1 00:00:02,240 --> 00:00:05,600 Speaker 1: From the heart of where innovation, money and power collive 2 00:00:06,360 --> 00:00:10,879 Speaker 1: in Silicon Valley and beyond. This is Bloomberg Technology with 3 00:00:10,960 --> 00:00:26,360 Speaker 1: Emily jay I met Lovelow in New York in for 4 00:00:26,440 --> 00:00:29,319 Speaker 1: Emily Chang. This is Bloomberg Technology. Coming up in the 5 00:00:29,360 --> 00:00:33,760 Speaker 1: next hour artificial intelligence, a humanoid robot, and a recruitment 6 00:00:33,840 --> 00:00:36,839 Speaker 1: drive to power Tesla's work on full self driving. The 7 00:00:36,880 --> 00:00:40,479 Speaker 1: ev maker hosts its second A I D will discuss 8 00:00:40,640 --> 00:00:44,559 Speaker 1: what to expect. Plus, Bloomberg learns that super agent Ari 9 00:00:44,680 --> 00:00:48,760 Speaker 1: Emmanuel is pushing a settlement between Twitter and Elon Musk. 10 00:00:49,040 --> 00:00:52,479 Speaker 1: The social media company shares jump as Musks texts. Two 11 00:00:52,560 --> 00:00:57,200 Speaker 1: key players are revealed, and Katie Hahn left Andres and 12 00:00:57,200 --> 00:01:00,400 Speaker 1: Horowitz to start her own firm, Han Ventures. I'll sit 13 00:01:00,440 --> 00:01:02,560 Speaker 1: down with her on how she plans to put the 14 00:01:02,640 --> 00:01:05,880 Speaker 1: one point five billion dollars of capital she raised to 15 00:01:06,000 --> 00:01:08,640 Speaker 1: work again to all that in a moment. The first 16 00:01:08,720 --> 00:01:11,520 Speaker 1: let's get to the markets and wow, what an end 17 00:01:11,600 --> 00:01:13,480 Speaker 1: to a week, What an end to a month? And 18 00:01:13,520 --> 00:01:16,200 Speaker 1: what an end to a quart of brutal for then 19 00:01:16,200 --> 00:01:18,960 Speaker 1: as that one hundred bloombergs, Baby lip Scholtz here with 20 00:01:19,040 --> 00:01:23,800 Speaker 1: the latest blimey, Yeah, pretty much everything under the sea 21 00:01:24,080 --> 00:01:27,280 Speaker 1: or everything trading sharply in the red today. It's really 22 00:01:27,319 --> 00:01:30,200 Speaker 1: just been a brutal September. When I have multiple emails 23 00:01:30,360 --> 00:01:34,000 Speaker 1: from analysts and strategists saying good riddance September, that really 24 00:01:34,000 --> 00:01:36,039 Speaker 1: is a signal of all you need to know for 25 00:01:36,080 --> 00:01:39,280 Speaker 1: the Nazza one, in particular, a third quarter trading in 26 00:01:39,280 --> 00:01:42,320 Speaker 1: the red. That's its longest quarterly losing streak since two 27 00:01:42,360 --> 00:01:45,400 Speaker 1: thousand and two. Obviously, this pain has really been across 28 00:01:45,440 --> 00:01:48,200 Speaker 1: the board. You're looking at stocks trading lower than the 29 00:01:48,240 --> 00:01:51,720 Speaker 1: likes of Apple, Microsoft providing the largest largest drag. Really 30 00:01:51,800 --> 00:01:54,640 Speaker 1: just bad when the market is so heavily skewed to 31 00:01:54,800 --> 00:01:56,880 Speaker 1: these tech giants. I think I took a bit of 32 00:01:56,880 --> 00:02:01,160 Speaker 1: a pause during Friday to think when things were slightly different. 33 00:02:01,160 --> 00:02:04,120 Speaker 1: We had the FED on September twenty one, and then 34 00:02:04,120 --> 00:02:06,720 Speaker 1: when I look at the NASTAC one hundred, I think 35 00:02:06,720 --> 00:02:09,320 Speaker 1: I'm right saying mid August we were up nineteen point 36 00:02:09,360 --> 00:02:11,600 Speaker 1: three percent for the quarter or something like that, and 37 00:02:11,680 --> 00:02:13,799 Speaker 1: things have changed so quickly. What is top of mind 38 00:02:13,880 --> 00:02:17,480 Speaker 1: right now for investors, particularly when we look across technology 39 00:02:17,480 --> 00:02:20,079 Speaker 1: and the equity markets. It's a lot of uncertainty obviously 40 00:02:20,120 --> 00:02:23,799 Speaker 1: around the Federal Reserve, the intent to continue raising interest rates, 41 00:02:23,880 --> 00:02:26,880 Speaker 1: keeping those interest rates higher for longer. But then when 42 00:02:26,880 --> 00:02:29,320 Speaker 1: you really look at the latest leg lower and you 43 00:02:29,360 --> 00:02:31,560 Speaker 1: look at Apple, Amazon, and Microsoft, there are a lot 44 00:02:31,560 --> 00:02:34,480 Speaker 1: of jitters around earning season. Earnings is right on the horizon. 45 00:02:34,520 --> 00:02:37,160 Speaker 1: You looked at that report from Nike that really blew 46 00:02:37,160 --> 00:02:39,160 Speaker 1: out the bottom for that stock, and wait on both 47 00:02:39,200 --> 00:02:42,760 Speaker 1: the Nasdaq one hundred and the down Industrial average. So 48 00:02:43,080 --> 00:02:45,639 Speaker 1: concerns are really getting to the point where price to 49 00:02:45,720 --> 00:02:47,880 Speaker 1: earnings are going to be something that falls under the 50 00:02:47,880 --> 00:02:50,360 Speaker 1: mic right, and earnings expectations have already come down. We 51 00:02:50,400 --> 00:02:53,160 Speaker 1: wait for the third quarter earnings and see how much 52 00:02:53,240 --> 00:02:56,400 Speaker 1: earnings expectations full further thank you to bilian Berg's Bailey 53 00:02:56,400 --> 00:02:59,560 Speaker 1: lip Sholts Right. On Friday night, Elon Musk will hold 54 00:02:59,600 --> 00:03:03,560 Speaker 1: testa second ever AI date. We expect updates on full 55 00:03:03,560 --> 00:03:08,160 Speaker 1: self driving, the company's in house supercomputer, and of course Optimus, 56 00:03:08,360 --> 00:03:12,040 Speaker 1: a humanoid Tesla box. You may remember the Mr Roboto 57 00:03:12,160 --> 00:03:15,440 Speaker 1: style dancer the joint Musk on stage last year. This 58 00:03:15,520 --> 00:03:20,320 Speaker 1: time he's expected to unveil a true prototypes. Sorry, it's 59 00:03:20,320 --> 00:03:22,280 Speaker 1: hard to keep a straight face with that. On the 60 00:03:22,320 --> 00:03:25,240 Speaker 1: screen to get what we expect. In the latest we're 61 00:03:25,280 --> 00:03:28,480 Speaker 1: joined by Bloomberg Shawn o'caine over in Austin. So, Sean, 62 00:03:28,800 --> 00:03:31,639 Speaker 1: what do we expect. It's about what it looks like 63 00:03:31,680 --> 00:03:33,720 Speaker 1: when I try to dance too. Um. Yeah, I think 64 00:03:33,720 --> 00:03:37,200 Speaker 1: the biggest thing that we're waiting for is what kinds 65 00:03:37,240 --> 00:03:40,560 Speaker 1: of demos. Musk has promised to show off a couple 66 00:03:40,600 --> 00:03:43,520 Speaker 1: of different actual working demos tonight, and so I think 67 00:03:43,520 --> 00:03:44,960 Speaker 1: that's going to be the thing that people take away 68 00:03:44,960 --> 00:03:47,520 Speaker 1: the most. More specifically, think a lot of people in 69 00:03:47,560 --> 00:03:51,000 Speaker 1: the community who are working on robotics and have studied 70 00:03:51,000 --> 00:03:53,000 Speaker 1: it for a long time, you know, I think we 71 00:03:53,040 --> 00:03:55,760 Speaker 1: all they all expect to see Tesla try to prove 72 00:03:56,200 --> 00:03:58,160 Speaker 1: that it can differentiate somehow from some of the other 73 00:03:58,200 --> 00:04:00,360 Speaker 1: companies like Boston Dynamics that are already working on this 74 00:04:00,440 --> 00:04:02,800 Speaker 1: kind of stuff. And one thing we are probably going 75 00:04:02,840 --> 00:04:04,800 Speaker 1: to get based on what Tesla has been tweeting and 76 00:04:04,840 --> 00:04:07,440 Speaker 1: Elon Musk has been tweeting, is some sort of demonstration 77 00:04:07,480 --> 00:04:10,440 Speaker 1: of manual dexterity and being able to show that the 78 00:04:10,520 --> 00:04:14,320 Speaker 1: robot can really do fine motor skills, uh, you know, 79 00:04:14,360 --> 00:04:16,760 Speaker 1: by doing some sort of demo whether that's you know, 80 00:04:16,800 --> 00:04:18,760 Speaker 1: cracking open a beer or something like that. I don't know, 81 00:04:18,839 --> 00:04:20,960 Speaker 1: I'm sure it will be something weird, maybe throwing up 82 00:04:21,279 --> 00:04:24,640 Speaker 1: a giant metal ball at a cybertruck. Bring us up 83 00:04:24,680 --> 00:04:26,479 Speaker 1: to speed on where Test is at with its work 84 00:04:26,520 --> 00:04:30,280 Speaker 1: around our official intelligence, Right, we're really focused on on Optimus, 85 00:04:30,279 --> 00:04:34,240 Speaker 1: but actually the focus to this point has really been 86 00:04:34,240 --> 00:04:38,279 Speaker 1: about full self driving. Yeah. I mean they have spent 87 00:04:38,400 --> 00:04:40,200 Speaker 1: a lot of time. They have a lot of engineers 88 00:04:40,240 --> 00:04:42,680 Speaker 1: who have been working on the sort of underlying the 89 00:04:42,920 --> 00:04:47,080 Speaker 1: fundamental research and development and engineering that goes into trying 90 00:04:47,120 --> 00:04:49,360 Speaker 1: to make their cars be able to drive themselves. And 91 00:04:49,360 --> 00:04:51,599 Speaker 1: they're not there yet, but they've done a ton of 92 00:04:51,640 --> 00:04:54,640 Speaker 1: work going down that path, more than any other automaker. 