1 00:00:02,200 --> 00:00:05,480 Speaker 1: From the heart of where innovation, money and power COLLI 2 00:00:06,320 --> 00:00:10,840 Speaker 1: in Silicon Valley and beyond. This is Bloomberg Technology with 3 00:00:10,920 --> 00:00:27,120 Speaker 1: Emily jay I. Remember ly check in San Francisco and 4 00:00:27,160 --> 00:00:30,400 Speaker 1: this is Bloomberg Technology coming up the next hour. Multiple 5 00:00:30,480 --> 00:00:35,800 Speaker 1: SpaceX employees fired after an open letter criticizing Elon Musk. 6 00:00:36,000 --> 00:00:38,800 Speaker 1: What this says about his leadership and what it means 7 00:00:39,120 --> 00:00:44,720 Speaker 1: for Twitter Plus? Does AI have feelings? Google just suspended 8 00:00:44,720 --> 00:00:47,240 Speaker 1: an engineer who claims the answer to that question is yes. 9 00:00:47,680 --> 00:00:50,560 Speaker 1: We will have an in depth conversation with Google's former 10 00:00:50,640 --> 00:00:55,880 Speaker 1: AI ethicist about the limits and power of this technology 11 00:00:56,040 --> 00:00:59,080 Speaker 1: and cryptotraders go from feeling the fear of missing out 12 00:00:59,080 --> 00:01:02,280 Speaker 1: to straight up fear. We'll talk to micro Strategy CEO 13 00:01:02,320 --> 00:01:05,520 Speaker 1: Michael Saylor about his big bet on bitcoin and if 14 00:01:05,560 --> 00:01:11,240 Speaker 1: he has any regrets. SpaceX now has fired several employees 15 00:01:11,280 --> 00:01:14,639 Speaker 1: involved in an open letter criticizing the behavior of CEO 16 00:01:14,720 --> 00:01:17,639 Speaker 1: Elon Musk. This according to an internal memo that began 17 00:01:17,760 --> 00:01:21,920 Speaker 1: circulating among staff this week. The letter, seen by Bloomberg 18 00:01:21,920 --> 00:01:25,840 Speaker 1: called Musk's behavior and tweets quote a frequent source of 19 00:01:25,880 --> 00:01:30,360 Speaker 1: distraction and embarrassment, and called on SpaceX leadership to condemn 20 00:01:30,360 --> 00:01:34,680 Speaker 1: and distance itself from his quote personal brand. Bloomberg's bad 21 00:01:34,760 --> 00:01:38,640 Speaker 1: Ludlow here now to discuss. So Bloombook obtained a copy 22 00:01:38,640 --> 00:01:41,440 Speaker 1: of the Soper letter. What exactly did it say? Yes, 23 00:01:41,520 --> 00:01:44,360 Speaker 1: so this is a small select group of employees who 24 00:01:44,440 --> 00:01:47,760 Speaker 1: called on SpaceX Is management, including Gwin short World, the 25 00:01:47,840 --> 00:01:53,600 Speaker 1: CEO and president, to basically publicly distance itself from Musk 26 00:01:53,800 --> 00:01:56,120 Speaker 1: what he says, what he believes, what he was doing, 27 00:01:56,400 --> 00:02:00,040 Speaker 1: because they felt that it was quote, an embarrassment and 28 00:02:00,480 --> 00:02:02,400 Speaker 1: that it was basically impacting the work that space It 29 00:02:02,480 --> 00:02:03,800 Speaker 1: was trying to do. This was the view of a 30 00:02:03,800 --> 00:02:06,240 Speaker 1: small number of employees. It was shared as sort of 31 00:02:06,280 --> 00:02:10,679 Speaker 1: something you could sign vera QR code through internal messaging channels, 32 00:02:10,760 --> 00:02:12,560 Speaker 1: and it picked up some momentum. You know, they did 33 00:02:12,560 --> 00:02:19,160 Speaker 1: get signatures until management acted. What exactly did management then? 34 00:02:20,600 --> 00:02:24,080 Speaker 1: So in a second internal memo which was sent by 35 00:02:24,120 --> 00:02:27,000 Speaker 1: Gwyn shot Well, the company's CEO, which Blomberg has seen, 36 00:02:28,120 --> 00:02:31,120 Speaker 1: they basically said this wasn't helpful. You know, they conducted 37 00:02:31,160 --> 00:02:35,280 Speaker 1: an investigation into the origins of this open letter from staff, 38 00:02:35,840 --> 00:02:38,840 Speaker 1: and as you said, and I think we had the 39 00:02:38,919 --> 00:02:40,920 Speaker 1: quote from Gwyn shot Well about sort of the broader 40 00:02:40,960 --> 00:02:44,320 Speaker 1: impact of it. They ended up firing that what they 41 00:02:44,320 --> 00:02:46,840 Speaker 1: said a number of employees, we don't have a firm 42 00:02:47,120 --> 00:02:49,360 Speaker 1: number and how many it is. But this is what's interesting. 43 00:02:49,639 --> 00:02:53,720 Speaker 1: She calls this overreaching activism. You know, so clearly there's 44 00:02:53,720 --> 00:02:56,360 Speaker 1: a group of employees within the business who have strong opinion. 45 00:02:56,400 --> 00:03:00,720 Speaker 1: And this what she says elsewhere in the memo that's 46 00:03:00,760 --> 00:03:02,720 Speaker 1: frankly too long for us to share on the screen, 47 00:03:02,800 --> 00:03:06,400 Speaker 1: is that this select group of employees were putting pressure 48 00:03:06,400 --> 00:03:09,920 Speaker 1: on their peers to sign something that they didn't believe in. 49 00:03:09,960 --> 00:03:12,760 Speaker 1: These are paraphrasing Gwin Shotwell's words, did a bunch of 50 00:03:12,800 --> 00:03:15,760 Speaker 1: employees sign it? And are we've seen arranging numbers up 51 00:03:15,800 --> 00:03:19,760 Speaker 1: there from several hundreds several thousand. SpaceX has twelve thousand 52 00:03:19,760 --> 00:03:24,639 Speaker 1: employees around the world essentially, so it gained traction um. 53 00:03:25,880 --> 00:03:28,480 Speaker 1: But ultimately, you know, what Gwyn Shotwell goes on to 54 00:03:28,520 --> 00:03:31,200 Speaker 1: say in that memo is that this is a distraction 55 00:03:31,240 --> 00:03:33,240 Speaker 1: from their end goal and their own goals we know 56 00:03:33,520 --> 00:03:36,080 Speaker 1: is getting to mars Well. This isn't the first story 57 00:03:36,360 --> 00:03:40,120 Speaker 1: in the last few weeks about Elon Musk's behavior at SpaceX. 58 00:03:40,160 --> 00:03:43,440 Speaker 1: There was another story about accusing him of sexual harassment. Right, 59 00:03:44,040 --> 00:03:47,080 Speaker 1: and Gwen shot Well also defended him in that instance 60 00:03:47,080 --> 00:03:50,680 Speaker 1: as well. Correct. So last month, Business Insider reported that 61 00:03:51,040 --> 00:03:54,680 Speaker 1: the company space X settled with a former contract employee 62 00:03:54,960 --> 00:03:58,360 Speaker 1: in two thousand eighteen for two fifty thousand U S 63 00:03:58,400 --> 00:04:03,720 Speaker 1: dollars and that employee in question was a contract air 64 00:04:03,760 --> 00:04:07,360 Speaker 1: steward aboard a space x jet that Must would used 65 00:04:07,360 --> 00:04:10,720 Speaker 1: to travel. And straight away we should point out must 66 00:04:10,760 --> 00:04:15,320 Speaker 1: denies not only denies the claims made, but he actually 67 00:04:15,360 --> 00:04:17,839 Speaker 1: goes on to say in a series of tweets earlier 68 00:04:17,880 --> 00:04:19,719 Speaker 1: in the month or last month that he basically saw 69 00:04:19,760 --> 00:04:21,760 Speaker 1: this as a political attack on him, that it was 70 00:04:21,800 --> 00:04:27,680 Speaker 1: sort of a calculated initiative to to impact his reputation. Um, 71 00:04:27,720 --> 00:04:30,640 Speaker 1: you know, and he questioned the source who is the 72 00:04:30,640 --> 00:04:33,839 Speaker 1: original source of the business inside of story. So regardless, 73 00:04:33,880 --> 00:04:35,400 Speaker 1: we don't get to the bottom of it. I should 74 00:04:35,440 --> 00:04:38,600 Speaker 1: point out SpaceX does have a comms team, a PR team. 75 00:04:38,920 --> 00:04:41,840 Speaker 1: I messaged them regularly. They are real people, but they 76 00:04:41,880 --> 00:04:44,919 Speaker 1: have not responded to multiple recomment on this story, on 77 00:04:44,960 --> 00:04:47,640 Speaker 1: the open letter story, so you know, what we have 78 00:04:47,720 --> 00:04:50,040 Speaker 1: to go on is these internal messages that Bloomberg has 79 00:04:50,000 --> 00:04:53,680 Speaker 1: scene interesting. There has been some other news for SpaceX 80 00:04:53,720 --> 00:04:56,240 Speaker 1: today they did have another successful life. I mean, this 81 00:04:56,279 --> 00:04:57,880 Speaker 1: is what it all comes back to down to. So 82 00:04:57,960 --> 00:05:00,719 Speaker 1: today was kind of another bog standard style launch, another 83 00:05:00,800 --> 00:05:04,440 Speaker 1: three fifty three Starlink satellites deployed to orbit. But it 84 00:05:04,480 --> 00:05:07,800 Speaker 1: was also a milestone for reusability. It was the hundred 85 00:05:07,839 --> 00:05:12,520 Speaker 1: of launch using a proven, a flight proven rocket or booster. 