Cisco gives engineer unlimited AI tokens to rewrite legacy code

Commenters spent less time marveling at unlimited AI tokens than asking what Cisco is actually trying to achieve by rewriting nearly all of a mature networking stack. Without a concrete target such as portability, feature parity, simpler maintenance, or measurable performance gains, the effort reads to many as AI theater dressed up as modernization. The worry is not merely that generated code will be buggy: old production networking software embodies decades of undocumented interoperability quirks, field fixes, hardware workarounds, and edge cases that tests rarely capture. A cleaner AI-generated implementation could therefore fail precisely where real networks are least forgiving. A smaller optimistic camp sees unusually good conditions for agentic development. Networking code has specifications, measurable throughput and latency goals, and at least some potential for regression comparison against existing behavior. One reader described an AI-assisted modernization of an orphaned embedded codebase completed cheaply and quickly, while another argued that rapidly improving models could make an initial feature-complete rewrite progressively viable. Even those sympathetic to AI generally insisted that validation, simulation, and domain expertise—not token volume—determine whether this is engineering or a costly outage generator. Cisco’s history of defects amplified the cynicism: some joked that AI may struggle to make the product worse, while others saw that as exactly the danger.