1 00:00:01,440 --> 00:00:05,080 Speaker 1: From my Heart where innovation, money and power Collie in 2 00:00:05,200 --> 00:00:10,080 Speaker 1: Silicon Valley, NBN. This is Bloomberg Technology with Caroline Hide 3 00:00:10,160 --> 00:00:11,559 Speaker 1: and Ed Ludlove. 4 00:00:24,520 --> 00:00:26,680 Speaker 2: And Caroline Heinda Blomberg's weldad quarters in New York and 5 00:00:26,760 --> 00:00:30,280 Speaker 2: Ludlow who's off. This is Bloomberg Technology coming up full 6 00:00:30,520 --> 00:00:31,840 Speaker 2: earnings coverage ahead. 7 00:00:31,880 --> 00:00:33,400 Speaker 3: Netflix sept for its worst day. 8 00:00:33,320 --> 00:00:36,479 Speaker 2: Of the year, that says its outlook full short of estimates. 9 00:00:36,640 --> 00:00:39,479 Speaker 2: Will break down the results. Plus, let's look at Tesla's 10 00:00:39,520 --> 00:00:41,879 Speaker 2: results as Elon Musk warns and more blows to the 11 00:00:41,880 --> 00:00:46,960 Speaker 2: company's profitability, and Chip Giant TSMC drops as it warns 12 00:00:47,000 --> 00:00:50,040 Speaker 2: the AI frenzy may not last. We'll have that and 13 00:00:50,200 --> 00:00:53,400 Speaker 2: so much more ahead. First, let's get you up to 14 00:00:53,440 --> 00:00:55,600 Speaker 2: speed with a lackluster day in the markets. 15 00:00:55,640 --> 00:00:57,480 Speaker 3: We send it over to Blomberg's affaguilty a little. 16 00:00:57,640 --> 00:00:59,880 Speaker 4: It is a little lackluster, Caroline. In fact, it feels 17 00:00:59,880 --> 00:01:02,920 Speaker 4: like the first worst down day in many days for 18 00:01:03,120 --> 00:01:05,240 Speaker 4: the major indexes here in the US, because we've had 19 00:01:05,240 --> 00:01:07,400 Speaker 4: this melt up. But some of the big tech reporters 20 00:01:07,400 --> 00:01:09,319 Speaker 4: reports while putting a bit of a wrinkle in that 21 00:01:09,480 --> 00:01:12,520 Speaker 4: right now we have Tesla down six point four to 22 00:01:12,600 --> 00:01:17,240 Speaker 4: two percent of course on its report. That's where profitability 23 00:01:17,280 --> 00:01:19,720 Speaker 4: is an issue. Elon Musk even talking about the idea 24 00:01:19,760 --> 00:01:21,760 Speaker 4: that if rates continue to rise, they will have to 25 00:01:21,760 --> 00:01:24,679 Speaker 4: bring prices down that could weigh on profitability more so. 26 00:01:24,959 --> 00:01:26,720 Speaker 4: And then the fears around some of the other big 27 00:01:26,760 --> 00:01:29,440 Speaker 4: tech companies that have not yet reported, Meta, Alphabet and 28 00:01:30,160 --> 00:01:33,840 Speaker 4: Apple all lower ahead of those reports. This idea that 29 00:01:33,880 --> 00:01:36,600 Speaker 4: we were talking about yesterday. So much movement to the upside, 30 00:01:36,680 --> 00:01:38,840 Speaker 4: but will these reports deliver? Now if we flip up 31 00:01:38,840 --> 00:01:41,080 Speaker 4: the boards, we are going to see that what this 32 00:01:41,200 --> 00:01:44,400 Speaker 4: means for the s and P five hundred and the 33 00:01:44,520 --> 00:01:47,320 Speaker 4: NASAC well, we are looking at a down day, of course, 34 00:01:47,360 --> 00:01:49,840 Speaker 4: and the nicy Fang Index down even more, down three 35 00:01:49,880 --> 00:01:52,920 Speaker 4: point two percent. The socks not immune from this. We 36 00:01:52,960 --> 00:01:54,880 Speaker 4: have been video down quite a bit. We also have 37 00:01:54,960 --> 00:01:57,360 Speaker 4: AMD down. And then finally, if we take a look 38 00:01:57,360 --> 00:02:00,000 Speaker 4: at the other big earnings a lagger that is Netflix, 39 00:02:00,040 --> 00:02:01,559 Speaker 4: So I think we have a chart of that here. 40 00:02:01,760 --> 00:02:04,160 Speaker 4: We're going to see, Wow, gosh, the worst day I 41 00:02:04,280 --> 00:02:07,680 Speaker 4: believe since April twenty twenty two. And Caroline, there's this 42 00:02:07,800 --> 00:02:09,800 Speaker 4: continued puzzle. I'm sure you're going to solve it on 43 00:02:09,840 --> 00:02:13,360 Speaker 4: this show. In the next hour. But record blowout subscribers. Well, 44 00:02:13,360 --> 00:02:15,760 Speaker 4: I don't know if a record, but blowout subscribers. But 45 00:02:15,800 --> 00:02:18,120 Speaker 4: where's the revenue And they're talking about still keeping that 46 00:02:18,120 --> 00:02:19,880 Speaker 4: double digit for the end of the year. 47 00:02:20,400 --> 00:02:22,000 Speaker 3: Putting a lot of pressure on the fourth quarter. 48 00:02:22,080 --> 00:02:24,760 Speaker 2: Quite frankly, Yeah, Bank of America looking optimistic towards that 49 00:02:24,800 --> 00:02:27,120 Speaker 2: second half, but as you say, for now, the revenue 50 00:02:27,120 --> 00:02:30,720 Speaker 2: not quite following that uptick in subscriptions. Let's go back 51 00:02:30,720 --> 00:02:33,519 Speaker 2: to Tesla for a moment, because it is falling hard too, 52 00:02:33,639 --> 00:02:36,640 Speaker 2: warning of more hits to its already shrinking profitability. CEO 53 00:02:36,680 --> 00:02:39,040 Speaker 2: Elon Musk said the company will have to keep lowering 54 00:02:39,040 --> 00:02:42,760 Speaker 2: the prices of its evs is interest rates continues to rise. 55 00:02:44,639 --> 00:02:46,640 Speaker 5: I think it makes it does make sense to sacrifice 56 00:02:46,720 --> 00:02:50,799 Speaker 5: margins in favor of making more vehicles because we think 57 00:02:50,880 --> 00:02:52,840 Speaker 5: in the not to do in the future they will 58 00:02:52,880 --> 00:02:57,680 Speaker 5: have a chromatic valuation increase. I think the Tesla fleet 59 00:02:57,960 --> 00:02:59,880 Speaker 5: value increase of the point which we can upload full 60 00:03:00,080 --> 00:03:02,799 Speaker 5: of you know, full self driving, had it's approved by regulators, 61 00:03:04,560 --> 00:03:08,760 Speaker 5: will be the single biggest step change in asset value 62 00:03:08,840 --> 00:03:09,639 Speaker 5: maybe in history. 63 00:03:10,400 --> 00:03:13,040 Speaker 2: He's got more analysis than mention. Akaine as an Austin 64 00:03:13,120 --> 00:03:15,799 Speaker 2: In Shawn, I don't really understand why the market's so 65 00:03:15,880 --> 00:03:19,000 Speaker 2: surprised by this. These price cuts, This focus on volume 66 00:03:19,080 --> 00:03:20,400 Speaker 2: over profitability has been. 67 00:03:20,280 --> 00:03:22,600 Speaker 3: A theme all year, But were they're hoping for some 68 00:03:22,760 --> 00:03:23,440 Speaker 3: sort of change. 69 00:03:25,800 --> 00:03:27,520 Speaker 6: I think, you know, if you look at the cuts 70 00:03:27,600 --> 00:03:30,160 Speaker 6: that they've made and the subsequent adjustments, there have been 71 00:03:30,160 --> 00:03:32,320 Speaker 6: a couple of little price pumps over the last month 72 00:03:32,400 --> 00:03:35,480 Speaker 6: or so, they have flattened out a bit, certainly not 73 00:03:35,560 --> 00:03:37,480 Speaker 6: as volatile as they were in the first quarter. So 74 00:03:37,560 --> 00:03:40,240 Speaker 6: I think maybe we have some investors who were expecting 75 00:03:40,320 --> 00:03:42,960 Speaker 6: that to be completely closed, and especially given the fact 76 00:03:42,960 --> 00:03:45,080 Speaker 6: that there was a chance maybe we were done with 77 00:03:45,200 --> 00:03:48,160 Speaker 6: interest rates hikes, people were thinking that maybe this was 78 00:03:48,200 --> 00:03:51,760 Speaker 6: all over. But obviously Elon Musk says that if interest 79 00:03:51,840 --> 00:03:53,800 Speaker 6: rates keep going up, they're willing to cut. I mean, 80 00:03:53,920 --> 00:03:56,080 Speaker 6: he said last quarter, and he sort of reiterated it. 81 00:03:56,080 --> 00:03:56,600 Speaker 1: In that clip. 82 00:03:57,000 --> 00:03:59,520 Speaker 6: He's willing to cut even deeper to the bone because 83 00:03:59,560 --> 00:04:02,480 Speaker 6: he believed so much in the ability to deliver full 84 00:04:02,560 --> 00:04:05,960 Speaker 6: autonomy and what that would do to Tesla's margins. You know, 85 00:04:06,080 --> 00:04:08,720 Speaker 6: he's so bought into that idea that he really kind 86 00:04:08,760 --> 00:04:11,280 Speaker 6: of doesn't care about the margins, right now, so I 87 00:04:11,360 --> 00:04:14,120 Speaker 6: think it's part that and also part Remember they've added 88 00:04:14,160 --> 00:04:16,680 Speaker 6: like five hundred billion dollars to their market cap this year. 89 00:04:17,040 --> 00:04:19,080 Speaker 6: They've had a pretty big rise over the last couple 90 00:04:19,120 --> 00:04:20,920 Speaker 6: of weeks and months, and so you know, maybe this 91 00:04:21,080 --> 00:04:22,760 Speaker 6: is just some resetting of expectations. 92 00:04:22,839 --> 00:04:24,559 Speaker 3: I mean, good context there, Sean. 93 00:04:24,600 --> 00:04:27,600 Speaker 2: What's interesting about this focus on autonomous driving is the 94 00:04:27,680 --> 00:04:30,840 Speaker 2: need for compute right now, and of course a supercomputer 95 00:04:31,040 --> 00:04:33,800 Speaker 2: Dojo takes limelight a billion in. 96 00:04:33,880 --> 00:04:35,479 Speaker 3: Terms of investment in that area. 97 00:04:35,800 --> 00:04:38,480 Speaker 2: That seemed to surprise people, even though the CFO tried 98 00:04:38,560 --> 00:04:39,400 Speaker 2: to calm some nerves. 99 00:04:40,720 --> 00:04:42,440 Speaker 6: Yeah, yeah, there was a funny moment. It was probably 100 00:04:42,440 --> 00:04:45,200 Speaker 6: one of the more maybe lively moments of the call, 101 00:04:45,440 --> 00:04:48,760 Speaker 6: which was otherwise pretty straightforward. You know, Elon coming out 102 00:04:48,760 --> 00:04:52,040 Speaker 6: and saying basically about exactly how much or at least 103 00:04:52,080 --> 00:04:53,560 Speaker 6: in the ballpark of what they want to spend on 104 00:04:53,640 --> 00:04:57,040 Speaker 6: a super coupeter over the next year. And you know, 105 00:04:57,120 --> 00:04:59,839 Speaker 6: something important to remember, this is a specific use case 106 00:05:00,120 --> 00:05:03,280 Speaker 6: type of supercomputer in the sense that he's not using 107 00:05:03,320 --> 00:05:05,320 Speaker 6: it to try to be something that they could throw 108 00:05:05,440 --> 00:05:08,479 Speaker 6: at his AI effort. But he just announced last week 109 00:05:08,520 --> 00:05:10,320 Speaker 6: and do large language models on this. He says, this 110 00:05:10,480 --> 00:05:13,720 Speaker 6: is very tailored to processing video and images. It's something 111 00:05:13,760 --> 00:05:17,360 Speaker 6: that is specifically made to help improve the driving software 112 00:05:17,760 --> 00:05:20,560 Speaker 6: Antesla's cars. And so now we finally had a ballpark. 