86 00:05:12,520 --> 00:05:15,039 Speaker 1: In other words, this is the hundred of time that 87 00:05:15,160 --> 00:05:18,240 Speaker 1: SpaceX has sent up a rocket that has previously flown 88 00:05:18,720 --> 00:05:22,279 Speaker 1: and landed it successfully, and it's just changed the game, right. 89 00:05:22,320 --> 00:05:24,360 Speaker 1: This is the whole point of the space X story 90 00:05:24,760 --> 00:05:28,640 Speaker 1: that this reusability angle allows them to go with such 91 00:05:28,680 --> 00:05:32,279 Speaker 1: regularity that it becomes routine, but also that it makes 92 00:05:32,600 --> 00:05:36,280 Speaker 1: access to space much more affordable for little satellite providers 93 00:05:36,360 --> 00:05:39,400 Speaker 1: and themselves building out the Starlink network. All right, well, 94 00:05:39,600 --> 00:05:43,800 Speaker 1: TBC to be continued as I'm sure this narrative will 95 00:05:43,839 --> 00:05:57,440 Speaker 1: be Thank you thanks. Earlier this week, a Google engineer 96 00:05:57,480 --> 00:06:00,320 Speaker 1: working on the company's AI development team was suspen ended 97 00:06:00,320 --> 00:06:04,680 Speaker 1: after claiming a chatbot actually has feelings. Blake Lemoyne was 98 00:06:04,720 --> 00:06:07,919 Speaker 1: placed on paid leave last week after he posted on 99 00:06:08,040 --> 00:06:12,000 Speaker 1: Medium that he had encountered a quote Sentien AI, igniting 100 00:06:12,040 --> 00:06:15,440 Speaker 1: a fiery debate about the possibilities and limits of this 101 00:06:15,560 --> 00:06:20,120 Speaker 1: cutting edge technology. Dr Margaret Mitchell, Hugging Face, Chief Ethics 102 00:06:20,160 --> 00:06:24,720 Speaker 1: Scientists and researcher and former Google AI employee who worked 103 00:06:24,760 --> 00:06:29,520 Speaker 1: on the AI development team, joins us now to discuss Margaret, 104 00:06:29,560 --> 00:06:31,720 Speaker 1: thank you so much for joining us, or I should say, 105 00:06:32,080 --> 00:06:38,640 Speaker 1: Dr Mitchell, given your expertise, do you think Blake Lemoyne 106 00:06:38,920 --> 00:06:46,000 Speaker 1: is right? Does this AI? Does this spot have feelings? Uh? Okay, 107 00:06:46,000 --> 00:06:50,120 Speaker 1: well no, I don't think it does. Um. I certainly 108 00:06:50,160 --> 00:06:55,360 Speaker 1: don't think it has feelings definitely not consciousness or sentience, 109 00:06:55,800 --> 00:07:00,240 Speaker 1: which which is what the claims have been. So what 110 00:07:00,360 --> 00:07:03,240 Speaker 1: does this, though tell us about the potential or power 111 00:07:04,279 --> 00:07:08,080 Speaker 1: four AI am bought to full human beings into thinking 112 00:07:08,279 --> 00:07:13,960 Speaker 1: that they're real. Yeah, there's a few things going on. UM. 113 00:07:14,040 --> 00:07:18,400 Speaker 1: On the one hand, we have uh psychological effects of 114 00:07:18,600 --> 00:07:23,679 Speaker 1: UM interacting with things that are human like UM. So uh, 115 00:07:23,720 --> 00:07:27,400 Speaker 1: we we tend to anthropomorphize UM. We tend to put 116 00:07:27,480 --> 00:07:32,240 Speaker 1: intentionality UM into things that we're that we're inter interacting 117 00:07:32,280 --> 00:07:34,800 Speaker 1: with that seem human like. I think people are sort 118 00:07:34,840 --> 00:07:37,160 Speaker 1: of used to doing this with our pets and things, 119 00:07:37,240 --> 00:07:40,520 Speaker 1: you know, creating like whole dialogues and conversations UM, but 120 00:07:40,600 --> 00:07:44,240 Speaker 1: also with like you know, uh, stuffed animals and tamagotchis 121 00:07:44,280 --> 00:07:48,080 Speaker 1: and things like that. And there's also been psychological studies 122 00:07:48,360 --> 00:07:53,000 Speaker 1: showing that we have a propensity to um impute intentionality 123 00:07:53,760 --> 00:07:58,920 Speaker 1: into um non conscious beings UM when they show some 124 00:07:59,000 --> 00:08:03,920 Speaker 1: sort of properties like speaking uh, like vulnerability UM or 125 00:08:04,080 --> 00:08:08,240 Speaker 1: or movement that's aligned with human like movement UM. On 126 00:08:08,280 --> 00:08:12,280 Speaker 1: the other hand, we also have a lot of companies 127 00:08:12,280 --> 00:08:17,800 Speaker 1: working in AI using the language of human cognition UM 128 00:08:18,000 --> 00:08:21,120 Speaker 1: so saying things like chain of thought, saying things like 129 00:08:21,240 --> 00:08:26,000 Speaker 1: reasoning um, you know, essentially comparing the models that they're 130 00:08:26,040 --> 00:08:29,200 Speaker 1: working with to the brain, which makes some sense, but 131 00:08:29,280 --> 00:08:32,520 Speaker 1: you really have to temper that with with the details 132 00:08:32,559 --> 00:08:35,400 Speaker 1: of this essentially being um a bunch of a bunch 133 00:08:35,440 --> 00:08:38,880 Speaker 1: of calculations UM. So we have a few things going on, 134 00:08:39,000 --> 00:08:43,480 Speaker 1: the psychological illusions UH and the language that companies are 135 00:08:43,600 --> 00:08:48,559 Speaker 1: using around the technology they're building. So, given the complexity 136 00:08:48,600 --> 00:08:53,160 Speaker 1: of this, what are your biggest concerns about? For example, 137 00:08:53,200 --> 00:08:57,040 Speaker 1: these transcripts that that Blake Lamoyne published where the computer 138 00:08:57,160 --> 00:08:59,880 Speaker 1: is saying I'm scared of dying, I'm scared of being 139 00:09:00,000 --> 00:09:05,760 Speaker 1: turned off. Yeah, I mean I I think I echo 140 00:09:06,240 --> 00:09:10,680 Speaker 1: UM a lot of researchers in this space, UM, where 141 00:09:11,400 --> 00:09:13,959 Speaker 1: I think we all sort of feel like sentience is 142 00:09:14,320 --> 00:09:17,959 Speaker 1: not the point here. UM. I think it's I think 143 00:09:17,960 --> 00:09:20,480 Speaker 1: it's important to note that we are not going to 144 00:09:20,520 --> 00:09:24,440 Speaker 1: get an agreement on sentience or consciousness anytime soon. People 145 00:09:24,480 --> 00:09:27,040 Speaker 1: are going to see sentience, people are going to see 146 00:09:27,040 --> 00:09:30,360 Speaker 1: consciousness UM, and that will probably go on, you know, 147 00:09:30,480 --> 00:09:34,800 Speaker 1: indefinitely where we just have a disagreement. UM. But when 148 00:09:34,880 --> 00:09:39,680 Speaker 1: you do have people starting to see sentience in consciousness, UM, 149 00:09:39,720 --> 00:09:42,840 Speaker 1: it starts to bring up things like, UM, you know, 150 00:09:42,880 --> 00:09:45,480 Speaker 1: like robot rights, all this work that's been done on 151 00:09:45,960 --> 00:09:50,600 Speaker 1: what the personhood of these models might be. UM. Well, 152 00:09:50,640 --> 00:09:53,800 Speaker 1: at the same time, you have technology that you know, 153 00:09:54,000 --> 00:09:58,880 Speaker 1: essentially discriminates, you know, against black, black and brown people, UM, 154 00:09:58,920 --> 00:10:04,000 Speaker 1: poorly represents women and reflects misogynistic viewpoints. Uh. So there's 155 00:10:04,000 --> 00:10:06,679 Speaker 1: something to be said for an obsession with the personhood 156 00:10:06,960 --> 00:10:11,160 Speaker 1: of AI and systems UM and thinking about the rights 157 00:10:11,160 --> 00:10:14,160 Speaker 1: that they might have while not actually doing good work 158 00:10:14,280 --> 00:10:18,400 Speaker 1: on the rights of actual people. UM. On top of Oh, 159 00:10:18,559 --> 00:10:20,480 Speaker 1: I have so much to say, but yeah, you have 160 00:10:20,520 --> 00:10:23,640 Speaker 1: another question. I'm sure well, you know, and of course 161 00:10:23,840 --> 00:10:25,559 Speaker 1: you know the history behind this is that you were 162 00:10:25,600 --> 00:10:29,079 Speaker 1: fired for your work and sounding the alarm about sexism 163 00:10:29,120 --> 00:10:33,000 Speaker 1: and racism in a in AI at Google. So it 164 00:10:33,000 --> 00:10:36,080 Speaker 1: sounds to me like you're saying, this isn't the problem. 