Nvidia, known for its graphics processing units (GPUs) crucial for training and scaling artificial intelligence (AI), is now focusing on memory chips, which CEO Jensen Huang highlighted as AI's biggest bottleneck. Initially, tech giants aimed to acquire as much compute power as possible, but the focus has shifted to specialized high bandwidth memory, essential for storing and quickly retrieving the trillions of data points used in AI training and applications. Nvidia is now building systems with multiple parts, including memory chips packed into its hardware, relying on suppliers such as Micron, SK Hynix, and Samsung for a continuous supply. The downside of the shortage is that Nvidia is at the mercy of these suppliers, potentially affecting its business if they cannot produce chips fast enough. However, Nvidia has the cash and purchasing scale to have priority on memory chips, as it generated $48.6 billion in free cash flow in its most recent quarter (ended April 26) and finished with $13.2 billion in cash and cash equivalents, allowing it to pay a premium and shut out smaller competitors. The underperformance of Nvidia's stock is largely due to overall sentiment toward major AI companies, like the "Magnificent Seven."