113 00:05:20,600 --> 00:05:23,200 Speaker 6: And yeah, immediately after we got this sort of lawyered 114 00:05:23,200 --> 00:05:25,040 Speaker 6: dep response from the CFO, who said, hey, you know, 115 00:05:25,360 --> 00:05:27,239 Speaker 6: that sounds like a lot of money. But we baked 116 00:05:27,279 --> 00:05:29,080 Speaker 6: this into the guidance that we've been giving you guys, 117 00:05:29,240 --> 00:05:30,840 Speaker 6: so you may have a little bit more clarity on 118 00:05:30,960 --> 00:05:33,719 Speaker 6: exactly how we're spending some of that. But don't get spooked. 119 00:05:33,760 --> 00:05:35,200 Speaker 6: This isn't some sort of new expense. 120 00:05:36,160 --> 00:05:40,599 Speaker 2: Nevertheless, expenses racking up, well, perhaps the profitability that margin 121 00:05:40,720 --> 00:05:43,719 Speaker 2: under pressure at just eighteen point one percent, seawann O Caine. 122 00:05:43,880 --> 00:05:46,320 Speaker 2: Great to get your analysis. We thank you, and let's 123 00:05:46,360 --> 00:05:48,040 Speaker 2: got to invest to Tate now a peace to Welcome 124 00:05:48,040 --> 00:05:51,400 Speaker 2: to the show. Sylvia Jabronski defines ets CEO CIO overseeing 125 00:05:51,440 --> 00:05:53,520 Speaker 2: nine hundred million dollars an assets onder management of course 126 00:05:53,520 --> 00:05:56,480 Speaker 2: Tesla when your key holdings and your pure electric vehicle 127 00:05:56,560 --> 00:06:00,640 Speaker 2: ETF and Sylvia, were you upended by this still focus 128 00:06:00,720 --> 00:06:04,280 Speaker 2: on volume of a profit or had you anticipated so? 129 00:06:04,480 --> 00:06:07,840 Speaker 7: I anticipated that you know, the earnings announcement and call 130 00:06:08,000 --> 00:06:09,600 Speaker 7: was sort of go like this. There was so much 131 00:06:09,640 --> 00:06:11,920 Speaker 7: focus on what will profit margins look like when you 132 00:06:12,000 --> 00:06:14,440 Speaker 7: take out kind of the tax credits and you know, 133 00:06:14,640 --> 00:06:17,400 Speaker 7: different types of incentives coupled with the lowering of prices, 134 00:06:17,480 --> 00:06:19,800 Speaker 7: and you know, the street didn't like it, but I 135 00:06:19,839 --> 00:06:21,920 Speaker 7: actually thought it was a great call, and I would 136 00:06:21,920 --> 00:06:24,799 Speaker 7: have said that there's actually calls for the stock to rally. 137 00:06:24,960 --> 00:06:27,720 Speaker 7: You know, what Elona Musk is doing is continuing to 138 00:06:27,800 --> 00:06:31,600 Speaker 7: corner the EV market by making EV vehicles accessible. You know, 139 00:06:31,640 --> 00:06:34,560 Speaker 7: there is a volume game here, so while you're sacrificing 140 00:06:34,640 --> 00:06:37,440 Speaker 7: some margin in the near term, you're increasing you know, 141 00:06:37,600 --> 00:06:40,920 Speaker 7: capacity to produce more vehicles, interest in purchasing vehicles because 142 00:06:40,920 --> 00:06:44,200 Speaker 7: they're affordable and really you know, taking that stake while 143 00:06:44,200 --> 00:06:45,760 Speaker 7: everybody else is trying to catch up. 144 00:06:45,920 --> 00:06:48,800 Speaker 1: I also think that you know, he has enough of 145 00:06:49,120 --> 00:06:52,360 Speaker 1: kind of you know, shots to fire here. 146 00:06:52,480 --> 00:06:55,320 Speaker 7: He has the cyber talk coming out, and that's arguably 147 00:06:55,400 --> 00:06:57,920 Speaker 7: going to be an expensive type of vehicle that people 148 00:06:57,960 --> 00:07:00,400 Speaker 7: seem to be you know, really seeking and waiting for. 149 00:07:01,080 --> 00:07:02,960 Speaker 7: And you know, so when you have days like this, 150 00:07:03,120 --> 00:07:06,640 Speaker 7: when when a stock like Tesla kind of falls precipitously 151 00:07:06,720 --> 00:07:08,880 Speaker 7: in one day from you know, what looks like bad 152 00:07:08,960 --> 00:07:10,720 Speaker 7: news but is actually really good news. 153 00:07:10,560 --> 00:07:12,440 Speaker 1: And then you know the other EV makers fall in 154 00:07:12,520 --> 00:07:12,920 Speaker 1: line with it. 155 00:07:13,000 --> 00:07:15,200 Speaker 7: I mean, this in my mind is a great kind 156 00:07:15,200 --> 00:07:17,960 Speaker 7: of dollar cost averaging day on you know, the top 157 00:07:18,040 --> 00:07:18,680 Speaker 7: EV stocks. 158 00:07:18,920 --> 00:07:22,920 Speaker 2: Okay, so buying in on the weakness even though it's 159 00:07:22,960 --> 00:07:27,200 Speaker 2: still priced what eighty times forward earnings? 160 00:07:27,440 --> 00:07:29,080 Speaker 3: How do you vindicate that valuation? 161 00:07:29,240 --> 00:07:32,200 Speaker 2: Are you the person who's also in this idea of 162 00:07:32,280 --> 00:07:35,840 Speaker 2: a ROBOTAXI the idea that ultimately there's a big bump 163 00:07:36,200 --> 00:07:36,800 Speaker 2: in valuation. 164 00:07:37,840 --> 00:07:39,240 Speaker 1: I am, and you have to be right. You have 165 00:07:39,320 --> 00:07:41,040 Speaker 1: to be if you're buying the stock at those valuations. 166 00:07:41,080 --> 00:07:41,800 Speaker 1: It's a fair point. 167 00:07:41,840 --> 00:07:43,720 Speaker 7: But there is a whole lot of evidence that, you know, 168 00:07:43,880 --> 00:07:46,040 Speaker 7: the R and D is moving there in the right direction. 169 00:07:46,160 --> 00:07:48,720 Speaker 7: Of course, you know investors want everything to happen more 170 00:07:48,840 --> 00:07:49,840 Speaker 7: quickly than it usually does. 171 00:07:49,960 --> 00:07:51,760 Speaker 1: But this is very much a growth stock. You know, 172 00:07:51,840 --> 00:07:54,640 Speaker 1: they're they're working on things that do not yet exist, 173 00:07:54,720 --> 00:07:56,560 Speaker 1: and this is what you want when you invest. 174 00:07:56,280 --> 00:07:59,480 Speaker 7: In innovation, the future of EV. So there's a few things. 175 00:07:59,520 --> 00:08:02,760 Speaker 7: One TV market was fourteen percent of sales globally. That's 176 00:08:02,800 --> 00:08:05,440 Speaker 7: projected to be thirty percent by twenty six percent. We 177 00:08:05,560 --> 00:08:07,800 Speaker 7: know that Tesla has you know, sixty percent of the market. 178 00:08:07,880 --> 00:08:10,480 Speaker 7: Now that shrinks, but they get more orders, more volume, 179 00:08:10,520 --> 00:08:12,760 Speaker 7: so I think they continue to kind of benefit there. 180 00:08:12,960 --> 00:08:15,320 Speaker 7: Number two is the new you know vehicle, the cyber truck. 181 00:08:15,400 --> 00:08:18,800 Speaker 7: Number three is you know that you mentioned the dojo before, 182 00:08:19,080 --> 00:08:22,480 Speaker 7: the potential for AI their their self you know, kind 183 00:08:22,520 --> 00:08:26,680 Speaker 7: of driving database, data center computer. The technology looks like 184 00:08:26,720 --> 00:08:28,360 Speaker 7: it's kind of being set up to support them. And 185 00:08:28,600 --> 00:08:30,680 Speaker 7: I think the robotaxi thing, if anyone is going to 186 00:08:30,680 --> 00:08:33,079 Speaker 7: get it done, it will be Tesla, and I you know, 187 00:08:33,280 --> 00:08:34,880 Speaker 7: we have no reasonably that he's not going to get 188 00:08:34,880 --> 00:08:35,160 Speaker 7: it done. 189 00:08:35,760 --> 00:08:39,240 Speaker 3: Sylvia, what about the global nature of this company? 190 00:08:39,360 --> 00:08:41,559 Speaker 2: Of course they're talking about perhaps having to slow down 191 00:08:41,600 --> 00:08:43,960 Speaker 2: on some of the factory output as they update them. 192 00:08:44,000 --> 00:08:46,840 Speaker 2: Of Course they've got exposure in Berlin here in the US, 193 00:08:46,960 --> 00:08:50,760 Speaker 2: but also China, and I noted that byd Lee Auto 194 00:08:50,840 --> 00:08:55,120 Speaker 2: Ready managing to ramp up their own deliveries at the moment. 195 00:08:55,200 --> 00:08:56,560 Speaker 2: What's competition like globally? 196 00:08:57,679 --> 00:08:59,760 Speaker 7: Yeah, and I think that you know, when you're in 197 00:08:59,800 --> 00:09:01,600 Speaker 7: bad staying in the EV space. This sort of makes 198 00:09:01,640 --> 00:09:03,640 Speaker 7: the argument for having a basket of all of these 199 00:09:03,720 --> 00:09:05,520 Speaker 7: names right, because you want the test, you want Tesla, 200 00:09:05,559 --> 00:09:07,760 Speaker 7: and then you also want who is the Tesla of China? 201 00:09:07,840 --> 00:09:07,920 Speaker 1: Right? 202 00:09:08,040 --> 00:09:09,720 Speaker 7: Is that Xkig, is that Li Auto? And it's good 203 00:09:09,760 --> 00:09:12,920 Speaker 7: to kind of have that broad based diversification. I think 204 00:09:12,960 --> 00:09:15,120 Speaker 7: the answer is that these are all going to be winners. 205 00:09:15,160 --> 00:09:15,280 Speaker 8: You know. 206 00:09:15,400 --> 00:09:19,480 Speaker 1: The piece of the entire pie of EV again is 207 00:09:19,559 --> 00:09:20,200 Speaker 1: growing globally. 208 00:09:20,320 --> 00:09:22,800 Speaker 7: Right, we talked about that fourteen percent going to thirty percent, 209 00:09:22,920 --> 00:09:25,600 Speaker 7: So massive upside for anyone who sort of gets it right. 210 00:09:25,640 --> 00:09:26,520 Speaker 1: In terms of Tesla. 211 00:09:26,920 --> 00:09:29,000 Speaker 7: The fact that they're talking about building a factory that's 212 00:09:29,120 --> 00:09:32,760 Speaker 7: larger than Volkswagen and BMW in you know, Germany, the 213 00:09:32,840 --> 00:09:35,360 Speaker 7: home of German autos, which is like you know, I mean, 214 00:09:35,559 --> 00:09:38,160 Speaker 7: the Michigan of Europe is just mind boggling to me. 215 00:09:38,320 --> 00:09:41,000 Speaker 7: So that tells you that the demand will continue there 216 00:09:41,040 --> 00:09:42,920 Speaker 7: and the opportunity for car sales continues there. 217 00:09:42,960 --> 00:09:44,520 Speaker 1: There's some amazing stats out there. 