1 00:00:00,120 --> 00:00:04,360 Speaker 1: Bloomberg is now on your dashboard with Apple CarPlay and 2 00:00:04,360 --> 00:00:08,160 Speaker 1: Android Auto. It gives you access to every Bloomberg podcast, 3 00:00:08,280 --> 00:00:11,560 Speaker 1: live audio feeds from Bloomberg Radio, print stories from Bloomberg 4 00:00:11,640 --> 00:00:14,920 Speaker 1: News in audio form, and the latest headlines of the 5 00:00:14,920 --> 00:00:18,600 Speaker 1: click of a button with Bloomberg News. Now it's free 6 00:00:18,680 --> 00:00:21,439 Speaker 1: with the latest version of the Bloomberg Business App. That's 7 00:00:21,680 --> 00:00:24,400 Speaker 1: the Bloomberg Business App. Get it on your phone in 8 00:00:24,440 --> 00:00:27,760 Speaker 1: the Apple App Store or on Google Play. Just download 9 00:00:27,800 --> 00:00:30,560 Speaker 1: the app, connect your phone to your car and get started. 10 00:00:30,960 --> 00:00:34,400 Speaker 1: And it's all presented by our sponsor, Interactive Brokers. 11 00:00:35,400 --> 00:00:38,600 Speaker 2: Welcome to the Bloomberg Markets Podcast. I'm Paul Sweeney alongside 12 00:00:38,640 --> 00:00:39,800 Speaker 2: my co host Matt Miller. 13 00:00:40,200 --> 00:00:44,279 Speaker 1: Every business day we bring you interviews from CEOs, market pros, 14 00:00:44,320 --> 00:00:48,160 Speaker 1: and Bloomberg experts, along with essential market moving news. 15 00:00:48,720 --> 00:00:51,839 Speaker 2: Find the Bloomberg Markets Podcast called Apple Podcasts or wherever 16 00:00:51,960 --> 00:00:54,560 Speaker 2: you listen to podcasts, and at Bloomberg dot com, slash 17 00:00:54,600 --> 00:00:58,320 Speaker 2: podcast open AI. This is the story of the morning. 18 00:00:58,360 --> 00:01:00,040 Speaker 2: I'm going to assume most people will figure it. I 19 00:01:00,080 --> 00:01:03,480 Speaker 2: have kind of seen the news right now. It looks 20 00:01:03,600 --> 00:01:06,680 Speaker 2: like Microsoft might be a little bit of a winner here. 21 00:01:06,680 --> 00:01:08,360 Speaker 2: So we're going to round Table to sing ed Ludlow, 22 00:01:08,440 --> 00:01:12,560 Speaker 2: he's host to Bloomberg Technology and Mandeep saying he's our 23 00:01:12,560 --> 00:01:16,520 Speaker 2: senior technology analyst for Bloomberg Intelligence. So Mandib at this 24 00:01:16,680 --> 00:01:19,240 Speaker 2: stage and it's still a very fluid situation, I'm not 25 00:01:19,280 --> 00:01:21,520 Speaker 2: even sure where the employees are going from open Ai, 26 00:01:21,600 --> 00:01:25,000 Speaker 2: whether they're staying at the existing thing or whether they're 27 00:01:25,200 --> 00:01:28,080 Speaker 2: going to go over to Microsoft. I'm not even sure 28 00:01:28,080 --> 00:01:31,479 Speaker 2: where Sam Altman is. But it appears that Microsoft hired 29 00:01:31,480 --> 00:01:35,160 Speaker 2: by Microsoft. We'll see. I'm waiting to see, like triple confirmation. 30 00:01:35,400 --> 00:01:39,080 Speaker 1: Zatcha n Adella confirmed it. He's the CEO of Microsoft. 31 00:01:39,160 --> 00:01:39,720 Speaker 3: It can be going. 32 00:01:39,800 --> 00:01:40,640 Speaker 2: I don't know where these guys. 33 00:01:40,680 --> 00:01:41,400 Speaker 4: It's Silicon Valley. 34 00:01:41,440 --> 00:01:43,400 Speaker 2: The kids don't know what they're doing. Mandib, it seems 35 00:01:43,400 --> 00:01:46,559 Speaker 2: like it's a win win win for Microsoft here, what's 36 00:01:46,600 --> 00:01:47,280 Speaker 2: your take. 37 00:01:47,200 --> 00:01:50,880 Speaker 5: Actually, we sort of disagree with that consensus. Few that 38 00:01:51,120 --> 00:01:55,640 Speaker 5: nice for Microsoft simply because you know, when you are 39 00:01:55,960 --> 00:01:59,320 Speaker 5: acquiring talent like this, it's not the best way to 40 00:01:59,600 --> 00:02:02,080 Speaker 5: you know, build a new product. And in this case, 41 00:02:02,120 --> 00:02:04,920 Speaker 5: the IP still remains with open Ai, so it's not 42 00:02:05,000 --> 00:02:07,320 Speaker 5: as if they can poured over the large anchorage model, 43 00:02:07,360 --> 00:02:11,000 Speaker 5: which is really the key to building the ecosystem, they 44 00:02:11,040 --> 00:02:13,839 Speaker 5: will have to build it from scratch. And remember, when 45 00:02:13,840 --> 00:02:17,840 Speaker 5: you're training AI, you need, yes, the infrastructure, but you 46 00:02:17,880 --> 00:02:21,320 Speaker 5: also need large amounts of data. And Microsoft mader like 47 00:02:21,360 --> 00:02:24,840 Speaker 5: three months to make Rock Well. So Twitter had a 48 00:02:24,880 --> 00:02:27,080 Speaker 5: lot of their own data. So that's what it comes 49 00:02:27,120 --> 00:02:29,240 Speaker 5: down to. Can they build a model just based on 50 00:02:29,320 --> 00:02:32,560 Speaker 5: being data? The answer is no. And that's where. 