Anthropic, Google and xAI will join OpenAI on the CDAO’s nascent effort to partner with industry on pioneering artificial intelligence projects focused on national security applications. Under the individual contracts — each worth up to $200 million — the Pentagon will have access to some of the most advanced AI capabilities developed by the four companies, including large language models, agentic AI workflows, cloud-based infrastructure and more.

“The adoption of AI is transforming the Department’s ability to support our warfighters and maintain strategic advantage over our adversaries,” Chief Digital and AI Officer Doug Matty said in a statement. “Leveraging commercially available solutions into an integrated capabilities approach will accelerate the use of advanced AI as part of our Joint mission essential tasks in our warfighting domain as well as intelligence, business, and enterprise information systems.”

OpenAI received the first contract for the effort June 17 and will create prototypes of agentic workflows for national security missions. According to CDAO, work with all four vendors will expand the Pentagon’s experience with emerging AI capabilities, as well as give the companies better insights into how their technology can benefit the department.

The contract with CDAO is also another win for xAI, which is owned by Musk — who previously led the Trump administration’s Department of Government Efficiency (DOGE) efforts but recently had a falling out with President Donald Trump over legislation and other issues — and develops the generative AI tool called Grok. The company announced Monday that it was launching a new suite of AI tools for U.S. government users known as “Grok for Government.” The platform is now available to purchase by federal agencies through the General Services Administration, according to a post on X, which Musk also owns.

In a blog post published Monday, Jim Kelly, Google Public Sector’s vice president of federal sales, noted that the company will provide the Pentagon its Cloud Tensor Processing Units for training AI models, AI-powered agents via Google’s Agentspace, and access to the company’s infrastructure based in the contiguous United States.

“These advanced AI solutions will enable the DoD to effectively address defense challenges and scale the adoption of agentic AI across enterprise systems to drive innovation and efficiency with agile, proven technology,” Kelly wrote.

The announcement is the latest step the Defense Department has taken in recent months to accelerate adoption of AI-enabled capabilities developed by commercial companies — many of which have recently announced new business ventures focused on national security.

In June, Anthropic introduced a custom set of its Claude Gov AI models that are tailored specifically to defense use cases, ranging from operational planning to intelligence analysis. The same month, OpenAI launched a new initiative called “OpenAI for Government” that expands on its current partnerships with the Defense Department and other U.S. government agencies — including custom AI models for national security.

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In its daily list of new contract awards, the department announced Monday evening that the CDAO had awarded a $200 million prototype other transaction agreement to OpenAI Public Sector to “develop prototype frontier AI capabilities to address critical national security challenges in both warfighting and enterprise domains.”

The estimated completion date for the work is July 2026.

The announcement did not provide any additional details about what those capabilities will entail or what specific mission sets they’ll be applied to.

Last year, the CDAO listed a variety of “warfighting” use cases that its newly launched Artificial Intelligence Rapid Capabilities Cell would focus on, including command and control, decision support, operational planning, logistics, weapons development and testing, uncrewed and autonomous systems, intelligence activities, information operations and cyber operations.

“Enterprise management” use cases include financial systems, human resources, enterprise logistics and supply chain, health care information management, legal analysis and compliance, procurement processes, and software development and cybersecurity.

On Tuesday, DefenseScoop sent the CDAO questions seeking more information about the deal with OpenAI.

In a statement, a CDAO official said the vendor will be expected to “prototype agentic workflows to address our hardest challenges.”

The official, who provided comment on the condition of anonymity, added that more announcements are expected in the near term.

“This award is another step on our journey toward accelerating the adoption of advanced AI capabilities across the Department, and, in the coming weeks, we will announce partnerships with other Frontier AI companies as well. Access to top tier talent from partners like OpenAI is critical for building the agentic workflows needed to increase Joint Force lethality and enterprise efficiencies,” they said.

They noted that the CDAO has recently partnered with the Army’s Enterprise Large Language Model (LLM) Workspace to provide military users across the combatant commands, Office of the Secretary of Defense, and the Joint Staff access to “industry-leading general purpose LLMs.”

In an press release Monday evening announcing the launch of a new “OpenAI for Government” initiative, the company noted the new $200 million deal with the CDAO, saying the organization will help the Pentagon “identify and prototype how frontier AI can transform its administrative operations, from improving how service members and their families get health care, to streamlining how they look at program and acquisition data, to supporting proactive cyber defense.”

