Is Nvidia Stock Overvalued? Why AI Slowdown Could Lead to a 50% Drop in Nvidia Stock

Nvidia has been the core of the artificial intelligence (AI) revolution, as its GPUs are the go-to choice for training big AI models. This status has driven revenue from FY’23’s $27 billion to a projected $200 billion this fiscal year, which is one of the most astounding growth tales in the archives of tech.

Besides its hardware, Nvidia’s CUDA software stack has locked in customer loyalty, along with building an impressive moat that rivals have been unable to overcome. But even as a market leader, Nvidia’s valuation at around 40x forward earnings suggests higher expectations. Investors are not only counting on leadership, but on prolonged, multi-year leadership in AI hardware.

The Valuation Deception

These high anticipations expose Nvidia and make it at risk to any disruption in demand or structural changes in the AI cycle. A change to inference-centric applications from training-focused workloads would dampen the intensity of GPU demand, which will damage the optimistic tale.

The company’s past provides it with a cautionary example. In the pandemic, Nvidia rode high on demand for cryptocurrency mining and gaming GPUs. But when inflation set in and crypto demand evaporated, its stock dropped almost 66% highs and lows, which is much more quickly than the S&P 500’s 25% drop in the same timeframe.

This volatility shows the risk that even small fractures in today’s AI narrative can cause excessively steep drops, particularly in view of the present premium valuation.

Training versus Inference, Future of AI

The initial stage of AI was mainly dependent on the process of training, during which Nvidia’s super-efficient GPUs had the leading role. To train large models, an enormous amount of computing power is required, thus Nvidia has gained the benefits by being the fastest and most efficient solution.

Nevertheless, the future could very well be different. Due to the stabilization of model sizes and the limited availability of high-quality training data, the AI training behemoth might be coming to a standstill.

Also, inference is increasingly important. In contrast to training, inference is persistent, distributed, and tends towards inexpensive solutions. This creates room for opponents like AMD, which is introducing more competitive GPUs such as its MI lineup

At the same time, ASICs (Application-Specific Integrated Circuits) are becoming popular, proposing even more cost-effectiveness and energy-efficiency for repeated inference tasks that is similar to their use in cryptocurrency mining. This trend may dethrone Nvidia’s leadership, especially in those markets where performance matters less as compared to price and efficiency.

Competitors & Hyperscalers Joining in

Inference warfare is already a crowded battlefield. AMD is claiming a position with the strategy of delivering good performance at a lower price, while semiconductors such as Marvell and Broadcom are similarly well-placed to provide custom silicon for hyperscalers.

The Big Tech titans are also hedging against reliance on Nvidia, as Amazon, Meta, and Google are also heavily investing in decent AI chips. Their rationale is obvious, which is to cut costs, secure supply, and gain leverage. Nvidia’s reliance on these hyperscalers is high, in Q2 only two customers accounted for almost 39% of its sales. If even one of those firms shifts more dramatically to in-house silicon, Nvidia’s base of revenues could suffer a major blow.

Chinese Competition Heightens the Stakes

Beyond the U.S, Chinese companies such as Alibaba, Baidu, and Huawei are accelerating development of AI chips, in order to avoid U.S export controls. Alibaba is set to introduce a new inference chip for its cloud business. While Nvidia GPUs will continue to be at the heart of training workloads in China, domestic companies are working to own inference workloads in the long term. This transition not only brings fresh competition but also geopolitical and supply chain threats that might intensify Nvidia’s volatility.

Nvidia at the Front, but with Fragile Hopes

For the moment, Nvidia’s dominance is unthreatened. Its GPUs continue to be unmatched for exclusive AI training, and its CUDA environment continues to tie developers and businesses to its ecosystem. It continues to pump a lot of money into R&D to keep ahead, but the larger risk is valuation and changing market forces. If the AI cycle shifts from training to inference, with higher competition and tighter margins, even if Nvidia continues to lead in technology, it could witness a valuation reset.

Investor’s Dilemma

Nvidia remains at the top, but its valuation already anticipates hyper-growth going forward. For investors, the question is not whether Nvidia will continue to lead, it surely will, but whether that leadership can support the sort of returns embedded in today’s valuation. The previous GPU cycle demonstrated how rapidly sentiment can shift, and with hyperscalers and competitors compressing, history could repeat itself.

Nvidia remains the unchallenged champion of AI hardware, but its shares trade in a delicate territory where hopes are nearly unrealistically high. Even the perfect company can undergo failure if market sentiment turns, and Nvidia’s history of volatility is a testament. Investors have a tricky choice, either to enjoy the ride of AI expansion, or hold on to their seats for a hit. A 50% plunge may be drastic, but in Nvidia’s world of demand, it’s not mind boggling.