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Seagate and WD published separate AI storage studies within days of each other; the headline numbers: Seagate says 99% of enterprises expect AI to increase their storage requirements over the next three years, while WD’s IDC research puts the comparable figure at 74%. Read the fine print, and both reports land in the same directional place. AI is generating more data, enterprises intend to keep more of it for longer, and storage is taking a larger share of AI infrastructure planning than the GPU-centric conversation of the past two years suggested. The two studies don’t measure the trend the same way. Seagate’s Data Infrastructure Readiness Report surveyed more than 2,700 enterprise technology decision-makers across seven markets, while WD commissioned IDC to survey 763 IT and business decision-makers responsible for AI architecture and storage across seven countries. Different questions, thresholds, and sample sizes make some of the main numbers look farther apart than they probably are. The Numbers Differ Because the Questions Do Seagate found that 99% of respondents expect AI to increase storage requirements over the next three years, with 70% anticipating an increase of at least 26% and 32% expecting requirements to rise by more than half. Only 38% consider their organizations fully prepared for AI’s long-term data demands, even though 83% describe themselves as fully or mostly prepared. WD’s study starts from what has already happened. IDC found that 94.7% of respondents are storing more data because of AI and generative AI adoption over the past 12 months, 61% saw data growth of 25% or more in the previous year, and 74% expect volumes to grow by at least 25% over the next three years. The gap between 99% and 74% looks substantial until you compare the questions. Seagate’s 99% covers respondents expecting any increase in storage requirements, while WD’s 74% counts only organizations expecting growth of at least 25%. Where the two reports line up more closely is on the changing value and lifespan of enterprise data. WD found that 74.3% of respondents are retaining data longer because of AI and GenAI, 75.9% are bringing increasing volumes of archived cold-tier data back online, and 96% expect faster archive retrieval to become necessary for AI inference and retrieval-augmented generation workloads. The same share, 75.9%, said augmenting datasets with synthetic data has both raised the value of existing data and produced new datasets, which is one more reason the pile keeps growing. WD also found that 74.6% of enterprise data resides in warm, cool, and cold tiers, and more than 60% of data lake capacity is cold or infrequently accessed. If AI workloads keep pulling historical information back into use, the line between active and archived data gets blurrier than tiering policies have assumed, a point that squares with the archive demand we saw in the Q1 LTO shipment numbers. Storage Is Part of a Larger AI Readiness Problem Seagate’s study places storage inside a wider set of infrastructure challenges. Data quality and readiness was cited by 53% of respondents as a leading AI deployment challenge, followed by storage infrastructure at 43%, compute availability at 27%, and energy constraints at 24%. That ordering matters because compute has dominated the AI infrastructure discussion. Seagate’s respondents still put GPUs high on their spending lists, but security and compliance ranked first at 44%, data management and governance second at 43%, and AI and GPU infrastructure tied with storage hardware refreshes at 39%. Energy is already shaping those plans: 77% said their organization has delayed or restructured an expansion over power and sustainability concerns. WD reaches a similar conclusion through the data lifecycle. Historical information that once sat in colder storage may need to come back quickly for inference, RAG, or other AI workloads, which pushes organizations to balance capacity, accessibility, performance, and cost across tiers. WD’s respondents also put security ahead of cost when asked about their biggest storage concerns for AI workloads: security and data protection led at 58.1%, followed by reliability and data durability at 49.7% and performance at 48.4%, with cost of storage media fourth at 44.2%. What Buyers Can Take From Both Studies Neither study makes a case for one storage technology over another. Both show AI increasing storage requirements, and WD’s adds that organizations are retaining data longer and pulling more archived information back into active use. Seagate frames the response as workload-aligned, multi-tiered architectures that balance performance, capacity, efficiency, and long-term value against the needs of each dataset. WD gets to the same place by showing that most enterprise data already sits outside the hottest tier while demand for fast access to archived data climbs. That leaves buyers with a planning problem that’s bigger than adding capacity. They have to decide how much data to retain, how quickly each class of data needs to be reachable, and what it will cost to store and manage those datasets as they grow. Both vendors would like the answer to include a lot of hard drives; the survey data suggests the buyers asking the question rank security, durability, and performance ahead of the price of the media. Flash vendors are working the other side of the same constraints. Seagate’s 77% who’ve delayed or restructured an expansion over power and sustainability is the opening for high-capacity QLC, and our Micron 6600 ION 245TB paper measured what that swap looks like: one 245TB SSD replaced eight 30TB nearline drives in the same Dell R5715, drew 170.2W under sequential writes against 173.5W for the HDD array at idle, and at exabyte scale fit in 6 racks against 22 for the densest HDD enclosures. Hard drives keep the acquisition cost advantage per terabyte, and both surveys show the cold and archive tiers where that still decides the purchase are growing. Where power and floor space are the binding constraints, flash is pricing itself against the GPUs it frees room for, a comparison neither HDD-funded study set out to make.
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