Analysts and researchers say Google's TurboQuant compression algorithm to make LLMs more efficient is more likely to expand memory chip demand than reduce it

Google built TurboQuant to make large language models leaner and meaner. The compression algorithm is designed to squeeze LLMs into smaller memory footprints, theoretically cutting the need for expensive semiconductor hardware. Sounds like bad news for chipmakers, right?

Not so fast. Analysts and researchers told the Financial Times the opposite is likely true. More efficient AI doesn't mean less hardware — it means more AI getting deployed everywhere. When you make models cheaper to run, people run a lot more of them.

It's a classic Jevons paradox playing out in silicon. Efficiency gains in AI workloads historically drive expansion, not contraction, of compute and memory demand. Experts say the semiconductor industry should expect growing appetite for memory chips as compression techniques like TurboQuant lower the barrier to widespread LLM deployment.

Chipmakers can breathe easy. For now.