When Interoperability Is Not Enough: Future Substitutability in Evolving AI-Native Networks

AI-native networks are expected to evolve through increasingly deep integration among communication, computing, AI execution, orchestration, control, and application components. This paper asks whether a system that integrates successfully, and that remains fully interoperable, also remains able to adopt the best configuration that exists later. Future substitutability is therefore defined not as the ability to migrate an entire system from one ecosystem to another, but as technical-frontier reachability: whether the installed base can instantiate the best technically available configuration at a future time. Co-optimization and roadmap expansion are treated as rational competitive responses rather than avoidable practices, so the question is not whether integration should occur but whether its trajectory preserves access to future alternatives. A minimal analytical model, in which every component combination stays interoperable by construction, shows that substitutability varies across functional positions. Weakly coupled peripheral positions can remain contestable throughout the horizon, alternatives at moderately coupled positions can be initially superior and later overtaken, and expansion-facing positions can lose access to a superior block while visible multi-vendor diversity persists elsewhere; which positions stay contestable is shaped by where expansion attaches rather than by integration depth alone. The resulting frontier-reachability gap is sawtooth-like rather than monotonic: lifecycle events reduce it abruptly, continued expansion rebuilds it, and it can close after expansion stops and lifecycles mature. A counterfactual decomposition shows that lifecycle locking alone can open a substantial frontier gap, while accumulated same-ecosystem coupling increasingly enlarges that gap and, under a sufficient condition given in the paper, can prevent incremental closure.