AI computing power demand crushes everything! The $3 trillion AI arms race is in full swing, and the semiconductor bull market narrative is far from over.

Although many semiconductor stocks closely related to AI computing infrastructure have risen significantly in 2026, the analyst team at Wall Street financial giant Bank of America Corp believes that this strong semiconductor bull market is far from over. The frenzy of AI computing infrastructure construction behind the list of semiconductor stocks essential to this wave still has plenty of room for further growth. This is mainly due to the continuous surge in AI computing demand, as the wave of artificial intelligence spending is likely to continue for a long time. In the view of the Bank of America Corp analyst team, AI computing infrastructure is entering a more enduring and wider capital expenditure cycle. Almost simultaneously, another Wall Street financial giant, Morgan Stanley, released a research report showing that the AI computing arms race is entering a phase of system-level expansion. AI infrastructure demand is showing a rare "inelastic" trendregardless of cost curves, tech giants continue to ramp up their construction of AI data centers, and this "inelastic demand" will continue to strengthen the resilience of the U.S. economy and overall profit growth of the S&P 500 index. The latest research report from JPMorgan Chase, another Wall Street banking giant, suggests that driven by the frenzy of technology stocks dominated by the AI computing theme, the S&P 500 index, one of the benchmark stock indices, may surpass the seemingly unattainable epic level of 9,000 points in the next year. This implies that the benchmark stock index, which has surged by over 60% since 2024, could rise by over 20%. Wall Street analysts are expanding the narrative of AI computing infrastructure from "GPU dominance/single core drive" to a "full-stack computing power reassessment dominated by AI GPU/ASIC+CPU+HBM/DRAM/NAND storage chips+optical interconnection system of data centers." It is predicted that global AI computing infrastructure spending will reach close to $3 trillion by 2028. This means that the popularity of AI agents worldwide, combined with the continuous high capital spending by North American tech giants, is driving the AI computing investment theme in the market towards the shift from a "competition around GPU for single-point computing power" to a "full-stack computing system driven by AI agents." The next round of excess alpha returns will no longer belong solely to the strongest leaders in the AI GPU/AI ASIC fields, but will systematically spread to high-performance CPUs in data centers, DRAM/NAND/HBM storage, AI PCBs, liquid cooling systems, data center optical interconnection systems, ABF substrates/glass substrates, and extensive wafer foundries, and other full-stack AI computing infrastructure layers. For the U.S. stock market, which has hit record highs in recent years and entered a new long-term bull market trajectory, as well as the MSCI global stock market benchmark indices, global investors have been increasingly enthusiastic about the "AI computing power investment theme" in recent years, playing the most core and strongest bullish drive, which can be said to be as long as this wave of "AI belief" continues to remain hot and sweep across global stock markets, the U.S. and even global stock markets will continue to witness an incredibly strong bullish trend. AI belief continues to fuel the semiconductor bull market wave! NVIDIA Corporation and AMD lead the AI computing infrastructure construction frenzy The analyst team at Bank of America Corp stated that from the perspective of AI data center engineering, the bottleneck of AI computing infrastructure is now expanding from "is there enough GPU" to "can the system-level throughput be delivered": GPU/ASIC is responsible for matrix calculations, CPU is responsible for scheduling and managing Agentic AI (AI agents), leaders in HBM/DRAM/NAND storage chips tackle large model weights, KV caches, vector databases, reasoning data lakes, and data migration, Marvell/Arista/Astera and other data center interconnections and multiple AI data center network chains solve cluster communications, Taiwan Semiconductor Manufacturing Co., Ltd. Sponsored ADR/Intel Corporation/ASML Holding NV ADR/Lam Research/KLA/Tektronix/Synopsys, Inc. and other semiconductor equipment giants and EDA links determine advanced processes, advanced packaging, and yield escalation. NVIDIA Corporation's latest financial report also confirmed that the underlying demand for AI computing power remains extremely strong: its quarterly revenue increased by 85% year-on-year to $81.62 billion, and data center revenue nearly doubled to $75.2 billion, with Huang