History Says This Is 1 of the Biggest Risks Nvidia Faces, and It Could Be About to Repeat Itself

Key Points

• Nvidia is the undisputed leader in AI chips.

• The shift toward ASICs seen in cryptocurrency mining is an example of a risk investors need to watch when owning Nvidia.

• However, there are some key differences between what happened in cryptocurrency mining versus what is happening today in AI.

• 10 stocks we like better than Nvidia ›

So far, Nvidia (NASDAQ: NVDA) has been the biggest winner of the artificial intelligence (AI) boom, but investors should not forget how quickly hardware leadership can shift when a market matures. The cryptocurrency mining industry is a great example. Graphics processing units (GPUs) were once the workhorse of crypto mining, at least until application-specific integrated circuits (ASICs) were developed to take mining to the next level.

These ASICs did only one thing, but they did it faster and cheaper, and in short order, GPU-based mining for currencies like Bitcoin fell far out of vogue. The economics were simply too compelling to ignore. If a crypto miner wanted to stay competitive, they had to switch to ASICs or get priced out.

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Now, ASICs are being designed for AI workloads.

Image source: Getty Images.

ASICs and AI

GPUs have been the dominant choice for training large language models (LLMs), and Nvidia has been the undisputed GPU king thanks to its powerful software platform, CUDA. The company has built an entire ecosystem around its chips, and it is the reason Nvidia's data center revenue has exploded. But AI workloads are massively expensive and energy-hungry, and for the largest hyperscalers (companies that own massive data centers) focused on AI, there is a huge incentive to find something cheaper and more efficient.

This is exactly why Alphabet built its Tensor Processing Units (TPUs), and why Amazon developed its Trainium and Inferentia chips. Others are now following suit. Meta Platforms and OpenAI have reportedly been working with Broadcom to develop their own custom chips, with OpenAI believed to be the customer that made a surprising $10 billion order for next year. Meanwhile, large Nvidia customer Microsoft has also been working to create its own custom AI chip.

The goal is clear: lower costs and reduce reliance on Nvidia. Meanwhile, with the market beginning to shift more toward inference, the landscape is changing. Nvidia's moat around inference isn't nearly as wide as the one it has for training. Inference isn't as technically demanding as training, so the years of code built on top of CUDA aren't as impactful. Meanwhile, inference is a continuing cost, so the total cost of ownership and cost per inference are much more important factors.

When the cost curve in Bitcoin mining forced the shift to ASICs, GPUs went from must-have to irrelevant almost overnight in the space. Nvidia's massive valuation today assumes that hyperscalers will keep buying ever more GPUs, but history says they will only do so as long as the economics make sense.

Now, there are some major differences between Bitcoin mining and inference that work in Nvidia's favor. Bitcoin mining is a brute force repetitive task, while inference is understanding the intent of an input, like a question, and using the information the LLM was trained on to execute. New AI techniques are also constantly being developed, like reasoning or multimodal AI, and GPUs are more adaptable to handle these tasks compared to ASICs, which can become obsolete more quickly.

Nvidia also sees this risk and is taking steps to protect itself. Its recent $100 billion investment partnership with OpenAI is a perfect example. Whether directly or indirectly, OpenAI is one of the biggest users of Nvidia's GPUs, but it has recently developed its own AI ASICs. With this investment, Nvidia is effectively paying to ensure OpenAI keeps using its GPUs.

Will AI ASICs replace GPUs?

Investors should watch the ASIC threat closely because it could be the single biggest risk to Nvidia's growth story. The company has a wide moat, but it is not unbreakable. The hyperscalers have the money and motivation to chip away at its dominance, and every dollar that moves to in-house AI chips is a dollar that doesn't go to Nvidia.

That doesn't mean GPUs are going away, as AI workloads are still evolving, and GPUs are flexible enough to handle new models and techniques. However, as the market shifts to inference, custom AI chips will likely take share.

Right now, the market looks big enough for there to be multiple future AI infrastructure winners, but Bitcoin showed how quickly the economics can flip, and AI could follow a similar pattern. Investors should keep that in mind before assuming Nvidia's growth is on autopilot for the next decade.

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Geoffrey Seiler has positions in Alphabet. The Motley Fool has positions in and recommends Alphabet, Amazon, Bitcoin, Meta Platforms, Microsoft, and Nvidia. The Motley Fool recommends Broadcom and recommends the following options: long January 2026 $395 calls on Microsoft and short January 2026 $405 calls on Microsoft. The Motley Fool has a disclosure policy.