Nvidia turmoil and financing risks weigh heavily! Yet Barclays remains bullish on the long-term outlook for AI trades.

Barclays Private Bank Chief Market Strategist Julien Lafargue said that despite the NVIDIA Corporation (NVDA.US) turmoil and financing risks testing the resilience of the artificial intelligence (AI) trade, investors should still stay involved in the AI boom. Lafargue previewed Barclays' upcoming 2027 outlook report titled "Until the Music Stops." He said the institution still believes there are ample reasons to continue holding related investments. "For us, this is not a bubble, but a technological revolution that will affect every industry. But we need to recognize that this process cannot happen overnight," Lafargue said in an interview. NVIDIA Corporation Turmoil Has Not Derailed the AI Trade In premarket trading on Thursday, NVIDIA Corporation shares fell more than 7%. Three days after a November 8 post acknowledging an extramarital affair, Jensen Huang was reported to have a second extramarital relationship, with the woman being an NVIDIA Corporation vice president who reports directly to him. Meanwhile, media reports citing people familiar with the matter said the NVIDIA Corporation board is moving quickly to prepare to remove Jensen Huang, with a board meeting as early as November 12 to decide his fate, and has already partnered with external executive search firms to identify a successor, considering first appointing an interim CEO as a transition. Some commentators believe NVIDIA Corporation may be able to weather the storm, but the process will be extremely difficult, and the probability of Jensen Huang being removed as chief executive is quite high. Data from prediction market Polymarket shows that the probability of bets on Jensen Huang leaving or being removed has surged. Currently, the probability of Jensen Huang leaving before November 30, 2026, has risen to 86%, to 91% before December 31, 2026, further to 92% before March 31, 2027, and to 93% before December 31, 2027. With NVIDIA Corporation set to report third-quarter results next week, "key-person risk" is worrying investors. Fiona Cincotta, senior analyst at Gain Capital, said that before the board investigation results are announced, related uncertainty may continue to weigh on NVIDIA Corporation's share price, especially with NVIDIA Corporation due to report earnings later this month. The risk that NVIDIA Corporation's governance turmoil poses to the entire AI industry lies in the fact that NVIDIA Corporation has become a core node in the global AI industry's capital, technology and supply chain networks. In addition to supplying chips to global AI companies, NVIDIA Corporation has also participated in the development of upstream and downstream parts of the industrial chain through investments. Its investment targets include companies such as OpenAI, Anthropic, xAI, CoreWeave and Nebius. In August this year, NVIDIA Corporation also signed preliminary agreements with institutions including Apollo Global Management Inc, BlackRock, Inc., Blackstone, Bruker Corporation, Goldman Sachs Group, Inc. and KKR, planning to mobilize more than US$500 billion in funding in the future. Jensen Huang previously said NVIDIA Corporation may support up to a quarter of that funding. This means that once a serious leadership crisis occurs at NVIDIA Corporation, the impact may not be limited to the company itself but could also spread to numerous companies that rely on its chips, investment and financing support. Well-known tech stock analyst Dan Ives believes that if Jensen Huang leaves the CEO position, NVIDIA Corporation's share price could fall 25% to 30%, with the corresponding share price potentially even dropping toward around US$122. Jay Goldberg, an analyst who has long been cautious on NVIDIA Corporation and is one of the few on Wall Street to give a "sell" rating, said that if the current pressure ultimately leads to Jensen Huang's departure, it could be a serious mistake for NVIDIA Corporation and all related parties. As a core bellwether in the AI industrial chain, a sharp decline in NVIDIA Corporation's share price would undoubtedly transmit to the broader AI sector, casting a shadow over the outlook for the AI trade once again. Speaking about NVIDIA Corporation, Lafargue said the company has been called "the central bank of the AI trade," but he believes that even if the company may change CEOs, it does not mean all trades must be re-examined and renegotiated. Lafargue acknowledged that national security considerations could prompt stricter measures restricting exports of chips made by NVIDIA Corporation or other U.S. manufacturers overseas, which could affect the entire industry. However, he added that the direction of development over the next two to three years is very clear, and competition and innovation in the United States and other regions will intensify further, driving the emergence of new technologies and ultimately weakening the impact of any short-term disruption. Lafargue compared the situation facing NVIDIA Corporation's leadership to leadership transitions at Microsoft Corporation (MSFT.US), Alphabet (GOOGL.US) and