The recently released 2025 VA AI Inventory catalogs cases of AI use across the department, from clinical tools used during patient care to systems that help process benefits, automate records management and support customer service functions.

Among the systems identified is Ambient AI Scribe, a clinical documentation tool used during medical appointments.

According to VA, the technology listens to clinician-patient conversations and generates clinical notes, reducing administrative workload and allowing providers to spend more time focused on veterans.

VA’s disclosure also details widespread use — by more than 50,000 personnel — of commercial AI products across the department, including Microsoft Copilot Chat, Microsoft Teams Premium, Grammarly GitHub Copilot and other platforms for support tasks such as meeting transcription, document drafting, report summarization, information retrieval and software development.

Nearly Every Major VA Function Features AI

Health and medical applications represent the largest category of use cases, followed by systems supporting benefits administration, information technology, service delivery, financial management and administrative operations.

Applications identified in the inventory assist clinicians with imaging analysis and diagnostic workflows, identify disease risk, summarize records, analyze feedback, process forms and provide information through chat-based tools. VA also classifies its VA.gov chatbot as a high-impact use case.

VA says the chatbot is a tool that helps veterans locate information, complete tasks and access support services through self-service interactions.

Another high-impact use case, TERA Memorandum Automation, helps claims processors complete toxic exposure memoranda by pre-populating answers using information already contained in veterans’ records.

VA reports the system achieved a 98.12% accuracy rate and was used to assist with more than 181,000 forms, saving an estimated 54,581 work hours since deployment.

“VA is using artificial intelligence to improve how veterans access care, benefits and services, while also helping employees work more efficiently and effectively,” said VA Press Secretary Quinn Slaven in a statement to Military Times.

According to Slaven, the inventory reflects “steady growth, stronger governance and expanding real-world impact, particularly in health care, benefits processing and operational efficiency.”

Inventory Provides A Detailed View Of Governance Requirements

For currently deployed high-impact systems, the inventory tracks whether agencies completed pre-deployment testing, impact assessments, independent reviews, monitoring processes, operator training, fail-safe mechanisms and appeal procedures.

Individual entries also identify potential risks associated with specific applications. For several health care systems, descriptions note that inaccurate outputs could contribute to downstream clinical or operational impacts if not detected and corrected, while also stating that providers receive training and oversight intended to mitigate those risks.

VA says all high-impact systems are required to complete an AI Impact Assessment and Risk Mitigation Plan before being put to work.

“Deployed high-impact AI use cases are continuously monitored,” said Slaven. “And any use case that cannot be brought into compliance is discontinued.”

Some AI systems previously reported by VA are now classified as retired. Those systems include healthcare, training and operational applications. The inventory does not specify why individual systems were deactivated.

Performance Data Varies Across Systems

The level of detail provided in the inventory varies from one system to another. Some entries provide measurable performance outcomes, testing results or operational metrics. Others provide only basic descriptions of intended functions and governance requirements.

The U.S. Government Accountability Office, which developed an AI Accountability Framework for federal agencies, says meaningful oversight requires documentation that can be independently reviewed.

“GAO’s AI Accountability Framework highlights the importance of documentation and a paper trail that an independent third party can follow,” GAO officials told Military Times. “As AI inputs and operations are not always visible, a central tenet to oversight is, ‘Show me, don’t just tell me.’”

GAO said organizations should be able to demonstrate who is responsible for AI systems, how those systems operate, whether they rely on quality and reliable data and how limitations are identified and addressed.

The agency also emphasized the importance of monitoring metrics such as accuracy and error rates and establishing thresholds that help determine when corrective action or retirement of a system is warranted.

Veterans Advocates Want Human Accountability

Chris Macinkowicz, deputy director of the Veterans of Foreign Wars National Veterans Service, said the organization’s concerns focus less on whether VA should use artificial intelligence and more on how those systems are managed after deployment.

“AI needs to be a tool, not an employee,” Macinkowicz told Military Times.

He said human review remains essential, particularly when systems are involved in processes affecting veterans.

“We need to make sure human eyes are on it afterwards,” he said. Macinkowicz also stressed the importance of continued evaluation after deployment.

“We need to have assurances from VA that they’re going to go back after these AI agents are deployed to make sure that they’re still accurate a month down, six months down, a year down [the road],” he said.

GAO officials said organizations should establish metrics that help determine when corrective action is needed and when an AI system should be retired because it no longer meets its intended objectives.

