Micron's HBM Bottleneck Could Boost Stock Despite AI Efficiency

Google's TurboQuant algorithm — which achieves a 6x reduction in key-value cache memory usage for large language models — briefly rattled MU shares, sparking concerns that AI efficiency gains would undercut demand for high-bandwidth memory chips . The reaction may be overblown: despite the compression breakthrough, Micron's entire HBM production for 2026 is reportedly sold out under binding contracts, and management disclosed during the most recent earnings call that the company can fill only 50-67% of its current order book.

The Jevons paradox offers a compelling lens for understanding why efficiency gains may not translate into lower memory demand. Just as cheaper computing historically expanded computing usage rather than reducing it, AI hyperscalers are likely to redirect freed memory capacity toward more complex models, longer context windows, and new applications — rather than simply purchasing fewer chips. Global HBM spending is projected to grow 58% year-over-year in 2026, reaching $54.6 billion, with demand expected to expand through 2028 and supply constraints keeping memory prices elevated.

MU shares have declined roughly 20% from recent highs, potentially creating a re-entry opportunity for investors who believe the HBM supply-constraint thesis remains intact. Competing with SK Hynix and Samsung in the HBM4 generation, Micron is ramping production capacity but remains constrained — a dynamic that supports elevated memory pricing and margins even in a world where individual AI models require less memory per inference run. The Google TurboQuant story may prove to be a near-term overhang that obscures a structurally undersupplied market.