Here's How Nvidia's Incredible AI Success Could Lead to Its Undoing

Nvidia (NVDA 5.27%) was able to ride the artificial intelligence (AI) wave kicked off by ChatGPT in late 2022 and dominate the market for AI accelerators, partly because the company had spent years building a strong foundation in accelerated computing. Nvidia's GPUs are useful not only for AI workloads, but also across academia and industry for running simulations and doing other computing-intensive tasks. CUDA, Nvidia's proprietary software toolkit that allows developers to harness the power of the company's GPUs, has been around for nearly two decades. The vast ecosystem around CUDA was an enormous advantage for Nvidia when the AI boom started. With tech giants and start-ups moving fast to pull ahead in the AI race, Nvidia's GPUs were the path of least resistance. The tide is starting to turn When the market for data center GPUs was much smaller, it made no sense for Nvidia's customers to spend the engineering resources necessary to free themselves from their dependence on CUDA. The return on investment just wasn't there. With the AI boom causing an explosion in demand for data center GPUs, the math is starting to change. Nvidia's data center segment generated more than $35 billion in revenue during the latest quarter alone, and the company's profit margins are through the roof. Customers like Microsoft, Meta Platforms, and OpenAI are spending billions, or even tens of billions, of dollars each year on Nvidia's GPUs as they race to meet demand for AI computing capacity. The stakes are now much higher. A company like Meta could save billions by cutting out Nvidia and designing its own AI chip and software stack from the ground up. This push to bring AI accelerators in-house appears to be picking up steam across the tech industry. Some companies, including Amazon and Alphabet, have already created multiple generations of custom AI chips. Other companies are now following suit. Meta is reportedly testing a custom AI chip aimed at AI training workloads, which are much more computationally intensive than AI inference workloads. OpenAI isn't far behind, reportedly working on finalizing its first custom AI chip design. Companies that design their own AI chips and reduce dependence on Nvidia's pricey AI accelerators could have a major cost advantage over competitors that don't follow the same path. This could compel even more companies to bring AI chips in-house. Eroding Nvidia's dominance While Nvidia does have some competition from AMD in the AI accelerator market, its own customers represent a far larger threat. The numbers are now big enough to prompt tech giants and AI companies to rethink their AI hardware and software stacks. Beyond reducing demand for Nvidia's chips, this trend could put pressure on Nvidia's pricing and margins. It's only because the market for data center GPUs became so large that the CUDA stranglehold over accelerated computing is under attack. Nvidia is still selling a lot of AI chips, and it will continue to do so as long as the AI boom keeps going. Once demand for AI computing capacity cools down -- trees don't grow to the sky, after all -- growth will be tough for Nvidia as its market share erodes due to competition from homegrown AI chips. Nvidia stock has already slumped from its all-time high as concerns about the resiliency of AI demand have collided with worries about tariffs and the state of the global economy. As custom AI chips make up a growing portion of the AI accelerator market, the stock could come under additional pressure.