As VA expands the use of AI across healthcare, benefits processing and other operations, GAO officials said meaningful oversight depends on agencies being able to demonstrate, through documentation and measurable performance data, that systems continue operating as intended.

The disclosure offered a glimpse into how generative AI is being used by the military. It came in a court filing supporting the Justice Department’s effort to dismiss a lawsuit targeting a Mississippi power plant, also owned by Musk, that supplies electricity to data centers used to run the AI system.

Cameron Stanley, the Defense Department’s chief digital and artificial intelligence officer, made the disclosure in a statement filed in support of the government’s motion. He called the example “a testament to the greatly increased operational efficiency made possible by the Grok Gov Model.”

He added that if the lawsuit succeeds in shutting down the power plant, Grok’s parent company, xAI, would lose capacity to train and develop future versions of the AI tool. “And if xAI is hindered from continuing to improve and upgrade Grok, including the Grok Gov Model, [the Department of Defense’s] ability to meet its national security mission and keep pace with adversaries will be impaired,” Stanley said.

Although Stanley did not elaborate on how military operators used Grok during combat missions, he instead described it as a “critical national security” tool.

“For example, it is deployed in Maven Smart Systems (MSS) to support vital national security missions, including targeting, intelligence, readiness, and recruitment,” he said.

Stanley explained that MSS users process nearly two billion tokens on the Defense Department’s top-secret network each day. Tokens are units used to measure AI processing. “This amount of AI usage is roughly equivalent to 1.5 billion words or up to 6 million pages of text being processed by the technology,” he said.

The Justice Department filed Stanley’s statement alongside its motion on June 15. In the filing, government attorneys argued the lawsuit “threatens American national, economic, and energy security by seeking to shut off the power supply for artificial-intelligence innovation that supports the Department of Defense’s military operations.”

The lawsuit, filed in April by the NAACP and its Mississippi chapter, alleges that xAI and MZX Tech failed to obtain required permits before installing and operating 27 gas-powered turbines at a facility in Southaven, Mississippi, roughly 13 miles south of Memphis, Tennessee.

The facility, known as the Colossus Gas Plant, powers two data centers — Colossus I and Colossus II — used to operate the Grok chatbot. The complaint said xAI also plans to build a third data center closer to the plant.

The organizations argued the turbines are operating without adequate pollution controls and are emitting harmful pollutants that pose risks to public health. They say the facility operates near an area with “tens of thousands of people” and has the potential to emit more than 1,700 tons of smog-forming nitrogen oxides.

They contend that a Clean Air Act permit would require the plant to use the “best available control technology,” or BACT, to reduce emissions to acceptable levels.

“Had Defendants obtained a permit, they would have been held to this standard,” the complaint said. “By simply not applying for — or receiving — a permit, xAI and MZX Tech have avoided subjecting these twenty-seven unpermitted turbines to a BACT determination.”

Associate Attorney General Stanley Woodward said the Justice Department intervened because enforcement of federal law falls to the executive branch, not private interest groups. “The Department of Justice is committed to maintaining that constitutional order while protecting national security and promoting American energy and innovation,” he said.

Justice Department officials added in a press release that the state of Mississippi is charged with administering the permitting program and decided no permit was required.

Adam Gustafson, principal deputy assistant attorney general for the Environment and Natural Resources Division, said the government “will not sit idly by while private organizations use environmental laws to undermine our national security.”

In a statement, officials with Earthjustice and the Southern Environmental Law Center, the legal groups representing the NAACP in the lawsuit, framed the motion as the Trump administration’s attempting to protect wealthy, well-connected companies from being held to account.

They noted that instead of disputing xAI’s permit violations and allegations of unlawful polluting, the Justice Department expressed “vague” concerns about national security.

“This isn’t about national security,” said Laura Thoms, director of enforcement for Earthjustice. “It’s a desperate attempt to protect wealthy corporations like xAI from obeying our bedrock environmental laws.”

Last year, the Defense Department awarded xAI — along with other AI companies — a contract of up to $200 million for developing “agentic AI workflows across a variety of mission areas.” Around that same time, the White House revealed its plan for integrating AI into workflows across the federal government.

Kym Meyer, SELC’s litigation director, said the motion contradicts “decades of well-established legal precedent,” and called it “an unprecedented attack on the public’s ability to defend themselves from illegal pollution.”

Ukraine, in the fifth year of fighting a full-scale Russian invasion, is already using AI for a plethora of battlefield functions, from flying drones at targets to helping plan combat operations and crunching data on Russian missile attacks.

