1 00:00:02,529 --> 00:00:04,210 Speaker 1: Bloomberg Audio Studios. 2 00:00:04,690 --> 00:00:07,050 Speaker 2: Podcasts. Radio. News. 3 00:00:12,530 --> 00:00:16,680 Speaker 1: This is the Bloomberg Surveillance Podcast. Catch us live weekdays 4 00:00:16,739 --> 00:00:20,220 Speaker 1: at 7 a.m. Eastern on Apple CarPlay or Android Auto 5 00:00:20,300 --> 00:00:23,880 Speaker 1: with the Bloomberg Business App. Listen on demand wherever you 6 00:00:23,920 --> 00:00:26,960 Speaker 1: get your podcasts or watch us live on YouTube. 7 00:00:28,720 --> 00:00:32,740 Speaker 3: Mr. Wang is out as CEO. The chief financial officer 8 00:00:32,820 --> 00:00:39,089 Speaker 3: I mentioned earlier, Collette Kress, is named the interim CEO. Mr. 9 00:00:39,290 --> 00:00:45,449 Speaker 3: Jensen Wang resigns effective immediately. whole series here. We are 10 00:00:45,490 --> 00:00:49,150 Speaker 3: advantaged to have with us now, Mandeep Singh with Anurag 11 00:00:49,190 --> 00:00:52,530 Speaker 3: Rana coming up here in a bit. Mandeep, this is 12 00:00:52,590 --> 00:00:56,380 Speaker 3: a first step in this crisis for NVIDIA. The next 13 00:00:56,420 --> 00:01:01,680 Speaker 3: step must be to find an operating CEO, right? Yes. 14 00:01:01,920 --> 00:01:05,880 Speaker 4: And it sounds like the CFO, Colette Kress, is going 15 00:01:05,900 --> 00:01:10,479 Speaker 4: to be the interim CEO. But I wouldn't be surprised 16 00:01:10,680 --> 00:01:13,979 Speaker 4: if they take, you know, much longer in terms of 17 00:01:14,050 --> 00:01:17,890 Speaker 4: the CEO search and have Colette kind of take care 18 00:01:18,030 --> 00:01:21,630 Speaker 4: of things for maybe, you know, one or two quarters. 19 00:01:21,910 --> 00:01:27,490 Speaker 4: And It will be hard for anyone to fill Jensen's shoes, 20 00:01:27,630 --> 00:01:31,140 Speaker 4: given his role at the company and what he has done. 21 00:01:31,240 --> 00:01:34,759 Speaker 4: But there are a lot of names that we have heard, 22 00:01:35,200 --> 00:01:37,640 Speaker 4: and it will be interesting to see if they go 23 00:01:37,740 --> 00:01:40,780 Speaker 4: for an internal or an external candidate in terms of 24 00:01:40,840 --> 00:01:41,920 Speaker 4: the final CEO pick. 25 00:01:42,470 --> 00:01:44,890 Speaker 5: Mandeep, I think what a lot of our listeners and 26 00:01:44,910 --> 00:01:48,570 Speaker 5: viewers kind of understand about AI is that so many 27 00:01:48,610 --> 00:01:55,330 Speaker 5: of these deals between chip makers, hyperscalers, software, hardware, there's 28 00:01:55,350 --> 00:01:59,970 Speaker 5: so many deals that are intra-company deals, circular deals, if 29 00:02:00,020 --> 00:02:03,520 Speaker 5: you will. And many of those deals, many of those 30 00:02:03,560 --> 00:02:09,480 Speaker 5: relationships have Jensen Wang as the centerpiece here. How does 31 00:02:09,520 --> 00:02:11,459 Speaker 5: that play out going forward? I mean, it just seems 32 00:02:11,500 --> 00:02:12,620 Speaker 5: like it's going to be so difficult. 33 00:02:14,190 --> 00:02:17,549 Speaker 4: It will be difficult for anyone, even if it's an 34 00:02:17,630 --> 00:02:24,350 Speaker 4: internal candidate, to kind of manage all the relationships in 35 00:02:24,389 --> 00:02:30,130 Speaker 4: terms of the ecosystem that Jensen had created. And I 36 00:02:30,150 --> 00:02:35,250 Speaker 4: think that's where anyone stepping in new may have their 37 00:02:35,410 --> 00:02:39,239 Speaker 4: own way of going about things on the product side 38 00:02:39,340 --> 00:02:41,700 Speaker 4: as well as the financing side that you alluded to. 39 00:02:42,210 --> 00:02:46,200 Speaker 3: Tell me with the GPU, folks, Mandeep Singh, we underplay it. 40 00:02:46,210 --> 00:02:50,239 Speaker 3: We ask him dumb questions. He is incredibly good at 41 00:02:50,740 --> 00:02:54,740 Speaker 3: the electrical engineering and computer science on this. What is 42 00:02:54,780 --> 00:02:59,639 Speaker 3: the skill set? to go from GPU chip whatever to 43 00:03:00,460 --> 00:03:05,680 Speaker 3: the next GPU chip. Is that a Mandeep Singh engineering decision? 44 00:03:06,380 --> 00:03:10,860 Speaker 3: Is it a guesstimate about the depreciation of the older chip? 45 00:03:11,180 --> 00:03:11,940 Speaker 3: How does that work? 46 00:03:13,480 --> 00:03:16,360 Speaker 4: Yeah, look, I mean, what NVIDIA has done is they 47 00:03:16,540 --> 00:03:20,650 Speaker 4: have launched, you know, new versions of their chips almost 48 00:03:20,810 --> 00:03:24,490 Speaker 4: at a six-month cadence. And we are talking about, you know, 49 00:03:24,570 --> 00:03:28,630 Speaker 4: their latest Vero Rubin architecture, which is, you know, five 50 00:03:28,690 --> 00:03:32,340 Speaker 4: times better in terms of throughput than their Blackwell architecture. So, 51 00:03:32,940 --> 00:03:37,200 Speaker 4: to be able to iterate on these things and stay 52 00:03:37,260 --> 00:03:39,520 Speaker 4: ahead of everyone else, which is why they had such 53 00:03:39,560 --> 00:03:44,120 Speaker 4: a big share in the GPU market, is very commendable 54 00:03:44,200 --> 00:03:49,190 Speaker 4: in terms of execution. But that's where, if it's someone technical, 55 00:03:50,100 --> 00:03:52,490 Speaker 4: it will make a difference in terms of how they 56 00:03:52,850 --> 00:03:54,390 Speaker 4: approach the product side of things. 57 00:03:54,530 --> 00:03:57,210 Speaker 3: Have you met Harvey Jones, the new chair? I mean, 58 00:03:57,370 --> 00:03:59,930 Speaker 3: the dominant feature there is he's one of, I believe, 59 00:04:00,010 --> 00:04:04,380 Speaker 3: off the Bloomberg Bandip for original directors? I mean, do 60 00:04:04,740 --> 00:04:09,760 Speaker 3: you have any perspective of Mr. Jones' relationship with Mr. 61 00:04:09,840 --> 00:04:11,430 Speaker 3: Wong and with the rest of the team? 62 00:04:13,160 --> 00:04:16,279 Speaker 4: Yeah, I mean, look, I think when it comes to 63 00:04:16,580 --> 00:04:21,140 Speaker 4: the board and, you know, the new chair, obviously, you know, 64 00:04:21,380 --> 00:04:26,480 Speaker 4: everyone is very close to Jensen, given he's the co-founder and, 65 00:04:26,920 --> 00:04:29,140 Speaker 4: you know, has been with the company for over 30 66 00:04:29,140 --> 00:04:33,460 Speaker 4: plus years. So from that perspective, I think I don't 67 00:04:33,520 --> 00:04:36,779 Speaker 4: know Harvey in particular, but at the same time, I 68 00:04:36,800 --> 00:04:40,300 Speaker 4: think it would be somebody who Jensen knows quite well. 69 00:04:40,540 --> 00:04:45,380 Speaker 3: I look, Mandeep, at the product cycle. Ian Buck seems 70 00:04:45,440 --> 00:04:51,250 Speaker 3: to be incredibly important here. He is the Stanford technologist 71 00:04:51,470 --> 00:04:55,700 Speaker 3: behind the moment here. How important is his position now, 72 00:04:56,040 --> 00:04:58,120 Speaker 3: and what does Ian Buck need to do in the 73 00:04:58,180 --> 00:04:58,859 Speaker 3: coming days? 74 00:05:00,040 --> 00:05:03,140 Speaker 4: Yes. So he's definitely the front runner when it comes 75 00:05:03,200 --> 00:05:09,030 Speaker 4: to internal candidates. And clearly he is the guy with 76 00:05:09,050 --> 00:05:15,429 Speaker 4: the CUDA aspect of the Bloomberg product lineup, which is why, sorry, 77 00:05:15,450 --> 00:05:18,650 Speaker 4: the NVIDIA lineup, which is why NVIDIA has done so well. 78 00:05:19,290 --> 00:05:24,450 Speaker 4: And from that perspective, you know, having someone technical will 79 00:05:24,490 --> 00:05:27,470 Speaker 4: be quite beneficial at this point of time to calm 80 00:05:27,510 --> 00:05:31,380 Speaker 4: down all the anxiety that investors have. 81 00:05:31,480 --> 00:05:37,059 Speaker 3: And Mandeep underplays the ginormous technology feat of Ian Buck. 82 00:05:37,420 --> 00:05:44,310 Speaker 3: This was Brook, B-R-O-O-K at Stanford. then went over to CUDA, C-U-D-A, 83 00:05:44,810 --> 00:05:47,350 Speaker 3: which Mandeep will explain to me over a beverage of 84 00:05:47,370 --> 00:05:51,120 Speaker 3: my choice at some point. And then you bring that 85 00:05:51,310 --> 00:05:54,719 Speaker 3: over to the miracle that is NVIDIA. Mandeep, what is 86 00:05:54,740 --> 00:06:00,180 Speaker 3: your experience of beast technology people, quite frankly, like you 87 00:06:00,260 --> 00:06:05,400 Speaker 3: with great respect, running as a CEO company? Can that 88 00:06:05,580 --> 00:06:06,380 Speaker 3: actually work? 89 00:06:08,170 --> 00:06:11,349 Speaker 4: Well, what Jensen was really good at was he was 90 00:06:11,750 --> 00:06:15,260 Speaker 4: both very good on the technology side and also great 91 00:06:15,300 --> 00:06:18,760 Speaker 4: in terms of that ecosystem that he had created and 92 00:06:18,800 --> 00:06:21,839 Speaker 4: the supply chain aspect of it. And that's a hard 93 00:06:21,940 --> 00:06:26,630 Speaker 4: combination to find in terms of at the CEO level. 94 00:06:26,710 --> 00:06:30,469 Speaker 4: So clearly, Ian... has all the credentials when it comes 95 00:06:30,589 --> 00:06:35,370 Speaker 4: to the technology side of the NVIDIA products. But I 96 00:06:35,410 --> 00:06:38,490 Speaker 4: think to have those kind of relationships with TSMC and 97 00:06:38,529 --> 00:06:41,650 Speaker 4: all the suppliers, which is why NVIDIA got to this 98 00:06:41,730 --> 00:06:45,130 Speaker 4: position in terms of ramping up capacity so fast, I 99 00:06:45,170 --> 00:06:48,380 Speaker 4: think that will be a true test of somebody who 100 00:06:48,420 --> 00:06:51,619 Speaker 4: has got all the different aspects of what is needed 101 00:06:52,000 --> 00:06:52,679 Speaker 4: for this company. 