The rapid expansion of open-source artificial intelligence models is reshaping enterprise deployment strategies without significantly undermining revenue at frontier laboratories like Anthropic. Decagon chief executive Jesse Zhang recently articulated a theory explaining this divergence, arguing that open-source and frontier models function as sequential phases within a single operational lifecycle rather than direct competitors. As organizations mature their AI implementations, they routinely migrate high-volume, predictable workloads to cost-effective open-source alternatives. Meanwhile, novel and complex use cases continue to drive sustained spending on premium, state-of-the-art models. Platform analytics confirm this two-tiered market structure. Recent data from Vercel and OpenRouter indicate that open-source models dominate token volume, with DeepSeek processing over a third of traffic and securing leading usage metrics. Despite this consumption shift, Anthropic remains responsible for more than half of the platform total AI expenditure. OpenRouter analytics reinforce the trend while revealing a stark pricing disparity. Anthropic Opus 4.8 averages $1.37 per million tokens compared to just six cents for cheaper alternatives, suggesting that premium inference costs continue to capture the majority of enterprise budgets. This dynamic suggests that the broader AI market is expanding faster than open-source migration can displace frontier revenue. Zhang characterizes the emerging equilibrium by noting that frontier laboratories will continue to lead in discovery and experimental deployment, while open-source ecosystems increasingly handle production-scale operations. The sustained demand for high-cost models also stems from inherently difficult tasks that resist full automation by lighter architectures. Although vertical AI applications have adopted lighter models and startup economics have stabilized, foundation labs have successfully preserved their most lucrative segment. Consequently, the enterprise AI economy appears to be settling into a durable structure where open-source models optimize cost and scalability, while frontier providers maintain profitability by dominating early-stage innovation and complex problem-solving.