“AI will form a new paradigm of warfare. It’s already actively doing so,” Danylo Tsvok, the head of the defense ministry’s AI center, told Reuters.

He predicted AI systems would eventually be unified into a single network overseeing the battlefield, leading to a “war of operating systems” with Russia in the next three to five years, if the conflict continues.

“The system that possesses more data and better understands that data, proposes solutions — that system will gain the advantage over the other,” he said.

The center was founded in March as Defense Minister Mykhailo Fedorov seeks to put AI and data-driven decision-making at the heart of Ukraine’s defenses.

Drones, still mostly flown by pilots, have already upended the way the war is being fought.

Ukrainian and Russian troops launch thousands of unmanned aerial vehicles (UAVs) a day at each other. Kyiv is also trying to solve its frontline troop shortage with ground robots.

The ability of drones to constantly surveil the battlefield and hit targets with precision has accelerated the “kill chain” – the process of planning and executing a strike on the enemy. AI decision-making would speed this up even more, Tsvok said.

Technological arms race

Ukraine, whose military numbers around a million personnel, is already using AI tools in its command systems.

But Tsvok said the goal was to create a single operating system to recommend battlefield decisions all the way up from individual frontline units to strategic command.

This would significantly speed up the analysis of data from the 1,200-kilometer (750-mile) front line to allow recommendations to human commanders, he said.

The aim, Tsvok said, is to unite weapons and data systems into “one single living organism that can operate in a coordinated manner.”

The technological arms race launched by Europe’s largest war since World War Two, which lasted from 1939 to 1945, has attracted interest from foreign AI companies hungry for combat data to train their models and the opportunity to test their systems.

Some, such as U.S. company Palantir, have provided Ukraine with their systems. Kyiv has created Brave1 Dataroom, a project to share battlefield data with allied countries for training their software.

“This is the place where you can understand whether your system works,” said Tsvok, wearing a black T-shirt and jeans.

Moscow is also developing its artificial intelligence capabilities. A senior Ukrainian air defense commander told Reuters in April he was concerned by Russia’s increasing use of AI in planning drone and missile attacks on cities, which could significantly reduce the planning time for each strike.

“The question is,” Tsvok said, “how quickly we build our solutions and how practically we apply them and achieve the primary impact on the battlefield from our side.”

He added that the defense ministry was developing an AI-driven recruitment and HR system as part of Fedorov’s push for data-driven reform of the vast government department.

Ukraine operates on the principle of having a human in the loop on combat decisions, but Tsvok said AI systems could eventually outrun humans, whose presence would then slow decisions down.

“Then the question arises: how do we keep up with making decisions that autonomous systems propose?” he said.

On the heels of President Donald Trump’s recent executive order on AI innovation, Emil Michael, the undersecretary of defense for research and engineering, said that he’s concerned about the category of “cyber weapons” that companies are releasing, such as Anthropic’s Mythos.

“These companies have a responsibility to ensure that their weapons, what they call weaponization potential of these models, to be careful and thoughtful about what they’re doing,” Michael said Thursday at The Washington Post’s inaugural Building America Summit.

Lawmakers have increasingly warned against the weaponization of AI models by U.S. adversaries against citizens, businesses and government agencies, calling for their crafting to include resilient security measures to safeguard against hacks.

On Tuesday, the White House released an executive order that establishes an “AI cybersecurity clearinghouse,” through which AI industry partners can volunteer to have the Defense Department scan their systems for software vulnerabilities before their release.

Michael said that companies that have models with a “weaponization capability” could allow the federal government to spend 30 days examining their systems. The government could potentially identify vulnerabilities across the country in systems with IP that could be susceptible to hacks, such as electricity grids or public hospitals.

“I think they’ve all agreed and think it’s a good idea to do that. That’s been a good constructive process,” Michael said. “I give all the companies, Open AI, even Anthropic, and Google credit for sort of agreeing that was a smart thing to do.”

Anthropic has been left out of deals with the Pentagon after the firm refused to allow unrestricted access to its Claude models for fully autonomous weapons and mass domestic surveillance.

The company sued the Trump administration over the federal government labeling the firm as a supply chain risk over its decision to restrict the military’s use of its technology.

Mythos, Anthropic’s new model, has drawn criticism as skeptics of the program point out that it could pose a danger with its hacking and cybersecurity capabilities. Anthropic previewed the model and announced that it was capable of finding ways to exploit vulnerabilities in software.