102 00:06:52,880 --> 00:06:56,460 Speaker 3: Mandeep Singh, thank you so much. Greatly appreciate it this morning. 103 00:06:56,560 --> 00:06:59,920 Speaker 3: I'm sure I have a long and hellacious day. It 104 00:06:59,980 --> 00:07:02,419 Speaker 3: is widely understood at Bloomberg that they can't be on 105 00:07:02,460 --> 00:07:05,140 Speaker 3: the same airplane. They can't even be in the same room. 106 00:07:05,240 --> 00:07:08,529 Speaker 3: Anurag Rana in Chicago, as we have Mandeep Singh in 107 00:07:08,589 --> 00:07:11,450 Speaker 3: New York, and we're thrilled that Anurag could join us 108 00:07:11,490 --> 00:07:15,490 Speaker 3: here for years at KeyBank and driving all of technology 109 00:07:15,670 --> 00:07:20,530 Speaker 3: at Bloomberg. He is our definitive force on cloud. What 110 00:07:21,010 --> 00:07:26,300 Speaker 3: is the challenge here, Anurag, in the relationships with all 111 00:07:26,340 --> 00:07:30,910 Speaker 3: the people that buy these chips for data centers, etc.? 112 00:07:31,020 --> 00:07:32,700 Speaker 3: Do you have to get out and sell it, as 113 00:07:32,920 --> 00:07:37,790 Speaker 3: Mandeep was talking about, or do they even get an interregnum. 114 00:07:37,090 --> 00:07:37,310 Speaker 6: Of 30 days? 115 00:07:39,210 --> 00:07:40,790 Speaker 7: Yeah, I think the biggest thing is going to be, 116 00:07:40,850 --> 00:07:43,950 Speaker 7: you know, NVIDIA has had a very massive, you could say, 117 00:07:44,410 --> 00:07:47,690 Speaker 7: gap between the others when it comes to AI accelerators. 118 00:07:48,110 --> 00:07:50,470 Speaker 7: But this is the chance for the others to catch up, 119 00:07:50,510 --> 00:07:55,180 Speaker 7: you know, whether that's Amazon or Microsoft or Google can 120 00:07:55,250 --> 00:07:57,160 Speaker 7: close the gap and say, well, why am I paying 121 00:07:57,220 --> 00:08:00,420 Speaker 7: so much money to NVIDIA for those chips when I 122 00:08:00,460 --> 00:08:03,620 Speaker 7: can have my own? So far, they are forced to 123 00:08:03,660 --> 00:08:06,830 Speaker 7: buy those chips because That's what the customers demand. 124 00:08:06,910 --> 00:08:09,120 Speaker 2: That is the best in the business. But if this 125 00:08:09,150 --> 00:08:10,320 Speaker 2: gives them a little bit of. 126 00:08:10,280 --> 00:08:12,280 Speaker 7: A chance to catch up, I think this could be 127 00:08:12,340 --> 00:08:14,190 Speaker 7: good for the hyperscale cloud providers. 128 00:08:15,100 --> 00:08:19,140 Speaker 5: Anurag, you've covered Apple forever. As I think about the 129 00:08:19,160 --> 00:08:22,850 Speaker 5: impossible task of filling the shoes of Jensen Wong, one 130 00:08:22,890 --> 00:08:25,890 Speaker 5: of the names I think of is Tim Cook. Talk 131 00:08:25,910 --> 00:08:26,470 Speaker 5: to us about that. 132 00:08:27,150 --> 00:08:28,750 Speaker 2: Oh, you are such a gossip beast. Yeah, I thought 133 00:08:28,770 --> 00:08:29,330 Speaker 2: about that as well. 134 00:08:29,770 --> 00:08:32,809 Speaker 3: Oh, come on. It would make, it would be, who 135 00:08:32,830 --> 00:08:34,890 Speaker 3: would play Tim Cook in the movie? No, I don't know. 136 00:08:34,910 --> 00:08:38,500 Speaker 3: Richard Gere. That would be it. Anurag, continue on Tim Cook. 137 00:08:39,340 --> 00:08:41,100 Speaker 7: Yeah, I think, you know, one of the biggest thing 138 00:08:41,220 --> 00:08:44,660 Speaker 7: is they just did the transition. The CEO is very young. 139 00:08:45,740 --> 00:08:48,160 Speaker 7: And Tim said he's going to be chairman for a while. 140 00:08:48,480 --> 00:08:51,959 Speaker 7: He's going to make sure that he navigates the political 141 00:08:52,040 --> 00:08:56,939 Speaker 7: headwinds or the political currents both in China and the U.S. 142 00:08:57,100 --> 00:09:00,380 Speaker 7: I feel very– I'll be surprised if he takes off 143 00:09:00,420 --> 00:09:03,220 Speaker 7: the responsibility of headwinds. ensuring that Apple's in a great 144 00:09:03,260 --> 00:09:06,400 Speaker 7: hand before leaving Apple. But to be honest, you don't 145 00:09:06,440 --> 00:09:07,140 Speaker 7: know at this point. 146 00:09:07,780 --> 00:09:09,900 Speaker 3: Can I impress Anurag with my vast knowledge? 147 00:09:09,950 --> 00:09:10,180 Speaker 2: Okay. 148 00:09:10,590 --> 00:09:13,410 Speaker 3: Anurag, I know you're big on Microsoft and them as 149 00:09:13,470 --> 00:09:19,450 Speaker 3: a holistic Mag7 future. So they have Azure Maya, M-A-I-A, 150 00:09:19,530 --> 00:09:22,130 Speaker 3: I'm probably pronouncing it wrong. And they're going to go 151 00:09:22,190 --> 00:09:27,850 Speaker 3: from Maya 200... out to their newest Maya 300. NVIDIA 152 00:09:27,880 --> 00:09:30,690 Speaker 3: has that same analog, right? Who's going to make the 153 00:09:30,750 --> 00:09:34,320 Speaker 3: decision on the next Maya 300 at NVIDIA? 154 00:09:35,640 --> 00:09:37,340 Speaker 7: Yeah, the thing is that I think is the biggest 155 00:09:37,360 --> 00:09:40,880 Speaker 7: question because as Mandeep was saying, NVIDIA has done a 156 00:09:41,000 --> 00:09:44,229 Speaker 7: phenomenal job of going from one generation to the other 157 00:09:44,530 --> 00:09:46,689 Speaker 7: in a very short period of time. And the gap 158 00:09:46,730 --> 00:09:49,030 Speaker 7: they have with, let's say, Maya. Now, Microsoft has had 159 00:09:49,050 --> 00:09:52,070 Speaker 7: Maya for years. But to be honest, nobody's rushing out 160 00:09:52,130 --> 00:09:56,819 Speaker 7: and buying that capacity on that chip. And Microsoft has 161 00:09:56,840 --> 00:09:59,180 Speaker 7: to be forced to go out and buy NVIDIA chips. 162 00:10:00,080 --> 00:10:04,579 Speaker 7: If we see some shortening of that innovation curve, you 163 00:10:04,640 --> 00:10:08,160 Speaker 7: could see down the road that people are using training 164 00:10:08,300 --> 00:10:11,599 Speaker 7: or inference on Microsoft's buyer chips. We've already seen that 165 00:10:11,700 --> 00:10:15,130 Speaker 7: with Amazon. Amazon chips are in demand by customers, and 166 00:10:15,200 --> 00:10:18,240 Speaker 7: Google's obviously far ahead in that game, and they are 167 00:10:18,280 --> 00:10:20,560 Speaker 7: trying to come up with their own systems by the 168 00:10:20,600 --> 00:10:21,300 Speaker 7: end of next year. 169 00:10:21,540 --> 00:10:23,199 Speaker 2: So they are already in the market. So I think 170 00:10:23,440 --> 00:10:25,940 Speaker 2: the competition is going to get very tough over the 171 00:10:25,980 --> 00:10:26,620 Speaker 2: next few years. 172 00:10:27,140 --> 00:10:29,800 Speaker 3: You say who's ahead. Who's behind? 173 00:10:31,559 --> 00:10:33,460 Speaker 7: I think if we were to do the math right now, 174 00:10:33,640 --> 00:10:37,960 Speaker 7: NVIDIA being number one in terms of technology, then Google, 175 00:10:38,040 --> 00:10:41,520 Speaker 7: then Amazon, and then Microsoft. And my math is only 176 00:10:41,620 --> 00:10:44,430 Speaker 7: driven by what the customer demand is. If the customer 177 00:10:44,490 --> 00:10:48,610 Speaker 7: is not demanding big Maya workloads all for their inference 178 00:10:48,730 --> 00:10:51,930 Speaker 7: and training, then I think they are behind. But Microsoft 179 00:10:51,970 --> 00:10:55,960 Speaker 7: has started to change their own internal first-party apps with 180 00:10:56,000 --> 00:10:59,580 Speaker 7: Maya chips, which means if you are running Copilot, or 181 00:10:59,600 --> 00:11:03,530 Speaker 7: for that matter, any other app that runs AI workloads, 182 00:11:03,870 --> 00:11:06,870 Speaker 7: they are slowly moving it in the background before the 183 00:11:06,910 --> 00:11:08,349 Speaker 7: customers get comfortable with it. 184 00:11:08,950 --> 00:11:12,250 Speaker 3: Anurag Rana with us, folks. Across America and worldwide, the 185 00:11:12,360 --> 00:11:16,640 Speaker 3: shock of a resignation at NVIDIA by Jensen Wang. An 186 00:11:16,780 --> 00:11:20,949 Speaker 3: interim CEO announced the chief financial officer and a new 187 00:11:21,050 --> 00:11:25,010 Speaker 3: chair of the board, Harvey C. Jones, who's been on 188 00:11:25,030 --> 00:11:28,449 Speaker 3: the board since, I believe, 1993 as well. We are 189 00:11:28,510 --> 00:11:32,640 Speaker 3: advantaged with the expertise of Mandeep Singh and now Anurag Rana. 190 00:11:32,740 --> 00:11:35,339 Speaker 3: And I just want to point out, Paul, that when 191 00:11:36,059 --> 00:11:39,600 Speaker 3: Anurag says he's doing the math, it's not our math. 192 00:11:39,760 --> 00:11:43,090 Speaker 3: I walked by his whiteboard in Chicago one day. Thank you. 193 00:11:43,370 --> 00:11:47,630 Speaker 3: And it was matrix algebra in the eigenspace. Exactly. 