--- title: "AI computing power demand crushes everything! TSMC CEO shouts \"no signs of demand slowing down\" as Wedbush supports a long-term bull market for chip stocks" type: "News" locale: "en" url: "https://longbridge.com/en/news/288869150.md" description: "Taiwan Semiconductor CEO C.C. Wei stated that the demand for AI computing power is strong and will be difficult to fully meet in the coming years, highlighting the long-term shortage of full-stack computing infrastructure. Wedbush's research report supports a long-term bull market for chip stocks, viewing this as a positive signal. Although Broadcom's performance fell short of expectations, leading to a short-term cooling in the market, the perspectives from Taiwan Semiconductor and Wall Street reinforced the logic of long-term positive AI demand" datetime: "2026-06-05T13:17:02.000Z" locales: - [zh-CN](https://longbridge.com/zh-CN/news/288869150.md) - [en](https://longbridge.com/en/news/288869150.md) - [zh-HK](https://longbridge.com/zh-HK/news/288869150.md) --- # AI computing power demand crushes everything! TSMC CEO shouts "no signs of demand slowing down" as Wedbush supports a long-term bull market for chip stocks According to the Zhitong Finance APP, renowned Wall Street investment firm Wedbush Securities released a research report on Friday stating that the latest positive information regarding the AI computing power supply chain conveyed by TSMC's (TSM.US) CEO C.C. Wei shows that this global leader in wafer foundry will be unable to meet AI demand for "many years" — which is a long-term positive signal for the stock prices and fundamental outlook of TSMC and participants in the global AI computing power and chip supply chains. As of the time of publication, Broadcom's latest disclosed growth prospects for AI semiconductors fell short of Wall Street expectations, and signs of a cooling investment frenzy in the AI computing power supply chain have recently emerged, with TSMC's U.S. ADR trading price dropping nearly 3% in pre-market trading on Friday. The signals conveyed by TSMC's management and Wedbush clearly reinforce the long-term logic that demand for AI computing infrastructure remains strong. C.C. Wei stated at the annual shareholders' meeting that even with new production capacity coming online in the U.S., TSMC will still struggle to fully meet AI-driven demand in the coming years; this indicates that the bottleneck is not just the short- to medium-term CoWoS capacity or HBM supporting capacity, but rather a systemic shortage at the wafer end of the global AI computing power supply chain, including advanced process logic chips, advanced packaging, AI accelerators, and high-performance computing wafer capacity. A series of positive messages revealed by C.C. Wei at this week's TSMC shareholders' meeting highlights that the supply bottleneck for AI computing infrastructure is expanding from a single chip/wafer capacity shortage to a long-term capital expenditure cycle that includes data center CPUs, high-performance network infrastructure, optical interconnect base chips, and 2.5D/3D advanced packaging capacity. According to the analyst team at Wall Street financial giant Bank of America, the AI computing infrastructure is entering a more durable and broader capital expenditure cycle. Almost simultaneously, another Wall Street financial giant, Morgan Stanley, released a report indicating that the AI computing arms race has entered a system-level expansion phase, with AI infrastructure demand showing a rare "inelastic" trend — meaning that regardless of cost curves, tech giants continue to ramp up investments in AI data centers, and this "inelastic demand" will continue to strengthen the resilience of the U.S. economy and the overall profit growth of the S&P 500 index. **From GPU to ASIC capacity expansion, TSMC is indispensable, and C.C. Wei signals a long-term gap in AI computing demand** Wedbush stated that TSMC CEO C.C. Wei's latest remarks align with Broadcom CEO Hock Tan's statement about major customer orders extending to 2028, indicating that leading logic and advanced packaging may remain in a state of "severe supply shortage" even after 2026. TSMC is also better positioned to exceed Wall Street's optimistic expectations for revenue and profit margins in 2026-2027 through ramping up 2nm-level capacity, improving product mix, and implementing moderate price increases. Matt Bryson, a senior analyst at Wedbush Securities, wrote in a report to clients: "Our view is that C.C. Wei's statement about 'demand exceeding supply for many years' is consistent with the information conveyed by Hock Tan last night (Broadcom's backlog is significantly increasing as large customers extend orders to 2028 to ensure product availability in a constrained supply environment) This pattern reinforces our long-held investment thesis: the most advanced process CPU/GPU logic chips and advanced packaging will actually continue to be in short supply, and the strong pricing status will extend far beyond 2026. For TSMC, we believe that regular price increases (even if the extent is controlled in the long term), positive product mix effects (the ramp-up of 2nm advanced process capacity, additional capacity close to leading process nodes, and stable output from mature nodes) should allow the company to continue to exceed Wall Street expectations in sales and profit margins in 2026 and 2027, provided that capacity and technology planning are executed