165 00:10:36,160 --> 00:10:38,680 Speaker 1: We shouldn't be asking if robots have feelings and rights. 166 00:10:38,720 --> 00:10:42,960 Speaker 1: We should be asking if AI is gender blind and 167 00:10:43,000 --> 00:10:47,040 Speaker 1: color blind and making sure, um that we're focusing on 168 00:10:47,040 --> 00:10:51,120 Speaker 1: all of these other things that are far more important. Yeah, 169 00:10:51,160 --> 00:10:54,160 Speaker 1: I mean, so it's not gender blind, it's actually uh 170 00:10:54,679 --> 00:10:58,560 Speaker 1: targeting gender and negative ways. UM. And so for example, 171 00:10:58,600 --> 00:11:00,880 Speaker 1: we know that a lot of these systems are trained 172 00:11:01,040 --> 00:11:06,400 Speaker 1: on UM text data from from websites that have misogynistic 173 00:11:06,440 --> 00:11:12,160 Speaker 1: tendencies UM and UH websites that are predominantly white and 174 00:11:12,280 --> 00:11:16,760 Speaker 1: male UM and and actually US based. UM. So there's 175 00:11:16,760 --> 00:11:19,840 Speaker 1: all these kinds of things that are being UM propagated 176 00:11:19,880 --> 00:11:22,800 Speaker 1: by these systems that are really problematic. UM. And they 177 00:11:22,920 --> 00:11:27,640 Speaker 1: become even more problematic when people start to be affected 178 00:11:27,640 --> 00:11:30,320 Speaker 1: by the systems as they interact with them. UM. So, 179 00:11:30,360 --> 00:11:33,199 Speaker 1: in the case of consciousness, UM, you have the concern 180 00:11:33,280 --> 00:11:37,200 Speaker 1: that people might be persuaded to do horrible things. UM. 181 00:11:37,280 --> 00:11:41,120 Speaker 1: You also have you know, concerns around bullying and hate bots, 182 00:11:41,679 --> 00:11:43,440 Speaker 1: uh and these kinds of things that can you know, 183 00:11:43,559 --> 00:11:47,480 Speaker 1: really hurt people. Um. And then you know you also 184 00:11:47,640 --> 00:11:52,000 Speaker 1: have um, these subtle effects of you know, in search 185 00:11:52,120 --> 00:11:55,120 Speaker 1: ranking results, what will tend to appear at the top 186 00:11:55,160 --> 00:11:57,520 Speaker 1: of that ranking And if it's a function of these 187 00:11:57,520 --> 00:12:00,240 Speaker 1: sorts of language models um as Google, for exap people 188 00:12:00,320 --> 00:12:03,480 Speaker 1: has said, UM, they are, then you're going to have 189 00:12:03,559 --> 00:12:07,120 Speaker 1: these bias effects influencing search results in such a way 190 00:12:07,160 --> 00:12:09,960 Speaker 1: that you tend to see the viewpoints of white men, 191 00:12:10,280 --> 00:12:13,200 Speaker 1: you know, at the top of the search drinking results 192 00:12:13,280 --> 00:12:16,040 Speaker 1: as opposed to you know, black women, And that is 193 00:12:16,080 --> 00:12:19,160 Speaker 1: sort of a echo a chamber effects where it's like 194 00:12:19,720 --> 00:12:23,400 Speaker 1: the privileged gets more privileged, right, privilege gets privileged, while 195 00:12:23,440 --> 00:12:27,400 Speaker 1: the marginalized become more marginalized. Now Google has come out 196 00:12:27,400 --> 00:12:29,400 Speaker 1: saying that in this particular case, when it comes to 197 00:12:29,440 --> 00:12:32,400 Speaker 1: Blake Lemoyne, that you know, hundreds of researchers have interacted 198 00:12:32,440 --> 00:12:36,079 Speaker 1: with the same technology haven't expressed these concerns. I also 199 00:12:36,120 --> 00:12:39,760 Speaker 1: sat down with Alphabet and Google CEO Sundar pacha I 200 00:12:40,559 --> 00:12:43,960 Speaker 1: last year and asked him about concerns around AI from 201 00:12:43,960 --> 00:12:47,559 Speaker 1: within Google itself. From people like yourself. I asked him 202 00:12:47,600 --> 00:12:49,840 Speaker 1: what keeps him up at night? Take a listen to 203 00:12:49,920 --> 00:12:54,480 Speaker 1: what he had to say. Anytime you're developing technology, there 204 00:12:54,559 --> 00:12:56,800 Speaker 1: is a dual site to it. I think the journey 205 00:12:56,800 --> 00:13:01,880 Speaker 1: of humanity is harnessing the benefits while minimizing the downsides. 206 00:13:02,559 --> 00:13:04,640 Speaker 1: The good thing with AI is it's both going to 207 00:13:04,679 --> 00:13:08,480 Speaker 1: take time. I think I've seen more focus on the 208 00:13:08,600 --> 00:13:11,800 Speaker 1: downsides early on than most of the technology we've developed. 209 00:13:11,800 --> 00:13:15,360 Speaker 1: So in some ways I'm encouraged by how much concerned 210 00:13:15,400 --> 00:13:19,320 Speaker 1: there is. And you're right, even within Google, you know, 211 00:13:19,840 --> 00:13:24,400 Speaker 1: you know people think about it deeply. Margaret, Do you 212 00:13:24,480 --> 00:13:27,840 Speaker 1: think he and Google are leading on these issues in 213 00:13:27,880 --> 00:13:33,200 Speaker 1: the right way? No, clearly not. I mean everyone I think, 214 00:13:33,320 --> 00:13:35,600 Speaker 1: at least in tech is familiar with this notion of 215 00:13:35,640 --> 00:13:40,800 Speaker 1: tech solutionism UM. And there's no lack of pr and 216 00:13:40,920 --> 00:13:44,440 Speaker 1: calms around the benefits of AI and really trying to 217 00:13:44,480 --> 00:13:47,600 Speaker 1: push it as beneficial for humanity and all these sorts 218 00:13:47,640 --> 00:13:51,120 Speaker 1: of things. It's it's a very small minority who speaks 219 00:13:51,200 --> 00:13:54,840 Speaker 1: up about the downside. So I would say that UM 220 00:13:55,120 --> 00:14:01,319 Speaker 1: soon Dar's characteration characterization is false UM and frustrating false UM. 221 00:14:01,640 --> 00:14:03,680 Speaker 1: And one of the reasons. I think that there's a 222 00:14:03,720 --> 00:14:06,920 Speaker 1: desire to stay away from the downsides. UM. In addition 223 00:14:06,960 --> 00:14:10,480 Speaker 1: to you know, concern around profit is that it also 224 00:14:10,520 --> 00:14:13,200 Speaker 1: starts to open up liability. Right, so if you have 225 00:14:13,280 --> 00:14:17,520 Speaker 1: systems that you can show work worse on black women, 226 00:14:17,880 --> 00:14:20,480 Speaker 1: then now it starts to bring up questions of discrimination 227 00:14:20,560 --> 00:14:25,480 Speaker 1: within the systems. UM. So it behooves companies to try 228 00:14:25,520 --> 00:14:27,920 Speaker 1: and say, oh, the downsides are you know, are being 229 00:14:28,040 --> 00:14:32,200 Speaker 1: over examined, and try and kind of shut that conversation down. 230 00:14:32,640 --> 00:14:35,080 Speaker 1: But I think what's actually happening is that the small 231 00:14:35,160 --> 00:14:38,800 Speaker 1: set of people who have been speaking about ethical concerns 232 00:14:39,320 --> 00:14:42,920 Speaker 1: are starting to be listened to because people are seeing 233 00:14:43,040 --> 00:14:46,720 Speaker 1: the negative effects. UM. And I think that's really what's 234 00:14:46,760 --> 00:14:49,760 Speaker 1: happening is a desire on the corporate side to shut 235 00:14:49,840 --> 00:14:52,280 Speaker 1: the conversation down for a lot of sort of incentives 236 00:14:52,320 --> 00:14:55,920 Speaker 1: they have, and then people actually seeing the downside it's 237 00:14:55,960 --> 00:14:59,320 Speaker 1: and not having an effect on what gets reported. Do 238 00:14:59,400 --> 00:15:04,120 Speaker 1: you think a little