For most of its history, Cisco Systems Inc. won by building vertically integrated network products and asking customers to buy all of it. The company did very well with that model. Security, meanwhile, was something Cisco sold as a separate product line rather than something that was inherent to its infrastructure. Patching a core switch meant scheduling a maintenance window, crossing your fingers and hoping nothing broke.
For as long as Cisco Systems Inc. has held its WebexOne event, the show has aimed to drive thought leadership in the collaboration industry. This year, with artificial intelligence on tap, it had its fair share of unified communications and contact center. But more importantly, it was somewhat of a coming-out party for the “new Cisco,” which consolidated much of its historically sprawling portfolio into a single AI platform, with Webex as the place where people and agents meet.
Since its founding in 2004, it’s arguable that no network vendor has made better switches than Arista Networks Inc. That was true then and remains true, as its prowess in this area helped it ride the cloud boom and the artificial intelligence wave. That model is changing as AI scales. Today, ahead of next week’s Open Compute Project Global Summit in San Jose, Arista announced an expansion of its AI network strategy from switches to rack scale.
Operating a network has not changed in decades. Every network engineer knows the drill. An alert fires. You open a dashboard. Then another. Then you pull logs, review client history, ping the wireless team, and, hopefully, an hour later, there is enough context to start solving the problem. The industry spent a decade building better dashboards, even though operations teams needed fewer.
The networking industry loves inflection points. Over the years, we have had many new compute models that require the network to evolve. For as long as I can remember, the holy war between InfiniBand and Ethernet was fought on a relatively simple battlefield: throughput versus ubiquity. But as artificial intelligence workloads scale from tens of thousands of processors to massive clusters approaching the million-graphics-processing-unit mark, the network is fundamentally changing. It is no longer just a standalone infrastructure layer; it has become the critical backplane of a tightly integrated AI supersystem.