New Survey Reveals Economic Model of AI Infrastructure is Being Reshaped

WD-sponsored global research indicates that as data continues to accumulate and compound growth, the economic model of AI infrastructure is being reshaped. According to IDC's latest survey, AI not only drives rapid data growth but also extends the retention time of data and reactivates the demand for historical data.

AI-Driven Data Growth

This study conducted in Hong Kong found that AI is facilitating structural growth in data storage demand. Among the surveyed organizations, 94.7% reported that their stored data volume has continued to increase over the past 12 months due to the widespread application of AI and generative AI. 61% of the organizations recorded a data growth of 25% or more and expect data volumes to continue to grow over the next three years.

Additionally, 85.4% of the surveyed organizations indicated that their data lake capacity has expanded over the past year, while 59.4% believe that AI-generated data is the primary driver of this growth in data lake capacity.

Extended Data Retention Time

The survey also noted that nearly 95% of the organizations reported an increase in the value of their data after adopting AI. 74.3% of organizations have extended their data retention time, and 75.9% are bringing more archived cold data back online to support AI workloads.

According to the research, 96% of organizations anticipate needing to retrieve archived data more quickly to support AI inference and retrieval-enhanced generative applications.

Design Requirements for AI Infrastructure

WD CEO Irving Tan stated that AI operates based on data, and the demand for organizations to store, manage, and access data at scale will continue to grow. This data foundation will play a crucial role in determining the future development potential of AI.

The research indicates that AI workloads generate data throughout their lifecycle, and this data will continue to persist even after the relevant workloads are completed, potentially serving as input data for future AI applications. As data volumes grow, retention times extend, and historical data is reactivated, organizations need to balance performance, capacity, accessibility, and cost-effectiveness.

Conclusion

The ability to manage data at scale in a cost-effective manner is no longer a secondary consideration for infrastructure; it is gradually becoming a key factor in determining the success or failure of AI. A comprehensive data lifecycle design will be an important consideration for future AI infrastructure.