wine should have been suspended? Uh? No, 239 00:15:04,440 --> 00:15:07,880 Speaker 1: I don't. UM. I so I should say that um. 240 00:15:08,280 --> 00:15:11,320 Speaker 1: Blake and I are really good friends. We worked together 241 00:15:11,400 --> 00:15:14,480 Speaker 1: at Google. We wrote a paper together actually on how 242 00:15:14,520 --> 00:15:21,640 Speaker 1: to um mitigate problematic biases in UM in machine learning systems. Um. 243 00:15:21,680 --> 00:15:26,040 Speaker 1: He's a very very bright guy. UM. So I'm a 244 00:15:26,040 --> 00:15:28,800 Speaker 1: little bit worried that there's sort of this reductive narrative 245 00:15:28,840 --> 00:15:32,640 Speaker 1: that there's something like fundamentally wrong with him or something. UM. 246 00:15:32,680 --> 00:15:35,440 Speaker 1: He he has a lot of dimensions. UM. And I 247 00:15:35,480 --> 00:15:38,400 Speaker 1: think Google could have done a much better job at 248 00:15:38,480 --> 00:15:43,480 Speaker 1: engaging with him rather than this you know, very alienating 249 00:15:43,920 --> 00:15:46,720 Speaker 1: sort of experience that they gave him instead. UM. I 250 00:15:46,720 --> 00:15:48,920 Speaker 1: think it shows a weakness on Google's part to be 251 00:15:49,040 --> 00:15:53,720 Speaker 1: able to um uh, to be able to be open 252 00:15:53,800 --> 00:15:58,120 Speaker 1: to different kinds of experiences and perspectives. So one, are 253 00:15:58,240 --> 00:16:02,800 Speaker 1: your biggest fears if Google continues to develop the technology 254 00:16:03,400 --> 00:16:06,840 Speaker 1: at the pace that it is developing, this technology continues 255 00:16:07,000 --> 00:16:10,280 Speaker 1: to you know, potentially not listen to this as you say, 256 00:16:10,400 --> 00:16:13,720 Speaker 1: minority of voices that are speaking up paint the picture 257 00:16:13,840 --> 00:16:16,680 Speaker 1: of of what you fear the world could look like 258 00:16:17,520 --> 00:16:22,480 Speaker 1: if Google continues on this path. Oh no, that is 259 00:16:22,520 --> 00:16:25,040 Speaker 1: a very big question. UM. And I'm not a good painter, 260 00:16:25,920 --> 00:16:28,680 Speaker 1: I should mention I'm a computer scientist, so I might 261 00:16:28,720 --> 00:16:31,400 Speaker 1: not you know, be as elephant at this UM. But 262 00:16:31,720 --> 00:16:35,280 Speaker 1: you know, we're already seeing a lot of what we 263 00:16:35,320 --> 00:16:39,240 Speaker 1: can expect to happen in the future, but even worse. UM. 264 00:16:39,320 --> 00:16:42,359 Speaker 1: So just recently, someone released a ton of hate thoughts 265 00:16:42,480 --> 00:16:46,320 Speaker 1: UM and then made the model available to the public UM. 266 00:16:46,400 --> 00:16:49,400 Speaker 1: And so we are going to likely see an increase 267 00:16:49,600 --> 00:16:56,320 Speaker 1: of hateful UM intelligence seeming systems across our interactions online 268 00:16:56,320 --> 00:16:59,120 Speaker 1: and on social media. UM. And this includes things like 269 00:16:59,160 --> 00:17:03,960 Speaker 1: bullying as is really problematic, persuasion into sort of more 270 00:17:04,320 --> 00:17:09,760 Speaker 1: extremist UM areas. UM. I think we're going to see 271 00:17:10,200 --> 00:17:15,720 Speaker 1: UH further sort of marginalization and worsening of power differentials. 272 00:17:15,760 --> 00:17:19,680 Speaker 1: So as you know, a company like Google a mass 273 00:17:19,800 --> 00:17:24,320 Speaker 1: is more and more um ability to affect people's sense 274 00:17:24,480 --> 00:17:27,640 Speaker 1: of of what's true in the world through search ranking results, 275 00:17:28,280 --> 00:17:32,160 Speaker 1: through the sort of UH products they're making. UM. It 276 00:17:32,200 --> 00:17:35,840 Speaker 1: means that the voices of people who have less access 277 00:17:35,880 --> 00:17:38,840 Speaker 1: to the Internet, for example, are going to disappear more 278 00:17:38,840 --> 00:17:41,280 Speaker 1: and more, while Google a mass is more and more 279 00:17:41,320 --> 00:17:45,800 Speaker 1: power UM. And so I'm very very concerned about how 280 00:17:45,880 --> 00:17:50,120 Speaker 1: much the sort of technology moving forward empowers Google UM 281 00:17:50,200 --> 00:17:54,159 Speaker 1: and sort of lack of respects that I've seen for 282 00:17:54,640 --> 00:17:58,960 Speaker 1: very serious ethical concerns. You know, misinformation obviously is one 283 00:17:59,040 --> 00:18:01,640 Speaker 1: alongside some of the models that have come out recently, 284 00:18:01,680 --> 00:18:04,080 Speaker 1: We're not gonna know what's real. There's going to be 285 00:18:04,320 --> 00:18:08,720 Speaker 1: text text based misinformation, so so news that's wrong, uh, 286 00:18:08,800 --> 00:18:12,600 Speaker 1: image based missed information so images that look real that 287 00:18:12,600 --> 00:18:16,119 Speaker 1: that are not real. UM. And video based as well, 288 00:18:16,359 --> 00:18:20,520 Speaker 1: and also audio based. So essentially all of the main 289 00:18:20,640 --> 00:18:26,040 Speaker 1: ways that we consume information online will now no longer 290 00:18:26,200 --> 00:18:31,680 Speaker 1: be very easy uh to trace back to reality. UM. 291 00:18:31,720 --> 00:18:39,480 Speaker 1: And that means mass misunderstanding. UM. So yeah, scary, Dr Mitchell. Uh. 292 00:18:40,280 --> 00:18:42,320 Speaker 1: This we could have this conversation for hours, and I 293 00:18:42,640 --> 00:18:44,560 Speaker 1: know we're gonna be having it for years. I'd love 294 00:18:44,720 --> 00:18:46,480 Speaker 1: to have you back to talk more about your work 295 00:18:46,480 --> 00:18:49,000 Speaker 1: at Hugging Face. I know that there you are taking 296 00:18:49,000 --> 00:18:51,280 Speaker 1: a different approach to a lot of these issues. UM, 297 00:18:51,280 --> 00:18:54,000 Speaker 1: But because of commercials, we're gonna have to leave it here. Um. 298 00:18:54,080 --> 00:18:57,720 Speaker 1: Dr Margaret Mitch and Mitchell Hugging Face chief ethics scientists 299 00:18:57,800 --> 00:19:01,520 Speaker 1: and researcher. UM, thank you uh for joining us today 300 00:19:01,520 --> 00:19:05,119 Speaker 1: and help us work through some of these very complex issues. 301 00:19:05,119 --> 00:19:07,920 Speaker 1: Will have much more ahead stay with us. This is Bloomberg. 302 00:19:17,440 --> 00:19:19,720 Speaker 1: TikTok says it has reached an agreement with Oracle to 303 00:19:19,800 --> 00:19:23,560 Speaker 1: store data from US users on Oracle servers. The deal 304 00:19:24,000 --> 00:19:27,440 Speaker 1: has been in the work since following concerns of security 305 00:19:27,560 --> 00:19:30,160 Speaker 1: risks linked to the Chinese owned app. This news comes 306 00:19:30,200 --> 00:19:34,840 Speaker 1: the same day BuzzFeed shared that leaked audio from dozens 307 00:19:34,840 --> 00:19:37,960 Speaker 1: of internal TikTok meetings revealed US user data has been 308 00:19:38,000 --> 00:19:50,520 Speaker 1: repeatedly accessed from China. Welcome back to bloomber Technology and 309 00:19:50,560 --> 00:19:52,480 Speaker 1: Emily Chang in San Francisco. I want to dig into 310 00:19:52,480 --> 00:19:55,280 Speaker 1: this and how the fed rate hike is impacting the 311 00:19:55,320 --> 00:19:59,879 Speaker 1: world of VC with Mike Volpi and partner at Index Ventures. 