More broadly, the company said the OpenAI for Government initiative will give customers access to its “most capable” models within “secure and compliant environments,” including through ChatGPT Enterprise and ChatGPT Gov.

It will also offer custom models for national security organizations on a limited basis, per the announcement.

Katrina Mulligan, the head of the company’s national security policy and partnerships who previously served as chief of staff to the secretary of the Army and principal deputy assistant secretary of defense for special operations and low-intensity conflict, told DefenseScoop that the new project with the CDAO marks a major milestone for OpenAI for Government as it moves to expand its work in the public sector.

“The $200 million ceiling reflects the Department’s trust in OpenAI to responsibly bring frontier AI into mission-critical settings. CDAO is also creating a framework for how OpenAI’s leading expertise can be used to develop and test solutions that support the Department of Defense in its mission to ensure the safety and security of Americans,” she wrote.

An OpenAI spokesperson said the goal of the pilot program is to “scope” and prototype potential applications for frontier AI.

“The contract aims to explore where OpenAI’s tools can improve military operations and cybersecurity, save time for staff by making tedious work more efficient, and help DoD better support service members. DoD requires this type of contract to scope future partnerships, and the project is structured to lead into a potential follow-on production agreement. The applications may take different functions and forms that are yet to be determined – but could include all of our services,” they told DefenseScoop, adding that all use cases must be consistent with the company’s usage policies and guidelines, which “prohibit its use for the development or use of weapons.”

The announcement of the $200 million deal came just a few days after OpenAI’s chief product officer Kevin Weil joined the Army Reserve to serve in Detachment 201, a new “Executive Innovation Corps,” along with other execs from the tech community.

Mikayla Easley contributed reporting for this story.

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“This is a new way for us on how to rapidly innovate and rapidly prototype,” Jacob Glassman, who serves as senior technical advisor to the assistant secretary of the Navy for research, development and acquisition, told DefenseScoop.

Glassman and Dr. Raj Dasgupta, a research scientist at the Naval Research Laboratory’s Distributed Intelligent Systems Section, shared new details Wednesday about the sea service’s ongoing pursuits developing and deploying AI, during an SNG Live event hosted by Scoop News Group.

“As we all know over the last, what, three years now, [generative AI] has been the main gamechanger in the field of AI. The Navy and our lab also have been able to use genAI techniques,” Dasgupta said.

GenAI encompasses the field of still-maturing technologies that can process huge volumes of data and perform increasingly “intelligent” tasks — like recognizing speech or producing human-like media and code based on human prompts. 

These capabilities are pushing the boundaries of what existing tech can achieve. Still, according to Glassman, the Navy has historically “struggled with AI adoption.”

Glassman and his team produced DoN GPT to ultimately help resolve some of the challenges causing that struggle. It’s essentially an enterprise-level offering that is hosted and deploys AI capabilities in the Navy’s Flank Speed cloud service, which is an enterprise Microsoft 365 environment that currently hosts unclassified but sensitive information at Impact Level 5, or IL5.

“All the technical people out there know that that’s really, really important for us. So that enables us to scale, but also enables us to reach every single enterprise user in the Navy and Marine Corps,” Glassman said.

The team hosted “alpha trials” over the course of 45 days to identify the best AI use cases to unleash DoN GPT with a beginning subset of the workforce.

“My favorite part of this was our demo to the [assistant] secretary [of the Navy]. No PowerPoint, none of the ‘here’s a big white paper.’ I pulled it up and we had trained and re-tuned it with all of our acquisition strategies over the past, like, 12 years or something. And I had it prompted in — I said, ‘I need an acquisition strategy on a program with sharks with freaking laser beams attached to their heads [a reference to a line in an Austin Powers movie] and an OV-1 [High Level Operational Concept Graphic],’ and it cranks it out right there. Now, there’s misspellings and you’re always going to have the rock throwers [but] I did that in five minutes — and with just me, right?” Glassman explained. “So those are the kind of things we’re seeing” in terms of use cases.

That accelerated trial period allowed DoN GPT’s makers to identify bugs that they typically wouldn’t find until years after launching an enterprise service, in his view. 

It also unfolded at the same time when the Pentagon and its components were offering options to incentivize more than 50,000 civilian personnel to retire early or resign, as part of a major campaign from the Trump administration to cut the federal workforce and what current leaders view as wasteful spending.

“AI can absolutely dramatically improve this. Why do I need three or four full-time equivalents in a program office to generate an acquisition strategy when DoN GPT may be able to do it with one person in the course of a week?” Glassman said.

