According to Zhitong Finance APP, Wall Street's bullish sentiment towards AMD (AMD.US), the leading high-performance chip maker for PCs and data centers in the U.S. and the biggest competitor to Nvidia's AI GPUs, has been growing stronger recently. Financial giants such as Bank of America, Goldman Sachs, and Barclays have recently upgraded their ratings or bullish outlooks for AMD. Goldman Sachs has significantly raised the target price for Advanced Micro Devices (AMD) from $450 to $640 while maintaining the most optimistic rating of "Buy" for the stock. Another firm, Cantor Fitzgerald, has set a target price of $700 (up from the previous $500), while the most optimistic target comes from KeyBanc, which has raised AMD's target price from $530 to $725 and maintained an "Overweight" rating.

The core bullish logic from Wall Street analysts is not merely about "chasing Nvidia's market share in the AI GPU market," but rather about the wave of agent-based AI driving a comprehensive explosion in AI CPU demand, narrowing the CPU-GPU ratio. With its data center server-level CPUs, AI accelerators, Helios rack-mounted server clusters, and cloud customer base, AMD is expected to become one of the most important beneficiaries of AI computing infrastructure, aside from Nvidia.

The bullish logic on Wall Street has expanded from the early view that "AMD is the second AI GPU training/inference accelerator supplier after Nvidia" to a resonance of three super growth curves: server CPUs, AI GPUs, and large-scale rack-level server clusters. Analysts unanimously bet that AMD, with its CPU and GPU production capacity, will jointly dominate the pricing power of AI computing infrastructure in the inference era alongside Nvidia, the world's highest-valued company and AI chip superpower.

It is reported that AMD has recently signed an important final agreement with Rackspace Technology to deploy the first 30 megawatts of AI computing infrastructure based on AMD technology in Rackspace's data centers worldwide. This legally binding contract formalizes the preliminary memorandum of understanding announced by the two companies in May. The related AI computing power plan is set to be phased into operation between the end of 2026 and 2028. The two companies stated that this deployment will enable workloads of regulated enterprises to utilize the computing infrastructure built with AMD Instinct AI GPUs and AMD EPYC central processing units (CPUs) within Rackspace's enterprise AI cloud architecture and AI agent deployment workflows.

Rackspace Technology CEO Gallien Candia stated that this collaboration integrates AI computing infrastructure with a regulated governance operating model, provided by a partner that assumes unified responsibility. Dan McNamara, Senior Vice President and General Manager of AMD's Computing and Enterprise AI Business, stated that this collaboration will help regulated enterprises deploy scalable AI computing infrastructure. The two companies also jointly committed to investing in combined sales and marketing resources to target enterprise clients in regulated industries As AI agents become popular worldwide, the main line of AI computing power investment is shifting from a "single-point computing power competition centered around GPUs" to a "full-stack computing power system driven by AI agents." The next round of excess alpha returns will no longer solely belong to the strongest leaders in the AI GPU/AI ASIC fields, but will systematically spread to the full-stack AI computing infrastructure layers such as high-performance CPUs in data centers, DRAM/NAND/HBM storage, AI PCBs, liquid cooling systems, data center optical interconnect systems, ABF substrates/glass substrates, MLCCs, electronic fabrics, and extensive wafer foundries. In this narrative shift of AI, data center CPUs, optical interconnects, and storage chips may emerge as the biggest winners.

From lithography machines, HBM to server CPUs, the computing power supercycle welcomes full-chain verification

The global demand outlook for AI computing power is increasingly optimistic, contrary to the expectations of some pessimists who foresee "computing power oversupply." This is especially true given the latest performance report from lithography machine giant ASML (ASML.US), which provides more forward-looking industrial evidence than the orders from a single AI chip company.