Amazon.com, Inc. (AMZN.US). He believes that these companies' transitions from charismatic leaders to successors did not weaken their corporate value. Financing Risks Rise Amid AI Return Concerns and Fed Policy Uncertainty Lafargue said Barclays has identified 12 factors that could disrupt markets, among which interest rates and the financing environment pose the most significant risks. Still, he said: "We want to stay invested and keep our investment exposure until there are signs that the music is starting to slow." Lafargue believes investors need to recognize that technological progress does not mean all companies will earn the same returns, and the competitive landscape and profitability within the AI industrial chain may diverge markedly. Looking at the broader AI investment trade, Lafargue believes the market needs to readjust expectations for the pace at which computing capacity comes online. He noted that the trillions of dollars in funding already committed cannot be converted into actually delivered computing infrastructure in a short period. In fact, AI infrastructure construction itself has long delivery cycles. Even after construction of a data center is completed, it does not mean it can immediately begin commercial operations. Grid connection, power supply, equipment installation, system commissioning and customer deployment can all cause delays. Rating agency Moody's Corporation recently noted that there is a clear time lag between the completion of a data center and the actual generation of stable cash flow, and insufficient power supply and transmission infrastructure are becoming important constraints on AI infrastructure expansion. At the same time, as the scale of AI infrastructure investment continues to expand, the market's focus is gradually shifting from "whether computing demand is real" to "whether massive capital expenditure can earn sufficient returns." Although demand for computing power from cloud computing companies and AI model developers remains strong, data centers, GPUs, networking equipment and power facilities all require huge capital investment, while the commercialization of AI applications on the enterprise and consumer sides is not fully synchronized. For investors, the long-term development prospects of AI technology and whether related companies can achieve investment returns within a reasonable timeframe are two different questions. Out of concern over whether massive AI investment can earn reasonable returns, investors are demanding more premium from related companies in financing. In addition, uncertainty over the Federal Reserve's policy outlook has also intensified financing risk. AI data center construction is capital-intensive, has long investment cycles and requires huge upfront investment. Companies not only need to pay high costs for GPUs, servers and networking equipment, but also must bear land, power, cooling system and long-term financing expenses. Therefore, changes in interest rate levels and credit market conditions will directly affect project funding costs, investment payback periods and final yields. After this week's higher-than-expected October CPI data, the market has increased bets that the Federal Reserve will further tighten monetary policy in the future. Higher interest rates have a dual impact. On the one hand, companies need to pay more interest, increasing the cost of debt-financed construction of data centers and computing facilities; on the other hand, rising interest rates raise the discount rate for future cash flows, reducing the valuation of not-yet-profitable AI businesses and related infrastructure assets. For companies expanding through long-term computing leasing contracts, fixed-price service agreements and debt financing, if financing costs rise while the prices customers pay cannot increase in tandem, project profit margins may narrow significantly. Moreover, rising financing costs may also create chain reactions: some emerging cloud service providers may be unable to obtain refinancing at acceptable interest rates and be forced to delay data center construction or cut GPU purchases; although large cloud service providers have stronger financing capacity, they may also reassess the pace of capital expenditure and prioritize funds for projects with higher returns and clearer commercialization paths. Once the industry as a whole slows investment, order growth expectations for chip suppliers, server manufacturers, data center operators and power equipment companies may also be affected. All these factors could cast a shadow over the outlook for the AI trade. However, large technology giants still have strong operating cash flow, higher profit margins and relatively solid balance sheets, which can to some extent buffer the impact of rising financing costs. JPMorgan believes that demand for AI computing power remains enormous, and as agent workflows and inference tasks continue to expand, future computing demand is still expected to keep growing. Tighter financing conditions may mainly change the industry's investment pace and the competitive landscape, rather than directly eliminating the long-term demand for AI infrastructure construction.