So far, he noted, the entire Navy has lost about 10% of its civilian staff in the government’s ongoing workforce reduction effort. 

“Now, the Navy and Marine Corps have a large workforce — but definitely in our contracting officer points, our business financial management and within our program management teams, it’s really, really significant. So, this capability will not replace them. AI does not replace people, but it augments the remaining force enough to mitigate [some of] that” strain, Glassman said.

Now, his team is “gearing up” for DoN GPT’s next “beta trials” of piloting.

“I’m actually briefing the [assistant] secretary later today about that,” Glassman told DefenseScoop, referring to acting Assistant Secretary of the Navy Brett Seidle.

Beyond this nascent tool, inside the Naval Research Laboratory, scientists and experts are studying and applying AI across a wide variety of use cases. 

Some, according to Dasgupta, are associated with reinforcement learning, attack simulations like through red-teaming and blue-teaming, large language models and even large reasoning models, which go further than text generation and can reveal more about systems’ thinking processes.

They are also working to help the Navy confront and counter risks that accompany applications of genAI.

“We want [the workforce] to use tools like DoN GPT and genAI, but we also want to give them a feel of what the risks of genAI are. So essentially, we are looking at tools that will take genAI and it will output the output of the genAI, plus what the risks of that output is going to be, so that the end user — like the fleet or the sailor — they have a good idea of what the risks could be of using that,” Dasgupta told DefenseScoop.

Updated on June 12, 2025, at 9:45 AM: This story has been updated to clarify that Jacob Glassman planned to brief acting Assistant Secretary of the Navy Brett Seidle, not Secretary of the Navy John Phelan.

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As a combined intelligence and combat support agency, NGA is tasked with collecting geospatial intelligence (GEOINT) from satellites and other sources and turning it into actionable data for military operators and decision makers. Although the organization is already well-versed in AI capabilities — as it runs the Pentagon’s high-profile Maven computer vision program — NGA Director Vice Adm. Frank Whitworth is pushing personnel to leverage the technology even more this year.

“As I was looking at the last several years, we use descriptors for the ‘A’ in NGA with a little bit of alliteration, and we would say ‘action’ or ‘acceleration,’” Whitworth told DefenseScoop in an exclusive interview on the sidelines of Space Symposium. “I wanted to put a finer point on it and say, the ‘A’ for this year is going to be AI.”

Research from NGA and industry on multimodal artificial intelligence has been one of the key factors driving his push for acceleration, he noted.

Similar to generative AI, multimodal models can simultaneously process information from multiple source types — including text, images and audio. But while generative AI is geared toward creating new content, multimodal AI expands on those capabilities and allows users to understand different types of data in a single, integrated output. 

For NGA, that could mean creating more holistic intelligence packages from sources beyond just imagery, which will be critical as available data is expected to exponentially increase in the coming years due to new sensors being fielded across domains.

“Distinction is really difficult — to distinguish enemy from non-enemy, combatant from non-combatant,” Whitworth said. “While GEOINT, imagery-derived intelligence typically is one of our primary forms of identifying and driving distinction, we always are looking for other forms of corroboration.”

NGA has already employed three test cases for multimodal AI, although Whitworth said the capabilities are still too early-on in their development to talk about them more specifically. However, he emphasized that the agency continues to explore other ways to collect and verify data beyond computer vision. 

“It’s a big Earth, and so while we have really good indications typically of where to look, there’s no guarantee,” he said. “Our business is to steal secrets, and when people are trying to keep things secret from us that could cause us harm or change the American way of life, we’re going to have to rely on all kinds of sources to ensure we’re looking at the right place.”

NGA also intends to leverage lessons from ongoing AI-focused targeting programs like Maven and apply them to its other roles, such as warning — which Whitworth described as a “behemoth” responsibility.

“Warning involves establishing a baseline of equipment, behaviors, activities around the world and being able to prioritize where you’re looking, and then ensuring you can find anomalies and announce them,” he said. “That’s going to take a lot of work. … Warning requires improvements in workflow, improvements in automation and yes, it will involve AI and ML.”

The warning mission is currently under the purview of the agency’s Analytic Services Production Environment for the National System for GEOINT program, also known as ASPEN, Whitworth said. 

Kickstarted in 2023 to address massive increases in data, ASPEN is a suite of analytic capabilities that leverages automation and AI to help NGA analysts provide more accurate warning indications to customers.

