In the age of artificial intelligence (AI), the network is no longer an auxiliary infrastructure but a "mission-critical core area (the most important area where a problem would have a fatal impact on an entire company or service)." Beyond mere consolidation, a "self-driving network" that autonomously identifies, diagnoses and resolves issues before users are affected will be the market's battleground.
Rami Rahim, executive vice president and general manager of networking at Hewlett Packard Enterprise Company (HPE), met with ChosunBiz on the 20th at the Grand Hyatt Seoul in Yongsan District and explained the competitiveness of networks in the AI era.
Rahim, who studied electrical engineering at the University of Toronto in Canada, joined AMD in 1994 and worked for a year as an application-specific integrated circuit (ASIC) engineer. He then went on to graduate school at Stanford University, earned a master's degree in electrical engineering, and joined Juniper Networks in 1997 as an ASIC engineer. Rahim joined HPE with HPE's acquisition of Juniper Networks in 2025 and had served as Juniper's chief executive officer (CEO) for 10 years until then. He now oversees the networking business that encompasses HPE Juniper (Juniper) Networking and HPE Aruba (Aruba) Networking. Juniper Networks, acquired by HPE, makes network equipment and software for corporations, telecommunications carriers and cloud service providers, with particular strengths in AI-based network operations and data centers and security. HPE strengthened its network competitiveness for the AI and Hybrid Cloud era through the acquisition of Juniper.
Rahim described HPE's AI network strategy along two axes: "AI for the network" and "the network for AI." He said, "One is to reduce operational complexity and boost user experience through AIOps (technology that automates IT system operations with AI and predicts and manages failures), and the other is to target the data center network and routing markets for AI training and inference infrastructure," adding, "HPE sees both opportunities at the same time."
The differentiator he emphasized for HPE is the "self-driving network." Rahim said, "It's no longer enough to have a model where people respond only after an outage occurs," adding, "We need a network that detects anomalies first, analyzes the causes, and then optimizes itself." He continued, "What we are aiming for is not simply tacking AI onto network management tools," adding, "It is AI-native networking that provides a consistent autonomous operations experience across campuses, branches and data centers."
Only five months after completing the Juniper acquisition, in Dec. last year HPE unveiled its AI-native networking portfolio combining HPE Aruba Networking and HPE Juniper Networking. Rahim said, "The core of the Juniper integration is not organizational consolidation but the evolution of operating methods that customers can actually feel," adding, "We are strengthening a common AI operations experience that spans Aruba Central and Mist."
He said the importance of networks in the AI era becomes clearer in data centers. "Even if you invest billions of dollars in an AI data center, if there is latency or bottlenecks in the network, you cannot use the graphics processing units (GPUs) optimally," Rahim said. "High-performance computing alone does not complete AI infrastructure." He added, "In an AI factory, you have to view everything as a single design—from the internal consolidation linking GPU to GPU, to long-distance interconnection between data centers, to on-ramps where AI workloads flow in from the edge, and the routing that supports it," noting, "Without a high-performance network, the return on GPU investment is also halved."
In line with this, HPE is designing AI infrastructure by bundling high-performance switches for GPU consolidation within data centers, long-distance data center interconnection, edge on-ramps and routing. The new QFX5250 switch unveiled in Dec. last year was designed to support ultra Ethernet transport. "AI workloads are far more sensitive to latency and congestion," Rahim said, adding, "It is important to ensure stable consolidation not only within data centers but also across multicloud and long-distance distributed cluster environments."
Collaboration with Nvidia is also one pillar of this strategy. In Dec. last year, HPE expanded its AI factory portfolio with Nvidia and added edge on-ramp and data center interconnection features based on HPE Juniper Networking. Rahim said, "AI is no longer a workload that stays only inside a single data center," adding, "You need to ensure stable consolidation across Multi-Cloud, distributed clusters and the "edge (a computing environment that processes data close to users or devices rather than in a central cloud data center)" for AI to work properly in real customer environments."
This strategy has been reflected in results. According to HPE's released results for the first quarter of fiscal year 2026 (Nov. 2025–Jan. 2026), total revenue was $9.3 billion (about 13.9054 trillion won), up 18% from a year earlier. Of that, networking revenue surged 151.5% to $2.7 billion (about 4.037 trillion won), and the operating margin was 23.7%. Campus and branch revenue rose 42% to $1.2 billion (about 1.7942 trillion won), data center networking revenue increased 382.6% to $444 million (about 663.9 billion won), and security revenue rose 114.3% to $255 million (about 381.3 billion won) from a year earlier, respectively.
Rahim said, "The impact of the Juniper integration is proven not by a simple message but by numbers," adding, "Customers now see networks not as simple consolidation equipment but as core infrastructure that determines competitiveness in the AI era." He added, "In both areas—'AI for the network,' which improves operational efficiency, and 'the network for AI,' which underpins AI infrastructure itself—demand is growing simultaneously among corporations, service providers and cloud service providers."
Reflecting this trend, HPE raised its networking institutional sector revenue growth forecast for fiscal year 2026 (Nov. 2025–Oct. 2026) to 68%–73%. Rahim said, "The competition in AI-era networks ultimately comes down to autonomy," adding, "The market landscape will be determined by who first properly implements a network that identifies and resolves issues on its own before users are affected, rather than responding after problems occur."