218 00:09:44,640 --> 00:09:47,200 Speaker 7: If you look at EV sales last year in China, 219 00:09:47,280 --> 00:09:49,720 Speaker 7: fifty percent of evs sold or fifty percent of car 220 00:09:49,840 --> 00:09:52,720 Speaker 7: sold were EV's and the Nordics, though it was eighty 221 00:09:52,840 --> 00:09:55,640 Speaker 7: nine percent of all vehicles sold and Europe that numbers 222 00:09:55,679 --> 00:09:59,760 Speaker 7: closer For sixty percent, So you know, he's following the 223 00:10:00,160 --> 00:10:00,839 Speaker 7: of interest. 224 00:10:00,640 --> 00:10:04,240 Speaker 3: There at a time where macro headwinds also there. 225 00:10:04,440 --> 00:10:06,080 Speaker 2: Just that's ran out of this conversation a little bit 226 00:10:06,160 --> 00:10:09,079 Speaker 2: with where you know, Musk started to focus on interest 227 00:10:09,160 --> 00:10:11,520 Speaker 2: rates still rising and the impact on the consumer. 228 00:10:11,600 --> 00:10:14,320 Speaker 3: How are you feeling about the macro picture right now? 229 00:10:15,320 --> 00:10:17,760 Speaker 1: Yeah, I'm actually a little more bullish on the macro picture. 230 00:10:17,960 --> 00:10:20,480 Speaker 7: And you know, we've talked Caroline before about some of 231 00:10:20,520 --> 00:10:22,520 Speaker 7: my stock picks that were getting crush last year in 232 00:10:22,559 --> 00:10:24,720 Speaker 7: twenty twenty two with interest rates rising. 233 00:10:24,600 --> 00:10:26,680 Speaker 1: On the tech space, and I think that that's playing 234 00:10:26,720 --> 00:10:27,000 Speaker 1: out now. 235 00:10:27,040 --> 00:10:28,599 Speaker 7: You know, there was a bear market last year and 236 00:10:28,679 --> 00:10:31,199 Speaker 7: tech interest rates were rising, and now we're coming to 237 00:10:31,280 --> 00:10:32,840 Speaker 7: the end of that. So I am in the camp 238 00:10:32,920 --> 00:10:35,520 Speaker 7: of if we get another hiker two, okay, it's it's 239 00:10:35,760 --> 00:10:38,800 Speaker 7: sort of manage what we're already there. I think that, 240 00:10:39,120 --> 00:10:42,400 Speaker 7: you know, the economy has withstood a major recession. Perhaps 241 00:10:42,400 --> 00:10:44,520 Speaker 7: you get a soft landing, but in terms of rates 242 00:10:44,559 --> 00:10:47,400 Speaker 7: specifically towards ev or other goods and services, you know, 243 00:10:47,480 --> 00:10:50,480 Speaker 7: I don't see prices necessarily kind of coming down, but 244 00:10:50,600 --> 00:10:54,319 Speaker 7: inflation is coming down. Prices will probably stabilize and it 245 00:10:54,440 --> 00:10:57,440 Speaker 7: will become you know, kind of easier to borrow money 246 00:10:57,480 --> 00:11:00,280 Speaker 7: to produce more vehicles, to you know, to take on 247 00:11:00,440 --> 00:11:02,240 Speaker 7: debt for innovation for growth companies. 248 00:11:02,320 --> 00:11:05,320 Speaker 1: So I think we'd have you know, some arrange bound volatility. 249 00:11:05,360 --> 00:11:07,559 Speaker 7: It will be kind of a slower year into the 250 00:11:07,679 --> 00:11:10,760 Speaker 7: year end, but if earnings hold up, then I think 251 00:11:10,840 --> 00:11:12,360 Speaker 7: the rally continues. 252 00:11:12,760 --> 00:11:15,240 Speaker 2: A little bit of dissinflation visa v deflation right now, 253 00:11:15,320 --> 00:11:18,320 Speaker 2: Sylvia Jablonsky, really great to have your take across Macro, 254 00:11:18,480 --> 00:11:20,920 Speaker 2: and of course so Micro and Tesla CEO and CIO 255 00:11:21,360 --> 00:11:22,600 Speaker 2: of Defiance ETF. 256 00:11:31,960 --> 00:11:33,880 Speaker 9: Only a small percentage of our members are on the 257 00:11:33,920 --> 00:11:36,280 Speaker 9: ads to here, even with the moves we just mentioned. 258 00:11:36,400 --> 00:11:38,360 Speaker 9: Nice growth in the ads to here, but still off 259 00:11:38,400 --> 00:11:41,640 Speaker 9: a small base, and we're really early in terms of 260 00:11:41,720 --> 00:11:45,000 Speaker 9: paid sharing impacts including extra member for the reasons that 261 00:11:45,080 --> 00:11:47,640 Speaker 9: Greg mentioned, that's going to build up over multiple quarters. 262 00:11:48,679 --> 00:11:49,400 Speaker 3: That's a newman there. 263 00:11:49,440 --> 00:11:53,000 Speaker 2: Netflix is CFO and trying to articulate why they're all 264 00:11:53,080 --> 00:11:56,079 Speaker 2: so early in this shift towards an advertising model. That's 265 00:11:56,120 --> 00:11:59,200 Speaker 2: continuing the conversation with Julie Alexander, director of strategy over 266 00:11:59,240 --> 00:12:01,400 Speaker 2: at Parent Analysts, and it was a really interesting set 267 00:12:01,440 --> 00:12:04,000 Speaker 2: of numbers. The fact that Netflix is down the most 268 00:12:04,040 --> 00:12:08,400 Speaker 2: it's Eightril twenty twenty two. Clearly people had some anxiety 269 00:12:08,480 --> 00:12:10,360 Speaker 2: around the fact that the revenue just doesn't seem to 270 00:12:10,400 --> 00:12:11,959 Speaker 2: be following the uptick in subscriptions. 271 00:12:13,520 --> 00:12:15,079 Speaker 1: Yeah, I think it speaks too. 272 00:12:15,200 --> 00:12:17,680 Speaker 10: I mean, let's be clear, Netflix had a really great 273 00:12:17,800 --> 00:12:20,120 Speaker 10: quarter when we're looking at Netflix's previous quarters. 274 00:12:20,400 --> 00:12:22,160 Speaker 11: This is the kind of uptic that we are hoping 275 00:12:22,200 --> 00:12:23,000 Speaker 11: to see from Netflix. 276 00:12:23,280 --> 00:12:25,360 Speaker 1: But that softer revenue is concerning. 277 00:12:25,679 --> 00:12:28,040 Speaker 10: It's only concerning if we think about this in terms 278 00:12:28,120 --> 00:12:31,520 Speaker 10: of that ARM number, that average revenue permember. Other companies 279 00:12:31,559 --> 00:12:34,360 Speaker 10: refer to this as typical RPO, that average revenue per user. 280 00:12:34,640 --> 00:12:36,840 Speaker 1: And so we see that three percent decline year over year. 281 00:12:37,200 --> 00:12:39,400 Speaker 10: Now that we know that as Netflix tries to onboard 282 00:12:39,440 --> 00:12:41,760 Speaker 10: these new subscribers, right, so we see that nice subscriber 283 00:12:41,800 --> 00:12:44,000 Speaker 10: growth in the most recent quarter. The question is what 284 00:12:44,120 --> 00:12:46,480 Speaker 10: are revenue are they generating for these customers that now 285 00:12:46,640 --> 00:12:49,280 Speaker 10: have different pricing options. They can come in at a 286 00:12:49,360 --> 00:12:52,240 Speaker 10: cheaper add tier, which actually is better for Netflix overall. 287 00:12:52,320 --> 00:12:54,920 Speaker 10: The ARM front because of that advertising revenue alongside the 288 00:12:54,960 --> 00:12:57,280 Speaker 10: subscription review is really strong. But a lot of these 289 00:12:57,360 --> 00:13:00,319 Speaker 10: countries where they're experimenting with the password sharing crack, and 290 00:13:00,360 --> 00:13:02,280 Speaker 10: where they're looking at how to kind of increase their 291 00:13:02,280 --> 00:13:05,040 Speaker 10: subscribers and leading into this immense demand that they see 292 00:13:05,080 --> 00:13:07,679 Speaker 10: across their portfolio. The questions are these members coming in 293 00:13:07,840 --> 00:13:10,240 Speaker 10: at a cheaper plan that maybe does not include subscribers 294 00:13:10,520 --> 00:13:13,360 Speaker 10: We know that Netflix includes options, for example in Latin America, 295 00:13:13,600 --> 00:13:15,440 Speaker 10: for some of these subscribers who are getting kicked off 296 00:13:15,480 --> 00:13:17,679 Speaker 10: their parents' plans, are getting kicked off their friends'. 297 00:13:17,440 --> 00:13:19,199 Speaker 11: Plans to go in and say, hey, I want to 298 00:13:19,200 --> 00:13:22,280 Speaker 11: come in a cheaper tier for the service. 299 00:13:22,480 --> 00:13:25,240 Speaker 10: And so how much revenue is Netflix really generating on 300 00:13:25,360 --> 00:13:28,160 Speaker 10: average across these different subscribers is going to be the 301 00:13:28,320 --> 00:13:31,120 Speaker 10: long term question. I think that's why you see Spencer 302 00:13:31,480 --> 00:13:34,439 Speaker 10: and you see Greg and you see tests Rando's. The 303 00:13:34,520 --> 00:13:36,880 Speaker 10: co CEOs, Greg Peters and teds Rando's speak to this 304 00:13:37,040 --> 00:13:40,360 Speaker 10: really important aspect of pushing people towards the advertising tier, 305 00:13:40,640 --> 00:13:43,720 Speaker 10: taking away certain plans like these basic ad free plans 306 00:13:43,800 --> 00:13:46,200 Speaker 10: right and kind of moving into this idea of having 307 00:13:46,440 --> 00:13:50,000 Speaker 10: a better position pricing power to really generate the revenue 308 00:13:50,040 --> 00:13:51,240 Speaker 10: that they need, especially. 309 00:13:50,920 --> 00:13:52,640 Speaker 1: As they kind of level out content spending. 310 00:13:52,960 --> 00:13:54,760 Speaker 2: So JUDI they did do that of course in the 311 00:13:54,920 --> 00:13:59,040 Speaker 2: UK and the US countries where people, of course canford 312 00:13:59,360 --> 00:14:01,760 Speaker 2: they uptake, can decide Okay, I'll go for the AD 313 00:14:01,920 --> 00:14:04,240 Speaker 2: supported cheap A model, or I'll have to pay more 314 00:14:04,600 --> 00:14:05,440 Speaker 2: to get AD free. 315 00:14:05,760 --> 00:14:07,440 Speaker 3: What do they do about emerging markets? 