In order to streamline adoption of AI across all of its roles and responsibilities, Whitworth noted that NGA has recently established a new program executive office for advanced analytics helmed by Rachael Martin, who previously served as the program lead for NGA Maven. The PEO intends to bring together the agency’s best practices of artificial intelligence and machine learning.

The new office is NGA’s latest organizational change made to help its AI adoption in 2025. The agency also recently named Mark Munsell as its first director of AI standards, while Trey Treadwell and Joseph O’Callaghan are serving as director of AI programs and director of AI mission, respectively.

“We didn’t actually have people who came to work and said, ‘I am the director of AI.’ And it was time to do that,” Whitworth said. “As the organization begins to mature, you at least have somebody who’s thinking about, what do I need in my organization? Do I need to change names? Do I need to change the organizational structure to ensure AI/ML is treated with the seriousness it deserves?”

At the same time, NGA is keeping an eye out for AI-enabled capabilities being developed by the commercial industry that could be incorporated into its workflows, while leveraging innovative procurement methods geared towards commercial solutions, Whitworth said.

For example, the agency used the Defense Innovation Unit’s commercial solutions opening (CSO) process to award pilot funding for Project Aegir, a program that focuses on commercial techniques to identify, monitor and track illicit maritime vessel activity in the U.S. Indo-Pacific Command area of responsibility. NGA is also exploring using bailment agreements, which allows the Defense Department to temporarily loan government-owned systems to smaller companies in order to test, research and develop new capabilities during a trial period.

“It is really focused on, how do we get industry into the door quickly? That’s why we did things like the CSO, that’s why we’re trying bailment agreements,” Whitworth said. “We’re leaning forward on trying to bring them in, because industry moves at the speed of light.”

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Pending accreditation, the chatbot will be deployed to U.S. Indo-Pacific Command and allow users to experiment with genAI models on the Secure Internet Protocol Router (SIPRNet), Jeff Marshall, director of DISA’s Hosting and Compute Center, said during a webinar broadcast Tuesday by Federal News Network. The platform is currently in the accreditation stage and is expected to open up “within the next month or so,” Marshall noted.

The capability was developed in close collaboration with the Air Force Research Lab, which launched its own experimental generative AI chatbot for the Department of the Air Force on unclassified networks — dubbed NIPRGPT — last year. Similar to AFRL’s program, AFRL and DISA are using the effort to evaluate and expedite delivery of commercial AI tools, but the agency’s initiative will be in classified realms, Marshall said.

“We’re not trying to deploy this on our own. We’re not trying to make it a production system. This is [a research-and-development] system that we’re using for Indo-Pacom in order to test large language models overseas,” he said.

Across the Pentagon, organizations have looked to capitalize on commercial large language models and other artificial intelligence capabilities. Although there have been various efforts over the last few years — ranging from task forces to experimental platforms — the department is still learning how the technology can be best used to improve back-office and tactical operations.

Marshall noted that DISA’s SIPR-based LLM will largely help “facilitate that demand signal of, what does an Indo-Pacom commander need and want to utilize AI for? And then, how do we then shape that to what industry can actually provide for us at scale?”

DISA plans to host the chatbot on one of the two Joint Operational Edge (JOE) cloud environments it has deployed to the Pacific. Initiated in 2023, the JOE cloud effort seeks to stand up commercial cloud environments at the agency’s overseas data centers, allowing DISA to place cloud-native applications in locations outside of the continental United States. Along with JOE, the agency is also providing its private cloud capability known as Stratus to areas overseas.

To date, DISA has put two JOE cloud nodes at Indo-Pacom and one at U.S. European Command, and will soon deploy another node in Southwest Asia, Marshall said.

Moving forward, DISA is looking to potentially provide additional JOE cloud environments in Europe in order to support operations for U.S. Africa Command, which is headquartered in Germany. But Marshall emphasized the agency is doing so while balancing demand signals with available resources.

“Let’s don’t just throw it all out there one time and hope that it sticks to the wall,” he said. “We’re taking in the demand signal, we’re making sure that there is a valid need that supports us doing the deployment and then, of course, there’s a budget to cover it.”

Updated on March 26, 2025, at 10:35 AM: This story has been updated to clarify AFRL’s role in the new chatbot initiative and to remove “acting” from Jeff Marshall’s job title.

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DIU — a Silicon Valley-headquartered organization which has embedded personnel at Indo-Pacom and Eucom to help tackle some of the combatant commands’ tech-related challenges.