According to the financial report, ASML's revenue in the second quarter reached €9.326 billion, exceeding the market expectation of €8 billion; net profit was €2.918 billion, also surpassing the expected €2.62 billion, with a gross margin of 54%. More critically, the company significantly raised its 2026 revenue guidance from €36 billion–€40 billion to €43 billion–€45 billion, and increased its gross margin guidance from 51%–53% to 54%–56%, expecting third-quarter revenue to further rise to €11 billion–€12 billion. ASML's management stated that orders in the first half of the year were "extremely strong," with customers accelerating the construction of production capacity for advanced process logic chips and memory chips. Therefore, they plan to increase the capacity of low numerical aperture extreme ultraviolet lithography machines and immersion deep ultraviolet lithography machines by about 30% by 2027, and are studying an additional increase of about 30% in 2028. This indicates that major customers like TSMC and Intel have committed to years of lithography equipment, rather than just verbal forecasts, voting for strong AI computing power demand in 2027–2028.

The performance warning from the established American tech giant IBM (IBM.US) further proves from the demand side that the AI computing power infrastructure procurement frenzy is spreading from hyperscale cloud providers to traditional business models. IBM disclosed that to avoid continued shortages and price increases of key AI computing power infrastructure such as AI chips, server CPUs, storage, and memory chips, clients are shifting their quarterly capital budgets from software projects to core hardware procurement related to AI infrastructure. This led to its revenue of only $17.2 billion, below the market expectation of $17.86 billion, with the stock price plummeting about 25% in a single day.

IBM's own large contract delays and sales execution errors are also significant reasons for the performance gap, so not all issues can be attributed to industry trends; however, the fact that enterprise clients are beginning to lock in infrastructure early indicates that AI computing power demand is no longer just a massive capital expenditure story for Microsoft, Meta, and Google, but is changing the priority allocation of IT budgets for ordinary enterprises Goldman Sachs has significantly raised AMD's target price from $450 to $640, not merely betting on its Instinct AI GPU accelerating market share in the AI chip sector to catch up with NVIDIA, but believing that the wave of AI intelligence will significantly increase the server CPU load required for data retrieval, task scheduling, model orchestration, and network and storage management, making EPYC CPU clusters an undervalued second core growth engine in the market.

ASML's rare and substantial capacity expansion indicates that advanced logic chip manufacturers like TSMC and Intel are preparing for a larger-scale demand for custom AI ASIC accelerators such as data center server-level CPUs, GPUs, and TPUs, with significantly larger wafer production capacity; meanwhile, Samsung's latest performance and the CEO of SK Hynix releasing a continuous severe shortage situation in the memory chip sector on the first day of its U.S. ADR listing further validate that the demand for AI server systems remains incredibly strong.

Wall Street is also bullish on memory chips. Last week, SK Hynix's U.S. ADR, which made a significant debut in the U.S. stock market, received a target price of up to $330 from Barclays, a major Wall Street financial giant, implying an upside of about 117% compared to the closing price before coverage began. Barclays stated that industry representatives generally expect DRAM manufacturers to currently meet only about 75%-80% of demand, with the fulfillment rate potentially dropping to about 60% by 2027. Long-term supply agreements are expected to improve profitability visibility in the next two to three years.

ASML, IBM, and SK Hynix collectively demonstrate from three directions—upstream equipment orders, downstream enterprise procurement, and key memory chip supply—that the super demand cycle for AI computing power has not peaked due to a large-scale correction in tech stocks or semiconductor stocks, and that the bottleneck in AI computing power is gradually shifting from GPU/TPU/AI ASIC to CPU, HBM, server memory, NAND storage components, widespread wafer manufacturing, and semiconductor equipment such as lithography machines. Therefore, AMD is expected to be revalued from a "NVIDIA alternative" to the second-largest full-stack computing platform spanning EPYC central processors, Instinct accelerators, and rack-level systems. However, whether this positive outlook can be realized still depends on advanced product mass production, software ecosystem, TSMC wafer allocation, HBM supply, and gross margins; additionally, the severe shortage of HBM/DRAM/NAND memory chips supports end-user demand but may also limit AMD's short-term shipments.

Are the Two Major x86 Chip Giants Heading into a Wild Bull Market Together?

In the eyes of Wall Street analysts, AMD is certainly not simply replicating NVIDIA's GPU dominance, but is attempting to reconstruct its valuation logic in the AI data center supply chain by focusing on the entire AI infrastructure ecosystem surrounding EPYC CPUs, Instinct GPUs, Helios/rack-scale platforms, 2nm Venice, and advanced packaging.