properly (especially in the 2nm process). Wang Chih-Kang emphasized multiple times at the company's annual shareholder meeting that even with the addition of advanced process or advanced packaging capacity in the United States, TSMC will be unable to meet the nearly endless AI-driven infrastructure demand for many years. He also reiterated that TSMC's overall revenue growth this year will exceed 30% and expressed a willingness to raise prices, although it will be done in a "moderate and sustainable manner." He compared this to the significant price increases by memory chip manufacturers and stated that the company would not "suddenly raise prices significantly like memory companies." Additionally, Wang Chih-Kang mentioned that AI humanoid robots and autonomous driving will become the next long-term performance growth drivers for TSMC. **Global Advanced Chip Process and Advanced Packaging Super Cycle Reconfirmed by TSMC's Leader** The sharp decline in the AI computing industry chain in the U.S. stock market on Thursday and the drop in the South Korean stock market on Friday seems more like a valuation digestion phase of a highly crowded trading theme, rather than a refutation of AI computing demand. The significant drop in Broadcom's stock price after its earnings report mainly reflects that the market's expectations for AI-related semiconductor demand have become extremely harsh; even if the company maintains a strong outlook for AI semiconductor revenue, as long as the guidance does not exceed expectations explosively, funds will take profits. The decline in the South Korean KOSPI benchmark index along with Samsung and SK Hynix also reflects that AI-related asset prices are highly sensitive to any marginal disappointments after significant gains this year. In other words, the secondary market is trading on the trend of "shifting from highly crowded AI trading themes to undervalued defensive leaders," while the signals given by super leaders in the AI computing supply chain, such as TSMC and Hon Hai, are still validating that "AI capital expenditures and computing orders are still being fulfilled." Specifically regarding AI capital expenditures, TSMC's leader Wang Chih-Kang's statements at the shareholder meeting, such as "I don't know where the peak is" and "I haven't seen any indicators of demand stopping," can be considered the most bullish remarks from a major industry player in the AI computing supply chain during this TSMC shareholder meeting. From the perspective of the engineering chain, TSMC's latest statements carry the highest level of industry credibility because it is at the core of the "physical bottleneck layer" of AI computing infrastructure: NVIDIA/AMD AI GPUs, Broadcom/Marvell ASICs and network chips, AMD AI accelerators, and cloud vendors' self-developed ASICs all revolve around advanced processes, advanced packaging, and high-yield mass production. If TSMC remains in short supply in the coming years, it indicates that the construction of AI data centers is not just a single quarter of inventory pull, but a multi-year capital expenditure cycle spreading from training GPUs to inference, Agentic AI, robotics, autonomous driving, and sovereign AI Taiwan Semiconductor Manufacturing Company (TSMC) also emphasized in its shareholder meeting materials that it will continue to invest in Taiwan's leading processes and advanced packaging, and promote multi-stage capacity construction for 2nm advanced processes to meet the strong computing power demand driven by AI. Regarding the revenue prospects of AI, Anthropic, which launched a series of heavyweight AI agents in February that hit global software stocks hard, recently released a significant profit trajectory. This strong revenue data and better-than-expected profit path will further accelerate tech giants in building an "AI computing power super empire." Anthropic expects its Q2 revenue to double, achieving operating profit for the first time, highlighting that the AI industry chain is shifting from a "burning money narrative" to a "cash flow cycle narrative." The most significant aspect is that Anthropic's monthly ARR surged by $11 billion, equivalent to the ten-year scale of three SaaS giants: Palantir, Snowflake, and DataBricks—an unprecedented miracle in the history of capitalism. Anthropic also expects Q2 revenue to jump from $4.8 billion in Q1 to $10.9 billion, achieving approximately $559 million in operating profit, indicating that cutting-edge AI applications are not just consuming computing power but are beginning to transform enterprise programming, agent workflows, cybersecurity, and data analysis into high-value token revenues. The explosive demand for Claude and AI tools is pushing Anthropic closer to its first profitable quarter, and its computing power cost per $1 of revenue has decreased from about $0.71 in Q1 to about $0.56 in Q2, showing that scale effects and reasoning efficiency are improving the economic model of AI applications. This is also why Wall Street financial giant Morgan Stanley is proclaiming that the AI computing power arms race has entered a system-level expansion phase. The institution has significantly revised its capital expenditure expectations for large U.S. tech giants in 2026 from $433 billion a year ago to $805 billion, with capital expenditures in 2027 expected to reach $1.1 trillion, up from the previous forecast of $950 billion. 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