312 00:20:00,160 --> 00:20:02,520 Speaker 1: Might great to have you back with us. Look, it's 313 00:20:02,560 --> 00:20:04,560 Speaker 1: been an incredibly vi all of the week, a lot 314 00:20:04,600 --> 00:20:08,040 Speaker 1: of uncertainty about the future, a lot of people saying 315 00:20:08,720 --> 00:20:13,560 Speaker 1: the R word is inevitable. What do you think. Yeah, 316 00:20:13,600 --> 00:20:15,680 Speaker 1: I'm gonna start by saying that I'm a Lakers fan, 317 00:20:15,760 --> 00:20:19,600 Speaker 1: so I'm relatively different all of the events. Well, we're 318 00:20:19,640 --> 00:20:25,440 Speaker 1: not sorry, but thank you for clarifying, Okay, So, um, yeah, 319 00:20:25,520 --> 00:20:29,840 Speaker 1: it's it's definitely a very tricky time right now. Um, 320 00:20:30,040 --> 00:20:33,159 Speaker 1: and I think that when we look at the portfolio 321 00:20:33,160 --> 00:20:36,400 Speaker 1: of companies that we look after, there are certainly ones 322 00:20:36,440 --> 00:20:38,399 Speaker 1: that are more in the consumer side of business that 323 00:20:38,440 --> 00:20:43,040 Speaker 1: are seeing some softness happened. They're seeing that consumers in 324 00:20:43,160 --> 00:20:47,080 Speaker 1: general have read enough in the news, seeing enough tweets 325 00:20:47,119 --> 00:20:51,200 Speaker 1: about inflation and interest rates and all that, and they 326 00:20:51,240 --> 00:20:54,080 Speaker 1: are moderating their behavior. And you can see that even 327 00:20:54,080 --> 00:20:56,000 Speaker 1: in the statements that the larger companies are making. So 328 00:20:56,119 --> 00:21:00,080 Speaker 1: whether it's Amazon or Target or Walmart saying they don't 329 00:21:00,080 --> 00:21:03,479 Speaker 1: the right inventory or they may not be expanding as 330 00:21:03,520 --> 00:21:06,680 Speaker 1: quickly as they thought, there's clearly something happening out there. 331 00:21:07,000 --> 00:21:08,800 Speaker 1: I think what we don't know is whether it's a 332 00:21:08,840 --> 00:21:11,280 Speaker 1: little Our recession, sort of like a quick one that's 333 00:21:11,359 --> 00:21:13,359 Speaker 1: you know, quick one a quarter or two where it 334 00:21:13,359 --> 00:21:16,600 Speaker 1: fixes things, or a big Our recession. But there's clearly 335 00:21:16,640 --> 00:21:19,800 Speaker 1: something signaling that's going on out there. And I would 336 00:21:19,800 --> 00:21:22,959 Speaker 1: say that on a relative basis, it's happening sooner than 337 00:21:23,000 --> 00:21:25,560 Speaker 1: people expected, because there was sort of this notion that, oh, 338 00:21:25,640 --> 00:21:29,240 Speaker 1: maybe next year. I don't think if something, if the 339 00:21:29,240 --> 00:21:32,520 Speaker 1: economy is to slow down with some significance, it's probably 340 00:21:32,560 --> 00:21:34,920 Speaker 1: not next year, but it's like next quarter or the 341 00:21:35,000 --> 00:21:38,600 Speaker 1: quarter after that. So just how bad then do you 342 00:21:38,640 --> 00:21:41,400 Speaker 1: think is the wreckage is going to be? How many 343 00:21:41,400 --> 00:21:44,320 Speaker 1: more companies will have layoffs? How many people will get 344 00:21:44,400 --> 00:21:47,199 Speaker 1: laid off? How many companies will make it out of this? 345 00:21:47,280 --> 00:21:51,879 Speaker 1: How long does this last? Yeah? In all candor, I 346 00:21:51,920 --> 00:21:53,800 Speaker 1: don't think it's going to be that bad. There will 347 00:21:53,880 --> 00:21:57,920 Speaker 1: be some high profile situations where well known companies are 348 00:21:58,000 --> 00:22:02,000 Speaker 1: letting people go, and that will be painful, undoubtedly, But 349 00:22:02,400 --> 00:22:06,119 Speaker 1: I think that generally speaking, over the last couple of years, 350 00:22:06,160 --> 00:22:11,440 Speaker 1: in and especially private companies have been able to raise 351 00:22:12,080 --> 00:22:15,399 Speaker 1: uh amounts of money that we've never seen in the past, 352 00:22:15,880 --> 00:22:18,600 Speaker 1: and that puts their balance sheets in a pretty good condition. 353 00:22:19,040 --> 00:22:21,840 Speaker 1: Now they may have over hired, so they might trim 354 00:22:21,880 --> 00:22:24,520 Speaker 1: a little bit here or there, but by and large, 355 00:22:24,560 --> 00:22:28,040 Speaker 1: I think the majority of companies have the strongest balance 356 00:22:28,080 --> 00:22:30,399 Speaker 1: sheets that they've had in a while, and in many 357 00:22:30,440 --> 00:22:33,080 Speaker 1: cases have enough in the tank to get through a 358 00:22:33,119 --> 00:22:36,000 Speaker 1: difficult period and to come out at the end of it. 359 00:22:36,240 --> 00:22:39,520 Speaker 1: So I do think that we will surely see uh 360 00:22:39,720 --> 00:22:42,840 Speaker 1: some challenges, and perhaps the ones that we most challenge 361 00:22:42,880 --> 00:22:45,520 Speaker 1: would be the public companies because they obviously have to 362 00:22:45,520 --> 00:22:48,320 Speaker 1: respond to a stock price and the fact that right 363 00:22:48,359 --> 00:22:52,240 Speaker 1: now investors want shorter term earnings or less losses in 364 00:22:52,280 --> 00:22:55,040 Speaker 1: the short term, so I think we'll see more from those, 365 00:22:55,080 --> 00:22:58,400 Speaker 1: But by and large. I don't see the kind of 366 00:22:58,760 --> 00:23:02,119 Speaker 1: quote wreckage that Mamy we saw in two thousand and 367 00:23:02,160 --> 00:23:05,320 Speaker 1: If anything, I would expect this to happen more quickly, 368 00:23:05,359 --> 00:23:08,800 Speaker 1: both the downturn and the upside to happen more quickly 369 00:23:08,840 --> 00:23:11,600 Speaker 1: than in the past. Let's talk about some of these 370 00:23:11,600 --> 00:23:15,280 Speaker 1: more high profile situations, and I want to focus on Cisco. 371 00:23:15,359 --> 00:23:18,760 Speaker 1: Cisco CEO Chuck Robbins was on the show earlier this week. 372 00:23:18,800 --> 00:23:21,520 Speaker 1: I asked for his perspective. Take a listen to what 373 00:23:21,560 --> 00:23:25,280 Speaker 1: he had to say. We are always planning for different scenarios, 374 00:23:25,280 --> 00:23:27,080 Speaker 1: but we've been around long enough and been through enough 375 00:23:27,119 --> 00:23:29,879 Speaker 1: downturns that we have playbooks and we know how to 376 00:23:30,000 --> 00:23:35,200 Speaker 1: we know how to deal with those appropriately. Now, Cisco 377 00:23:35,359 --> 00:23:37,320 Speaker 1: is one of the companies that hit its peak in 378 00:23:37,400 --> 00:23:41,399 Speaker 1: the dot com boom, and the stock Mike has never recovered. 379 00:23:41,440 --> 00:23:44,240 Speaker 1: And what's interesting is you worked there for over a 380 00:23:44,320 --> 00:23:48,200 Speaker 1: decade through the dot com boom and bust, and I'm 381 00:23:48,280 --> 00:23:52,800 Speaker 1: just so curious how you reflect on on the fact 382 00:23:52,840 --> 00:23:55,639 Speaker 1: that Cisco, you know, never has, at least from a 383 00:23:55,720 --> 00:24:00,960 Speaker 1: stock perspective, gone back to what it was. Yeah, I 384 00:24:01,000 --> 00:24:04,240 Speaker 1: think that there's a couple of things though that contribute 385 00:24:04,240 --> 00:24:05,639 Speaker 1: to that, and it's true, by the way, it was 386 00:24:05,680 --> 00:24:08,960 Speaker 1: incredibly unpleasant to go from eighty some dollars a share 387 00:24:09,040 --> 00:24:11,040 Speaker 1: to like nine dollars a share and the span of 388 00:24:11,080 --> 00:24:13,720 Speaker 1: six months when I used to work there. Um, But 389 00:24:13,880 --> 00:24:15,760 Speaker 1: I think that there's a couple of factors that are 390 00:24:15,760 --> 00:24:19,080 Speaker 1: happening in Cisco's case. If you look at where Cisco 391 00:24:19,160 --> 00:24:21,560 Speaker 1: is today, they're a single digit grower, you know, five 392 00:24:21,560 --> 00:24:24,960 Speaker 1: percent grow or three percent grower. And fundamentally what happened 393 00:24:24,960 --> 00:24:28,000 Speaker 1: there is, yes, there was a downturn, but in parallel 394 00:24:28,040 --> 00:24:31,800 Speaker 1: with that, the technologies that they were pervading became much 395 00:24:31,920 --> 00:24:36,320 Speaker 1: more broadly available commoditized, competitive, etcetera, etcetera, and the company 396 00:24:36,359 --> 00:24:41,040 Speaker 1: never really reachieved the kind of growth rate that occurred 397 00:24:41,240 --> 00:24:44,680 Speaker 1: during the pre dot COM's scenario. If you take another 398 00:24:44,720 --> 00:24:48,840 Speaker 1: example like Amazon all Contrair, they did much better afterwards 399 00:24:48,960 --> 00:24:53,160 Speaker 1: because they were able to strategically expand the product lines, 400 00:24:53,200 --> 00:24:56,520 Speaker 1: the capabilities, the offerings, the sectors that the company was in. 401 00:24:56,960 --> 00:24:58,960 Speaker 1: And so really what comes out of this is that 402 00:24:59,400 --> 00:25:02,920 Speaker 1: you know, companies go into a difficult downturn like this, 403 00:25:03,080 --> 00:25:05,159 Speaker 1: and when they come out of it, do they have 404 00:25:05,240 --> 00:25:08,160 Speaker 1: the correct strategy for coming out of it. Most people 405 00:25:08,160 --> 00:25:10,639 Speaker 1: will say exactly what Chuck said, which is, we have 406 00:25:10,720 --> 00:25:13,640 Speaker 1: a playbook, we know how to deal with crises, We're 407 00:25:13,640 --> 00:25:16,040 Speaker 1: gonna cut this, we're gonna cut that, and so forth. 