Defense Secretary Pete Hegseth addressed AUSA attendees at the Project Agincourt panel on Wednesday, sharing that the Pentagon is requesting roughly $50 billion in reconciliation funding to support the push to scale autonomous and robotic systems.

This request is part of an effort to make the technology available in far greater numbers.

The figure is separate from the Pentagon’s fiscal 2027 budget request, which includes $53.6 billion for the Drone Dominance initiative. That request covers $16.9 billion for unmanned-system procurement, $14.4 billion for counter-unmanned systems, $13.5 billion for contested logistics, $4.5 billion for collaborative autonomy and $4.3 billion for institutional systems and personnel. The budget request does not establish that the reconciliation funding Hegseth cited is the same money or that it is dedicated entirely to drone purchases

Hegseth characterized the funding as a one-time investment rather than a recurring annual allocation, intended to accelerate the development and production of autonomous and robotic systems.

“Ultimately we will be dealing with hundreds of thousands of one-way attack systems this year and into the millions in years to come at a pace that this department’s never ever seen before.”

The Defense Secretary said the Pentagon must make it easier for new companies to compete in a defense market long dominated by established contractors and costly platforms. Traditional defense firms remain important, he said, but the department also needs lower-cost systems that can be manufactured at scale.

The Army’s Drone Dominance program offers a concrete measure of that ambition. In a December 2025 announcement, the Army said a $1 billion initiative would fund approximately 340,000 small unmanned aircraft systems for combat units over two years. The service said it expected to deliver tens of thousands of drones in 2026 and hundreds of thousands by 2027.

Acting Army Secretary Adam Telle said the service is broadening access to its programs for companies outside the traditional defense industrial base. The Army, he said, must make room for new entrants while continuing to work with established suppliers.

Telle also described plans to coordinate the Army’s autonomous-systems efforts while distributing capabilities to operational units. The service is developing an approach that incorporates drones, autonomous ground systems, autonomous fires and autonomous lift into future combat operations.

“We have an MOS in the Army, 390A, where warrant officers could keep the speciality and the skills over the duration to teach our soldiers how to do robotics,” Telle added.

Expanding access to Army test ranges is another part of that effort, Telle said. Greater access gives developers more opportunities to test systems and receive feedback from military users. He also described efforts to share lessons across the force rather than have individual units and organizations solve the same problems independently.

Hegseth said the department must remove bureaucratic barriers that slow the adoption of new technology, telling the panel, “I want you to tell me which rules can be signed away.” He urged officials to identify rules and directives that could be changed without involving new legislation to accelerate progress.

The concern, critics say, is not limited to weapons that can select and engage targets on their own.

At the hearing, titled “Artificial Intelligence in the Military Domain: Implications for Human Rights,” witnesses told members of the Tom Lantos Human Rights Commission that AI can influence human decisions leading to a strike while leaving the individual formally responsible for the final call.

“The approval button alone does not demonstrate the control,” Anna Mysyshyn, a Ukrainian AI-governance researcher, told lawmakers.

The issue comes as the Pentagon pushes to use AI to accelerate military decision-making. The Pentagon’s 2023 AI adoption strategy identified “fast, precise and resilient kill chains” as a desired outcome, while its 2026 AI strategy calls for becoming an “AI-first” warfighting force by redesigning military workflows and operational concepts around current AI capabilities.

Mysyshyn questioned whether a person supervising multiple AI-enabled systems can recognize an incorrect recommendation and intervene fast enough before harm occurs. She cited Ukraine as an example of AI compressing decisions from hours to minutes.

In many scenarios, a human making the final decision does not necessarily have enough information, time or the ability to challenge the system’s recommendation, she said.

The Defense Department’s 2023 directive on autonomy in weapon systems requires autonomous and semi-autonomous weapons to allow commanders and operators to exercise appropriate levels of human judgment over the use of force.

Wednesday’s testimony focused on a broader question: whether that judgment remains meaningful if AI is influencing targeting faster than a human can authenticate.

Amanda Klasing, national director of government relations and advocacy at Amnesty International USA, warned that AI can compress the military “kill chain” in ways that make it difficult to determine whether meaningful human discernment actually occurred before a strike.

Joseph Chapa, a former military officer and scholar of military ethics, argued that the debate should not focus solely on how autonomous a system is. He urged policymakers to consider whether an AI system relies on statistical inference, what happens when it fails in a particular operational environment and whether its decisions can be audited afterward.