Meanwhile, the DoD has integrated AI throughout the department.

When posed a question at the summit about the government’s usage of AI, Michael said that six months ago, only about 80,000 federal employees were AI users each month. But now, there are 1.5 million, he stated, saying that the government has “raced” to ramp up usage among workers for efficiency, intelligence and warfighting.

“I think by the end of this year, I’d be shocked if three quarters of the department isn’t using AI in some way,” Michael said. “We’ve integrated all the biggest AI companies over the last few months, so, we’re, in one year, going to make progress more than the five years before it.”

Speaking at a hearing before the Senate Armed Services Committee Tuesday, Lt. Gen. Michael Conley said “necessity had been the mother of invention” in spurring the service to apply available machine learning tools to combat operations.

“On the first day, they realized that our MQ-9 [Reaper drones] and other aircraft were at risk in a very hostile environment, and they were able to take some smart people, use artificial intelligence tools, and put humans on the loop, instead of in the process the whole time, and move Top Secret national-level intel,” Conley said, referring to his “small team” of AI and tech specialists.

The process of moving intelligence would have taken human operators “20 to 30 minutes to get that to a crew, into a cockpit, or into a ground control station,” he said.

Instead, Conley said, nine crew members were able to use AI “bots” to convert applicable data to a Secret clearance level and make it available in the cockpits of aircraft — all within two to three seconds.

“We have data that indicates that we’ve saved a lot of aircraft over the last 60 days using that tool, just to provide a better battlefield situational awareness,” Conley said.

Conley also confirmed the Air Force was using its AI-powered exploitation and dissemination tools to help intelligence analysts process huge amounts of complex data, including full-motion video.

“[In] a very heavy, human-intensive process, they’ve been able to use AI again, just smart humans getting together, figuring out solutions in order to proliferate that information across the intel community in seconds, rather than what could take hours in normal processes,” he said. “So we’re learning every day, and we’re getting better every day.”

To date, two U.S. aircraft have been downed by hostile fire over Iran: an F-15E Strike Eagle fighter jet and an A-10 Thunderbolt attack plane. Three more F-15s were lost to friendly fire over Kuwait.

Reportedly, some 24 Reaper drones have been destroyed by Iran over the Strait of Hormuz and elsewhere. An Air Force F-35A Joint Strike Fighter also sustained severe damage over Iran.

While aviation losses have been significant, Conley’s comments offer a rare window into prospective casualties that may have been averted, and how AFSOC is employing emerging tools to expand its operational picture.

The Air Force recently released contracting documents for a desired “Next-Generation Air Operations Center Weapon System” that would deliver more AI tools to planners at the geographic combatant commands, an effort for which it has not specified a procurement timeline.

Conley hailed MQ-9 operations in Epic Fury particularly noteworthy.

“Our MQ-9 enterprise proved that adaptive airmen can transform any platform and mission envelope, destroying hundreds of targets in contested operating areas,” he said. “I believe recent operations have been the MQ-9 community’s finest hour.”

He also hailed the work of AFSOC in rescuing two downed F-15 pilots in April in a daring and widely praised joint operation. The mission resulted in the loss of two MC-130 aircraft as well as four Army MH-6 Little Bird helicopters. Conley said he’d like to see the aircraft replaced quickly, although he said he expected it to “take some time.”

The service’s goal is to familiarize Marines with generative artificial intelligence platforms and large language models, such as Google’s Gemini, OpenAI’s ChatGPT, and xAI’s Grok, which are among the tools available through the Defense Department’s recently launched GenAI.mil platform.

The requirement follows broader DoD efforts to integrate AI across training, administration and operational planning while establishing standards for responsible use of the technology.

In an email, Maj. Hector Infante, the communications director for Marine Training and Education Command, said the course is intended to give Marines a “foundational understanding of artificial intelligence and its relevance to today’s operating environment.”

Infante said the 45-minute online course will introduce “key AI concepts, practical application, and responsible use considerations” through interview-style segments featuring experts discussing policy and operational applications.

He added that the course will “focus on how AI can support decision-making and mission effectiveness” and promote awareness of the tool rather than technical expertise.

According to the MARADMIN, all Marines have until Dec. 31, 2026, to complete the course. They can enroll and access the course through the Marine Corps eLearning Ecosystem, or MCeLE (formerly MarineNet). Civilians working within the Corps are “highly encouraged” to take the course as well.

Additionally, the Marines said that intermediate and advanced AI courses are currently in development and will be available sometime this fiscal year.

