The Trillion Dollar Stock Club is a pretty exclusive group. Only 10 U.S.-listed stocks are currently above this valuation level, and several are still far away from doing so.
However, one stock that’s a bit far off is AMD (NASDAQ:AMD). AMD has a market capitalization of $330 billion, so investors may not be paying attention to whether it breaks through the $1 trillion valuation range. However, AMD’s hardware is starting to become more popular in the field of artificial intelligence (AI), perhaps faster than many think.
How long will it take for AMD to reach $1 trillion? Well, if its predictions come true, it could happen in just four years.
NVIDIA (NASDAQ: NVDA) It is the undisputed king of graphics processing units (GPUs). GPUs are ideal for AI workloads because they can process multiple calculations in parallel. When the AI ​​boom begins in 2023, Nvidia’s GPUs, control software, and other hardware that supports them are far superior to AMD’s. As a result, Nvidia’s products became the first choice, while AMD’s products became only alternatives.
However, these trends are changing. AMD has made massive improvements to its control software, ROCm. It noted that downloads increased 10x year-on-year in November 2025. This is a big deal because it shows developers are exploring their hardware. This could be a sign that AMD’s products are starting to emerge as viable alternatives and could be poised to take some market share away from Nvidia.
There is limited funding available to build data centers. Compute hardware can account for nearly half of the build cost, and while Nvidia’s products are the best, they aren’t cheap.
There are no ready prices for data center flagship GPUs yet, and these numbers are estimates based on reports. Nvidia’s Blackwell B200 GPUs cost between $30,000 and $50,000 per chip, depending on options. Its AMD rival, the MI350, sells for $25,000. This allows AI hyperscale enterprises to get greater price/performance on cloud GPUs, but whether this cheaper price is worth it from a performance perspective remains to be seen.
However, AI hyperscalers may not have a choice whether to continue using AMD’s chips. During Nvidia’s third-quarter earnings release, the company announced that its cloud GPUs were “sold out.” While that sounds like a good question, the bigger issue is that if customers can’t get the computing power they need from Nvidia, they may turn to AMD’s products as an alternative. If AMD’s products can deliver similar results at a lower price, more customers may choose AMD hardware in the future.
