1 00:00:02,360 --> 00:00:06,680 Speaker 1: Bloomberg Audio Studios, podcasts, radio news. 2 00:00:06,920 --> 00:00:10,040 Speaker 2: Joining us now after extensive travel, thrilled you? Good, fine 3 00:00:10,080 --> 00:00:13,480 Speaker 2: time to start us off this morning, Daniel Day us 4 00:00:13,520 --> 00:00:15,720 Speaker 2: in Singapore. How do you get back to like the 5 00:00:15,760 --> 00:00:18,239 Speaker 2: golf stream always got the web bush golf stream blowblight 6 00:00:18,239 --> 00:00:21,279 Speaker 2: out of technology for web bushes, Stan I just I 7 00:00:21,320 --> 00:00:25,799 Speaker 2: want to look forward. What did you learn from Nvidia yesterday? 8 00:00:26,640 --> 00:00:30,600 Speaker 2: That's part of the AI story of the other ten stocks. 9 00:00:31,840 --> 00:00:34,479 Speaker 3: I mean it's all about Blackwell that's fueling. I mean 10 00:00:34,560 --> 00:00:38,720 Speaker 3: eleven billion that shows. I mean this is manage rejectory 11 00:00:38,800 --> 00:00:42,239 Speaker 3: fifteen to twenty billion a quarter. So if you had 12 00:00:42,360 --> 00:00:47,000 Speaker 3: any concerns, it's the opposite Big tech, other enterprises, they're 13 00:00:47,040 --> 00:00:51,120 Speaker 3: doubling down. That's the multiplier factor cross tech. For every 14 00:00:51,159 --> 00:00:54,000 Speaker 3: dollar spent on a video chip, eight ten dollars spent 15 00:00:54,080 --> 00:00:55,080 Speaker 3: across to us attack. 16 00:00:55,560 --> 00:00:58,160 Speaker 2: What is your ex exis do when you look at 17 00:00:58,600 --> 00:01:01,720 Speaker 2: technology develop and we'll talk to man deep sing about 18 00:01:01,720 --> 00:01:05,039 Speaker 2: this at a moment, dan Ie, do you have like 19 00:01:05,360 --> 00:01:08,720 Speaker 2: a timeline out on Nvidia? Do you have a terminal 20 00:01:08,840 --> 00:01:11,720 Speaker 2: value on in video that's appropriate seven years? 21 00:01:11,760 --> 00:01:12,200 Speaker 1: What is it? 22 00:01:13,360 --> 00:01:16,720 Speaker 3: I think two to two twenty five to me is 23 00:01:16,840 --> 00:01:18,840 Speaker 3: ultimately going to be sort of the base cases this 24 00:01:18,920 --> 00:01:21,399 Speaker 3: plays out, and again you're not gonna need like the 25 00:01:21,480 --> 00:01:25,640 Speaker 3: massive blowouts. They stay in this trajectory outside of some 26 00:01:25,760 --> 00:01:27,840 Speaker 3: black swan and the Bears will always hold for that 27 00:01:27,920 --> 00:01:30,679 Speaker 3: like they have the last decade. I think this is 28 00:01:30,720 --> 00:01:33,880 Speaker 3: a four an ultimately five trillion dollars markup as it 29 00:01:33,920 --> 00:01:37,800 Speaker 3: plays out. There is one red food in the world 30 00:01:38,000 --> 00:01:40,760 Speaker 3: for these chips, and he's wearing a black leather jacket. 31 00:01:40,840 --> 00:01:42,880 Speaker 3: His name is Jensen Dan. 32 00:01:42,959 --> 00:01:45,160 Speaker 4: Talk to us about the just catalysts for this name. 33 00:01:45,360 --> 00:01:48,520 Speaker 4: You know, it's it's really unusual for Nvidia shareholders to 34 00:01:48,520 --> 00:01:51,080 Speaker 4: look down at their Bloomberg screen and see year to 35 00:01:51,200 --> 00:01:53,640 Speaker 4: date sucks, you know, down a couple of percent here, 36 00:01:54,000 --> 00:01:55,600 Speaker 4: not to see green on the screen here. What's the 37 00:01:55,640 --> 00:01:58,240 Speaker 4: next catalyst do you think for this stock? 