51 00:02:32,440 --> 00:02:33,120 Speaker 6: I think why not? 52 00:02:33,360 --> 00:02:36,600 Speaker 1: Because Bing has a lot of data. 53 00:02:36,919 --> 00:02:40,720 Speaker 5: Look at how long Bing had had integration with open 54 00:02:40,760 --> 00:02:43,720 Speaker 5: ai Chat GPT. Still they haven't been able to take 55 00:02:43,840 --> 00:02:47,840 Speaker 5: any share from Google, and Microsoft's CEO admitted that that 56 00:02:48,120 --> 00:02:52,359 Speaker 5: despite the integration with open Ali and and so clearly 57 00:02:52,600 --> 00:02:55,480 Speaker 5: there is a lot more to open AI's IP than 58 00:02:55,520 --> 00:02:59,959 Speaker 5: what we know. And that's why it's a proprietary foundational model, 59 00:03:00,080 --> 00:03:02,200 Speaker 5: unlike Madawslama, which is open source. 60 00:03:02,280 --> 00:03:04,480 Speaker 2: So there all right, So I want to bring in 61 00:03:04,680 --> 00:03:07,639 Speaker 2: Ed Ludlow if for no other reason then he's literally 62 00:03:07,680 --> 00:03:10,800 Speaker 2: in Silicon Valley and Ed, your job right now is 63 00:03:11,040 --> 00:03:14,440 Speaker 2: explain to me why I or anybody else in this 64 00:03:14,480 --> 00:03:18,280 Speaker 2: country should care about what's happening it opened ai and 65 00:03:18,480 --> 00:03:19,399 Speaker 2: Sam Maltman. 66 00:03:19,919 --> 00:03:21,760 Speaker 1: Have you cared about anything else all weekend? 67 00:03:22,320 --> 00:03:22,519 Speaker 6: Yeah? 68 00:03:22,560 --> 00:03:23,080 Speaker 4: That's well. 69 00:03:23,120 --> 00:03:26,320 Speaker 7: I mean, you know, to my credit, chaps, I am 70 00:03:26,360 --> 00:03:29,760 Speaker 7: on almost every single byline that we've written since Friday lunchtime. 71 00:03:29,880 --> 00:03:36,760 Speaker 7: So forgive my dower mood. But look, this is what's 72 00:03:36,800 --> 00:03:40,280 Speaker 7: at stake, billions of dollars of value in the private markets. 73 00:03:41,840 --> 00:03:46,800 Speaker 7: The elimination of a company that is the clear market 74 00:03:46,840 --> 00:03:51,520 Speaker 7: incumbent for both consumer facing and enterprise tools that are 75 00:03:51,560 --> 00:03:54,200 Speaker 7: powered by a large language model and are presented in 76 00:03:54,240 --> 00:03:56,520 Speaker 7: the form of a chatbot or generator of AI tool. 77 00:03:57,080 --> 00:03:58,800 Speaker 4: And third of all, there will be a. 78 00:03:58,760 --> 00:04:04,200 Speaker 7: Massive spotlight on my Microsoft because the question will remain 79 00:04:04,320 --> 00:04:05,960 Speaker 7: do they buy the rest of it? Well, what we 80 00:04:06,080 --> 00:04:08,160 Speaker 7: reported over the weekend is that when all of this 81 00:04:08,200 --> 00:04:09,800 Speaker 7: wrangling was go on, and we can get into it, 82 00:04:09,840 --> 00:04:12,760 Speaker 7: I've got some great gossip for you guys. But the 83 00:04:13,080 --> 00:04:17,040 Speaker 7: principal consideration was Sam wanted the board to resign. They didn't. 84 00:04:17,080 --> 00:04:19,680 Speaker 7: They dug in and he did not come back. But 85 00:04:19,800 --> 00:04:22,400 Speaker 7: one offer on the table was whether Microsoft or not 86 00:04:22,440 --> 00:04:24,799 Speaker 7: took a board seat, And the reason that they didn't, 87 00:04:24,839 --> 00:04:27,280 Speaker 7: I'm told by sources, is that there would just be 88 00:04:27,320 --> 00:04:31,680 Speaker 7: too much scrutiny from an antitrust perspective, too much concentration 89 00:04:31,760 --> 00:04:35,520 Speaker 7: of influence of a company that is leading the direction 90 00:04:35,640 --> 00:04:36,960 Speaker 7: of AI in the real world. 91 00:04:37,040 --> 00:04:38,280 Speaker 4: That's what we're talking about here. 92 00:04:38,520 --> 00:04:41,760 Speaker 1: Well, now they get most of the company for free, right. 93 00:04:41,839 --> 00:04:44,200 Speaker 1: I mean, I'm interested to hear what you've been hearing, 94 00:04:44,200 --> 00:04:47,080 Speaker 1: and I know that you. First of all, Ed, I've 95 00:04:47,120 --> 00:04:49,560 Speaker 1: been reading everything you've been writing, and thank you for 96 00:04:49,600 --> 00:04:51,479 Speaker 1: the work. I can't believe you've had any sleep, and 97 00:04:51,520 --> 00:04:52,440 Speaker 1: you look amazing. 