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Everyone's been focused on making AI chips faster, but Seagate Technology Holdings PLC (STX) thinks the next big leap might come from something far less glamorous: storage.
The hard drive maker argues that smarter data management can help Nvidia Corp. (NVDA)'s expensive GPUs do more work without needing more of them. It's a pitch that flips the usual AI narrative—instead of just throwing more compute at problems, maybe we should think about how data is stored and retrieved.
The argument comes from a white paper Seagate published with SK hynix Inc. (SKHY), looking at how inference and agentic AI workloads are changing data management. Instead of regenerating the same information over and over, modern AI apps increasingly rely on retaining context that can be reused.
"Our recent white paper with SK hynix illustrates the importance of tiered storage for inference and agentic AI workloads, which show a direct benefit to hard drive storage," CEO Dave Mosley said on the company's fiscal fourth-quarter earnings call.
At the heart of this is something called key-value (KV) cache. It stores previously generated context so AI models can grab it instead of recreating it each time. "Key-value, or KV cache, is used to retain and reuse that context efficiently," Mosley said.
According to Seagate, that simple shift has an outsized impact on AI economics. By moving context across memory, SSDs and hard drives—instead of forcing GPUs to recompute it—AI infrastructure can make better use of its most expensive hardware.
"This drives the need for increased hard drive storage and reduces GPU usage during the most compute-intensive phases of an agentic application. As a result, GPU resources are available for additional revenue-generating workloads," Mosley said.
The message isn't that GPUs become less important. Rather, Seagate argues that storage is becoming a bigger contributor to AI performance as inference workloads expand and models retain more context over time. That makes storage architecture an increasingly important part of the AI stack alongside compute and memory.
The comments also reinforce Seagate's broader investment thesis that AI is creating structural demand for high-capacity storage. Management said cloud data centers now account for roughly 90% of the company's exabyte shipments, while customers continue extending long-term supply commitments into 2029 and beyond as AI infrastructure scales.
For investors, the takeaway is that the next phase of the AI race may not be won solely by building bigger GPU clusters. As companies look to squeeze more value out of every Nvidia accelerator they buy, the biggest upgrade could come from the storage systems working quietly behind the scenes.
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