87 stories
AI data centers have spent heavily on faster accelerators, denser racks and liquid cooling to keep that hardware running. The data pipeline that feeds those processors has not kept up, and expensive compute ends up waiting on memory and storage.
As AI factories scale, hardware interoperability is becoming as important as raw performance. Accelerators, memory and storage now come from many different suppliers, and every mismatch between them slows deployment across the data center.
AI factory deployment has long been a race to secure graphics processing units, but the builders of those systems are finding that compute is only half the problem. Data pipelines, storage costs and the clock on getting a cluster running now weigh as heavily as raw horsepower.
Memory has become the scarcest resource in the AI data center, and hyperscalers are rethinking how they hand it out. Composable memory, which pools capacity beyond individual servers and assigns it to workloads on demand, is emerging as one of the most practical answers to that squeeze.
Server consolidation is getting a second look as agentic AI drives a surge in demand for general-purpose compute. Graphics processing units dominate the AI infrastructure conversation, but the agents running on those systems lean heavily on central processing units, and data centers are short on…
Data sovereignty is becoming an architectural problem as enterprises push artificial intelligence inference toward their most sensitive information. Pipelines assembled from separate tools struggle to keep embeddings, permissions and source data aligned, leaving many valuable data sets out of AI’s…
AI factories run on graphics processing units that cost a fortune and work at blistering speed, yet those chips increasingly sit idle waiting for data. As the bottleneck moves from compute to memory and storage, NAND flash is being asked to do far more than hold files.
AI infrastructure networking is approaching a physical turning point as the copper links that tie GPUs together inside a rack strain under ever-faster signaling speeds. The move to light-based interconnects could redraw the boundaries of what counts as a single AI system. Cisco Systems Inc.
AI-powered knowledge management systems face a fundamental challenge when businesses need accurate answers rather than plausible ones. Imago Platform Ltd. encountered that problem while developing an application to help customers navigate complicated product warranty policies.
Sovereign AI control is becoming a business continuity issue as autonomous agents take on essential enterprise workflows. Keeping data within national boundaries addresses part of the challenge, but organizations also need control over agent behavior, external dependencies and the economics of…
Physical AI needs context about its surroundings to adapt beyond a fixed set of instructions. Munich-based Spranic is building a spatial memory layer that records physical spaces and how they change.
Americans have big concerns about artificial intelligence, but that hasn’t stopped OpenAI Inc., Nvidia Corp., IBM Corp. and others from charging ahead. Last week saw shares of Nvidia, CoreWeave Inc., Oracle Corp. and Nebius Group N.V.
Enterprise AI scalability is running into a hard budget wall for some customers. As agentic workloads multiply, cloud token bills are consuming allocations faster than planned, prompting companies to rethink where inference should run.
As enterprises move from debating artificial intelligence adoption to deploying it, AI governance has become the harder question. Autonomous agents now run around the clock across a sprawl of platforms, and controls built for deterministic software were never designed for them.
Financial institutions are adopting artificial intelligence faster than many compliance teams can establish the controls needed to oversee it. That gap is raising new questions about AI-powered compliance and regulatory accountability.
Voice AI is quickly becoming core infrastructure as people swap screens and clicks for conversations with their devices. That shift puts new pressure on the compute, storage and partnerships behind the models doing the listening and talking.
Silicon Valley’s AI partner ecosystem is changing as enterprises push artificial intelligence projects out of pilots and into production. In the region where much of the AI stack is invented, infrastructure providers are learning that selling to builders takes a different playbook.
When the founders at Phasis Inc. sought to develop a platform for orienting AI agents, they turned to graphs as a way to obtain a measurement of culture. The company wanted to better understand relations not just between people, but between a person and their values, beliefs and subcultures.
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