Tech · MIT Technology Review
Traditional enterprise IT has been able to rely on relatively stable infrastructure assumptions
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“Data centers must now support continuous, distributed, and increasingly real-time AI services—none of which are a single workload,” says McGregor.
Key facts
- Systems for AI need to be rearchitected because shoehorning modern AI systems into legacy infrastructure limits AI’s transformative potential
- This content was produced by Insights, MIT Technology Review’s custom content arm, not its editorial staff
- It’s thousands, it’s millions, it’s billions of different workloads,” says Jim McGregor, founder and principal analyst, Tirias Research
- The team tend to think of AI as a single workload, and it’s not
Summary
The era of AI inference has arrived. This shift changes what infrastructure must deliver. “The team tend to think of AI as a single workload, and it’s not. For business leaders, the priority is clear: AI infrastructure decisions must balance cost, flexibility, and future readiness. Systems for AI need to be rearchitected because shoehorning modern AI systems into legacy infrastructure limits AI’s transformative potential.