Renxun even calling AI data center construction the "largest infrastructure expansion in human history." NVIDIA Corporation's performance clearly highlights that the global frenzy of AI computing infrastructure construction is far from over and is expanding from AI GPU/AI ASIC to data center CPUs, high-performance network infrastructures, enterprise-level HBM/DRAM/NAND storage, entire server clusters, AI super factories, and large-scale enterprise AI cloud computing systems. On Wall Street, analysts are increasingly bullish on the "global AI leader" NVIDIA Corporation, with the average target price suggesting that NVIDIA Corporation's market value could surpass $7 trillion, with unanimous expectations that capital expenditure on AI training/inference computing infrastructure will reach at least $3 trillion by 2030. It is understood that a report from the Bank of America Corp's senior analyst team, led by analyst Wamsi Mohan, writes to clients, "We have an unprecedented level of confidence in the strong momentum of AI computing infrastructure and expect that NVIDIA Corporation and AMD, the two AI chip giants, will continue to be the dominant forces in the AI infrastructure frenzy. Driving factors include: (1) Sales of cutting-edge AI labs expected to increase 3 to 5 times year-on-year; (2) Significant improvement in AI monetization prospects, exponential expansion of tokens (Google Token statistics show a 7-fold increase year-on-year), as well as ongoing concerns and potential panic from super-large-scale cloud computing service providers being disrupted by AI technology; (3) Supply continues to be tight, with comprehensive utilization rates approaching full load for all deployed infrastructure; and (4) Underestimated sovereign, corporate, and industrial-level AI computing demands." "We expect that by 2030, the total addressable market (TAM) of AI-related semiconductors will more than double, reaching about $1.7 trillion." This latest forecast from Bank of America Corp is even higher than the estimate given by ASML Holding NV ADR. Christophe Fouquet, the CEO of the Dutch lithography giant ASML Holding NV ADR ( ASML.US ), stated last week that the booming global semiconductor market is likely to face a long-term supply shortage in the foreseeable future, and predicted that the global semiconductor market could reach an astonishing $1.5 trillion by 2030. Advanced process chip manufacturers such as Taiwan Semiconductor Manufacturing Co., Ltd. Sponsored ADR and Intel Corporation have a strong demand for ASML Holding NV ADR's EUV equipment, or extreme ultraviolet lithography equipment, under the almost endless demand for AI GPU/ASIC and HBM/DRAM storage chips. This strong demand has helped this Dutch semiconductor equipment manufacturer become the highest valued company in Europe. In addition, analysts point out that the rapid rise in semiconductor stocks is being driven by earnings, as the forward price-to-earnings ratio is still around 25.6 times, largely unchanged since the beginning of the year, and significantly lower than the historical peak of around 30 times during the internet bubble. Analysts added, "Multiple pieces of evidence indicate that NVIDIA Corporation and AMD, the two AI chip giants, as well as a number of strong-performing leaders in the AI computing industry, remain the dominant forces in the AI infrastructure frenzy. Therefore, we see limited evidence of speculative valuation multiple expansion, and this profit-driven fundamental factor supports the sustainability of this uptrend in the background of significantly expanding AI computing infrastructure in the long term." Therefore, the Bank of America Corp analyst team stated that their most favored areas in the semiconductor market are still the leaders in AI chips (led by NVIDIA Corporation, AMD, and Broadcom Inc.), storage chip leaders (led by Micron), analog chips (Analog Devices, Inc. and Texas Instruments Incorporated), semiconductor equipment (Lam Researchi.e., KLA Group and KLA), EDA electronic design automation (led by Synopsys, Inc.), optical interconnects (led by Marvell Technology, Inc.), and some leading consumer electronics companies. Regarding the central processing unit (i.e., data center CPU) computing market, Bank of America Corp stated that they expect several CPU-related releases at the upcoming Computex event in Taipei, China next month, including more details on the total addressable market (TAM) of the data center CPU predicted by NVIDIA Corporation to be about $200 billion. In the analog chip market, the organization added that in addition to the two major analog chip and MCU leaders, Analog Devices, Inc. and Texas Instruments Incorporated, ON Semiconductor Corporation and Microchip also have relevant exposures to AI data center infrastructure, with