It’s not your model — AI has a data movement problem
For the past two years, the checkbook has been wide open as enterprises have poured billions into AI models, compute, licenses and GPUs.
For the past two years, the checkbook has been wide open as enterprises have poured billions into AI models, compute, licenses and GPUs.
The short version
- Yet despite those massive investments, a growing number of organizations are running into the same problem: they can’t reliably access what they just built.
- When those expensive AI workflows collapse, it’s easy for leaders to assume the models aren’t working.
- However, the blame often lies elsewhere: leaders consistently underestimate the connectivity requirements beneath their AI strategy.
What happened
According to recent research , 59% of organizations now report bandwidth issues, up from 43% just a year ago. Latency challenges have nearly doubled, surging from 32% to 53%.
Why it matters
Enterprises are paying for that gap in performance, in user experience and often in unnecessary spend aimed at the wrong part of the stack.
Summary by Nerd News Network. Read the full article at Network World via the links above and below.