We recently wrote about “How AI Chips Are Changing the Semiconductor Industry,” noting that AI chips come in all shapes and sizes. From smartphone AI chips that perform computations at the edge to larger chips that are used in data centers, the trend is moving toward increased specialization. Large lots of general-purpose AI chips are taking a back seat to small batches of chips that are highly customized. As long-time investors in NVIDIA (NVDA), we’re curious about how they’re reacting to these changes.
For quite a while NVIDIA was the only pure-play AI stock for retail investors who wanted exposure to the growth of artificial intelligence. They were a market-leading producer of graphical processing units (GPUs) that were used ubiquitously by anyone training AI algorithms. Now, we’re seeing specialized AI chips like ASICs being used for training as well. In order to understand how this affects NVIDIA, it’s helpful to know where their revenues are coming from.
What Does NVIDIA Actually Do?
Trying to figure out exactly what NVIDIA does is becoming increasingly difficult, depending on how you look at it. First, there are two