Core Insights - The article highlights the critical challenge faced by AI companies: the scarcity of computational resources, which is a fundamental constraint in the industry [1][5]. Pricing Dynamics - AI service pricing is fundamentally a trade-off among three performance metrics: latency, throughput, and context window [2][3]. - By adjusting these three parameters, service providers can achieve any price level, making simple price comparisons less meaningful [4][24]. DeepSeek's Strategy - DeepSeek adopted an extreme configuration with high latency, low throughput, and a minimal context window to offer low prices and maximize R&D resources [4][28]. - Despite DeepSeek's low pricing strategy, its official platform has seen a decline in user engagement, while third-party hosted models have surged in usage by nearly 20 times [16][20]. Competitive Landscape - Anthropic, another leading AI company, faces similar resource constraints, leading to a 30% decrease in API output speed due to increased demand [34][36]. - Both DeepSeek and Anthropic illustrate the complex trade-offs between computational resources, user experience, and technological advancement in the AI sector [5][53]. Market Trends - The rise of inference cloud services and the popularity of AI applications are reshaping the competitive landscape, emphasizing the need for a balance between technological breakthroughs and commercial success [5][45]. - The article suggests that the ongoing price war is merely a surface-level issue, with the real competition lying in how companies manage limited resources to achieve technological advancements [53].