38 00:01:59,600 --> 00:02:04,800 Speaker 3: Yeah, I think catalysts now clearly going forward, you're going 39 00:02:04,880 --> 00:02:08,080 Speaker 3: to continue to focus on what Blackwell demand looks like. 40 00:02:08,240 --> 00:02:10,560 Speaker 3: Right and in the video you know, they'll have their 41 00:02:10,600 --> 00:02:13,320 Speaker 3: conference in March. I think that's going to be a 42 00:02:13,400 --> 00:02:16,280 Speaker 3: sort of spectacle that's gonna you know, they're launching pad 43 00:02:16,760 --> 00:02:20,440 Speaker 3: for Jensen Foreign video and look, and I think there 44 00:02:20,480 --> 00:02:23,160 Speaker 3: was all smoke, no fire, and I just continue. And 45 00:02:23,200 --> 00:02:27,600 Speaker 3: we've talked about on the show Deep Seek is bullish 46 00:02:28,040 --> 00:02:31,800 Speaker 3: for in video and big tech. It's not the opposite, 47 00:02:31,960 --> 00:02:34,079 Speaker 3: despite you know many of the barrizone fire in a 48 00:02:34,120 --> 00:02:34,639 Speaker 3: crowd theater. 49 00:02:34,919 --> 00:02:37,000 Speaker 2: If you're just joining us, folks at Man Deep sign 50 00:02:37,040 --> 00:02:39,200 Speaker 2: will be with us in a moment. From Bloomberg Intelligence, 51 00:02:39,200 --> 00:02:41,960 Speaker 2: we start with Daniel Ives of web Bush. They're head 52 00:02:42,000 --> 00:02:44,840 Speaker 2: of technology. For those of you on Bloomberg Radio, it 53 00:02:44,880 --> 00:02:48,600 Speaker 2: is critical to point out Ives with the Penn State 54 00:02:48,720 --> 00:02:54,359 Speaker 2: Knitney Lions merch in the background this morning at Penn's State, 55 00:02:54,520 --> 00:02:56,800 Speaker 2: Dan Eyes, they're going to have to do a rebuy 56 00:02:57,000 --> 00:03:00,959 Speaker 2: of Apple technology. We have Mark Germi in four or five, 57 00:03:01,080 --> 00:03:04,960 Speaker 2: six days ago and we started with a chip and 58 00:03:05,160 --> 00:03:08,160 Speaker 2: you know it's like Man Deep sing talk and Dan 59 00:03:08,200 --> 00:03:13,600 Speaker 2: and I's German was riveting about the technological veracity of 60 00:03:13,639 --> 00:03:16,000 Speaker 2: the Apple story. Do you agree? 61 00:03:17,320 --> 00:03:17,760 Speaker 1: Oh? 62 00:03:17,919 --> 00:03:22,160 Speaker 3: I think the best is yet to come because I did, 63 00:03:22,240 --> 00:03:25,080 Speaker 3: and German's talked about it as well. But the consumer 64 00:03:25,160 --> 00:03:29,600 Speaker 3: AI revolution comes from Coupertino. They're a toll collector. China 65 00:03:29,639 --> 00:03:33,120 Speaker 3: app us APP Australia doesn't matter. In other words, it's 66 00:03:33,160 --> 00:03:35,640 Speaker 3: about that. In stallwas I continue to think that is 67 00:03:35,680 --> 00:03:39,720 Speaker 3: going to incrementally add sixty seventy dollars per share the 68 00:03:39,760 --> 00:03:43,560 Speaker 3: AI story, the incremental services, and that doesn't include a 69 00:03:43,560 --> 00:03:46,040 Speaker 3: new form factor chip and just more and more control 70 00:03:46,080 --> 00:03:47,040 Speaker 3: of their ecosystem. 71 00:03:48,760 --> 00:03:52,000 Speaker 4: Dan, how did the company kind of position the deep 72 00:03:52,080 --> 00:03:56,280 Speaker 4: seek issue as a competitor out there in the marketplace? 