98 00:04:52,880 --> 00:04:55,159 Speaker 7: I apologize for that because I haven't had any sleep. 99 00:04:55,160 --> 00:04:55,720 Speaker 4: That's why I'm. 100 00:04:56,279 --> 00:05:00,200 Speaker 1: Secondly, to me, it looks like Microsoft now gets that's 101 00:05:01,400 --> 00:05:06,320 Speaker 1: Altman and Brockman and maybe five hundred other employees pretty 102 00:05:06,360 --> 00:05:07,360 Speaker 1: much for salaries. 103 00:05:07,480 --> 00:05:07,640 Speaker 8: Right. 104 00:05:08,000 --> 00:05:10,600 Speaker 1: I guess maybe a signing bonus, but they don't have 105 00:05:10,680 --> 00:05:13,320 Speaker 1: to spend eighty six billion dollars or even if you 106 00:05:13,360 --> 00:05:17,000 Speaker 1: take out their thirteen you know, seventy three billion dollars. 107 00:05:17,839 --> 00:05:19,159 Speaker 1: Isn't that a huge win? 108 00:05:20,160 --> 00:05:20,360 Speaker 4: Right? 109 00:05:21,279 --> 00:05:25,560 Speaker 7: Open AI has seven hundred and seventy employees. Five hundred 110 00:05:25,560 --> 00:05:27,800 Speaker 7: of those have signed a letter to the board this morning, 111 00:05:27,839 --> 00:05:30,760 Speaker 7: which I've seen, saying that if the board doesn't resign, 112 00:05:31,480 --> 00:05:34,880 Speaker 7: those five hundred will resign and they will go to Microsoft. 113 00:05:35,279 --> 00:05:38,919 Speaker 7: When Satya Nadella posted on X last night, he buried 114 00:05:38,920 --> 00:05:41,760 Speaker 7: the lead. It was a very long statement. The idea 115 00:05:41,800 --> 00:05:44,680 Speaker 7: that Sam and Greg Brockman, the former chair of open 116 00:05:44,720 --> 00:05:47,120 Speaker 7: AI's board, were joining Microsoft, was right at the bottom. 117 00:05:47,480 --> 00:05:49,800 Speaker 7: But there was very clear language there, and I'd ask 118 00:05:49,839 --> 00:05:51,760 Speaker 7: the audience to go and look out for it. It 119 00:05:51,880 --> 00:05:55,680 Speaker 7: said those two with others joining from open Ai. 120 00:05:56,040 --> 00:05:59,240 Speaker 4: Satya said it last night. So that's a very real risk. 121 00:05:59,320 --> 00:06:01,159 Speaker 7: And what I would say is that at the core 122 00:06:01,240 --> 00:06:04,679 Speaker 7: of this story is the open aies almost the entirety 123 00:06:04,720 --> 00:06:07,279 Speaker 7: of its value. And Mande may disagree here, but the 124 00:06:07,440 --> 00:06:10,120 Speaker 7: entirety of its value that the investors outlined to me 125 00:06:10,680 --> 00:06:14,120 Speaker 7: is its intellectual capital. If you take those top ten 126 00:06:14,560 --> 00:06:17,880 Speaker 7: data scientists and the top product guys, which is Sam Outman, 127 00:06:18,279 --> 00:06:20,840 Speaker 7: and you take them somewhere else, open has ai has 128 00:06:20,880 --> 00:06:23,239 Speaker 7: nothing left. That's what we're talking about here. 129 00:06:23,480 --> 00:06:25,800 Speaker 1: Okay, I'm looking at the ip that mandplay. 130 00:06:25,839 --> 00:06:27,880 Speaker 2: That's what I'm saying. Many if I'm looking at Microsoft stock, 131 00:06:27,920 --> 00:06:30,280 Speaker 2: it's only up seven ten to one percent, I would 132 00:06:30,279 --> 00:06:32,000 Speaker 2: have thought it a up ten percent or more. 133 00:06:32,720 --> 00:06:33,719 Speaker 6: Why do you think that is? 134 00:06:34,080 --> 00:06:37,279 Speaker 5: And look, Opening Eye has a lot of other investors too, 135 00:06:37,360 --> 00:06:41,000 Speaker 5: So granted Microsoft owns forty nine percent of the company, 136 00:06:41,040 --> 00:06:43,840 Speaker 5: but there are other investors, the private rounds that they 137 00:06:43,880 --> 00:06:45,799 Speaker 5: had before Microsoft got on board. 138 00:06:45,839 --> 00:06:47,520 Speaker 1: And so I think, by the way, can I just 139 00:06:47,560 --> 00:06:51,360 Speaker 1: ask clearly clarify the Microsoft owns forty nine percent of 140 00:06:51,400 --> 00:06:56,440 Speaker 1: the for profit unit in the nonprofit structure or do 141 00:06:56,480 --> 00:06:58,440 Speaker 1: they own forty nine percent of the whole thing? 142 00:06:58,800 --> 00:07:00,800 Speaker 5: Forty nine percent of the whole thing? But Ed can 143 00:07:00,839 --> 00:07:04,200 Speaker 5: confirm if that's he has a different point of view. 144 00:07:04,240 --> 00:07:08,320 Speaker 7: Well, on paper, on paper, the on the balance sheet 145 00:07:08,320 --> 00:07:12,240 Speaker 7: of Microsoft, it will show that the open aillc or 146 00:07:12,280 --> 00:07:14,600 Speaker 7: the incorporated for profit business. 