194 00:11:47,650 --> 00:11:50,209 Speaker 5: I mean, it was painful. Stock, I'm just looking at 195 00:11:50,230 --> 00:11:52,869 Speaker 5: NVIDIA stock here. It was down about 7% when the 196 00:11:52,929 --> 00:11:55,829 Speaker 5: news broke. It's traded down even more. We're down a 197 00:11:55,850 --> 00:12:00,890 Speaker 5: little more than 10% now in the pre-market trading for NVIDIA. Anurag, 198 00:12:00,970 --> 00:12:03,230 Speaker 5: when I think about key man risk out there, just 199 00:12:03,290 --> 00:12:06,230 Speaker 5: in general, I think Elon Musk, certainly with all of 200 00:12:06,530 --> 00:12:11,880 Speaker 5: his companies, but right there. is Jensen Wong here. He 201 00:12:11,900 --> 00:12:14,599 Speaker 5: is central. It just seems like, I'm not sure if 202 00:12:14,620 --> 00:12:16,380 Speaker 5: it's really the case, but it seems like he's central 203 00:12:16,420 --> 00:12:20,820 Speaker 5: to every single deal that happens in the AI space. 204 00:12:21,700 --> 00:12:25,000 Speaker 5: How does that stop on a dime? I can't, I mean, 205 00:12:25,240 --> 00:12:27,760 Speaker 5: how does that play out? 206 00:12:27,780 --> 00:12:29,800 Speaker 7: Paul, I feel the exact same thing, to be honest, 207 00:12:29,840 --> 00:12:33,520 Speaker 7: because look at the entire neocloud space. They're all there 208 00:12:33,559 --> 00:12:36,150 Speaker 7: because they know at the back of it, Jensen's there 209 00:12:36,230 --> 00:12:39,209 Speaker 7: to do a backstop if something bad happens. Now, to 210 00:12:39,230 --> 00:12:41,510 Speaker 7: their advantage, the last two things that have happened, as 211 00:12:41,530 --> 00:12:44,540 Speaker 7: Nebia just reported right now, and we had Corviva a 212 00:12:44,550 --> 00:12:47,500 Speaker 7: couple of days ago, they've started to ask for prepayments. 213 00:12:47,780 --> 00:12:50,340 Speaker 7: So that's a good thing for them, frankly. Oracle did 214 00:12:50,380 --> 00:12:53,440 Speaker 7: the same thing. But you are absolutely right. The entire 215 00:12:53,559 --> 00:12:57,020 Speaker 7: AI trade, as much as it's related to the US-related assets, 216 00:12:57,420 --> 00:13:00,970 Speaker 7: it's all backstopped by Jensen. So we're going to see 217 00:13:00,990 --> 00:13:03,790 Speaker 7: a big, big vacuum in this thing. And to be honest, 218 00:13:04,070 --> 00:13:07,010 Speaker 7: maybe things slows down. And sometimes it's not a bad 219 00:13:07,030 --> 00:13:09,710 Speaker 7: thing because that would give some time to digest what's happening. 220 00:13:09,990 --> 00:13:11,530 Speaker 7: Free cash flow gets positive again. 221 00:13:11,960 --> 00:13:15,120 Speaker 2: Maybe CapEx gets pushed out. But maybe that's not a 222 00:13:15,120 --> 00:13:15,500 Speaker 2: bad thing. 223 00:13:16,320 --> 00:13:19,040 Speaker 3: And one final question here as we stagger through the 224 00:13:19,080 --> 00:13:22,800 Speaker 3: morning here. And we haven't talked about this. Paul alluded 225 00:13:22,860 --> 00:13:29,990 Speaker 3: to it once. All of the interdependencies. cross-ownerships of transactions 226 00:13:30,530 --> 00:13:35,150 Speaker 3: and combinations involved here, they do center around Jensen Wang 227 00:13:35,170 --> 00:13:40,310 Speaker 3: because everybody needs NVIDIA. What happens to that matrix this morning? 228 00:13:41,450 --> 00:13:45,500 Speaker 7: Yeah, you're absolutely right. It's that ecosystem that's getting shaken up. 229 00:13:45,940 --> 00:13:49,940 Speaker 7: But guess what ecosystem is not getting shaken up? The 230 00:13:50,000 --> 00:13:53,239 Speaker 7: one that's around Google, one that's around Amazon, and then 231 00:13:53,300 --> 00:13:56,750 Speaker 7: what's around Microsoft. These three companies are forced to buy 232 00:13:56,800 --> 00:13:59,470 Speaker 7: NVIDIA chips. But if they can prove to the market 233 00:13:59,510 --> 00:14:02,189 Speaker 7: that they can run some workloads on their own chips, 234 00:14:02,610 --> 00:14:04,770 Speaker 7: I think that changes the equation altogether. 235 00:14:04,790 --> 00:14:06,569 Speaker 3: Can they do that? I've got to get out of 236 00:14:06,590 --> 00:14:08,440 Speaker 3: that box. I'm saying I've got to get out ahead 237 00:14:08,480 --> 00:14:11,520 Speaker 3: of this. Anurag Rana, do they have the ability right 238 00:14:11,559 --> 00:14:15,900 Speaker 3: now to say, look at this, you need stability, buy Amazon, 239 00:14:16,059 --> 00:14:17,490 Speaker 3: buy Microsoft Silicon? 240 00:14:18,690 --> 00:14:21,350 Speaker 7: I think Google's already there, to be honest. Google TPU 241 00:14:21,490 --> 00:14:24,710 Speaker 7: is doing very well. And Amazon's done a very good 242 00:14:24,730 --> 00:14:28,040 Speaker 7: job with their Tranium chips. So it's the third person 243 00:14:28,100 --> 00:14:31,820 Speaker 7: is Microsoft. And from what we have heard, the Maya 300, 244 00:14:31,820 --> 00:14:34,320 Speaker 7: when it comes out, it should be at par with 245 00:14:34,380 --> 00:14:36,120 Speaker 7: at least doing the base. 246 00:14:35,900 --> 00:14:37,560 Speaker 2: Level stuff that what people are looking for. 247 00:14:37,880 --> 00:14:40,260 Speaker 7: So it's time for them to gain market share if 248 00:14:40,340 --> 00:14:44,750 Speaker 7: we do see some kind of delays in the tech 249 00:14:44,810 --> 00:14:45,950 Speaker 7: curve of NVIDIA chips. 250 00:14:46,510 --> 00:14:48,470 Speaker 3: I mean, we'll just have to. This is just too much. 251 00:14:48,490 --> 00:14:51,530 Speaker 3: Anurag Rana, thank you so much. You've got to go. 252 00:14:51,570 --> 00:14:53,930 Speaker 3: He's going to Travell at the Langham. Oh, is that right? 253 00:14:53,960 --> 00:14:55,740 Speaker 3: That's the power breakfast in Chicago. 254 00:14:55,800 --> 00:14:56,400 Speaker 5: Very good. 255 00:14:56,440 --> 00:14:59,300 Speaker 3: He's got his own table and the whole thing. Anurag Rana, 256 00:14:59,380 --> 00:15:03,740 Speaker 3: thank you. Thank you so much here. Stay with us. 257 00:15:04,060 --> 00:15:07,280 Speaker 3: More from Bloomberg Surveillance coming up after this. 258 00:15:14,550 --> 00:15:18,150 Speaker 1: You're listening to the Bloomberg Surveillance Podcast. Catch us live 259 00:15:18,230 --> 00:15:20,310 Speaker 1: weekday afternoons from 7 to 10 a.m. 260 00:15:20,350 --> 00:15:20,750 Speaker 8: Eastern. 261 00:15:20,850 --> 00:15:24,160 Speaker 1: Listen on Apple CarPlay and Android Auto with the Bloomberg 262 00:15:24,180 --> 00:15:26,700 Speaker 1: Business app. Or watch us live on YouTube. 263 00:15:27,400 --> 00:15:31,800 Speaker 3: Joining us from London now, sort of bringing in the financial, 264 00:15:32,400 --> 00:15:35,500 Speaker 3: along with the imagery that he covers with WPP and 265 00:15:35,620 --> 00:15:40,830 Speaker 3: Omnicron in that. Matt, finesse for me the image disaster. 266 00:15:41,500 --> 00:15:45,080 Speaker 3: total train wreck of nvidia how do they pick up 267 00:15:45,140 --> 00:15:48,860 Speaker 3: the pieces of an image without jensen wong. 268 00:15:50,060 --> 00:15:52,340 Speaker 6: Yeah it's a it's a tool order but you know 269 00:15:52,560 --> 00:15:55,580 Speaker 6: i guess the most important place is where you start 270 00:15:55,780 --> 00:15:58,280 Speaker 6: and you know they've started by you know cutting ties 271 00:15:58,300 --> 00:16:01,540 Speaker 6: with him so um that that's kind of a really 272 00:16:01,590 --> 00:16:04,030 Speaker 6: important move for them but you know there's still i 273 00:16:04,070 --> 00:16:06,410 Speaker 6: think probably a lot of chapters of this story to 274 00:16:06,450 --> 00:16:09,150 Speaker 6: play out you know we've We've not really talked so much, 275 00:16:09,230 --> 00:16:12,770 Speaker 6: I don't think, yet about what the U.S. investigations could be. 276 00:16:12,830 --> 00:16:15,710 Speaker 6: And there's a number of predecessors that. 277 00:16:15,590 --> 00:16:16,160 Speaker 2: I can think of. 278 00:16:16,190 --> 00:16:18,910 Speaker 6: And the one at the forefront of my mind is 279 00:16:19,090 --> 00:16:23,150 Speaker 6: Ericsson that ended up with a deferred prosecution agreement and 280 00:16:23,350 --> 00:16:28,580 Speaker 6: big fines because of breaking U.S. international kind of trade 281 00:16:28,620 --> 00:16:32,680 Speaker 6: laws a few years back. So things like that could 282 00:16:32,700 --> 00:16:35,900 Speaker 6: still be coming up. down the line for Nvidia. So 283 00:16:35,980 --> 00:16:38,780 Speaker 6: it's going to be a very, very busy, busy time 284 00:16:38,800 --> 00:16:42,830 Speaker 6: for them on a number of fronts. Here, reassuring customers, 285 00:16:43,750 --> 00:16:47,510 Speaker 6: talking to the US government, trying to hold back this 286 00:16:47,590 --> 00:16:50,150 Speaker 6: wave of competitors that you were just talking to Anurag about. 287 00:16:50,710 --> 00:16:53,850 Speaker 6: One name that wasn't mentioned there was SpaceX and Tesla 288 00:16:53,890 --> 00:16:58,120 Speaker 6: with their TerraFab, who are also trying to create capacity. So, 289 00:16:58,150 --> 00:17:02,060 Speaker 6: you know, there's a lot. It's a big, big challenge. 