73 00:03:57,440 --> 00:03:59,920 Speaker 3: I think actually someone of the rest of the big ten. 74 00:04:00,400 --> 00:04:04,560 Speaker 3: It's actually it's a positive the models. The more innovative 75 00:04:04,640 --> 00:04:08,119 Speaker 3: the models get, the cheaper they get, the more use case, 76 00:04:08,200 --> 00:04:11,400 Speaker 3: the more GPU capacity. In other words, like that's why 77 00:04:11,520 --> 00:04:14,360 Speaker 3: like that because everyone here and you guys talk about 78 00:04:14,400 --> 00:04:17,200 Speaker 3: all the time, like everyone's waiting for where's the cracks 79 00:04:17,200 --> 00:04:20,640 Speaker 3: in the armor smoke? Is this the AI bubble? But 80 00:04:20,680 --> 00:04:23,080 Speaker 3: we've talked about this two trillion of cap backs next 81 00:04:23,080 --> 00:04:25,919 Speaker 3: three years, Big Tech span three hundred and thirty billion 82 00:04:26,040 --> 00:04:29,599 Speaker 3: this year. You can find cracks, but you're not going 83 00:04:29,680 --> 00:04:31,840 Speaker 3: to find it here. And I think that's what you 84 00:04:31,880 --> 00:04:35,720 Speaker 3: saw last night in terms of really the demand there 85 00:04:35,800 --> 00:04:39,360 Speaker 3: was no I thought Gens was actually more bullish than 86 00:04:39,440 --> 00:04:41,240 Speaker 3: even what he was a few months ago. 87 00:04:41,520 --> 00:04:43,560 Speaker 2: Daniel, I thank you so much. Get some sleep too 88 00:04:43,600 --> 00:04:48,320 Speaker 2: early traveling worldwide with US with wet Bush well rested. 89 00:04:48,400 --> 00:04:51,840 Speaker 2: Mandeep Singh joins us right now, senior technology analysts for 90 00:04:51,880 --> 00:04:55,520 Speaker 2: Bloomberg Intelligence. The distinction here man deep designs is doing 91 00:04:55,600 --> 00:04:58,200 Speaker 2: by old cell trying to make money. You're looking at 92 00:04:58,200 --> 00:05:02,880 Speaker 2: the industry holistically. If I'm such in a della and 93 00:05:02,960 --> 00:05:08,680 Speaker 2: I'm listening in Vidia yesterday, what did Microsoft learn from 94 00:05:08,680 --> 00:05:10,920 Speaker 2: the the Nvidia conference call? 95 00:05:12,240 --> 00:05:15,760 Speaker 1: Yeah, look, I think Dan mentioned about black Vell ramping 96 00:05:15,839 --> 00:05:19,440 Speaker 1: up nicely. Contributed eleven billion dollars in the quarter, So 97 00:05:19,520 --> 00:05:23,159 Speaker 1: that means that video is really ramping up nicely with 98 00:05:23,279 --> 00:05:25,680 Speaker 1: its new architecture. In fact, they laid out the plan 99 00:05:25,800 --> 00:05:29,960 Speaker 1: for black Belt Ultra, so they're moving very fast when 100 00:05:29,960 --> 00:05:33,360 Speaker 1: it comes to the advances with the chips. So one 101 00:05:33,440 --> 00:05:36,400 Speaker 1: I would say, you know, data point that didn't do 102 00:05:36,520 --> 00:05:39,159 Speaker 1: very well was on the networking side, so they had 103 00:05:39,640 --> 00:05:43,400 Speaker 1: a second sequential decline in networking revenue. And if I 104 00:05:43,640 --> 00:05:46,040 Speaker 1: try to tie that in, you know with the size 105 00:05:46,080 --> 00:05:49,799 Speaker 1: of clusters. Again, in AI, there are these two main concepts, 106 00:05:49,839 --> 00:05:54,120 Speaker 1: training versus inferencing. Training is driven by the size of clusters, 107 00:05:54,160 --> 00:05:56,599 Speaker 1: and then if you have a bigger cluster, you have 108 00:05:56,760 --> 00:06:00,160 Speaker 1: to network it in a way where it's coher and 109 00:06:00,200 --> 00:06:03,680 Speaker 1: then you know you could use it for your training purposes. 