93 00:04:55,080 --> 00:04:57,719 Speaker 1: And you know, the thinking is and and the pitch 94 00:04:57,760 --> 00:05:00,280 Speaker 1: from Elon Musk is like, hey, we're doing all this work, 95 00:05:01,000 --> 00:05:03,679 Speaker 1: there's a way that we could leverage that into other 96 00:05:04,440 --> 00:05:07,320 Speaker 1: lines of business. And so whether or not that's taking 97 00:05:07,360 --> 00:05:10,680 Speaker 1: the supercomputer that they've developed and making space for other 98 00:05:10,720 --> 00:05:14,080 Speaker 1: companies to use it in some capacity because maybe in 99 00:05:14,120 --> 00:05:17,080 Speaker 1: some ways it exceeds what other sort of chip makers 100 00:05:17,080 --> 00:05:20,400 Speaker 1: are out there doing, or taking the virtual worlds that 101 00:05:20,400 --> 00:05:23,680 Speaker 1: they're building to train those AI models to power the 102 00:05:23,680 --> 00:05:26,120 Speaker 1: full self driving system and finding a way to use 103 00:05:26,560 --> 00:05:28,320 Speaker 1: what they're doing there, and so you know, I think 104 00:05:28,560 --> 00:05:30,480 Speaker 1: that's probably one thing we maybe get a little more 105 00:05:30,520 --> 00:05:33,720 Speaker 1: clarity on tonight. Musk is always very proud of the 106 00:05:33,760 --> 00:05:37,279 Speaker 1: supercomputing team, and some of these things are happening, you know, 107 00:05:37,360 --> 00:05:39,279 Speaker 1: far more in the background compared to sort of the 108 00:05:39,279 --> 00:05:43,360 Speaker 1: electric vehicle divories and sales. So I hope we hear 109 00:05:43,360 --> 00:05:45,200 Speaker 1: more about some of that tonight, but he's promised it's 110 00:05:45,200 --> 00:05:47,679 Speaker 1: going to be very technical. Tesla treats all these events 111 00:05:47,680 --> 00:05:50,359 Speaker 1: as recruiting events, so you know, if you're watching as 112 00:05:50,360 --> 00:05:52,120 Speaker 1: a lay person, it might be hard to follow, but 113 00:05:52,160 --> 00:05:54,159 Speaker 1: I'm sure there will be some some sort of dancing 114 00:05:54,240 --> 00:05:56,640 Speaker 1: robot to keep you entertained. Hey, show them real quick, 115 00:05:56,760 --> 00:05:59,160 Speaker 1: give us some color. What what? What do these events 116 00:05:59,160 --> 00:06:01,279 Speaker 1: tend to be like? How do they tend to play out? 117 00:06:03,160 --> 00:06:05,600 Speaker 1: I mean they're overwhelming when you're there. Even the vehicle 118 00:06:05,720 --> 00:06:08,440 Speaker 1: reveals are always a little bit um, you know, like 119 00:06:08,560 --> 00:06:11,560 Speaker 1: rock concerts, I mean they're always it's always a big stage, 120 00:06:11,600 --> 00:06:14,479 Speaker 1: there's always a lot of loud music. Uh. And I 121 00:06:14,560 --> 00:06:17,320 Speaker 1: was at the opening of the Austin factory earlier this year, 122 00:06:17,320 --> 00:06:20,080 Speaker 1: and it was you know, Tesla took that to its 123 00:06:20,120 --> 00:06:24,080 Speaker 1: most extreme and like actually hosting concerts during the event 124 00:06:24,160 --> 00:06:26,440 Speaker 1: before and after Elon Musk's folks. So I don't think 125 00:06:26,440 --> 00:06:29,040 Speaker 1: we're gonna get quite as much of that element tonight. 126 00:06:29,279 --> 00:06:31,600 Speaker 1: It's gonna be a lot nearlier, but you never know. 127 00:06:31,760 --> 00:06:34,040 Speaker 1: With Elon Musk, it can always go in a really 128 00:06:34,080 --> 00:06:36,480 Speaker 1: wild direction. And so I'm I think I think I'm 129 00:06:36,480 --> 00:06:39,880 Speaker 1: most interested to see is how far has the development 130 00:06:39,880 --> 00:06:41,960 Speaker 1: of this robot com Does it looked like someone dancing 131 00:06:41,960 --> 00:06:43,840 Speaker 1: at a suit or will that actually looks like a robot? 132 00:06:44,120 --> 00:06:46,280 Speaker 1: All right, Bloomberg, Seawan Okaine, thank you very much. I 133 00:06:46,320 --> 00:06:48,200 Speaker 1: want to get some more analysis and bring in p F. 134 00:06:48,240 --> 00:06:51,000 Speaker 1: Farrago of New Street Research. She currently has a by 135 00:06:51,120 --> 00:06:54,000 Speaker 1: rating on Tessa Stock. And I'm delighted to have you 136 00:06:54,080 --> 00:06:56,080 Speaker 1: here in New York with me because you cover Tesla, 137 00:06:56,800 --> 00:06:59,920 Speaker 1: but from the tech perspective, you also cover in video 138 00:07:00,480 --> 00:07:02,440 Speaker 1: a chip maker that's done a lot of work around 139 00:07:02,520 --> 00:07:07,240 Speaker 1: artificial intelligence, and we'll bring those themes in. What is 140 00:07:07,320 --> 00:07:10,280 Speaker 1: your interest in the work that Tester is doing around AI? 141 00:07:10,600 --> 00:07:15,240 Speaker 1: What do you see is the opportunity there so let's 142 00:07:15,280 --> 00:07:18,440 Speaker 1: talk about like the chip space. What's very interesting is 143 00:07:18,520 --> 00:07:21,680 Speaker 1: that test slat today they have these giants and your 144 00:07:21,800 --> 00:07:25,160 Speaker 1: network that is driving the car for you more or less, right, 145 00:07:25,280 --> 00:07:28,720 Speaker 1: and these thing still has to train to learn. And 146 00:07:29,000 --> 00:07:32,920 Speaker 1: the learning cycle today on giant clusters of Nvida GPUs 147 00:07:33,440 --> 00:07:37,120 Speaker 1: takes let's say five days, and the objective of dojos 148 00:07:38,320 --> 00:07:40,080 Speaker 1: has developed in nose is to be able to do 149 00:07:40,200 --> 00:07:43,640 Speaker 1: that over one night, right. And that's basically instead of 150 00:07:43,680 --> 00:07:46,320 Speaker 1: having people working and waiting a week, is people working, 151 00:07:46,560 --> 00:07:49,360 Speaker 1: going to bed, and the next morning working again. So 152 00:07:49,480 --> 00:07:51,640 Speaker 1: it's a game changer in terms of the pace at 153 00:07:51,680 --> 00:07:54,120 Speaker 1: which you can improve the system. That's that's all the 154 00:07:54,240 --> 00:07:56,440 Speaker 1: vision of e learn and that's what the investment has 155 00:07:56,480 --> 00:08:00,240 Speaker 1: been made there. It's interesting you went straight to the hardware, yes, 156 00:08:00,720 --> 00:08:04,480 Speaker 1: component of this. You know a lot of Tesla fans 157 00:08:04,760 --> 00:08:07,680 Speaker 1: and investors and owners that I've spelling to, they want 158 00:08:07,720 --> 00:08:11,360 Speaker 1: to know about the latest beta version of full self 159 00:08:11,480 --> 00:08:13,440 Speaker 1: driving updates. They want to know when there will be 160 00:08:13,920 --> 00:08:17,160 Speaker 1: a wide public release. Yes, what do you expect on 161 00:08:17,280 --> 00:08:20,160 Speaker 1: that front? Yeah, it's a very good questions. So Tesla 162 00:08:20,240 --> 00:08:22,480 Speaker 1: has already made a lot of fo guys on the system. 163 00:08:22,520 --> 00:08:26,920 Speaker 1: I personally drive the latest version of every Day I 164 00:08:27,040 --> 00:08:30,680 Speaker 1: like it Alert. My assessment is that on the perception front, 165 00:08:31,240 --> 00:08:35,000 Speaker 1: Tesla has made achievements nobody and you need its ability 166 00:08:35,040 --> 00:08:37,200 Speaker 1: to read the world around it. The GAK can figure 167 00:08:37,240 --> 00:08:43,200 Speaker 1: out what's around it, like the traffic lights or the cars, obstacles, pedestrians, bikers. 168 00:08:43,920 --> 00:08:46,280 Speaker 1: I can't remember a single time my car would not 169 00:08:46,640 --> 00:08:49,800 Speaker 1: perceive something around it um and I think it has 170 00:08:49,840 --> 00:08:51,959 Speaker 1: been the focus of Tesla in the last in the 171 00:08:52,080 --> 00:08:55,240 Speaker 1: last few years, and everybody that Sweatyland calls like real 172 00:08:55,320 --> 00:08:58,240 Speaker 1: world AI interacting with the real world, and they've achieved 173 00:08:58,280 --> 00:09:00,760 Speaker 1: a lot. And what's very interesting that now now that 174 00:09:00,840 --> 00:09:03,679 Speaker 1: the car can deceive the world around it's the next 175 00:09:03,760 --> 00:09:08,079 Speaker 1: challenge to me is what we call representation, which is 176 00:09:08,160 --> 00:09:11,040 Speaker 1: making sense of what what you perceive, what you see 177 00:09:11,520 --> 00:09:13,480 Speaker 1: and figuring out, okay, is that's the stuff I need 178 00:09:13,520 --> 00:09:15,800 Speaker 1: to wait and wait for this car and then sneak 179 00:09:15,840 --> 00:09:18,079 Speaker 1: between these two cards and things like that, And that's 180 00:09:18,120 --> 00:09:21,439 Speaker 1: where I feel fs D. But as a user, I 181 00:09:21,520 --> 00:09:24,000 Speaker 1: feel there's still a lot of work. It's already very impressive, 182 00:09:24,080 --> 00:09:25,559 Speaker 1: but there's still a lot of work to do for 183 00:09:25,960 --> 00:09:28,120 Speaker 1: really the car to step to level three where I 184 00:09:28,160 --> 00:09:32,360 Speaker 1: can stop supervising it. And what I would expect today 185 00:09:32,880 --> 00:09:36,320 Speaker 1: is actually Testalla to show these challenges and to show 186 00:09:36,800 --> 00:09:40,959 Speaker 1: how difficult it is and what they're doing to to 187 00:09:41,080 --> 00:09:43,160 Speaker 1: address the challenges, because that's the way you're getting to 188 00:09:43,240 --> 00:09:45,400 Speaker 1: hire the best people on that point, what they're doing 189 00:09:45,480 --> 00:09:48,079 Speaker 1: to address it, you know, Elon Musk builds. This is 190 00:09:48,120 --> 00:09:51,280 Speaker 1: a recruitment event. I did a Twitter pole, you know, 191 00:09:51,360 --> 00:09:55,640 Speaker 1: and I asked Tesla owners, fans, investors, what are they 192 00:09:55,720 --> 00:09:58,080 Speaker 1: interested in? And of course you see on your screen 193 00:09:59,040 --> 00:10:02,319 Speaker 1: they're interested in lot mess the humanoid robot. I mean, 194 00:10:03,240 --> 00:10:06,000 Speaker 1: how interested you in the robot itself? You know, what 195 00:10:06,120 --> 00:10:08,560 Speaker 1: do you see? Do you see that as a product 196 00:10:08,920 --> 00:10:11,280 Speaker 1: or it's just an in house tool or a gimmick. 