Last December, the DoD unveiled the GenAI.mil platform as part of the White House’s plan to expand AI infrastructure and introduce AI tools throughout the government.

About a month later, the Marines made the platform its official chatbot and began retiring its previous tool, the Non-classified Internet Protocol Router Generative Pre-training Transformer, or NIPRGPT.

Kyle ‘KMo’ Moschetto, a former Marine cyber officer turned technology consultant, explained in a video tutorial that GenAI.mil makes commercial AI tools available in a secure network.

“Think of this as a military-grade version of the AI tools that you’ve heard about from ChatGPT to Anthropic Claude to Google Gemini, but it lives inside government-level security and policy enforcement,” Moschetto said.

When describing GenAI.mil, military officials often use terms like “efficient” and “force multiplier” but they’re light on practical use.

However, Defense Secretary Pete Hegseth used layman’s terms to explain the capability’s possibilities early on.

In a memo released alongside the announcement, Hegseth wrote that it “can help you write documents, ask questions, conduct deep research, format content, and unlock new possibilities across your daily workflows.”

“It removes wasted time and focuses more of our energy into decisive results for the warfighter,” he added.

The same censorship apparatus the Party built to control its people is now corrupting the AI systems its leaders depend on. The United States, by leaning into an open marketplace of information and ideas, will gain advantage as it takes a different path.

AI is increasingly training newer, faster AI models. This typically involves scraping the internet for content and then loading it into datasets for new programs. The problem: online content used for training is increasingly generated by AI. As a result, each generation of technology drifts from reality.

AI researchers refer to this as “model collapse,” a phenomenon in which models trained on their own synthetic outputs degrade over successive generations. The only defense is a constant influx of fresh, honest, human-generated information. Without it, the system folds in on itself.

China’s Great Firewall cuts off that influx, expediting the impact of model collapse within its borders. The first generation of large language models worldwide was trained on massive datasets of publicly available human-generated text. These human-derived pieces of information built an algorithmic approximation of how people think, argue, explain, and communicate.

Now, the internet is filling with AI-generated content at a rate inconceivable five years ago. Marketing copy, product descriptions, social media captions and news summaries are increasingly produced by AI systems and published online. They’ve all become generic and detached from human signals.

With each cycle of new AI, the models drift further from their human origins. The newer systems amplify the patterns AI systems favor while losing nuance and amplifying existing biases. Each successive AI generation is one step further removed from the humans these programs were originally built to serve.

In China, the Great Firewall accelerates this problem. The Chinese Ministry of Public Security built the Great Firewall of China in the late 1990s to censor the Chinese people. It is now the most sophisticated information control infrastructure in human history. The Great Firewall does not just restrict what Chinese users can see. It shapes the data used to train Chinese AI systems. By design, it strips out politically sensitive events, dissenting viewpoints, and independent reporting. Left behind is a curated record of reality aligned with the Party’s narrative. That filtered record of information becomes the raw material for LLMs.

The training data fed into LLMs in China does not contain any criticism of the government, fair reportage of controversial topics, or accurate information about Chinese history. Events such as the the Uyghur detention camps exist inside the Firewall only as the state chose to describe them.

Now add model collapse on top of this. Chinese AI companies such as Baidu, Alibaba, ByteDance and dozens of others aggressively deploy AI-generated content across their platforms. This material becomes training data for the next generation of Chinese AI models. With independent reporting locked out, model collapse expedites inside the Great Firewall with no escape valve. The practical consequences of this divergence are already visible and will accelerate.

Chinese LLMs struggle with tasks that require new observation, original synthesis or human complexity that the training data was designed to suppress. If asked about repression in Chinese history or controversial current events such as the Uyghur detentions, these LLMs either do not answer or produce a response indistinguishable from a Party press release. A Chinese trade official relying on domestic AI to model the economic impact of Western sanctions is working from a system incapable of providing an honest account of how those sanctions functioned or failed in comparable historical cases.

The West has a milder version of this problem.

Western AI models are trained on increasingly synthetic content, but human reporters regularly push new information into the ecosystem. Free and open societies have structural advantages to retain the capacity to reason about what is happening in the world rather than what previous AI systems described. When asked about Tiananmen Square, Western accounts typically focus on the 1989 protests and the crackdown. By contrast, Chinese models either refuse to answer or return state-aligned language. This information gap becomes part of the training data for the next generation.