On Wednesday, DIU announced that Scale AI was awarded a prototype contract for the new Thunderforge capability, which will include the company’s agentic applications, Anduril’s Lattice software platform and Microsoft’s large language model technology.

“The Thunderforge technology solution will provide AI-assisted planning capabilities, decision support tools, and automated workflows, enabling military planners to navigate evolving operational environments. By leveraging advanced large language models (LLMs), AI-driven simulations, and interactive agent-based wargaming, Thunderforge will enhance how the U.S. military prepares for and executes operations,” the unit said in a release.

DIU issued a solicitation for the program last year via its commercial solutions opening contracting mechanism.

“The joint planning process is complex, time-consuming, and resource-intensive. Planners and other staff members must synthesize large amounts of information from diverse sources, consider multiple courses of action (COA), and produce detailed operational plans and orders – often under significant time pressure. As the operational environment becomes more complex and dynamic, there is a need to accelerate and enhance joint planning capabilities while maintaining rigor and human judgment,” the document stated.

In a statement Wednesday, Bryce Goodman, Thunderforge program lead and contractor with DIU, noted that current military planning processes rely on decades-old technology and methodologies.

The U.S. military wants new tech that can quickly ingest, process and summarize large volumes of information relevant to military planning; identify key insights, patterns and relationships; produce draft operations plans, concept plans and operations orders; and perform automated wargaming of courses of action and provide comparative analysis of advantages, disadvantages and risks.

“Our AI solutions will transform today’s military operating process and modernize American defense. Working together with DIU, Combatant Commands, and our industry partners, we will lead the Joint Force in integrating AI into operational decision-making. DIU’s enhanced speed will provide our nation’s military leaders with the greatest technological advantage,” Scale AI founder and CEO Alexandr Wang said in a statement.

According to DIU, initial deployments of the system to Indo-Pacom and Eucom are expected to support “mission-critical” planning activities such as campaign development, theater-wide resource allocation and strategic assessment.

If the tech meets expectations, plans call for scaling the Thunderforge capability across the U.S. military’s combatant commands in the future.

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As our world becomes increasingly powered by generative AI, the most sought-after resource is no longer oil or gold — it’s data. Staying ahead in the AI race requires constant troves of new data to create better generative AI models.

However, we cannot treat AI as just another consumer good designed to make life or work easier, where we simply choose the cheapest or most convenient assistant. The AI technologies we use shape the knowledge we absorb, influence our beliefs, and could become geopolitical tools for misinformation — a national security concern that cannot be overlooked.

For example, the recent high-performing chatbot developed by Chinese company DeepSeek does not provide information about Tiananmen Square and purveys common Chinese Communist Party propaganda about Taiwan and other topics. And yet, days after DeepSeek launched, it became the most popularly downloaded free application in the U.S.

In response, U.S. legislators proposed a bipartisan bill to ban DeepSeek from government devices. The aim is to prevent users from sending heaps of information to DeepSeek and to Chinese state-owned entities. By interacting with DeepSeek over the internet, we are surrendering the single most important resource in maintaining leadership in AI: data.

However, there are three big problems with the data required to train generative AI. First, the world is running out of the high-quality, real-world data required to train models, with Epoch AI predicting we may run out by 2028.

The second problem is that real-world data is inherently flawed and biased because it’s simply a collection of society’s beliefs and actions. Therefore, AI is liable to perpetuate existing political, racist, sexist, and other biases. The current administration has underscored the importance of developing “AI systems that are free from ideological bias or engineered social agendas.”

Third, real-world data is often incomplete. Within the Department of Defense (DOD), intelligence, surveillance and reconnaissance (ISR) systems can face collection gaps, resulting in incomplete data sets (e.g. incomplete satellite image data). In addition, computer security needs — such as identifying network intrusions or malware — may be weakened by incomplete data sets.

The solution to these problems is synthetic data. Synthetic data can augment real-world data by filling in critical gaps, help provide the volume of data needed to train AI and mitigate intrinsic biases. Synthetic data is designed to resemble real-world data and is artificially engineered by computers using algorithms, simulations, or machine learning models. Gartner predicts that more than half of the data used to train AI will be synthetic by 2030.

The benefits of synthetic data generated by today’s “classical” computers are that it’s widely available, affordable, and ideal for small to mid-scale problems with well-structured data. The drawbacks are that classical synthetic data can be less complex and diverse than real-world data, can struggle to capture the high-dimensional patterns needed for training AI, and may face future challenges in scaling quickly to meet growing data demands.

