Companies are spending millions on AI data strategy while their most valuable historical data sits on tapes they can't read.
This is a pattern I keep running into, and it's genuinely frustrating to watch.
The org has decades of proprietary data, like documents, video, internal records, customer interactions, whatever. This data is genuinely unique, as competitors don't have it, you can't buy it, and it represents real institutional history. In the current environment, it's exactly the kind of thing that would differentiate a proprietary model or a fine-tuned system from generic alternatives.
It's on LTO tapes from 2004-2017, so nobody's touched them in years. The hardware to read the older formats may or may not still exist in the building. Meanwhile, the same org is paying for a generic foundation model API and wondering why the outputs don't reflect their domain knowledge.
The link between legacy tape archives and AI training assets is not a consideration that the average data organization has yet come to grips with. It's an issue in the infrastructure team's problem basket, not the machine learning team's.
I came across Tape Ark while looking into the tape migration space. They work on exactly this problem at scale, getting the data off the physical medium and into a format that's actually usable. The migration is the unsexy conditions that unlocks everything else.
The orgs that solve the physical access problem in the next couple of years are going to be in a meaningfully different position for proprietary AI development than the ones that don't.
Has anyone here dealt with this in practice, getting legacy physical archives into a usable state for ML work?