1 00:00:02,520 --> 00:00:08,840 Speaker 1: Bloomberg Audio Studios, podcasts, radio news, The. 2 00:00:08,880 --> 00:00:13,160 Speaker 2: Stock Movers podcast, your roundup of companies making moves in 3 00:00:13,200 --> 00:00:17,599 Speaker 2: the stock market harnessing the power of Bloomberg Data. 4 00:00:17,720 --> 00:00:20,599 Speaker 3: I'm Tim Stenebeck along with Carol Master. Let's take a 5 00:00:20,600 --> 00:00:23,959 Speaker 3: look at some stocks. Meta Platforms taking a forty nine 6 00:00:24,040 --> 00:00:28,840 Speaker 3: percent steak in scale AI with Alexander Wang, who's twenty 7 00:00:28,880 --> 00:00:31,760 Speaker 3: eight years old, joining Meta as a top executive to 8 00:00:31,760 --> 00:00:33,159 Speaker 3: build AI super intelligent. 9 00:00:33,280 --> 00:00:36,040 Speaker 2: Mistake, Right, it's a steak's g ak. 10 00:00:36,760 --> 00:00:38,360 Speaker 3: It's not an acquisition. 11 00:00:38,040 --> 00:00:40,920 Speaker 1: Yet, Okay, If it walks like an acquisition. 12 00:00:40,560 --> 00:00:42,519 Speaker 3: Dave Lie says, do not call it an acquisition for 13 00:00:42,560 --> 00:00:45,400 Speaker 3: a host of reasons. Okay, yeah. Davely is US technology 14 00:00:45,400 --> 00:00:48,080 Speaker 3: columnists for Bloomberg Opinion. He spent about fifteen years reporting 15 00:00:48,080 --> 00:00:50,720 Speaker 3: on tech at the FT and BBC before joining Bloomberg. 16 00:00:50,720 --> 00:00:52,640 Speaker 3: He joins us, here, do we just not call this 17 00:00:52,640 --> 00:00:54,760 Speaker 3: an acquisition? Like? Why why are we not acquired? Why 18 00:00:54,760 --> 00:00:56,360 Speaker 3: aren't companies not acquiring things right now? 19 00:00:56,440 --> 00:00:59,080 Speaker 4: Because they're afraid. They're afraid that the courts are going 20 00:00:59,160 --> 00:01:02,640 Speaker 4: to undo their acquisition. So at this time they're thinking 21 00:01:03,000 --> 00:01:07,160 Speaker 4: probably too risky to complicated. If the stake was fifty 22 00:01:07,160 --> 00:01:09,160 Speaker 4: percent as opposed the forty nine percent. That would of 23 00:01:09,160 --> 00:01:13,119 Speaker 4: course be a takeover rather than an investment, and immediately 24 00:01:13,200 --> 00:01:15,640 Speaker 4: that would trigger all kinds of reviews. They'd have to wait, 25 00:01:15,680 --> 00:01:17,880 Speaker 4: they'd have to get clearances, not only in the US 26 00:01:17,920 --> 00:01:19,840 Speaker 4: but also around the world different regulators. 27 00:01:20,360 --> 00:01:22,200 Speaker 5: There's no time for that in the AI era. 28 00:01:22,280 --> 00:01:24,160 Speaker 4: They've got they've got to get to work straight away. 29 00:01:24,560 --> 00:01:26,840 Speaker 4: And so we're seeing this sort of new approach which 30 00:01:26,880 --> 00:01:29,440 Speaker 4: I sort of likened in a column I wrote to 31 00:01:29,480 --> 00:01:31,600 Speaker 4: sort sort of when a football team buys a great 32 00:01:31,640 --> 00:01:34,560 Speaker 4: young prospect, you know, they pay a transfer fee to 33 00:01:34,640 --> 00:01:38,240 Speaker 4: compensate wherever he's leaving. In this case, for Wang, it's about, 34 00:01:38,360 --> 00:01:40,760 Speaker 4: you know, making investors whole at the scale AI the 35 00:01:40,800 --> 00:01:43,559 Speaker 4: company he founded, and they bring on this young talent 36 00:01:43,600 --> 00:01:45,200 Speaker 4: with the view that he's going to be, you know, 37 00:01:45,280 --> 00:01:45,720 Speaker 4: the what. 38 00:01:45,880 --> 00:01:47,240 Speaker 5: He's going to run a super. 39 00:01:47,000 --> 00:01:50,280 Speaker 4: Intelligence team at Meta and this is obviously what Mark 40 00:01:50,360 --> 00:01:53,680 Speaker 4: Zuckerbo thinks. He's needed to bring his team up to 41 00:01:53,800 --> 00:01:56,200 Speaker 4: up to speed to compete against the other big teams. 42 00:01:57,080 --> 00:02:00,240 Speaker 3: He's going to be sitting by. Mark Zuckerberg reportedly what 43 00:02:00,400 --> 00:02:01,480 Speaker 3: is superintelligence? 