When the Model Moves: Entrepreneurial Adaptation to Foundation-Model Dependency in AI Startups

Abstract

Foundation models allow artificial intelligence (AI) startups to develop sophisticated products without owning the underlying model, but this speed creates dependence on providers that control model behaviour, availability, pricing, safety settings and lifecycle decisions. This paper examines how the AI venture ecosystem publicly constructs that dependence as manageable and which adaptation strategies it makes available to downstream ventures. The study uses qualitative content analysis of 15 organisation-controlled online sources published by model providers, AI gateways and routers, developer frameworks, and observability and evaluation platforms. Sources were selected using explicit criteria concerning ecosystem role, public accessibility, recency and substantive treatment of model lifecycle, switching, routing, fallback, evaluation or integration. Provider materials are treated as strategic discourse rather than neutral evidence of implementation; interpretations are therefore bounded, compared across ecosystem roles and contextualised using recent independent research on platform power, transparency, competition and AI business models. The findings identify model volatility as the operating condition and five responses: multi-model hedging, abstraction-layer building, failure routing and fallback, evaluation-based capability monitoring, and workflow ownership. Together these responses form a dependency-management architecture. The paper defines workflow ownership as venture-specific control over the customer process, data context, integration logic, evaluation criteria, governance and assurance surrounding model inference, so that customer value can persist when the upstream model changes. The analysis develops the concept of resilient dependence: ventures may remain reliant on external models while reducing the ability of any single provider or model version to determine continuity. Economically, the architecture trades additional integration, evaluation and governance costs for lower exposure to supplier power, service interruption, margin volatility and forced migration. The study contributes a process model for research on AI entrepreneurship and a practical framework for assessing whether a downstream venture can continue delivering and capturing value when its upstream model moves.