Recent Advances in Artificial Intelligence and the Department of Energy’s Role in Ensuring U.S. Competitiveness and Security in Emerging Technologies

Chairman Manchin, Ranking Member Barrasso, and members of the Senate Energy and Natural Resources Committee. I appreciate the opportunity to submit this statement underpinning the Department of Energy’s visions to shape our strategic investments in AI.

The Federation of American Scientists (FAS) is a catalytic, non-partisan, and nonprofit organization committed to using science and technology to benefit humanity by delivering on the promise of equitable and impactful policy. FAS believes that society benefits from a federal government that harnesses science, technology, and innovation to meet ambitious policy goals and deliver impact to the public.

I am the Associate Director for Emerging Technologies and National Security at FAS where I lead our work on emerging technologies’ policy from the lens of our national security innovation base, as well as focusing on the strategic competition between the United States and the Chinese Communist Party. I wish to commend your work in bringing the Committee together to discuss the Department of Energy (DOE)’s role in ensuring U.S. competitiveness and security in emerging technologies. This hearing could not have come at a more opportune time. 

In March, the Chinese Communist Party (CCP) held its yearly “two sessions” meeting—referring to the coming together of China’s principal political bodies, the National People’s Congress (NPC) and the National Committee of the Chinese People’s Political Consultative Conference (CPPCC)—during which they not only confirmed Xi Jinping’s third term as president but also introduced a set of new policies and government appointments. During this meeting, Xi emphasized the importance of self-reliance in science and technology as a strategic goal to combat Western influence. Meanwhile, the Central Committee revealed plans to restructure the Chinese government to better position China’s national innovation system for driving advancements in both commercial and dual-purpose military-civilian technologies. This latest initiative underscores two decades of unwavering CCP commitment toward indigenous innovation, calibrated specifically to outflank its Western competitors like the United States. And it’s getting results: a recent analysis by the Australian Strategic Policy Institute found that China now leads in 37 out of 44 critical technology areas globally, while Chinese production of high-value patents in the global marketplace has increased by 400% over the past decade.

The Committee’s hearing is exploring a question that is of vital national interest. The two proposals—creating an Office of Critical and Emerging Technology within the DOE and the Frontiers in Artificial Intelligence for Science, Security and Technology—could change this trajectory for the better.

First, the creation of an Office of Critical and Emerging Technology within the DOE. This office would enable a robust assessment of U.S. technological competitiveness and prepare us for emerging technology surprises conveying a potential threat to national security. This framework will refine our strategic direction, facilitate rapid threats-response coordination with interagency collaboration from entities like DoD, DNI and NSF amongst others, while advancing proactive countermeasure strategies.

The Office should serve as a hub for innovative practices across all 17 National Labs and 34 user facilities that the DOE stewards. The DOE labs and user facilities have expertise and capabilities that are important in national and international science policy challenges. This office should promote greater participation from our labs to better inform these discussions, thereby effectively fostering a diversity of perspectives within national science policy discourse and international forums, which is ever-critical given the ascending competition from nations including China and Russia in domains like AI, quantum computing, and biotechnology. 

Secondly, the FASST initiative—Frontiers in Artificial Intelligence for Science, Security, and Technology—is another imperative. AI’s transformative potential is undeniable but demands substantial improvement in fundamental aspects like explainability, trustworthiness, reliability, especially for mission-critical applications and privacy-sensitive issues.

The DOE, with its high-performance computing prowess, is uniquely positioned to deliver secure and dependable AI solutions for the challenging problems of the century. By leveraging DOE’s world-leading exascale computing capabilities while working synergistically with key stakeholders from academia, industry, and interagency groups, we can unlock groundbreaking AI innovations.

Efforts must be made to accelerate integrated math and science R&D, particularly foundational AI research to develop secure, trustworthy techniques. Rigorous verification and validation processes, guided by scientific validity, can vet new technologies for their societal implications before widespread deployment.

Moreover, expanding on foundational research in physics-informed AI could lead to better integration of AI models with our understanding of real-world phenomena. This involves cooperative research among diverse specialties, an endeavor DOE labs and associated universities are equipped for.

The proposed multi-billion-dollar annual program involving DOE Office of Science, National Nuclear Security Administration, and applied energy programs aims to leverage unique leadership capabilities in computing to create transformative AI hubs focused on solving grand challenge problems, innovate world-class AI technologies, and harness cutting-edge testbeds for developing energy-efficient AI hardware platforms in concert with US industry.

Adding to the testimony, I would like to emphasize the pivotal role the FASST initiative will play in the development of unique open and secure foundation models for discovery and national security. The objective is to harness unique and highly-curated datasets to foster advancements and ensure that the United States remains at the helm of science and technology. 