290 00:17:02,710 --> 00:17:04,570 Speaker 5: You know, I'm probably like a lot of people, Tom, 291 00:17:04,670 --> 00:17:07,169 Speaker 5: listeners and viewers, we don't know who this Colette Kress is. 292 00:17:07,230 --> 00:17:08,689 Speaker 5: So what do you do on the Bloomer Terminal? You 293 00:17:08,710 --> 00:17:12,209 Speaker 5: go bio Colette Kress. And I see that she's got 294 00:17:12,230 --> 00:17:14,230 Speaker 5: a lot of experience in the tech space. But there 295 00:17:14,600 --> 00:17:17,440 Speaker 5: are 1,600 other people who use the bio function to 296 00:17:17,480 --> 00:17:20,720 Speaker 5: figure out who Colette Kress is. Matt, talk to us 297 00:17:20,740 --> 00:17:25,740 Speaker 5: about visibility for the business of NVIDIA here as it 298 00:17:26,020 --> 00:17:29,320 Speaker 5: deals with this uncertainty here. How much visibility do they 299 00:17:29,340 --> 00:17:31,379 Speaker 5: have on their revenue stream for the coming quarters? 300 00:17:33,170 --> 00:17:37,880 Speaker 6: I think a lot have been probably visibility through 2027, 301 00:17:37,880 --> 00:17:43,240 Speaker 6: possibly even into 2028, because the industry has been thinking 302 00:17:43,540 --> 00:17:46,020 Speaker 6: two or three, maybe four or five steps ahead for 303 00:17:46,080 --> 00:17:49,860 Speaker 6: quite some time now. When you hear the likes of 304 00:17:49,960 --> 00:17:54,140 Speaker 6: TSMC and Micron and the kind of Semi's names all 305 00:17:54,180 --> 00:17:57,040 Speaker 6: talk about being sold out for at least the next 306 00:17:57,040 --> 00:18:00,320 Speaker 6: 12 months, that's because people have got orders in the 307 00:18:00,359 --> 00:18:05,650 Speaker 6: pipes with NVIDIA. So I think for now, you know, 308 00:18:05,690 --> 00:18:09,660 Speaker 6: the business kind of hums along. It's probably more, you know, 309 00:18:09,800 --> 00:18:12,740 Speaker 6: those kind of two to three year kind of orders. And, 310 00:18:12,820 --> 00:18:14,720 Speaker 6: you know, as you also mentioned, you know, these kind 311 00:18:14,740 --> 00:18:18,360 Speaker 6: of investments that they've made across companies, were they backed 312 00:18:18,420 --> 00:18:21,020 Speaker 6: by just Jensen or was there consensus within the organization 313 00:18:21,080 --> 00:18:24,060 Speaker 6: about funding those startups? 314 00:18:24,580 --> 00:18:26,699 Speaker 3: A cross section of people today that we speak to 315 00:18:26,940 --> 00:18:31,380 Speaker 3: within and outside Bloomberg are on this historic moment for technology. 316 00:18:31,400 --> 00:18:33,150 Speaker 3: And I really mean it. I think of Chip Ward, 317 00:18:33,490 --> 00:18:35,410 Speaker 3: my book of the summer. I think it was three 318 00:18:35,850 --> 00:18:39,409 Speaker 3: years ago. This is truly an historic day for technology. 319 00:18:39,490 --> 00:18:42,440 Speaker 3: Everybody has a different skill set. Matt Block, some folks, 320 00:18:42,980 --> 00:18:49,170 Speaker 3: brilliantly pulls together razor-sharp financials He's a CFA with sort 321 00:18:49,210 --> 00:18:53,879 Speaker 3: of the imagery, the structure, the grind of it. Matt Blockson, 322 00:18:53,940 --> 00:19:00,939 Speaker 3: I'm counting 82 sell-side analysts on NVIDIA. They're sitting in 323 00:19:00,980 --> 00:19:05,190 Speaker 3: an office right now with their head of research, and importantly, 324 00:19:05,230 --> 00:19:08,930 Speaker 3: their head of compliance about, okay, what do we do? 325 00:19:09,390 --> 00:19:13,649 Speaker 3: How does that process work? Yeah. 326 00:19:14,580 --> 00:19:18,960 Speaker 6: Very differently, I think, at different banks, depending on their 327 00:19:18,980 --> 00:19:22,020 Speaker 6: jurisdiction and their size. Some analysts just end up with 328 00:19:22,060 --> 00:19:24,940 Speaker 6: a huge amount of freedom to largely write what they 329 00:19:24,980 --> 00:19:29,270 Speaker 6: want and take personal responsibility for what's out there. 330 00:19:29,290 --> 00:19:31,510 Speaker 2: You look at some of the big bulge brackets. It's 331 00:19:31,570 --> 00:19:32,550 Speaker 2: a tricky process. 332 00:19:32,810 --> 00:19:35,590 Speaker 6: And you may well find that people are sitting there 333 00:19:35,690 --> 00:19:38,630 Speaker 6: frustrated right now that the research they want to put 334 00:19:38,750 --> 00:19:41,980 Speaker 6: out is not going out or it's going out without 335 00:19:42,020 --> 00:19:44,500 Speaker 6: the real kind of cutting edge. angle that they want 336 00:19:44,520 --> 00:19:48,859 Speaker 6: to give. So, yeah, it can be frustrating back and 337 00:19:48,890 --> 00:19:50,429 Speaker 6: forward for sure when you're in that scene. 338 00:19:50,900 --> 00:19:54,100 Speaker 3: One, two, three, four, five, six, seven, eight, nine, ten. 339 00:19:54,760 --> 00:19:58,179 Speaker 3: It's too many numbers to count at 9, 12 a.m. 340 00:19:58,260 --> 00:20:01,820 Speaker 3: I'm going to say 14 changes of opinion today. 341 00:20:02,060 --> 00:20:02,240 Speaker 2: Yeah. 342 00:20:02,640 --> 00:20:05,340 Speaker 3: Or reaffirmations of opinion just in the Bloomberg. 343 00:20:05,840 --> 00:20:08,399 Speaker 5: Matthew, give our listeners and viewers kind of a sense 344 00:20:08,440 --> 00:20:13,640 Speaker 5: of Just Jensen Huang and how does he get deals done? 345 00:20:13,700 --> 00:20:18,409 Speaker 5: How much is Jensen versus NVIDIA? I mean, because some 346 00:20:18,430 --> 00:20:20,689 Speaker 5: people are trying to just get a sense of how 347 00:20:20,730 --> 00:20:22,109 Speaker 5: important this guy is to this company. 348 00:20:22,990 --> 00:20:23,230 Speaker 3: Yeah. 349 00:20:23,609 --> 00:20:26,050 Speaker 6: I mean, I think you have to say that NVIDIA 350 00:20:26,369 --> 00:20:29,420 Speaker 6: really is Jensen Huang and that may feel like a 351 00:20:29,430 --> 00:20:33,119 Speaker 6: bit of disservice to all the people in that company that. 352 00:20:32,760 --> 00:20:33,400 Speaker 2: Make it work. 353 00:20:33,500 --> 00:20:36,940 Speaker 6: But clearly he's co-founder and he's kind of recreated that 354 00:20:37,000 --> 00:20:42,150 Speaker 6: company into the AI powerhouse that it is. So I think, 355 00:20:42,230 --> 00:20:44,330 Speaker 6: you know, everything they've done in the last two or 356 00:20:44,369 --> 00:20:46,910 Speaker 6: three years, really, I think has been inspired by him 357 00:20:46,990 --> 00:20:52,010 Speaker 6: and his own personal energy and insight and creativity and, 358 00:20:52,330 --> 00:20:54,629 Speaker 6: you know, expertise in the industry and his ability to 359 00:20:54,670 --> 00:20:58,170 Speaker 6: kind of create personal relationships with the most important people 360 00:20:58,190 --> 00:21:01,030 Speaker 6: in the world, both in, you know, politics and in 361 00:21:01,070 --> 00:21:05,450 Speaker 6: business and really drive those deals through. Before we let 362 00:21:05,470 --> 00:21:08,859 Speaker 6: you go, I think it's going to be super interesting 363 00:21:08,900 --> 00:21:10,139 Speaker 6: to see what he does next. 364 00:21:11,100 --> 00:21:14,859 Speaker 3: Matt, before we let you go within the blur that 365 00:21:14,880 --> 00:21:19,000 Speaker 3: we're dealing with right now, futures negative 53, the arch 366 00:21:19,240 --> 00:21:22,480 Speaker 3: issue here, and I see it like in the Financial Times, 367 00:21:23,030 --> 00:21:27,550 Speaker 3: the Telegraph, all the other British papers, is this technology 368 00:21:27,650 --> 00:21:32,550 Speaker 3: insecurity within the United Kingdom, which I don't get because 369 00:21:32,570 --> 00:21:35,690 Speaker 3: I know the Cambridge math and I really, really know 370 00:21:35,730 --> 00:21:39,800 Speaker 3: the imperial technology. Math, you know, our folks, our definitive 371 00:21:39,880 --> 00:21:46,460 Speaker 3: quantitative finance program, arguably worldwide. Matt Bloxham, how does the 372 00:21:47,260 --> 00:21:50,760 Speaker 3: United Kingdom get its Technology Act together? 373 00:21:53,440 --> 00:21:55,619 Speaker 2: Yeah, well, like a really tricky question. 374 00:21:56,700 --> 00:21:58,920 Speaker 6: I think that the UK needs to kind of have 375 00:21:58,960 --> 00:22:02,119 Speaker 6: a clear view of the role it wants to play 376 00:22:02,180 --> 00:22:03,560 Speaker 6: in the global technology industry. 377 00:22:04,070 --> 00:22:08,350 Speaker 2: Market. And once it's made that decision, really back it. 378 00:22:08,490 --> 00:22:15,010 Speaker 6: With a combination of funding, tax incentives and policy that 379 00:22:15,130 --> 00:22:18,409 Speaker 6: really kind of allows companies to kind of build on 380 00:22:18,450 --> 00:22:23,450 Speaker 6: that platform and to encourage inward investment into the UK. 381 00:22:23,970 --> 00:22:25,649 Speaker 6: And then you start to get into also all the 382 00:22:25,670 --> 00:22:29,020 Speaker 6: geopolitical issues around, you know, how has Brexit impacted that? 383 00:22:29,880 --> 00:22:32,359 Speaker 6: But the clarity needs to start with What is the 384 00:22:32,420 --> 00:22:35,320 Speaker 6: role that this country wants to play in the global 385 00:22:35,359 --> 00:22:36,360 Speaker 6: technology industry? 386 00:22:36,720 --> 00:22:39,110 Speaker 2: And everything should flow from there. 387 00:22:39,170 --> 00:22:41,970 Speaker 3: Thank you so much, Matt Blocks. I'm greatly, greatly appreciate it. 388 00:22:43,690 --> 00:22:47,840 Speaker 3: Stay with us. More from Bloomberg Surveillance coming up after this. 389 00:22:55,109 --> 00:22:58,690 Speaker 1: You're listening to the Bloomberg Surveillance Podcast. Catch us live 390 00:22:58,770 --> 00:23:01,949 Speaker 1: weekday afternoons from 7 to 10 a.m. Eastern. Listen on 391 00:23:02,050 --> 00:23:05,310 Speaker 1: Apple CarPlay and Android Auto with the Bloomberg Business app 392 00:23:05,530 --> 00:23:07,250 Speaker 1: or watch us live on YouTube. 