The Illusion of Efficiency, Why Cheaper AI Will Consume More of Everything

Another week, another reminder that in AI, efficiency rarely means contraction, more likely expansion. As explored previously (When Less Becomes More,When Less Becomes More, Can AI Algorithmic Minimalism Topple GPU Dominance?), we are now seeing technical constraints being worked around in ways that will materially reshape the trajectory of AI’s evolution.

The emergence of TurboQuant, outlined in recent Google Research, is a case in point. By compressing the key value cache that underpins how large language models retain conversational context, TurboQuant promises to reduce memory requirements by up to sixfold and cut inference costs by as much as 4 to 8 times. On the surface, that reads like a direct threat to the high bandwidth memory (HBM) ecosystem powering today’s AI boom.

Markets reacted accordingly. Semiconductor stocks wobbled and the familiar narrative resurfaced. If AI becomes more efficient, surely demand for compute and memory must fall. History suggests the opposite.

What TurboQuant really does is lower the cost per token, fundamentally shifting the economics of inference. Tasks previously deemed too expensive. Persistent AI agents, real-time copilots, long-context reasoning amongst other capabilities suddenly become viable at scale. This is not optimisation at the margins, it is the unlocking of entirely new classes of workload.

It is Jevons’ paradox, replayed in silicon. Efficiency does not dampen demand, it democratises it. We saw the same pattern with Kubernetes. Greater utilisation of infrastructure did not reduce demand for servers, it accelerated it. AI appears to be following the same trajectory, only faster.

The strategic implication is clear, the battleground is shifting from raw capability to cost efficient ubiquity. Those who interpret efficiency gains as a signal to retrench risk are missing the far larger opportunity. AI not as a scarce resource but as an always on, pervasive layer embedded across every process, interaction and decision.

In classic fashion, we celebrate using less, only to justify doing far more. Efficiency becomes the licence to expand, not restrain and the pattern repeates. We use less per unit, then proceed to use far more in aggregate. Efficiency becomes the enabler of expansion, not restraint. The unit cost falls, the workloads proliferate and the overall bill edges upward. Cloud computing taught us that much, progress has a habit of finding new ways to cost more.