44 00:02:01,520 --> 00:02:01,840 Speaker 2: What is? 45 00:02:02,240 --> 00:02:07,320 Speaker 3: And also scally Eyes not building its own superintelligence either of. 46 00:02:07,240 --> 00:02:10,560 Speaker 5: That's what's danger. That's what's confusing to me. So Scale 47 00:02:10,680 --> 00:02:11,960 Speaker 5: Eye is an interesting company. 48 00:02:12,000 --> 00:02:14,480 Speaker 4: It's kind of right at the very early stages of 49 00:02:14,520 --> 00:02:18,040 Speaker 4: AI development. It's about hiring humans to do data labeling 50 00:02:18,080 --> 00:02:20,520 Speaker 4: and say that's a tree, that's a bus, and so forth. 51 00:02:20,600 --> 00:02:22,360 Speaker 3: It doesn't sound like super intelligence to me. 52 00:02:23,080 --> 00:02:25,640 Speaker 4: The crucial thing and a colleague, one of my colleagues, 53 00:02:25,680 --> 00:02:28,720 Speaker 4: Ellen who Hewitt, wrote this fantastic piece about Alexander Wang 54 00:02:28,760 --> 00:02:33,120 Speaker 4: today sort of talking about how this guy knows everybody 55 00:02:33,160 --> 00:02:36,040 Speaker 4: in AI and he knows everything that's going on. And 56 00:02:36,080 --> 00:02:38,440 Speaker 4: one of the things about SCALEI that makes it particularly 57 00:02:38,480 --> 00:02:41,720 Speaker 4: useful for Meta is that it's a company that other 58 00:02:41,720 --> 00:02:43,960 Speaker 4: companies would go to when they needed data. So they 59 00:02:43,960 --> 00:02:46,160 Speaker 4: were working with open Ai, they were working with Google, 60 00:02:46,240 --> 00:02:48,760 Speaker 4: They're working with several other companies, and so they have 61 00:02:48,840 --> 00:02:51,200 Speaker 4: a bit of an insight into what those companies are 62 00:02:51,240 --> 00:02:53,280 Speaker 4: working on and what sort of data they were needing 63 00:02:53,320 --> 00:02:56,800 Speaker 4: to have. So now Mark Zuckerberg gets a bit of 64 00:02:56,800 --> 00:02:59,240 Speaker 4: that intelligence as we want what you. 65 00:02:59,240 --> 00:03:02,120 Speaker 1: Have, We want to we want what you know about 66 00:03:02,120 --> 00:03:04,840 Speaker 1: what kind of the whole landscape is doing when it 67 00:03:04,880 --> 00:03:05,480 Speaker 1: comes to AI. 68 00:03:05,600 --> 00:03:07,920 Speaker 5: What have these companies come to you for yeah, exactly. 69 00:03:08,000 --> 00:03:11,800 Speaker 4: And also Alexander Wung is just known as a great networker, right, 70 00:03:11,840 --> 00:03:14,280 Speaker 4: and so when so much of this AI race is 71 00:03:14,320 --> 00:03:18,360 Speaker 4: about talent and bringing people on, we're hearing Zuckerberg is 72 00:03:18,600 --> 00:03:21,639 Speaker 4: emailing people personally and saying, come and join us. I'll 73 00:03:21,639 --> 00:03:24,600 Speaker 4: give you ten million bucks if you do it. This 74 00:03:24,680 --> 00:03:28,040 Speaker 4: is this is just a signal of how fierce the 75 00:03:28,080 --> 00:03:28,639 Speaker 4: AI rate. 76 00:03:28,919 --> 00:03:30,959 Speaker 1: Different type of signing bonus. 77 00:03:30,720 --> 00:03:36,520 Speaker 4: Right, like a smart star, but considerably bigger in terms 78 00:03:36,560 --> 00:03:37,280 Speaker 4: of the numbers. 79 00:03:37,720 --> 00:03:41,000 Speaker 3: What's the promise, though, I mean, what is this super 80 00:03:41,040 --> 00:03:43,680 Speaker 3: intelligence that they're working toward. What is the world Dave 81 00:03:44,400 --> 00:03:48,400 Speaker 3: that Meta Platforms wants to build and be ready for. 82 00:03:48,880 --> 00:03:50,800 Speaker 5: I'm not sure I want to imagine it, frankly, but. 83 00:03:50,880 --> 00:03:51,760 Speaker 3: It's you and me both. 84 00:03:51,760 --> 00:03:54,000 Speaker 5: I mean, there's various names for it. 85 00:03:54,040 --> 00:03:54,200 Speaker 2: Now. 86 00:03:54,240 --> 00:03:57,480 Speaker 5: Superintelligence or artificial general intelligence is. 87 00:03:57,600 --> 00:04:00,360 Speaker 3: Very a g I and super intelligence are the same thing. 