A handful of big tech companies operate a large, global network of massive AI data centers.<\/p>\n
Those companies, chief among them Meta, Amazon, Google, Microsoft, and Oracle, are often referred to as hyperscalers. As the top AI chipmaker, Nvidia provides the hardware for these hyperscalers, and in turn, the hyperscalers are the chip giant\u2019s biggest customers.<\/p>\n
During the rise of the AI hype era, any investment made into AI infrastructure by these hyperscalers was met with market fervor, and any good news for these hyperscalers meant good news for Nvidia.<\/p>\n
But recently that dynamic has started to change. The hyperscalers\u2019 financial commitments for the year topped $725 billion, a figure that has doubled<\/a> since a year ago. Meanwhile, this AI commitment is still showing limited returns, causing investors to grow wary<\/a> of this record AI spending and wonder if it is warranted or signifies a bubble. Earlier this year, Evercore analysts warned that the investment commitments could turn these tech giants\u2019 cash flow negative, a fate that, if it came to pass, would certainly hit Nvidia\u2019s profits as well. It seems to be a major reason why some experts think Nvidia\u2019s revenue growth may be peaking.<\/p>\n This pressure may have been the driver of a major change in the way Nvidia reports its most valuable financial metric, data center revenue. In an attempt to prove revenue diversification to investors, Nvidia announced on Wednesday that, going forward, it will break down data center revenue into two: hyperscalers and \u201cACIE,\u201d which stands for AI Clouds, Industrial, and Enterprise, aka a catch-all category that encompasses pretty much everything else.<\/p>\n \u201cIt\u2019s really about the fact that our business has now evolved and grown to such a large scale, it\u2019s helpful to segment it, so that you have a better understanding of how our business works,\u201d Nvidia CEO Jensen Huang said at the company\u2019s earnings call on Wednesday evening.<\/p>\n On the call, Huang spent considerable time hammering in the point that Nvidia\u2019s revenue is not as dependent on the hyperscalers as once thought, even though in the past quarter hyperscalers accounted for half of all data center revenue.<\/p>\n Huang assured investors that while hyperscaler growth came first and both categories will \u201cgrow incredibly fast,\u201d ultimately the second category would \u201cbe larger over time.\u201d<\/p>\n \u201cHyperscale developed AI first for a lot of reasons, you know, they have great computer science, they have excellent data center capability, and they also focus largely on consumer applications, which, if not perfect, is not the end of the world,\u201d Huang said. \u201cFor many of the other applications\u2026until the AI is very capable and does really productive work, and does it safely, and it could do it in a way that can actually generate impact and income, it doesn\u2019t really get used.\u201d<\/p>\n Nvidia could back up those growth promises with its results so far. Hyperscaler data center revenue grew 12% quarter over quarter, while the second category grew 31%.<\/p>\n Huang has spent the past few months trying to battle the negative investor outlook on the hyperscaler capex commitments to save Nvidia\u2019s reputation, but this new reporting method signals a change in strategy. In the previous earnings report, he spent much of the call assuring investors that the spending commitments were warranted, offering an alternative way to measure revenue by correlating it directly with compute power instead.<\/p>\n \u201cCompute equals revenue,\u201d<\/a> Huang proclaimed at that earnings call repeatedly.<\/p>\n