From GPU to CPU: How Will the AI Agent Revolution Ignited by Meta (META.US) Muse Reshape the Chip Industry Landscape?

Title context: From GPU to CPU: How Will the AI Agent Revolution Ignited by Meta (META.US) Muse Reshape the Chip Industry Landscape? Text: The news that Meta's personal AI agent Muse surpassed 2.5 million downloads within six days of launch and topped Apple Inc.'s U.S. App Store free chart is like a stone tossed into the global AI stock pool, stirring up ripples layer by layerAMD surged past a trillion-dollar market capitalization on Monday, staging a rare "CPU rally" alongside Meta, Intel Corporation, and Arm. On the surface, this is a sector frenzy driven by a single hit product; in essence, it is the market repricing the structural migration of computing power as AI moves from "generation" to "action." Major banks including Wedbush, Morgan Stanley, Goldman Sachs Group, Inc., and Jefferies Financial Group Inc. have formed a systematic judgment on this: as AI agents move toward large-scale adoption, the computing bottleneck is shifting from GPUs to CPUs and memory, and the server CPU market will usher in a tens-of-billions-of-dollars incremental opportunity. Muse Hit Ignites Chip Stock Sentiment Meta's personal AI agent Muse, launched on September 8, surpassed 902,000 downloads within six days of launch, beating the 773,000 for the previous-generation Meta AI over the same period, quickly topping Apple Inc.'s U.S. App Store free chart and holding the top spot for multiple consecutive days. According to Sensor Tower data, Muse's cumulative downloads have exceeded 2.5 million, ranking ahead of ChatGPT and Claude. Muse's initial consumer popularity ignited market expectations for a surge in computing power demand following the large-scale adoption of AI agents, with capital pouring heavily into chip stocks such as AMD (AMD.US), Intel Corporation (INTC.US), and Arm (ARM.US). Catalyzed by this, AMD surged nearly 10% on Monday to $615.52, with its market capitalization breaking through $1 trillion for the first time, becoming the fourth U.S. chip company to cross that threshold after NVIDIA Corporation, Broadcom Inc., and Micron. Meta's stock jumped 11%, Intel Corporation soared more than 12%, Arm surged over 17%, and the Philadelphia Semiconductor Index closed up 4.3%, its largest single-day gain since August 4 and its fifth consecutive trading day of gains. South Korean chipmakers Samsung Electronics and SK Hynix rose about 3.5% in early trading, while Taiwan's weighted index rose 1.8% to a record high. Allspring Global Investments portfolio manager Gary Tan said the rise in the Taiwan market "reflects growing belief that hyperscalers will continue to expand their own chip scale as AI adoption accelerates." "If a product like Muse gains traction, hyperscalers will need more computing capacity, further accelerating demand for custom AI chips and supporting Taiwan's ASIC ecosystem." Gary Tan, portfolio manager at Allspring Global Investments This strong debut helps revive confidence in the AI tradeafter earlier concerns about excessive valuations and, more recently, existential threats from advanced models. Muse's early traction provides new evidence that demand remains strong. "The market quickly realized this is not just a product success storyit is a repricing of structural demand for AI computing power. Muse's large-scale adoption could significantly boost demand across the entire AI infrastructure supply chain, making chipmakers key beneficiaries of this trend." Dilin Wu, strategist at Pepperstone How Agents Work Determines the CPU's Role Leap The focus of market excitement is not the short-term ranking of an app, but the fact that the AI agents represented by Muse are changing the underlying structure of computing power demand. The workflow of a traditional chat Siasun Robot&Automation is "user asks a questionmodel generates an answertask ends"; while the workflow of an Agent is a loop of "user proposes a goalmodel breaks down the plancalls browsers and toolsexecutes operationsadjusts when blockedcontinues execution." In this loop, the GPU handles model inference and matrix operations, while the CPU undertakes a large amount of execution-layer work such as task orchestration, virtual machine operation, browser control, API calls, database reads and writes, and sandbox security isolation. As Fujitsu described in its Hot Chips 2026 technical presentation, orchestration, retrieval, database calls, and conditional branching are increasingly falling on the CPU, with the GPU handling only the batch matrix operation steps among them. The quantitative significance of this change lies in the migration of the CPU/GPU ratio. Traditional training-oriented servers often use 1:4 or even 1:8 to describe