147 00:07:15,120 --> 00:07:17,720 Speaker 4: But again the audience, there's a. 148 00:07:17,760 --> 00:07:20,080 Speaker 7: Ven diagram or a diagram out there, sorry, not a 149 00:07:20,120 --> 00:07:23,240 Speaker 7: ven diagram that sets it out. And you can say, 150 00:07:23,240 --> 00:07:25,960 Speaker 7: but man Deep is right, forty nine percent Microsoft holds. 151 00:07:25,960 --> 00:07:29,160 Speaker 7: I'd make one observation that the other fifty one percent 152 00:07:29,200 --> 00:07:32,200 Speaker 7: on the cap table. My understanding is that even the 153 00:07:32,200 --> 00:07:36,320 Speaker 7: biggest individual shareholders from a venture firm perspective own less 154 00:07:36,360 --> 00:07:39,040 Speaker 7: than one percent each, which is another reason that this 155 00:07:39,160 --> 00:07:40,440 Speaker 7: is so chaotic right now. 156 00:07:40,360 --> 00:07:41,920 Speaker 6: Right an Alman says he on none. 157 00:07:42,160 --> 00:07:44,560 Speaker 5: Well, so, I mean, look, at the end of the day, 158 00:07:44,920 --> 00:07:47,600 Speaker 5: there is a lot of ip within open the eye. 159 00:07:47,640 --> 00:07:50,360 Speaker 5: If the employees leave, they can't take that IP. And 160 00:07:50,440 --> 00:07:53,120 Speaker 5: that's the thing about a tech company is you know, 161 00:07:53,240 --> 00:07:56,400 Speaker 5: if I have an algorithm or a model that's within 162 00:07:56,440 --> 00:07:59,880 Speaker 5: a company until unless Microsoft ends up buying that company, 163 00:08:00,200 --> 00:08:03,200 Speaker 5: they don't have rights to the open ai foundational model 164 00:08:03,520 --> 00:08:06,400 Speaker 5: and for employees to recreate that. Let's say the five 165 00:08:06,480 --> 00:08:11,040 Speaker 5: hundred employees recreate that model within six months, what is 166 00:08:11,080 --> 00:08:13,640 Speaker 5: your IP? I mean any other company can do it then. 167 00:08:13,920 --> 00:08:17,160 Speaker 5: So it puts Microsoft in a tough spot because one, 168 00:08:17,280 --> 00:08:20,680 Speaker 5: on the one hand, it could show that AI large 169 00:08:20,720 --> 00:08:23,360 Speaker 5: arid models are a commodity. On the other hand, they 170 00:08:23,400 --> 00:08:26,280 Speaker 5: may have to buy the whole company for eighty billion 171 00:08:26,360 --> 00:08:29,080 Speaker 5: because the other fifty one percent want a good exit. 172 00:08:29,160 --> 00:08:30,480 Speaker 5: They don't want the company to die. 173 00:08:30,480 --> 00:08:34,679 Speaker 2: Act at third thirty seconds, what are you reporting on? 174 00:08:34,720 --> 00:08:36,120 Speaker 2: What are you guys working on today? What are you 175 00:08:36,200 --> 00:08:36,680 Speaker 2: chasing today? 176 00:08:36,720 --> 00:08:39,240 Speaker 4: Okay, the big thing is going to be private markets. 177 00:08:39,360 --> 00:08:41,680 Speaker 7: There was a tender that was open for hundreds of 178 00:08:41,679 --> 00:08:44,400 Speaker 7: billions of dollars for employees of open ai to sell 179 00:08:44,440 --> 00:08:47,640 Speaker 7: shares to thrive capital. My understanding is that is very 180 00:08:47,720 --> 00:08:51,040 Speaker 7: much in doubt. Friday, the market for private open ai 181 00:08:51,120 --> 00:08:52,920 Speaker 7: stock was incredibly liquid, very. 182 00:08:52,920 --> 00:08:54,960 Speaker 6: Un in doubt. It's probably a way to say it. 183 00:08:55,800 --> 00:08:59,080 Speaker 7: Yes, that liquidity slams shot Friday while I was having 184 00:08:59,160 --> 00:09:01,200 Speaker 7: lunch with a so who is trying to buy in 185 00:09:01,240 --> 00:09:04,400 Speaker 7: that market, look out for that. People are going to 186 00:09:04,440 --> 00:09:07,000 Speaker 7: get burned. These are transactions that were pending and will 187 00:09:07,000 --> 00:09:07,720 Speaker 7: now not go through. 188 00:09:08,000 --> 00:09:10,560 Speaker 6: I'd say got burned. Yeah, story is over ed. 189 00:09:10,760 --> 00:09:12,079 Speaker 2: Thank you so much for joining us, and I know 190 00:09:12,120 --> 00:09:14,080 Speaker 2: you're busy ed Ludlow and your team out there for 191 00:09:14,120 --> 00:09:18,199 Speaker 2: Bloomberg Technology man deep seeing senior technology annels for Bloomberg Intelligence. 