Core Viewpoint - Jensen Huang has established a $4.6 trillion empire through Nvidia, focusing on AI infrastructure, but there are three significant threats to the company's future that are not addressed in earnings calls [1] Group 1: Threats to Nvidia - **Threat 1: Major Customers Developing In-House Chips** Microsoft, Meta, Amazon, and Alphabet account for 40-50% of Nvidia's revenue and are all creating custom AI chips, which could replace Nvidia's offerings. Inference workloads, which represent 80% of long-term AI compute, are at risk if these companies build their own chips [2][3] - **Threat 2: AMD as a Competitive Alternative** AMD's MI300X chips have gained traction, offering competitive performance at 20-30% lower costs compared to Nvidia. Microsoft Azure and Oracle Cloud are adopting AMD technology, and OpenAI is reportedly testing AMD chips to reduce dependency on Nvidia [4][5][6] - **Threat 3: Geopolitical Risks from China** China's approval of H200 chips may seem positive, but it poses a risk as the country has a history of extracting technology and then developing domestic alternatives. If Nvidia becomes too reliant on the Chinese market, future bans could severely impact revenue [7][8] Group 2: Nvidia's Strategic Omissions - **Lack of Discussion on Customer Developments** Jensen Huang focuses on AI demand and partnerships in earnings calls but avoids discussing customer chip development, AMD's market share, and the implications of inference versus training margins [9][10] - **Market Realities Ignored** The optimistic view assumes AI growth benefits all players, while the pessimistic view recognizes that customers are building their own solutions, AMD is providing cheaper options, and geopolitical tensions could threaten Nvidia's market position [10]

Investment Rating - The semiconductor production equipment industry is rated as Attractive [5][70]. Core Insights - The imminent launch of DeepSeek R2, which features 1.2 trillion parameters and significant cost efficiencies, is expected to positively impact the Japanese semiconductor production equipment (SPE) industry [3][7][11]. - The R2 model's capabilities include enhanced multilingual support, broader reinforcement learning, multi-modal functionalities, and improved inference-time scaling, which could democratize access to high-performance AI models [7][9][11]. - The development of efficient AI models like R2 is anticipated to increase demand for AI-related SPE, benefiting companies such as DISCO and Advantest [11]. Summary by Sections DeepSeek R2 Launch - DeepSeek's R2 model is reported to have 1.2 trillion parameters, a significant increase from R1's 671 billion parameters, and utilizes a hybrid Mixture-of-Experts architecture [3][7]. - The R2 model offers cost efficiencies with input costs at $0.07 per million tokens and output costs at $0.27 per million tokens, compared to R1's $0.15-0.16 and $2.19 respectively [3][7]. Industry Implications - The launch of R2 is expected to broaden the use of generative AI, leading to increased demand for AI-related SPE across the supply chain, including devices like dicers, grinders, and testers [11]. - The report reiterates an Overweight rating on DISCO and Advantest, which are positioned to benefit from the anticipated increase in demand for AI-related devices [11]. Company Ratings - DISCO (6146.T) is rated Overweight with a target P/E of 25.1x [12]. - Advantest (6857.T) is also rated Overweight, with a target P/E of 14.0x [15].

Investment Rating - The semiconductor production equipment industry is rated as Attractive [5] Core Insights - The imminent launch of DeepSeek R2, which features 1.2 trillion parameters and significant cost efficiencies, is expected to positively impact the Japanese semiconductor production equipment (SPE) industry [3][7] - The development of lightweight, high-performing AI models like DeepSeek R2 is anticipated to democratize access to generative AI, thereby expanding the market for AI-related SPE [11] Summary by Sections DeepSeek R2 Characteristics - DeepSeek R2 is reported to have 1.2 trillion parameters, with 78 billion active parameters and utilizes a hybrid Mixture-of-Experts architecture [3] - The input cost for R2 is $0.07 per million tokens, significantly lower than R1's $0.15-0.16, while the output cost is $0.27 compared to R1's $2.19 [3][7] - Enhanced multilingual capabilities and broader reinforcement learning are key upgrades in R2, allowing it to handle various data types including text, image, voice, and video [9][11] Market Implications - The anticipated launch of R2 is expected to boost demand for AI-related devices, including GPU and HBM, as well as custom chips and other AI devices [11] - The report reiterates an Overweight rating on DISCO and Advantest, which are expected to benefit from increased demand for AI-related devices [7][11] Company Ratings - Advantest (6857.T) is rated Overweight with a target price of ¥10,300 based on expected earnings peak [16] - DISCO (6146.T) is also rated Overweight with a target P/E of 25.1x based on earnings estimates [13]