the relative ratio of CPU to GPU, while Agent inference places more emphasis on high concurrency and tool calls, giving the CPU share an opportunity to rise toward 1:1 to 1:2, with some institutions even giving higher projected ranges. Major Bank Views: Structural Judgment Has Formed, but the Transmission Chain Has Not Yet Been Verified Around this structural shift, major Wall Street investment banks are forming an increasingly clear consensus. Wedbush analyst Matthew Bryson put it most directly: "The only manufacturers of substantive computing devices for AI agents are Intel Corporation and AMD." This judgment pulls the server CPU market back into the core view of AI investment. Jefferies Financial Group Inc. analyst Jacky He also noted that as AI agents gain broader consumer adoption, higher inference and orchestration loads will directly benefit server CPU demand, and emphasized that this trend has profound significance for the long-neglected x86 ecosystem. Morgan Stanley's judgment is more systematic. The bank estimates that agentic AI could bring an additional $32.5 billion to $60 billion in incremental space to the data center CPU market by 2030, while this market itself already exceeds $100 billion. Morgan Stanley's core argument is that "the computing bottleneck is shifting from GPUs to CPUs and memory," and that AI's shift from the generation stage to the autonomous action stage will bring a structural leap in general-purpose computing intensity. Its research team further noted that when AI workloads shift from one-off inference to continuous task execution, the CPU's orchestration and coordination functions will become more strategically valuable than the GPU's raw computing power. JPMorgan added a view from the perspective of the industry landscape, arguing that the rise of AI agents will accelerate a reset in the balance hyperscalers strike between custom chips and general-purpose CPUsincreasing investment in ASICs and custom accelerators while also raising demand for high-core-count server CPUs. The two are not substitutes but complements. Citi's analyst team emphasized in its latest report the logic of "second-round beneficiaries"as CPU loads rise, accompanying DDR5 memory, enterprise SSDs, and high-bandwidth memory controllers will also see demand pull, creating tailwinds for memory makers such as Micron, Samsung Electronics, and SK Hynix. But it must be made clear that the current market move still has significant sentiment-driven characteristics. Some analysts pointed out that this rally is built on a "transmission chain that has not yet produced disclosed orders." Oppenheimer analyst Jason Helfstein's calculation provides a sober reference: Meta would need about 115 million Muse paid subscribers ($20 per month) to generate about $27.5 billion to $28 billion in annualized AI agent revenue, and he judged this outcome "unlikely" to be achieved, citing questionable paid conversion, intense competition, and low consumer trust in sharing passwords with Meta. Industrial Chain Spillover and the "Cracks" in Meta's Frenzy If the structural upward shift in CPU demand holds, the beneficiaries will not be limited to the two CPU giants. Meta is AMD's second-largest customer, contributing about 5.5% of its revenue, and the two further expanded cooperation in February this year, planning to deploy up to 6 gigawatts of AMD Instinct GPUs. Arm also occupies a key position in data center CPU architecture in the Agent era. More deeply, ASIC custom chips, optical communications, memory, and other segments will also benefit from changes in the computing power structure. But Meta's own financial condition is bearing the cost of AI investment. In the second quarter of 2026, Meta's revenue was $60.8 billion, up 28% year over year, but free cash flow plunged to $784 million from $8.55 billion in the same period last year, a year-over-year drop of 91%; operating margin compressed to 31% from 43%, and full-year capital expenditure guidance is as high as $130 billion to $145 billion. In addition, Amazon.com, Inc. has blocked Muse from accessing its retail website on the grounds that Muse failed to identify itself, was suspected of obtaining user credentials, and scraped account data, revealing that the battle among platforms over control of user relationships in the Agent era is escalating. The Meta Connect conference opening this Wednesday will become the first checkpoint for testing the sustainability of this "CPU rally." If Muse's user growth CKH HOLDINGS commercialization path can be further verified at the conference, the market's repricing of AI inference computing power structure will gain a more solid anchor; otherwise, the current valuation expansion driven by a single product catalyst may take longer to digest.