The AI infrastructure discussion is typically framed around the cost of data centers, the power requirements, and the compute needed to train and run models, including GPUs and high-performance storage. That’s hardly surprising given the eye-watering investment numbers occupying the headlines.
The other key commodity, of course, is data to fuel those models. According to Stanford University’s 2025 AI Index Report, dataset sizes for training LLMs are doubling every eight months. In practical terms, as each model is built, some data will move quickly into curation and model-development environments, where fast access is essential.
Much of it, however, will wait longer while teams establish its relevance to a particular AI use case – not sitting idle, but held securely and ready to move quickly into curation, training and transformation pipelines when needed.
From a storage perspective, this raises a point that is easy to overlook: a dataset does not need the same performance at every stage of the AI pipeline. What matters is that it is ready when it is needed – not that it sits on always-on, high-performance infrastructure throughout, which at scale becomes unnecessarily expensive.
The question for infrastructure planners, then, is not whether AI needs fast storage, but where organizations should keep the very large datasets that will be required in future, before they are ready to be processed. That choice is a strategic one, not a housekeeping one.
The right capacity tier should keep data protected and readily recoverable into AI, training and transformation pipelines, puts performance only where the work is actually happening, and returns the difference to the budget.
Your data portfolio as strategic advantage
As every organization’s mission is different, so too each will be at a different stage of the AI journey. Some have raced ahead with systems already in production, while many others continue to explore how the data they already hold could support AI initiatives – documents, images and video, operational records, information collected through connected systems; the list goes on.
This is why knowing your own data is fast becoming a competitive lever rather than an IT chore. Models are available to everyone, so proprietary data is your competitive advantage – if you can access it and use it at scale.
The organizations that will move fastest are the ones that already know what they hold, where it sits, and how quickly it can be put to work. Shortening the distance between a business question and the data that answers it is now a measure of how fast a company can execute and succeed.
So data is not simply an input to AI: it is what shapes the model. The more of an organization’s own data it can bring to bear, the sharper and more specific the resulting tools become, which is why the working assumption should be that almost anything the business holds is potentially useful.
The conventional approach has been to hold large datasets in a disk-based data lake until they are needed for further processing. Yet as data sets grow ever larger, so too could cost. If every candidate dataset has to live on always-on, high-performance infrastructure, cost sets the ceiling on how much data an organization can afford to keep in play at all.
The challenge, then, is to keep everything available to workflows as needed, so that the deciding factor is the use case, not the storage bill.
Tale of the tape
The smart play therefore is not to spend more, but to stop overspending where there is a better way. And it turns out one of the strongest answers here is a technology that has never stopped innovating: tape. Most people still associate it with backup and long-term archive – a role it continues to play well – but successive LTO generations have transformed its capacity, throughput and security while the industry looked elsewhere.
The latest tape technology and systems now behave like any other tier in the stack, ready to stream data into fast storage when curation or training is ready for it. And tape’s economics get better as it grows. At the multi-petabyte scale AI programs now reach, cost per terabyte is a fraction of flash or even HDD infrastructure. Performance and capacity can also scale independently, adding more drives for throughput and more cartridges for capacity.
When considered with tape’s extraordinary energy efficiency, this storage technology emerges as a strategic capability to build into the data center, allowing an organization to keep its entire data estate in play, at a cost that scales predictably.
A safer place for valuable data
Cost of storage and operation often gets projects approved, yet protection is the one that keeps people up at night. Here, tape offers something the online tiers structurally cannot. Encryption is handled in hardware on the cartridge. Write-Once-Read-Many (WORM) media makes a dataset immutable in the physical sense, so that irreplaceable data cannot be rewritten.
And for the most valuable material, tape sets can leave the library altogether and be stored in a secure location or offsite – fully offline, fully air-gapped, and insulated from anything that happens to the production environment.
What counts now in building data and AI pipelines for your organization is ensuring data is ready to move into the right performance tier the moment it is needed. Data is the fuel for the models an organization builds, the decisions it makes, and how fast it can act on either.
Tape is what makes it affordable to keep all of that ‘data fuel’ at scale, protect what cannot be replaced, and put any of it to work on demand. Build it in now, and what you can do with your data is no longer limited by what you can afford to keep online, and instead becomes the means to get, and stay, ahead of your competition.
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