Wall Street analysts are expanding the narrative of AI computing power infrastructure from "GPU monopoly/single-core driven" to a full-stack computing revaluation dominated by "AI GPU/ASIC + CPU + HBM/DRAM/NAND memory chips + optical interconnect leading data center high-speed connection system collaboration." According to Goldman Sachs, the AI computing power super bull market is far from over; instead, it has transitioned from the "AI chip purchasing frenzy" to the second phase of "large-scale construction of AI factories"—meaning that the next round of excess alpha returns will no longer be limited to the strongest leaders in the AI GPU/AI ASIC fields but will systematically spread to the entire stack of AI computing power infrastructure layers, including high-performance CPUs for data centers, DRAM/NAND/HBM storage, AI PCBs, liquid cooling systems, data center optical interconnect systems, ABF substrates/glass substrates, MLCCs, electronic fabrics, and extensive wafer foundries.

A senior analyst team led by Brian Nowak from Wall Street financial giant Morgan Stanley released the latest research report on July 12, significantly raising the capital expenditure forecasts for the five largest hyperscale cloud computing and technology giants (Meta, Amazon, Microsoft, Google, SpaceX) for 2027/2028 to approximately $1.2 trillion and $1.4 trillion, respectively. The firm also revised its expectations for the capital expenditures of major U.S. tech giants in 2026 from $433 billion a year ago to $805 billion.

Wall Street's bullish sentiment towards AMD is growing stronger, and the same is true for another x86 CPU giant, Intel. Against the backdrop of a massive explosion in demand for data center CPUs, several Wall Street financial giants have recently significantly raised their 12-month target stock prices for the two x86 architecture CPU super giants—Intel (INTC.US) and AMD (AMD.US). These financial giants have also substantially increased their outlook for the data center CPU and overall CPU market size in their latest research reports.

At the 54th Global Technology, Media, and Telecommunications Conference hosted by Wall Street financial giant JP Morgan, Intel CEO Pat Gelsinger stated that Intel 18A (the advanced chip process below 2nm, at 1.8nm level) has supported the mass production of Panther Lake, with yield rates improving by about 7% each month, exceeding Intel's internal expectations. Gelsinger also mentioned that as the focus of AI computing power infrastructure shifts from training to inference, CPUs are becoming increasingly important and indispensable in the AI era, with the configuration ratio of CPUs to GPUs accelerating from 1:8 towards 1:1, and potentially reaching 4:1.

It is noteworthy that Intel's investment logic is undergoing a fundamental change: the market no longer views it merely as a traditional consumer electronics CPU manufacturer waiting for a recovery in the personal computer cycle but is beginning to reprice it as a full-stack AI computing power infrastructure platform based on "data center server CPUs + advanced process chip manufacturing/foundry/advanced packaging." This is why the international financial giant HSBC has raised its target price for Intel by 100% to $200—this is also the highest target among Wall Street analysts for Intel. Intel has once again become one of the most watched semiconductor stocks among global retail and institutional investors Based on Intel's stock price of approximately $107.76 on July 15 and a market capitalization of about $547.7 billion, a target price of $200 implies a potential upside of about 85.6%. Assuming the number of shares outstanding remains roughly unchanged, this corresponds to a market capitalization of approximately $1.02 trillion, indicating that HSBC believes Intel is likely to re-enter the ranks of "trillion-dollar chip giants."

As AI agents (i.e., Agentic AI) rapidly gain popularity worldwide, and as cloud computing giants and AI leaders accelerate their spending on AI infrastructure, along with the booming global AI data center computing power infrastructure, HSBC has provided this aggressive bullish outlook. Although NVIDIA Corporation (NVDA.US) has dominated the AI GPU infrastructure market, spending on CPUs, advanced packaging, and wafer/semiconductor manufacturing capacity is becoming an increasingly critical part of the AI computing supply chain. HSBC believes that Intel can benefit from the sustained surge in data center CPU demand driven by AI agents and the global AI semiconductor capacity expansion wave led by Musk's Terafab "super chip factory."