Large-scale distributed wireless networks provide infrastructure-free and cost-effective connectivity, supporting applications from disaster recovery to global digital inclusion. However, multi-hop communication poses scalability challenges, as point-to-point (P2P) capacity decreases with the number of intermediate relays (hop count). Thus, we focus on the critical role of multi-hop communication and user interaction probability, which empirical evidence indicates decays as a power-law with geographic distance. We present a comprehensive analysis of network scalability, from capacity estimation to empirical evaluation of real-world interaction patterns. The capacity estimation problem is decomposed using a novel analytical methodology, along with symmetric topology selection and geometric partitioning, to overcome the analytical complexities inherent in previous models. The estimated P2P capacity bounds, derived from expected hop count, surpass previous benchmarks. Specifically, when the power-law exponent exceeds a critical threshold, the expected hop count remains stable and P2P capacity is sustained; otherwise, the hop count grows and capacity declines as the network scales. Thus, an analytical method is devised to relate real-world interaction patterns to the power-law exponent, quantified by the contact distribution. Then, analysis of multiple empirical datasets confirms that the exponent falls within a range that naturally supports scalability. Consequently, multi-hop communication does not fundamentally hinder the wide-scale deployment of distributed wireless networks. This capacity-based analysis provides a clear perspective on scalability under realistic assumptions and underscores the promising future of such networks, as well as their potential for widespread deployment.