Nvidia earnings countdown: 2 things investors need to see for the stock to climb higher

00:00 Speaker A

What do you think investors need to hear in order to see some sort of move in this stock post close?

00:10 Speaker B

I think uh two things I would call out. One is uh clearly the hyperscale exposure is big for Nvidia, still more than 50% of the sales. And you want to see all of the hyperscalers continuing to spend on Nvidia even though they have their own chip ambitions and obviously, Google is the furthest along. The second big thing is the China sales. And that’s where a lot of the upside could come from in terms of where consensus is at because so far the way company has talked about is, they have really set minimal expectations when it comes to selling to China on a sustainable basis and you know, growing that segment of their sales. So from that perspective, any good news on that side would reflect in the stock.

01:14 Speaker A

Mandeep, but what if we don’t hear anything about China? Cuz it seems like there’s just this ongoing narrative about whether or not they will be able to eventually use this videos H200 uh processors in the country. And of course, right now we also have China really ramping up their own competition when it comes to AI.

01:40 Speaker B

Well, that’s the one thing you kind of know about Nvidia through the past few quarters is even though, uh, you know, there is competition, even yesterday Open AI talked about releasing a chip that is comparable in performance. But when it comes to selling to enterprises and you know, even what hyperscalers are deploying on their own cloud, Nvidia’s chips are still being used at scale and that’s where this company will, uh, you know, grow its data center revenue more than 100% uh in the quarter. So that kind of growth at the base that Nvidia is at, just goes to show the dominance they have when it comes to accelerators for AI data centers, even with all the competition. So, in China, yes, there are local chips, but when it comes to, you know, what the leading labs prefer to train their uh models on, it is still Nvidia and that is what we see across the board.