Andrew Chen, an AI investor, has historically positioned early in paradigm shifts, investing in AI infrastructure in 2020, generative AI in 2022, and open-source models in 2024. By 2026, he proposed that open-source models will dominate the mass market, contrasting with Sam Altman's and Marc Andreessen's views. Chen argues the market is layered, not zero-sum, with cutting-edge models serving 10% high-value scenarios and open-source serving 90% of the mass market. By 2026, enterprise AI spending will reach $200 billion, with 60% for general tasks and 40% for high-value tasks. If open-source models dominate 90% of general tasks, open-source model spending would be $180 billion (cost $0.1/million tokens), while cutting-edge model spending would be $20 billion (cost $10/million tokens). Funding is shifting from subscription fees to local deployment. The market share of open-source models is expected to rise from 30% in 2026 to 80% by 2030, while cutting-edge models drop from 70% to 20%, but profits remain concentrated in cutting-edge models (gross margin 80% vs open-source 20%). Chen draws analogies to cloud computing and Android versus iOS, noting that open-source models like Llama 3 and Mistral approach cutting-edge performance at 100 times lower cost, driving mass adoption. This represents a democratization