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NVDA

Bull vs. Bear: Is Nvidia a Buy or Sell? Let's Look at the Bullish and Bearish Cases for the Stock.

April 11, 2026 — 01:27 pm EDT

Written by

Geoffrey Seiler for

The Motley Fool->

Key Points

- Nvidia has created a wide moat and proven to be a forward-looking company.

- But peak AI infrastructure spending and increased competition are major risks.

- 10 stocks we like better than Nvidia ›

When you're looking to invest in a stock, it's always good to know both the bearish and bullish sides. That way, there tend to be fewer surprises, and you can make better-informed decisions as new information presents itself. The first stock I want to look at in an ongoing series of articles is Nvidia (NASDAQ: NVDA). Here are two perspectives.

The bull case

Nvidia is at the center of one of the most powerful technological trends the world has seen in artificial intelligence (AI). Its graphics processing units (GPUs) are the main chips used to power artificial AI infrastructure, where it commands an approximate 90% market share.

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The company has formed a wide moat through the ecosystem it has built around its GPUs. This starts with its CUDA software platform, where virtually all early foundational AI code was written on its platform and optimized for its chips. At the same time, its proprietary NVLink interconnect system essentially lets its chips act as one powerful unit.

Image source: The Motley Fool.

The most powerful part of the Nvidia story, though, has been the company's ability to predict market trends and evolve. It created CUDA about a decade before Advanced Micro Devices developed its competing software, and wisely seeded it into institutions that were doing early research on AI. Then, in 2020, it acquired a leading-edge networking company called Mellanox that became the basis for its powerful networking segment.

More recently, the company has set itself up better for the age of inference and agentic AI with its "acquisitions" of Groq and SchedMD. This has led to the introduction of language processing units (LPUs) designed specifically for inference and its NemoClaw platform to deploy AI agents. It has even developed its own central processing units (CPUs). As a result, it can now deliver complete server racks tailored for specific AI tasks, such as training, inference, and agentic AI. This has helped turn it into a complete AI infrastructure company and not just a chipmaker.

Meanwhile, the AI race still looks like it is in its early innings, with some of the largest companies in the world and global governments racing to not be left behind. This creates a long runway of growth for Nvidia.

The bear case

While Nvidia has dominated the AI infrastructure market, it is seeing more competition than it has in the past. Custom AI ASICs (application-specific integrated circuits), which are hardwired chips designed for specific tasks, are starting to make inroads, especially in inference, given their superior power efficiency characteristics.

Just this month, Anthropic announced it would expand its capacity with Alphabet's Tensor Processing Units (TPUs), while it already has a large data center running on Amazon's Trainium chips. More and more hyperscalers, meanwhile, are looking to design their own custom chips, often with the help of partners like Broadcom or Marvell Technology.

No. 2 GPU player AMD is also starting to make some inroads. Its ROCm software platform has vastly improved in the past few years, and it's formed partnerships with both OpenAI and Meta Platforms to deliver GPUs in exchange for warrants in the company. Meanwhile, the shift to newer code being written on open-source platforms helps open the door to gain share, particularly in the less demanding inference market.

The biggest case against Nvidia, though, is that the AI infrastructure market could be hitting peak spending levels. The five largest hyperscalers alone are set to spend a whopping $700 billion on AI infrastructure this year. That's about 1.5% of GDP (gross domestic product), which is around where past tech investment cycles have peaked. Cloud computing providers and other hyperscalers will need to see strong returns on their investment to maintain this spending.

The verdict

In my view, while Nvidia will inevitably lose some market share, it will remain the most important player in AI infrastructure given its strong and growing ecosystem. Meanwhile, I believe that hyper