In the AI era, Gillis said, the network performs the role the PCI bus once played inside a single server. Distributed AI systems require memory, compute, GPU, and storage to work together across physical infrastructure at scale. The network is the backplane that makes that possible. The result, he said, is that customers have come to see it as the one thing they can count on.
“There is a new operating model necessary for infrastructure,” Gillis told Network World.
AI changed how Cisco builds its own products
The shift shows up inside Cisco’s own development organization. Gillis runs a team of roughly 12,000 software developers, and AI coding tools have fundamentally changed how that team works.
Earlier generations of AI coding tools produced significant gains on new projects, where a team of five to 10 developers could accomplish what once required 100 people working for a year. But those tools hit a ceiling on complex legacy products. A Catalyst switch or Cisco firewall can contain 50 to 100 million lines of code, more context than prior models could handle at once. Newer AI coding tools have removed that ceiling, allowing Cisco to accelerate development across its entire product portfolio.
