Which AI future should shape network upgrades—cloud-first, agent-led, or fully immersive—and how much risk can be absorbed if the bet proves wrong when the most valuable, latency-sensitive traffic increasingly happens near people rather than inside faraway data centers? In many enterprises, the
A surge in AI demand met a hard limit this week: power, not silicon, became the gating factor just as Microsoft lost two leaders who had been central to bridging that gap between compute ambition and physical reality. The exits landed while Copilot and Azure AI usage climbed, turning routine
Chloe Maraina has spent her career turning messy operational data into clear, visual stories that leaders can act on. As a Business Intelligence expert with a data science toolkit, she’s been inside the planning cycles that connect product roadmaps, AI-enabled workflows, and vendor economics to
In a world where a half-second delay can mean a missed hazard in traffic, a spoiled batch on a factory line, or a broken shopping experience at the register, moving AI decisions closer to where data is created has started to look less like an option and more like an imperative across industries
In sprawling cloud estates where telemetry is the nervous system and logs arbitrate truth, a fresh set of Fluent Bit flaws turned routine observability into an attack surface large enough to warp incident response, blind monitoring, and even sway production traffic. The findings, attributed to
Investors questioned whether the AI surge had outrun fundamentals even as usage spiked, but inside Google the mandate hardened around a simple, audacious rule: double compute capacity every six months or risk ceding the platform shift to faster movers with deeper pipelines and fewer bottlenecks.
