More Is Different: Toward a Theory of Emergence in AI-Native Software Ecosystems

TL;DR

This paper proposes viewing AI-native software ecosystems as complex adaptive systems to better understand emergent failures and guide ecosystem-level monitoring and governance.

Contribution

It introduces a CAS-based framework for analyzing AI-native ecosystems, mapping properties to observable dynamics, and linking theory to software evolution.

Findings

Defines micro-level variables and coarse-graining for causal emergence measurement.

Maps CAS properties onto ecosystem dynamics, distinguishing from other architectures.

Proposes seven falsifiable propositions connecting CAS theory to software evolution.

Abstract

Software engineering faces a fundamental challenge: multi-agent AI systems fail in ways that defy explanation by traditional theories. While individual agents perform correctly, their interactions degrade entire ecosystems, revealing a gap in our understanding of software evolution. This paper argues that AI-native software ecosystems must be studied as complex adaptive systems (CAS), where emergent properties like architectural entropy, cascade failures, and comprehension debt arise not from individual components, but from their interactions. We map Holland's six CAS properties onto observable ecosystem dynamics, distinguishing these systems from microservices or open-source networks. To measure causal emergence, we define micro-level state variables, coarse-graining functions, and a tractable measurement framework. Seven falsifiable propositions link CAS theory to software evolution, challenging or extending Lehman's laws where agent-level assumptions fail. If confirmed, these findings would demand a radical shift: ecosystem-level monitoring as the primary governance mechanism for AI-native systems. If refuted, existing theories may only need incremental updates. Either way, this work forces us to ask: Can software engineering's core assumptions survive the age of autonomous agents?