Core Insights - Amazon Web Services (AWS) is significantly aiding cross-border logistics companies in applying AI technologies to reduce costs by 10% [1][20] - The implementation of AI solutions has led to substantial improvements in logistics efficiency and cost savings across various operational aspects [21] Company Overview - Walltag, a platform-based company specializing in international logistics and SaaS software, targets freight forwarders and cross-border e-commerce logistics service providers [3][13] - The company has developed a comprehensive solution that integrates various logistics services, including TMS, OMS, and WMS, and has accumulated over 2,000 clients in the logistics sector [4][3] AI Applications - The company has utilized AWS's large model capabilities to enhance document recognition accuracy from 85% to 98%, significantly improving operational efficiency [5][20] - AI applications include an intelligent assistant for intent recognition and automated business process management, which reduces manual operations [6][20] - The Type to SQL feature allows for automated BI data analysis and report generation, streamlining data management for logistics companies [8][20] Cost Reductions - Logistics costs have been reduced by 10% through optimization of service provider pricing [15][20] - Labor costs have decreased by 5% due to automation and improved accuracy in processes, with AI achieving a recognition accuracy of 99% in the logistics sector [15][18] - Procurement costs have been lowered through intelligent comparisons and selections of delivery service providers [15][20] - Business risk costs have been mitigated via intelligent alerts that help identify and reduce the likelihood of anomalies, enhancing customer service team efficiency by 40% [15][20] Future Outlook - The integration of AI technologies is positioned as a key driver for digital transformation in the logistics industry, with a shift from cost optimization to innovation-driven strategies [21] - Amazon plans to invest $100 billion in AI computing power and cloud infrastructure to support businesses in navigating global opportunities [21]

Core Viewpoint - The article emphasizes that artificial intelligence (AI) is entering a new phase of cost reduction and efficiency improvement, creating new investment opportunities in various sectors, particularly in consumer-centric applications and computational infrastructure [1]. Group 1: AI Development and Cost Efficiency - DeepSeek has introduced a cost-effective method for running large language models, which is expected to lower operational expenses for AI service providers [2]. - The training cost of China's DeepSeek R1 model is reported to be under $6 million, significantly lower than the hundreds of millions spent by U.S. companies, while achieving comparable performance [2]. - The adoption of DeepSeek's technology by Chinese telecom companies and data centers demonstrates China's capability to create competitive AI solutions despite a lack of advanced semiconductor hardware [2]. Group 2: Competitive Advantages in Emerging Markets - China possesses significant advantages in consumer-facing AI applications due to its large digital ecosystem and cost efficiency, forming a solid competitive barrier [3]. - The demand for global computational power remains strong, with new AI application scenarios emerging, supported by favorable industrial policies [3]. - Investment focus areas include AI technology applications in embodied intelligence, consumer electronics, smart driving, and the infrastructure that supports AI applications [3]. Group 3: Market Volatility and Investment Caution - Despite the efficiency gains from DeepSeek, there are risks associated with AI investments, particularly regarding the technology's ability to enhance productivity globally [4]. - The lack of widely adopted "killer" AI applications raises concerns among investors about the return on AI investments [5]. - Companies should be evaluated based on their defensive capabilities, value-driven potential, and long-term growth records to ensure stable returns while avoiding overvaluation in uncertain market conditions [5].