393 00:23:07,790 --> 00:23:11,070 Speaker 3: He is the dean of technology. The only one I 394 00:23:11,109 --> 00:23:14,100 Speaker 3: can think of is Rick Sherlin would be close. Ted 395 00:23:14,140 --> 00:23:15,420 Speaker 3: Mortensen joins us now. 396 00:23:16,740 --> 00:23:16,960 Speaker 4: Mr. 397 00:23:17,000 --> 00:23:20,080 Speaker 3: Mortensen, thank you so much for joining us this morning. 398 00:23:20,560 --> 00:23:24,459 Speaker 3: What did Jensen Wang do with silicon that no one 399 00:23:24,540 --> 00:23:25,840 Speaker 3: else did before him? 400 00:23:28,220 --> 00:23:31,620 Speaker 9: He's been three years ahead of anybody in the industry, 401 00:23:31,720 --> 00:23:35,040 Speaker 9: both from a system standpoint, and that would be not 402 00:23:35,100 --> 00:23:39,740 Speaker 9: only silicon, but networking and the access to memory. What 403 00:23:39,790 --> 00:23:44,050 Speaker 9: he's put out with Vera Rubin is probably the biggest 404 00:23:44,230 --> 00:23:47,130 Speaker 9: advance in compute technology in history. 405 00:23:47,770 --> 00:23:48,830 Speaker 3: So not only is. 406 00:23:48,810 --> 00:23:49,909 Speaker 2: He the. 407 00:23:51,590 --> 00:23:59,990 Speaker 9: Architect of NVIDIA, But he's also really the superintelligence lead 408 00:24:00,310 --> 00:24:05,750 Speaker 9: and the capital allocator and the political face of AI 409 00:24:05,950 --> 00:24:06,869 Speaker 9: into Washington. 410 00:24:06,950 --> 00:24:10,330 Speaker 3: So what he has done in. 411 00:24:10,290 --> 00:24:13,909 Speaker 9: The last decade will go down in history as one 412 00:24:13,930 --> 00:24:17,280 Speaker 9: of the greatest CEOs X of this news. 413 00:24:20,180 --> 00:24:24,520 Speaker 5: Ted, what does the company do now, given that he 414 00:24:24,600 --> 00:24:27,600 Speaker 5: has stepped down here? I can't even imagine trying to 415 00:24:27,619 --> 00:24:29,909 Speaker 5: fill those shoes. How does this proceed here. 416 00:24:29,920 --> 00:24:30,240 Speaker 3: Do you think? 417 00:24:31,590 --> 00:24:35,390 Speaker 9: They have to appoint somebody at the CEO level that 418 00:24:35,510 --> 00:24:43,030 Speaker 9: is recognized by the buy side as a luminary, an 419 00:24:43,290 --> 00:24:51,520 Speaker 9: expert in superintelligence, and who has a pedigree Similar like 420 00:24:51,859 --> 00:24:56,440 Speaker 9: some of the DeepMind guys. This is a very coveted job. 421 00:24:57,700 --> 00:25:00,730 Speaker 9: I bet there's a bunch of people being contacted right now. 422 00:25:01,410 --> 00:25:04,550 Speaker 9: But they've got to put an A + + player 423 00:25:04,590 --> 00:25:05,050 Speaker 9: in the seat. 424 00:25:05,710 --> 00:25:08,469 Speaker 3: Ted, I look at this and it just screams to 425 00:25:08,510 --> 00:25:08,770 Speaker 3: me of. 426 00:25:11,119 --> 00:25:16,220 Speaker 3: You know, different candidates. Is Marissa Myers with her operational 427 00:25:16,280 --> 00:25:20,919 Speaker 3: research at Stanford? Is she more CEO McKinsey like to 428 00:25:21,000 --> 00:25:25,050 Speaker 3: take that position wrapped around Ian Buck doing the engineering? 429 00:25:26,420 --> 00:25:28,180 Speaker 9: I tell you, if I had one guy that I'd 430 00:25:28,220 --> 00:25:32,530 Speaker 9: put in the seat of the Demis Hassabis from DeepMind, 431 00:25:32,810 --> 00:25:37,369 Speaker 9: I mean, that's the kind of individual. There has to 432 00:25:37,410 --> 00:25:42,700 Speaker 9: be a technology mind involved in the CEO position, not 433 00:25:42,740 --> 00:25:46,060 Speaker 9: a person that is consultant, not a person from an 434 00:25:46,140 --> 00:25:50,720 Speaker 9: adjacent industry. This has got to be a technology appointment. 435 00:25:51,800 --> 00:25:53,780 Speaker 5: What does Jensen Wong do now? I mean, he is 436 00:25:53,940 --> 00:25:57,379 Speaker 5: central to this whole thing. It's not just his seat. 437 00:25:57,420 --> 00:25:59,720 Speaker 5: It was him. Uh, I'm not sure what he does now. 438 00:26:01,200 --> 00:26:04,330 Speaker 9: Well, I, you know, I think he's going to be 439 00:26:04,390 --> 00:26:07,310 Speaker 9: mired in investigation for months upon months. 440 00:26:07,609 --> 00:26:10,270 Speaker 3: And I, my heart goes out to him because we're 441 00:26:10,310 --> 00:26:10,850 Speaker 3: all human. 442 00:26:10,910 --> 00:26:11,170 Speaker 4: Right. 443 00:26:11,970 --> 00:26:17,129 Speaker 9: And, um, everybody makes a mistake here and there. And, um, I, 444 00:26:17,310 --> 00:26:20,640 Speaker 9: I hope that, uh, he just didn't walk into a buzzsaw. 445 00:26:20,760 --> 00:26:25,629 Speaker 9: I mean, um, You know, if he got into a 446 00:26:25,650 --> 00:26:30,010 Speaker 9: bad situation and it's blowing up like an atomic bomb, 447 00:26:30,430 --> 00:26:33,750 Speaker 9: there's nothing he can do. What I'm most worried is 448 00:26:34,070 --> 00:26:38,470 Speaker 9: anything that has espionage around it. We're now in a 449 00:26:38,530 --> 00:26:39,629 Speaker 9: different zip code. 450 00:26:39,670 --> 00:26:41,149 Speaker 3: We're in a criminal zip code. 451 00:26:41,859 --> 00:26:45,280 Speaker 9: And nobody can avoid that, especially with the change in 452 00:26:45,440 --> 00:26:49,260 Speaker 9: the political landscape that we saw post the midterms. 453 00:26:49,280 --> 00:26:51,700 Speaker 3: Well, we've got a time constraint with you. We're so 454 00:26:51,740 --> 00:26:53,820 Speaker 3: happy that you could join us this morning. But let 455 00:26:53,840 --> 00:26:56,080 Speaker 3: me get this finally in. And I want to go 456 00:26:56,140 --> 00:26:59,510 Speaker 3: back to Sir Dennis, of course. I mentioned earlier, Ted, 457 00:26:59,680 --> 00:27:04,030 Speaker 3: the academics of Imperial College and the University of Cambridge. 458 00:27:04,390 --> 00:27:10,070 Speaker 3: Dennis is arguably the greatest British export of technology. I 459 00:27:10,109 --> 00:27:15,270 Speaker 3: would suggest as a founder of Google Gemini, he's more software-based. 460 00:27:15,690 --> 00:27:21,050 Speaker 3: How does he handle the silicon and the cards of NVIDIA? 461 00:27:21,340 --> 00:27:24,100 Speaker 3: Does he bolt that onto his abilities or does he 462 00:27:24,140 --> 00:27:25,659 Speaker 3: have to learn that in real time? 463 00:27:26,980 --> 00:27:29,780 Speaker 9: The people at NVIDIA have a hardware pedigree, so that's 464 00:27:29,859 --> 00:27:32,600 Speaker 9: not a problem. It's just somebody that has to tie 465 00:27:32,680 --> 00:27:35,240 Speaker 9: both the hardware and the software stack together. 466 00:27:35,990 --> 00:27:38,250 Speaker 3: And I think talking to clients, they are looking. 467 00:27:38,310 --> 00:27:42,830 Speaker 9: I mean, NVIDIA has a 6.6% weighting in the S & 468 00:27:42,210 --> 00:27:46,430 Speaker 9: P and NASDAQ. I can tell you after conversations last night, 469 00:27:47,920 --> 00:27:51,240 Speaker 9: all the portfolio managers need to see that A-plus player, 470 00:27:51,560 --> 00:27:53,060 Speaker 9: and it's got to be relatively quick. 471 00:27:54,480 --> 00:27:58,180 Speaker 5: Competitive threats here is... How vulnerable is NVIDIA right now? 472 00:27:59,690 --> 00:28:03,030 Speaker 9: I think they're vulnerable on price and gross margin because 473 00:28:03,130 --> 00:28:04,330 Speaker 9: to maintain. 474 00:28:06,030 --> 00:28:08,830 Speaker 9: I think they're going to see price concessions. They're going 475 00:28:08,850 --> 00:28:14,129 Speaker 9: to have to give price concessions to some customers. I think, obviously, 476 00:28:14,260 --> 00:28:18,020 Speaker 9: AMD ad nauseum has been talked about the last two days. 477 00:28:18,410 --> 00:28:23,660 Speaker 9: They're in the catbird seat. Broadcom should get some business. 478 00:28:24,840 --> 00:28:28,420 Speaker 9: I would say some of my clients are really looking 479 00:28:28,540 --> 00:28:35,960 Speaker 9: at Google on the TPU side, AWS on Tranium's side. Marvell, Celestica, 480 00:28:36,700 --> 00:28:40,560 Speaker 9: you know, a bunch of names. You're going to see 481 00:28:40,660 --> 00:28:47,060 Speaker 9: portfolio managers reduce their way overweight positions on the index side, 482 00:28:47,360 --> 00:28:48,620 Speaker 9: and they're going to spread out risk. 483 00:28:49,420 --> 00:28:51,740 Speaker 3: This has been wonderful, Ted. Wonderful to see you in 484 00:28:51,780 --> 00:28:55,310 Speaker 3: the blur of your Baird day. Ted Mortensen, definitive in 485 00:28:55,370 --> 00:29:01,710 Speaker 3: American technology. Stay with us. More from Bloomberg Surveillance coming 486 00:29:01,830 --> 00:29:02,790 Speaker 3: up after this. 487 00:29:10,070 --> 00:29:13,650 Speaker 1: You're listening to the Bloomberg Surveillance Podcast. Catch us live 488 00:29:13,730 --> 00:29:16,910 Speaker 1: weekday afternoons from 7 to 10 a.m. Eastern. Listen on 489 00:29:17,010 --> 00:29:20,280 Speaker 1: Apple CarPlay and Android Auto with the Bloomberg Business app 490 00:29:20,500 --> 00:29:22,200 Speaker 1: or watch us live on YouTube. 