The rapid expansion of artificial intelligence is driving a convergence toward an AI operating system that governs how data, compute and intelligence move through enterprise infrastructure. The conversation has moved past experimentation and into the hard questions of inference at scale, agent sprawl and the operational mechanics of running AI in production. Companies, including Vast ...

AI infrastructure is running into a hard physical barrier, and diamond cooling is emerging as a new way to push more compute through existing power limits. As AI systems grow larger and hotter, the challenge is no longer simply installing more GPUs but managing the heat and electricity they demand. Companies such as Akash Systems ...

AI isn’t just adding horsepower to data centers — it’s redefining what they are built to do. The shift is structural, not incremental. Traditional racks and retrofit upgrades can’t keep pace with large-scale AI workloads, forcing a redesign of power, cooling and network architecture from the ground up. Companies such as GlobalAI are building around ...

Quantum sensing is stepping out of the lab and into real-world systems — with Nvidia tightly woven into the playbook. For years, much of the quantum narrative has focused on distant computing breakthroughs, but Infleqtion Inc. is charting a more immediate path, generating revenue from deployed systems and using today’s products to finance longer-term milestones. ...

Enterprise networking has hit a turning point — it’s no longer judged by port speeds but by how well it keeps artificial intelligence running across clouds, data centers and the edge. As MWC Barcelona 2026 approaches under the banner of “The IQ Era,” the transition is unmistakable: Intelligence is no longer layered on top of ...

The hyperconverged edge is where AI factories collide with wireless networks. That shift is forcing telecom infrastructure, enterprise networking and compute architecture to converge in ways the industry has long discussed but rarely executed. Nvidia’s push to extend artificial intelligence beyond centralized data centers is accelerating that change, and companies such as Veea Inc. are ...

The Nvidia ecosystem is quickly becoming the control plane for AI infrastructure. The shift isn’t just about GPUs anymore. As enterprises move from experimentation to scaled deployment, the market is consolidating around a standardized stack where Linux and Kubernetes integrate tightly with Nvidia hardware, and the Red Hat–Nvidia partnership reflects customers’ push for repeatable AI ...

The autonomous enterprise is taking shape as companies move from AI that informs to systems that actually act. Across the enterprise market, the question around AI has shifted from “does this work?” to “does this actually move the business forward?” That evolution is playing out in partnerships throughout the enterprise, such as the one between ...

Across large enterprises, AI adoption is shifting from experimentation to operational pressure, with customer service often emerging as the first system that must work at scale. The difference between pilots and production is no longer theoretical — it shows up in resolution rates, satisfaction scores and revenue impact. Zendesk Inc., a customer service software company, sits ...

Metadata management has become the practical dividing line between AI systems that scale and those that stall. As organizations push AI from experimentation into sustained production, the limiting factor is no longer models, but visibility into sprawling data estates. Survey results and field experience from companies such as HighFens Inc. show that without usable metadata, ...