Core Viewpoint - DeepSeek has released an update to its model, DeepSeek-V3.1-Terminus, which aims to improve stability and consistency based on user feedback, but the anticipated release of the next-generation model, DeepSeek-R2, has been delayed, causing disappointment in the industry [1][2][3] Group 1: Market Expectations and Delays - The initial release of DeepSeek-R1 was a significant success, outperforming top models from OpenAI and establishing high expectations for the subsequent model, R2 [3][5] - Since the launch of R1, there have been over ten rumors regarding the release of R2, with initial expectations set for May 2025, but these have not materialized, leading to a sense of frustration in the market [5][6] - The delay in R2's release is attributed to internal performance issues and external pressures, including supply chain challenges related to NVIDIA chips [6][12] Group 2: Strategic Developments - Despite the delay of R2, DeepSeek has made significant strides in building an open-source ecosystem, launching several models and tools that lower the cost of AI technology [8][9] - The company has introduced various components aimed at enhancing training and inference efficiency, such as FlashMLA and DeepGEMM, which reportedly improve inference speed by approximately 30% [9][11] - DeepSeek's open-source strategy has positioned it as a key player in promoting accessible AI technology in China, although the absence of R2 raises concerns about its competitive edge [8][17] Group 3: Challenges Faced by DeepSeek - DeepSeek faces a "triple dilemma" regarding the delay of R2, including the need for technological breakthroughs, managing high market expectations, and navigating intense competition from domestic rivals like Alibaba and Baidu [11][12][13] - The company must overcome technical challenges related to transitioning from NVIDIA to Huawei's Ascend chips, which has hindered R2's development [11][12] - DeepSeek's lack of a robust content ecosystem compared to larger tech companies limits its ability to continuously improve its models, leading to issues such as "hallucinations" in its outputs [15][16]