197 00:10:11,679 --> 00:10:14,760 Speaker 1: What's your read I'm going to be interested in exactly 198 00:10:14,800 --> 00:10:17,760 Speaker 1: in the same thing. I want the test let's him 199 00:10:17,840 --> 00:10:20,800 Speaker 1: to tell me how far have they're gone. And I 200 00:10:21,280 --> 00:10:24,120 Speaker 1: don't explain them to to have been to have gone 201 00:10:24,200 --> 00:10:27,559 Speaker 1: that far. It's quite a challenging project. But I want 202 00:10:27,640 --> 00:10:31,679 Speaker 1: them to articulate what the challenges are because that's the 203 00:10:31,720 --> 00:10:33,640 Speaker 1: best way they're going to be able to hire the 204 00:10:33,720 --> 00:10:35,839 Speaker 1: best people like you can work with it on most 205 00:10:35,880 --> 00:10:38,600 Speaker 1: you can have exposure exposure to the Tesla stock options, 206 00:10:39,040 --> 00:10:41,240 Speaker 1: and you're going to work on the most interesting challenges. 207 00:10:41,520 --> 00:10:43,480 Speaker 1: That's really I think what they're going to try to have. 208 00:10:43,679 --> 00:10:45,240 Speaker 1: So it might be a bit disappointing for like an 209 00:10:45,240 --> 00:10:47,880 Speaker 1: atlyst and investors because you're not going to see the 210 00:10:47,920 --> 00:10:50,319 Speaker 1: product getting into the market anytime soon. You're going to 211 00:10:50,440 --> 00:10:53,520 Speaker 1: have like very technical discussions about how do you achieve 212 00:10:53,640 --> 00:10:57,319 Speaker 1: you know, like the dexterity, how do you achieve perception 213 00:10:57,400 --> 00:11:00,400 Speaker 1: for a robot or like UM as I say, um 214 00:11:01,160 --> 00:11:04,840 Speaker 1: representation and things like that. You've said in your previous 215 00:11:04,960 --> 00:11:08,520 Speaker 1: research that Tesla is a matter of years ahead of 216 00:11:08,600 --> 00:11:10,679 Speaker 1: others when it comes to ours official intelligence, and I 217 00:11:10,720 --> 00:11:13,320 Speaker 1: said earlier you also cover in video and the video 218 00:11:13,440 --> 00:11:15,400 Speaker 1: not just on the semi co inductor side, but broadly 219 00:11:15,440 --> 00:11:17,400 Speaker 1: has done a lot of work in AI. Can you 220 00:11:17,520 --> 00:11:21,360 Speaker 1: compare and contrast those two, you know, where does Tesla 221 00:11:21,440 --> 00:11:24,880 Speaker 1: stand in your estimation and what has it achieved today 222 00:11:24,920 --> 00:11:29,520 Speaker 1: in the field of artificial intelligence. So in the field 223 00:11:29,559 --> 00:11:33,280 Speaker 1: of like real world artificial intelligence, I don't think anybody 224 00:11:34,040 --> 00:11:37,640 Speaker 1: stands close to test is ahead of everybody in terms 225 00:11:37,679 --> 00:11:41,440 Speaker 1: of developing systems, but remember the videos a platform players, 226 00:11:41,440 --> 00:11:43,199 Speaker 1: they develop a chip and like a Nichol system for 227 00:11:43,240 --> 00:11:45,079 Speaker 1: people can develop their own systems, so they are not 228 00:11:45,160 --> 00:11:48,000 Speaker 1: training the same game. And what's happening today is that 229 00:11:48,120 --> 00:11:51,079 Speaker 1: Tesla being so ahead of the curves, the same with 230 00:11:51,160 --> 00:11:53,160 Speaker 1: Goodglele is ahead of the curves. These two players, whatever 231 00:11:53,200 --> 00:11:56,120 Speaker 1: they done, they've developed their own chips and like the 232 00:11:56,200 --> 00:12:00,480 Speaker 1: tipu at Google and Dodgo for for training at at Tesla, 233 00:12:00,760 --> 00:12:02,719 Speaker 1: and they've done that for this reason that they need 234 00:12:02,800 --> 00:12:05,559 Speaker 1: to accelerate the pace at which they develop their systems. 235 00:12:06,880 --> 00:12:09,600 Speaker 1: So of course, like mainstreat, like common wisdom is that's 236 00:12:09,640 --> 00:12:12,840 Speaker 1: not good for video because like the dream of Tesla 237 00:12:12,920 --> 00:12:15,640 Speaker 1: is actually to turn off the Nvidia cluster and replace 238 00:12:15,679 --> 00:12:18,839 Speaker 1: it by Dodgo. But I have a different take. I 239 00:12:18,920 --> 00:12:22,000 Speaker 1: think it's very encouraging. It's very positive because at the 240 00:12:22,080 --> 00:12:23,640 Speaker 1: end of the day, what Tesla wants is to be 241 00:12:23,720 --> 00:12:27,520 Speaker 1: able to do over night what takes them several days today. 242 00:12:27,840 --> 00:12:30,199 Speaker 1: So they need more and more and more processing power 243 00:12:30,800 --> 00:12:33,280 Speaker 1: and as you can imagine, very few people will be 244 00:12:33,320 --> 00:12:35,599 Speaker 1: able to develop their own systems. So with Tesla and 245 00:12:35,679 --> 00:12:38,679 Speaker 1: Google do it, and that's the market for nvideo. So 246 00:12:38,920 --> 00:12:43,120 Speaker 1: I actually see Google developing the TPU Tesla developing Dodgo 247 00:12:43,600 --> 00:12:47,280 Speaker 1: as very positive leading indicator of where the market of 248 00:12:47,440 --> 00:12:50,920 Speaker 1: Nvidia is going. Everybody needs more processing power to develop 249 00:12:50,960 --> 00:12:52,719 Speaker 1: these models. Here. I wish we had more time, but 250 00:12:52,920 --> 00:12:56,360 Speaker 1: very quickly we might get the court deliveries that this weekend. 251 00:12:56,400 --> 00:12:59,640 Speaker 1: What are you expecting on that front? So I sense 252 00:13:00,000 --> 00:13:04,600 Speaker 1: the SLA has produced a lot of car Wow, that's high, 253 00:13:04,760 --> 00:13:07,920 Speaker 1: that's very high. I also suspect they've been struggling a 254 00:13:08,000 --> 00:13:10,640 Speaker 1: bit to deliver all this car. So I think delivery 255 00:13:10,800 --> 00:13:13,439 Speaker 1: is are more like a three sixty. And I think 256 00:13:13,480 --> 00:13:15,480 Speaker 1: you have about twenty thousand cars that might be on 257 00:13:15,600 --> 00:13:18,240 Speaker 1: boots between China and the rest of the world. All right, 258 00:13:18,520 --> 00:13:29,840 Speaker 1: Pierre Ferrague, New Street Research, thank you very much. Hollywood 259 00:13:29,880 --> 00:13:32,400 Speaker 1: super agent Ari Emmanuel is trying to pave the way 260 00:13:32,480 --> 00:13:35,839 Speaker 1: for a potential settlement between Elon Musk and Twitter. That's 261 00:13:35,840 --> 00:13:39,000 Speaker 1: according to sources, The news coming one day after Musk's 262 00:13:39,080 --> 00:13:44,000 Speaker 1: text between Twitter executives, close friends and potential investors came 263 00:13:44,080 --> 00:13:47,600 Speaker 1: to light. Bloombergs Max Chafkin joins us discussed, just when 264 00:13:47,679 --> 00:13:52,599 Speaker 1: you think that this saga cannot get any weirder, we 265 00:13:52,760 --> 00:13:56,000 Speaker 1: have a Hollywood super agent. You know, I'm not sure 266 00:13:56,080 --> 00:13:58,000 Speaker 1: how seriously we should take this. I think one of 267 00:13:58,040 --> 00:14:00,520 Speaker 1: the things we learned in these texts that dropped um 268 00:14:00,880 --> 00:14:02,240 Speaker 1: is that there are a lot of people out there 269 00:14:02,440 --> 00:14:06,520 Speaker 1: trying out trying to use their friendship with Elon Musk 270 00:14:06,600 --> 00:14:08,960 Speaker 1: and sort of wheedle their way into this deal. Um. 271 00:14:09,040 --> 00:14:11,160 Speaker 1: You know Ari Emmanuel of course, very very well known, 272 00:14:11,280 --> 00:14:13,880 Speaker 1: very famous dealmaker. This is potentially a huge deal. I 273 00:14:13,880 --> 00:14:16,160 Speaker 1: don't think it's hugely surprising that he might try to 274 00:14:16,400 --> 00:14:18,400 Speaker 1: put himself in the middle of this. I don't think 275 00:14:18,440 --> 00:14:20,160 Speaker 1: that's a reason to think it's going to go anywhere. 276 00:14:20,480 --> 00:14:23,040 Speaker 1: So let's get onto those texts. These are texts that 277 00:14:23,240 --> 00:14:26,160 Speaker 1: emerge from court filings relating to the upcoming trial, which 278 00:14:26,160 --> 00:14:29,200 Speaker 1: starts October seventeenth. They go all the way back to April, 279 00:14:29,680 --> 00:14:32,880 Speaker 1: which predates Musk's offer. I think we have a series 280 00:14:32,960 --> 00:14:36,720 Speaker 1: of texts starting with the current Twitter CEO, Paragagrool and 281 00:14:36,920 --> 00:14:39,520 Speaker 1: and basically what they seem to demonstrate is how that 282 00:14:39,640 --> 00:14:46,240 Speaker 1: relationship deteriorated quite quickly, and then Elon Musk basically said, 283 00:14:46,240 --> 00:14:47,960 Speaker 1: all right, I'm going to put a deal in too, 284 00:14:48,040 --> 00:14:50,680 Speaker 1: make take Twitter private. Yeah, I mean one thing you 285 00:14:50,760 --> 00:14:52,960 Speaker 1: learn when you read these texts that wow, like Elon 286 00:14:53,040 --> 00:14:55,080 Speaker 1: Musk is talking to a lot of people. He's having 287 00:14:55,160 --> 00:14:58,480 Speaker 1: direct relationships with many many people in in sort of 288 00:14:58,520 --> 00:15:02,480 Speaker 1: all different domains, politics, all these billionaires. Um. And he's 289 00:15:02,560 --> 00:15:05,560 Speaker 1: you know, just texting away with with Twitter ceo and 290 00:15:05,640 --> 00:15:08,320 Speaker 1: it's board um and it's kind of working things out 291 00:15:08,360 --> 00:15:10,920 Speaker 1: as he goes. And as you said with the with 292 00:15:11,080 --> 00:15:14,320 Speaker 1: the text with Twitter ceo, you see, you know what 293 00:15:14,520 --> 00:15:17,400 Speaker 1: seems like pretty diligent efforts to work with him. Uh 294 00:15:17,480 --> 00:15:20,880 Speaker 1: and then uh, the deterioration the relationship happened, you know, 295 00:15:20,960 --> 00:15:23,560 Speaker 1: almost immediately. We saw this play out, by the way, 296 00:15:23,960 --> 00:15:26,360 Speaker 1: on Twitter as well, when Elon Musk's famously you know, 297 00:15:26,440 --> 00:15:29,400 Speaker 1: tweeted a poop emoji at at the ceo of Twitter. 