Seagate Technology Holdings plc (NASDAQ: STX) management will participate in two major investor conferences in early September 2026.

The company, a provider of mass-capacity data storage, confirmed its schedule for upcoming presentations at industry events hosted by Citi and Goldman Sachs.

Conference Schedule

Management is scheduled to present at the following events:

Accessing the Webcasts

Live webcasts of the presentations will be accessible via Seagate’s Investor Relations website. A replay of the sessions will remain available for at least 30 days following each event.

About Seagate Technology

Seagate is a pioneer in mass-capacity data storage, helping hyperscale cloud providers, enterprises, and consumers manage data. The company has operated for more than 45 years, focusing on sustainable, high-performance storage solutions.

Seagate Technology Holdings PLC (NASDAQ: STX) CEO Dave Mosley argues that the next leap in artificial intelligence performance will not come from compute power alone, but from smarter storage architecture that maximizes the efficiency of expensive Nvidia Corp. (NASDAQ: NVDA) GPUs. During the company’s fiscal fourth-quarter earnings call, Mosley highlighted a joint white paper with SK hynix Inc. (NASDAQ: SKHY) demonstrating how tiered storage and key-value (KV) cache can reduce GPU usage during intensive inference workloads, thereby lowering infrastructure costs and boosting productivity.

The core of this strategy involves retaining context across interactions rather than repeatedly generating it. By moving context across memory, solid-state drives, and hard drives, AI systems can retrieve previously generated information instead of forcing GPUs to recompute it. This approach frees up GPU resources for additional revenue-generating tasks, making storage an increasingly vital component of the AI stack alongside compute and memory.

The Role of KV Cache in AI Economics

Mosley emphasized that modern AI applications, particularly agentic AI and inference workloads, rely heavily on reusing context. "Key-value, or KV cache, is used to retain and reuse that context efficiently," Mosley stated. This mechanism allows systems to store previously generated context in high-capacity hard drives, reducing the computational burden on accelerators.

This shift has significant implications for AI economics. By minimizing redundant computation, organizations can extend the utility of their existing hardware investments. "This drives the need for increased hard drive storage and reduces GPU usage during the most compute-intensive phases of an agentic application," Mosley explained. Consequently, storage architecture is becoming a primary driver of performance gains as models retain more context over time.

Impact on Infrastructure Efficiency

Physical AI and Data Demand

Beyond inference efficiency, Mosley reiterated that physical AI—driven by humanoid robots and autonomous vehicles—represents a massive long-term growth catalyst for storage demand. He described robots as networks of cameras and sensors generating continuous unstructured video data. This footage must be retained to train "world models" that enable machines to understand their physical environment.

"People focus very much on the end product—the robot—but I actually think it’s a lot more of a data play," Mosley said. The requirement to preserve years of historical video for future training cycles creates sustained demand for enterprise-grade storage capacity, reinforcing Seagate’s position as a critical infrastructure provider.

What the Numbers Show

The strategic pivot toward storage as an active enabler of AI performance is supported by strong market signals. Management reported that cloud data centers now account for roughly 90% of Seagate’s exabyte shipments. Furthermore, customers are extending long-term supply commitments into 2029 and beyond, indicating confidence in the structural demand for high-capacity storage as AI infrastructure scales.

For investors, the narrative shifts from viewing storage as a passive commodity to recognizing it as a lever for optimizing expensive compute resources. As companies seek to squeeze more value from every Nvidia accelerator, the integration of efficient storage solutions like KV cache becomes a decisive competitive advantage in the AI race.