Three cost curves are converging and the military is on the wrong side of all of them
Cloud AI inference pricing is going up. OpenAI has raised prices on frontier-class models multiple times since 2024. Anthropic's Claude costs went up 30% at the last generation release. Google's Gemini API increased across every tier. The pattern is consistent: as models get more capable, running them gets more expensive, and demand is growing faster than compute capacity. There is no price relief coming.
Dedicated GPU hardware for the battlefield is going up too. NVIDIA's data center GPUs are backordered. Ruggedized edge compute platforms like the Klas Voyager that Anduril acquired cost $15,000-25,000 per node with six-month procurement lead times. The global competition for AI-capable silicon, from hyperscalers to autonomous vehicle companies to defense, is driving hardware costs higher and availability lower.
Consumer AI hardware is going the opposite direction. Apple's M5 chip delivers 38 TOPS of neural engine performance with Neural Accelerators embedded in every GPU core, providing over 4x the GPU AI throughput of the previous generation. A MacBook Air starts at $1,099. The A19 Pro in an iPhone 17 Pro matches that 38 TOPS figure in a phone. Google's Tensor G4 runs Gemini Nano on-device. Qualcomm's Snapdragon X Elite pushes 45 TOPS. Consumer silicon vendors are in an AI performance war that drives capability up and price down every product cycle.
The military needs AI compute at the edge. The cloud path is getting more expensive every year. The ruggedized hardware path is expensive and scarce. The consumer hardware path is getting cheaper, faster, and more capable every year. One of these cost curves works for a force that needs to scale to thousands of nodes. The other two do not.