316 00:14:07,480 --> 00:14:10,200 Speaker 2: How do they ensure that they can have the revenue 317 00:14:10,240 --> 00:14:12,920 Speaker 2: they need from areas that they're still able to grow in. 318 00:14:13,160 --> 00:14:17,480 Speaker 10: Ultimately, I think what's really smart about Netflix's plan, And 319 00:14:17,559 --> 00:14:19,720 Speaker 10: let's be clear, I think there would actually be more 320 00:14:19,760 --> 00:14:23,800 Speaker 10: of a concern if the ARM didn't change, in part 321 00:14:23,840 --> 00:14:26,680 Speaker 10: because Netflix was not experimenting with its pricing power. I 322 00:14:26,720 --> 00:14:28,480 Speaker 10: think the fact that Netflix is saying we're going to 323 00:14:28,560 --> 00:14:31,520 Speaker 10: experiment with pricing power, We're going to experiment in markets 324 00:14:31,560 --> 00:14:34,080 Speaker 10: that we know local content is really important, and we 325 00:14:34,240 --> 00:14:37,560 Speaker 10: know that customers don't necessarily have the disposable income that 326 00:14:38,000 --> 00:14:41,000 Speaker 10: customers in the US, the UK, Canada tend to have. 327 00:14:41,440 --> 00:14:43,000 Speaker 10: We really want to figure out how we can be 328 00:14:43,120 --> 00:14:46,200 Speaker 10: the local, dominant entertainment source and all these different countries 329 00:14:46,280 --> 00:14:48,880 Speaker 10: right where Netflix is trying to monopolize attention is not 330 00:14:49,040 --> 00:14:51,200 Speaker 10: just as the global distributor of one type. 331 00:14:51,000 --> 00:14:54,240 Speaker 11: Of content, but being a local powerhouse and then sitting under. 332 00:14:54,080 --> 00:14:55,840 Speaker 1: This global umbrella that is Netflix. 333 00:14:56,120 --> 00:14:58,360 Speaker 10: And so when we think about that, what Netflix is 334 00:14:58,400 --> 00:15:00,640 Speaker 10: really I think building is the s of having a 335 00:15:00,840 --> 00:15:03,800 Speaker 10: prestige type of platform, a premium platform and then a 336 00:15:03,920 --> 00:15:07,200 Speaker 10: casual platform, and the casual platform and the premium platform, 337 00:15:07,360 --> 00:15:10,760 Speaker 10: unlike in other companies where that might be limited access 338 00:15:10,840 --> 00:15:12,600 Speaker 10: to certain titles, whether that might be. 339 00:15:14,440 --> 00:15:17,280 Speaker 1: The inability to actually access certain things on the platform. 340 00:15:17,320 --> 00:15:20,680 Speaker 10: And think about how Peacock has Premium Plus, how HBO Max, 341 00:15:20,880 --> 00:15:23,760 Speaker 10: how they differentiate between or now max rather how they 342 00:15:23,840 --> 00:15:27,560 Speaker 10: differentiate between the ad free platform and the ad supported platform. 343 00:15:27,920 --> 00:15:30,600 Speaker 10: Netflix is taking this into account of well, if we 344 00:15:30,680 --> 00:15:33,280 Speaker 10: get if we get rid of these basic plants that 345 00:15:33,280 --> 00:15:35,920 Speaker 10: don't have advertising, and we can make up that kind 346 00:15:36,000 --> 00:15:39,440 Speaker 10: of average revenue per member by bringing people into that 347 00:15:39,520 --> 00:15:42,120 Speaker 10: ad free plan, sorry, that ad supported plan, and then 348 00:15:42,160 --> 00:15:44,960 Speaker 10: continuing to build upon that and actually bringing an additional revenue. 349 00:15:45,160 --> 00:15:45,800 Speaker 1: Then in these. 350 00:15:45,680 --> 00:15:47,960 Speaker 10: Markets where we can keep our pricing really low and 351 00:15:48,120 --> 00:15:52,160 Speaker 10: see increased household penetration, especially in markets like in VR 352 00:15:52,200 --> 00:15:54,600 Speaker 10: or parts of Europe where the linear and paid TV 353 00:15:54,720 --> 00:15:57,040 Speaker 10: system is still pretty fundamental. 354 00:15:57,360 --> 00:15:59,760 Speaker 1: These are countries where they don't necessarily need streaming. 355 00:16:00,200 --> 00:16:02,920 Speaker 10: Internet access is still being developed in many ways, or 356 00:16:02,960 --> 00:16:05,280 Speaker 10: they're still kind of moving on to those types of plans. 357 00:16:05,560 --> 00:16:07,720 Speaker 10: Netflix is saying, we know that we need to penetrate, 358 00:16:07,760 --> 00:16:10,040 Speaker 10: and we know that that is our first goal. You'll 359 00:16:10,040 --> 00:16:12,240 Speaker 10: got a country like India where they're seeing strong penetration, 360 00:16:12,360 --> 00:16:15,080 Speaker 10: but they're taking it at a stronger loss on the 361 00:16:15,120 --> 00:16:15,760 Speaker 10: revenue front. 362 00:16:15,960 --> 00:16:19,520 Speaker 11: And unlike Disney, who's who's suggesting with Bob Buyer's recent. 363 00:16:19,320 --> 00:16:21,560 Speaker 10: Comments, we might move out of this market a little bit, 364 00:16:21,800 --> 00:16:24,080 Speaker 10: you have Netflix saying we really want to double down. 365 00:16:24,360 --> 00:16:28,280 Speaker 10: How can we use two different pricing power plans globally 366 00:16:28,560 --> 00:16:30,680 Speaker 10: to ensure that our revenue does not kind of see 367 00:16:30,720 --> 00:16:33,680 Speaker 10: these consistent slowdowns as we bring on these new customers. 368 00:16:33,840 --> 00:16:38,560 Speaker 2: Juday, what about content, Because what's interesting is is perhaps 369 00:16:38,920 --> 00:16:41,040 Speaker 2: Netflix less exposed to some of the strikes here in 370 00:16:41,040 --> 00:16:43,840 Speaker 2: the United States because they can make much more content 371 00:16:44,080 --> 00:16:46,000 Speaker 2: locally in these local markets. 372 00:16:47,280 --> 00:16:47,480 Speaker 1: Yeah. 373 00:16:47,520 --> 00:16:49,320 Speaker 10: I mean, you know, just some quick data points from 374 00:16:49,360 --> 00:16:51,880 Speaker 10: our firm, parent Analytics. You know, Netflix leads the pack 375 00:16:51,920 --> 00:16:54,600 Speaker 10: when it comes to on platform demand share, and what 376 00:16:54,760 --> 00:16:58,640 Speaker 10: that really suggests to us is this ability to retain customers. 377 00:16:58,840 --> 00:17:01,360 Speaker 10: This idea that when people are looking at an entire 378 00:17:01,440 --> 00:17:05,119 Speaker 10: platform across original licensed programming, rarely spending the majority of 379 00:17:05,160 --> 00:17:05,560 Speaker 10: their time. 380 00:17:05,840 --> 00:17:08,520 Speaker 11: Netflix has actually beaten out Hulu in Q one twenty 381 00:17:08,600 --> 00:17:09,080 Speaker 11: twenty three. 382 00:17:09,320 --> 00:17:11,879 Speaker 10: In Q two it beat out HBO Max again now 383 00:17:11,960 --> 00:17:15,000 Speaker 10: Max and with those combined Discovery plus Max assets, and 384 00:17:15,080 --> 00:17:17,840 Speaker 10: so Netflix still is kind of the home in many ways, 385 00:17:17,960 --> 00:17:20,160 Speaker 10: especially in the United States, to a lot of these customers. 386 00:17:20,760 --> 00:17:24,120 Speaker 10: Where Netflix is most insulated is that Netflix has two 387 00:17:24,640 --> 00:17:27,320 Speaker 10: or three main advantages. It has this global pipeline, and 388 00:17:27,359 --> 00:17:30,080 Speaker 10: we've seen demand for global content to increase. Now, I 389 00:17:30,160 --> 00:17:32,440 Speaker 10: want to be very clear here, the story of squid 390 00:17:32,520 --> 00:17:35,760 Speaker 10: games should not be that Netflix can create global hits 391 00:17:35,800 --> 00:17:37,680 Speaker 10: on the fly. It should be that they can create 392 00:17:37,760 --> 00:17:39,960 Speaker 10: really strong local hits that might be able to find 393 00:17:40,040 --> 00:17:44,040 Speaker 10: audiences outside of those territories and therefore increase the revenue 394 00:17:44,080 --> 00:17:45,760 Speaker 10: that they that they originally had planned for that. 395 00:17:46,440 --> 00:17:48,880 Speaker 1: And so if we think about we'll have to wrap 396 00:17:48,920 --> 00:17:49,159 Speaker 1: it up. 397 00:17:49,240 --> 00:17:51,639 Speaker 2: We've loved there's so much meat to the bones of 398 00:17:51,680 --> 00:17:54,840 Speaker 2: everything that you're bringing in terms of your proprietary data 399 00:17:54,880 --> 00:17:57,480 Speaker 2: and analytics, and we'll get into it a lot more later. 400 00:17:57,640 --> 00:17:59,640 Speaker 2: But thank you so much that that was a very 401 00:18:00,080 --> 00:18:02,480 Speaker 2: wide ranging conversation when it comes to all things Netflix 402 00:18:02,520 --> 00:18:06,119 Speaker 2: parent analytics Judio Alexander. Meanwhile, coming up a conversation with 403 00:18:06,280 --> 00:18:08,200 Speaker 2: our investment CEO and founder Kath Wood. 404 00:18:08,080 --> 00:18:18,800 Speaker 3: This is a bit back technology with tech earnings upon us. 405 00:18:18,920 --> 00:18:22,120 Speaker 2: How much will we actually see AI driver of revenue 406 00:18:22,440 --> 00:18:24,639 Speaker 2: or more just a driver of conversation? And what are 407 00:18:24,680 --> 00:18:27,520 Speaker 2: the risks still posed by the technology? Talked about all 408 00:18:27,560 --> 00:18:29,280 Speaker 2: of this and much more with the Arch investment CEO 409 00:18:29,359 --> 00:18:32,919 Speaker 2: and founder Kathy would and that Christie's art and tech summuch. 410 00:18:33,000 --> 00:18:33,480 Speaker 3: Take a listen. 411 00:18:34,440 --> 00:18:37,080 Speaker 12: One of the questions we get we're asked a lard 412 00:18:37,320 --> 00:18:42,199 Speaker 12: is about the displacement of jobs. I think you're worried 413 00:18:42,240 --> 00:18:46,040 Speaker 12: about the nefarious uses of artificial intelligent of humanity. 414 00:18:46,280 --> 00:18:47,600 Speaker 3: The people seem to be talking. 415 00:18:47,600 --> 00:18:54,800 Speaker 12: Yeah, so that on the second, all technologies can be 416 00:18:55,000 --> 00:18:59,080 Speaker 12: used for nefarious purposes. I think what's scaring people is 417 00:18:59,480 --> 00:19:03,040 Speaker 12: that this one is evolving so quickly. And one of 418 00:19:03,080 --> 00:19:07,080 Speaker 12: the reasons it's evolving so quickly is the rate at 419 00:19:07,119 --> 00:19:12,119 Speaker 12: which costs are declining. So AI training costs are dropping 420 00:19:12,480 --> 00:19:17,760 Speaker 12: seventy percent per year. And why is that it's actually 421 00:19:17,880 --> 00:19:20,760 Speaker 12: twice as fast as Morse law. It's the pace is 422 00:19:20,840 --> 00:19:24,960 Speaker 12: the learning curve associated with this technology, So just to 423 00:19:25,040 --> 00:19:30,240 Speaker 12: get put help people understand what this means. So chat 424 00:19:30,359 --> 00:19:34,879 Speaker 12: GPT came out of GPT three. If that model had 425 00:19:34,960 --> 00:19:38,680 Speaker 12: been developed in twenty fifteen, it would have cost eight 426 00:19:38,800 --> 00:19:44,640 Speaker 12: hundred million dollars. Instead it was twenty twenty cost four 427 00:19:44,680 --> 00:19:48,680 Speaker 12: and a half million dollars inceday, energy usees, intent, the 428 00:19:48,800 --> 00:19:52,680 Speaker 12: cost yes, the cost of yes, developing the model to 429 00:19:52,920 --> 00:19:58,560 Speaker 12: hardware software today, So eight hundred million eight years ago, 430 00:20:00,320 --> 00:20:02,320 Speaker 12: two and a half years ago, four and a half 431 00:20:02,400 --> 00:20:07,280 Speaker 12: million today less than four hundred thousand dollars. Now we're 432 00:20:07,320 --> 00:20:13,159 Speaker 12: building bigger models, but the costs are collapsing and the 433 00:20:13,240 --> 00:20:14,920 Speaker 12: opportunities therefore are exploding. 