AI交易迎来「验票时刻」：微软、Meta、高通财报将于今晚盘后公布
Related
Bank of America raised its capital expenditure forecasts for Alphabet, Meta, and AWS.
According to BlockBeats, on July 8th, Bank of America revised its capital expenditure forecasts for Alphabet, Meta, and AWS upwards for 2026 and 2027. Alphabet's 2026 capital expenditure forecast was revised upwards from $187 billion to $195 billion, and its 2027 forecast from $257 billion to $290 billion. Meta's 2026 forecast was revised upwards from $130 billion to $145 billion, and its 2027 forecast from $157 billion to $185 billion. AWS's 2026 forecast remained at $159 billion, while its 2027 forecast was revised upwards from $196 billion to $230 billion.
Research institution releases AI enterprise application rankings: Nvidia, Meta, and Amazon tied for first place.
According to a CNBC report on June 1st, BlockBeats reported that the AI-Driven Enterprise Institute (AIDE) released a new research report assessing the level of artificial intelligence (AI) application among S&P 500 companies. The report uses publicly available data such as earnings calls, job postings, and patent applications, scoring companies across four dimensions: AI awareness, advocacy, strategic orientation, and implementation. The results show that Nvidia, Meta, Amazon, and energy services giant Schlumberger (now SLB) received a perfect score of 100, tying for the highest level of AI application. Walmart, AES, NextEra Energy, Alphabet, and Microsoft also ranked highly. By industry, Alphabet ranked first in communications services, Amazon in consumer discretionary, Walmart in consumer staples, SLB in energy, Block in finance, and Microsoft in information technology. AIDE CEO Paul Cheek stated that the index aims to provide corporate boards and management with an objective reference point to help assess the gap between their AI strategies and those of their peers, rather than measuring the direct contribution of AI to corporate financial returns. The report also points out that there is still significant room for improvement in the AI literacy of current corporate boards and executives. --------------------------------- Click the original link below to join the Beating · Lark AI news channel and monitor global AI hotspots and news 24/7.
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.
UBS: Meta's sale of AI computing power may not be bad news; it could actually alleviate profit pressure.
According to BlockBeats, on July 2nd, UBS believes that Meta selling AI computing power or model access services to external customers should not necessarily be interpreted by the market as a negative signal of "AI infrastructure overcapacity." On the contrary, this could be a path for Meta to more quickly convert its massive AI investments into revenue. In its First Read report released on July 1st, UBS mentioned that Meta is reportedly considering two commercialization methods: selling "raw" computing power to external companies and providing access to AI models hosted on Meta's infrastructure. The report states that Zuckerberg has previously mentioned similar options publicly, so this is not entirely new information. However, this direction may still make some investors uneasy. Meta has previously described long-term growth opportunities to the market primarily including advertising, more immersive content experiences, business messaging, Meta AI, and AI devices, rather than directly becoming a cloud computing or computing power provider. Therefore, if the company does sell computing power externally, the market may question: Is this proactive monetization, or a passive digestion of excessive AI capital expenditure? UBS's assessment is more pragmatic. The bank believes that one of Meta's core problems is the excessively long investment cycle in AI, coupled with an unclear revenue realization timeline. Compared to waiting for Meta's AI chatbot or enterprise intelligent agent business to gradually scale up, selling cloud computing power or model access could potentially generate more immediate revenue, thus alleviating investor concerns about flat or even declining EPS in 2027. The report maintains its Buy rating on Meta with a 12-month target price of $865, while the listed share price is $601.85. UBS projects Meta's diluted GAAP EPS for 2026 and 2027 to be approximately $32.60 and $33.00, respectively, and states that it will not adjust its earnings forecast until the company confirms the relevant information. Its target price remains based on a 26x multiplier of its full-year diluted GAAP EPS forecast ending in Q1 2028. The significance of this report is that Meta's AI deals are entering a new phase: the market is no longer just asking how much it will spend on GPUs and data centers, but is beginning to demand to see how these investments will generate returns. For UBS, selling computing power is not a strategic retreat, but rather an additional cash flow outlet for its AI investment cycle.
Opinion: Meta's sale of computing power may be intended to boost confidence in the company's valuation; we reiterate the investment opportunities in the neocloud sector.
BlockBeats reported on July 2nd that renowned analyst degentrading (@degentradingLSD) commented on the news of "Meta selling computing power." He believes Meta's own computing resources are severely strained. The simplest reason for yesterday's news that "Meta plans to build its own cloud business and sell surplus AI computing power" is that Zuckerberg sees Xai leasing out computing resources and being able to revalue its stock. For Zuckerberg to continue capital expenditures, he must signal to investors that these expenditures will ultimately generate cash returns. Additionally, degentrading cited Serenity's view that the market seems to have forgotten that Meta is buying computing power from neocloud. Stocks like NBIS and CRWV were oversold yesterday and currently offer excellent risk-reward ratios.
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.