192 00:09:18,240 --> 00:09:20,880 Speaker 2: Appreciate getting you two folks together as we continue to 193 00:09:20,920 --> 00:09:23,200 Speaker 2: report on this dynamic technology story. 194 00:09:24,400 --> 00:09:27,760 Speaker 8: You're listening to the team Ken's are Live program Bloomberg 195 00:09:27,880 --> 00:09:31,240 Speaker 8: Markets weekdays at ten am Eastern on Bloomberg dot com, 196 00:09:31,320 --> 00:09:34,480 Speaker 8: the iHeartRadio app and the Bloomberg Business app, or listen 197 00:09:34,520 --> 00:09:36,760 Speaker 8: on demand wherever you get your podcasts. 198 00:09:38,600 --> 00:09:40,280 Speaker 2: All Right, So Matt and I were just talking like 199 00:09:40,440 --> 00:09:42,720 Speaker 2: one of the key fundamental issues here in this open 200 00:09:42,760 --> 00:09:45,760 Speaker 2: AI seems to be the debate within open AI and 201 00:09:45,800 --> 00:09:48,280 Speaker 2: within the tech industry and with I guess just kind 202 00:09:48,320 --> 00:09:52,000 Speaker 2: of the tech world in general about AI weighing commercialization 203 00:09:52,760 --> 00:09:55,240 Speaker 2: versus the fact that this in could end civilization. So 204 00:09:55,880 --> 00:09:57,520 Speaker 2: that seems to be getting a little bit lost here 205 00:09:57,520 --> 00:09:59,160 Speaker 2: in some of the reporting here today. Maybe our next 206 00:09:59,200 --> 00:10:01,480 Speaker 2: guest can help us out. Jim Anderson. He's the CEO 207 00:10:01,480 --> 00:10:03,240 Speaker 2: of Beacon Software. He joins his life here in our 208 00:10:03,240 --> 00:10:06,360 Speaker 2: Bloomberg Interactive Brooker's studio. So, Jim, how do you, guys, 209 00:10:06,600 --> 00:10:08,880 Speaker 2: you know, in that space, in the software space, how 210 00:10:08,880 --> 00:10:12,199 Speaker 2: do you think about AI in terms of commercializing it 211 00:10:12,240 --> 00:10:16,400 Speaker 2: but by also being cognizant of the potential risk it 212 00:10:16,440 --> 00:10:16,960 Speaker 2: could posed. 213 00:10:17,520 --> 00:10:19,520 Speaker 9: Yeah, Paul, it won't surprise you to hear that we 214 00:10:19,600 --> 00:10:21,880 Speaker 9: think first about commercializing it, right, Because if you can't 215 00:10:21,880 --> 00:10:24,840 Speaker 9: commercialize it, you don't have anything to really worry about, right. 216 00:10:24,920 --> 00:10:28,079 Speaker 9: So in the eyes of a sort of capitalist or 217 00:10:28,080 --> 00:10:30,600 Speaker 9: a private sector person, you tend to focus on that first. 218 00:10:30,920 --> 00:10:33,400 Speaker 9: I think one of the things social media has taught 219 00:10:33,480 --> 00:10:36,400 Speaker 9: us is, hey, let's not collectively be so naive about 220 00:10:36,440 --> 00:10:38,719 Speaker 9: the negatives. Let's not wait a decade and see what 221 00:10:39,280 --> 00:10:41,559 Speaker 9: damage we've wrought and then see if we can undo it. 222 00:10:41,760 --> 00:10:45,640 Speaker 9: So I do think there's including with private sector, capitalistic companies, 223 00:10:45,679 --> 00:10:48,600 Speaker 9: there's a lot more willingness to try to anticipate those 224 00:10:48,640 --> 00:10:52,000 Speaker 9: negative effects. But you know, the open eye example is 225 00:10:52,000 --> 00:10:55,360 Speaker 9: an extreme example of a capitalistic company worth reportedly eighty 226 00:10:55,400 --> 00:10:58,520 Speaker 9: billion dollars according to Microsoft, run by a nonprofit with 227 00:10:58,559 --> 00:11:01,800 Speaker 9: a very different sensibility. And it's not really surprising that 228 00:11:02,000 --> 00:11:03,880 Speaker 9: you ended up with a very different view of the world. 229 00:11:03,960 --> 00:11:06,280 Speaker 1: Well, and as we know, with social media, you just 230 00:11:06,320 --> 00:11:08,520 Speaker 1: can't put the genie back in the bottle, right. And 231 00:11:08,600 --> 00:11:13,000 Speaker 1: the problem is where social media seems to now in hindsight, 232 00:11:13,080 --> 00:11:17,040 Speaker 1: be a cancer on society that we can't cure. AI 233 00:11:17,400 --> 00:11:22,440 Speaker 1: could be like a massive cardiac arrest. I mean, it 234 00:11:22,440 --> 00:11:26,240 Speaker 1: could just kill us all. But no one can care 235 00:11:26,320 --> 00:11:29,240 Speaker 1: about that anymore since we can make money on it today. 236 00:11:29,240 --> 00:11:32,640 Speaker 1: It doesn't matter if it's going to destroy society in uh, 237 00:11:32,760 --> 00:11:34,000 Speaker 1: you know, decades. 