88 00:04:00,400 --> 00:04:01,640 Speaker 5: It's all basically the same goal. 89 00:04:01,720 --> 00:04:04,800 Speaker 4: This idea that you can create this essentially one machine 90 00:04:05,080 --> 00:04:07,680 Speaker 4: or you know, one technology that people can use for 91 00:04:08,040 --> 00:04:10,480 Speaker 4: whatever sort of varied us as they want an AI 92 00:04:10,600 --> 00:04:10,920 Speaker 4: to do. 93 00:04:11,400 --> 00:04:14,480 Speaker 5: And there is this feeling that somebody will get there first. 94 00:04:14,840 --> 00:04:18,120 Speaker 4: Right now, I'm of the view personally that there'll be 95 00:04:18,160 --> 00:04:20,520 Speaker 4: several players doing this and that's how it's going to 96 00:04:20,640 --> 00:04:23,720 Speaker 4: the land's going to turn out. However, I think there 97 00:04:23,760 --> 00:04:25,320 Speaker 4: is a sort of panic to make sure that we've 98 00:04:25,320 --> 00:04:28,160 Speaker 4: got to be you know, first, or very very close. 99 00:04:27,920 --> 00:04:28,839 Speaker 5: Behind, to be fair. 100 00:04:29,320 --> 00:04:31,680 Speaker 1: I mean Microsoft has done some similar moves right to 101 00:04:31,760 --> 00:04:35,080 Speaker 1: try and position themselves by acquiring you know, either different 102 00:04:35,080 --> 00:04:39,000 Speaker 1: people that seem to have certainly some AI insight or 103 00:04:39,040 --> 00:04:41,560 Speaker 1: know how. I think Alphabet has done the same thing right, 104 00:04:41,600 --> 00:04:43,160 Speaker 1: So they're all kind of jocking for position. 105 00:04:43,320 --> 00:04:45,599 Speaker 4: They're all truking a position, and they're also taking the 106 00:04:45,600 --> 00:04:48,200 Speaker 4: same approach of we can't be seen to be acquiring 107 00:04:48,320 --> 00:04:51,080 Speaker 4: companies so we can try and acquire people. 108 00:04:51,279 --> 00:04:51,400 Speaker 1: Now. 109 00:04:51,480 --> 00:04:53,960 Speaker 4: I think they're not as clever as they think in 110 00:04:54,000 --> 00:04:56,800 Speaker 4: this regard. I think the Federal Trade Commission, the Department 111 00:04:56,839 --> 00:05:00,919 Speaker 4: of Justice, they know what's happening here. This is competition, 112 00:05:01,080 --> 00:05:03,719 Speaker 4: just in a different form. Because if you've got companies 113 00:05:03,720 --> 00:05:07,360 Speaker 4: that are spending crazy money on wages, does that mean 114 00:05:07,400 --> 00:05:10,720 Speaker 4: a smaller company just can't compete for talent? I guess not, right, 115 00:05:10,800 --> 00:05:13,920 Speaker 4: So there's going to be and we already know the 116 00:05:13,960 --> 00:05:15,679 Speaker 4: FEC is looking into some of these deals. 117 00:05:16,080 --> 00:05:17,080 Speaker 5: I'm looking at what they're going to do. 118 00:05:17,200 --> 00:05:19,120 Speaker 3: Yeah, what's going on when it comes to innovation at 119 00:05:19,120 --> 00:05:21,880 Speaker 3: these huge companies. I mean, I read your calm and 120 00:05:21,960 --> 00:05:23,920 Speaker 3: I think to myself, this is turning into like a 121 00:05:24,680 --> 00:05:28,080 Speaker 3: PEPSI buys Poppy for two billion dollars type of thing, 122 00:05:28,120 --> 00:05:30,920 Speaker 3: because we want to let the startups duke it out 123 00:05:30,920 --> 00:05:33,840 Speaker 3: for shelf space. When it comes to probiotic soda, we're 124 00:05:33,880 --> 00:05:37,359 Speaker 3: not going to develop it ourselves. Is that happening? A 125 00:05:37,440 --> 00:05:41,080 Speaker 3: decade ago, I thought Facebook killed the startup ecosystem. 126 00:05:41,560 --> 00:05:43,680 Speaker 4: Well, I mean, look, I guess it's a question of 127 00:05:44,040 --> 00:05:46,920 Speaker 4: the shifting purpose of the ecosystem to those big companies, right. 128 00:05:46,960 --> 00:05:48,760 Speaker 4: I mean it used to be if you started a startup, 129 00:05:48,800 --> 00:05:53,120 Speaker 4: you had several exit opportunities U IPO, you get acquired. 