History Warns: Nvidia’s Biggest Threat Looms Again - And It’s About to Repeat

Nvidia faces a ghost from its past—the same cyclical threat that crushed its valuation twice before. Chip demand cycles turn faster than Wall Street analysts change their price targets.

The Inventory Avalanche

When AI hype peaks, manufacturers overproduce. Then the hangover hits—warehouses stuffed with unsold GPUs as demand plateaus. Nvidia's 2018 inventory crisis saw shares drop 54% in three months.

Margin Compression Tsunami

Competitors flood the market with cheaper alternatives. Suddenly those 70% gross margins look like fantasy football stats. Remember when AMD undercut pricing by 40% during the crypto mining collapse?

Innovation Treadmill

Nvidia must outrun its own success—each new chip must be exponentially better while maintaining backward compatibility. Tech history shows even market leaders stumble when R&D cycles accelerate too fast.

Wall Street's amnesia about semiconductor cycles is almost impressive—they'll downgrade the stock six months after upgrading it based on the same data. Nvidia either breaks the pattern or becomes another cautionary tale in an industry that eats its young.

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 whybuilt its Tensor Processing Units (TPUs), and whydeveloped its Trainium and Inferentia chips. Others are now following suit.and OpenAI have reportedly been working withto 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 customerhas 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.