110 00:06:03,760 --> 00:06:07,760 Speaker 1: So the fact that in Vidia's networking revenue decline sequentially, 111 00:06:07,800 --> 00:06:11,440 Speaker 1: again it's off a very strong base, it tells you 112 00:06:11,560 --> 00:06:14,000 Speaker 1: that there may be a slight pause when it comes 113 00:06:14,080 --> 00:06:18,719 Speaker 1: to building these bigger clusters and Nvidio is the most 114 00:06:18,760 --> 00:06:22,919 Speaker 1: exposed to that you know trend. So from that perspective, 115 00:06:23,000 --> 00:06:27,480 Speaker 1: I think you could ask yourself given their valuation, I mean, 116 00:06:27,520 --> 00:06:30,560 Speaker 1: clearly a lot is baked in in terms of growth 117 00:06:30,640 --> 00:06:34,080 Speaker 1: and the outer years is what everyone is worried about. 118 00:06:34,320 --> 00:06:37,680 Speaker 1: Can Nvidia sustain this momentum in you know, the outer 119 00:06:37,760 --> 00:06:41,320 Speaker 1: years twenty twenty seven, twenty eight and beyond. And I 120 00:06:41,320 --> 00:06:43,520 Speaker 1: don't think we got an answer last night in terms 121 00:06:43,600 --> 00:06:45,920 Speaker 1: of how this could drive it. And the other data 122 00:06:45,960 --> 00:06:48,600 Speaker 1: point I would call is the China sales. I mean 123 00:06:48,640 --> 00:06:51,960 Speaker 1: it's half of where it was before they export controls, 124 00:06:52,240 --> 00:06:55,400 Speaker 1: and their guide doesn't bake in the fact that if 125 00:06:55,520 --> 00:06:58,760 Speaker 1: tariffs go into effect then it would be a much 126 00:06:58,760 --> 00:07:01,800 Speaker 1: bigger hit on the roast margin, which actually fell three 127 00:07:01,839 --> 00:07:02,520 Speaker 1: hundred paces on. 128 00:07:03,480 --> 00:07:05,680 Speaker 4: So, following up on that, mat Man Deep, how did 129 00:07:05,720 --> 00:07:09,480 Speaker 4: Gensi Zwang kind of characterize the whle tariff environment, that 130 00:07:09,640 --> 00:07:13,880 Speaker 4: trade environment between China and the West. 131 00:07:15,120 --> 00:07:19,120 Speaker 1: Yeah, he didn't explain much. Neither did the CFO in 132 00:07:19,200 --> 00:07:22,680 Speaker 1: terms of what could go wrong. I mean, as of now, 133 00:07:22,760 --> 00:07:26,720 Speaker 1: what they said was China sales is half of before 134 00:07:26,760 --> 00:07:30,760 Speaker 1: the export controls went into effect under Biden. And look, 135 00:07:30,880 --> 00:07:33,520 Speaker 1: if they tightened the export controls, which is a very 136 00:07:33,680 --> 00:07:36,480 Speaker 1: likely scenario given what we have heard from the administration, 137 00:07:37,200 --> 00:07:40,400 Speaker 1: then China sales could go to zero by the end 138 00:07:40,440 --> 00:07:44,120 Speaker 1: of a year. And when you think about upside, I mean, 139 00:07:44,160 --> 00:07:46,800 Speaker 1: given Nvidia has said this trend of beat and races, 140 00:07:46,880 --> 00:07:49,280 Speaker 1: which they did last night as well, you have to 141 00:07:49,320 --> 00:07:52,080 Speaker 1: ask to yourself where will that upside come from. If 142 00:07:52,200 --> 00:07:54,600 Speaker 1: China sales, which was as big as you know, twenty 143 00:07:54,640 --> 00:07:57,480 Speaker 1: percent of the overall sales at one point, now it's 144 00:07:57,600 --> 00:08:01,640 Speaker 1: much smaller around ten percent. Will will that have an 145 00:08:01,680 --> 00:08:04,560 Speaker 1: impact in terms of you know, the BDA and a restaurant. 146 00:08:04,640 --> 00:08:07,280 Speaker 2: Joining us on your commuter crass nation. Mandeep Singh with 147 00:08:07,320 --> 00:08:11,160 Speaker 2: us with Bloomberg Intelligence on your technology holdings