Statistical AI systems can fail differently from traditional software, Chapa said, including producing substantially different outputs from relatively small changes in inputs. More complex systems can also make it difficult to reconstruct how a particular recommendation was produced after the fact.

He called for greater AI education across the military, including specialized training for personnel working with targeting and intelligence systems.

Witnesses on Wednesday also pointed to a larger accountability problem: determining what happens when an AI-assisted decision results in civilian harm.

The Defense Department established its Civilian Harm Mitigation and Response Action Plan in 2022, including a Civilian Protection Center of Excellence intended to serve as a hub for analysis and training. Federal law requires the Pentagon to operate the center.

But a Defense Department inspector general review released in May assessed the department’s implementation of the civilian-harm framework and found problems including the loss or reassignment of personnel, the end of funding for a civilian-harm data-management platform and the failure of a senior steering committee to continue meeting.

That matters in the case of AI because, as Klasing noted, the military currently has limited ability to determine whether AI may contribute to improper targeting of civilians.

On the first day of the U.S.-Iran war, a Tomahawk cruise missile struck Shajareh Tayyebeh elementary school in Minab, southern Iran. At least 168 people were killed — more than 100 of them under the age of 12, according to UN and Iranian officials.

The school building sat fewer than 100 yards from a long-time Islamic Revolutionary Guard Corps naval installation and was previously located within the IRGC compound perimeter until a wall appeared between 2013 and 2016, according to an analysis of satellite imagery by Amnesty International.

By the time the U.S. and Israel launched their first strikes on Feb. 28, the school had been established several years prior. It was active on social media and had its own website, a Reuters investigation found.

More than 120 House Democrats sent a letter to the Pentagon on March 12, demanding clarity on whether artificial intelligence, “including the use of the Maven Smart System, [was] used to identify the Shajareh Tayyebeh school as a target.”

U.S. military investigators in the wake of the incident stated that the strike was likely carried out by U.S. forces. The Pentagon, however, has still not released its investigative findings.

Klasing proposed adding information about AI involvement to the Pentagon’s annual civilian-casualty reports required under Section 1057 of the National Defense Authorization Act. The existing reporting requirement calls for information about civilian casualty mitigation efforts and investigations, but does not specifically require the department to identify whether AI contributed to an incident.

Steve Feldstein, a senior fellow at the Carnegie Endowment for International Peace, said the Pentagon should collect data from military operations and conduct after-action reviews to determine whether AI-assisted systems accurately identified intended targets and whether errors contributed to civilian casualties.

“A bigger question is to what extent is the Pentagon collecting data and verifying the target accuracy of all these 1000s of strikes?” Feldstein said.

Feldstein also cautioned against treating AI itself as the source of every potential problem. AI can be used to help verify intelligence, detect changes in imagery and prioritize defensive responses to threats such as drones and ballistic missiles.

But if a human-influenced system is built around assumptions that certain demographic traits make someone a lawful target, Mysyshyn said, AI would not correct that underlying premise.

Feldstein similarly pointed to the rules humans establish before an AI system is used. If commanders determine that a particular level of civilian harm is acceptable in pursuit of a military objective, he said, the machine is operating within a human-defined framework.

The challenge is particularly acute when systems operate faster than people can meaningfully assess recommendations.

The commission opened Wednesday’s hearing with an example from Iraq. In 2004, Army squad leader Nick Ellison encountered two young men firing a machine gun from a car during a census patrol in Kirkuk.

Ellison made eye contact with one of the men, saw panic and ordered his soldiers to hold fire. The men turned out to be brothers of a newly married groom, and the gunfire was celebratory.

Mysyshyn said testing should examine bias, reliability, civilian protection, realistic time pressure, whether operators can interrupt systems and whether decisions can be traced throughout an AI system’s lifecycle.

Chapa said some AI systems may be difficult to audit after the fact, while Deborah Brown of Human Rights Watch said information about military AI use has often emerged through investigative reporting and whistleblowers rather than government disclosures.

The growing challenge, according to witnesses, is no longer making a machine pull a trigger, but whether the human who approves the trigger pull has enough time, information and authority to make a meaningful choice before disaster.

Military Times reporter Katie Livingstone contributed to this report.

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.