298 00:15:29,560 --> 00:15:32,400 Speaker 1: But but it's it's pretty interesting to see this, um, 299 00:15:32,600 --> 00:15:34,640 Speaker 1: you know, kind of in black and white. The other 300 00:15:34,720 --> 00:15:37,360 Speaker 1: thing we learned is some of the background I suppose 301 00:15:37,440 --> 00:15:40,600 Speaker 1: of how Elon Musk went out to raise tapits or 302 00:15:40,640 --> 00:15:43,640 Speaker 1: outside equity financing for this deal. And in exchange with 303 00:15:43,760 --> 00:15:46,680 Speaker 1: Larry Edison, he basically says, you want to get involved. 304 00:15:47,160 --> 00:15:50,000 Speaker 1: He says, sure, how much? Elon Musk says, oh, two billion. 305 00:15:50,120 --> 00:15:52,040 Speaker 1: Yeah you think they were, you know, talking about buying 306 00:15:52,040 --> 00:15:55,200 Speaker 1: a cup of coffee or something. Larrie Elson says, oh billion, 307 00:15:55,320 --> 00:15:58,160 Speaker 1: you know ish uh the I think what this shows 308 00:15:58,360 --> 00:16:01,200 Speaker 1: first of all, number one, this is a hot deal. 309 00:16:01,400 --> 00:16:04,760 Speaker 1: I mean, and we can debate whether the whether they're 310 00:16:04,760 --> 00:16:07,280 Speaker 1: fundamentals to back that up, or whether it's just Elon Musk. 311 00:16:07,360 --> 00:16:10,200 Speaker 1: But you look down the list and you see Ellison, 312 00:16:10,520 --> 00:16:13,640 Speaker 1: Mark and Reason, a bunch of lesser investors, all of 313 00:16:13,720 --> 00:16:16,520 Speaker 1: them basically willing to get in and on this without 314 00:16:16,800 --> 00:16:19,680 Speaker 1: much of a many questions asked, um which I think 315 00:16:20,000 --> 00:16:22,800 Speaker 1: you know that tells you something about how things turned out. 316 00:16:23,320 --> 00:16:26,600 Speaker 1: It also tells you just the gravity around Elon Musk h. 317 00:16:26,880 --> 00:16:29,440 Speaker 1: He is clearly at the top of the Silicon valley. 318 00:16:29,480 --> 00:16:33,080 Speaker 1: Totem Pole. You have Mark and Resent, very influential venture 319 00:16:33,160 --> 00:16:36,080 Speaker 1: capitalists basically putting up a quarter of a billion dollars, 320 00:16:36,280 --> 00:16:38,640 Speaker 1: no questions asked, and and and in seeing this over 321 00:16:38,680 --> 00:16:40,800 Speaker 1: and over again, including Larry Elson, another you know, rich 322 00:16:40,840 --> 00:16:43,600 Speaker 1: and powerful guy. So when the news broke that Aria 323 00:16:43,600 --> 00:16:47,080 Speaker 1: Emmanuel was, according to sources, brokering some kind of settlement, 324 00:16:47,120 --> 00:16:49,640 Speaker 1: the shares rose at one point to their highest level 325 00:16:49,720 --> 00:16:51,280 Speaker 1: since man I think we have a chart which shows 326 00:16:51,360 --> 00:16:52,960 Speaker 1: the spread. You know, I look at it from time 327 00:16:53,000 --> 00:16:55,960 Speaker 1: to time. It's kind of a soft indication of where 328 00:16:56,000 --> 00:16:59,680 Speaker 1: wool Street sees this deal's likelihood. Where do all these 329 00:16:59,720 --> 00:17:02,520 Speaker 1: tech leave us ahead of the trial? Is my question? Well, 330 00:17:02,640 --> 00:17:04,480 Speaker 1: I think one of the things people have observed this 331 00:17:04,560 --> 00:17:06,359 Speaker 1: already about Elon Musk, But you know, he has some 332 00:17:06,480 --> 00:17:08,879 Speaker 1: things in common with the former president. And one of 333 00:17:08,960 --> 00:17:11,800 Speaker 1: those things is he tends to tweet what he's actually feeling. 334 00:17:11,880 --> 00:17:14,000 Speaker 1: I mean, and and so if you look at these texts, 335 00:17:14,280 --> 00:17:16,280 Speaker 1: they are very interesting. They give you a sense of 336 00:17:16,320 --> 00:17:18,879 Speaker 1: what is it like to live as Elon Musk. You 337 00:17:19,000 --> 00:17:22,280 Speaker 1: have a lot of people hangers on, rich guys bothering 338 00:17:22,320 --> 00:17:25,080 Speaker 1: you all the time. But it doesn't actually change the 339 00:17:25,160 --> 00:17:27,960 Speaker 1: narrative all that much. Elon Musk has said many of 340 00:17:28,000 --> 00:17:30,000 Speaker 1: the things that we learned in these texts he's already 341 00:17:30,040 --> 00:17:32,960 Speaker 1: said publicly, including this thing about bots, where he was 342 00:17:33,040 --> 00:17:35,800 Speaker 1: already you know, talking about bots publicly before you have 343 00:17:36,080 --> 00:17:38,919 Speaker 1: a business week call him out. Recently, Twitter's woes come 344 00:17:38,960 --> 00:17:41,960 Speaker 1: from Jack Dorsey being too busy with bitcoin. Very quickly, 345 00:17:42,080 --> 00:17:45,040 Speaker 1: give me the overview of that. So Dorsey and and 346 00:17:45,200 --> 00:17:47,280 Speaker 1: this is one interesting thing that that came out in 347 00:17:47,320 --> 00:17:49,679 Speaker 1: these texts. You see Dorsey, who had just resigned from 348 00:17:49,720 --> 00:17:53,440 Speaker 1: Bitcoin from Twitter, probably to spend more time with his 349 00:17:53,520 --> 00:17:55,840 Speaker 1: Bitcoin u talking to Musk and saying, you know, we 350 00:17:55,920 --> 00:17:57,919 Speaker 1: really need to change things. We need to we need 351 00:17:57,960 --> 00:17:59,800 Speaker 1: to shake this up. Maybe it should be a protocol. 352 00:18:00,200 --> 00:18:04,000 Speaker 1: He's very much whispering and Musks here encouraging him to 353 00:18:04,040 --> 00:18:07,240 Speaker 1: pursue this deal and encouraging the idea that there needs 354 00:18:07,320 --> 00:18:10,240 Speaker 1: to be significant change, which is kind of strange because 355 00:18:10,320 --> 00:18:13,159 Speaker 1: he was the CEO until very recently. It's and it 356 00:18:13,280 --> 00:18:15,520 Speaker 1: kind of tells you again the gravity around Elon Musk 357 00:18:15,720 --> 00:18:18,359 Speaker 1: that Jack Dorsey thinks that this outsider can come in 358 00:18:18,600 --> 00:18:20,600 Speaker 1: and do something that he, as a founder and CEO 359 00:18:20,720 --> 00:18:23,199 Speaker 1: cannot do. All right, So much more to learn, Bloomberg's 360 00:18:23,200 --> 00:18:26,600 Speaker 1: Max Chafkin, Thank you much more ahead, stay with us. 361 00:18:26,880 --> 00:18:53,879 Speaker 1: This is Bloomberg. Google continues to collect ad revenue from 362 00:18:54,000 --> 00:18:58,800 Speaker 1: crisis pregnancy centers on abortion related search terms, while failing 363 00:18:59,119 --> 00:19:03,399 Speaker 1: to properly lay them as organizations that do not provide abortions. 364 00:19:03,480 --> 00:19:07,680 Speaker 1: Bloom The Quick takes Medicine Mills paints the picture is 365 00:19:07,800 --> 00:19:11,960 Speaker 1: Google keeping its promises on abortion information After Roe v 366 00:19:12,200 --> 00:19:16,680 Speaker 1: Wade was overturned. The information that's displayed on Google products 367 00:19:16,720 --> 00:19:24,159 Speaker 1: across maps, search and ads became really crucial information. Google 368 00:19:24,160 --> 00:19:27,399 Speaker 1: announced that it would adjust its search engine so users 369 00:19:27,480 --> 00:19:31,760 Speaker 1: could find verified abortion providers. But are Google's changes actually 370 00:19:31,880 --> 00:19:35,400 Speaker 1: working in practice. Let's say we're in Lincoln, Nebraska, where 371 00:19:35,400 --> 00:19:37,560 Speaker 1: it can be hard to get an abortion. If I 372 00:19:37,600 --> 00:19:41,359 Speaker 1: search abortion help, the contextual labels provided by Google seem 373 00:19:41,480 --> 00:19:44,480 Speaker 1: to work. You can see that the first search result 374 00:19:44,600 --> 00:19:48,119 Speaker 1: clearly says does not provide abortions. In this case, the 375 00:19:48,200 --> 00:19:51,000 Speaker 1: labels are working. All of these search results are for 376 00:19:51,160 --> 00:19:54,679 Speaker 1: crisis pregnancy centers. That's the type of non medical organization 377 00:19:54,800 --> 00:19:58,160 Speaker 1: that aims to persuade women to not get abortions. These 378 00:19:58,200 --> 00:20:01,240 Speaker 1: centers pay to appear in searchers for women horse seeking 379 00:20:01,280 --> 00:20:05,080 Speaker 1: abortion information. The problem is that this approach from Google 380 00:20:05,240 --> 00:20:09,160 Speaker 1: doesn't seem to work in searches for adjacent terms. In September, 381 00:20:09,280 --> 00:20:12,480 Speaker 1: when I searched for planned parenthood, for example, I found 382 00:20:12,560 --> 00:20:15,919 Speaker 1: that Google provided no labels on any of the ads 383 00:20:16,000 --> 00:20:18,760 Speaker 1: that came up in search. The top shown result was 384 00:20:18,840 --> 00:20:22,800 Speaker 1: for Nebraska Parents Center, which is a known crisis pregnancy center, 385 00:20:22,880 --> 00:20:25,919 Speaker 1: and there was no label. According to an investigation by 386 00:20:26,000 --> 00:20:29,680 Speaker 1: Bloomberg and the Center for Countering Digital Hate. Google also 387 00:20:29,800 --> 00:20:32,960 Speaker 1: failed to add these labels on other abortion related search 388 00:20:33,160 --> 00:20:37,600 Speaker 1: terms like pregnancy help, abortion pills, and n a F hotline. 