I have followed Google since its founding, and the company has always pushed the boundaries of research. Their latest artificial intelligence infrastructure and research effort continue this trend. Last week, the company announced its new compression algorithm, TurboQuant, in development. This technology could reduce the memory required to run large language models by up to six times. It optimizes the key-value cache, enabling the model to recall prior results rather than recalculate. This streamlines the process.
On the surface, this development appears disruptive enough to potentially impact the semiconductor industry, much as DeepSeek's compression algorithm from China did last year, causing a downturn in AI-related stocks. Both TurboQuant and DeepSeek's algorithms aim to improve efficiency—in DeepSeek's case, with notable effects on costs and model performance, and in TurboQuant's case, with the promise of significant memory reduction. In both scenarios, increased efficiency could reduce the need for as many costly semiconductor chips.
As a tech analyst with over four decades of writing about technology, I have seen a pattern. When engineers solve a problem, the market does not simply sit back and say, "Job well done." The goalposts move.
For comparison, consider the shift from hard drives to solid-state drives. Faster data access did not reduce the need for data storage. Instead, it enabled more applications that demanded much more data. The same is happening now. More efficient models do not always mean more compact models. Instead, they enable developers and businesses to build more complex models and run more inference-based applications. This is crucial for business.
TurboQuant underscores a subtle but important point: Google is taking the economics of AI deployment seriously. Training large language models has always been costly, but it’s becoming clear that operating them is increasingly expensive too. That’s where TurboQuant’s sixfold reduction in memory requirements stands out—it’s not just a matter of saving money, but of enabling new applications that were previously out of reach, particularly in on‑device AI-related applications, where memory constraints are a real limitation.
Google has a strong incentive to make its AI infrastructure more efficient. This is not simply a cost savings for Google. This is a cost savings that could make its cloud infrastructure more competitive. This is a major development for Google, regardless of its impact on chip stocks.
While this comparison is more satisfying, it is incomplete. For one, the efficiency savings from DeepSeek were linked to legitimate claims about cost savings in training. These cost savings, in turn, challenged conventional wisdom about the capital expenditures required for frontier AI. What is at play in TurboQuant is a more limited optimization on the inference side. Is this a big deal? Absolutely.
What's clear is that the era of brute-force scaling of AI—throwing more chips at every problem—is giving way to something more sophisticated. The top labs are now competing not only on model size and capability but also on efficiency and cost. Google's announcement is a signal that optimization is now a first-class problem, not an afterthought.
For the industry as a whole, that’s a win. Greater efficiency makes AI more accessible, supports stronger business models and drives sustainable growth. Investors fixated on chip demand may be missing the real story: efficient, accessible AI isn’t just a potential upside—it’s the path forward.
Disclosure: Google subscribes to the research reports from the company I founded, Creative Strategies, along with many other high-tech companies around the world.