434 00:20:15,960 --> 00:20:21,480 Speaker 2: Is it exploding just for private companies for closed source 435 00:20:21,560 --> 00:20:25,760 Speaker 2: foundational models? Are you thinking that open source which in 436 00:20:25,920 --> 00:20:28,879 Speaker 2: many ways the art world is trying to struggle with 437 00:20:28,960 --> 00:20:33,520 Speaker 2: this sort of ownership versus sharing model as well. What 438 00:20:33,640 --> 00:20:36,280 Speaker 2: do you think will end up being the AI models 439 00:20:36,480 --> 00:20:37,879 Speaker 2: or can they all live together? 440 00:20:38,760 --> 00:20:43,200 Speaker 12: So we're studying this very carefully. So foundation models, the 441 00:20:43,320 --> 00:20:50,439 Speaker 12: open AI anthropic metas Lama, Google's palm. Now we have AJX, 442 00:20:52,080 --> 00:20:56,600 Speaker 12: the foundation models. We thought we're going to commoditize, and 443 00:20:56,880 --> 00:21:00,800 Speaker 12: we actually still think they will. You do open AI 444 00:21:00,960 --> 00:21:04,600 Speaker 12: and Microsoft together now announcing charges for all of these 445 00:21:04,640 --> 00:21:08,240 Speaker 12: AI assistants and so forth. But you have challengers. You 446 00:21:08,400 --> 00:21:11,600 Speaker 12: have Meta out there saying no, we're not, We're not. 447 00:21:11,880 --> 00:21:16,320 Speaker 12: This is open source. You have academic researchers, You've got 448 00:21:16,440 --> 00:21:22,600 Speaker 12: people in the technology community who who are passionate about 449 00:21:22,840 --> 00:21:24,520 Speaker 12: this open source movement. 450 00:21:25,119 --> 00:21:27,280 Speaker 2: Kathy with there, we'll talk much more about open source 451 00:21:27,359 --> 00:21:37,320 Speaker 2: in our VC spotlight. Wellcome back to bling back technology. 452 00:21:37,320 --> 00:21:39,200 Speaker 2: I'm Caroline Hyde in New York. Let's get you a 453 00:21:39,280 --> 00:21:41,320 Speaker 2: check on the market's halfway through this trading day, and 454 00:21:41,440 --> 00:21:44,840 Speaker 2: we are seeing negativity warries, particularly in the tech space. 455 00:21:45,200 --> 00:21:47,040 Speaker 2: We're looking over all, and then as that pulls down 456 00:21:47,080 --> 00:21:49,440 Speaker 2: one point three percent, those earnings looking lackluster for some 457 00:21:49,560 --> 00:21:51,560 Speaker 2: of the key tech names. How much can we justify 458 00:21:51,680 --> 00:21:54,760 Speaker 2: a record first half for the NASA one hundred in particular. 459 00:21:54,960 --> 00:21:57,320 Speaker 2: Now remember this is just falling that little bit lower, 460 00:21:57,359 --> 00:21:58,440 Speaker 2: but it is the worst day that it was see 461 00:21:58,440 --> 00:22:00,640 Speaker 2: since Dune the seventh for this index two year yield. 462 00:22:00,880 --> 00:22:03,879 Speaker 2: Also a movement away from the bond market is we're 463 00:22:03,920 --> 00:22:07,600 Speaker 2: seeing basis points move nine almost ten basis points on 464 00:22:07,680 --> 00:22:10,040 Speaker 2: the two years. So once again perhaps we see that 465 00:22:10,200 --> 00:22:12,280 Speaker 2: there's going to need to be a tackle of inflation. 466 00:22:12,480 --> 00:22:15,119 Speaker 2: Maybe this is more what's just ultimately happening in some 467 00:22:15,240 --> 00:22:17,840 Speaker 2: of the sell off that we've seen in terms of 468 00:22:18,040 --> 00:22:20,320 Speaker 2: yields pushing lower over the last few weeks, that we've 469 00:22:20,320 --> 00:22:22,440 Speaker 2: seen the CPI print come in, and we look forward 470 00:22:22,480 --> 00:22:24,680 Speaker 2: to all other inflationary prints we've had. Over in the UK, 471 00:22:24,800 --> 00:22:26,840 Speaker 2: it was looking good, and indeed in Europe we're seeing 472 00:22:26,840 --> 00:22:29,520 Speaker 2: the Bloomberg Commodity Index though up on the six tenths percent. 473 00:22:29,640 --> 00:22:31,760 Speaker 2: Maybe this is what's feeding into anxiety as to why 474 00:22:32,000 --> 00:22:34,760 Speaker 2: the Fed might still have to continue to increase interest rates. 475 00:22:34,880 --> 00:22:37,480 Speaker 2: It's because inflation is still evident, particularly in grains. We're 476 00:22:37,480 --> 00:22:40,440 Speaker 2: thinking of wheat pushing higher. Therefore, the Bloomberg Commodity Index 477 00:22:40,520 --> 00:22:42,280 Speaker 2: just on the upside. Moving on, let's look at the 478 00:22:42,320 --> 00:22:44,119 Speaker 2: individual names when it comes to our sector. Right here, 479 00:22:44,160 --> 00:22:46,359 Speaker 2: when I'm looking at tech IBM, look I focused on 480 00:22:46,440 --> 00:22:48,480 Speaker 2: one of the very few big tech names are on 481 00:22:48,520 --> 00:22:50,879 Speaker 2: the higher side today, IBM up more than three percent 482 00:22:50,960 --> 00:22:54,360 Speaker 2: as they managed to actually releave some anxiety about their 483 00:22:54,480 --> 00:22:57,400 Speaker 2: forward guidance in terms of yes, their second quarter didn't 484 00:22:57,600 --> 00:23:01,560 Speaker 2: perhaps match expectations on current but people really feel that 485 00:23:01,600 --> 00:23:03,600 Speaker 2: this is still a turnaround story that they're managing to 486 00:23:03,640 --> 00:23:05,880 Speaker 2: bring in the free cash flow at the moment. 487 00:23:06,000 --> 00:23:07,440 Speaker 3: So some analysts liking that number. 488 00:23:07,600 --> 00:23:12,159 Speaker 2: SAP, I'm showing the American Depository receipts of this German company. 489 00:23:12,359 --> 00:23:14,320 Speaker 2: It's the biggest software company over in Europe with down 490 00:23:14,359 --> 00:23:17,760 Speaker 2: more than five percent, even upgrade their outlooks for profitability, 491 00:23:17,840 --> 00:23:19,640 Speaker 2: but not enough and maybe some of the second quarter 492 00:23:19,680 --> 00:23:21,879 Speaker 2: looked a little bit shy, were worried about spending on 493 00:23:21,960 --> 00:23:25,000 Speaker 2: it over in Europe. And Tesla, I mean, down by 494 00:23:25,080 --> 00:23:27,919 Speaker 2: more than seven percent. Pretty crushing day. But remember how 495 00:23:28,000 --> 00:23:30,600 Speaker 2: far we have rallied on this particular stock as we 496 00:23:30,680 --> 00:23:32,919 Speaker 2: look at a company that's still focuses. 497 00:23:32,560 --> 00:23:34,080 Speaker 3: On volume over profitability. 498 00:23:34,480 --> 00:23:37,200 Speaker 2: Interesting that also Tesla was talking about that supercomputer that's 499 00:23:37,200 --> 00:23:39,440 Speaker 2: going to be driving its autonomous driving. We understand the 500 00:23:39,520 --> 00:23:44,200 Speaker 2: billion being spent there, dojo. Let's move across to another supercomputer, Cerebras, 501 00:23:44,560 --> 00:23:48,120 Speaker 2: known for its AI compute processes and cloud services. It's 502 00:23:48,440 --> 00:23:52,359 Speaker 2: operating and managing one of the largest AI supercomputers in 503 00:23:52,440 --> 00:23:52,840 Speaker 2: the world. 504 00:23:53,000 --> 00:23:57,000 Speaker 3: It's called Condoor Galaxy one or CG one for short 505 00:23:57,280 --> 00:23:59,200 Speaker 3: hows in Santa Clara, California. 506 00:23:59,400 --> 00:24:02,479 Speaker 2: It can be built to help train generative AI models 507 00:24:02,640 --> 00:24:05,120 Speaker 2: at a faster rate than ever before. They claim, CG 508 00:24:05,280 --> 00:24:08,200 Speaker 2: one has already been purchased by one of its strategic 509 00:24:08,280 --> 00:24:11,200 Speaker 2: partners based over an Abidabi. Let's talk now to the CEO, 510 00:24:11,560 --> 00:24:14,119 Speaker 2: Andrew Feldman of Cerebras. Andrew, it's great to have some 511 00:24:14,240 --> 00:24:16,040 Speaker 2: time with you. And look, it's a lot of money, 512 00:24:16,119 --> 00:24:18,320 Speaker 2: what one hundred million for one of these supercomputers, and 513 00:24:18,320 --> 00:24:19,920 Speaker 2: I'm interested as to what Abu Dabi is going to 514 00:24:19,960 --> 00:24:20,399 Speaker 2: be using it for. 515 00:24:22,040 --> 00:24:22,200 Speaker 1: Well. 516 00:24:22,280 --> 00:24:25,359 Speaker 13: First, you know, today we announced a strategic partnership with 517 00:24:25,720 --> 00:24:29,080 Speaker 13: G forty two and the first deliverable from that strategic 518 00:24:29,160 --> 00:24:32,359 Speaker 13: partnership was one of the largest AI supercomputers in the world. 519 00:24:33,200 --> 00:24:38,320 Speaker 13: And as you and your audience knows, there's a yawning 520 00:24:38,400 --> 00:24:43,440 Speaker 13: demand right now for AI compute. And this demand is 521 00:24:43,520 --> 00:24:47,040 Speaker 13: not just domestic in the US. We're seeing it globally, 522 00:24:47,960 --> 00:24:51,000 Speaker 13: and by working together with G forty two, we think 523 00:24:51,080 --> 00:24:54,520 Speaker 13: we can build a constellation not just one, but nine 524 00:24:55,720 --> 00:25:00,480 Speaker 13: AI supercomputers and fundamentally change the global inventory of compute. 525 00:25:00,840 --> 00:25:04,280 Speaker 2: Okay, Chris, many might rightly or wrongly have really put 526 00:25:04,320 --> 00:25:06,359 Speaker 2: all their eggs in the in video basket, feeling that 527 00:25:06,680 --> 00:25:08,639 Speaker 2: is where we're going to be satiated in terms of 528 00:25:08,680 --> 00:25:11,320 Speaker 2: our compute power and necessity. And you're saying that's not true, 529 00:25:11,440 --> 00:25:13,280 Speaker 2: or at least we don't need to just focus on 530 00:25:13,440 --> 00:25:15,879 Speaker 2: some of these large, big tech American players. 