491 00:29:22,800 --> 00:29:25,940 Speaker 3: An economic story with Anna Wong is always wonderful with 492 00:29:25,980 --> 00:29:29,840 Speaker 3: Bloomberg U.S. Economics. Eight ways to go here, but we 493 00:29:29,900 --> 00:29:34,040 Speaker 3: must look at our technology. It's part of our nominal 494 00:29:34,120 --> 00:29:38,230 Speaker 3: GDP boom, this moment that the haves and have-nots of 495 00:29:38,310 --> 00:29:39,270 Speaker 3: America are living. 496 00:29:39,870 --> 00:29:40,209 Speaker 2: Dr. 497 00:29:40,250 --> 00:29:44,510 Speaker 3: Wong, the economic impact of this crisis at NVIDIA. 498 00:29:45,560 --> 00:29:45,740 Speaker 2: Yes. 499 00:29:46,200 --> 00:29:49,340 Speaker 8: One way to think about this is to ask ourselves, 500 00:29:49,590 --> 00:29:54,290 Speaker 8: what if this NVIDIA shock becomes more of a given 501 00:29:54,370 --> 00:29:58,210 Speaker 8: other impulse to this AI sell-off and leading to a 502 00:29:58,350 --> 00:30:03,150 Speaker 8: broader equity correction? So if you simulate the shock in 503 00:30:03,270 --> 00:30:07,860 Speaker 8: the Fed's Fervus general equilibrium model, so suppose that S & 504 00:30:07,820 --> 00:30:08,940 Speaker 8: P 500 fall by another 20%. So we found that 505 00:30:09,020 --> 00:30:19,420 Speaker 8: it will roughly subtract one percentage point. To GDP it 506 00:30:19,460 --> 00:30:25,490 Speaker 8: will barely do anything to inflation because typically- equity price 507 00:30:25,510 --> 00:30:30,150 Speaker 8: correction just do not- just not just equity but- any 508 00:30:30,570 --> 00:30:35,290 Speaker 8: demand shock just do not really move the- dial on inflation. 509 00:30:35,870 --> 00:30:40,620 Speaker 8: Unemployment rate will rise by roughly point five percent. Point 510 00:30:40,840 --> 00:30:44,350 Speaker 8: in the next year so that pushes. if the baseline 511 00:30:44,390 --> 00:30:47,690 Speaker 8: is 4.2% on the street, then that could push it 512 00:30:47,710 --> 00:30:53,220 Speaker 8: to 4.7%. And the mechanism of all this is primarily 513 00:30:53,320 --> 00:30:58,520 Speaker 8: through the consumer wealth channel, right? So currently about 40% 514 00:30:58,520 --> 00:31:01,739 Speaker 8: of household net worth is tied to equity and So 515 00:31:01,760 --> 00:31:05,200 Speaker 8: when you have an equity correction like this, it could 516 00:31:05,220 --> 00:31:12,830 Speaker 8: affect the GDP consumption through that channel. But I think 517 00:31:12,870 --> 00:31:16,090 Speaker 8: the risk for this particular time that we are living 518 00:31:16,190 --> 00:31:19,130 Speaker 8: in is that it could be a bigger impact because 519 00:31:19,430 --> 00:31:22,580 Speaker 8: the Fed is not going to react. So even in 520 00:31:22,660 --> 00:31:25,700 Speaker 8: the Fed's own model, it says the Fed should be– 521 00:31:26,480 --> 00:31:29,840 Speaker 8: reducing rates by 50 pips. But right now, the Fed 522 00:31:29,900 --> 00:31:32,719 Speaker 8: is thinking about or Chris Waller is talking about. 523 00:31:32,580 --> 00:31:34,520 Speaker 3: Raising rates by 50 pips. 524 00:31:35,200 --> 00:31:40,270 Speaker 8: So it actually removed this very powerful offsetting channel against 525 00:31:40,330 --> 00:31:43,390 Speaker 8: these drag on the economy. And so if the Fed, 526 00:31:43,830 --> 00:31:47,610 Speaker 8: if the equity correction would not do anything to inflation 527 00:31:47,670 --> 00:31:51,050 Speaker 8: and the Fed is on track to not hike, as 528 00:31:51,110 --> 00:31:54,130 Speaker 8: the SEP says, or even hike by 80 bps, as 529 00:31:54,530 --> 00:31:58,670 Speaker 8: the Warp or the future suggests, then the GDP hit 530 00:31:58,730 --> 00:32:03,780 Speaker 8: could be even larger at 1.5 percentage point, basically reducing 531 00:32:03,800 --> 00:32:04,620 Speaker 8: GDP to zero. 532 00:32:04,740 --> 00:32:06,700 Speaker 3: Have you ever heard Anna Wong this gloomy? 533 00:32:06,900 --> 00:32:07,220 Speaker 2: No. 534 00:32:07,400 --> 00:32:09,940 Speaker 3: I have never heard it. Paul, save the interview. 535 00:32:10,550 --> 00:32:12,630 Speaker 5: Anna, so talk to us just about, you know, we 536 00:32:12,670 --> 00:32:16,030 Speaker 5: got some inflation data here. Talk to us about your 537 00:32:16,050 --> 00:32:19,930 Speaker 5: thoughts of underlying inflation here. We do have crude oil 538 00:32:19,970 --> 00:32:22,690 Speaker 5: that's been coming down over the past several days, but 539 00:32:22,780 --> 00:32:25,220 Speaker 5: still there's that underlying what a lot of people call 540 00:32:25,300 --> 00:32:26,740 Speaker 5: sticky inflation. Talk to us about that. 541 00:32:28,080 --> 00:32:28,920 Speaker 2: Yeah. 542 00:32:29,180 --> 00:32:33,340 Speaker 8: So, Paul, inflation truly is a very complex subject as 543 00:32:33,400 --> 00:32:36,760 Speaker 8: there's so much detail to look at. So we have 544 00:32:37,200 --> 00:32:40,180 Speaker 8: So as a result, we constructed the Bloomberg Price Project, 545 00:32:40,740 --> 00:32:44,370 Speaker 8: which looks at millions of prices each month, right? And 546 00:32:44,410 --> 00:32:47,410 Speaker 8: what it's saying is that the headline inflation is actually 547 00:32:47,470 --> 00:32:53,180 Speaker 8: stronger than BLS is saying. So BLS numbers would imply 548 00:32:53,480 --> 00:32:59,239 Speaker 8: that year-over-year inflation is probably growing at 3.9%. But our 549 00:32:59,310 --> 00:33:04,050 Speaker 8: Bloomberg Price Project says it's 4.6%. But it also says, 550 00:33:04,270 --> 00:33:07,230 Speaker 8: our project also says the underlying pace of inflation is 551 00:33:07,370 --> 00:33:12,490 Speaker 8: not as broad-based as the BLS number suggests. So once 552 00:33:12,530 --> 00:33:16,460 Speaker 8: you trim the outliers, we are actually at 2.3%. for 553 00:33:16,620 --> 00:33:22,260 Speaker 8: underlying inflation and core is at roughly 2.5%. So I 554 00:33:22,820 --> 00:33:25,800 Speaker 8: think the Fed is in a tough place here because 555 00:33:25,860 --> 00:33:28,720 Speaker 8: on one hand, you just can't ignore the really strong 556 00:33:28,740 --> 00:33:32,900 Speaker 8: headline number and people are feeling the inflation squeeze from 557 00:33:33,280 --> 00:33:35,700 Speaker 8: the strong headline number. But on the other hand, the 558 00:33:35,810 --> 00:33:38,870 Speaker 8: underlying is not showing much of the broadening. 559 00:33:40,200 --> 00:33:42,860 Speaker 3: Anna, thank you so much. Anna Wong, really, really appreciate 560 00:33:42,880 --> 00:33:45,520 Speaker 3: it this morning. Dr. Wong driving all of our U.S. 561 00:33:45,580 --> 00:33:50,209 Speaker 3: economics and particularly to the December 9th meeting as well. 562 00:33:50,930 --> 00:33:53,229 Speaker 3: It is on, and I hope I'm going to get 563 00:33:53,270 --> 00:33:57,570 Speaker 3: this pronunciation right, Ed Ludlow's there three and four times 564 00:33:57,810 --> 00:34:03,750 Speaker 3: a week. It is Berryessa Road, San Jose, California. And 565 00:34:03,770 --> 00:34:06,760 Speaker 3: there's a Denny's. with a plaque, and I think it's 566 00:34:06,800 --> 00:34:10,700 Speaker 3: table four. This is where Jensen and the NVIDIA clan 567 00:34:11,140 --> 00:34:15,029 Speaker 3: hung out and invented it all. We are thrilled with 568 00:34:15,070 --> 00:34:17,790 Speaker 3: Ted Mortensen just on with us with all of his 569 00:34:17,850 --> 00:34:21,609 Speaker 3: decades of experience truly in the trenches on this day 570 00:34:21,650 --> 00:34:26,190 Speaker 3: after day. Ed Ludlow joins us with Bloomberg Technology. Ed Ludlow, 571 00:34:26,270 --> 00:34:31,149 Speaker 3: how will the employees, 40-some thousand of NVIDIA, how will 572 00:34:31,170 --> 00:34:32,890 Speaker 3: they respond to this moment? 573 00:34:35,040 --> 00:34:37,320 Speaker 10: I spoke to several of them yesterday. You know, it 574 00:34:37,340 --> 00:34:39,720 Speaker 10: was Veterans Day yesterday, right? So to take you inside 575 00:34:39,900 --> 00:34:42,940 Speaker 10: that building at NVIDIA HQ, it was a holiday for 576 00:34:42,980 --> 00:34:46,040 Speaker 10: most staff. And as one engineer put it to me, 577 00:34:46,060 --> 00:34:47,890 Speaker 10: he said, I don't want to log on because I 578 00:34:47,950 --> 00:34:53,190 Speaker 10: don't want to face what is out there, you know, 579 00:34:53,210 --> 00:34:56,029 Speaker 10: in the reporting at that time. Now there is confirmation. 580 00:34:56,110 --> 00:34:59,630 Speaker 10: Jensen Wong has resigned as CEO. Colette Kress, who is 581 00:34:59,810 --> 00:35:03,589 Speaker 10: very familiar to Wall Street, very familiar with is interim 582 00:35:03,620 --> 00:35:08,630 Speaker 10: CEO and they've made these adjustments to the board. But yeah, 583 00:35:08,770 --> 00:35:13,910 Speaker 10: you know, the way that Jensen ran that business, it 584 00:35:13,950 --> 00:35:16,550 Speaker 10: was structured so deliberately where he had like 60 direct 585 00:35:16,590 --> 00:35:19,540 Speaker 10: reports and then the tens of thousands of people underneath 586 00:35:19,560 --> 00:35:21,899 Speaker 10: that and it worked, you know, it worked that way. 587 00:35:21,940 --> 00:35:25,480 Speaker 10: But I think many people are in shock, even though Sunday, 588 00:35:25,620 --> 00:35:29,160 Speaker 10: this past Sunday, Jensen Wang had communicated to all staff 589 00:35:30,550 --> 00:35:31,469 Speaker 10: what had taken place. 