In the fast-evolving world of military technology, speed is not just a competitive edge—it’s increasingly a necessity. As artificial intelligence (AI) becomes an integral part of modern warfare, the defense sector faces a pivotal challenge: how to rapidly adopt and deploy these cutting-edge solutions without succumbing to the pitfalls of overreliance or losing sight of the human judgment that has long underpinned military decision-making. Two recent analyses, published on October 24 and 25, 2025, by TokenRing AI and researchers Hyeyoon Jeong and Mathew Jie Sheng Yeo, offer a comprehensive look at how the United States and its allies are racing to transform defense procurement and policy to keep up with the relentless pace of AI innovation—while grappling with profound ethical and strategic questions.
At the heart of this transformation is BMNT, an advisory firm co-founded by Dr. Alison Hawks and Pete Newell. BMNT has introduced a set of proprietary frameworks—most notably “Hacking for Defense” (H4D) and “Hacking for X”—designed to overhaul the Department of Defense’s notoriously sluggish acquisition process. According to TokenRing AI, the typical defense procurement cycle can stretch to an astonishing 14 years, a timeline wholly incompatible with the rapid development cycles of AI technology. BMNT’s approach, inspired by Silicon Valley’s startup culture, aims to slash these timelines, emphasizing early collaboration with innovative founders and a shift away from rigid, prescriptive requirements toward a more agile, evidence-based system.
This shift is more than just a tweak to bureaucracy—it’s a fundamental reimagining of how the military identifies and acquires new capabilities. Instead of dictating specific technical solutions and drowning in paperwork, BMNT’s frameworks focus on the real needs of warfighters. By acting as a bridge between the defense sector and the commercial tech world, BMNT is making it significantly easier for early-stage and commercial AI companies to engage with the government. This not only accelerates the delivery of practical, relevant solutions to the field but also broadens the defense industrial base, encouraging a wider variety of companies—including startups and tech giants like Google, Microsoft, and Amazon—to contribute to national security.
The benefits of this new approach are already being felt across the industry. AI companies now have clearer pathways and stronger incentives to enter the defense market, while startups, often stymied by long, opaque procurement cycles, are gaining access to mentorship, non-dilutive funding through programs like Small Business Innovation Research (SBIR), and direct connections to government customers. TokenRing AI highlights the story of Offset AI, a startup that, through BMNT’s H4XLabs, not only developed vital drone communication solutions for the Army but also discovered commercial opportunities in agriculture—a testament to the power of dual-use innovation.
But it’s not just about getting new tools into the hands of soldiers faster. The integration of AI into defense brings with it a host of new risks and responsibilities. As Jeong and Yeo observe in their analysis, AI-enabled systems offer unprecedented speed and agility on the battlefield, allowing decisions to be made at “machine speed and scale.” This transition from human-driven to AI-driven warfare promises to overcome longstanding limitations like decision-making latency and the constraints of human resources. Yet, it also introduces a significant danger: automation bias.
Automation bias refers to the growing tendency for human operators to place uncritical trust in the outputs of automated systems. In the high-pressure context of warfare, where milliseconds matter, there’s a real risk that human oversight becomes a mere formality. Jeong and Yeo cite the example of the Israeli Defence Force’s use of the AI-based targeting system “Lavender,” which generated kill lists for operators to approve—sometimes with as little as 20 seconds of review per target. In such scenarios, human judgment risks being reduced to a rubber stamp, with potentially fatal consequences if the AI’s recommendations are flawed.
This risk is not hypothetical. The U.S. Defense Advanced Research Projects Agency (DARPA) has demonstrated, through simulations and live trials, that AI can outperform human pilots in certain tactical situations. As AI systems become more capable and more deeply integrated into military operations, the temptation to delegate critical decisions to algorithms will only grow. Jeong and Yeo warn that, without robust safeguards, this could lead to a dangerous erosion of “meaningful human control”—a principle supported by international agreements like the Convention on Certain Conventional Weapons (CCW), but one that remains ambiguously defined and inconsistently applied.
The accelerating AI arms race between the U.S. and China adds another layer of complexity. Both countries are investing heavily in military AI, and as their systems become more advanced, the incentives to rely on machine judgment increase. Yet, as Jeong and Yeo point out, this mutual vulnerability could actually serve as a foundation for cooperation. They propose that Washington and Beijing formally acknowledge the dangers of automation bias and work together to clarify what constitutes “meaningful human control” in military AI applications. Such a joint declaration—building on the Biden-Xi summit agreement to maintain human control over nuclear decisions—could establish vital guardrails against the unchecked delegation of life-and-death decisions to autonomous systems.
Practical steps could include the development of a shared glossary of AI-related terms, structured dialogues to refine the definition of “meaningful” control, and the enhancement of training programs for military personnel operating AI systems. By strengthening AI literacy and fostering transparency, both sides could reduce the risk of catastrophic errors and build the confidence needed for future military exchanges.
Meanwhile, BMNT’s process innovations are driving a broader cultural shift within the defense establishment. By embedding Mission Deployment Teams within government commands and scaling H4D programs globally, BMNT aims to create a more agile, responsive, and technologically advanced defense ecosystem. The long-term vision includes the development of fully autonomous systems—unmanned aerial vehicles, ground robots, naval vessels, and even AI-piloted fighter jets like Shield AI’s X-BAT—capable of complex operations with minimal human intervention. By 2030, intelligence officers may routinely rely on AI-enabled tools to model threats and automate briefing documents, while multimodal AI agents streamline security operations.
Yet, the challenges are formidable. Data availability and quality, especially for classified battlefield information, remain significant hurdles for AI training. The armed forces face a shortage of AI talent and robust infrastructure, and ethical, legal, and societal concerns about autonomous weapons and AI bias loom large. Ensuring model robustness, cybersecurity, and interoperability with legacy systems is crucial, as is fostering a culture of continuous innovation and risk-taking.
Experts agree that the next two decades will see AI fundamentally transform warfare, with military dominance increasingly defined by algorithmic performance. But as TokenRing AI and Jeong and Yeo both emphasize, the key to harnessing this potential lies not just in technological breakthroughs, but in the ability to adapt processes, policies, and culture. BMNT’s “Hacking for Defense” approach, while not an AI milestone in itself, represents a vital catalyst for this transformation—bridging the gap between Silicon Valley’s rapid innovation and the Pentagon’s operational needs, and setting the stage for a new era of agile, responsible defense innovation.
The pace of change in military AI is relentless, but the enduring challenge will be ensuring that speed and agility do not come at the expense of human judgment, oversight, and ethical responsibility. The future of defense may well depend on how successfully this balance is struck.
