Google just gave Wall Street a reason to rethink the biggest AI trade available.

Alphabet's Google Research said earlier in March that it had developed a new family of compression algorithms, TurboQuant, PolarQuant and Quantized Johnson-Lindenstrauss, or QJL.

What is the point of all of these? They aim to slash the memory required to run large language models and vector search systems.

In Google's tests, TurboQuant reduced key-value cache memory needs by at least six times while preserving accuracy, raising important questions regarding the bigger issue for investors. What happens to memory and storage demand if AI models become dramatically better? That question hit a nerve fast. Micron Technology (MU), Western Digital (WDC), Seagate Technology (STX) and SanDisk (SNDK) all moved lower as investors digested the possibility that AI workloads may not need as much firepower.

Story Date: March 30, 2026