Cisco

Cisco’s intent to acquire Seattle-based NeuralFabric signals a decisive shift toward practical, domain-specific AI that meets real-world constraints around data, compliance, and infrastructure. Cisco plans to acquire NeuralFabric, an enterprise AI platform focused on building small language models (SLMs) from proprietary data with deployment across SaaS and on-premises environments. By focusing on SLMs trained on enterprise data and deployable in hybrid environments, Cisco aims to shorten time-to-value while keeping control where it belongs—inside the business. They reduce inference cost, improve latency, and can be deployed on-premises or at the edge—critical for sectors like telecom, financial services, and healthcare.
Amazon has moved its low Earth orbit broadband effort out of code-name mode and into a market-facing brand with strategic implications for telecom and enterprise buyers. Project Kuiper is now Amazon Leo, a direct reference to the low Earth orbit constellation underpinning the service. The rebrand signals a transition from R&D to commercial execution. Amazon reports more than 150 satellites in orbit today—roughly 153 by recent counts—following a string of successful launches and a completed prototype mission. The company says it will light up service as it adds coverage and capacity.
Enterprises adopting private 5G, LTE, or CBRS networks need more than encryption to stay secure. This article explains the 4 pillars of private network security: core controls, device visibility, real-time threat detection, and orchestration. Learn how to protect SIM and device identities, isolate traffic, secure OT and IoT, and choose the right vendors for a robust private network security strategy.
The Bethpage Black Ryder Cup turned a 1,500‑acre golf course into a pop-up smart city, giving HPE a high-stakes stage to showcase end-to-end AI, networking, and edge operations at scale. Golf is a network planner’s stress test: fans are constantly moving, crowd density swings hole-to-hole, and the venue is built from scratch for a few intense days. More than 250,000 spectators demanded seamless connectivity, broadcast-grade reliability, and instant digital services. This environment forced an enterprise-grade blueprint - fast deployment, elastic capacity, airtight security, and automated operations, mirroring the requirements of modern campuses, arenas, and industrial sites.
Gartner’s latest outlook points to global AI spend hitting roughly $1.5 trillion in 2025 and exceeding $2 trillion in 2026, signaling a multi-year investment cycle that will reshape infrastructure, devices, and networks. This is not a short-lived hype curve; it is a capital plan. Hyperscalers are pouring money into data centers built around AI-optimized servers and accelerators, while device makers push on-device AI into smartphones and PCs at scale. For telecom and enterprise IT leaders, the message is clear: capacity, latency, and data gravity will dictate where value lands. Spending is broad-based. AI services and software are growing fast, but the heavy lift is in hardware and cloud infrastructure.
Cisco’s Secure AI Factory with NVIDIA, now integrated with VAST Data’s InsightEngine, targets the core blocker to agentic AI at scale: getting proprietary data to models quickly, securely, and at enterprise breadth. The new joint solution aims to collapse RAG pipeline delays from minutes to seconds, reduce integration risk with validated reference designs, and keep every interaction within security and compliance controls. By aligning Cisco’s AI PODs, NVIDIA’s AI Data Platform and DPUs, and VAST’s data intelligence layer, the offering provides a turnkey workload data fabric for production-grade AI agents. Cisco AI PODs now ship with VAST InsightEngine using NVIDIA’s AI Data Platform reference design, turning raw enterprise data into AI-ready indices and vectors in near real time.
Fresh polling signals rising public concern that AI could upend employment, destabilize politics, and strain social and energy systems. A recent Reuters/Ipsos survey of 4,446 U.S. adults found that 71% worry AI will permanently displace too many workers. Seventy-seven percent of respondents fear AI will fuel political instability if hostile actors exploit the technology. The poll also shows broad worry about AIs indirect costs: 66% are concerned about AI companions displacing human relationships, and 61% are concerned about the technology's energy footprint. Bottom line: Public concern is high, and that increases the cost of missteps.
The telecom sector once hailed AI as a game-changer, but is it delivering? This article explores why many operators report low ROI on AI tools, and how legacy systems, cultural resistance, and regulatory hurdles stall adoption. Despite challenges, AI shows targeted promise in predictive maintenance, fraud detection, and 5G network slicing.
A new Ciena and Heavy Reading study signals that AI will become a primary source of metro and long-haul traffic within three years while most optical networks remain only partially prepared. AI training and inference are shifting from contained data center domains to distributed, edge-to-core workflows that stress transport capacity, latency, and automation end-to-end. Expectations are even higher for long-haul: 52% see AI surpassing 30% of traffic and 29% expect AI to account for more than half. Yet only 16% of respondents rate their optical networks as very ready for AI workloads, underscoring an execution gap that will shape capex priorities, service roadmaps, and partnership models through 2027.
Deutsche Telekom, Orange, and the Linux Foundation outline their 2025 cloud-native telecom roadmap, highlighting Kubernetes-native workloads, AI integration, observability, and zero-trust security models. Learn how open-source tooling, GitOps automation, and cultural transformation are reshaping next-gen telco operations.
Indian telecom companies such as Jio and Airtel are moving beyond internal AI use cases to co-develop monetizable, India-focused AI applications in partnership with tech giants like Google, Nvidia, Cisco, and AMD. These collaborations are enabling sector-specific AI tools across healthcare, education, and agriculture, boosting operational efficiency, customer experience, and creating new revenue streams for telecom operators.
NVIDIA is partnering with telecom leaders like T-Mobile, Cisco, and MITRE to develop AI-powered 6G networks, integrating artificial intelligence into next-gen wireless infrastructure. Announced at NVIDIA GTC, this initiative leverages AI-RAN and Open RAN technologies to enhance spectral efficiency, optimize network performance, and enable seamless 6G connectivity.

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