What Google's TurboQuant can and can't do for AI's spiraling cost

Google's TurboQuant technology reduces AI memory usage by up to six times through real-time data compression, targeting the memory-intensive key-value cache that stores recent AI interactions. This innovation uses a two-stage compression approach called PolarQuant and QJL to shrink data without losing accuracy, potentially making AI more affordable and accessible for local deployment. However, experts predict this efficiency gain will likely lead to increased AI usage rather than actual cost reductions, following the Jevons paradox where improved efficiency drives higher overall consumption.