238 00:11:34,480 --> 00:11:36,200 Speaker 6: Yeah, I don't know that. I would agree that nobody 239 00:11:36,240 --> 00:11:37,439 Speaker 6: can care about it anymore. 240 00:11:37,440 --> 00:11:38,480 Speaker 4: But you can care. 241 00:11:38,559 --> 00:11:41,280 Speaker 9: But you can't do anything if you can't stop stop, right. 242 00:11:41,400 --> 00:11:43,200 Speaker 9: But I think it's it's interesting. I mean, you know 243 00:11:43,240 --> 00:11:45,760 Speaker 9: that we could have a debate about Skynet and whether 244 00:11:45,880 --> 00:11:48,360 Speaker 9: you know the end of humanity, how likely that is 245 00:11:48,800 --> 00:11:51,240 Speaker 9: to happen. I think the risk of that is it 246 00:11:51,280 --> 00:11:54,959 Speaker 9: misses you know, if everything is the extinction of humanity. 247 00:11:54,600 --> 00:11:56,040 Speaker 6: Or or or not. 248 00:11:56,200 --> 00:11:58,040 Speaker 9: I mean, you know, you've you've sort of dialed it 249 00:11:58,120 --> 00:12:00,160 Speaker 9: up to eleven, right, there's nothing less than that. I 250 00:12:00,160 --> 00:12:02,360 Speaker 9: think there are plenty of near term harms that are 251 00:12:02,440 --> 00:12:05,839 Speaker 9: much more likely that will happen much more quickly that 252 00:12:05,880 --> 00:12:08,400 Speaker 9: we run the risk of glossing over and think about it, right, 253 00:12:08,440 --> 00:12:11,280 Speaker 9: remember the early days of email, right, you know, Wow, 254 00:12:11,320 --> 00:12:15,600 Speaker 9: this is amazing. I can communicate with people, and then scammers, grifters, fraudsters, 255 00:12:15,640 --> 00:12:18,319 Speaker 9: and marketers decided, hey, I can send a billion emails 256 00:12:18,320 --> 00:12:20,240 Speaker 9: for the same price as one and all of our 257 00:12:20,240 --> 00:12:23,400 Speaker 9: inboxes started being flooded. Well now, basically content creation is 258 00:12:23,440 --> 00:12:27,440 Speaker 9: effectively free with the chat, GPT and those kinds of tools. 259 00:12:27,440 --> 00:12:30,400 Speaker 9: With AI, now, I think the entire Internet runs the 260 00:12:30,520 --> 00:12:34,480 Speaker 9: risk of being flooded with spam, misinformation, and just garbage. 261 00:12:34,720 --> 00:12:37,160 Speaker 9: And we're all going to have a very pragmatic challenge, 262 00:12:37,320 --> 00:12:39,760 Speaker 9: you know, sort of managing through that. Never mind the 263 00:12:40,400 --> 00:12:43,559 Speaker 9: terrible potential long term use cases. Just in the near term, 264 00:12:43,559 --> 00:12:45,520 Speaker 9: we're going to have a lot of real problems because 265 00:12:45,760 --> 00:12:47,600 Speaker 9: bad actors are going to take these tools and do 266 00:12:47,720 --> 00:12:48,560 Speaker 9: bad things with them. 267 00:12:48,960 --> 00:12:52,000 Speaker 2: So open or just AI in general, I mean with 268 00:12:52,160 --> 00:12:56,640 Speaker 2: open AI, I guess as we speak part of Microsoft. 269 00:12:56,640 --> 00:13:00,400 Speaker 2: Now what are the competitors out there and just an artificialgens? 270 00:13:00,400 --> 00:13:02,679 Speaker 2: What are the competitors and does it change? It seems 271 00:13:02,720 --> 00:13:05,040 Speaker 2: like it's got to change the competitive landscape. 272 00:13:05,200 --> 00:13:05,960 Speaker 6: No doubt it will. 273 00:13:06,000 --> 00:13:07,520 Speaker 9: I mean you you want to say, okay, what are 274 00:13:07,520 --> 00:13:10,559 Speaker 9: the chatbot competitors who are building large language models that 275 00:13:10,600 --> 00:13:14,720 Speaker 9: you know Google's got Barred Anthropic was a former AI person. Uh, 276 00:13:14,800 --> 00:13:16,280 Speaker 9: you know, there's a there's a couple of them out there, 277 00:13:16,320 --> 00:13:18,199 Speaker 9: but you know, building your own large language models like 278 00:13:18,200 --> 00:13:20,200 Speaker 9: a billion dollar proposition. So it's not going to be 279 00:13:20,600 --> 00:13:22,640 Speaker 9: dozens of these things. It's a it's a big tech 280 00:13:22,720 --> 00:13:25,360 Speaker 9: kind of investment. But I think what's interesting is you 281 00:13:25,440 --> 00:13:27,160 Speaker 9: raise a point about you know, we all tend to 282 00:13:27,160 --> 00:13:29,680 Speaker 9: think of AI in terms of chat, GPT and chatbots, 283 00:13:29,720 --> 00:13:32,280 Speaker 9: and that's really amazing and it's got some really interesting 284 00:13:32,360 --> 00:13:37,080 Speaker 9: use cases, but think about other worlds like transportation, healthcare, manufacturing. 