Market opinions are diverging regarding the impact of Google's new algorithm on the storage industry chain. A recent research report from Morgan Stanley indicates that while the TurboQuant algorithm launched by Alphabet can reduce some memory usage, it is more likely to drive AI applications towards requiring even greater computational power, rather than diminishing overall demand for storage and memory. The report, authored by Morgan Stanley analyst Joseph Moore, suggests that the widespread interpretation of a "6x reduction in memory usage" is exaggerated, as the optimization primarily targets KV Cache memory, not total memory requirements. He noted, "The recent decline in storage stocks is partly due to this overinterpretation." The report further mentions that Alphabet's latest models, Gemini 3 and Gemini 2.5 Pro, already feature a 1 million token context window. Previously, Gemini 1.5 Pro was tested with support for up to 10 million tokens but was not publicly released due to prohibitively high inference costs. Analysis suggests that as technologies like TurboQuant reduce costs, future AI models will tend to utilize larger-scale contexts and more complex computations, thereby driving up overall demand for computing power and storage. Based on this assessment, Morgan Stanley has reaffirmed its "Overweight" ratings on Micron Technology and SanDisk Corp. The analyst pointed out that the core bottleneck facing AI development remains insufficient memory supply, particularly in the DRAM sector. Rapidly growing demand from data centers is already putting pressure on memory supply for end products like PCs and smartphones. Moore emphasized, "From our industry chain checks, there is no indication whatsoever that memory or storage demand is declining."