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

According to Investing.com, Meta's personal AI agent Muse surpassed 2.5 million downloads in just 6 days since its launch, topping the U.S. Apple App Store Free Chart—like a stone thrown into the global AI stock pool, rippling across the market. On Monday, AMD surged past the $1 trillion market cap milestone, joining Meta, Intel, and Arm in staging a rare "CPU rally." On the surface, this boom appears to be driven by a single blockbuster product; in fact, the market is repricing the structural migration of computing power prompted by AI’s shift from "generation" to "action."

Major institutions such as Wedbush, Morgan Stanley, Goldman Sachs, and Jefferies have already issued systematic judgments: As AI agents achieve large-scale adoption, computational bottlenecks are shifting from GPUs to CPUs and memory, and the server CPU market is set to see incremental opportunities worth tens of billions of dollars.

Muse’s blockbuster launch ignites chip stock sentiment

Meta launched its personal AI agent Muse on September 8. Within 6 days, downloads exceeded 902,000—outpacing the 773,000 of its predecessor Meta AI in the same period—and quickly claimed the top spot on the U.S. Apple App Store free chart, holding the position for several days. According to Sensor Tower data, Muse's total downloads have surpassed 2.5 million, ranking ahead of ChatGPT and Claude.

Muse’s initial popularity among consumers has fueled expectations of surging computational demand after AI agents’ mass adoption, prompting significant capital inflows into chip stocks like AMD (AMD.US), Intel (INTC.US), and Arm (ARM.US). Catalyzed by this trend, AMD surged nearly 10% to $615.52 on Monday, crossing the $1 trillion market cap for the first time—becoming the fourth U.S. chip company to reach this milestone after Nvidia, Broadcom, and Micron. Meta’s stock soared 11%, Intel jumped over 12%, Arm skyrocketed more than 17%, and the Philadelphia Semiconductor Index closed up 4.3%, marking the biggest one-day gain since August 4 and rising for the fifth consecutive trading day.

South Korean chip makers Samsung Electronics and SK Hynix saw their shares rise by about 3.5% in early trading, while Taiwan’s TAIEX index climbed 1.8% to a record high.

Gary Tan, portfolio manager at Allspring Global Investments, said the rally in Taiwan’s market "reflects growing market confidence that, as AI adoption accelerates, hyperscale cloud providers will continue to expand their proprietary chip capacity."

"If products like Muse gain traction, hyperscale cloud providers will need greater compute 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 restore confidence in AI trading—after previous concerns of overvaluation and recent fears of existential risks from advanced models. The early traction of Muse provides new evidence that demand remains robust. "The market quickly realized this is not just a story about a successful product—it’s a repricing of structural demand for AI computing power. Muse’s widespread adoption could significantly boost demand across the AI infrastructure supply chain, making chip makers key beneficiaries." — Dilin Wu, Pepperstone strategist

How agents work signals a transformative role for CPUs

The market’s excitement is not about an app’s short-term chart-topping but the underlying change in computational demand structure represented by AI agents like Muse.

Traditional chatbots operate via "user asks—model generates answer—task ends"; agents follow a cycle of "user sets goal—model decomposes plan—calls browser and tools—executes action—adapts on obstacles—continues execution."

In this loop, GPUs handle model inference and matrix operations, while CPUs take on a vast array of execution-layer tasks such as task orchestration, virtual machine operation, browser control, API calls, database read/write, and sandbox isolation. As described by Fujitsu at the Hot Chips 2026 technology showcase, orchestration, retrieval, database calls, and conditional branching are increasingly handled by CPUs, with GPUs used only for batch matrix calculations.

The quantitative significance of this shift lies in the changing CPU/GPU ratio. Traditional training servers often use a CPU:GPU ratio of 1:4 or even 1:8, but agent inference, with its emphasis on high concurrency and tool invocation, may see the CPU ratio rise to 1:1 or 1:2, with some institutions projecting even higher ratios.

Major institutions’ views: Structural consensus formed, but transmission chain yet unverified

Wall Street’s leading investment banks are reaching an increasingly clear consensus around this structural shift.

Wedbush analyst Matthew Bryson put it most directly: "There are only two substantial AI agent computing device manufacturers—Intel and AMD." This judgment brings server CPUs back to the forefront of AI investment. Jefferies analyst Jacky He also pointed out that as AI agents gain broader consumer adoption, higher inference and orchestration loads will directly benefit server CPU demand—and stressed the long-term significance of this trend for the long-overlooked x86 ecosystem.

Morgan Stanley’s judgment is more systematic. The firm estimates that agent-based AI could bring an additional $32.5–60 billion to the data center CPU market by 2030, on top of a market already exceeding $100 billion. The core view is that "the computing bottleneck is shifting from GPU to CPU and memory," and as AI transitions from generation to autonomous action, general processing intensity will structurally leap. Their research team further notes that as AI workloads shift from single inference runs to continuous task execution, the orchestration and coordination functions of CPUs become more strategically valuable than the raw computing power of GPUs.

J.P. Morgan supplements this perspective from an industry structure angle, suggesting that the rise of AI agents will accelerate hyperscale cloud providers’ rebalancing between custom chip investments and general-purpose CPUs—increasing both investment in ASICs and custom accelerators and demand for high-core-count server CPUs. The two are complementary, not substitutes.

Citi’s analyst team highlighted the logic of "secondary beneficiaries" in a recent report—as CPU loads rise, supporting DDR5 memory, enterprise SSDs, and high-bandwidth memory controllers will also see boosted demand, benefiting memory vendors like Micron, Samsung Electronics, and SK Hynix.

However, it is crucial to note that the current rally is still significantly sentiment-driven. Some analysts pointed out this rise is built on a "transmission chain with no disclosed order flow yet." Oppenheimer analyst Jason Helfstein provided a sobering reference: Meta would need about 115 million Muse paid subscribers ($20 per month) to generate approximately $27.5–28 billion in annual AI agent revenue—a result he deems "unlikely" due to doubts about paid conversions, fierce competition, and consumers’ low trust in sharing credentials with Meta.

Industry chain spillover and "cracks" within Meta’s exuberance

If the structural shift in CPU demand holds, the beneficiaries will not be limited to the CPU duopoly. Meta is AMD’s second-largest customer, contributing about 5.5% of its revenue, and the two expanded cooperation in February to plan deployment of up to 6 GW of AMD Instinct GPUs. Arm also occupies a key position in data center CPU architectures in the Agent era. On a deeper level, links such as ASIC custom chips, optical communication, and memory will also benefit as computing power structure changes.

However, Meta’s own finances are bearing the cost of AI investment. In Q2 2026, Meta’s revenue grew 28% to $60.8 billion year-over-year, yet free cash flow plunged from $8.55 billion a year ago to $784 million—a 91% drop; operating margin shrank from 43% to 31%, with full-year capital expenditure guidance soaring to $130–145 billion.

Furthermore, Amazon has blocked Muse from accessing its retail website on grounds that Muse failed to identify itself, allegedly obtained user credentials, and scraped account data—highlighting the intensifying battle for user relationship control among platforms in the Agent era.

This Wednesday’s Meta Connect conference will be the first key checkpoint for testing the sustainability of this "CPU rally." If Muse’s user growth and monetization path can be further validated at the event, the market's repricing of AI inference computing infrastructure will gain a stronger anchor; otherwise, the current valuation expansion—driven by a single product catalyst—may take longer to play out.