Industrial Securities: The news regarding Meta's external leasing and sales of computing power should not be interpreted overly pessimistically as a sign of computing power surplus.
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Everbright Securities: Meta computing power leasing is a resource optimization method and does not change the medium-to-long-term expansion plan.
According to Odaily Odaily, Meta is venturing into external computing power leasing. A recent research report from Everbright Securities states that Meta's underlying logic for entering the cloud computing market is to create a new monetization channel for its massive AI infrastructure investments. This reflects that cloud computing is currently the fastest and most reliable way to recover huge upfront investments and improve current returns. It also indicates that the ability to monetize externally has become a cash flow guarantee for Meta to continue expanding its AI investments. Meta's overall plan to continuously increase its investment in AI computing power in the medium to long term has not changed. (Jinshi)
Zuckerberg signals the commercialization of computing power, suggesting that Meta infrastructure may enter a dual-track model of "self-use + external leasing".
According to BlockBeats, on July 1st, Bloomberg reported that Mark Zuckerberg stated in a May shareholder call, "Almost every week, different companies come to us, either wanting us to build an API service or asking if we have computing power to sell to them, at a higher price than we paid. We haven't done that yet because we have our own need for that computing power," Zuckerberg said at the time. "But obviously, if we realize we've overbuilt, then that's an option, which partly gives us the confidence to invest in scaling up." In the rapidly evolving AI race, Zuckerberg has repeatedly stated that he believes the entire industry is facing a bottleneck in computing power, and that Meta should accumulate as much computing power as possible before deciding how to use it. Despite facing numerous complex challenges, Meta CEO Mark Zuckerberg has signaled to investors that he is willing to sell excess computing infrastructure and even launch so-called API services, allowing customers to pay based on AI usage—a business typically measured in "tokens" (i.e., the amount of data generated and consumed when a customer queries). Discussions surrounding Meta Platforms' potential commercialization of some of its AI computing power have raised concerns about a potential "oversupply" of computing power. However, analysts argue that this assessment is oversimplified. Meta currently does not have any significant surplus computing power available for sale. On one hand, it continues to expand its investment in AI infrastructure and has signed large-scale computing power agreements with companies like Crusoe. On the other hand, its existing H100/H200 resources are primarily used for internal recommendation systems and AI model training, and demand remains tight. Therefore, the so-called "selling computing power" is more of a future option for asset utilization than a current reality. Overall, it reflects a shift in AI computing power from a single scarcity to a multi-generational, tiered use and long-term capacity planning phase.
Galaxy Securities: We recommend focusing on leading computing power leasing companies.
According to Mars Finance, Galaxy Securities points out that with the explosive growth in demand for AI inference computing power, the token factory business model is accelerating. Compared with traditional computing power leasing, token factories place greater emphasis on binding with downstream large-scale model inference needs. Pricing is gradually shifting from "computing power resources and duration" to "token output and unit cost." Upstream computing infrastructure can participate more in the value distribution of downstream model manufacturers, strengthening the bargaining power of the upstream computing infrastructure segment and sharing the dividends of the token economy era. Galaxy Securities believes that computing power leasing companies are currently exploring the "token factory" cooperation model with large model manufacturers, which will gradually evolve from the underlying "selling resources" model of computing power leasing to a "selling output" model. Computing power leasing companies will provide MaaS services and share the dividends of the token boom by adopting a token revenue-sharing profit model. This is expected to improve the profit margin of computing power leasing companies, and the valuation center is expected to further increase. It is recommended to pay attention to leading computing power leasing companies.
CITIC Securities: Significant fluctuations will not alter the AI supercycle; emphasize the importance of domestic computing power as a "Plan B".
