本轮AI算力行情的驱动因素

Core Viewpoint - The AI market has evolved through significant phases, with a current shift from training-driven demand to inference-driven demand, leading to a new wave of growth in capital expenditure related to AI [1][2]. Group 1: Scaling Law and Demand - The "scaling law" indicates that increased investment in GPUs and computational power enhances AI performance, transitioning from pre-training to post-training and now focusing on inference [2][4]. - In 2023, the scaling law is primarily evident in the pre-training phase, while in 2024, it will shift towards post-training, optimizing models for specific tasks [2]. - The demand for inference has surged, with applications in programming, search, and image processing, leading to a 50-fold increase in monthly token consumption for Google's Gemini in just one year [4][7]. Group 2: Capital Investment Trends - The AI industry is witnessing annual capital investments amounting to hundreds of billions, benefiting upstream sectors including GPUs, high-speed interconnect solutions, power supply, and cooling systems [7][8]. - Investment in computing power can be categorized into overseas and domestic sectors, each with distinct investment logic [7]. Group 3: Overseas Computing Power - Product upgrades in overseas computing power focus on higher performance products, enhancing value in specific segments, driven by chip and interconnect upgrades [8][10]. - Price-sensitive upstream segments are affected by downstream demand fluctuations, leading to supply bottlenecks and price increases, exemplified by the PCB industry [9]. Group 4: Domestic Computing Power - The gap in computing power between U.S. and Chinese internet companies is widening, with U.S. companies doubling their computing reserves annually, while domestic growth, though rapid, lags behind due to high-end chip export restrictions [13][15]. - Domestic GPUs are improving, with some models now matching the performance of NVIDIA's lower-tier offerings, indicating potential for competitiveness [15]. - The shift in AI demand from training to inference favors domestic computing power, allowing it to meet specific customer needs in certain scenarios [15][16]. Group 5: Market Dynamics and Future Outlook - The AI industry is characterized by high uncertainty, with rapid changes in trends, necessitating a cautious yet proactive approach to investment in AI computing power [16].