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AI’s hidden storage bill: What inference leaves behind

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Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 22/10/2026, 09:20:24 UTC.

Briefings

Research indicates that AI inference generates substantial back-end data storage costs, often exceeding the output file size by 130x for video generation. A 2026 survey of 763 IT leaders found 95% reported increased data volume due to generative AI, with 61% seeing growth of 25% or more. McKinsey projects AI inference demand will grow 35% annually through 2030, surpassing training demand and accounting for over 40% of total data center demand by 2030. Storage budgets currently focus on compute, but retention policies for AI data are often overlooked, leading to hidden operational costs.

Key points

• An 8-second AI-generated video clip left more than 500MB of associated data behind on the back end, including logs, cached context, and safety records, compared to the 3.97MB output file.
• A recent IDC survey of 763 IT leaders found that nearly 95% reported an increase in data volume due to generative AI over the past 12 months, with roughly 61% reporting growth of 25% or more.

Context

This briefing addresses the critical gap in AI infrastructure budgeting where compute costs are often budgeted but storage costs are overlooked. The finding matters because AI inference is continuous, unlike training, meaning data accumulates indefinitely and can become an input for future workloads, requiring dynamic storage strategies rather than static capacity planning.

Limitations

The evidence relies on a single 2026 survey of 763 respondents and internal measurements from one vendor (WD). Some data points, such as the exact 130x increase ratio for video clips, are based on internal measurements rather than independent verification. The study covers the past 12 months of data volume growth but does not provide historical baseline data for comparison. Future projections from McKinsey are estimates rather than confirmed facts.

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