531 00:25:16,880 --> 00:25:19,120 Speaker 13: I think that's exactly right. I think first, all your 532 00:25:19,119 --> 00:25:22,000 Speaker 13: eggs in one basket has historically been a poor strategy. 533 00:25:22,480 --> 00:25:26,320 Speaker 13: I think dependence is anywhere in your supply chain is 534 00:25:26,359 --> 00:25:30,200 Speaker 13: probably not the right strategy. But there are visionary companies 535 00:25:30,760 --> 00:25:35,680 Speaker 13: like G forty two around the globe. We're seeing models 536 00:25:35,760 --> 00:25:39,760 Speaker 13: both foundation in closed source and open source in Europe, 537 00:25:40,640 --> 00:25:44,000 Speaker 13: coming from the Middle East, coming from Abudhabi, coming from Singapore, 538 00:25:44,920 --> 00:25:49,680 Speaker 13: from Japan, from South Korea. And this is a phenomena 539 00:25:49,720 --> 00:25:55,080 Speaker 13: that extends well beyond the six or seven US hyperscalers. Right, 540 00:25:55,359 --> 00:26:00,080 Speaker 13: this is a need for compute that we thought a 541 00:26:00,200 --> 00:26:03,879 Speaker 13: partnership with G forty two could help me to a 542 00:26:03,960 --> 00:26:04,760 Speaker 13: global demand. 543 00:26:05,160 --> 00:26:06,760 Speaker 3: Can you push us into the future a little bit. 544 00:26:06,800 --> 00:26:09,840 Speaker 2: We were talking with Kathy Wood about closed source open 545 00:26:09,920 --> 00:26:14,040 Speaker 2: source and you over at Cerebris are basically helping train 546 00:26:14,200 --> 00:26:17,280 Speaker 2: GPT models and then putting them into the open source community. 547 00:26:17,359 --> 00:26:17,920 Speaker 3: Why are you doing that? 548 00:26:18,040 --> 00:26:22,440 Speaker 2: What do you think ultimately the AI landscape looks like 549 00:26:22,840 --> 00:26:23,720 Speaker 2: in five years or so. 550 00:26:25,200 --> 00:26:28,440 Speaker 13: Ok. I think one of the things she spoke about 551 00:26:28,480 --> 00:26:33,280 Speaker 13: in her session was just how fast the AI community 552 00:26:33,359 --> 00:26:36,120 Speaker 13: is moving. And it's been moving that quickly in part 553 00:26:36,240 --> 00:26:40,000 Speaker 13: because of the open source and not just open source, 554 00:26:40,080 --> 00:26:44,520 Speaker 13: but open publishing mentality. People have put ideas into the 555 00:26:44,560 --> 00:26:48,120 Speaker 13: space again and again, and within weeks or months, those 556 00:26:48,160 --> 00:26:52,560 Speaker 13: ideas are built on, they're improved, they're furthered and this 557 00:26:52,840 --> 00:26:58,600 Speaker 13: is produced a sort of a Cambrian explosion of ideas. 558 00:26:59,359 --> 00:27:03,200 Speaker 13: And we have been leaders in putting large models into 559 00:27:03,240 --> 00:27:07,440 Speaker 13: the open source community. Our partner G forty two through 560 00:27:07,480 --> 00:27:11,480 Speaker 13: its companies, has been pushing open source models into the community, 561 00:27:12,400 --> 00:27:15,240 Speaker 13: and many others have. We're not alone at all, other 562 00:27:15,359 --> 00:27:19,879 Speaker 13: hardware makers, other software makers. I think the community, the 563 00:27:19,960 --> 00:27:24,480 Speaker 13: ecosystem is more healthy if it's not just a small 564 00:27:24,600 --> 00:27:27,919 Speaker 13: number of compute providers and a small number of model makers. 565 00:27:28,400 --> 00:27:30,600 Speaker 3: What about the China US divide, though. 566 00:27:33,280 --> 00:27:37,760 Speaker 13: It's an important divide, and you know there we have 567 00:27:38,000 --> 00:27:41,800 Speaker 13: to think carefully that there are no easy answers about 568 00:27:42,680 --> 00:27:47,000 Speaker 13: powerful technology and the right way to manage their distribution 569 00:27:47,119 --> 00:27:52,560 Speaker 13: around the world. I think it's it's an extraordinarily challenging 570 00:27:52,680 --> 00:27:55,879 Speaker 13: problem and one that the politicians have to have to 571 00:27:56,000 --> 00:27:56,480 Speaker 13: work through. 572 00:27:57,080 --> 00:27:59,520 Speaker 2: Meanwhile, though, the politicians have to balance the risks with 573 00:27:59,680 --> 00:28:02,240 Speaker 2: the real wards. Many would say key rewards being the 574 00:28:02,280 --> 00:28:05,280 Speaker 2: application of AI in science and healthcare and the like. 575 00:28:05,440 --> 00:28:07,760 Speaker 2: Which industries have you been most impressed with the way 576 00:28:07,800 --> 00:28:11,360 Speaker 2: in which they've adopted and used AI, particularly using your supercomputer. 577 00:28:13,320 --> 00:28:14,080 Speaker 1: That's exactly right. 578 00:28:14,280 --> 00:28:19,000 Speaker 13: I think the regulation has to balance the drive for 579 00:28:19,080 --> 00:28:22,879 Speaker 13: innovation and how startups like ourselves and large partners of 580 00:28:23,600 --> 00:28:26,719 Speaker 13: startups like G forty two can drive innovation. I mean, 581 00:28:26,920 --> 00:28:30,960 Speaker 13: just recently we've seen through our partnership with G forty 582 00:28:31,000 --> 00:28:35,680 Speaker 13: two use of AI models and healthcare. We have partners 583 00:28:36,000 --> 00:28:41,280 Speaker 13: who do drug design with AI models. We see digital 584 00:28:41,320 --> 00:28:47,760 Speaker 13: assistance helping the elderly. We see in nearly every realm 585 00:28:47,800 --> 00:28:49,800 Speaker 13: of life and how we live, or work or play, 586 00:28:50,640 --> 00:28:54,600 Speaker 13: there's a role for AI. And you know you're showing 587 00:28:54,800 --> 00:28:58,840 Speaker 13: some of our customers, ask Zeneca, Total Bear, Jasper all 588 00:28:59,000 --> 00:29:04,000 Speaker 13: using AI in profoundly different ways, I think, and G 589 00:29:04,240 --> 00:29:06,760 Speaker 13: forty two will use it in still different ways too. 590 00:29:07,760 --> 00:29:11,040 Speaker 13: I think they are underrepresented languages. Arabic is one of 591 00:29:11,080 --> 00:29:16,120 Speaker 13: them where there's an opportunity for tremendous expansion in. 592 00:29:16,160 --> 00:29:17,640 Speaker 3: The role of AI. 593 00:29:18,320 --> 00:29:22,040 Speaker 13: But I think we have to work together. I think 594 00:29:22,080 --> 00:29:25,800 Speaker 13: your point about export is real and a challenge. But 595 00:29:25,880 --> 00:29:27,360 Speaker 13: the community has come together. 596 00:29:27,160 --> 00:29:27,640 Speaker 1: In the past. 597 00:29:27,760 --> 00:29:30,640 Speaker 13: We've built standards together, competitors come together and said this 598 00:29:30,800 --> 00:29:34,960 Speaker 13: is the standard. We can overcome geopolitical challenges as well. 599 00:29:35,280 --> 00:29:37,960 Speaker 2: Andrew Fellman, thanks for the time and the insights. The 600 00:29:38,040 --> 00:29:39,920 Speaker 2: CEO A cerebris No, it's a pleasure. 601 00:29:39,960 --> 00:29:40,720 Speaker 13: Thank you for having me. 602 00:29:41,160 --> 00:29:44,360 Speaker 2: Meanwhile, let's talk about the power of artificial intelligence and 603 00:29:44,760 --> 00:29:48,080 Speaker 2: what is US lawmakers doing about it? Scrambling to impose 604 00:29:48,160 --> 00:29:50,880 Speaker 2: limits on AI disinformation in particular ahead of twenty twenty 605 00:29:50,920 --> 00:29:54,520 Speaker 2: fours elections. So far, Democrats have unveiled some pretty modest 606 00:29:54,560 --> 00:29:57,800 Speaker 2: proposals calling for more transparency and campaign ads. One bill 607 00:29:57,880 --> 00:30:01,080 Speaker 2: seeks labeling on political ads the use of deep fakes 608 00:30:01,120 --> 00:30:04,040 Speaker 2: and other forms of AI. But Republicans it's been kind 609 00:30:04,080 --> 00:30:06,920 Speaker 2: of slow to sign on. Nevertheless, both actual sides of 610 00:30:06,960 --> 00:30:09,040 Speaker 2: the aisle have been pretty slow to grasp for really 611 00:30:09,160 --> 00:30:13,240 Speaker 2: comprehensive understanding, Like many of us are the rebby evolving technology, 612 00:30:13,560 --> 00:30:15,560 Speaker 2: and in fact we understand there at least months away 613 00:30:15,560 --> 00:30:21,920 Speaker 2: from introducing comprehensive legislation to mitigate AI's most serious disinformation threats. Meanwhile, 614 00:30:22,080 --> 00:30:25,320 Speaker 2: let's look at us social media giants or ready taking 615 00:30:25,440 --> 00:30:28,640 Speaker 2: action in India complying with the government. There, Facebook, Google 616 00:30:28,680 --> 00:30:31,680 Speaker 2: and Twitter are already removing a violent video There was 617 00:30:31,680 --> 00:30:35,000 Speaker 2: actually from May, but that's gone viral overnight. According to 618 00:30:35,040 --> 00:30:38,000 Speaker 2: people familiar, some social media companies began removing photos and 619 00:30:38,080 --> 00:30:41,000 Speaker 2: videos of an incident as it violated their rules even 620 00:30:41,040 --> 00:30:44,320 Speaker 2: before New Delhi issued emergency blocking orders. Video triggered the 621 00:30:44,360 --> 00:30:46,840 Speaker 2: first public comments from Prime Minister Modi on the violence 622 00:30:47,000 --> 00:30:50,160 Speaker 2: in Manipur state, where ethnic groups have clashed for nearly 623 00:30:50,200 --> 00:30:52,960 Speaker 2: two months. Though some journalists have voiced concerned about blocking 624 00:30:53,040 --> 00:30:55,600 Speaker 2: orders are obstructing news publishers' ability. 625 00:30:55,320 --> 00:30:56,640 Speaker 3: To report on the event. 626 00:30:57,640 --> 00:31:01,320 Speaker 2: Meanwhile, coming up, why all companies will need to incorporate 627 00:31:01,400 --> 00:31:04,960 Speaker 2: II into that operations to survive. Calling to a Gray 628 00:31:05,000 --> 00:31:07,680 Speaker 2: Lot partner Jerry Chen, we'll discuss that this is blue 629 00:31:07,720 --> 00:31:08,360 Speaker 2: meg technology. 630 00:31:23,120 --> 00:31:24,640 Speaker 3: Time Now for VC roundup. 631 00:31:24,680 --> 00:31:27,400 Speaker 2: First up, Sequoia Capital shaking up its team after a 632 00:31:27,440 --> 00:31:31,200 Speaker 2: turbulentlyar of market upheaval, a breakup, and at least five 633 00:31:31,280 --> 00:31:35,720 Speaker 2: investor departures, including two crypto focused investors Michelle Frodin Daniel Chen. 634 00:31:36,080 --> 00:31:38,720 Speaker 2: In the breakup, the firm also spun off Sequoia Heritage. 