The Pentagon Needs AI Talent, but Can’t Attract & Keep It with Yesterday’s Bureaucracy

The Pentagon took off sprinting in a race to accelerate the adoption of artificial intelligence in our defense ecosystem over the first five months of this year. The Department of War was clear about its goal to transform the U.S. military into an “AI-first” fighting force.

The Department issued sweeping memorandums, launched new AI tools, and created over 100,000 AI agents to support its tech overhaul.

It’s an impressive start for America’s largest employer and biggest bureaucracy. But in the Pentagon’s rush to keep pace with a revolutionary technology, it’s overlooking something mundane but just as essential: hiring.

There are many talented U.S. citizens concerned about AI technology who want to lend AI-related skills to the Department of War and Intelligence Community to ensure the country maintains its technological edge against adversaries. But despite the interest, several important institutional barriers are preventing these highly skilled people from having maximum impact.

This Policy Byte examines the bureaucratic hurdles that are stymying hiring and the Pentagon’s push to adopt AI, sketching out the typical hiring process that two highly qualified prospective employees might face in seeking to bring their tech talents to the Department of War. Our piece then zooms in on the significant challenges that might cause potential hires like these to walk away.

Meet Maya and Spencer, two fictitious personas we use to illustrate some current barriers preventing the Department from being as “AI-first” as it could be.

It is instructive to start with Maya. She recently graduated top of her undergraduate class from a respected data science program, spent two years building machine learning models, and interned at a defense-adjacent research lab. She wants to serve her country, but she doesn’t think uniformed military paths are right for her. As she makes the decision on her entry-level job in this high-demand sector, a mid-tier tech firm is offering $145,000 with equity and a signing bonus. The Department of War’s 1560/Data Scientist posting on USAJOBS lists a GS-7 salary of $55,000, almost one third the private-sector offer. She follows her heart and applies for the government role, but doesn’t hear back for more than five weeks. Her student loans can’t wait for a hiring timeline measured in seasons rather than days. Maya reluctantly accepts the industry offer.

Next, Petty Officer Second Class Spencer Sullivan. He’s an enlisted Navy Information Systems Technician who taught himself to code Python while on deployment. He is extremely proud of a prestigious innovation award he won for a predictive maintenance tool he built, which saved his command thousands of man-hours. His leadership praised the initiative, but due to rigid personnel timelines he was routed back into the standard career path. While the Navy introduced a Robotics Warfare Specialist rating in 2024, Sullivan wasn’t in one of the seven ratings that were transfer-eligible. No occupational code captures the totality of what Spencer does, or his potential impact on the force in the future. He cannot laterally move into a headquarters civilian AI billet or a research laboratory without first separating from service, earning a qualifying degree, and re-entering through the hiring process that may take over a year. Spencer’s talent is trapped inside a personnel system designed for the industrial age, which is undergoing a well-intentioned but insufficiently paced effort to modernize.

The two vignettes presented above don’t represent real people, but the fictionalized scenarios illustrate a frustrating point: the government’s AI talent acquisition and retention systems are working exactly as designed, but for a different era. The national security enterprise’s fragmented AI talent ecosystem can’t recruit fast enough, pay competitively enough, or move people fluidly enough to ensure the right person with the right qualifications in the right role. At this critical time, an urgent, bespoke set of layered solutions is required for at least two important structural failures identified by numerous studies over the past several years.

The federal/private-sector AI-related pay gap is a structural deterrent that shapes career decisions at the point of entry and accelerates mid-career attrition. Under standard policy, a highly talented, young AI software engineer like Maya without a master’s degree typically can’t enter the government above GS-7, a significant disadvantage compared to competitive salaries in the tech sector. The existing hiring system also makes it difficult for talented experts without a traditional educational or career path to be hired at a grade commensurate with their level of expertise.

A separate RAND study approached the issue by interviewing private-sector recruiting experts. The emphatic consensus was that the federal pay scale is not sufficient, with one recruiter highlighting the fact that their company pays software engineers higher salaries than any GS-15 or Senior Executive Service members are even legally permitted to earn.

The Department of War’s January 2026 AI Strategy directs military services to attract and retain talent via the use of special hiring/pay authorities and novel talent programs, but none have been publicly released yet. Other efforts like the Army’s “Detachment 201” to directly commission a small number of senior AI advisors on small projects, or the Army’s new 49B AI and Machine Learning occupational code are well intentioned, but as currently scoped are too small to provide a holistic solution for the wider force, particularly as the very moment that the Secretary of War has emphasized speed and rapid adoption.

The service-specific, stove-piped talent management patchwork cannot keep up with the pace of change because the system isn’t designed to sufficiently see, classify, or move AI talent. A 2021 report noted systemic issues: AI positions are frequently filled when the billet is funded and available, not when there is a skill match with the role. AI talent identification relies on word-of-mouth and personal networking instead of reliable, repeatable processes. And many service members possess relevant AI skills but end up assigned to non-technical roles where they are either underleveraged or completely unrecognized.

This underutilization often stems from legacy processes that fail to prioritize important technical expertise. In the Navy, the researchers noted anecdotal sentiment that “the Navy doesn’t value its AI talent” because personnel who invest time developing AI skills instead of serving in traditional roles become “unpromotable.”

The lack of a DoW-wide framework to manage AI talent is revealed in the way the individual military services are building independent AI-specialist occupational tracks. There isn’t currently a way to manage AI talent enterprise-wide via cross-component detailing to vital billets. More concerning is the fact that there is no comprehensive data on AI talent management for the department that provides fidelity who is leaving, why, and what could be done to retain them. The Department of War could be losing its most talented and valuable AI-savvy civilians, soldiers, sailors, airmen, and marines without even knowing it.

AI growth is exploding with as-of-yet unknown impacts on the entry-level job market, and many experts contend the global AI race won’t be won by the side with the best hardware, but instead with the best tech-savvy talent. One key report suggests that “digital expertise is the most important requirement for government modernization, but few parts of the government have adequately invested in building a digital workforce.” A group of experts assess bluntly: “the government will not be able to recruit its way out of its technology workforce deficit.” Without an expanded pipeline from universities to government service, the talent gap (and negative operational impact it causes) will continue to create risk for U.S. national security.

The barriers that the real Mayas and Petty Officer Sullivans face are serious, and without bold action, could add up to strategic failure. There is a large body of research from the past few years sitting on the shelf, full of well-developed recommendations ready to be implemented immediately if properly resourced. So far, Congress and the Pentagon haven’t demonstrated sufficient political will to make the required holistic, transformative personnel policy choices to reduce the strategic risk.