AI, autonomy, and the future of naval warfare with Captain Jon Haase, United States Navy
Summary
Haase’s mine-hunting system shows that defense AI is an edge-integration problem before it is a model problem. An ensemble of deep learners runs on an NVIDIA processor aboard unmanned underwater vehicles, identifies likely mines, and triggers deterministic mission branches without surfacing; the same models accelerate laptop-based human review. Underwater, where GPS is unavailable and acoustic communications are unreliable, power efficiency, inference latency, navigation, and onboard sensing all constrain how the models can be used.
The commercial-to-military hardening gap can turn an expected 10% modification into a 90% change. Encrypting one underwater acoustic message lengthened transmission, kept a DSP operating longer, increased voltage and heat, and ultimately caused a circuit card to fail—forcing changes from software through physical architecture. Haase’s blunt warning to defense-tech vendors: products that appear to work “never actually work” until reliability, cybersecurity, sustainment, and fielding are solved.
A working prototype represents only “5% of the problem” in military deployment. Effective technology is table stakes; professionals ask whether it can survive adversarial attack, meet policy, be fielded, and be sustained. This makes acquisition knowledge and systems engineering durable sources of differentiation even as models and robotics evolve.
Haase’s team should build bespoke capabilities only where the commercial market cannot supply them. Mine recognition requires custom development, but for large language models Haase would rather change the requirement to fit a scaled commercial product than fund a government-specific substitute whose cost rises “exponentially.” Open-source models further accelerate contractors that already understand the mission, shifting value toward safe integration and deployment.
Haase supports offensive AI integration but rejects framing military effectiveness and ethical restraint as alternatives. His standard for taking life is direct, specific human involvement, explainability, oversight, and protection of civilians; after Lukas Biewald pressed the adversarial-arms-race problem, Haase answered that aggression, victory, and moral legitimacy are “an and, not an or.” AI may increase pace and decision advantage, but it should “enable and empower humans, not replace us.”
A technology lead matters, but Haase argues that integration and training matter more than a 10–20% model-performance edge. His pushback on anxiety about DeepSeek R1 and R10 was categorical: “Software has never won a war.” Over the next 5 to 10 years he expects more robots, agents, real-time data, and remote operations, while reserving “about 30% for something we just don’t see yet.”
A promising application is a hierarchy of interoperable agents that clears the fog of war. A vehicle-level agent could help distinguish a mine from a rock and replan a mission; higher-level agents could compress those missions into fleet positioning, with shared data flowing dynamically through the chain of command. Haase sees “completely outsized returns” in making exhausted humans communicate and decide more clearly—and demonstrated internally that visible leadership use can change AI adoption within “a week or two.”
Deep dive
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