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Recently, during a highly anticipated investor meeting at DeepSeek, the founder Liang Wenfeng systematically explained the company's pricing strategy, technical roadmap, and commercial considerations. Liang Wenfeng clearly stated that DeepSeek has always adhered to earning reasonable profits rather than maximizing profits. The core logic lies in leveraging extreme cost efficiency to unlock stronger model capabilities, thereby promoting the widespread application of technology.
In terms of product pricing practices, one of DeepSeek's models had initially set a high price due to concerns about excessive demand, but then directly reduced its price to one-fourth of the original. Liang Wenfeng pointed out that, given limited computing resources, computational efficiency is the decisive factor in training larger-scale models. Compared with the traditional approach of large companies continuously adding computing resources, DeepSeek chose to prioritize cost efficiency first.
Regarding competition in the API market, Liang Wenfeng believes that significantly lowering prices puts considerable pressure on competitors. However, DeepSeek's operational mechanism is extremely lightweight, requiring only a small number of personnel for maintenance. It does not need dedicated sales or customer service teams, and can attract users to access the product autonomously. When talking about the commercialization path, he admitted that although DeepSeek has been pushing for commercial implementation, it is not the ultimate goal, and the time point for a complete shift to commercialization is still far off.
As a hard-core player in the field of large models, DeepSeek has restructured the traditional business cycle by emphasizing high cost efficiency and lightweight operations. This development model, which prioritizes technological efficiency over commercial harvesting, has not only significantly lowered the entry barrier for artificial intelligence but is also driving the generative AI industry from blind resource accumulation towards a new competitive phase focused on deepening algorithm and architecture efficiency.