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first_img Anthropic's prospectus names TSMC, warning that advanced chip production capacity is concentrated in Taiwan

The AI laboratory Anthropic, which developed Claude, named TSMC in its IPO prospectus, warning that the world's most advanced chip manufacturing capacity is highly concentrated in Taiwan. The prospectus states that if TSMC's operations are disrupted, it could severely impact the supply of AI chips and affect Anthropic's business, financial condition, and operational results.According to MoneyDJ citing the prospectus, the global AI industry heavily relies on TSMC's advanced processes. Suppliers such as NVIDIA and AMD, as well as companies that design their own chips like Amazon and Google, all need TSMC to turn their designs into physical chips. The listed risks include earthquakes, typhoons, interruptions in power and water supply, as well as cross-strait geopolitical tensions, military conflicts, blockades, tariffs, and trade restrictions.The prospectus also states that if a significant event occurs in Taiwan, the supply of advanced chips could be severely hindered; due to the time-consuming nature of building and certifying alternative foundries, the impact could last for several years. The expansion of advanced processes outside Taiwan is still in its early stages, making it difficult to effectively reduce concentration risks in the short term. TSMC has been unable to fully meet market demand due to strong demand, resulting in Anthropic not being able to obtain a sufficient supply of chips in a timely manner.

first_img Samsung's foundry 4-nanometer capacity is nearing full load, expanding 5-nanometer operations

According to a report by North Korea Biz on October 20, Samsung Electronics' foundry division has seen its 4-nanometer process capacity nearing full load due to an increase in customer orders, while the company is also expanding efforts to attract new customers for its 5-nanometer process. The report states that contracts for the most advanced 2-nanometer process with major global tech companies are still delayed, prompting Samsung to increase the utilization rates of its relatively stable 4-nanometer and 5-nanometer processes to improve profitability.The 4-nanometer process is used for smartphone application processors, artificial intelligence semiconductors, and high-performance computing chips. The report indicates that not all chips in the expanding artificial intelligence semiconductor market require the most advanced processes; the manufacturing costs of 4-nanometer and 5-nanometer processes are relatively low while still achieving high performance, making them suitable for certain artificial intelligence inference chips and application-specific integrated circuits.Regarding the 2-nanometer process, Samsung is in discussions with major global tech companies about production contracts, but some negotiations are taking longer than originally expected. In addition to yield rates, the most advanced processes also require comprehensive verification of energy efficiency, design assets, and stable mass production capabilities, which means a longer time frame from contract signing to mass production. Samsung views the 2-nanometer process as the core growth driver for its next-generation foundry business and plans to continue using the fully surrounded gate transistor structure first introduced in the 3-nanometer process to improve energy efficiency and performance.

Vitalik compared the mining costs of BTC and the inference costs of AI: the former has electricity costs accounting for nearly half, while the latter relies more on capital expenditures for computing power

Ethereum co-founder Vitalik Buterin shared that the cost of Bitcoin mining is roughly half derived from capital expenditures on computing power equipment and the other half from electricity consumption; in contrast, the cost of AI inference is almost entirely dominated by capital expenditures on computing power. This difference reflects the distinct cost structures of the two computing businesses. Bitcoin miners need to continuously pay for electricity and determine their profit margins based on mining machine efficiency, coin prices, and network difficulty changes; AI inference, on the other hand, relies more on investments in expensive hardware like GPUs, as well as equipment utilization and computing efficiency.The industrial boundaries of both are also changing. As the demand for AI computing power grows, some Bitcoin mining companies are beginning to shift their existing electricity resources and data center facilities towards AI and high-performance computing businesses, hoping to transition from pure mining to computing infrastructure services. Some mining companies are leveraging previously allocated electricity capacity for mining to enter the AI data center market. However, transformation does not mean that cost pressures disappear: mining operations are more directly exposed to fluctuations in electricity prices and Bitcoin prices, while AI computing businesses must bear risks related to GPU procurement, depreciation, and underutilization. Both types of businesses compete for electricity and computing resources, but ultimately, profitability depends on how these resources are converted into revenue.
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