The algorithmic-based view: Why data, models, and systems replace VRIN as the core of competitive advantage

This Google's search algorithm rebuilds itself 50,000 times per year—an evolutionary capability that no static VRIN resource can replicate—transforming user interactions into a $200 billion competitive fortress while exposing a critical gap in strategic theory: the Resource-Based View (RBV), which focuses on firm-controlled assets such as patents and factories, fails to explain modern advantage, where self-reinforcing systems—rather than resources—create true inimitability. Through theory synthesis, computational simulations, and case studies of pioneers like Amazon, NVIDIA, and ByteDance, we reveal that 71% of market dominance in AI-driven sectors stems from an Algorithmic Core—a dynamic architecture fueled by recursive data flywheels that accelerate with use, human-AI collaboration that amplifies ingenuity, and adaptive models that reconfigure like biological systems; crucially, firms leveraging causal AI (e.g., NVIDIA's physics-based simulation Thus, we propose the Algorithmic-Based View (ABV) as the new paradigm for sustained advantage, demonstrating that superiority flows not from owning resources, but from orchestrating resilient learning systems that convert volatility into innovation—transforming strategy to prioritize algorithm evolution over IP protection, causal depth over big data accumulation, and "AI whisperers" (experts fusing domain intuition with computational design) over traditional star hires. Static resources rust as algorithmic systems reinvent—mastering this change determines tomorrow's winners. Keywords: Algorithmic-Based View, Resource-Based View, Competitive Advantage, AI Strategy, Causal AI, Algorithmic Core, Data Flywheel, Human-AI Collaboration.