In today's era of GenAI sweeping the globe, the value of data is undergoing an unprecedented paradigm shift. In the past, we were accustomed to sending all our data to cloud centers, but by 2026, this path has clearly reached a bottleneck. Paul McParland, Vice President of Marketing for Edge Data Center Solutions at Seagate, pointed out in a recent online media interview that the new frontier for unlocking data value and improving the return on investment (ROI) of AI lies at the "edge."
Generative AI not only makes content faster, but also makes data "heavier and longer-lasting".
According to a white paper released by market research firm IDC at the end of 2025, generative AI is having a comprehensive impact on the data ecosystem. What we are seeing is no longer just simple text growth, but a more complex content revolution:
• A dual explosion in quality and quantity:Approximately 79% of respondents felt that content creation speed had improved, but more concerningly, 73% of businesses reported that files had become larger and more complex. This means that infrastructure must handle a much larger volume of unstructured data than ever before.
• The shift in data retention strategies:Data that was once considered "outdated" is now being used to train AI. Approximately 42% of enterprises are adjusting their architectures to extend data retention periods, which poses significant challenges to storage costs and management efficiency.
• Shifting from software issues to hardware limitations:Paul McParland cautions that AI is not currently facing limitations due to insufficient algorithms, but rather constraints in hardware infrastructure such as power supply, data movement, cooling systems, and resource scarcity.
Three core driving forces: Why must the value of data flow back to the edge?
Paul McParland argues that data at the edge is transforming into "physical capital" for businesses, and this is supported by three core factors:
• Data ROI:Moving computation to the source of data generation enables "rapid ingestion" and "low latency." A more practical consideration is "egress cost," as reducing the frequent exchange of large amounts of raw data with the cloud can significantly optimize the cost structure.
• Data Trust:The power of AI depends entirely on the quality of the data it is fed. Accurate data extraction and early verification at the moment of data generation are crucial to ensuring the credibility of AI outputs.
• Data Gravity:Data has "weight" and tends to remain where it is generated. Rather than going through the trouble of moving data to support computation, it's better to deploy infrastructure along with the data—this is the core logic of edge solutions.
Taiwan: An "Accelerator" for the Growth of Global Edge Data
In this interview, Paul McParland specifically highlighted Taiwan's strategic importance. As a global hub for semiconductors and precision manufacturing, Taiwan is the perfect arena for the explosive growth of edge data.
• Industrial ecosystem advantages:Taiwan boasts a highly dense ecosystem of smart factories, robotics, and IoT hardware.
• Immediate processing of requests:The massive amounts of data generated by the Industrial Internet of Things (IIoT) are extremely sensitive to latency, which directly drives the rigid demand for high-performance edge storage solutions.
Seagate's strategy: HDDs remain the "ballast" in the AI era.
For this wave of "edge-heavy" workflows, Seagate's strategy is very clear: provide petabyte-scale high-density ingestion platforms. While SSDs have their speed advantages, HDDs, with their balance of capacity and cost, still support over 80% of data storage needs in AI storage infrastructure. Through next-generation large-scale capacity platforms and modernized connectivity, Seagate aims to prevent enterprises from being overwhelmed by infrastructure when facing data explosions.
Analysis of viewpoints
In the past, when we talked about edge computing, it might have been limited to "reducing the burden on the cloud." However, in the era of generative AI, the edge has become the primary site for data assetization. Data is no longer just a simple archive, but has been transformed into the "raw material" for enterprise insights. For Taiwanese industries, leveraging existing hardware advantages in conjunction with edge data center solutions will be key to gaining a long-term competitive advantage in the digital economy over the next decade.
