Concerns about AI energy use ranks last in global enterprise survey, highlighting the challenges which lie ahead

Seagate study claims security and storage are top of agenda for AI infrastructure
Energy is a distant last, preceded by LLM viability and regulations
Debates over AI energy usage will continue until compromise is met

AI energy consumption is becoming an increasingly hot topic, with industry stakeholders and critics voicing concerns over the environmental impact of the technology.

But a recent survey from Seagate points toward more pressing concerns for IT leaders, claiming energy usage ranked bottom of the agenda behind regulatory considerations, the viability of LLMs, and network capacity.

Notably, security and storage were among the key focus points for business leaders looking ahead, with nearly two-thirds (61%) of respondents who predominately use cloud storage to host AI workloads said their cloud-based storage will increase by over 100% in the next three years.

Cost effective storage is key

This sharpened focus on AI adoption is expected to prompt a surge in demand for data storage, with hard drives emerging as the “clear winner,” said Roger Entner, founder and lead analyst of Recon Analytics, which carried out the survey.

“The survey results generally point to a coming surge in demand for data storage,” he said. “When you consider that the business leaders we surveyed intend to store more and more of this AI-driven data in the cloud, it appears that cloud services are well-positioned to ride a second growth wave.”

A key factor in this push is the cost efficiency of hard drives, the study found, which offer better scalability and improve per-dollar-per-terabyte cost.

Another contributory factor to the appeal of hard drives is data retention, the survey found. Organizations embracing AI typically hold data for longer periods of time to train and optimize AI models.

This lengthy data retention practice plays a critical role in ensuring accuracy when training models, with 90% of respondents already using AI believing that holding onto data for longer helps improve outcomes.

“With the vast majority of survey respondents saying they need to store data for longer periods of time to improve quality outcomes of AI, we’re focused on a real density innovation needed to increase storage capacity for each platter in our HAMR-based hard drives,” Entner said.

“We have a clear pathway to more than double per-platter storage capacity over the next few years.”

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