285 00:13:37,320 --> 00:13:40,840 Speaker 9: AI has the ability to transform those industries. Traffic, Like, 286 00:13:40,880 --> 00:13:43,240 Speaker 9: imagine a world where autonomous vehicles and those have their 287 00:13:43,240 --> 00:13:47,040 Speaker 9: own sets of challenges, can untangle traffic in big cities 288 00:13:47,080 --> 00:13:49,160 Speaker 9: and we no longer have to sit in traffic jams. 289 00:13:49,200 --> 00:13:53,079 Speaker 9: Imagine a world where you know, radiologists don't miss scans 290 00:13:53,160 --> 00:13:55,960 Speaker 9: on X rays or MRIs and things like that. I mean, 291 00:13:56,040 --> 00:13:58,079 Speaker 9: you know, there's plenty of use cases where AI can 292 00:13:58,160 --> 00:14:02,600 Speaker 9: truly help humanity and substantive powerful ways that have nothing 293 00:14:02,600 --> 00:14:05,320 Speaker 9: to do with chatbots that are powered by AI and 294 00:14:05,360 --> 00:14:07,000 Speaker 9: these underlying large language models. 295 00:14:07,400 --> 00:14:10,240 Speaker 1: So the large language model or the data set, I 296 00:14:10,240 --> 00:14:12,959 Speaker 1: guess is the most important issue that you deal with. 297 00:14:13,000 --> 00:14:16,600 Speaker 1: How do you look at this from Beacon software perspective? 298 00:14:16,720 --> 00:14:19,960 Speaker 1: Like what data sets are the most important? You know, 299 00:14:20,000 --> 00:14:21,520 Speaker 1: which ones do you want to get a hold of? 300 00:14:21,560 --> 00:14:25,600 Speaker 1: Which ones can we not touch? Yeah, it's such a huge. 301 00:14:25,360 --> 00:14:28,600 Speaker 9: Issue, right, Yeah, it is, And we've specialized in transportation, right, 302 00:14:28,640 --> 00:14:31,280 Speaker 9: so we're working with state dots Georgia and other states 303 00:14:31,320 --> 00:14:33,360 Speaker 9: to say, okay, how are you going to untangle traffic 304 00:14:33,440 --> 00:14:35,400 Speaker 9: mainly in your big cities, which is where you've got it. 305 00:14:35,600 --> 00:14:37,400 Speaker 6: Well, they're actually a wash in data. 306 00:14:37,440 --> 00:14:39,640 Speaker 9: They've got more data than they know what to do with, 307 00:14:39,800 --> 00:14:42,440 Speaker 9: than they can effectively store, than they can effectively harness. 308 00:14:42,480 --> 00:14:45,800 Speaker 9: And what's AI great at taking a massive data set 309 00:14:45,880 --> 00:14:48,440 Speaker 9: and making sense of it? And these connected vehicles now, 310 00:14:48,520 --> 00:14:50,640 Speaker 9: think about it. Every time you've got a Tesla on 311 00:14:50,640 --> 00:14:52,600 Speaker 9: the road. Every time you turn your windshield wipers on, 312 00:14:53,440 --> 00:14:55,520 Speaker 9: there's a signal right there that's been captured. 313 00:14:55,600 --> 00:14:57,640 Speaker 6: And you say, well, who cares? Well, guess what if 314 00:14:57,640 --> 00:14:59,520 Speaker 6: you harnessed every vehicle on the road and you knew 315 00:14:59,520 --> 00:15:01,520 Speaker 6: when they're winch wipers were on? At what level? 316 00:15:01,800 --> 00:15:04,960 Speaker 9: You now have a hyper local precipitation map that's the 317 00:15:05,080 --> 00:15:06,440 Speaker 9: likes of which nobody else has. 318 00:15:06,520 --> 00:15:07,120 Speaker 6: Right, It is awesome. 319 00:15:08,560 --> 00:15:10,160 Speaker 9: So those times are that right, But you've got to 320 00:15:10,160 --> 00:15:11,640 Speaker 9: be able to capture the data. You've got to be 321 00:15:11,640 --> 00:15:14,120 Speaker 9: able to intelligently use the data, and you've got to 322 00:15:14,160 --> 00:15:15,960 Speaker 9: not be able to drown in the data, which is 323 00:15:15,960 --> 00:15:19,000 Speaker 9: what everybody is facing right now. And AI, to oversimplify, 324 00:15:19,200 --> 00:15:22,280 Speaker 9: AI has the AI ability to keep you from drowning 325 00:15:22,280 --> 00:15:23,360 Speaker 9: and data like you have been. 326 00:15:24,080 --> 00:15:29,520 Speaker 2: Is there who's thinking about how AI should be managed? 327 00:15:29,600 --> 00:15:30,160 Speaker 4: Regulated? 328 00:15:30,240 --> 00:15:31,960 Speaker 2: Is anybody thinking about that out there? 