389 00:20:37,800 --> 00:20:41,440 Speaker 1: What we're finding is that Google's tiny policy changes do 390 00:20:41,720 --> 00:20:44,880 Speaker 1: actually have a big impact on whether people are able 391 00:20:44,960 --> 00:20:49,360 Speaker 1: to find accurate information about abortions or if they're misled. 392 00:20:49,680 --> 00:20:52,680 Speaker 1: In a statement, Google said it has clear and longstanding 393 00:20:52,760 --> 00:20:56,280 Speaker 1: policies that govern abortion related ads on its platform, and 394 00:20:56,359 --> 00:20:59,480 Speaker 1: it applies these rules to all advertisers. It added that 395 00:20:59,560 --> 00:21:03,520 Speaker 1: the company is constantly reviewing and updating its pauses as needed. 396 00:21:07,680 --> 00:21:11,040 Speaker 1: That was Bloomberg Quick Takes. Madison Mills another story we 397 00:21:11,119 --> 00:21:13,520 Speaker 1: continue to watch. Our Trier has ended a deal that 398 00:21:13,640 --> 00:21:16,560 Speaker 1: barred it from competing with Jewel that opens the door 399 00:21:16,640 --> 00:21:18,960 Speaker 1: for the maker of Marlboro to buy an e cigarette 400 00:21:19,000 --> 00:21:23,280 Speaker 1: company or develop its own vaping products. In Trier announced 401 00:21:23,280 --> 00:21:25,760 Speaker 1: a twelve point eight billion investment in Jewel. As of 402 00:21:25,840 --> 00:21:29,480 Speaker 1: June that Steak was worth four and fifty million. And 403 00:21:29,600 --> 00:21:32,080 Speaker 1: just this hour we got the headline that Intel's Mobile 404 00:21:32,119 --> 00:21:34,360 Speaker 1: I has filed for an I p O. We knew 405 00:21:34,400 --> 00:21:37,320 Speaker 1: that Intel had planned the spinoff, and according to sources, 406 00:21:37,359 --> 00:21:39,919 Speaker 1: that would be a lower valuation of thirty billion dollars. 407 00:21:40,359 --> 00:21:42,960 Speaker 1: Very interesting time in considering the markets and a lack 408 00:21:43,000 --> 00:21:45,680 Speaker 1: of companies going public. The company seeking to list on 409 00:21:45,720 --> 00:21:56,639 Speaker 1: the NASDAG under the ticker m b l Y. This 410 00:21:56,760 --> 00:21:59,879 Speaker 1: is Bloomberg Technology, im Ed Ludlow in New York. Rising 411 00:22:00,000 --> 00:22:04,560 Speaker 1: interest rates and repeated market volatility has created a funding void, 412 00:22:04,680 --> 00:22:08,159 Speaker 1: leaving many cash hungry startups scrambling for backup plans. So 413 00:22:08,320 --> 00:22:10,680 Speaker 1: how long are the hard time is gonna last? Joining 414 00:22:10,760 --> 00:22:14,480 Speaker 1: us for more insight is Kathy Gau, partner at Sapphire Dventures, 415 00:22:14,520 --> 00:22:17,639 Speaker 1: whose investments include the likes of Fitbit and twenty three 416 00:22:17,720 --> 00:22:19,840 Speaker 1: and me. Kathy is good to catch up. I've been 417 00:22:19,920 --> 00:22:23,760 Speaker 1: asking venture capitalists all week, where is your head at 418 00:22:23,880 --> 00:22:27,560 Speaker 1: right now? Well ed? As we know, the markets have 419 00:22:27,680 --> 00:22:31,520 Speaker 1: been absolutely crazy, But I will also say there's a 420 00:22:31,640 --> 00:22:35,080 Speaker 1: dynamic in the venture capital industry right now where VC 421 00:22:35,359 --> 00:22:40,040 Speaker 1: firms are sitting on an unprecedented amount of dry powder, 422 00:22:40,280 --> 00:22:44,760 Speaker 1: about three billion right now, sitting on the sidelines waiting 423 00:22:44,840 --> 00:22:48,000 Speaker 1: to be invested into private companies. But as we sit 424 00:22:48,080 --> 00:22:50,520 Speaker 1: here on the last day of Q three and look 425 00:22:50,600 --> 00:22:54,119 Speaker 1: back at the quarter, deal activity is still well below 426 00:22:54,200 --> 00:22:57,440 Speaker 1: that of last year, but in the past month or 427 00:22:57,480 --> 00:23:00,720 Speaker 1: so I am starting to see certain pause of signals 428 00:23:01,200 --> 00:23:04,840 Speaker 1: that indicate a pickup from the summer slowdown. So, first 429 00:23:04,880 --> 00:23:08,040 Speaker 1: of all, more and more companies are starting to raise 430 00:23:08,680 --> 00:23:11,439 Speaker 1: growth stage rounds and that's what I focused on Series 431 00:23:11,520 --> 00:23:13,680 Speaker 1: B all the way to pre I p O or 432 00:23:13,720 --> 00:23:16,920 Speaker 1: they're at least more open to talking to investors about 433 00:23:16,920 --> 00:23:20,879 Speaker 1: a potential funding round. Number two blockbuster M and A. 434 00:23:21,280 --> 00:23:25,480 Speaker 1: A few weeks ago, Adobe acquired the popular design platform 435 00:23:25,600 --> 00:23:29,439 Speaker 1: Figma for twenty billion dollars and fun fact, it's actually 436 00:23:29,520 --> 00:23:33,600 Speaker 1: the third largest acquisition of a subscription software company. The 437 00:23:33,640 --> 00:23:37,480 Speaker 1: first one was Salesforce acquiring Slack for about twenty seven billion, 438 00:23:37,960 --> 00:23:41,680 Speaker 1: and the second one was Microsoft acquiring LinkedIn for twenty 439 00:23:41,760 --> 00:23:44,800 Speaker 1: six billion, So the Adobe Figma deal is definitely up 440 00:23:44,800 --> 00:23:47,560 Speaker 1: there in terms of large M and A. And lastly, 441 00:23:48,160 --> 00:23:52,399 Speaker 1: the I p O market is slowly showing signs of revival. 442 00:23:52,680 --> 00:23:55,840 Speaker 1: Earlier this year, Instacar did file to go public, but 443 00:23:55,960 --> 00:24:01,000 Speaker 1: more recently, the corporate trip management company trip Actions also filed. 444 00:24:01,160 --> 00:24:04,320 Speaker 1: So all in all, the world is crazy, the markets 445 00:24:04,359 --> 00:24:07,240 Speaker 1: are crazy, but as a venture capitalists, I am encouraged 446 00:24:07,320 --> 00:24:09,720 Speaker 1: by some of these signals. Let's let's talk a little 447 00:24:09,720 --> 00:24:11,639 Speaker 1: bit in a moment about the kind of I P 448 00:24:11,800 --> 00:24:14,680 Speaker 1: O winter that we've been seeing. As you look across 449 00:24:14,840 --> 00:24:18,639 Speaker 1: the startup curve, what are the headwinds right now to 450 00:24:18,800 --> 00:24:21,920 Speaker 1: both deal making and also I guess the temptation to 451 00:24:22,440 --> 00:24:27,040 Speaker 1: proceed with an I P A Absolutely well, you know, 452 00:24:27,600 --> 00:24:33,960 Speaker 1: founders and CEOs continue to postpone fundraising number one due 453 00:24:34,000 --> 00:24:37,560 Speaker 1: to the very large amount of money that they raised 454 00:24:37,640 --> 00:24:41,240 Speaker 1: last year. Many companies raise mega rounds last year at 455 00:24:41,480 --> 00:24:45,160 Speaker 1: very very high valuations, so they have plenty of runway 456 00:24:45,560 --> 00:24:49,439 Speaker 1: to continue to grow their business commensurate with the valuations 457 00:24:49,480 --> 00:24:52,359 Speaker 1: they last raise. And at the same time, of course, 458 00:24:52,440 --> 00:24:56,520 Speaker 1: there's global headwinds to a lot of businesses. Uncertainty has 459 00:24:56,640 --> 00:24:59,400 Speaker 1: caused a lot of fear in the market, and companies 460 00:24:59,440 --> 00:25:03,960 Speaker 1: as a result are cutting back on spend, including software spend. Well, Cathy, 461 00:25:04,040 --> 00:25:07,119 Speaker 1: I know you to be this kind of long term optimist, 462 00:25:07,280 --> 00:25:09,720 Speaker 1: right you look at the global economy, you look long term, 463 00:25:09,800 --> 00:25:13,000 Speaker 1: and you see opportunity. So what is the opportunity down 464 00:25:13,080 --> 00:25:17,080 Speaker 1: the line? You know? And I'm still very long term 465 00:25:17,200 --> 00:25:22,160 Speaker 1: bullish on enterprise fas I fundamentally believe that current macro 466 00:25:22,359 --> 00:25:27,320 Speaker 1: challenges can actually be a catalyst for increased innovation and 467 00:25:27,440 --> 00:25:32,120 Speaker 1: optimization at many companies and enterprises. Of course, buyers will 468 00:25:32,200 --> 00:25:35,280 Speaker 1: continue to be very careful with spend because burn is 469 00:25:35,440 --> 00:25:38,440 Speaker 1: very important, but they'll also be looking for the next 470 00:25:38,640 --> 00:25:41,760 Speaker 1: and best, greatest solution, right They're looking for the best solutions, 471 00:25:42,200 --> 00:25:45,400 Speaker 1: and I think it might trigger a soft software upgrade 472 00:25:45,440 --> 00:25:48,600 Speaker 1: buying cycle across many of the sectors that we cover. 473 00:25:49,359 --> 00:25:51,440 Speaker 1: And I think it's also important really to take the 474 00:25:51,560 --> 00:25:55,480 Speaker 1: long run view. Some of the best companies have started 475 00:25:55,920 --> 00:25:59,919 Speaker 1: and we're built and emerged from economic downturns, companies like Microsoft, 476 00:26:00,080 --> 00:26:03,560 Speaker 1: Apple and Adobe. So caffy real quick, we were just 477 00:26:03,640 --> 00:26:05,680 Speaker 1: looking at some of your portfolio companies. What is the 478 00:26:05,760 --> 00:26:08,400 Speaker 1: advice to found is right now in terms of cash 479 00:26:08,480 --> 00:26:13,280 Speaker 1: preservational how they use cash's talent key in this environment. Absolutely, 480 00:26:13,359 --> 00:26:15,960 Speaker 1: And you know the four main things I chat with 481 00:26:16,119 --> 00:26:20,359 Speaker 1: my CEOs about, uh every week is Number one cash, right, 482 00:26:20,480 --> 00:26:23,600 Speaker 1: just you've got to make sure you have enough runway 483 00:26:24,000 --> 00:26:26,879 Speaker 1: to hit the milestones you need if you need to 484 00:26:26,960 --> 00:26:30,200 Speaker 1: raise the subsequent funding round, So CEOs are all keeping 485 00:26:30,240 --> 00:26:34,840 Speaker 1: cash firm in mind. Number two is really customer attention, right, 486 00:26:34,960 --> 00:26:38,520 Speaker 1: make sure your current customers are happy by delivering a 487 00:26:38,640 --> 00:26:41,760 Speaker 1: great product and a great service and really listening to 488 00:26:41,880 --> 00:26:44,280 Speaker 1: your needs. This is not a time to be neglecting 489 00:26:44,320 --> 00:26:47,320 Speaker 1: your current customers. Um. And lastly, and this might be 490 00:26:47,400 --> 00:26:50,840 Speaker 1: the most important of all in my opinion, your employees right. 