a focus on ON Semiconductor Corporation's emerging AI data center power business pipeline. "AI belief" disregards cost? The frenzy of AI computing infrastructure spending heading towards the "inelastic demand" era Other financial giants have also given similar signals about the semiconductor sector. International bank UBS Group AG significantly raised the target price for Micron to a new high on Wall Street this week, citing the ongoing tight supply of HBM, DRAM, and NAND brought about by AI. As of the close of the U.S. stock market on Tuesday, Micron's market value also surpassed $1 trillion for the first time, driven by strong bullish forces and the investment frenzy of the "storage chip super-cycle". Over the past year, Micron's stock price has risen by over 800%. Against this backdrop of a bullish market for storage chips, UBS Group AG raised Micron's target price from the previous $535 to $1625, and UBS Group AG expects that under the new valuation framework, Micron's market value will move towards an astonishing $1.8 trillion in the next 12 months, surpassing the market values of companies such as Meta, Tesla, Inc., and Berkshire Hathaway. In the view of the analyst team at Morgan Stanley, AI capital expenditure is altering the price transmission mechanisms of the macroeconomy. Traditional macroeconomic investment frameworks suggest that rising copper, power, memory, gas turbines, and financing costs will naturally suppress corporate investment. However, super-large-scale cloud computing firms and cutting-edge AI tech companies consider AI computing infrastructure as a strategic-level armament and not just ordinary capital goods. Morgan Stanley forecasts that by 2028, nearly $3 trillion of AI-related infrastructure investment will flow through the global economy, and over 80% of the expenditures are still ahead. A recent research report released by Morgan Stanley shows that AI computing infrastructure is upgrading to become the primary variable in global macro pricing. Starting from the practical engineering and capital expenditure levels of AI, Morgan Stanley emphasizes that the demand for AI computing infrastructure is showing a rare "inelastic" trend: that is, regardless of how the financing costs/sales prices of AI GPU/AI ASICs, data center CPUs, DRAM/NAND/HBM, AI PCBs, liquid cooling systems, data center optical interconnections, ABF substrates/glass substrates, advanced packaging, extensive wafer foundries, gas turbines, data center power chain, and other AI computing infrastructure components increase, tech giants are still resolutely continuing to build new or expand large-scale AI data centers and the extensive delivery process that supports the data center. Morgan Stanley states that the AI arms race is entering a phase of systemic expansion, and the institution has significantly raised its capital expenditure expectations for U.S. tech giants for 2026 from $433 billion a year ago to $805 billion, with capital expenditure expected to reach $1.1 trillion in 2027, up from the previous estimate of $950 billion. Morgan Stanley's latest expectations highlight that the supply chain bottlenecks of the AI computing infrastructure layer have expanded from "massive GPU/ASIC purchases" to "simultaneously addressing the delivery processes of the complete set of data center power equipment, liquid cooling, data center CPUs, DRAM/NAND/HBM, optical communication/optical interconnect, high-performance network interconnect, transformers, gas turbines, and other components." The analyst team at Morgan Stanley states that from a macro perspective, "inelastic demand" will strengthen the resilience of the U.S. economy but also increase uncertainty in the bond market. AI capital expenditures directly boost business fixed investment, leading to an upward revision of the U.S. GDP growth forecast for 2026 from 1.8% to 2.3% and for 2027 from 2.0% to 2.6%; at the same time, the profit growth forecast for the S&P 500 has been revised from 17% to 23%. Therefore, the analyst team at Morgan Stanley emphasizes that if the AI wave can not only drive corporate investments but also improve enterprise productivity and profit margins, high valuations are not purely a bubble, but will receive strong support from the profit realization. In conclusion, the artificial intelligence computing infrastructure is expected to continue to play a pivotal role in shaping the global economic landscape. The demand for AI computing power, driven by advancements in artificial intelligence, is reshaping the semiconductor industry and propelling growth in related sectors globally. This trend is expected to drive significant capital investment in AI infrastructure, leading to new technological advancements and opportunities for investors in the coming years.