Quantum computing is the solution to these challenges. Quantum computers will generate higher volumes of data and higher quality data than classical synthetic data.

“The future of generative AI training lies in combining real-world data with both classical and quantum synthetic data,” says Dr. Graham Enos, vice president of quantum solutions at Strangeworks and a former DOD mathematician. “As quantum computing advances, quantum synthetic data will increasingly dominate the synthetic data used to train AI. What’s exciting is that synthetic data generation is one of the most immediate and practical applications of quantum computers.”

The seemingly otherworldly properties of quantum computers make them ideal for the machine learning and simulation tasks that generate synthetic data. Unlike classical computers, which rely on bits that are either 0 or 1, quantum computers use qubits that can exist in a superposition of both states simultaneously, providing exponentially greater computing power. Entanglement is another critical property of quantum computing that allows qubits to represent more complex data distributions, enabling more complicated calculations than classical computers. By leveraging both superposition and entanglement, quantum computers can double their compute power simply by adding one qubit — in contrast, classical systems require doubling the number of transistors to double compute power.

Five years ago, the largest quantum computer was Google’s 53-qubit Sycamore chip that demonstrated “beyond classical” performance on a computational benchmark. The largest machines built today, from IBM and Atom Computing, boast upwards of 1,000 qubits. While quantum computers are not yet outperforming classical computers for practical applications, including generating meaningful quantum synthetic data for commercial AI training, they are quickly approaching that moment.

Recently, quantum computing company Quantinuum, a spinoff from Honeywell, announced that data from its H2 quantum computer can train AI systems using its Generative Quantum AI framework.

In the announcement, Dr. Thomas Ehmer from the healthcare business sector of Merck KGaA, Darmstadt is quoted as saying, “While some may suggest that a standalone quantum computer is still years away, the commercial opportunities from this breakthrough are here and now…the [Quantinuum] Helios system, launching later this year will hopefully enable AI to be used in unprecedented ways and unlocking transformative potential across industries.”

Similarly, in work partially funded by the U.S. government, Rigetti (RGTI) used a quantum neural network to generate synthetic data and fill gaps in global weather radar coverage, matching the performance of a classical baseline model. (These quantum machine learning methods from Rigetti are available on Strangeworks.)

This type of work is directly applicable to enhancing C4ISR capabilities by leveraging advanced computer vision techniques to analyze complex sensor data. For example, programs like Project Maven use synthetic data to train AI models that interpret full-motion video (FMV), synthetic aperture radar (SAR) imagery, and other intelligence sources.

The impacts of quantum computing go beyond improving AI models. Additional defense-related applications include cybersecurity threat detection, adversarial intent prediction, cryptanalysis, electromagnetic spectrum operations, and many more.

Yet, China is outspending the U.S. four to one in federal quantum technology investment and is steadily closing the technology gap. If the U.S. wants to continue to lead the quantum computing race and be the first to fully leverage the power of quantum synthetic data, we must bolster public and private investment in quantum technologies.

First, due to the time and effort needed to develop quantum workflows, government and private organizations should start working with experts today to identify use cases and build the hybrid infrastructure needed for rapid adoption of quantum data.

Second, the U.S. must reauthorize the National Quantum Initiative (NQI) Act, which accounts for about half of federal investment in quantum technologies. The initial NQI Act, which expired in 2023, was signed into law by President Donald Trump in 2018 and resulted in meaningful progress.

Lastly, Congress should increase funding to both DOD and the Department of Energy by passing the newly introduced bipartisan DOE Quantum Leadership Act as well as the Defense Quantum Acceleration Act. The latter was introduced by Rep. Elise Stefanik and Sen. Marsha Blackburn in April, and the legislation is intended to “supercharge the Department of Defense’s approach to quantum technology and advance U.S. national security.”

Without a recommitment to federal investment in quantum technologies, the U.S. risks another DeepSeek moment with both quantum and AI.

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GenAI models can leverage input data to create new content such as images, videos, audio and text.

“These systems have the potential to revolutionize mission processes by enhancing operational speed and efficiency, improving decisionmaking accuracy, reducing human involvement in redundant, tedious, and dangerous tasks, and enabling real-time adaptability to dynamic operational environments. This technological advancement can significantly boost mission effectiveness and operational readiness, providing a strategic edge in modern warfare. Commanders and senior leaders should advocate for the use of GenAI tools for their appropriate use cases,” Lt. Gen. Melvin “Jerry” Carter, deputy commandant for information, wrote in new guidance that he issued in December.

