This paper compares financial-market reactions to the releases of DeepSeek R1 in January 2025 and Kimi K3 in July 2026. Both AI models were presented as credible Chinese competitors near the capability frontier, but their releases conveyed different economic information. DeepSeek R1 combined open weights with unusually low reported training and inference costs, prompting investors to reassess expected demand for computing infrastructure and producing large stock-price decreases among semiconductor and data-centre-related firms. Kimi K3 instead entered at broadly frontier prices and generated a market reaction concentrated more heavily among rival Chinese AI laboratories. Drawing on descriptive event-window evidence, model capability and cost comparisons, subsequent industry developments, and the historical diffusion of open-source software, the paper argues that open-weight models redistribute rents across the AI value chain rather than eliminating them. Incumbents remain protected by complementary assets including distribution, product bundling, switching costs, proprietary data, serving capacity, and contractual trust. The analysis suggests that investors and policy makers should assess risks looking not only at model capability, but also at the ownership and durability of complementary assets across the AI value chain.
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