1 00:00:02,520 --> 00:00:07,360 Speaker 1: Bloomberg Audio Studios, podcasts, radio news. 2 00:00:08,560 --> 00:00:12,440 Speaker 2: The Stock Movers Report, your roundup of companies making moves 3 00:00:12,480 --> 00:00:15,880 Speaker 2: in the stock market. Harnessing the power of Bloomberg Data. 4 00:00:16,720 --> 00:00:18,320 Speaker 3: Let's take a look at some of the stocks on 5 00:00:18,360 --> 00:00:22,000 Speaker 3: the move. We can do that, bloombergs Natalia Keanan Jevich, Natalie, 6 00:00:22,000 --> 00:00:23,919 Speaker 3: what are you looking at today in the marketplace? 7 00:00:24,000 --> 00:00:26,920 Speaker 4: Yeah, let's start with Meta. Shares are up by almost 8 00:00:26,920 --> 00:00:29,680 Speaker 4: two percent. Meta has agreed to buy Manus. This is 9 00:00:29,680 --> 00:00:33,360 Speaker 4: a single pore based AI start up with Chinese roots. 10 00:00:33,440 --> 00:00:36,320 Speaker 4: The deal values Manus at about two billion dollars. This 11 00:00:36,400 --> 00:00:39,720 Speaker 4: is according to sources familiarle bit Matter, who also told 12 00:00:39,720 --> 00:00:44,280 Speaker 4: Bloomberg that this deal was struck in just about ten days. 13 00:00:44,520 --> 00:00:48,400 Speaker 4: So according to the statement, Meta wants to continue operating 14 00:00:48,520 --> 00:00:52,360 Speaker 4: and selling Manus service, but also it wants to integrate 15 00:00:52,440 --> 00:00:56,560 Speaker 4: these services into its own products. This startup is backed 16 00:00:56,560 --> 00:00:59,680 Speaker 4: by some of the biggest Chinese names, including ten Cent, 17 00:01:00,560 --> 00:01:03,560 Speaker 4: and it has AI agent which can do different tasks 18 00:01:03,640 --> 00:01:08,399 Speaker 4: like screening resumes or analyzing stocks, so it is a 19 00:01:08,400 --> 00:01:12,520 Speaker 4: profitable business. Manus had an annual revenue of one one 20 00:01:12,640 --> 00:01:16,760 Speaker 4: hundred and twenty five million dollars earlier this year. This 21 00:01:16,840 --> 00:01:19,640 Speaker 4: is from selling, of course, its products, and it's also 22 00:01:19,680 --> 00:01:24,280 Speaker 4: a good indicator because Mad can get immediate returns after 23 00:01:24,319 --> 00:01:27,840 Speaker 4: this acquisition. I'm just curious why Chinese or Singapore based 24 00:01:27,880 --> 00:01:31,759 Speaker 4: startup not US, because we have so many similar startups 25 00:01:31,760 --> 00:01:32,399 Speaker 4: here and. 26 00:01:32,480 --> 00:01:34,520 Speaker 3: They're all over the place. You never know where you're 27 00:01:34,520 --> 00:01:34,920 Speaker 3: going to find it. 28 00:01:34,959 --> 00:01:37,920 Speaker 1: But diversification, sure, maybe. 29 00:01:37,520 --> 00:01:39,440 Speaker 4: Cheaper, maybe cheaper yep. 30 00:01:40,000 --> 00:01:42,399 Speaker 1: And you know what. Dan Ives was on with Paul 31 00:01:42,400 --> 00:01:45,199 Speaker 1: again earlier in Alexis and made a really good point, 32 00:01:45,319 --> 00:01:49,639 Speaker 1: which was that for many years China has been ahead 33 00:01:49,640 --> 00:01:52,360 Speaker 1: of the US on a number of technologies. He would say, 34 00:01:52,720 --> 00:01:54,720 Speaker 1: he said, you know, you'd go to Taiwan and see 35 00:01:54,760 --> 00:01:56,280 Speaker 1: what they were doing, and you'd come back to New 36 00:01:56,360 --> 00:01:57,960 Speaker 1: York and see somebody get into a fight at the 37 00:01:58,000 --> 00:02:03,000 Speaker 1: dunkin donutsks. Right now, we're ahead in this tech or 38 00:02:03,080 --> 00:02:04,640 Speaker 1: so we tell ourselves. 39 00:02:04,840 --> 00:02:06,600 Speaker 3: Right, Yeah, I'm not sure how you measure that, but 40 00:02:06,640 --> 00:02:09,600 Speaker 3: that's how it feels. How about Tesla. 