2. NVIDIA Corp (NASDAQ:NVDA)
Number of Hedge Fund Investors: 193
Dan Niles, Niles Investment Management founder, said in a latest program on CNBC that NVIDIA’s Corp (NASDAQ:NVDA) $5.5 billion inventory write-down following US export restrictions on the company shows that the company is facing demand issues.
“The first thing that crossed my mind was okay so you’re trying to sell a Ferrari whose top speed is 90 mph not 200. Well, you have to write that down to zero. You can’t find any other takers for this when supposedly the bull case is demand is far outstripping supply and you can sell—you know, supply is the limiting issue. So that’s the first thing that struck me is why are they taking a $5.5 billion write-down? But again, we’ve had a lot of data points on the other side such as Microsoft, you know, walking away from data center leases, power contracts, etc. And the innovations by DeepSeek which cuts cost to produce tokens by over 90% that are now being used in US models like Meta and you go “Okay, you know, I’ve been saying for a while there’s an AI digestion phase coming this year and there’s probably some signs that it—that it’s actually here, that they can’t resell these chips anywhere else in the world, despite supposedly demand being so amazing.”
The market will keep punishing Nvidia for not coming up to its gigantic (and sometimes unrealistic) growth expectations. About 50% of the company’s revenue comes from large cloud providers, which are rethinking their plans amid the DeepSeek launch and looking for low-cost chips. Nvidia’s Q1 guidance shows a 9.4% QoQ revenue growth, down from the previous 12% QoQ growth. Its adjusted margin is expected to be down substantially as well to 71%. The market does not like it when Nvidia fails to post a strong quarterly beat. The stock will remain under pressure in the coming quarters when the company will report unimpressive growth.
Nvidia is facing challenges at several levels. Competition is one of them. Major competitors like Apple, Qualcomm, and AMD are vying for TSMC’s 3nm capacity, which could limit Nvidia’s access to these chips. Why? Because Nvidia also uses TSMC’s 3nm process nodes. Nvidia is also facing direct competition from other giants that are deciding to make their own chips. Amazon, with its Trainium2 AI chips, offer alternatives. Trainium2 chips could provide cost savings and superior computational power, which could shift AI workloads away from Nvidia’s offerings. Apple is reportedly working with Broadcom to develop an AI server processor. Intel is also trying hard to get back into the game with Jaguar Shores GPU, set to be produced on its 18A or 14A node.
Alger Spectra Fund stated the following regarding NVIDIA Corporation (NASDAQ:NVDA) in its Q1 2025 investor letter:
“NVIDIA Corporation (NASDAQ:NVDA) is a leading supplier of graphics processing units (GPUs) for a variety of end markets, such as gaming, PCs, data centers, virtual reality, and high-performance computing. The company is leading in most secular growth categories in computing, and especially artificial intelligence and super-computing parallel processing techniques for solving complex computational problems. In our view, Nvidia’s computational power is a critical enabler of AI and therefore essential to AI adoption. During the quarter, shares detracted from performance due to several factors. In January 2025, investor concerns grew regarding the emergence of advanced AI models from China, reportedly developed at lower costs and with reduced computing requirements, raising doubts about Nvidia’s market dominance. Additionally, U.S. President Donald Trump’s announcement of new tariffs targeting industries increased worries about higher operational costs. Despite these headwinds, Nvidia reported robust fiscal fourth-quarter results, highlighted by significant revenue growth driven by its data center segment. On the earnings call, CEO Jensen Huang emphasized the increasing computational requirements of future AI models, noting, “The more computation, the more the model thinks, the smarter the answer,” and adding that future reasoning models could demand substantially more compute resources. We believe Nvidia’s leadership in scaling AI infrastructure—including advancements in inference and reasoning during inference—continues to drive adoption among enterprises and startups, ensuring sustained demand for its high performance chips and software solutions. As older-generation chips are repurposed and new clusters deployed, we see Nvidia as well-positioned to capitalize on rising computational needs across AI applications.”
