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The aircraft carrier USS Gerald R Ford will be part of Washington’s expanded military presence – Copyright AFP Jaime REINA
The U.S. government is pumping more money into AI research, especially in relation to defence, making AI central to the future strategy. U.S. Department of War (DoW), including the Department of Defense (DoD), is actively developing, testing, and fielding artificial intelligence (AI) capabilities at the edge, meaning the technology runs directly on local devices and in operational environments. The goal is to achieve a decision advantage over adversaries by processing information with greater speed and accuracy on the battlefield.
To help to understand these development, Digital Journal caught up with Sek Chai, CTO, Latent AI.
Digital Journal: With major investments in physical AI (or edge AI) infrastructure from NVIDIA, AMD, and Qualcomm, and others, what overall market evolution do you predict in the edge AI ecosystem by 2026? Which developments do you believe will have the greatest impact on real-world deployment?
Sek Chai: Investments in physical or edge AI are dwarfed by investments in hyperscalar data centres. Smart investors are now realising that $1T expenditures for massive data centres will not bring in immediate returns, if at all. Instead, investments in Edge AI are actually much more logical and less risky. New developments to enable the reliability and robustness of Edge AI will bring Edge AI to the forefront with real-world deployment.
Such a fundamental shift to Edge First approach will bring about standardisation, interoperability, and security to the Edge AI market. These issues have been key elements that are addressed with Latent AI’s product offering.
DJ: From your vantage point, is the software stack (inference runtimes, model compression tools, deployment tools, and secure update pipelines) finally maturing fast enough to enable developers to utilise new hardware in production by 2026 fully? What gaps remain?
Chai: The tools and software stack are maturing, but not fast enough. Especially in edge hardware, it is still a wild-west ecosystem with heterogeneous solutions that are not interoperable. This is where Latent AI shines, by offering a standardized AI runtime with services layers, much akin to how Java offers the necessary abstractions for software.
DJ: What structural bottlenecks inside organizations (data ownership, integrator lock-in, lack of model governance) will most limit edge AI adoption in 2026? How can these be overcome?
Chai: Enterprises fear the unknown and the uncertainty. Thus, they are not readily adopting an Edge First approach because the cloud still offers a platform that is familiar. This bottleneck is now being overcome when they realize the economic, logistical, and sustainability issues to rely solely on the cloud.
DJ: With hardware becoming more broadly available, do you expect 2026 to be the year when Department of War (DoW) can overcome integration, interoperability, and accreditation challenges in edge AI software platforms and finally move the current wave of Edge AI pilots to fielded capabilities?
Chai: DoW are already fielding AI capabilities on the edge. However, these systems cost hundreds of millions of dollars and with many years of development. Furthermore, there’s vendor-lock from integrators that sell entire platforms, from algorithms to hardware. DoW seeks to procure the best of breeds of AI algorithms, without being locked-in to any vendor. Edge AI will scale in deployment as DoW elevates the urgency with an alternative procurement strategy with interoperability in mind.
DJ: Which autonomy initiatives today are showing the clearest pathway from experimentation to fielding, and what inflection points do you expect in 2026 as these programs scale?
Chai: The battlefield is changing and there is an urgent need to build solutions that can adapt to new operational environments. These are evident in the new type of warfare currently in Ukraine, where adaptation is key to mission success. Adversaries now adjust signatures, tactics, and decoys on commercial timelines, not acquisition timelines, so static edge AI models that rely on long, centralized retraining cycles fall behind almost as soon as they are deployed.
The SecWar and DoW’s emphasis on speed, adaptability, and AI-enabled decision dominance highlights a clear pathway where adaptive AI is part of the fielding requirements.
Dr. Tim Sandle Read More