The creation of uniquely crafted models, possible only through supercomputing, will offer unprecedented insights into complex processes like molecular dynamics crucial for additive manufacturing or power grid dynamics, leading to a more resilient energy infrastructure. Moreover, it’s crucial for the DOE to develop classified models to manage threats to our national security, from maintaining space situational awareness to advancing biodefense, nuclear deterrence, and nonproliferation efforts. However, I would also urge caution as this could provide our adversaries with a single point of attack to extract classified data if they were to gain access to the frontier model trained on classified data.

We are observing an unprecedented deployment of large language models and other advanced AI models like AlphaFold 2, AlphaGo, amongst others, across the country. AI tools and foundational models developed by the DOE could test and validate these AI tools. This capability is imperative to ensuring AI models deployed meet safety and ethical standards that align with our societal values. Furthermore, it will allow DOE to assess risks posed by other AI models that are outside of U.S. regulatory jurisdictions.

In terms of tool and software development, FASST could develop common platforms for safe, trustworthy AI suitable for high-stake usage scenarios. This would involve crafting tools and methodologies that enhance the trustworthiness and reliability of AI systems while preserving privacy. It also involves an acute focus on cybersecurity, establishing classified platforms capable of evaluating potential adversarial AI systems. 

The harnessing of both classified and unclassified scientific datasets will be instrumental in this endeavor. By transforming DOE’s leading-edge facilities into a nationwide integrated research infrastructure, we will cultivate a common platform for training and evaluation, thereby deriving valuable findings from the world’s largest volumes of scientific data.

Furthermore, FASST will be instrumental in bolstering state-of-the-art production capabilities for our nuclear stockpile by advancing the state-of-the-art in foundation models to rapidly validate AI technologies addressing emerging nuclear security missions. In addition, FASST’s aims to develop new foundation models for unique types of data such as seismic and electromagnetic are worthy of support as these areas where current capabilities are lacking.

Through these concerted efforts, we aim to combine the strides in AI innovation with critical missions in science, security, and technology—encompassing scientific discovery, energy sustainability, and national security. We will continue to boldly ride the tidal wave of AI evolution while ensuring that we stay ahead of possible detriments that could compromise our nation’s security and leadership in technology.

Eventually, the transformation of DOE facilities into a nationwide integrated research infrastructure can stimulate advanced AI research deployment across sectors, enhance resource utility, drive unprecedented growth potential, and reinforce U.S.’s techno-economic leadership.

In conclusion, championing these proposed provisions underscores the urgent need for research, development, and deployment to ensure our ongoing global competitiveness within the critical emerging technology fields. Proactive investments today promise substantial strategic dividends for our nation’s future by maintaining its vital role in technological innovation while robustly addressing potential risks tied to these technological breakthroughs. At the same time, we must proceed with caution as our adversaries try to gain access to our classified information every hour of every day. Creating frontier models with classified information could provide significant benefits to our national security apparatus, yet at the same time, it could also provide our adversaries an easier path to gain access to our secrets, hence we must do it in a way that ensures our systems are safe, secure, and reliable.

In the end, this is not just about maintaining a competitive edge; this is about national security, about establishing ethical guidelines for technology usage; it’s about mission-critical deployments where failure is unimaginable, about enhancing global standings through technological supremacy.

We believe this strategic investment into critical and emerging technologies will empower our nation to confront 21st-century challenges with solutions that are timely, scientifically rigorous, and security-enhancing. We express our unwavering support towards these provisions and encourage their decisive endorsement. 

Thank you for considering our views on these pressing topics.

If you have any questions, please reach out to me at

Divyansh Kaushik

Associate Director for Emerging Technologies and National Security

Federation of American Scientists

Strengthening the Integrity of Government Payments Using Artificial Intelligence


Tens of billions of taxpayer dollars are lost every year due to improper payments to the federal government. These improper payments arise from agency and claimant errors as well as outright fraud. Data analytics can help identify errors and fraud, but often only identify improper payments after they have already been issued.

Artificial intelligence (AI) in general—and machine learning (ML) in particular (AI/ML)—could substantially improve the accuracy of federal payment systems. The next administration should launch an initiative to integrate AI/ML into federal agencies’ payment processes. As part of this initiative, the federal government should work extensively with non-federal entities—including commercial firms, nonprofits, and academic institutions—to address major enablers and barriers pertaining to applications of AI/ML in federal payment systems. These include the incidence of false positives and negatives, perceived and actual fairness and bias issues, privacy and security concerns, and the use of ML for predicting the likelihood of future errors and fraud.