Recently, the Large Language Model (LLM) DeepSeeker V3 developed by Chinese startup DeepSeek has attracted widespread attention from the American and European industries. The outstanding performance of the model in terms of technical performance, open source mode, cost-effectiveness, and other aspects has received positive evaluations. The open-source DeepSeeker V3 is an important transformation in the global AI ecosystem, which helps countries and regions outside the United States to independently develop in the AI field and promote global AI technology towards a more open, diverse, and efficient direction. Multiple indicators are comparable to top models. According to independent analysis by evaluation agency Artificial Analysis, DeepSeeker V3 can compete with the world's top AI models. In terms of text understanding, encoding, mathematics, and subject knowledge, DeepSeeker V3 outperforms open-source models such as Meta's Llama 3.1-405B and Alibaba's Qwen 2.5-72B, and is on par with the world's top closed source models OpenAI's GPT-4o and Anthropic's Claude 3.5 Sonnet in performance. The advantages of DeepSeeker V3 in Chinese processing, encoding, and mathematical computation make it highly promising in the fields of education and research. The development and training costs of DeepSeeker V3 are also significantly lower than other large models. One of the founding members of OpenAI, Andrei Capatti, emphasized this extraordinary efficiency: models with performance and level like DeepSeeker V3 typically require clusters of 16000 to 100000 GPUs to train, while Chinese startups only used 2048 GPUs to complete training in 57 days. Its cost is about 5.576 million US dollars, which is only about 1/10 of other mainstream models such as GPT-4. On the other hand, the media has focused on issues such as so-called 'AI pollution', privacy protection, and security. With the proliferation of AI generated content on the Internet, AI models will produce "hallucinations" or misleading answers in the training process. The case of DeepSeeker V3 highlights the urgent need to ensure the purity and reliability of training data in the context of the increasing popularity of AI generated content. The open source strategy changes the AI ecosystem. The key to DeepSeek's significant efficiency improvement lies in the collaborative design of its algorithms, framework, and hardware. At a time when the development cost of AI models for American enterprises is becoming increasingly expensive, the emergence of DeepSeeker V3 indicates that top-level AI can be developed even without a budget of billions of dollars. This is good news for companies and research institutions with limited budgets. This efficient training method is referred to by the media as a "paradigm shift in the AI field," which is particularly important in the current context of computing power shortages. In addition, DeepSeeker V3's open-source strategy has also been warmly welcomed by the industry. Companies like OpenAI, which have a large number of users, charge high fees and are not open source, yet still lose billions of dollars; DeepSeeker V3 offers top-level models at a cheaper price and is also open-source. Such low development costs and aggressive pricing policies have put a lot of pressure on many AI companies. In the long run, this will impact the dominant position of American tech giants in the AI market. DeepSeeker V3 has demonstrated that open-source models have the ability to compete with closed source models in terms of performance and cost. This will prompt more companies to reassess their technology strategies, especially in balancing open source and closed source models, hardware dependencies, and software innovation. This will help promote the development of the AI ecosystem towards a more open direction, encourage more enterprises to join the open source camp, provide valuable resources for the global AI community, and thus promote the popularization of AI technology. Breaking through the "small courtyard high wall" blockade in recent years, there has been fierce competition between China and the United States around AI. The United States is attempting to restrict the development of Chinese AI through increasingly stringent chip export bans. On January 13th, the United States launched a new round of AI chip export restrictions, hoping to concentrate the development of AI technology in so-called "friendly countries and regions", prevent advanced chips from flowing into "rival" countries, and set American standards for global AI development. The policy makers in the United States assume that no country other than the United States can provide high-end GPU chips and corresponding computing power, or develop alternative algorithms and products. However, the emergence of DeepSeeker V3 proves that the "small courtyard high wall" blockade in the United States will eventually be defeated by technological innovation. It has been proven that various restrictions imposed by the United States have actually spurred innovation in algorithms and architecture in China. China's chip technology is gradually improving, and innovation in the field of AI applications has made significant progress. DeepSeeker V3 provides new impetus for the development of China's AI ecosystem and is also beneficial for global AI technology to move towards a more open, diverse, and efficient direction. On the other hand, in Europe, although governments around the world attach great importance to the development of AI technology and industries, Europe has overly relied on investment and technological support from large American technology companies in the process of AI development, leading to a weakening of technological sovereignty and market competitiveness. The role of AI in driving a new round of technological revolution and industrial transformation is becoming increasingly prominent. For Europe, which advocates technological sovereignty, promoting the development of local AI technology and reducing dependence on the United States may be the key to the sustainable development of AI in Europe. (New Society)