Google's AI memory-saving algorithm cuts memory use by eight times, spurring concerns among chipmakers as Samsung posts record Q1 earnings

The semiconductor market has seen weeks of volatility after reports emerged about Google’s TurboQuant, an AI memory-saving algorithm. The technology is designed to reduce the memory required to run artificial intelligence, potentially challenging major memory-chip suppliers such as Samsung Electronics and SK Hynix. Rather than triggering a collapse in demand, the latest signals point to a more complex outcome for the memory market.

Samsung’s guidance and the “supercycle” narrative

According to the Financial Times, investor concern that TurboQuant could cool the memory-chip boom has been met with evidence from Samsung’s earnings performance. Samsung guided first-quarter profit higher than the total profit from the previous year combined, easing shareholder concerns and reinforcing the view that the AI-related memory bottleneck remains active.

Samsung’s reporting also frames the market as entering an “unprecedented supercycle,” with demand for durable, high-performance memory chips stronger than ever.

How TurboQuant works and what it could change

TurboQuant compresses a KV cache, a form of short-term memory used by large language models to retain conversation context. Google says the approach can cut memory usage by up to six times without significantly compromising accuracy.

Han In-su, a researcher whose work underpins TurboQuant, said the algorithm could enable tasks previously not feasible—such as handling much longer contexts within limited memory, or running high-performance AI on smaller devices.

Professor Kwon Seok-joon of Sungkyunkwan University added that TurboQuant could reduce the operating costs of running language models by four to eight times. He also warned that this cost reduction could threaten demand for high-bandwidth memory chips.

From efficiency fears to Jevons paradox

In theory, if systems require less memory to run, chip demand could fall. This logic aligns with the market reaction last month, when Samsung and SK Hynix shares dropped sharply as investors sold amid fears that the memory gold rush could end.

However, analysts point to the Jevons paradox, an economic theory from 1865, which holds that when efficiency improves, total consumption of a resource can rise because lower effective costs make new uses viable.

Professor Kwon linked this to AI inference: cheaper inference could unlock larger volumes of work that were previously too expensive, including real-time code assistants or many AI agents running in parallel. In that scenario, total compute and storage demand would increase, lifting memory-chip demand alongside broader usage.

The article draws a parallel to Google’s Kubernetes technology. While early expectations were that it would reduce server demand by enabling more applications per hardware unit, adoption expanded as costs fell, contributing to growth in data-center infrastructure.

Market structure shift: longer contracts

Another factor supporting chipmakers’ confidence is a change in how AI demand is being secured. Ray Wang, a SemiAnalysis analyst, said memory is gradually losing its cyclical character due to sustained AI demand, as AI service providers seek supply through longer commitments.

Jun Young-hyun, co-CEO of Samsung, said the company is moving from quarterly or annual terms to three- to five-year contracts with major customers. The shift is intended to create a revenue cushion and help Samsung and SK Hynix better withstand short-term fluctuations driven by algorithm announcements or market sentiment.

Timing and outlook

TurboQuant remains at the ideation stage in Google’s academic post and is expected to be presented in detail at the International Conference on Learning Representations (ICLR) in Brazil at the end of April.

Even so, the article argues that demand for memory chips remains strong. Professor Han said the work began as an effort to achieve more perfect data compression, but has produced a significant macroeconomic and social ripple.

Sources: Financial Times; The Verge