130 00:05:53,480 --> 00:05:56,920 Speaker 4: You know, both of those channels have almost been closed off. 131 00:05:56,920 --> 00:05:59,200 Speaker 4: We're not seeing so many tech companies IPO anymore. That's 132 00:05:59,240 --> 00:06:02,080 Speaker 4: really dried up, and it's becoming much harder for tech 133 00:06:02,080 --> 00:06:06,279 Speaker 4: companies to acquire companies at all. But at the same time, 134 00:06:06,360 --> 00:06:10,640 Speaker 4: the startup ecosystem still has this fantastic ability to work 135 00:06:10,720 --> 00:06:13,560 Speaker 4: quickly and bring out new ideas. And I wrote in 136 00:06:13,640 --> 00:06:16,240 Speaker 4: that Columny reference that, you know, I'm not sure if 137 00:06:16,279 --> 00:06:20,479 Speaker 4: Alexander Wang joins Meta as like a junior engineer, I'm 138 00:06:20,480 --> 00:06:22,320 Speaker 4: not sure he writeses through the ranks. 139 00:06:22,000 --> 00:06:22,800 Speaker 5: That becomes this guy. 140 00:06:22,880 --> 00:06:24,800 Speaker 4: I think that's only something you could do outside of 141 00:06:24,839 --> 00:06:28,200 Speaker 4: the company, and that's just the nature of startups. And 142 00:06:28,279 --> 00:06:30,760 Speaker 4: one of the sort of moments of real reflection in 143 00:06:30,800 --> 00:06:33,799 Speaker 4: Silica Value was when open ai came along and launched 144 00:06:33,839 --> 00:06:36,520 Speaker 4: chat GBT and everyone's going, well, hold on a second, 145 00:06:36,560 --> 00:06:39,240 Speaker 4: why Google's been working on this for years? You know, 146 00:06:39,839 --> 00:06:42,320 Speaker 4: Microsoft's been doing it. Apple, you know, you'd think would 147 00:06:42,320 --> 00:06:44,240 Speaker 4: have been doing it, and it just wasn't at all really, 148 00:06:44,680 --> 00:06:46,760 Speaker 4: So there is this sort of understanding that the way 149 00:06:46,800 --> 00:06:48,840 Speaker 4: to incubate a lot of this talent, particularly of AI, 150 00:06:48,920 --> 00:06:53,039 Speaker 4: which is so talent and engineering driven, it's easier to 151 00:06:53,080 --> 00:06:55,400 Speaker 4: find that in the ecosystem rather than spend all your 152 00:06:55,440 --> 00:06:57,680 Speaker 4: own R and D for you know, ideas that might 153 00:06:57,720 --> 00:06:58,480 Speaker 4: come toughing. 154 00:06:58,520 --> 00:07:00,200 Speaker 1: It's funny, I was thinking about Amazon when they used 155 00:07:00,200 --> 00:07:01,920 Speaker 1: to just buy all these companies. I feel like kind 156 00:07:01,960 --> 00:07:03,840 Speaker 1: of almost a catch and kill kind of thing, right 157 00:07:03,920 --> 00:07:06,880 Speaker 1: to make sure that they didn't kind of lose out 158 00:07:06,960 --> 00:07:09,400 Speaker 1: on a marketplace. This sounds like it's kind of catch 159 00:07:09,440 --> 00:07:11,280 Speaker 1: and not miss out as well when it comes to 160 00:07:11,320 --> 00:07:15,040 Speaker 1: AI a little bit right, they want to make sure 161 00:07:15,080 --> 00:07:17,640 Speaker 1: that rather, you know, rather than take something out so 162 00:07:17,640 --> 00:07:19,760 Speaker 1: that it's not competitive. It's to like, wait, what am 163 00:07:19,800 --> 00:07:22,840 Speaker 1: I missing with people who really might have some insight 164 00:07:22,840 --> 00:07:23,240 Speaker 1: into this? 165 00:07:23,400 --> 00:07:26,239 Speaker 4: Just quickly, Yeah, I think it's it's they're not worried 166 00:07:26,280 --> 00:07:28,800 Speaker 4: that these smaller companies are going to steal their lunch, 167 00:07:29,040 --> 00:07:31,040 Speaker 4: worried that these smaller companies might go and help. 168 00:07:31,120 --> 00:07:32,560 Speaker 5: Somebody else they steal that lunch. 169 00:07:32,640 --> 00:07:37,760 Speaker 2: Check the Stock Movers podcast from Bloomberg Radio. Check back 170 00:07:37,800 --> 00:07:40,200 Speaker 2: with us throughout the day for the latest roundup of 171 00:07:40,240 --> 00:07:43,440 Speaker 2: companies making news on Wall Street and for the latest 172 00:07:43,480 --> 00:07:47,640 Speaker 2: market moving headlines. Listen to Bloomberg Radio Live. 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