408 00:25:16,440 --> 00:25:20,240 Speaker 1: It's actually much more about how do you strategically align 409 00:25:20,320 --> 00:25:23,080 Speaker 1: your business to come out of it than it is 410 00:25:23,119 --> 00:25:25,439 Speaker 1: how to survive that period. And if you look at 411 00:25:25,480 --> 00:25:29,639 Speaker 1: today's technology companies, the very very large majority of will 412 00:25:29,680 --> 00:25:32,680 Speaker 1: survive it. The question is do they have the strategy 413 00:25:32,720 --> 00:25:36,480 Speaker 1: to thrive afterwards? And the strategy has to be aligned 414 00:25:36,520 --> 00:25:39,600 Speaker 1: with the fact that things that may have been hot 415 00:25:39,920 --> 00:25:43,560 Speaker 1: before are no longer hot later. I don't have a 416 00:25:43,600 --> 00:25:46,959 Speaker 1: crystal ball. I couldn't say exactly what the differences will be, 417 00:25:47,000 --> 00:25:50,480 Speaker 1: but I'm pretty sure that pre the themes that matter 418 00:25:51,800 --> 00:25:55,320 Speaker 1: will not be exactly mirrored in the post. And the 419 00:25:55,400 --> 00:25:58,439 Speaker 1: companies that are more thoughtful and strategic about how to 420 00:25:58,480 --> 00:26:02,359 Speaker 1: be aggressive and answer in the recovery cycle, well, the 421 00:26:02,359 --> 00:26:04,879 Speaker 1: ones that will benefit and look more like Amazon, and 422 00:26:04,920 --> 00:26:06,840 Speaker 1: the ones that stick to their knitting and do the 423 00:26:06,880 --> 00:26:09,320 Speaker 1: same thing they were doing before, we'll probably end up 424 00:26:09,320 --> 00:26:11,320 Speaker 1: looking a little more aft Mole Cisco in terms of 425 00:26:11,320 --> 00:26:16,000 Speaker 1: their stock performance. Interesting, we'll really appreciate having your historical 426 00:26:16,200 --> 00:26:19,359 Speaker 1: perspective there, and I know you've been sharing some of 427 00:26:19,359 --> 00:26:22,760 Speaker 1: that advice with founders as well. Mike Volpi of Index 428 00:26:22,840 --> 00:26:26,080 Speaker 1: Venture is good to have you back with us coming 429 00:26:26,160 --> 00:26:31,160 Speaker 1: up micro Strategies Bitcoin Strategy. As crypto crashes, judds chairman 430 00:26:31,200 --> 00:26:36,000 Speaker 1: and CEO Michael Saylor, have any regrets, he joins me. Next, 431 00:26:36,359 --> 00:26:52,159 Speaker 1: this is Bloomberg time now for our crypto report, with 432 00:26:52,160 --> 00:26:56,760 Speaker 1: cryptocurrency and the market still seeing major fluctuations, prompting companies 433 00:26:56,800 --> 00:27:01,440 Speaker 1: as biggest coin base to cut costs. Bitcoin ow over 434 00:27:01,560 --> 00:27:04,760 Speaker 1: just the last five days, it's worse week in a year. 435 00:27:05,200 --> 00:27:08,199 Speaker 1: Let's bring in Michael Saylor now of micro Strategy for 436 00:27:08,280 --> 00:27:10,639 Speaker 1: more on his take. And Michael, I know this is 437 00:27:10,680 --> 00:27:14,960 Speaker 1: probably a rhetorical question, but do you have any regrets? 438 00:27:16,840 --> 00:27:19,920 Speaker 1: You know, UM we did a lot of a lot 439 00:27:19,960 --> 00:27:22,440 Speaker 1: of back testing, and I've gone back and I've looked 440 00:27:22,440 --> 00:27:27,080 Speaker 1: at the numbers and on August ten when we announced 441 00:27:27,080 --> 00:27:33,080 Speaker 1: our two million dollar bitcoined by UM. Since then, bitcoins up, 442 00:27:34,720 --> 00:27:40,080 Speaker 1: the money supplies up, the nazacs down two percent, goals 443 00:27:40,119 --> 00:27:44,520 Speaker 1: down nine, the S and P is up nine and 444 00:27:44,560 --> 00:27:46,560 Speaker 1: the only thing that looks better than the money supply 445 00:27:46,600 --> 00:27:52,080 Speaker 1: expansion is single family homes. I couldn't have bought billions 446 00:27:52,119 --> 00:27:55,399 Speaker 1: of dollars of single family homes and so that's not 447 00:27:55,480 --> 00:27:58,679 Speaker 1: been practical. So the bottom line is the bitcoin strategies 448 00:27:58,720 --> 00:28:02,440 Speaker 1: ten x better than any other alternative, and so now 449 00:28:02,760 --> 00:28:05,959 Speaker 1: I don't regret it. We've got two point eight billion 450 00:28:06,000 --> 00:28:08,440 Speaker 1: dollars worth the bitcoin on our balance sheet right now, 451 00:28:08,440 --> 00:28:11,560 Speaker 1: and we feel like we're positioned well for when the 452 00:28:11,640 --> 00:28:14,560 Speaker 1: markets turn around. And our only other choice would be 453 00:28:14,600 --> 00:28:16,800 Speaker 1: to give all the capital back to the shareholders, in 454 00:28:16,840 --> 00:28:20,560 Speaker 1: which case we would have nothing and we would be struggling, 455 00:28:21,240 --> 00:28:25,000 Speaker 1: uh to get by without any assets. Okay, how about 456 00:28:25,000 --> 00:28:30,399 Speaker 1: this is cash still trash? Yeah? I mean the money 457 00:28:30,400 --> 00:28:36,600 Speaker 1: supplies expanded by since January one, when we went into 458 00:28:36,640 --> 00:28:39,800 Speaker 1: this kind of COVID crisis, and we know that scarce 459 00:28:39,840 --> 00:28:42,479 Speaker 1: desirable assets are getting bit up in price. I mean 460 00:28:42,520 --> 00:28:45,760 Speaker 1: everybody wants to buy Rolex watches, they're buying luxury real estate, 461 00:28:45,800 --> 00:28:48,840 Speaker 1: they're buying everything to get their hands on, creating shortages. 462 00:28:49,640 --> 00:28:52,720 Speaker 1: So you know, we are an institution. We have to 463 00:28:52,720 --> 00:28:55,160 Speaker 1: take a ten year of you. And the only thing 464 00:28:55,200 --> 00:28:58,200 Speaker 1: that's for sure is if we hold cash over a decade, 465 00:28:58,240 --> 00:29:01,080 Speaker 1: we're gonna have a negative real yield. The only question 466 00:29:01,160 --> 00:29:03,719 Speaker 1: is how much. So we have to invest in something, 467 00:29:04,360 --> 00:29:07,560 Speaker 1: and we've chosen as a business strategy to FoST focus 468 00:29:07,680 --> 00:29:12,360 Speaker 1: on what we believe is the most exciting investment idea 469 00:29:12,440 --> 00:29:16,480 Speaker 1: because it's a digital commodity that's absolutely scarce and only 470 00:29:16,480 --> 00:29:20,440 Speaker 1: getting technically better every year. So are you considering buying 471 00:29:20,480 --> 00:29:25,320 Speaker 1: more bitcoin at these prices? I mean it's bitcoin on sale? Yeah, 472 00:29:25,360 --> 00:29:28,640 Speaker 1: I think it is on sale. Um. You know that 473 00:29:28,720 --> 00:29:31,720 Speaker 1: the number that I look at to figure out sort 474 00:29:31,720 --> 00:29:34,760 Speaker 1: of the a surrogate for the book value of bitcoin 475 00:29:35,040 --> 00:29:38,200 Speaker 1: is the four year simple moving average because it trades 476 00:29:38,280 --> 00:29:41,200 Speaker 1: billions of dollars a day, and so after fourteen hundred 477 00:29:41,320 --> 00:29:44,640 Speaker 1: days of billions of dollars a day, that number is 478 00:29:44,720 --> 