Coexistence of AI Generations: Lessons from Mobile Network Evolution and Their Security Implications
Year: 2026
Type: preprint
Abstract: Telecom taught the world how to run heterogeneous generations in parallel for decades (1G–5G and beyond). This paper formalizes that analogy for artificial intelligence (AI), where rule‑based, statistical/ML, deep learning, foundation models, and agentic systems will coexist for the foreseeable future. We make five contributions: (1) a formalized mapping between coexistence phenomena in mobile networks and AI multi‑version ecosystems; (2) a layered architecture for version‑aware AI orchestration with capability negotiation and sandboxed execution; (3) two analytical risk models quantifying interoperability and security exposure under coexistence; (4) a structured threat analysis with attack paths unique to cross‑model and cross‑agent amplification; and (5) a quantitative evaluation protocol encompassing semantic drift, safety policy violation rates, and change‑failure risk during phased upgrades. We detail engineering and governance controls that mirror proven telecom practices (multi‑RAT, phased migration, telemetry‑driven operations) which can help multi variant AI systems.
Source: Preprints.org
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7213772182