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Clear out junk files and repair common Windows errorsFree Scan →Scan for outdated or missing drivers - takes under a minuteDriver Scan →Repair Windows errors before they cause bigger problemsFix Now →Flash storage will eventually improve more slowly, but not soon, according to John “Coz” Colgrove, Pure Storage founder and chief visionary officer. In an interview published by ITPro on June 21, 2024, he said he did not expect a viable storage successor before the 2030s. He sees flash as particularly useful for AI inference, where organizations need responsive access to data alongside existing workloads.
When does Colgrove expect flash efficiency to flatten?
Colgrove described the slowdown as a distant prospect, not a near-term inflection point. “At some point in the future – far distant future – it will flatten out, because no technology goes forever,” he told ITPro. He also said, “At some point beyond the horizon … flash will stop improving, or it will slow down.” These are his forecasts, not a verified technology timetable.
To explain the long runway, Colgrove compared flash with disk storage, which he characterized as having improved over a 70-year trajectory. He expects flash to follow a similar pattern: continued gains for now, followed eventually by slower progress and, after that, the possibility of a new storage technology. He did not expect a genuine long-term alternative to flash before the 2030s.
Why does he think flash can keep improving?
Colgrove pointed to two continuing engineering paths: making flash cells smaller and stacking more layers. Those approaches can extend the technology’s development, but the interview does not quantify their potential gains or establish when either path will reach practical limits.
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He also described a change in how Pure Storage manages flash. Rather than managing each direct flash module independently, he said the company moved flash management into the host so it can be handled across the modules in that host. Colgrove said tracking flash condition and write life across an enterprise estate can help improve efficiency and extend flash life. The interview provides no independent benchmark for those benefits.
Why does Colgrove see a role for flash in AI?
His argument centers on the difference between AI training and inference:
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| Workload | Colgrove’s view | Storage implication |
|---|---|---|
| Training | Most training will take place at hyperscale cloud providers, a small number of very large technology companies, and GPU cloud providers. | The interview does not make enterprise flash the central storage story for this concentrated training activity. |
| Inference | He sees a larger storage opportunity as organizations use trained AI systems to respond to requests. | Organizations need responsive, scalable access to data while preserving ordinary access for other uses. |
In this account, inference does not replace existing enterprise data needs. It adds another reason to make useful data available quickly and reliably, without disrupting the ways organizations already use it.
What does AI change about data stewardship?
Colgrove argued that as data becomes more valuable to AI applications, the storage holding it matters more. “Efficient storage that is resilient, reliable, available, durable … becomes more important for that because your data’s more valuable.” That is his rationale for emphasizing dependable storage alongside performance and scale.
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He also warned about AI’s energy demands: “AI destroys sustainability because the GPUs use so much power,” Colgrove says. The interview offers no measured energy comparison, so this is a warning rather than a quantified finding about AI’s environmental impact or the savings a particular storage system might deliver.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What the interview does—and does not—establish
The interview captures Colgrove’s outlook at Pure Accelerate 2024; it is not a comparative test of storage systems. Its forecast about flash’s eventual slowdown, the timing of a successor, and the benefits of Pure Storage’s management approach should therefore be read as attributed views and claims, not independently demonstrated results. It does not establish a date for flattening, quantify AI inference storage demand, or measure how much energy efficient storage can save.
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