00:29:50,320 Speaker 1: twenty one thousand, seven hundred. Bitcoin touched that in the 479 00:29:50,360 --> 00:29:55,960 Speaker 1: March crisis. It touched it around sev it's touching it 480 00:29:56,120 --> 00:29:59,840 Speaker 1: right now. Generally a trades above there. You know. Our 481 00:30:00,000 --> 00:30:03,600 Speaker 1: strategy is, uh, we're going to acquire bitcoin with our 482 00:30:03,640 --> 00:30:05,840 Speaker 1: free cash flows from time to time, so we're kind 483 00:30:05,880 --> 00:30:09,440 Speaker 1: of dollar cost averaging into bitcoin, and we're gonna hold 484 00:30:09,480 --> 00:30:13,040 Speaker 1: a bitcoin for the long term. And uh, and so 485 00:30:13,120 --> 00:30:18,400 Speaker 1: it wouldn't really matter whether the price was ten more, more, 486 00:30:18,560 --> 00:30:22,680 Speaker 1: fifty more. We're just going to progressive. We acquire more 487 00:30:22,720 --> 00:30:27,400 Speaker 1: bitcoin because that's our strategy. But you want in terms 488 00:30:27,400 --> 00:30:30,040 Speaker 1: of for sale, Yeah, I mean it's like not a 489 00:30:30,040 --> 00:30:33,280 Speaker 1: bad price and we will keep buying more. Okay, what 490 00:30:33,360 --> 00:30:38,000 Speaker 1: if it gets below that nineteen thousand, five eleven number, 491 00:30:38,040 --> 00:30:43,360 Speaker 1: which was that top of the I believe will run. Yeah, 492 00:30:43,400 --> 00:30:45,400 Speaker 1: what you know that is that a time to panic? 493 00:30:46,920 --> 00:30:49,960 Speaker 1: We don't panic. We have a we have a strategy. 494 00:30:50,440 --> 00:30:53,400 Speaker 1: We're not traders. If your time arising is less than 495 00:30:53,440 --> 00:30:55,520 Speaker 1: four years, you're sort of a trader. If it's in 496 00:30:55,560 --> 00:30:58,640 Speaker 1: the months, you're definitely a trader. I'm not an expert trader. 497 00:30:58,680 --> 00:31:00,440 Speaker 1: I don't have a crystal ball. I don't where the 498 00:31:00,440 --> 00:31:03,800 Speaker 1: market's going to go week by week, month by month. Uh. 499 00:31:03,840 --> 00:31:06,360 Speaker 1: If you're time arizon is more than four years, you're 500 00:31:06,360 --> 00:31:08,880 Speaker 1: an investor. And when you're time arizing is ten years, 501 00:31:08,920 --> 00:31:11,800 Speaker 1: you're kind of a saver. So we have a very 502 00:31:11,840 --> 00:31:16,160 Speaker 1: long term tenure time horizon, and our view is over 503 00:31:16,200 --> 00:31:19,200 Speaker 1: the ten years, bitcoin is going to be a good 504 00:31:19,240 --> 00:31:23,080 Speaker 1: idea and it's just going to keep a creating and value. Uh. 505 00:31:23,200 --> 00:31:26,200 Speaker 1: You know, I can't tell you whether it'll go down 506 00:31:26,280 --> 00:31:29,360 Speaker 1: a bit here and there. It's in the near term, Emily. 507 00:31:29,560 --> 00:31:32,960 Speaker 1: It trades like a high beta risk asset, and there's 508 00:31:32,960 --> 00:31:36,160 Speaker 1: no denying that. Over the long term, we believe it's 509 00:31:36,200 --> 00:31:40,280 Speaker 1: a low risk store of value asset. There's about ten 510 00:31:40,440 --> 00:31:43,680 Speaker 1: things that have to happen over the next decade to 511 00:31:43,760 --> 00:31:45,680 Speaker 1: make it a better asset, and we kind of know 512 00:31:45,720 --> 00:31:49,200 Speaker 1: what those ten things are. And so we're waiting and 513 00:31:49,400 --> 00:31:51,600 Speaker 1: uh and biding our time, and we think that it's 514 00:31:51,640 --> 00:31:55,520 Speaker 1: going to improve as an asset class over time, and 515 00:31:55,560 --> 00:31:59,920 Speaker 1: we're not in a hurry. So what do you see 516 00:32:00,120 --> 00:32:03,240 Speaker 1: in the let's talk to take this tenure horizon. For example, 517 00:32:03,360 --> 00:32:06,120 Speaker 1: we've seen what the FED is doing with rate hikes. 518 00:32:06,160 --> 00:32:08,840 Speaker 1: There's all of this concern we're heading into a recession, 519 00:32:08,880 --> 00:32:12,800 Speaker 1: whether it's a capital are or a lower case our recession. 520 00:32:13,360 --> 00:32:16,920 Speaker 1: What do you see on the road ahead? And how 521 00:32:17,040 --> 00:32:21,400 Speaker 1: is that impacting your strategy too? You know, just buy 522 00:32:21,480 --> 00:32:25,640 Speaker 1: more and hold. Yeah, so let's take the ten sources 523 00:32:25,680 --> 00:32:29,560 Speaker 1: of my pain. Um, there's no wash trading rules, so 524 00:32:29,640 --> 00:32:32,840 Speaker 1: people can they can sell their bitcoin and buy it 525 00:32:32,920 --> 00:32:35,320 Speaker 1: back and harvest the tax gain. And that's not the 526 00:32:35,360 --> 00:32:38,320 Speaker 1: same with Apple, So if that gets fixed by the 527 00:32:38,360 --> 00:32:40,880 Speaker 1: House Ways and Means Committee, that's a big plus for 528 00:32:40,920 --> 00:32:45,720 Speaker 1: the asset. There's five twenty unregistered crypto exchanges offering twenty 529 00:32:45,920 --> 00:32:49,000 Speaker 1: x leverage. That's a negative for the asset class. As 530 00:32:49,040 --> 00:32:51,560 Speaker 1: they get regulated, and I expect they will, and as 531 00:32:51,600 --> 00:32:54,360 Speaker 1: the twenty x leverage disappears, that will be a positive. 532 00:32:55,160 --> 00:32:59,080 Speaker 1: There's nineteen thousand unregistered securities in the crypto industry cross 533 00:32:59,120 --> 00:33:03,080 Speaker 1: collateralized to get bitcoin. As as those things have to 534 00:33:03,760 --> 00:33:06,160 Speaker 1: have to get eliminated or they have to convert them 535 00:33:06,200 --> 00:33:10,080 Speaker 1: into publicly traded instruments, that's going to decrease the volatility 536 00:33:10,120 --> 00:33:13,920 Speaker 1: to be a big shakeout. The wildcat banks like the 537 00:33:14,360 --> 00:33:17,520 Speaker 1: you know, the terrorism and Lunas and Celsius, they actually 538 00:33:17,560 --> 00:33:21,480 Speaker 1: create massive volatility. And as they get regulated and they 539 00:33:21,560 --> 00:33:25,360 Speaker 1: disappear and they grow up and become institutionalized banks. Uh, 540 00:33:25,440 --> 00:33:28,720 Speaker 1: the asset class, I'm mature. There's a lot of ignorance 541 00:33:28,720 --> 00:33:31,320 Speaker 1: and fear. People think crypto is the same as bitcoin. 542 00:33:31,800 --> 00:33:34,200 Speaker 1: If they think that, that means they don't understand either 543 00:33:34,280 --> 00:33:37,400 Speaker 1: of those two things. We don't have a stable coin 544 00:33:37,440 --> 00:33:41,800 Speaker 1: Emily like us t isn't a stable coin. Tether is 545 00:33:41,840 --> 00:33:46,120 Speaker 1: an opaque security. No one understands if we ever have 546 00:33:46,280 --> 00:33:49,480 Speaker 1: an f d i C issued stable coin or something 547 00:33:49,480 --> 00:33:52,800 Speaker 1: from a public entity that's endorsed by the SEC, that's 548 00:33:52,800 --> 00:33:56,400 Speaker 1: gonna be very bullish for the industry. There's no spot 549 00:33:56,480 --> 00:33:59,360 Speaker 1: et F. I think it's only a matter of time 550 00:33:59,440 --> 00:34:01,640 Speaker 1: before there is. Want to prove that will be very 551 00:34:01,720 --> 00:34:05,960 Speaker 1: bullish for the industry. The fast by accounting is detrimental. 