To maintain a competitive AI advantage over China, Washington should treat human-generated data as a strategic asset and invest in journalism, open web archives, and synthetic-content labeling. Preserving the integrity of American training data is a defense imperative, not a tech problem.

Chinese leaders deploying AI products to make decisions about economics, geopolitics, and public health will make those decisions based on systems trained on what China’s information control apparatus wants people to believe. That is not an intelligence system. It is a mirror. And the tragedy of model collapse is that a mirror that has been looking at itself long enough no longer reflects anything.

Joe Buccino is a retired U.S. Army colonel and the author of “When Every Word Counts: How to Earn Trust, Command Attention, and Communicate Clearly in Any Situation.”

The move has stakeholders urging not to lean too hard on algorithms to distinguish, for instance, a scientist-spy sharing secret nano-energy plans with China from a Chinese professor publishing AI safety studies. Hanging in the balance lies troops’ technological edge, veteran intelligence officials and academics say.

AI’s confusion over the timing and nature of research partnerships may obscure real espionage if humans are not the ultimate judge of foreign influence, they warn, with some academics fearing an AI-work slop redux of the “China Initiative,” where the first Trump administration charged dozens of ethnic Chinese scientists with fraud, only to drop nearly all charges.

“Automated vetting tools are extremely useful for vetting large datasets and identifying patterns of concern. But those tools are for decision support to help the people, the human analysts, assess context and intent,” said David Cattler, who, until September, led the Defense Counterintelligence and Security Agency, which screens personnel seeking security clearances.

The renewed focus on academic research security comes as tensions between the U.S. and China over AI intensify, with China ramping up both the development of low-cost AI models and its alleged exploitation of U.S. models.

The U.S. intelligence community’s annual threat assessment reports that China “aims to displace the U.S. as the global AI leader by 2030,” in part, “by using its sizeable talent pool, extensive datasets, government funding and burgeoning global partnerships.”

Partnership is a complicated term in military “fundamental research” — studies that are publishable, rather than proprietary or classified, with potential defense applications.

For instance, mere co-authorship with a China-based scientist does not suffice to deny a U.S. researcher money, according to evolving academic research rules targeting foreign influence. Rather, to make the call, the Pentagon must assess each academic’s disclosures of external funding sources and affiliations.

According to a recently-declassified May 2025 inspector general report, such disclosures went unchecked because the Pentagon had not “requested additional government full-time equivalent employees to thoroughly review… and to conduct oversight of over 27,000 academic research awards.”

Defense News first obtained the report and the Pentagon’s response to a draft version through an open records request.

When asked about additional staffing, a Pentagon official pointed to orders in a January research security directive for the Chief Digital and AI Office (CDAO) to identify “automated vetting and continuous monitoring capabilities” and create a common research grant database.

The mandate also calls for a year-long “damage assessment” of selected research transactions, including cases that the House Select Committee on China flagged, in part, using AI tools.

A September GOP-led Committee report alleged that the Pentagon subsidized 1,400 academic papers published between June 2023 and June 2025 involving partnerships with the Chinese government.

The Pentagon said in an emailed statement on Thursday that the department “is committed to protecting the integrity of U.S. research while fostering international collaboration. Our approach leverages advanced analytical tools to augment human expertise, ensuring a rigorous and fair review process.”

Machines Make the Same Mistakes

Several university representatives, who requested anonymity due to the sensitivity of policy discussions, note that AI-assisted risk assessments have, in the past, drawn false assumptions about U.S.-China research collaborations.

For instance, the AI-aided GOP House report mislabeled the state-backed Wuhan National Laboratory for Optoelectronics as the sponsor of a collection of nine essays, whereas the Wuhan lab supported only one essay that involved no U.S. authors or federal funding.

The report also mistook a Chinese military-affiliated author’s publication on single-electron transistors for a DOD-funded project, when it merely referenced work funded by the U.S. military.

House Select Committee officials declined to comment on the cases.

Averting foreign interference “is not as easy as just having better AI capacities, because we know that some of those AI mistakes were the same human mistakes that led to inaccurate charges brought against researchers under the China Initiative,” Toby Smith, senior vice president for government relations and public policy at the Association of American Universities, said after learning of the AI gaffes.

About 30% of the China Initiative’s 77 cases involved academics not disclosing Chinese partnerships or funding sources, though disclosure was often not required, or no such partners or funding existed.

A jury found only one professor, Harvard nanochemist Charles Lieber, guilty, after he lied to Pentagon investigators about his participation in a Chinese talent recruitment program and made other false statements.