491 00:26:50,960 --> 00:26:54,400 Speaker 1: Continue to hire the best and develop the most talented 492 00:26:54,480 --> 00:26:57,720 Speaker 1: people you can find, all right. Thanks to Caffie Gaal 493 00:26:57,920 --> 00:27:02,720 Speaker 1: partner of course at Sapphire Avenches, now General Motors is 494 00:27:02,800 --> 00:27:06,800 Speaker 1: taking its next dive into electric vehicles. GM president Mark 495 00:27:06,920 --> 00:27:10,440 Speaker 1: Royce took my mate Bloombergs Matt Miller on an exclusive 496 00:27:10,520 --> 00:27:14,359 Speaker 1: first ride into the company's e V truck, the Silverado RST. 497 00:27:14,520 --> 00:27:17,879 Speaker 1: Miller joined him from the GM headquarters in Detroit, Michigan. 498 00:27:19,600 --> 00:27:23,520 Speaker 1: M What people need to do is get into it 499 00:27:23,920 --> 00:27:26,600 Speaker 1: and see what an electric truck really can do. And 500 00:27:26,800 --> 00:27:29,480 Speaker 1: this is a dedicated platform with Ultium. You know, we're 501 00:27:29,520 --> 00:27:32,640 Speaker 1: just bringing our cell plant online right now in Ohio 502 00:27:33,119 --> 00:27:35,359 Speaker 1: and the second one will be in spring Hill. But 503 00:27:35,520 --> 00:27:37,680 Speaker 1: the center of gravity is different. You know, We've got 504 00:27:37,960 --> 00:27:40,080 Speaker 1: this will beyond sale here um in the spring with 505 00:27:40,160 --> 00:27:42,280 Speaker 1: our work truck, which is uh, you know what you're 506 00:27:42,280 --> 00:27:45,720 Speaker 1: probably in today, and so you know, for under forty 507 00:27:45,800 --> 00:27:48,440 Speaker 1: thousand bucks and that's a that's a heck of a deal. 508 00:27:49,080 --> 00:27:51,120 Speaker 1: And uh, you know, we're gonna start off with about 509 00:27:51,160 --> 00:27:53,399 Speaker 1: ten thousand pounds of towing with it. We'll follow it 510 00:27:53,520 --> 00:27:57,320 Speaker 1: up with twenty pounds and it's just a very capable truck. 511 00:27:57,680 --> 00:27:59,560 Speaker 1: What's demand like right now, I mean, not just for 512 00:27:59,680 --> 00:28:02,840 Speaker 1: the business in general, the American consumer. What's demanded? Well, 513 00:28:02,920 --> 00:28:06,360 Speaker 1: we haven't seen a lot of demand UM fall off 514 00:28:06,440 --> 00:28:08,600 Speaker 1: even with some of the you know, sort of the 515 00:28:08,720 --> 00:28:11,560 Speaker 1: ups and downs of the economic situation that or we're 516 00:28:11,600 --> 00:28:14,960 Speaker 1: seeing in in the United States. But the demand is there. 517 00:28:15,040 --> 00:28:17,480 Speaker 1: I mean we we are. We are selling every single 518 00:28:17,600 --> 00:28:19,920 Speaker 1: thing that we make, and we're selling it deep into 519 00:28:19,960 --> 00:28:22,600 Speaker 1: the pipeline. So our dealers have really done a good 520 00:28:22,720 --> 00:28:25,800 Speaker 1: job of being able to UM show people what the 521 00:28:25,880 --> 00:28:29,000 Speaker 1: truck or whatever the product is UM that they want, 522 00:28:29,160 --> 00:28:31,160 Speaker 1: and then when it's built, and then when it's coming 523 00:28:31,600 --> 00:28:34,160 Speaker 1: and that'll just get get better as the chip UM, 524 00:28:34,560 --> 00:28:37,960 Speaker 1: the chip piece of this becomes begins to level off 525 00:28:38,000 --> 00:28:40,120 Speaker 1: and be more consistent. Are you seeing any signs of 526 00:28:40,200 --> 00:28:41,479 Speaker 1: that light at the end of the tunnel in terms 527 00:28:41,520 --> 00:28:43,640 Speaker 1: of chips? I think so. I think we're seeing you know, 528 00:28:43,800 --> 00:28:46,360 Speaker 1: fourth quarter here UM A little bit of leveling off, 529 00:28:46,800 --> 00:28:48,880 Speaker 1: it's nowhere near what the demand is. The demand is 530 00:28:48,920 --> 00:28:51,920 Speaker 1: still going to outstrip what we can actually supply because 531 00:28:52,520 --> 00:28:55,160 Speaker 1: the inventory levels are still extremely low. So it takes 532 00:28:55,200 --> 00:28:57,160 Speaker 1: a while for us to get the inventory back. But 533 00:28:57,400 --> 00:29:01,959 Speaker 1: that means no incentives obviously. Um. In fact, people are 534 00:29:01,960 --> 00:29:03,959 Speaker 1: paying more than M S r P in a lot 535 00:29:04,000 --> 00:29:07,200 Speaker 1: of situations. How long can that last? I won't last forever. 536 00:29:07,520 --> 00:29:10,320 Speaker 1: There's no way that can last forever. But you know, again, 537 00:29:10,600 --> 00:29:14,880 Speaker 1: until we begin to fill the pipeline again with some inventory, UM, 538 00:29:15,040 --> 00:29:17,040 Speaker 1: that's going to be the case. I think, what about 539 00:29:17,040 --> 00:29:19,440 Speaker 1: the electric pickup I'm not the pickup truck, I mean 540 00:29:19,480 --> 00:29:22,320 Speaker 1: to pick up in terms of consumers buying electric cars. 541 00:29:22,360 --> 00:29:26,280 Speaker 1: I drove the Bolt out here and it was surprisingly 542 00:29:26,440 --> 00:29:29,680 Speaker 1: luxurious for a car that you basically get into for 543 00:29:29,840 --> 00:29:32,760 Speaker 1: thirty five thousand after rebates, right, and it's even lower 544 00:29:32,800 --> 00:29:35,120 Speaker 1: than that now. We dropped the price on it here 545 00:29:35,360 --> 00:29:37,760 Speaker 1: a few months ago, and uh, you know, we're seeing 546 00:29:37,840 --> 00:29:40,320 Speaker 1: I think you'll see in our quarter quarterly results that 547 00:29:40,760 --> 00:29:43,200 Speaker 1: people are really interested in the Bolt as one of 548 00:29:43,280 --> 00:29:46,239 Speaker 1: the probably the lowest cost ev that you can get 549 00:29:46,320 --> 00:29:49,160 Speaker 1: into in the market. And you know, strategically that's great 550 00:29:49,200 --> 00:29:51,680 Speaker 1: for us because our dealers begin to learn how to 551 00:29:51,760 --> 00:29:54,360 Speaker 1: sell e V sum into the into the dealer footprint 552 00:29:54,600 --> 00:29:57,080 Speaker 1: with our customers, and then our customers are looking for 553 00:29:57,160 --> 00:30:00,280 Speaker 1: the cars so um, you know, and their supply um, 554 00:30:00,680 --> 00:30:03,280 Speaker 1: and you know, we we're matching that the best we 555 00:30:03,360 --> 00:30:06,040 Speaker 1: can right now. So it's a very very compelling vehicle, 556 00:30:06,600 --> 00:30:09,000 Speaker 1: always has been, particularly with super cruise on the e 557 00:30:09,080 --> 00:30:11,000 Speaker 1: u V, and we're seeing a lot of demand for that. 558 00:30:11,240 --> 00:30:14,360 Speaker 1: What about how does the business look to you as 559 00:30:14,440 --> 00:30:18,480 Speaker 1: we see rising rates, as we hear concerns about a recession, 560 00:30:19,040 --> 00:30:21,680 Speaker 1: how do you plan into that? We plan into it 561 00:30:21,800 --> 00:30:25,120 Speaker 1: with our cost structure and so we um, you know, 562 00:30:25,320 --> 00:30:27,120 Speaker 1: four or five years ago, we really went at our 563 00:30:27,160 --> 00:30:30,680 Speaker 1: cost structure and you know, we're in good shape, but 564 00:30:30,920 --> 00:30:33,320 Speaker 1: we can always be better, and so we'll continually take 565 00:30:33,400 --> 00:30:36,680 Speaker 1: costs unnecessary, you know, uh, cost out of the business 566 00:30:36,720 --> 00:30:40,080 Speaker 1: from an efficiency standpoint, and next year will be better 567 00:30:40,160 --> 00:30:41,960 Speaker 1: than this year. And that's the way we're gonna work 568 00:30:41,960 --> 00:30:44,960 Speaker 1: at That was GM president Mark Royce with bloom Bags 569 00:30:45,120 --> 00:30:59,520 Speaker 1: Matt Miller earlier today in the latest episode of Studio 570 00:30:59,640 --> 00:31:04,240 Speaker 1: One Point, former federal prosecutor turned crypto venture capitalist Katie 571 00:31:04,280 --> 00:31:07,960 Speaker 1: Horn shares her outlook on cryptocurrency markets. Horn sits down 572 00:31:08,200 --> 00:31:11,480 Speaker 1: with Bloomberg Technologies Emily Chang to discuss why she left 573 00:31:11,760 --> 00:31:14,720 Speaker 1: story venture capital firm Andreas and Horro Wrists to launch 574 00:31:14,800 --> 00:31:17,480 Speaker 1: her own firm, Horn Ventures, and just how hard it 575 00:31:17,680 --> 00:31:21,120 Speaker 1: was to do so. Take a listen. What I set 576 00:31:21,160 --> 00:31:24,800 Speaker 1: out to do was to continue to invest in this 577 00:31:24,920 --> 00:31:27,760 Speaker 1: ecosystem that I think is so broad. That decision was 578 00:31:27,880 --> 00:31:30,880 Speaker 1: very purposeful and the timing was very purposeful. We have 579 00:31:30,960 --> 00:31:33,920 Speaker 1: an early stage fund that does seed stage, Series A, 580 00:31:34,200 --> 00:31:36,320 Speaker 1: even Series B, and then we have what we call 581 00:31:36,360 --> 00:31:38,960 Speaker 1: an acceleration fund, which is it's not a growth fund, 582 00:31:39,000 --> 00:31:40,720 Speaker 1: it's a crypto growth fund, and I think those are 583 00:31:40,800 --> 00:31:44,400 Speaker 1: different things. But later stage it might be crypto publics. 