41 00:02:09,639 --> 00:02:13,600 Speaker 4: Tesla, Yes, so shares are actually down today after the 42 00:02:13,639 --> 00:02:18,760 Speaker 4: company posted estimates for vehicle deliveries which came Actually shares 43 00:02:18,800 --> 00:02:22,600 Speaker 4: are mostly flat, we should say, are more pessimistic compared 44 00:02:22,680 --> 00:02:26,760 Speaker 4: to data from Bloomberg. So by Tesla's count, analysts on 45 00:02:26,880 --> 00:02:31,560 Speaker 4: average expect the company to deliver slightly above four hundred 46 00:02:31,600 --> 00:02:34,920 Speaker 4: and twenty two thousand vehicles in the fourth quarter. That 47 00:02:35,000 --> 00:02:38,800 Speaker 4: would show at fifteen percent decline versus a year ago. 48 00:02:39,080 --> 00:02:42,280 Speaker 4: Bloomberg calculations showed that that could potentially be a ten 49 00:02:42,400 --> 00:02:45,440 Speaker 4: percent decline. Tesla is, of course, on pace to post 50 00:02:45,440 --> 00:02:49,520 Speaker 4: a second straight year of vehicle sales declines. And we 51 00:02:49,560 --> 00:02:52,760 Speaker 4: know that there was some rush in the third quarter 52 00:02:52,840 --> 00:02:55,520 Speaker 4: to buy cars ahead of this tax credit. 53 00:02:55,639 --> 00:02:58,240 Speaker 3: Yeah, a little pool pull ahead there that's probably playing out there. 54 00:02:58,800 --> 00:03:02,359 Speaker 3: What's this next one? Heretf Yeah, it's awesome. 55 00:03:02,600 --> 00:03:07,280 Speaker 4: Twenty four our shares that software company exactly. So I 56 00:03:07,320 --> 00:03:09,800 Speaker 4: think this is Matt's favorite sector of software services. 57 00:03:10,000 --> 00:03:13,800 Speaker 1: Software services are going to get eaten by AI, you know, 58 00:03:13,880 --> 00:03:16,040 Speaker 1: That's what I think. I could be wrong. I could 59 00:03:16,040 --> 00:03:20,400 Speaker 1: be wrong, because I've noticed lately really access to data 60 00:03:21,000 --> 00:03:23,960 Speaker 1: is a huge is a huge point, you know, because 61 00:03:23,960 --> 00:03:26,720 Speaker 1: I'm always looking for cars in the aftermarket, and I 62 00:03:26,760 --> 00:03:29,160 Speaker 1: look on an auto trader, and I look at car Gurus, 63 00:03:29,200 --> 00:03:32,120 Speaker 1: and I look at cars dot com, and when I 64 00:03:32,200 --> 00:03:37,960 Speaker 1: use Chat, GPT or Google Gemini, it's useless it's absolutely powerless, 65 00:03:37,960 --> 00:03:39,880 Speaker 1: and even the AI box will say, hey, listen, we 66 00:03:39,880 --> 00:03:43,320 Speaker 1: don't have any access to the data. So interesting if 67 00:03:43,360 --> 00:03:45,920 Speaker 1: you're a SaaS company and you have also an interesting 68 00:03:46,040 --> 00:03:48,440 Speaker 1: data set, like for example, there's a company called gong 69 00:03:48,920 --> 00:03:54,320 Speaker 1: Right which has access to customer feedback that no one 70 00:03:54,320 --> 00:03:57,040 Speaker 1: else has. And I always thought, well, gong is going 71 00:03:57,080 --> 00:03:59,320 Speaker 1: to get destroyed by AI, but maybe not because no 72 00:03:59,320 --> 00:04:01,080 Speaker 1: one else has all that data. 73 00:04:01,200 --> 00:04:02,600 Speaker 3: All the day, it's all about the data as we 74 00:04:02,640 --> 00:04:02,960 Speaker 3: know about. 75 00:04:03,000 --> 00:04:03,840 Speaker 1: So what's this company on. 76 00:04:04,320 --> 00:04:07,560 Speaker 4: So shares are up by thirty seven percent today after 77 00:04:07,600 --> 00:04:10,960 Speaker 4: the company agreed to be bought by Cevent and other 78 00:04:11,000 --> 00:04:14,440 Speaker 4: software solutions company for a total consideration of about four 79 00:04:14,680 --> 00:04:18,599 Speaker 4: one hundred million dollars in cash and expects to close 80 00:04:18,640 --> 00:04:22,320 Speaker 4: transaction in the second half of twenty twenty six. However, 81 00:04:22,400 --> 00:04:24,400 Speaker 4: it is a little bit strange because we see this 82 00:04:24,600 --> 00:04:27,440 Speaker 4: huge jump in stock prices, but the company when it 83 00:04:27,560 --> 00:04:31,560 Speaker 4: reported earnings last time, they already said that they received 84 00:04:31,640 --> 00:04:35,640 Speaker 4: some proposals. Okay, so maybe it is a surprise for 85 00:04:35,720 --> 00:04:36,159 Speaker 4: some people. 86 00:04:37,279 --> 00:04:41,320 Speaker 2: The Stock Movers report from Bloomberg Radio. Check back with 87 00:04:41,400 --> 00:04:44,000 Speaker 2: us throughout the day for the latest roundup of companies 88 00:04:44,040 --> 00:04:47,160 Speaker 2: making news on Wall Street and for the latest market 89 00:04:47,200 --> 00:04:51,640 Speaker 2: moving headlines. Listen to Bloomberg Radio Live. 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