Smith said that relying on proxies — such as co-authorship, affiliation or nationality — to deduce security concerns led investigators to confuse co-authorship with direct interaction or shared data access, and to misinterpret historical ties as active partnerships.

“AI systems trained on bibliometric data,” or citation analytics, “and affiliation records can inherit the same flawed assumptions that underpinned the China Initiative,” he said. “The core lesson from the China Initiative is that identifying genuine research security risk requires judgment, context and proportionality — qualities that automated systems should support, not replace.”

Gisela Perez Kusakawa, executive director of the Asian-American Scholar Forum, said that the Pentagon has not communicated the types of data that the new AI tools will collect, use and share, nor sought community feedback, heightening concerns about ethnic profiling.

In an emailed statement, the Pentagon responded, “It is the standard practice of the department to not provide specifics regarding the criteria and weighting for threat assessments conducted, whether by manual or automated process.”

Kusakawa said, “We should be making sure that our research environment is welcoming, that we are encouraging these talents, especially in AI, to come to the United States and, frankly, have family in the United States, and make this a country that they contribute to and invest their and their children’s future in.”

Increasingly, that AI talent is not coming. Statistics suggest that Chinese AI experts are staying in China, even as America has managed to keep earlier expats.

Of about 100 Chinese-origin AI scholars who were researching at U.S. institutions in 2019 — when their papers were accepted at the world’s most elite AI conference, NeurIPS — 87% remained stateside as of 2025, according to Carnegie Endowment for International Peace figures.

At the same time, the share of Chinese home-grown talent not moving abroad has skyrocketed since 2019, based on an Economist tally of similar data. In 2019, about 30% of NeurIPS authors educated in China were still in China. By 2022, that population had jumped to 58%, and up to 68% in 2025.

‘Key’ Financial Disclosures Were Missing

To ensure that no country has a stranglehold on general knowledge, “the goal cannot be to close the U.S. research system altogether,” said Cattler, the former Defense Counterintelligence and Security Agency director and now founder of consultancy Ironhelm Works. “The goal must be to ensure that collaboration strengthens national security rather than inadvertently weakening it.”

Last year’s inspector general report concluded that Pentagon agencies that partner with academic institutions “could be at an increased risk of exposure to foreign influence” because military units were “missing key documents” that can help discern scientists’ foreign financial sources, outside employers and other details related to potential conflicts.

For instance, about 80% of Air Force funding transactions sampled in the report were missing documents that can reveal problematic relationships. Also, the Air Force and the Defense Advanced Research Projects Agency — which bankrolled the Internet’s inventors — told evaluators that they had not, as required, annually checked that researchers named in progress reports were not involved in banned talent recruitment programs.

In response to questions about the screening lapses, the Pentagon said in an emailed statement that the department “is aware of the OIG’s findings in the report and the impacted directorates are working with [the Office of the Undersecretary for Defense and Engineering] to address the items identified.”

Jeffrey Stoff, a Chinese linguist and intelligence community analyst for 18 years who resigned in 2021 due to frustration with research security, maintains that DOD still needs more human expertise in language, culture and the minutiae of research restrictions.

“AI can and should be used for unsophisticated, labor-intensive tasks,” such as cross-referencing foreign organizations in financial disclosure forms against the names of blacklisted parties, but humans still need to inspect AI’s work, as not all nefarious ties are machine-readable, said Stoff, who now advocates for tighter safeguards as head of the nonprofit Center for Research Security and Integrity.

Cattler, giving more credit to AI, said that the scale of research collaboration demands that the Pentagon upgrade its approach to clocking potential spies.

With such a large population of research institutions and affiliated scientists, as well as a bombardment of often duplicative alerts, automation “improves the signal within that noise and can help orient the humans on really important matters,” he said.

“Sometimes people are deliberately deceptive when they are processed, and that could happen in a human exchange just as much as it could happen in something that a computer can see, but together, a human review and a computer review are incredibly powerful.”

The armed forces are working with partners on a system to provide what Desmeulles called “true distributed working capability” centered on data and using advanced AI, “a sovereign system that will essentially be the equivalent of Maven.”

The system could be available within a few months, and available for exercises in September 2027, the general said, declining to provide specifics.

Project Maven is a Pentagon program that uses AI to process drone and surveillance data to automatically detect and track objects, using technology provided by contractors including Palantir Technologies. Maven has faced controversy amid questions about AI-assisted targeting in Iran, with concerns about speed, accountability, and harm to civilians related to automated kill chains.