584 00:31:44,920 --> 00:31:48,560 Speaker 1: Um certain kinds of public tokens were set up to 585 00:31:49,040 --> 00:31:52,200 Speaker 1: hold tokens and participate in the token ecosystem or later 586 00:31:52,320 --> 00:31:54,960 Speaker 1: stage companies. I mean, you know there are now several 587 00:31:55,080 --> 00:31:59,480 Speaker 1: crypto many crypto unicorns. The space has become really competitive. 588 00:31:59,560 --> 00:32:02,360 Speaker 1: Even though you say, um, you know crypto has had 589 00:32:02,440 --> 00:32:05,240 Speaker 1: its kind of ups and downs. The thing is a 590 00:32:05,360 --> 00:32:07,400 Speaker 1: lot of people in some of those last cycles have 591 00:32:07,760 --> 00:32:10,440 Speaker 1: seen the kind of venture style returns that can be 592 00:32:10,560 --> 00:32:12,880 Speaker 1: had in crypto and so you've had a lot of 593 00:32:13,000 --> 00:32:16,400 Speaker 1: new funds enter the space and that's driven up competition. 594 00:32:16,680 --> 00:32:20,600 Speaker 1: So what differentiates on ventures that and from you know, 595 00:32:20,680 --> 00:32:22,760 Speaker 1: all of these other crypto funds or even the more 596 00:32:22,800 --> 00:32:25,600 Speaker 1: traditional venture capitalists like Entries and Herowitz or Sekoia they 597 00:32:25,640 --> 00:32:28,239 Speaker 1: also have crypto fun sure well, I think a lot 598 00:32:28,320 --> 00:32:31,760 Speaker 1: of traditional venture capital funds now have crypto funds. It 599 00:32:31,880 --> 00:32:34,160 Speaker 1: used to just be like a regulatory designation. I was like, 600 00:32:34,280 --> 00:32:37,680 Speaker 1: are you in r i A, which is Registered investment Advisor? 601 00:32:38,040 --> 00:32:41,120 Speaker 1: I would say for crypto founders today, um, it's not 602 00:32:41,280 --> 00:32:43,760 Speaker 1: enough to just be an r I A. Right they frankly, 603 00:32:43,800 --> 00:32:45,240 Speaker 1: I don't even know if a lot of them care 604 00:32:45,320 --> 00:32:49,000 Speaker 1: that's a regulatory designation. What crypto founders today want to 605 00:32:49,040 --> 00:32:51,360 Speaker 1: know is do you live in breath crypto? Do you 606 00:32:51,440 --> 00:32:54,040 Speaker 1: inhale the discords? Are you part of the community, do 607 00:32:54,120 --> 00:32:56,760 Speaker 1: you participate in governance? Are you going to be um 608 00:32:56,840 --> 00:33:00,720 Speaker 1: staking these kind of crypto verbs um out there? And 609 00:33:00,920 --> 00:33:03,760 Speaker 1: if you're not really in this space full time, I 610 00:33:03,800 --> 00:33:07,760 Speaker 1: think it's very hard to run a successful crypto fund. 611 00:33:08,040 --> 00:33:10,440 Speaker 1: We don't have a hedge fund component, so we're not 612 00:33:10,680 --> 00:33:13,480 Speaker 1: sitting here buying and trading and selling. UM. That's a 613 00:33:13,520 --> 00:33:16,719 Speaker 1: hedge fund structure, and there are crypto hedge funds. Um, 614 00:33:16,760 --> 00:33:19,240 Speaker 1: we're not one. We're making seven to ten ure bets. 615 00:33:19,720 --> 00:33:21,400 Speaker 1: Just how hard is it to launch a crypto fund 616 00:33:21,600 --> 00:33:23,920 Speaker 1: from scratch? You know it's hard, but it's not impossible. 617 00:33:23,960 --> 00:33:25,600 Speaker 1: And we don't have a crystal ball. I don't know. 618 00:33:26,000 --> 00:33:28,040 Speaker 1: I can't predict cycles, but I knew that we were 619 00:33:28,120 --> 00:33:30,560 Speaker 1: in a cycle where you saw so much forever an 620 00:33:30,600 --> 00:33:33,120 Speaker 1: excitement around the space. And I already talked about what 621 00:33:33,240 --> 00:33:35,600 Speaker 1: makes us different, But I think one thing also is 622 00:33:35,880 --> 00:33:38,120 Speaker 1: we are a nimble strike force. We don't fish in 623 00:33:38,160 --> 00:33:40,480 Speaker 1: the same pond we have the crypto natives. We have 624 00:33:40,640 --> 00:33:44,200 Speaker 1: a fish and execution. We have operators, seasoned operators who 625 00:33:44,280 --> 00:33:46,280 Speaker 1: really know how to stick the landing. And I think 626 00:33:46,320 --> 00:33:48,640 Speaker 1: that's reflected on our culture. I would say the only 627 00:33:48,720 --> 00:33:50,880 Speaker 1: thing that's changed in our strategy as a result of 628 00:33:50,960 --> 00:33:54,120 Speaker 1: the market correction is really more of a focus on 629 00:33:54,320 --> 00:33:57,800 Speaker 1: early stage. But we still have our late stage fund. 630 00:33:58,360 --> 00:34:01,280 Speaker 1: And when we see valuations, which I think will still 631 00:34:01,320 --> 00:34:05,120 Speaker 1: see correct um spoiler, I think we'll continue to see 632 00:34:05,160 --> 00:34:08,920 Speaker 1: some corrections and UM, so we might deploy our later 633 00:34:09,000 --> 00:34:11,040 Speaker 1: stage fund a little bit more slowly. It might not 634 00:34:11,200 --> 00:34:14,719 Speaker 1: be on an even cadence. And that's okay. Long term, 635 00:34:14,800 --> 00:34:17,640 Speaker 1: our strategy hasn't changed. Long term, we're committed to this space. 636 00:34:17,719 --> 00:34:19,520 Speaker 1: How much do you think valuations are going to correct? 637 00:34:19,880 --> 00:34:21,560 Speaker 1: You know, it's just I can't give you a one 638 00:34:21,600 --> 00:34:23,800 Speaker 1: size fits all answer because crypto is not a monolith. 639 00:34:24,040 --> 00:34:26,960 Speaker 1: You have some crypto companies that really follow more of 640 00:34:27,000 --> 00:34:30,239 Speaker 1: an enterprise sas business model. You have others that are 641 00:34:30,360 --> 00:34:33,719 Speaker 1: layer one protocols UM. You have still others that are 642 00:34:33,719 --> 00:34:36,080 Speaker 1: consumer facing applications. And I think one of the things 643 00:34:36,160 --> 00:34:39,040 Speaker 1: we're seeing right now is the infrastructure layer, and that's 644 00:34:39,040 --> 00:34:40,759 Speaker 1: where we're spending a lot of our time, by the way, 645 00:34:40,800 --> 00:34:43,160 Speaker 1: and we think more and more use cases will come 646 00:34:43,200 --> 00:34:46,279 Speaker 1: about when the infrastructure layers in place. UM So, my 647 00:34:46,480 --> 00:34:50,080 Speaker 1: own view is we are not rushing to deploy. We're 648 00:34:50,080 --> 00:34:53,759 Speaker 1: certainly not getting caught up. Do you get try not 649 00:34:53,880 --> 00:34:55,920 Speaker 1: to avoid that, you know, Look, I think it's very 650 00:34:56,040 --> 00:34:59,920 Speaker 1: easy to get into that mindset, right. It's because it's 651 00:35:00,120 --> 00:35:04,160 Speaker 1: competitive sport. It's a competitive space, and I think I'd 652 00:35:04,200 --> 00:35:06,520 Speaker 1: be lying if I said that that doesn't influence anyone. 653 00:35:06,640 --> 00:35:08,239 Speaker 1: I try to what I do and I think I 654 00:35:08,320 --> 00:35:10,680 Speaker 1: do differently, is I try to take stock of that. Oh, 655 00:35:11,200 --> 00:35:14,759 Speaker 1: that's the foe mo mentality kicking in. That's bad again. 656 00:35:14,800 --> 00:35:16,799 Speaker 1: You don't want to over correct though. What we look 657 00:35:16,880 --> 00:35:21,040 Speaker 1: for is we look for amazing founders. It doesn't matter 658 00:35:21,080 --> 00:35:23,480 Speaker 1: if I think valuations will correct. If there are amazing founders, 659 00:35:23,760 --> 00:35:26,600 Speaker 1: there's a huge tam We also look for what's your 660 00:35:26,719 --> 00:35:29,440 Speaker 1: regulatory plan if you're launching a token, what's your plan 661 00:35:29,520 --> 00:35:31,920 Speaker 1: to comply with the law, what's your plan for security. 662 00:35:32,239 --> 00:35:33,960 Speaker 1: By the way, we've seen a lot of hacks in 663 00:35:34,040 --> 00:35:36,360 Speaker 1: the space. We want to really dive in there and 664 00:35:36,480 --> 00:35:40,160 Speaker 1: make sure that the founders have been thoughtful. That was 665 00:35:40,200 --> 00:35:43,919 Speaker 1: Horn Benches founder and CEO Katie Horn with Bloomberg Technologies 666 00:35:44,080 --> 00:35:46,480 Speaker 1: Emily Chang. You can check out more of that conversation 667 00:35:46,600 --> 00:35:51,040 Speaker 1: tonight at seven thirty pm Eastern four pm Pacific here 668 00:35:51,239 --> 00:35:54,880 Speaker 1: on Bloomberg Television. Coming up, one company focusing on hiring 669 00:35:55,000 --> 00:35:58,480 Speaker 1: hourly workers get sixty million dollars for its series B. 670 00:35:58,640 --> 00:36:02,000 Speaker 1: I'll chat with the CEO of Workstream about the digitization 671 00:36:02,080 --> 00:36:05,360 Speaker 1: of recruitment and state of the labor market. This is 672 00:36:05,440 --> 00:36:42,839 Speaker 1: Bloomberg Workstream, a texting based hiring platform working with big 673 00:36:42,880 --> 00:36:46,680 Speaker 1: brands like McDonald's and Marriott. Just raised sixty million dollars 674 00:36:46,800 --> 00:36:49,480 Speaker 1: in fresh capital to help it expand to even more 675 00:36:49,600 --> 00:36:52,880 Speaker 1: industries that are employing hourly workers. Let's get right to 676 00:36:52,960 --> 00:36:57,600 Speaker 1: Workstream co founder and CEO Desmond Limn. Desmond, how does 677 00:36:57,680 --> 00:37:03,840 Speaker 1: Workstream work? Yeah, So we are this texting base hiring 678 00:37:03,960 --> 00:37:07,560 Speaker 1: software that helps you do actually saw screen actually onboard 679 00:37:07,560 --> 00:37:12,080 Speaker 1: hourly workers