半导体，最新预测

Core Insights - The article discusses the rapid evolution of the semiconductor industry, particularly in the context of artificial intelligence (AI) and custom chip development, predicting significant changes by 2026 [1][2]. Group 1: AI and Custom Chips - By 2026, the shipment of custom-designed chips (ASICs) is expected to surpass that of GPUs, driven by the need for data center operators to invest heavily to avoid falling behind [1]. - The performance metrics for chips will evolve beyond just floating-point operations to include interconnects, memory, and compilers, which will determine overall performance [1]. - The demand for AI-driven virtual twin simulations and model-based systems engineering (MBSE) will enable companies to optimize designs digitally, reducing reliance on physical prototypes [2]. Group 2: Market Dynamics and Trends - The global semiconductor sales reached $772 billion in the previous year, with a projected growth of 26% to $975 billion by 2026, and some analysts predicting a stronger annual growth rate of 30% [5]. - The AI data center market is expected to grow to $1.2 trillion by 2030, with a significant portion of this growth (approximately $900 billion) coming from AI accelerator chips like GPUs and custom processors [5]. Group 3: Competitive Landscape - NVIDIA currently holds a dominant position in the AI chip market with an estimated market share of 90%, and this is unlikely to change significantly by 2026 [2][3]. - AMD may become more competitive with the release of its MI400 series and the maturation of its ROCm software stack, but its success remains uncertain [3]. - The pricing of GPUs is on a downward trend, yet the increasing demand for AI workloads means that the total cost of AI infrastructure will continue to rise [3]. Group 4: Interconnect Technologies - High-speed interconnect technologies will gain renewed focus in modern data centers to support AI and machine learning workloads [3][4]. - Co-packaged optics (CPO) technology, developed by NVIDIA and Broadcom, is crucial for high bandwidth density interconnects in AI-driven architectures [4].