THE REAL COST OF AI INFRASTRUCTURE IS BECOMING IMPOSSIBLE TO HIDE. South Korea's $880B chip plan requiring a quarter of Seoul's power demand, Trump's pivot to fossil fuels to power data centers, and the emerging water/grid constraints aren't abstractions anymore—they're hard limits that will reshape which countries can actually compete in AI. The hardware bottleneck everyone obsesses over is real, but the energy and resource bottleneck will hit first and harder, forcing a reckoning between AI ambitions and physical reality that no amount of software optimization fixes.
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llama.cpp's relentless release cadence (five builds in one window) signals where the real AI competition lives: efficient inference at the edge, not frontier models. While everyone watches OpenAI and Anthropic theater, the infrastructure that actually matters—making models run locally, faster, cheaper—gets built in open-source commits by a handful of developers. This is where hardware innovation gets tested in the real world.