Core Viewpoint - The debate between open-source and closed-source models in AI continues, with open-source gaining traction as a more favorable option for digital transformation and cost efficiency [2][3][4]. Group 1: Open-Source Advantages - Open-source models allow companies to better control technology and innovate based on their specific needs, contrasting with the limitations of closed-source systems [3][4]. - Companies like Wells Fargo and Haokang Medical have successfully implemented open-source AI solutions, enhancing operational efficiency and compliance while reducing costs [5][6]. - IDC predicts that AI investments in the Asia-Pacific region, including China, will reach $175 billion by 2028, with a compound annual growth rate of 33.6%, indicating a rapid growth in AI commercialization [4]. Group 2: Cost and Efficiency - Open-source technology helps companies balance cost, efficiency, and innovation, breaking the "impossible triangle" and fostering a positive cycle of knowledge sharing and commercial feedback [3][4]. - SUSE AI provides a scalable and open enterprise-level AI infrastructure, enabling companies to easily expand AI applications and meet future challenges [6][7]. Group 3: Challenges of Open-Source - Despite its advantages, open-source technology presents security risks due to its open nature, making it challenging for companies to manage and secure their systems effectively [8][9]. - Companies must ensure their IT teams are knowledgeable about open-source technologies and focus on practical applications to address real business problems [8][9]. Group 4: Security Concerns - Security issues are a significant challenge for open-source AI, with 57% of IT decision-makers citing privacy and data security as primary concerns, and 84% of code repositories containing known vulnerabilities [9][10]. - SUSE's "AI guardrails" technology aims to help companies comply with regulations, monitor AI models, and prevent data poisoning, addressing some of the security challenges associated with open-source AI [10].

Core Insights - The emergence of open-source large models, such as DeepSeek-R1, has sparked a revolution in the AI industry, challenging the debate over the merits of open-source versus closed-source models [2][3] - Open-source AI is still in its early stages, with significant potential for industry transformation, but challenges related to security and commercialization remain [2][11] Group 1: Open-Source AI Impact - Open-source models are reshaping global industry dynamics, enabling lower-cost access to advanced AI capabilities for small and medium enterprises [3][4] - The cost of using DeepSeek-R1 for inference is only 1/30 of OpenAI's model, allowing developers to create applications at a fraction of the cost, such as a legal document generation tool that saw a 90% cost reduction [4] - The rise of "Model as a Service" (MaaS) is changing the service model of traditional cloud providers, making it easier for startups to deploy AI applications without building their own infrastructure [4][5] Group 2: Security Challenges - Security has become a major concern with the rise of open-source AI, with 57% of IT decision-makers citing privacy and data security as top issues [6][9] - High-profile security incidents, such as unauthorized access to Hugging Face's platform, highlight the vulnerabilities associated with open-source models [6][7] - DeepSeek has faced significant security threats, including DDoS attacks and data breaches, indicating the challenges that open-source platforms must address [7][8] Group 3: Future Considerations - The demand for computational power remains high, even with reduced costs, necessitating better observability and security measures from cloud service providers [5][9] - The integration of edge computing with AI is creating new security challenges, requiring companies to develop more complex security frameworks [10] - As data becomes a critical asset, ensuring data privacy and security in AI deployments is essential for companies [10][11]