Source: The TurboQuant Market Illusion
This is one of those risks that sounds abstract until you imagine explaining it after the fact. Then it suddenly becomes very concrete, very expensive, and very difficult to hide behind a slide deck.
In early 2026, two researchers published a blog post describing TurboQuant — an algorithm that reduces KV cache memory requirements by a factor of 6. Within 48 hours, Seoul’s KOSPI index had fallen sharply, memory stock prices had crashed globally, and billions of dollars had been wiped off the market. The reasoning: if AI needs 6× less memory, memory demand collapses. The category mistake: assuming that a reduction in per-unit resource consumption implies a proportional reduction in total resource demand. This is almost never true in technology markets. Cheaper transistors didn’t reduce semiconductor demand — they expanded computing to new use cases. More fuel-efficient engines didn’t reduce total fuel consumption — they made driving accessible to more people. Algorithmic efficiency in AI is almost certainly going the same direction. The deeper point: when you see a “problem solved” headline, the first question to ask is whether solving that problem reduces demand or expands market size. In technology, the answer is almost always the latter. Efficiency gains remove friction, lower price points, enable new use cases, and ultimately generate more total activity — not less. But the episode is a useful reminder of how even sophisticated investors can make category errors when a technology domain moves faster than their mental models can update.
My takeaway: the danger is rarely the dramatic thing in the headline. It is the quiet gap between knowing a risk exists and assigning someone to do something about it. Very unglamorous. Very important.