635 00:31:38,760 --> 00:31:41,280 Speaker 2: It's a wealth management business, which Michael Morrits will be 636 00:31:41,320 --> 00:31:44,320 Speaker 2: focusing on following his departure. Meanwhile, m and A and 637 00:31:44,400 --> 00:31:47,320 Speaker 2: investment activity at the largest tech in life science firms 638 00:31:47,520 --> 00:31:49,200 Speaker 2: finished the first half of twenty twenty three on a 639 00:31:49,240 --> 00:31:52,600 Speaker 2: disappointing streak, with pending and completed deals down forty five 640 00:31:52,640 --> 00:31:55,080 Speaker 2: percent compared to the start of twenty twenty two. If 641 00:31:55,120 --> 00:31:58,040 Speaker 2: it continues on twenty twenty three, total deal count could 642 00:31:58,040 --> 00:32:01,280 Speaker 2: be one of the lowest for Silicon Valley decades. Plus, 643 00:32:01,360 --> 00:32:04,960 Speaker 2: a US Congressional committee is investigating for Benure Capital firms 644 00:32:05,040 --> 00:32:09,040 Speaker 2: for their investments in Chinese tech companies GGV Capital, GSR Ventures, 645 00:32:09,040 --> 00:32:11,800 Speaker 2: Walden International, and qual Con Ventures. We're all being probed 646 00:32:11,800 --> 00:32:13,480 Speaker 2: at the moment. This is both the White House and 647 00:32:13,600 --> 00:32:16,800 Speaker 2: members of Congress acrafting policies to track and potentially block 648 00:32:16,920 --> 00:32:20,920 Speaker 2: US investments in certain fields in China. Well, let's get 649 00:32:21,160 --> 00:32:23,400 Speaker 2: more into the micro of what checks are being written 650 00:32:23,480 --> 00:32:25,040 Speaker 2: right now in the world of Benure Capital, and on 651 00:32:25,120 --> 00:32:27,680 Speaker 2: today's VC Spotlight, I'm pleased to bring in Jerry Chen, 652 00:32:27,880 --> 00:32:31,880 Speaker 2: partner at gray Lot Partners. Who's focus I'm sure, along 653 00:32:31,920 --> 00:32:34,400 Speaker 2: with a lot of your capadres at the moment, has 654 00:32:34,440 --> 00:32:37,800 Speaker 2: been artificial intelligence. How much are your current portfolio companies 655 00:32:37,880 --> 00:32:41,600 Speaker 2: managing to embrace and a D maybe pivot towards it. 656 00:32:42,640 --> 00:32:46,400 Speaker 14: Hey, Caroline, thanks for having me today. Everything's AI today. 657 00:32:46,440 --> 00:32:48,440 Speaker 14: So I think we said in a recent blog the 658 00:32:48,560 --> 00:32:51,280 Speaker 14: new new modes, it's AI or die. So it's not 659 00:32:51,480 --> 00:32:55,120 Speaker 14: like am I AIVC or is this AI VC fund? 660 00:32:55,480 --> 00:33:01,120 Speaker 14: Pretty much AIS touching healthcare, fin tech, consumer tech, door, storage, security, 661 00:33:01,280 --> 00:33:04,360 Speaker 14: So pretty much every partner at graylock folks on AI, 662 00:33:04,520 --> 00:33:05,360 Speaker 14: my self included. 663 00:33:06,200 --> 00:33:11,040 Speaker 2: Can you help us understand where the immediate opportunities are? 664 00:33:11,120 --> 00:33:12,080 Speaker 3: What's sort of been interesting. 665 00:33:12,120 --> 00:33:15,640 Speaker 2: There's been various reporting about perhaps how Jasper's been having 666 00:33:15,680 --> 00:33:19,840 Speaker 2: to cut back on its headcount, how actually experimental CEOs 667 00:33:20,120 --> 00:33:23,640 Speaker 2: and their engineers within big companies are willing to adopt 668 00:33:23,640 --> 00:33:27,360 Speaker 2: maybe open source models rather than purchasing the sort of 669 00:33:27,520 --> 00:33:30,400 Speaker 2: easier just plugins that many anticipated they would go with. First, 670 00:33:30,560 --> 00:33:33,040 Speaker 2: how are you seeing actual adoption of some of the 671 00:33:33,080 --> 00:33:34,200 Speaker 2: companies you've been investing in. 672 00:33:35,000 --> 00:33:36,280 Speaker 3: So I would say two things. 673 00:33:36,360 --> 00:33:40,320 Speaker 14: One, your interest from customers consumers is spot On. So 674 00:33:40,400 --> 00:33:43,880 Speaker 14: it'd say, like fifteen years ago, every at large customer 675 00:33:43,960 --> 00:33:46,239 Speaker 14: header had a cloud strategy, right, they had to talk 676 00:33:46,280 --> 00:33:48,200 Speaker 14: about how we're going to move to Amazon or as 677 00:33:48,360 --> 00:33:51,360 Speaker 14: or a Google cloud. Now every company out there, big 678 00:33:51,440 --> 00:33:54,160 Speaker 14: and small, has to have an AI strategy. So first 679 00:33:54,200 --> 00:33:57,960 Speaker 14: and foremost every portfolio company, Graylock is kind of benefiting 680 00:33:58,000 --> 00:34:00,800 Speaker 14: from the instant AI. But then the second question asked 681 00:34:00,920 --> 00:34:04,960 Speaker 14: is the difference between plugins, between models, between open source 682 00:34:05,320 --> 00:34:07,479 Speaker 14: and that's a question we had Graylock have been asking, 683 00:34:07,640 --> 00:34:09,800 Speaker 14: like where are the motes right? Where are the defential 684 00:34:09,840 --> 00:34:12,920 Speaker 14: business models in AI going forward? These some world where 685 00:34:12,960 --> 00:34:17,360 Speaker 14: we have chat GBT or Lama tea for Meta or 686 00:34:17,480 --> 00:34:19,919 Speaker 14: Palm from Google, Like what can you do to build 687 00:34:19,960 --> 00:34:22,080 Speaker 14: a mote around the AI models? 688 00:34:22,120 --> 00:34:23,279 Speaker 3: And so we've been debating that. 689 00:34:23,840 --> 00:34:27,479 Speaker 14: In one hand, you can talk about system engagement like chat, 690 00:34:27,640 --> 00:34:30,800 Speaker 14: like chat Gibt or bard. Chat has also become the 691 00:34:30,880 --> 00:34:33,600 Speaker 14: way to interact to AI, and it's debatable what that 692 00:34:34,080 --> 00:34:36,640 Speaker 14: persists or not. We're investors in a company called Pie 693 00:34:37,160 --> 00:34:40,399 Speaker 14: Inflection starre by Mustafa and my partner Ried Hoffmann. That's 694 00:34:40,480 --> 00:34:45,160 Speaker 14: all about using chat to talk to AI. There's infrastructure layers. 695 00:34:45,280 --> 00:34:47,600 Speaker 14: Companies like Lama Index, a new investment we just did 696 00:34:47,840 --> 00:34:50,839 Speaker 14: that talks about taking your enterprise data, interacting with these 697 00:34:50,880 --> 00:34:53,720 Speaker 14: open source models, and then comes like adept or inflection 698 00:34:53,800 --> 00:34:55,800 Speaker 14: and building these fundamental models themselves. 699 00:34:55,920 --> 00:34:56,240 Speaker 15: Carolyn. 700 00:34:56,360 --> 00:34:59,239 Speaker 14: So it's pretty cool because we're investing up and down 701 00:34:59,239 --> 00:35:01,680 Speaker 14: the stack. I think it's early innings to try to 702 00:35:01,719 --> 00:35:03,480 Speaker 14: figure out where all the money's going to accrue to. 703 00:35:04,200 --> 00:35:05,799 Speaker 3: Do you think at the moment some of the money 704 00:35:05,880 --> 00:35:07,480 Speaker 3: is being spent wrongly? 705 00:35:07,800 --> 00:35:09,560 Speaker 2: Have we got a sort of mini bubble on our 706 00:35:09,600 --> 00:35:12,400 Speaker 2: hands in terms of valuations and where it's being allocated. 707 00:35:13,360 --> 00:35:14,879 Speaker 15: I don't think it's it's right or wrong. 708 00:35:14,920 --> 00:35:16,880 Speaker 14: I think we're going to look back in time in 709 00:35:16,960 --> 00:35:18,960 Speaker 14: five or ten years to say that was a mistake. 710 00:35:19,000 --> 00:35:21,359 Speaker 14: I think there's a lot of experimentation going on right. 711 00:35:21,640 --> 00:35:25,080 Speaker 14: I think we're experimenting at the foundation models. We're experimenting 712 00:35:25,120 --> 00:35:28,120 Speaker 14: as a chat layer, we're experimented infrastructural layer. So I 713 00:35:28,200 --> 00:35:30,840 Speaker 14: think there's a lot of experimentation on how AI is 714 00:35:30,880 --> 00:35:34,960 Speaker 14: going to change a consumer tech, enterprise, tech security. I 715 00:35:35,040 --> 00:35:36,840 Speaker 14: think in the next two or three years we're going 716 00:35:36,920 --> 00:35:40,439 Speaker 14: to figure out where one which models work too, which 717 00:35:40,520 --> 00:35:43,080 Speaker 14: verticals make sense, be I, legal, be a healthcare be 718 00:35:43,200 --> 00:35:45,160 Speaker 14: a CRM. I think we're going to figure that out 719 00:35:45,200 --> 00:35:47,680 Speaker 14: over time, and then I think as investors we're going 720 00:35:47,760 --> 00:35:50,399 Speaker 14: to learn also, you know what business models make sense, 721 00:35:50,520 --> 00:35:53,040 Speaker 14: And you know my bias or my spoiler alert is 722 00:35:53,440 --> 00:35:55,840 Speaker 14: I think you know the old modes of our network 723 00:35:55,880 --> 00:36:00,480 Speaker 14: effects go to market strength, brand marketing. All those weapons 724 00:36:00,520 --> 00:36:03,320 Speaker 14: you employ as a startup founder in the past apply 725 00:36:03,480 --> 00:36:06,560 Speaker 14: even more going forward in AI. Right, So AI is great, 726 00:36:06,800 --> 00:36:09,000 Speaker 14: but I think Carolyn, like, the things that made money 727 00:36:09,000 --> 00:36:10,800 Speaker 14: in the past will make money in the future. 728 00:36:11,360 --> 00:36:14,880 Speaker 3: Where are those founders, sorry, where are the founders? 729 00:36:16,200 --> 00:36:19,000 Speaker 14: Well, I mean I think founders today AI are largely 730 00:36:19,040 --> 00:36:22,440 Speaker 14: coming out of two areas. One, they're coming from research 731 00:36:22,520 --> 00:36:25,520 Speaker 14: around AI. So you see a cluster of AI researchers 732 00:36:25,560 --> 00:36:28,800 Speaker 14: from open AI, from meta, from Google, folks that actually 733 00:36:28,880 --> 00:36:31,480 Speaker 14: know the deep technology. Number Two, I think you see 734 00:36:31,480 --> 00:36:33,520 Speaker 14: a cluster of founders coming out of domains. 735 00:36:33,840 --> 00:36:35,680 Speaker 3: So people are thinking like, Okay, I want to. 736 00:36:35,640 --> 00:36:40,840 Speaker 14: Apply AI, but I really know healthcare, legal, CRM customer 737 00:36:40,880 --> 00:36:44,200 Speaker 14: support very very well, and trying to marry the two together. 