590 00:35:32,500 --> 00:35:35,560 Speaker 5: So, Ed, we were just talking with Ted Mortensen, a 591 00:35:35,920 --> 00:35:38,990 Speaker 5: tech analyst at Baird, and he made the really strong 592 00:35:39,030 --> 00:35:42,390 Speaker 5: comment that what the company needs right now in terms 593 00:35:42,410 --> 00:35:47,430 Speaker 5: of a successor is a very, very well-respected tech leader, 594 00:35:47,489 --> 00:35:50,860 Speaker 5: as opposed to maybe a Wall Street type of hire. 595 00:35:51,430 --> 00:35:52,620 Speaker 5: What are you hearing out there in the Valley? 596 00:35:53,320 --> 00:35:56,140 Speaker 10: Well, look, I mean, Colette Cress is CEO on an 597 00:35:56,300 --> 00:35:59,359 Speaker 10: interim basis. And in the statement, the board was clear 598 00:35:59,380 --> 00:36:04,330 Speaker 10: that they will review external candidates. And in the media 599 00:36:04,370 --> 00:36:08,160 Speaker 10: reports that we have, the external candidates are technology people. 600 00:36:08,660 --> 00:36:11,359 Speaker 10: One of them is Arm CEO, Rene Haas, who you 601 00:36:11,380 --> 00:36:14,200 Speaker 10: guys will be familiar with. But he was at Nvidia 602 00:36:14,219 --> 00:36:17,660 Speaker 10: for seven years. 2006, he ran the compute business. He 603 00:36:17,700 --> 00:36:22,910 Speaker 10: knows the semiconductor industry. NVIDIA's systems are based on ARM architecture. 604 00:36:23,260 --> 00:36:25,310 Speaker 10: So people see that as a likely front runner. There 605 00:36:25,350 --> 00:36:30,009 Speaker 10: are several names out there. And internally, Ian Buck, did 606 00:36:30,050 --> 00:36:32,149 Speaker 10: I hear Tom say the name Ian Buck earlier or 607 00:36:32,170 --> 00:36:32,850 Speaker 10: did I mishear that? 608 00:36:33,070 --> 00:36:36,510 Speaker 3: Ed, we're pretending it's Bloomberg Technology. I've nailed the board 609 00:36:36,890 --> 00:36:38,330 Speaker 3: this morning. I mean, continue. 610 00:36:38,350 --> 00:36:41,270 Speaker 10: No, super smart. You know, Ian led the team that 611 00:36:41,390 --> 00:36:44,720 Speaker 10: wrote the CUDA software language. That is the software that 612 00:36:44,760 --> 00:36:48,760 Speaker 10: allows GPUs to be used for AI training and inference. 613 00:36:49,900 --> 00:36:51,819 Speaker 10: And he has been there for a long time and 614 00:36:51,840 --> 00:36:55,520 Speaker 10: is essentially a co-founder. And maybe, you know, he lacks 615 00:36:55,820 --> 00:37:00,620 Speaker 10: the characteristics that Jensen Wong has. But people talk about him, too. 616 00:37:00,989 --> 00:37:03,850 Speaker 3: OK, but to help me here, because Ted Mortensen brought 617 00:37:03,890 --> 00:37:06,089 Speaker 3: this up, I guess that my puny brain would have 618 00:37:06,150 --> 00:37:08,850 Speaker 3: picked it up at some point. But Mortensen out front 619 00:37:08,890 --> 00:37:11,150 Speaker 3: of Ludlow, out front of Keene in Sweden. 620 00:37:11,210 --> 00:37:11,310 Speaker 5: Yeah. 621 00:37:11,830 --> 00:37:16,000 Speaker 3: The combination of Sir Demas Hassabis, Nobel laureate in chemistry, 622 00:37:16,480 --> 00:37:20,799 Speaker 3: Google deep mind and the rest, founder of Google Gemini, arguably, 623 00:37:21,280 --> 00:37:26,030 Speaker 3: the combination of Sir Demas and Ian Buck sounds like 624 00:37:26,090 --> 00:37:28,669 Speaker 3: a rapid solution. Am I off the mark? 625 00:37:29,980 --> 00:37:35,810 Speaker 10: Well, any great software and hardware combination will be talked 626 00:37:35,870 --> 00:37:38,170 Speaker 10: about until it's confirmed right. You're not off the mark. 627 00:37:38,510 --> 00:37:43,700 Speaker 10: I mean, increasingly, NVIDIA has accrued incredible software talent anyway. 628 00:37:44,120 --> 00:37:45,680 Speaker 10: And what I would say in answer to your question 629 00:37:45,719 --> 00:37:49,120 Speaker 10: is the 60 people that were reported directly into Jensen 630 00:37:49,160 --> 00:37:54,610 Speaker 10: Wang include an astonishing number. astonishing experience and talent in 631 00:37:54,650 --> 00:37:57,410 Speaker 10: the domains of AI models and in the compute that 632 00:37:57,450 --> 00:38:00,890 Speaker 10: powers them. There's no shortage of that. But I would 633 00:38:00,930 --> 00:38:04,560 Speaker 10: also just say, remember recent reporting that Demis Hassabis was 634 00:38:04,640 --> 00:38:09,060 Speaker 10: frozen out at Google DeepMind for a wide range of reasons. 635 00:38:09,660 --> 00:38:12,739 Speaker 3: Okay. And this question comes in, I mean, it's 22 636 00:38:12,739 --> 00:38:15,640 Speaker 3: degrees this weekend in Stillwater, Minnesota. 637 00:38:15,680 --> 00:38:16,050 Speaker 2: Oh boy. 638 00:38:16,530 --> 00:38:20,089 Speaker 3: I mean, winter is upon us here. Steve in Stillwater 639 00:38:20,610 --> 00:38:23,700 Speaker 3: does a compare-contrast, and Ed, you're perfect for this. 640 00:38:24,380 --> 00:38:24,700 Speaker 2: Mr. 641 00:38:24,800 --> 00:38:28,380 Speaker 3: Wang, as compared to Steve Jobs, the impact of Jobs 642 00:38:28,560 --> 00:38:32,820 Speaker 3: on Apple, I guess they did okay. And then, Wang, 643 00:38:33,739 --> 00:38:37,450 Speaker 3: give us your take here on a rational comparison of 644 00:38:37,550 --> 00:38:38,950 Speaker 3: Jobs and Wang. 645 00:38:39,989 --> 00:38:47,010 Speaker 10: So, both engineers, you know, that's really important. Unique style 646 00:38:47,270 --> 00:38:53,620 Speaker 10: and approach. Steve Jobs, the turtleneck. If you're watching this 647 00:38:53,660 --> 00:38:55,940 Speaker 10: show right now on YouTube, as you see on the screen, NVIDIA, 648 00:38:55,980 --> 00:39:00,910 Speaker 10: the leather jacket of Jensen Wang. But I think the 649 00:39:00,950 --> 00:39:03,529 Speaker 10: main point of difference that I would flag to you 650 00:39:03,969 --> 00:39:06,610 Speaker 10: is that Jensen Wang made a fundamental change in how 651 00:39:06,650 --> 00:39:10,489 Speaker 10: the semiconductor industry works. Historically, you don't just walk into 652 00:39:10,530 --> 00:39:12,810 Speaker 10: the Nvidia store, pick up a bag of chips and 653 00:39:12,830 --> 00:39:15,680 Speaker 10: walk out. It never worked like that. You go through 654 00:39:15,719 --> 00:39:18,759 Speaker 10: a middleman and that middleman was Dell or it was 655 00:39:18,960 --> 00:39:22,660 Speaker 10: HPE or it was Supermicro. There was no business of 656 00:39:22,739 --> 00:39:25,230 Speaker 10: Jensen flying around the world selling chips to everyone. It 657 00:39:25,270 --> 00:39:26,450 Speaker 10: just doesn't work that way. 658 00:39:26,469 --> 00:39:26,990 Speaker 5: And it didn't. 659 00:39:27,250 --> 00:39:28,290 Speaker 10: But he made it so. 660 00:39:28,310 --> 00:39:29,129 Speaker 3: Very different. 661 00:39:30,530 --> 00:39:34,040 Speaker 10: Business model and that now going forward will be interesting 662 00:39:34,100 --> 00:39:36,680 Speaker 10: because he has the role of being the face of 663 00:39:36,719 --> 00:39:39,420 Speaker 10: the company, but also the relationships with the president of 664 00:39:39,440 --> 00:39:43,060 Speaker 10: the United States, the leaders in Southeast Asia and the 665 00:39:43,120 --> 00:39:43,640 Speaker 10: CEOs of. 666 00:39:43,620 --> 00:39:44,260 Speaker 5: All the companies. 667 00:39:44,480 --> 00:39:45,680 Speaker 10: You know who the first, well, I don't wanna get 668 00:39:45,700 --> 00:39:48,020 Speaker 10: in trouble. The first people I heard from last night 669 00:39:48,100 --> 00:39:52,239 Speaker 10: when the reports came out, about the board looking at 670 00:39:52,260 --> 00:39:54,940 Speaker 10: how StingJets won, where the leaders of the companies he 671 00:39:54,960 --> 00:39:57,950 Speaker 10: does business with, you know, before we really heard anything 672 00:39:57,969 --> 00:39:58,830 Speaker 10: from NVIDIA at all. 673 00:39:59,310 --> 00:40:01,550 Speaker 3: I think that says a lot. Ed, you're not going 674 00:40:01,570 --> 00:40:04,710 Speaker 3: to get in trouble. No one listens to Bloomberg surveillance. 675 00:40:05,210 --> 00:40:07,950 Speaker 3: No one watches us on YouTube. So you're not going 676 00:40:07,969 --> 00:40:12,040 Speaker 3: to get in trouble. Paul Sweeney with Bloomberg Technologies, Ed Ludlow. 677 00:40:12,680 --> 00:40:17,399 Speaker 5: Ed, give us a sense of how integral it is Mr. 678 00:40:17,460 --> 00:40:20,750 Speaker 5: Jensen was to getting all of these deals. A lot 679 00:40:20,770 --> 00:40:22,850 Speaker 5: of people refer to them as circular deals with all 680 00:40:22,890 --> 00:40:26,640 Speaker 5: these technology companies. How much of it was Jensen Wong 681 00:40:27,080 --> 00:40:30,780 Speaker 5: himself personally versus I got to do business with NVIDIA 682 00:40:30,800 --> 00:40:33,140 Speaker 5: because they're just the biggest and best out there? 683 00:40:33,620 --> 00:40:36,070 Speaker 10: I think that there's an element of both, but Jensen 684 00:40:36,090 --> 00:40:40,509 Speaker 10: Wang was very directly involved in many of the transactions 685 00:40:40,570 --> 00:40:42,529 Speaker 10: that NVIDIA does. One thing I'd point out to the 686 00:40:42,590 --> 00:40:45,310 Speaker 10: audience that I had to remind myself of quite recently 687 00:40:45,430 --> 00:40:50,370 Speaker 10: is NVIDIA has an investment arm, Nventures, NVIDIA Ventures. And 688 00:40:50,430 --> 00:40:52,710 Speaker 10: those guys are like a team of very capable people. 