According to Mars Finance, CITIC Securities points out that news of Meta's plan to lease out some of its computing power has once again triggered market concerns about computing power oversupply. This, coupled with concerns about cloud vendors' cash flow pressures, the continued rise in upstream prices, the slowdown in Capex growth, and overcrowding in AI transactions in recent months, is the main reason for the significant volatility in tech stocks. In the short term, Meta's move is primarily aimed at revitalizing its existing, outdated computing power assets. Considering its continued development of advanced models and investment in next-generation computing hardware, leasing out computing power is not contradictory to further increasing its investment in computing power. Furthermore, since computing power rental fees have continued to rise recently, concerns about computing power oversupply are unfounded. This round of tech stock adjustments is more of a deleveraging and rebalancing process in the recent global liquidity tightening environment, rather than a reversal of the AI industry trend. For the medium to long term, it is crucial to pay close attention to whether the next few months will see a similar breakthrough in AI capabilities as seen with OpenClaw and Coding Agent at the beginning of the year. In addition, it has been observed that after overseas AI assets entered a phase of high crowding, high correlation, and high volatility, international funds are beginning to seek differentiated sources of return. Domestic computing power with differentiated value, dubbed "Plan B," remains resilient and is expected to attract foreign investment. With the earnings season approaching, we recommend focusing on sub-sectors with high earnings certainty and reasonable valuations: In terms of growth prospects, we recommend domestic FAB (Featured Adhesives and Materials) and equipment sectors with positive narratives, as well as the optical communication sector with relatively low valuations; in the price increase chain, segments with high AI exposure and those that have already experienced price increases have a higher probability of realizing their earnings gains, such as memory and upstream PCB industries. (Cailian Press)
Glass substrates are entering a critical verification phase; yield rate will determine the inflection point for industrialization.
Mars Finance reported on July 7th that there has been a string of major news regarding glass substrates. Samsung Electro-Mechanics announced on July 2nd that it had signed a master contract with Dongyu Fine Chemicals, a wholly-owned subsidiary of Sumitomo Chemical Group of Japan, to jointly establish a joint venture called "Glassem" for the production of "Glass Core," a key material for glass substrates. On the same day, BOE Technology Group made its first public appearance in the market at its 2026 Investor Day event. The glass substrate concept continues to be a hot topic in the A-share market and has gained significant attention in the advanced semiconductor packaging industry chain. Interviewees stated that with traditional organic substrates gradually approaching the performance and reliability limits, the industrialization process of glass substrates has accelerated significantly. (Shanghai Securities News)
SemiAnalysis: Meta will accelerate, rather than slow down, its computing power procurement; it is in talks with Anthropic to build its own AI model service platform.
According to BlockBeats, on July 3rd, SemiAnalysis stated in its latest report that after news broke that Meta might become a new Neocloud, the market's initial reaction was to sell off computing power cloud companies like CoreWeave and Nebius, and to renewed concerns about "AI computing power oversupply." However, the firm's assessment is the opposite: this concern may be wrong. Meta's data center and computing power procurement will not slow down, but rather continue to accelerate. The article mentions that in the first half of this year alone, Meta has already signed contracts for over 5GW of capacity in the cloud services and managed data center sectors, and this does not include its accelerating self-built projects. SemiAnalysis states that Meta is in final negotiations with Anthropic, hoping to obtain access to Claude private instances. If this comes to fruition, the significance goes beyond "Meta buying more computing power"; it suggests that Meta may be building its own AI model service platform. This model is somewhat similar to AWS's Bedrock, Microsoft's Foundry, and Google's Vertex. Meta can initially use Claude internally, or it can package the model capabilities as a token-as-a-service to provide services externally in the future. In the short term, it might use its own models externally and Anthropic models internally; in the long term, Meta might even incorporate Anthropic and OpenAI models into its external service system. SemiAnalysis states that the underlying logic is that Meta has computing power, advertisers, social network distribution capabilities, and consumer-end entry points. If it can combine cutting-edge models, intelligent agents, and sales and marketing SaaS, it will not just be a company that buys GPUs, but will be moving towards the upper layers of AI application and model distribution.