faster. In the world of of trying to 680 00:37:12,160 --> 00:37:15,640 Speaker 1: work with with with with this hourly folks, there are 681 00:37:15,760 --> 00:37:17,560 Speaker 1: much more on the phone, they are much less tech 682 00:37:17,640 --> 00:37:20,239 Speaker 1: like Salvy. So we actually built a software that's really 683 00:37:20,360 --> 00:37:22,279 Speaker 1: built for them to be able to help them be 684 00:37:22,400 --> 00:37:26,040 Speaker 1: able to hire onboard faster. So it's to me about 685 00:37:26,080 --> 00:37:30,480 Speaker 1: the kind of circumstances that the workers themselves, who who 686 00:37:31,080 --> 00:37:34,200 Speaker 1: you hope to hire through your platform operating? What kind 687 00:37:34,280 --> 00:37:38,399 Speaker 1: of workers are these and in which industries? Yeah? Yeah, 688 00:37:38,480 --> 00:37:41,359 Speaker 1: I mean we all work across a really broad, broad 689 00:37:41,520 --> 00:37:43,880 Speaker 1: broad broad spens of the sacnis as. So now we 690 00:37:43,920 --> 00:37:49,240 Speaker 1: all work with more than four thousand customers from Burger, King, Dairy, Queen, 691 00:37:49,800 --> 00:37:52,680 Speaker 1: Merit and more. And I always say that as as 692 00:37:52,719 --> 00:37:55,239 Speaker 1: of now, our main focus has been in this restaurants, 693 00:37:55,280 --> 00:37:59,759 Speaker 1: but we have grewed very quickly into retail, healthcare order 694 00:38:00,200 --> 00:38:02,920 Speaker 1: and like you know, much more alvious. Yeah, there's a 695 00:38:03,040 --> 00:38:05,440 Speaker 1: very big difference in terms of folks who work in 696 00:38:05,480 --> 00:38:08,759 Speaker 1: there's hourly death less world and those who work in 697 00:38:08,880 --> 00:38:12,439 Speaker 1: tech office worker. I was saying, there's actually hourly world. 698 00:38:12,520 --> 00:38:16,200 Speaker 1: People are not as tech savvy. There's very high like turnover, 699 00:38:16,800 --> 00:38:19,279 Speaker 1: very high churned. Um, so there is a need to 700 00:38:19,360 --> 00:38:23,360 Speaker 1: actually tell the alwhere there's really built for them. I 701 00:38:23,440 --> 00:38:26,560 Speaker 1: think back to the August jobs data. You know, there's 702 00:38:26,600 --> 00:38:29,480 Speaker 1: there's tens of millions of people in this economy who 703 00:38:29,520 --> 00:38:32,279 Speaker 1: are working part time but by choice, and of course 704 00:38:32,320 --> 00:38:37,160 Speaker 1: there are many people on the sidelines who are trying 705 00:38:37,280 --> 00:38:39,600 Speaker 1: to find some part time work. You must have a 706 00:38:39,719 --> 00:38:43,240 Speaker 1: very interesting lens into the state of the jobs market. 707 00:38:43,719 --> 00:38:45,880 Speaker 1: What can you tell me about what your customers and 708 00:38:46,000 --> 00:38:48,400 Speaker 1: clients are looking for. Are they able to get the 709 00:38:48,480 --> 00:38:51,880 Speaker 1: workers that they need? Yeah, I would say that it 710 00:38:52,040 --> 00:38:54,920 Speaker 1: is still very very tough to actually find talent and 711 00:38:55,000 --> 00:38:59,359 Speaker 1: difine people. Staffing is to the top challenge for many 712 00:38:59,440 --> 00:39:04,680 Speaker 1: of this businesses, from restaurants to retail, hotels and and more. 713 00:39:04,760 --> 00:39:06,600 Speaker 1: But I think that's the very first trend that we see. 714 00:39:07,160 --> 00:39:09,800 Speaker 1: Second trend that we see there many of this business 715 00:39:09,840 --> 00:39:12,800 Speaker 1: they are much more open to actually embrace and to 716 00:39:13,000 --> 00:39:15,960 Speaker 1: try it out software and tech. We have seen a 717 00:39:16,120 --> 00:39:19,200 Speaker 1: very strong growth, almost like a tanext growth in terms 718 00:39:19,239 --> 00:39:23,160 Speaker 1: of how the business owners and and really ops leaders 719 00:39:23,200 --> 00:39:26,000 Speaker 1: are much more open to actually use software. So I 720 00:39:26,080 --> 00:39:28,279 Speaker 1: think that that that that's been very good. I think 721 00:39:28,320 --> 00:39:30,320 Speaker 1: the very the trend that we see more it's like 722 00:39:30,560 --> 00:39:32,800 Speaker 1: more and more folks are trying to focus on this 723 00:39:32,920 --> 00:39:35,960 Speaker 1: actually hourly worker. I would say that in the past 724 00:39:36,040 --> 00:39:39,120 Speaker 1: few years that this hasn't been much focused on this 725 00:39:39,239 --> 00:39:42,319 Speaker 1: audi worker, which really powers the world. Right, there's more 726 00:39:42,360 --> 00:39:45,840 Speaker 1: than two point seven billion business workers in the world. 727 00:39:46,040 --> 00:39:48,839 Speaker 1: There's more than eighty million auti workers in the US. 728 00:39:48,920 --> 00:39:51,920 Speaker 1: It's a very big market, but it hasn't been enough 729 00:39:52,040 --> 00:39:55,160 Speaker 1: good software and tech built for them. So I'll say 730 00:39:55,160 --> 00:39:58,000 Speaker 1: that there's more and more needs to look towards them. Now. 731 00:39:58,920 --> 00:40:02,000 Speaker 1: So you've got sixty million dollars in the bank. What 732 00:40:02,120 --> 00:40:04,640 Speaker 1: do you need to do with that cash to expand 733 00:40:04,719 --> 00:40:06,720 Speaker 1: your offering? What are you going to do to develop 734 00:40:07,160 --> 00:40:12,080 Speaker 1: your tech? Yeah? Yeah, we are very thankful and very 735 00:40:12,160 --> 00:40:14,239 Speaker 1: humble that we raised this right. I think this is 736 00:40:14,280 --> 00:40:17,920 Speaker 1: really with this funding it grows out, is be the 737 00:40:18,000 --> 00:40:22,960 Speaker 1: actually overall million dollars. And we have triating goals for this. 738 00:40:23,400 --> 00:40:26,360 Speaker 1: First's really want to expand from our main focus of 739 00:40:26,480 --> 00:40:33,080 Speaker 1: this restaurant into new vote goals, into retail, healthcare, warehouse 740 00:40:33,200 --> 00:40:35,440 Speaker 1: and more, and so now beyond some of the brands 741 00:40:35,480 --> 00:40:38,719 Speaker 1: that share we all work with like merit ups and 742 00:40:38,880 --> 00:40:41,160 Speaker 1: many many more brands. So that's the very first goal 743 00:40:41,480 --> 00:40:44,360 Speaker 1: part of this funding. Second goal that we have is 744 00:40:44,400 --> 00:40:51,000 Speaker 1: really really go ahead. Yeah, I think the the goal 745 00:40:51,080 --> 00:40:53,680 Speaker 1: next we have is really to build new products. Our 746 00:40:53,760 --> 00:40:56,640 Speaker 1: first product is hiring. Our second product is this on 747 00:40:57,000 --> 00:41:01,120 Speaker 1: on boarding helping you to actually like really really help 748 00:41:01,200 --> 00:41:04,759 Speaker 1: we do smooth yarders people work faster. So I would 749 00:41:04,760 --> 00:41:07,360 Speaker 1: say the next goal is really you're built new products. 750 00:41:07,440 --> 00:41:10,520 Speaker 1: The final goal is really leading into our company mission, 751 00:41:10,880 --> 00:41:13,800 Speaker 1: which is really to serve this to really serve this 752 00:41:14,280 --> 00:41:17,359 Speaker 1: doth Las Ali worker, to build better software and tag 753 00:41:17,719 --> 00:41:21,200 Speaker 1: for them. Give me very quickly, desn't really have that 754 00:41:21,360 --> 00:41:23,919 Speaker 1: thirty seconds? A sense of how many workers are using 755 00:41:23,960 --> 00:41:28,280 Speaker 1: your platform? Yeah, we have now more than four thousand 756 00:41:28,760 --> 00:41:34,160 Speaker 1: um customers across more than stars. We have now actually 757 00:41:34,200 --> 00:41:37,720 Speaker 1: helped to all domit more than ten ten ten million 758 00:41:37,800 --> 00:41:41,760 Speaker 1: hourly folks with been through our our this software alright. 759 00:41:41,840 --> 00:41:45,200 Speaker 1: Workstream co founder and CEO Desmond Lynn, thank you very much. 760 00:41:45,239 --> 00:41:47,680 Speaker 1: A good lens on the labor market. That does it 761 00:41:48,000 --> 00:41:50,920 Speaker 1: for this edition of Bloomberg Technology. Have a great weekend, 762 00:41:51,040 --> 00:41:53,880 Speaker 1: and don't forget to check out our podcast. You can 763 00:41:53,960 --> 00:41:57,560 Speaker 1: find it on the terminal as well as online on Apple, Spotify, 764 00:41:57,960 --> 00:42:00,640 Speaker 1: and of course, as always on I Hot Radio. We 765 00:42:00,760 --> 00:42:02,880 Speaker 1: made it, guys. The end of a tough week, the 766 00:42:03,120 --> 00:42:05,319 Speaker 1: end of a tough month, the end of a tough 767 00:42:05,440 --> 00:42:10,040 Speaker 1: quarter with volatility and financial markets, venture capitalists sitting on 768 00:42:10,160 --> 00:42:12,080 Speaker 1: the sidelines for now. But we'll have a whole lot 769 00:42:12,120 --> 00:42:15,239 Speaker 1: more next week. From San Francisco and New York. This 770 00:42:16,160 --> 00:42:30,759 Speaker 1: is Bloomberg. No one covers the world like Bloomberg. Well 771 00:42:30,800 --> 00:42:34,000 Speaker 1: that absorb these territories. What is NATO's reaction. It's lest 772 00:42:34,040 --> 00:42:36,560 Speaker 1: judge Y wants to majority of ushering in the nation's 773 00:42:36,640 --> 00:42:40,440 Speaker 1: most right with governments, since work offers no sign that 774 00:42:40,600 --> 00:42:43,680 Speaker 1: it is ready to stop pipening. With unmatched reach and 775 00:42:43,840 --> 00:42:48,800 Speaker 1: resources from more than one countries, the moment news breaks 776 00:42:49,280 --> 00:42:54,040 Speaker 1: twenty four hours a day, Bloomberg, your global Business Authority,