Obama’s administration slashed funding for Nevada’s Yucca Mountain disposal facility in 2009 and suspended development of a nuclear waste repository there. Despite the first Trump administration’s requests to fund the disposal program between 2018 and 2020, Congress has yet to approve a plan. Any rapid increase in nuclear energy must be accompanied by a commensurate spike in disposal capacity.

In addition to these concerns, the United States closed thirteen reactors between 2013 and 2022, which has encouraged the current administration to reverse course. Last year, the Department of Energy pledged to quadruple America’s nuclear output from 100 GW to 400 GW by 2050. President Trump also issued an executive order to unburden AI companies of federal regulations and requested that they shoulder the burden of energy costs. The next step is to fuse these developments with a theory of success that explains what “winning” the AI race looks like and then align that vision with the energy requirements needed to support it—much of which will be nuclear.

The Long Shadow of 1945

In her historical account of U.S. citizenship during the early atomic age, Sarah Robey explains how “American culture has never truly partitioned the difference between ‘atoms for peace’ and ‘atoms for war.’” Over the last eighty years, these blurred lines generated both hyperbolic and apathetic responses to the nation’s relationship with nuclear power. The atom became equal parts provider and destroyer, but these conversations disappeared once public fears of a Cold War going hot subsided. With American optimism toward nuclear energy now sitting at 61 percent, there is no better time to reignite the discussion about the atom’s role in American society.

Despite the Trump administrations’ efforts to break ground on new nuclear plants over the last ten years, AI theory has outpaced the long-term realities of AI application, especially regarding the energy equation. Advancing AI research will force western societies to embrace the atom for the purpose of sustaining life rather than destroying it much as Eisenhower theorized in 1953. Accepting this reality by establishing deeper connections between energy generation and AI strategy is the first step toward finding sustainable solutions to AI’s role in war and peace.

MAJ Michael P. Ferguson, U.S. Army, is an instructor in the Department of History and War Studies at the United States Military Academy and a Ph.D. student at the University of North Carolina at Chapel Hill. Specializing in early Cold War history and nuclear strategy, he has published several dozen articles and columns on a wide range of topics. His latest research appeared in the International Journal of Military History and Historiography and Texas National Security Review. The views expressed here are those of the author and do not reflect the policies or position of the U.S. Army, the U.S. Department of War, or the U.S. Government.

Learning to Love the Atom Again: Why the Future of Artificial Intelligence is Nuclear was originally published on Global Security Review.

That gap undermines American extended deterrence because Washington cannot reassure allies or deter aggressors without accounting for South Asia’s threat calculus. AI arms developments in this region stem from colonial legacies and mistrust of great power intentions, creating a volatile strategic environment.

India’s Governance Innovation in Defense AI

India’s governance model integrates civilian oversight with defense research and ensures ethical deployment of AI. The Responsible AI Certification Pilot evaluated algorithms for explainability before clearance. Its National Strategy for AI mandates ethical review boards for dual-use systems. Developers must document bias-mitigation measures and escalation pathways. Embedding accountability at design phase stabilizes deterrence signals by reducing inadvertent algorithmic behaviors.

The Evaluating Trustworthy AI (ETAI) Framework advances defense AI governance. It enforces five principles: reliability, security, transparency, fairness, privacy, and sets rigorous criteria for system assessment. Chief of Defense, Staff General Anil Chauhan, stressed resilience against adversarial attacks, highlighting the challenge of balancing effectiveness and safety. By mandating continuous validation against evolving threat scenarios, ETAI prevents mission creep and maintains operational integrity under stress.

India’s dual use by design philosophy embeds safeguards within prototypes from inception. This contrasts with reactive models that regulate AI after deployment. Civilian launch-authorization channels separate political intent from technical execution, ensuring decisions remain under human control and reinforcing credibility in crisis moments. Regular red-team exercises involving independent experts further validate system robustness and reduce risks of false positives in autonomous targeting.

Strengthening Extended Deterrence through Cooperation

US-India collaboration on AI verification can reinforce extended deterrence by aligning technical standards and testing protocols. The iCET fact sheet outlines secure information sharing and joint safety trials. Launched in January 2023, iCET has already enabled co-production of jet engines and transfer of advanced drone technologies. Building on this foundation, specialized working groups could develop common benchmarks for adversarial-resistance testing and automated anomaly detection.

A Center for Strategic and International Studies report recommends a trilateral verification cell blending American evaluation tools with India’s ethical reviews. Joint trials of autonomous air-defense algorithms would demonstrate interoperability and resolve. A shared “AI Red Flag” system would alert capitals to anomalous behaviors and reduce strategic surprise. Embedding cryptographically secure logging of decision path data ensures an immutable audit trail for post-event analysis and confidence building.

The INDUS-X initiative, launched during Prime Minister Narendra Modi’s 2023 US visit, integrates responsible AI principles into defense innovation. By aligning standards, both countries ensure AI systems enhance strategic stability rather than undermine it. Expanding INDUS-X to include scenario-based wargaming with allied partners can stress-test ethical frameworks and calibrate thresholds for human intervention under duress. This model can extend under the Quad framework, pressuring authoritarian regimes to adopt transparency measures.

Institutionalizing Global AI Arms Control

A formal arms control dialogue should adopt India’s baseline standards for ethical AI governance. The UNIDIR report calls for universal bias audits and incident-reporting obligations to prevent unintended escalation. Carnegie scholars propose a tiered certification process under a new protocol for autonomous systems within the Convention on Certain Conventional Weapons, requiring peer review of algorithms before deployment. Embedding such certification in national export-control regimes would create global incentives for adherence.

The UN General Assembly has established an Independent AI Scientific Panel and a Global Dialogue on AI Governance to issue annual assessments on risks and norms. This mechanism can evaluate military AI applications and recommend confidence-building measures. Procedural transparency would coexist with confidentiality requirements, balancing security with mutual reassurance. Regular joint workshops on risk-assessment methodologies can diffuse best practices and diffuse mistrust among major powers.

Regional Applications and Future Prospects

India’s responsible AI framework must inspire regional adoption and confidence-building measures. Pakistan and China should engage transparency initiatives to prevent dangerous asymmetries in AI capabilities. Proposed measures include joint research on AI safety, shared performance databases, and collaborative development of detection algorithms.

Successful tests of India’s hypersonic ET-LDHCM system, capable of Mach 8 and a 1,500-kilometer range, underscore the urgency of governance frameworks before fully autonomous weapons deploy. The Quad’s model of Indo-Pacific cooperation provides a template for multilateral norms on responsible AI in defense. Extending these norms to confidence-building measures such as pre-deployment notifications and automated backchannels can reduce the risk of inadvertent escalation.

Looking ahead to the United Nations General Assembly meeting on AI governance in September 2024, American policymakers can leverage India’s experience. Joint verification exercises and an ethical audit regime will establish global norms for military AI. Integrating lessons from ETAI and iCET into the assembly’s resolutions can produce enforceable standards that bind both democratic and authoritarian states. This approach will reaffirm American extended deterrence and help prevent destabilizing AI-driven arms races worldwide.

By demonstrating that ethical AI development strengthens rather than weakens deterrence credibility, India’s model provides both technical solutions and normative frameworks for managing the military applications of artificial intelligence. Sustained international cooperation on these principles is pivotal for securing strategic stability in a rapidly evolving technological landscape.

Vaibhav Chhimpa is a researcher who previously worked with the Department of Science & Technology (DST), India. Views expressed are the Author’s own.

The Artificial Intelligence (AI) Arms Race in South Asia was originally published on Global Security Review.