689 00:40:52,750 --> 00:40:56,290 Speaker 10: Think about them as like, you know, your business development team, 690 00:40:56,570 --> 00:40:58,989 Speaker 10: your M & A guys. And they were busy, like 691 00:40:59,010 --> 00:41:01,970 Speaker 10: they were out there thinking about all of this. But, 692 00:41:01,989 --> 00:41:04,859 Speaker 10: you know, Jensen Huang was very consistent on pushing back 693 00:41:04,900 --> 00:41:08,020 Speaker 10: against the circular financing thing as a bad problem, as 694 00:41:08,060 --> 00:41:10,160 Speaker 10: an issue. You know, the way that he looked at 695 00:41:10,219 --> 00:41:13,540 Speaker 10: it is that NVIDIA has lots of capital. They're throwing 696 00:41:13,580 --> 00:41:15,580 Speaker 10: off cash, as you know. You and I've talked about 697 00:41:15,620 --> 00:41:18,440 Speaker 10: that very regularly. What does NVIDIA do with all the cash? 698 00:41:19,030 --> 00:41:22,200 Speaker 10: And these companies that are relatively small right through to 699 00:41:22,239 --> 00:41:26,900 Speaker 10: some of their giant partners needed liquidity essentially to grease 700 00:41:26,940 --> 00:41:31,960 Speaker 10: the wheels, to buy compute and install it. And the 701 00:41:32,000 --> 00:41:34,600 Speaker 10: way that Jensen Wang would always say is that that's 702 00:41:34,640 --> 00:41:38,260 Speaker 10: no bad thing. It got people to actually move, get 703 00:41:38,300 --> 00:41:38,810 Speaker 10: stuff done. 704 00:41:39,850 --> 00:41:44,489 Speaker 3: What has changed now at Amazon, Google, AMD, all the 705 00:41:44,550 --> 00:41:47,430 Speaker 3: rest of them, is they try to plan out, I'm 706 00:41:47,450 --> 00:41:51,890 Speaker 3: going to say, Ed, amateurly, a six-month plan. We've had 707 00:41:51,950 --> 00:41:54,739 Speaker 3: others today say this is a moment where they can 708 00:41:54,820 --> 00:41:56,560 Speaker 3: seize the high ground. Do you buy that? 709 00:41:58,000 --> 00:42:01,270 Speaker 10: Well, so one just piece of reporting is that NVIDIA's 710 00:42:01,370 --> 00:42:03,529 Speaker 10: earnings are going to go ahead next Tuesday, which, you know, 711 00:42:03,550 --> 00:42:07,210 Speaker 10: I don't think anyone thought they wouldn't. But this change, 712 00:42:07,290 --> 00:42:11,020 Speaker 10: Jensen Wang resigning immediately and Colette Crest leading the company 713 00:42:11,040 --> 00:42:12,800 Speaker 10: on an interim basis, like in the here and now, 714 00:42:13,100 --> 00:42:16,260 Speaker 10: nothing changes for anyone, including those customers that you just mentioned. 715 00:42:16,700 --> 00:42:19,420 Speaker 10: talked about, they were already working on their own chips. 716 00:42:19,800 --> 00:42:23,130 Speaker 10: They were already trying to diversify away from relying on NVIDIA. 717 00:42:23,690 --> 00:42:26,790 Speaker 10: And it's highly unlikely that in the next fiscal year, NVIDIA, 718 00:42:27,050 --> 00:42:29,950 Speaker 10: that the picture changes dramatically. Maybe it will. It's so 719 00:42:29,989 --> 00:42:34,700 Speaker 10: hard to calculate. Again, I would say it was not 720 00:42:35,040 --> 00:42:39,239 Speaker 10: Colette Crest, the CFO, making the bold growth predictions. Um, 721 00:42:39,620 --> 00:42:43,200 Speaker 10: it was often gents among, so I guess whether they're 722 00:42:43,219 --> 00:42:45,219 Speaker 10: on the same page or not, we don't know, but yeah, 723 00:42:45,760 --> 00:42:46,840 Speaker 10: I hope that answers the question. 724 00:42:47,120 --> 00:42:50,650 Speaker 5: So Ed, I guess next Wednesday post-close is going to 725 00:42:50,670 --> 00:42:54,610 Speaker 5: be must see, you know, conference call with this company 726 00:42:54,650 --> 00:42:57,950 Speaker 5: reporting earnings here. How do you think they play it here? 727 00:42:59,969 --> 00:43:05,770 Speaker 10: Um, prepared remarks. Do they take questions from the analysts, Paul? 728 00:43:05,830 --> 00:43:10,310 Speaker 10: You know, in history, sometimes they don't. Sometimes they do, 729 00:43:10,710 --> 00:43:12,549 Speaker 10: or sometimes the IR team will say, we will not 730 00:43:12,590 --> 00:43:16,760 Speaker 10: be taking any further questions on a particular subject. Sometimes 731 00:43:16,840 --> 00:43:21,960 Speaker 10: legal counsel gets involved. One thing, I think it's actually 732 00:43:22,420 --> 00:43:26,629 Speaker 10: next Tuesday that they report earnings, which is unusual because 733 00:43:26,670 --> 00:43:28,530 Speaker 10: if you go on Ask B on the Bloomberg terminal, 734 00:43:28,969 --> 00:43:31,049 Speaker 10: it will tell you that in NVIDIA's history with the 735 00:43:31,070 --> 00:43:34,930 Speaker 10: Bloomberg data available, it's only reported earnings on a Tuesday, 7% 736 00:43:34,930 --> 00:43:37,610 Speaker 10: of the time. They always do Wednesday. So that was 737 00:43:37,630 --> 00:43:38,810 Speaker 10: kind of odd going into it anyway. 738 00:43:39,050 --> 00:43:42,200 Speaker 3: And you got to dash. Let me do this. I 739 00:43:42,239 --> 00:43:45,140 Speaker 3: just think it's important. We opened with this iconic Denny's 740 00:43:45,620 --> 00:43:49,720 Speaker 3: in San Francisco where these NVIDIA geniuses put it all together. 741 00:43:50,100 --> 00:43:52,500 Speaker 3: When you go to Denny's, Ed, is it the Philly 742 00:43:52,560 --> 00:43:55,490 Speaker 3: cheesesteak omelet or the T-bone steak and eggs? 743 00:43:57,469 --> 00:43:58,390 Speaker 10: T-bone steak and eggs. 744 00:43:58,880 --> 00:44:01,260 Speaker 3: I'm a big protein guy. It's like, you know, it's 745 00:44:01,350 --> 00:44:04,930 Speaker 3: full British, full whales. It's like the way they go. 746 00:44:05,310 --> 00:44:06,790 Speaker 3: What are you going to do on the show today? 747 00:44:06,830 --> 00:44:10,490 Speaker 3: I can't imagine. You must be blowing it up. 748 00:44:10,719 --> 00:44:14,140 Speaker 10: I'm going to seek the person that argues fiercely that 749 00:44:14,219 --> 00:44:18,759 Speaker 10: Jensen should have stayed, irrespective of his infidelity, because there 750 00:44:18,780 --> 00:44:21,410 Speaker 10: are so many of those people out there. They really 751 00:44:21,430 --> 00:44:24,529 Speaker 10: there really are. So so there's a scandal here. There's 752 00:44:24,550 --> 00:44:27,730 Speaker 10: some national security questions. But most people I've spoken to 753 00:44:27,770 --> 00:44:29,969 Speaker 10: believe that the board should not have done this. 754 00:44:30,170 --> 00:44:32,950 Speaker 3: I mean, Alexis, I mean, Ed Ludlow on entertainment tonight. 755 00:44:33,010 --> 00:44:34,279 Speaker 3: I can see I can see it. 756 00:44:34,300 --> 00:44:35,860 Speaker 11: You know, I actually have a question for Ed. Is 757 00:44:35,920 --> 00:44:37,940 Speaker 11: it out of the question, Ed, that Wong at some 758 00:44:37,960 --> 00:44:41,480 Speaker 11: point could be brought on, retained as a consultant to 759 00:44:41,500 --> 00:44:44,120 Speaker 11: the company? So at least investors feel like he has 760 00:44:44,390 --> 00:44:46,310 Speaker 11: input into the company's future. 761 00:44:47,610 --> 00:44:48,109 Speaker 2: Well, yeah. 762 00:44:49,140 --> 00:44:49,560 Speaker 3: He doesn't. 763 00:44:49,820 --> 00:44:53,279 Speaker 10: The statement says nothing about that. No advisory role, no 764 00:44:53,340 --> 00:44:57,439 Speaker 10: chairman emeritus. And one Silicon Valley consideration or actually big 765 00:44:57,580 --> 00:45:01,509 Speaker 10: global consideration, Jensen doesn't have any massive voting power. He 766 00:45:01,550 --> 00:45:03,990 Speaker 10: doesn't even have that much economic ownership of the company. 767 00:45:05,310 --> 00:45:07,529 Speaker 10: So there's no sort of class of shares that give him, 768 00:45:08,350 --> 00:45:11,650 Speaker 10: you know, the deciding vote. And, you know, that has 769 00:45:11,670 --> 00:45:13,089 Speaker 10: been the big point of difference with him. 770 00:45:13,770 --> 00:45:16,850 Speaker 3: See, the control room thought Alexis's question was brilliant. She's 771 00:45:16,890 --> 00:45:19,280 Speaker 3: a pro. They never say that to me. Ed Ludlow 772 00:45:19,360 --> 00:45:23,800 Speaker 3: can't wait for Bloomberg Technology. Truly, folks, the arc going 773 00:45:23,840 --> 00:45:26,640 Speaker 3: back to, I'm going to say just after World War II, 774 00:45:26,760 --> 00:45:32,770 Speaker 3: truly an historic day for American 20th and 21st century technology. 775 00:45:32,810 --> 00:45:35,770 Speaker 3: Look to Ed Ludlow, Bloomberg Technology, all of his great 776 00:45:35,810 --> 00:45:37,910 Speaker 3: work out on social as well. 777 00:45:39,380 --> 00:45:44,299 Speaker 1: This is the Bloomberg Surveillance Podcast, available on Apple, Spotify, 778 00:45:44,430 --> 00:45:48,690 Speaker 1: and anywhere else you get your podcasts. Listen live each weekday, 779 00:45:48,690 --> 00:45:54,130 Speaker 1: 7 to 10 a.m. Eastern, on Bloomberg.com, the iHeartRadio app, TuneIn, 780 00:45:54,450 --> 00:45:57,509 Speaker 1: and the Bloomberg Business app. You can also watch us 781 00:45:57,610 --> 00:46:01,610 Speaker 1: live every weekday on YouTube and always on the Bloomberg Terminal.