Marines with the 15th Marine Expeditionary Unit — which deployed to the Pacific with the Boxer Amphibious Ready Group for much of 2024 and participated in a variety of exercises and other activities in the region — are already taking advantage of these types of capabilities.

“We used a generative AI model to be able to assist with some staff functions they’re offering [for] both our unclassified and classified systems on using this. And some of the things that they would do is we get a lot of information, situation reports, different things like that, [and] we were making summaries using the Gen AI to help facilitate that,” Maj. Victor Castro, a communications officer with 15th MEU, told DefenseScoop during a call with reporters on Tuesday to discuss the deployment.

Capt. Kristin Enzenauer, a space operations officer, noted that an AI tool from Vannevar Labs can sift through media.

“That filters all mentions of 15th MEU … throughout the foreign media and provides us a summary to see how the 15th MEU was being mentioned or impact across the information environment, across foreign media. So we would present that probably biweekly … to the commanders,” she said.

Carter in his recent directive stated that Marine Corps commands will be expected to establish task forces or cells consisting of data, knowledge management, AI and digital operations experts to assess the applicability of existing and in-development offerings and produce a list of “forthcoming preferred GenAI capabilities.”

Col. Sean Dynan, who was the commanding officer of 15th MEU during last year’s deployment, said the unit’s use of artificial intelligence technology was just “the tip of the iceberg” for what Marines and amphibious ready groups could potentially do with it in the future.

“When we look at how complex it is to … prepare and supply an ARG/MEU deployed forward, I think that there’s significant opportunity for more use of AI,” he told reporters.

Gen. Eric Smith, commandant of the Corps, recently expressed confidence that Marines — especially the younger cohorts — will embrace genAI effectively.

“I don’t have any concerns about integrating it into the force,” he told DefenseScoop during a Defense Writers Group meeting last month. “My Marines are digital natives. I mean, they grew up with an iPhone 14 in their hands. You know, I grew up with a cord. So the young Marines will figure out how to use that and they’re the ones that are telling us how to do it. They’re completely comfortable with generative AI, with machine-to-machine learning. They’re completely comfortable with using a pad and doing targeting off a pad and passing that targeting data ubiquitously across the force through a sensor cloud and then down to another user through GEO satellites. They’re completely comfortable with that, so I don’t have any concerns about that other than the security of our communications links.”

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The Corps, like the Defense Department writ large, is keen on tapping into emerging genAI capabilities for various military purposes.

Last month, Lt. Gen. Melvin “Jerry” Carter, the deputy commandant for information, issued new guidance on the technology, writing that these types of tools present “unique and exciting opportunities” for the Marines. Such genAI models can use input data to create new content such as images, videos, audio, and text.

“These advanced AI algorithms possess the remarkable ability to provide humanlike responses to user prompts, leveraging the vast datasets on which they were trained,” Carter noted.

“These systems have the potential to revolutionize mission processes by enhancing operational speed and efficiency, improving decisionmaking accuracy, reducing human involvement in redundant, tedious, and dangerous tasks, and enabling real-time adaptability to dynamic operational environments. This technological advancement can significantly boost mission effectiveness and operational readiness, providing a strategic edge in modern warfare. Commanders and senior leaders should advocate for the use of GenAI tools for their appropriate use cases,” he wrote.

Commands will be expected to establish AI task forces or cells consisting of data, knowledge management, artificial intelligence and digital ops experts to assess the applicability of existing and in-development offerings and produce a list of “forthcoming preferred GenAI capabilities,” he noted.

Commandant Gen. Eric Smith told DefenseScoop at a Defense Writers Group meeting Wednesday that he doesn’t anticipate the need for a major organizational shakeup for the Corps as it looks to integrate new artificial intelligence capabilities. And he’s not looking to invest a lot of money into building bespoke platforms for Marines.

“We’re not going to reorganize based on AI. We are going to use the tools that are available, and we use the Marine Corps Warfighting Lab, along with deputy commandant for information … to make sure that what is out there is utilized by us so that what I don’t want to do is I don’t want to pay the S&T costs, I don’t want to pay the R&D costs. I just want to take off-the-shelf technology and have my Marines use it because the off-the-shelf technology is better than anything that I could come up with. I mean, it’s not a matter of the DOD having to lead this effort. I mean, it’s a matter of being a fast follower,” Smith said.