738 00:36:44,360 --> 00:36:46,120 Speaker 1: So I think we're investing in deep. 739 00:36:46,000 --> 00:36:49,360 Speaker 14: Tech founders, you're really academic researchers. And then we're investing 740 00:36:49,440 --> 00:36:52,360 Speaker 14: in domain experts that say, hey, I understand what the 741 00:36:52,440 --> 00:36:55,439 Speaker 14: problem here is. Oh holy shoot, Like I can solve 742 00:36:55,520 --> 00:36:57,759 Speaker 14: this problem with AI that I could in the past. 743 00:36:57,840 --> 00:37:00,640 Speaker 14: And AI is unlocking these domain ex first to do 744 00:37:00,760 --> 00:37:01,480 Speaker 14: things they can do. 745 00:37:01,600 --> 00:37:05,200 Speaker 16: Three four years ago, jey Chen with the applications of AI, 746 00:37:05,760 --> 00:37:07,759 Speaker 16: still trying to come to some really where all the 747 00:37:07,840 --> 00:37:09,960 Speaker 16: value is going to come from, as we will our partner, 748 00:37:09,960 --> 00:37:11,719 Speaker 16: a great up partner, is really interesting to have some 749 00:37:11,760 --> 00:37:12,120 Speaker 16: time with you. 750 00:37:12,239 --> 00:37:14,400 Speaker 3: Thank you. Meanwhile, let's stick with. 751 00:37:14,520 --> 00:37:16,680 Speaker 2: The world of bench Capital because mag so how many 752 00:37:16,719 --> 00:37:19,680 Speaker 2: changs sat down with Benchmark general partner Bill Gurley to 753 00:37:19,719 --> 00:37:22,239 Speaker 2: discuss the keys to his success over the decades long 754 00:37:22,320 --> 00:37:23,520 Speaker 2: career as a tech investor. 755 00:37:23,840 --> 00:37:25,440 Speaker 3: Is his take on what it takes to be a 756 00:37:25,480 --> 00:37:25,879 Speaker 3: great VC. 757 00:37:27,280 --> 00:37:30,280 Speaker 15: To be great at it requires a level of hustle 758 00:37:30,400 --> 00:37:35,799 Speaker 15: that's really hard to explain. And the reason is you're 759 00:37:35,920 --> 00:37:39,520 Speaker 15: trying to maximize the optionality that you get in front 760 00:37:39,560 --> 00:37:43,440 Speaker 15: of a home run pitch, and to do that, you 761 00:37:43,680 --> 00:37:47,200 Speaker 15: have to look under every rock you possibly can. And 762 00:37:47,360 --> 00:37:51,600 Speaker 15: so if you're not studying some new business model or 763 00:37:51,680 --> 00:37:55,320 Speaker 15: new platform, you know, AI, if you're not just in 764 00:37:55,600 --> 00:37:59,360 Speaker 15: every meeting, then you're lowering the chance that you're going 765 00:37:59,440 --> 00:38:02,240 Speaker 15: to be success. So it's an exhausting games. 766 00:38:02,840 --> 00:38:05,080 Speaker 2: Catch the whole conversation circle with them and chang tonight 767 00:38:05,160 --> 00:38:07,919 Speaker 2: ten pm A New York on Bloomberg Television, or stream 768 00:38:07,960 --> 00:38:10,120 Speaker 2: it at eight pm on Bloomberg Originals. 769 00:38:18,840 --> 00:38:23,520 Speaker 17: Going to have a profound impact on virtually every element 770 00:38:23,600 --> 00:38:27,000 Speaker 17: of the global economies. Obviously some areas more than others. 771 00:38:27,360 --> 00:38:30,200 Speaker 17: There are businesses that will be more vulnerable. If you 772 00:38:30,320 --> 00:38:33,879 Speaker 17: think about lower value add service businesses, maybe a call 773 00:38:34,000 --> 00:38:37,480 Speaker 17: center business, You've got to be cautious. There'll be businesses 774 00:38:37,760 --> 00:38:41,080 Speaker 17: like data centers where we've seen a step function increase 775 00:38:41,160 --> 00:38:41,680 Speaker 17: in demand. 776 00:38:43,040 --> 00:38:46,320 Speaker 2: Backstam president John Gray on the impact, of course of AI, 777 00:38:46,680 --> 00:38:49,359 Speaker 2: and let's stick with it again, artificial intelligence and well, 778 00:38:49,400 --> 00:38:53,320 Speaker 2: the infrastructure that sort of powers it TSMC out with earnings. 779 00:38:53,760 --> 00:38:56,839 Speaker 2: The chip maker is cutting its annual outlook for revenue more. 780 00:38:56,960 --> 00:39:01,080 Speaker 2: Let's bring in Kunjin Sivanni from Bloomberg Intelligence. Well, it 781 00:39:01,200 --> 00:39:04,000 Speaker 2: seemed to me that we had first and foremost a 782 00:39:04,120 --> 00:39:06,840 Speaker 2: high profile executive saying we don't know if AI is 783 00:39:06,880 --> 00:39:09,680 Speaker 2: here to stay, if this frenzy in demand is going 784 00:39:09,719 --> 00:39:10,279 Speaker 2: to be long term. 785 00:39:12,960 --> 00:39:16,120 Speaker 8: Yeah, So I think from TSMC perspective. What the guidance 786 00:39:16,160 --> 00:39:20,800 Speaker 8: suggests that the macroeconomic headwinds, especially demand weakness in China, 787 00:39:21,200 --> 00:39:23,680 Speaker 8: is expected to result in a continued slow down in 788 00:39:23,760 --> 00:39:27,160 Speaker 8: consumer markets like smartphones and PCs, where we are seeing 789 00:39:27,200 --> 00:39:30,600 Speaker 8: a sluggish inventory digestion playing out. On the positive side, 790 00:39:30,640 --> 00:39:34,040 Speaker 8: the AI frenzy driven growth was a positive tailwind, but 791 00:39:34,280 --> 00:39:37,200 Speaker 8: was not enough to offset the global weakness in other markets. 792 00:39:37,800 --> 00:39:40,480 Speaker 2: And remind us, I mean, there was the double whammy 793 00:39:40,560 --> 00:39:43,600 Speaker 2: of perhaps slow down, certainly for consumer based chips, but 794 00:39:43,640 --> 00:39:46,800 Speaker 2: there's also the fact that they're trying to boost supply, 795 00:39:47,280 --> 00:39:49,759 Speaker 2: particularly here in US and Arizona, but they can't get 796 00:39:49,760 --> 00:39:50,400 Speaker 2: the right labor. 797 00:39:50,520 --> 00:39:54,080 Speaker 3: They're seeing spiraling costs. This supply chain issue is clearly 798 00:39:54,120 --> 00:39:54,560 Speaker 3: a headache. 799 00:39:55,920 --> 00:39:58,840 Speaker 8: It definitely is. But you know, the Arizona project is 800 00:39:58,880 --> 00:40:01,360 Speaker 8: again of course come out in the future, so it 801 00:40:01,440 --> 00:40:05,120 Speaker 8: does not impact their supply currently. However, speaking of supply 802 00:40:05,239 --> 00:40:07,400 Speaker 8: and going back to your question about the AI frenzy, 803 00:40:07,480 --> 00:40:10,759 Speaker 8: whether it's short term or not, I think we are 804 00:40:10,800 --> 00:40:14,120 Speaker 8: in the very early, early leaning subgenerative AI cycle. I 805 00:40:14,200 --> 00:40:16,520 Speaker 8: don't think this is a short term frenzy. In fact, 806 00:40:16,600 --> 00:40:19,759 Speaker 8: we are seeing very strong demand and a higher percentage 807 00:40:19,800 --> 00:40:23,360 Speaker 8: of wallets spent in data center and enterprise shift towards AI. 808 00:40:23,800 --> 00:40:27,880 Speaker 8: But what we are limited right now is the supply interesting. 809 00:40:28,239 --> 00:40:31,360 Speaker 2: So would what do you think would give t SMC 810 00:40:31,560 --> 00:40:35,000 Speaker 2: the confidence to see and gain your sort of optimism 811 00:40:35,480 --> 00:40:37,960 Speaker 2: that generator AI is just at its early innings that 812 00:40:38,000 --> 00:40:41,040 Speaker 2: they should be focused on supply that this is ultimately 813 00:40:41,239 --> 00:40:42,719 Speaker 2: demand that steady for the long term. 814 00:40:43,800 --> 00:40:46,000 Speaker 8: I think one would be orders from the chip makers, 815 00:40:46,040 --> 00:40:48,879 Speaker 8: which again translates into the backlocks of the chip maker. 816 00:40:48,920 --> 00:40:49,800 Speaker 13: And we are seeing. 817 00:40:49,880 --> 00:40:53,360 Speaker 8: Significant demand for the AI chip makers, like in media, 818 00:40:53,560 --> 00:40:56,480 Speaker 8: they're having backlog and they're just not able to ship enough. 819 00:40:56,920 --> 00:40:58,879 Speaker 8: So I think that's a good proof point that gives 820 00:40:58,960 --> 00:41:02,359 Speaker 8: TSMC to backup supply and start shipping towards it. But again, 821 00:41:02,440 --> 00:41:05,360 Speaker 8: it doesn't happen over a quarter, so I don't expect 822 00:41:05,600 --> 00:41:08,439 Speaker 8: chip makers in AI to see a significant revenue step 823 00:41:08,560 --> 00:41:10,879 Speaker 8: up quarter over quarter as we saw with in Vida 824 00:41:10,960 --> 00:41:13,640 Speaker 8: last quarter. It will more be a steady growth until 825 00:41:13,719 --> 00:41:14,920 Speaker 8: supply really catch yourself. 826 00:41:15,560 --> 00:41:18,400 Speaker 2: And to that end, do you think that more supply 827 00:41:18,560 --> 00:41:20,719 Speaker 2: will be being based here in the United States? Will 828 00:41:20,760 --> 00:41:23,200 Speaker 2: we see more openings such as Arizona despite the hold. 829 00:41:23,120 --> 00:41:26,000 Speaker 8: Up I'll be shuled. I mean, there might be some 830 00:41:26,480 --> 00:41:29,880 Speaker 8: delay in the timing of the milestones, but we definitely 831 00:41:29,960 --> 00:41:33,080 Speaker 8: don't think it will be postponed or I mean or canceled. 832 00:41:33,120 --> 00:41:36,000 Speaker 8: We definitely will see. And beyond TSMC, we have players 833 00:41:36,040 --> 00:41:38,960 Speaker 8: like Intel right which have announced their foundry strategy and 834 00:41:39,080 --> 00:41:41,520 Speaker 8: are going all in on that, so we'll definitely see 835 00:41:41,560 --> 00:41:44,719 Speaker 8: steps taken in that direction the near term. Gyrations might 836 00:41:44,800 --> 00:41:46,000 Speaker 8: just slow it down and. 837 00:41:46,080 --> 00:41:49,320 Speaker 2: Jen great perspective context. Thank you so much. Conjen Savanni. 838 00:41:49,320 --> 00:41:53,880 Speaker 2: There bloombag Intelligence all Things and the chip and AI sphere. Meanwhile, 839 00:41:54,400 --> 00:41:56,320 Speaker 2: that does it for this edition of Bloombag Technology. Do 840 00:41:56,440 --> 00:41:58,520 Speaker 2: not forget, though, to check out our podcast. You can 841 00:41:58,600 --> 00:42:00,960 Speaker 2: find it on the terminal have one as well as 842 00:42:01,040 --> 00:42:05,040 Speaker 2: online on Apple, Spotify, and iHeart from New York. 843 00:42:05,600 --> 00:42:06,720 Speaker 3: This is pretty bad technology.