552 00:34:06,120 --> 00:34:08,840 Speaker 1: The lack of f d i C guidance makes it difficult, 553 00:34:08,880 --> 00:34:12,279 Speaker 1: if not impossible, for banks to hold this stuff. We're 554 00:34:12,280 --> 00:34:17,080 Speaker 1: waiting for clear SEC CFTC guidance, and those ten things 555 00:34:17,719 --> 00:34:20,080 Speaker 1: they're gonna get cured over the next decade. They're just 556 00:34:20,120 --> 00:34:23,280 Speaker 1: not gonna get cured over the next ten weeks. Okay, 557 00:34:23,320 --> 00:34:25,640 Speaker 1: So how are you looking then, more broadly at what 558 00:34:25,680 --> 00:34:28,680 Speaker 1: happens to the industry after this? You know, we're seeing 559 00:34:28,800 --> 00:34:33,160 Speaker 1: coin Base and a number of different crypto companies having 560 00:34:33,719 --> 00:34:36,520 Speaker 1: major layoffs. Do you think we'll look back on this 561 00:34:36,560 --> 00:34:39,040 Speaker 1: moment as some sort of inflection point for the industry 562 00:34:39,080 --> 00:34:42,000 Speaker 1: And if so, how does it look different in the 563 00:34:42,000 --> 00:34:46,080 Speaker 1: fewer we're crossing the chasm? Uh, there's about a trillion 564 00:34:46,080 --> 00:34:49,480 Speaker 1: dollars in the asset class. Four billion is bitcoin, the 565 00:34:49,520 --> 00:34:54,320 Speaker 1: other four billion is nineteen thousand unregistered securities. We're moving 566 00:34:54,480 --> 00:35:00,040 Speaker 1: from the era of the off shore entrepreneur to the 567 00:35:00,520 --> 00:35:05,160 Speaker 1: to the onshore public institution. And it's pretty clear from 568 00:35:05,160 --> 00:35:07,680 Speaker 1: share gains Lare's comments that he made in the last 569 00:35:07,680 --> 00:35:10,560 Speaker 1: few days that uh, they want to see all the 570 00:35:10,560 --> 00:35:14,080 Speaker 1: crypto exchanges regulated. Uh, they want to they want to 571 00:35:14,120 --> 00:35:16,719 Speaker 1: clean up this industry. The stable coin is gonna have 572 00:35:16,719 --> 00:35:20,400 Speaker 1: to be cleaned up as well. And uh, the winners 573 00:35:20,440 --> 00:35:23,000 Speaker 1: are going to be the public investors in public banks 574 00:35:23,000 --> 00:35:25,920 Speaker 1: and public companies, and the losers are going to be 575 00:35:26,000 --> 00:35:30,680 Speaker 1: the wildcatters, you know, and the entrepreneurs the gots got started. 576 00:35:30,680 --> 00:35:33,279 Speaker 1: They're flying by the seat of their pants. And I 577 00:35:33,320 --> 00:35:35,359 Speaker 1: think it's essential for us to move from a one 578 00:35:35,360 --> 00:35:39,000 Speaker 1: trillion dollar industry to a ten trillion dollar industry, so 579 00:35:39,120 --> 00:35:42,080 Speaker 1: I welcome it. I think the bitcoin has been held 580 00:35:42,120 --> 00:35:46,040 Speaker 1: back by its association with the with the anything goes 581 00:35:46,080 --> 00:35:51,000 Speaker 1: crypto industry, and as that gets regulated, then that's going 582 00:35:51,080 --> 00:35:55,440 Speaker 1: to actually create a green light for public institutions and 583 00:35:55,600 --> 00:35:59,000 Speaker 1: public companies to get much more heavily involved in bitcoin 584 00:35:59,280 --> 00:36:01,880 Speaker 1: and is going to catalyze the next leg of the 585 00:36:01,920 --> 00:36:07,160 Speaker 1: bull run All right, Michael Sailor, who apparently has no regrets. Michael, 586 00:36:07,239 --> 00:36:09,840 Speaker 1: always good to have you here on the show. Chair 587 00:36:09,920 --> 00:36:22,120 Speaker 1: and CEO of micro Strategy, have a great weekend. Every 588 00:36:22,160 --> 00:36:25,520 Speaker 1: new generation of media has been subsidized by advertising, making 589 00:36:25,560 --> 00:36:28,920 Speaker 1: it cheaper or free for consumers. There's little reason to 590 00:36:28,960 --> 00:36:31,879 Speaker 1: think that the metaverse will be any different. But our 591 00:36:31,920 --> 00:36:36,200 Speaker 1: brands actually taking the metaverse seriously quick takes Alex Webb 592 00:36:36,280 --> 00:36:39,759 Speaker 1: walks us through what the future of advertising may or 593 00:36:40,040 --> 00:36:44,000 Speaker 1: may not look like in the metaverse. We've heard a 594 00:36:44,040 --> 00:36:46,120 Speaker 1: lot about the metaverse in the past year, but is 595 00:36:46,160 --> 00:36:48,640 Speaker 1: it really going to be creating new digital economies or 596 00:36:48,680 --> 00:36:53,919 Speaker 1: is it just about selling existing real world goods. Every 597 00:36:53,920 --> 00:36:57,399 Speaker 1: new generation of media has been subsidized by advertising, making 598 00:36:57,440 --> 00:37:00,719 Speaker 1: it cheap or even free for consumers, from newspapers to 599 00:37:00,800 --> 00:37:03,960 Speaker 1: the radio, to TV and indeed the Worldwide Web. There's 600 00:37:03,960 --> 00:37:06,160 Speaker 1: little reason to think that the metaverse will be any 601 00:37:06,200 --> 00:37:09,319 Speaker 1: different meta platforms. That's Facebook, You or me said so 602 00:37:09,360 --> 00:37:12,480 Speaker 1: explicitly when it unveiled its vision for the metaverse last year. 603 00:37:12,760 --> 00:37:16,120 Speaker 1: Businesses will be creators to building out digital spaces or 604 00:37:16,160 --> 00:37:18,560 Speaker 1: even digital worlds, and they'll be able to use ads 605 00:37:18,680 --> 00:37:21,640 Speaker 1: to ensure the rate customers find what they've created. So 606 00:37:21,680 --> 00:37:24,440 Speaker 1: there we have it. For Mark Zuckerberg and co. The 607 00:37:24,520 --> 00:37:26,600 Speaker 1: business model for the metaverse will be the same as 608 00:37:26,600 --> 00:37:29,960 Speaker 1: it was for social media ads. Pair that with a 609 00:37:30,160 --> 00:37:33,120 Speaker 1: R and VR's ability to track your eyeballs and maybe 610 00:37:33,160 --> 00:37:35,960 Speaker 1: even gauge your mood, and it starts to become a 611 00:37:35,960 --> 00:37:38,600 Speaker 1: little creepy. But to what extent a brand's taking the 612 00:37:38,640 --> 00:37:42,280 Speaker 1: metaverse seriously? Big names like Nike and Samsung are building 613 00:37:42,360 --> 00:37:47,160 Speaker 1: virtual worlds. Facebook meta has teams selling virtual billboards in 614 00:37:47,200 --> 00:37:50,560 Speaker 1: those worlds, but they remain a relatively small slice of 615 00:37:50,640 --> 00:37:54,040 Speaker 1: firms overall marketing governments, and you can see why. Just 616 00:37:54,200 --> 00:37:57,400 Speaker 1: sixty seven million Americans will experience v our content at 617 00:37:57,440 --> 00:38:00,360 Speaker 1: least once a month in two and they'll be split 618 00:38:00,400 --> 00:38:03,360 Speaker 1: between a range of different platforms and roadblocks to minecraft 619 00:38:03,360 --> 00:38:07,080 Speaker 1: and beyond. Facebook alone had two hundred and sixty three 620 00:38:07,239 --> 00:38:10,800 Speaker 1: million users in the start of this year in North America. Crucially, 621 00:38:10,800 --> 00:38:13,279 Speaker 1: though most of the efforts have so far focused on 622 00:38:13,320 --> 00:38:17,240 Speaker 1: selling real world goods, Nike wants you to buy actual sneakers, Samsung, 623 00:38:17,520 --> 00:38:20,520 Speaker 1: real cell phones. The luxury brand Burbery might have made 624 00:38:20,520 --> 00:38:23,919 Speaker 1: four hundred thousand dollars selling digital skins with an associated 625 00:38:24,040 --> 00:38:26,200 Speaker 1: n f T last year, but that's really just a 626 00:38:26,320 --> 00:38:29,320 Speaker 1: rounding era in its three billion dollars in annual revenue. 627 00:38:29,440 --> 00:38:32,280 Speaker 1: In other words, they were a marketing gimmick to attract 628 00:38:32,320 --> 00:38:36,120 Speaker 1: young crypto enthusiasts you might hope as a brand are wealthy. 629 00:38:36,360 --> 00:38:39,880 Speaker 1: Metas ideal is of brands to pay to advertise virtual 630 00:38:39,960 --> 00:38:42,720 Speaker 1: goods in the virtual world that will let it monitor 631 00:38:42,800 --> 00:38:46,000 Speaker 1: the entire customer journey from seeing the ad campaign to 632 00:38:46,120 --> 00:38:48,680 Speaker 1: buying the product, then even seeing how they use it. 633 00:38:48,680 --> 00:38:51,320 Speaker 1: It feels like we're a long way away from that happening. 634 00:38:51,440 --> 00:38:53,960 Speaker 1: Just yes, if the metaverse takes off, there of course 635 00:38:54,040 --> 00:38:56,600 Speaker 1: will be big money if you made in advertising. It 636 00:38:56,719 --> 00:38:59,440 Speaker 1: just seems we're a long way right now from that happening, 637 00:38:59,600 --> 00:39:05,920 Speaker 1: and face Swooke's vision is a distant prospect. H