DOD officials and others have voiced concerns about the technology, including so-called “hallucinations” where genAI models produce inaccurate, misleading or biased results that could create problems for the humans who are trying to leverage them.

Smith noted that there needs to be an effective and continuous testing and evaluation methodology to ensure the output of the algorithms meets “reasonable expectations.”

Data privacy and security are also top of mind for DOD officials looking to bring these cutting-edge capabilities into the Pentagon and field them to military units. The department doesn’t want its sensitive data leaking out into the public domain or where adversaries can find it.

The commandant of the Corps expressed confidence that the Marines — especially the younger cohorts — will embrace genAI effectively and figure out the best use cases.

“I don’t have any concerns about integrating it into the force,” Smith told DefenseScoop at the Defense Writers Group meeting. “My Marines are digital natives. I mean, they grew up with an iPhone 14 in their hands. You know, I grew up with a cord. So the young Marines will figure out how to use that and they’re the ones that are telling us how to do it. They’re completely comfortable with generative AI, with machine-to-machine learning. They’re completely comfortable with using a pad and doing targeting off a pad and passing that targeting data ubiquitously across the force through a sensor cloud and then down to another user through GEO satellites. They’re completely comfortable with that, so I don’t have any concerns about that other than the security of our communications links.”

He noted that he recently spoke with leaders and Marines at U.S. Cyber Command about these types of security issues.

“I have concerns about our security layers, but they [and] we have to operate. You know, you can’t be so defensive that you can’t use your systems,” Smith said.

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Known as Project Athena, the pilot aims to evaluate the use cases and cost models of commercially available genAI tech that can be used to support the service’s back-end office work. The effort is being led by the Army’s Chief Information Officer Leonel Garciga alongside the Office of Enterprise Management (OEM). The assessment is slated to end in April, after which the department hopes to create a list of capabilities that can be purchased by various service components based on their needs and mission requirements.

“We’re going to assess different tools so that we can equip Army organizations with information. What capabilities should you consider based on your use cases? What is the cost model and what do you need to know about that?” Jennifer Swanson, deputy assistant secretary of the Army for data, engineering and software, said Tuesday during a roundtable with reporters.

The goal for Project Athena isn’t to choose a single generative AI platform and mandate its use across the service, but rather provide options for Army offices that detail the pros and cons of each capability — including their different features, use cases, cost models and deployment architectures, Swanson said.

Over the last year, the Army and others at the Pentagon have worked to understand how emerging genAI platforms that leverage large language models (LLMs) can be integrated into the department.

While some efforts have looked into the technology’s applicability for warfighting functions, the most immediately promising use cases are those that support daily business operations. In October, the service announced a new pilot dubbed #CalibrateAI focused on simplifying repetitive and arduous tasks, that has since been brought under Project Athena.

“We really wanted to focus on where we had this opportunity to employ capability at scale, get some of those use cases operating [and] look at some different models,” Garciga said during the roundtable. He added that Project Athena is evaluating a range of tools — from commercial-off-the-shelf software that can be deployed on existing environments to niche, integrated LLMs for existing capabilities.

Garciga noted that generative AI has been very useful in supporting the Army’s legal teams, public affairs offices and recruiting efforts. The technology has also shown promise in assessing documents related to requests for information (RFI) and sifting through the Pentagon’s vast inventory of policy documents, Swanson added.

Because funding for the genAI tools will come from individual Army organizations that choose to purchase them, a large part of Project Athena has been dedicated to informing leaders about the actual cost of implementing the capabilities — which can require additional cloud compute and storage infrastructure that might become too expensive for some offices to manage. 

“We want to make sure that we’re informing them from the standpoint of, this is really what you need to consider when you’re spending that money to make sure that we’re getting the best deal for the Army, and to make sure that we are aware of all the bills that may come with tools,” Swanson said.

Some genAI tools and platforms are already operationalized, accredited and on the network through Project Athena, according to Garciga. Once the pilot concludes in April, the Army plans to publish guidance based on lessons learned that were documented through the effort and work on what the service needs to do at the enterprise level moving forward.

“We want to throw stuff on the network and just operationalize it, but a lot of this has also been, what does this mean from an enterprise perspective? How do I hook up identities to it? How do we work on where we put the data?” Garciga said. “We could get that a little bit standardized so it makes sense.”

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