“We’ve really positioned data as the center of everything we do,” Desmeulles said in a briefing with three reporters on Saturday at the Montmorillon military camp in western France, describing data as the ammunition of the command post.

“The centrality of data is something that’s well understood by the corps, the Army, and the French forces,” he said. “So, we’re really focused on that.”

The armed forces are on track to develop “a true distributed working capability, based on highly advanced artificial intelligence and centered on data,” Desmeulles said. “We’re following that logic, to remain sovereign, and that’s an area where we are strong.”

Desmeulles said his corps is already seeing “very, very good” results from a data-centric approach, even if there is “still a little way to go before it’s practically perfect in my eyes.”

France has several AI companies that are active in defense, including Comand AI, ChapsVision and Safran’s AI business, and is also home to a major developer of large-language models with Mistral AI. France in 2024 created an agency under the Armed Forces Ministry that works on AI for defense.

The website cited a state-run media report and experts calling the vessel a “major leap in integrated combat capability” that “positions the vessel among the most advanced frigates in service today”.

A slew of announcements such as that one from March 30 shows AI expanding across a military that aims to “intelligentize” as it prepares for potential conflicts in the South China Sea and Taiwan Strait. But analysts say China is picking its AI battles carefully rather than expecting quick domination of the technology or short-term parity with the United States.

China is taking a “cautious official posture” toward AI in the armed forces, said Sophie Wushuang Yi, postdoctoral teaching fellow with Schwarzman College at Tsinghua University.

“China’s concept of intelligentized warfare has been embedded in official defense white papers since 2019,” Yi said. “But the open-source academic literature is frank that China cannot currently close the overall gap with the United States in military AI capability.”

Still, AI is becoming a force within the forces.

An institution under the People’s Liberation Army in January used AI to test drone swarms and, according to a test run shown on Chinese state television, one soldier supervised some 200 of the autonomous vehicles at the same time.

AI is taking on a greater role as well in the military’s use of space and cyberspace, said Malcolm Davis, a senior analyst for defense strategy and national security with the Australian Strategic Policy Institute. In space, he said, it can manage “complex orbital operations,” while in cyberspace it can plan and conduct operations against critical information infrastructure.

The military’s ability to use AI at machine speed would potentially let it exploit a faster “observe-orient-decide-act” loop compared to purely human-controlled systems, Davis said.

“That’s something that’s being demonstrated by the U.S. and Israel now in operational planning in the Iran war, where AI is playing a key role in identifying targets and planning mission packages,” the Canberra-based analyst said. “There’s no reason that the PLA won’t learn from that and utilize a similar capability.”

A testament to AI’s reach throughout the military, a March 26 PLA Daily report notes its use in battlefield perception, intelligent decision support and autonomous control systems.

PLA leaders particularly value AI decision-making because most of their people lack battlefield experience, unlike American counterparts, said Sam Bresnick, a research fellow with the Center for Security and Emerging Technology at Georgetown University.

He said its priorities include layering AI on top of computer networks, gathering volumes of data and the autonomy of unmanned systems such as uncrewed underwater vehicles.

Chinese officials want to surpass the U.S. in military AI use, Bresnick noted, but the government today fears information that AI could use or generate. “The data could go against Xi Jinping and Communist Party ideals,” he said. “They don’t want to lose control over it.”

The U.S. armed forces now have a “commanding” AI lead over China, the Modern War Institute at West Point said in a March 17 study.

It says the United States has more than 4,000 data centers versus some 400 in China. Four-year-old U.S. export controls on advanced semiconductors shipped to China limit Beijing’s access to AI-related hardware, the study adds.

“China’s publicly stated position is considerably more cautious and more hedged than is commonly assumed in Western coverage,” Yi said.

“The PLA lacks the volume of real operational data that the U.S. military has accumulated over decades of expeditionary warfare, and there are unresolved doctrinal tensions between the decentralized decision-making that effective AI-enabled operations require and the PLA’s deeply embedded centralized command culture,” said Yi of the Schwarzman College.

China may have surpassed the United States in AI for drone swarms, however, said Chen Yi-fan, assistant professor in the Diplomacy and International Relations Department at Tamkang University in Taiwan.

“With the addition of drone carriers already in service, the PLA has taken the lead over the U.S. military in this category of AI military applications,” he said.

The Qinzhou frigate was commissioned last year and did a combat drill in the South China Sea, where Beijing disputes maritime sovereignty with five other governments.