329 00:15:32,120 --> 00:15:34,120 Speaker 9: There's lots of thinking that. The question is anybody doing 330 00:15:34,160 --> 00:15:36,360 Speaker 9: anything about it? So I think, you know, again, let's 331 00:15:36,360 --> 00:15:40,240 Speaker 9: give credit where credits do. I think regulators, legislators, and 332 00:15:40,280 --> 00:15:42,760 Speaker 9: even the companies themselves are thinking about it, are talking 333 00:15:42,760 --> 00:15:45,200 Speaker 9: about it, and I think there's legitimate criticism. Great, you 334 00:15:45,240 --> 00:15:46,520 Speaker 9: just want to think and talk about it and never 335 00:15:46,560 --> 00:15:48,160 Speaker 9: do anything as a way of deflecting. 336 00:15:49,320 --> 00:15:51,840 Speaker 6: You know, the EU of course takes a leadership. 337 00:15:51,520 --> 00:15:53,920 Speaker 9: Role and a lot of things around privacy and data protection, 338 00:15:54,040 --> 00:15:56,040 Speaker 9: all that would stand a reason they might take a 339 00:15:56,120 --> 00:15:58,280 Speaker 9: more aggressive and they already have, you know, started to 340 00:15:58,320 --> 00:16:02,280 Speaker 9: take steps towards a more aggressive position than the US. 341 00:16:02,440 --> 00:16:05,280 Speaker 9: I think you're going to ask the big We're going 342 00:16:05,320 --> 00:16:07,680 Speaker 9: to ask the big tech companies to be more responsible 343 00:16:07,720 --> 00:16:08,440 Speaker 9: and maybe sign up. 344 00:16:08,400 --> 00:16:10,520 Speaker 6: To a higher standard than we would have a decade ago. 345 00:16:10,760 --> 00:16:14,880 Speaker 9: Okay, Microsoft, Okay, Google, Okay, Facebook, Meta, you know those 346 00:16:14,920 --> 00:16:17,640 Speaker 9: types of companies, We're going to expect more out of them. 347 00:16:17,640 --> 00:16:19,360 Speaker 9: But I think at the end of the day, especially 348 00:16:19,400 --> 00:16:21,320 Speaker 9: in the US, ultimately Congress is going to have to 349 00:16:21,320 --> 00:16:24,600 Speaker 9: get involved in Yeah, that they have a few. 350 00:16:24,480 --> 00:16:26,840 Speaker 2: Issues questioning coming from our Congress. 351 00:16:27,800 --> 00:16:30,640 Speaker 1: I won't mention there are a few I can definitely imagine, 352 00:16:30,640 --> 00:16:32,480 Speaker 1: but it's a horror show thinking about it. 353 00:16:32,640 --> 00:16:34,000 Speaker 6: So how long, Jim. 354 00:16:33,920 --> 00:16:40,720 Speaker 1: Until we have real leaps forward in like autonomous driving? 355 00:16:41,000 --> 00:16:44,040 Speaker 1: And I mean the real question is how long is 356 00:16:44,080 --> 00:16:46,400 Speaker 1: it going to be until we have far fewer deaths 357 00:16:46,400 --> 00:16:47,440 Speaker 1: from traffic accidents? 358 00:16:47,520 --> 00:16:49,640 Speaker 9: Yeah, I'm glad you mentioned that because We've got forty 359 00:16:49,640 --> 00:16:52,200 Speaker 9: three thousand people a year die on US roads. 360 00:16:52,360 --> 00:16:54,200 Speaker 6: It is a public health crisis. 361 00:16:54,200 --> 00:16:56,040 Speaker 9: I mean, people don't typically think about it that way, 362 00:16:56,160 --> 00:16:58,920 Speaker 9: and those are human driven cars, so there's a crying 363 00:16:59,040 --> 00:17:01,680 Speaker 9: need for that. But we saw lost over the weekend 364 00:17:02,120 --> 00:17:05,440 Speaker 9: in all of the open AI news cruise. The Robotaxi 365 00:17:05,440 --> 00:17:07,920 Speaker 9: company CEO stepped down. I don't know if you saw that. Yeah, yeah, 366 00:17:07,920 --> 00:17:11,720 Speaker 9: but controversy because they were not open and transparent. There 367 00:17:11,760 --> 00:17:14,280 Speaker 9: was an incident involving a robot taxi in San Francisco. 368 00:17:14,359 --> 00:17:17,959 Speaker 9: Somebody was seriously injured. They didn't really quite disclose what happened, 369 00:17:18,000 --> 00:17:20,320 Speaker 9: and so they lost trust there. And so it's striking 370 00:17:20,359 --> 00:17:23,000 Speaker 9: to me this sort of everybody wants to move fast, 371 00:17:23,000 --> 00:17:25,280 Speaker 9: but if you move too fast, you end up going slower, right, 372 00:17:25,280 --> 00:17:27,120 Speaker 9: because all you're going to do is create a backlash. 373 00:17:27,119 --> 00:17:30,160 Speaker 9: You're going to set the entire industry back year or two. 374 00:17:30,160 --> 00:17:32,520 Speaker 9: I don't know what the timeframe will be, but I 375 00:17:32,520 --> 00:17:35,120 Speaker 9: think it's critical that companies involved in Tesla is being 376 00:17:35,119 --> 00:17:37,600 Speaker 9: maybe the most notable of this around US autonomous driving 377 00:17:37,880 --> 00:17:40,520 Speaker 9: is running the risk of sort of poisoning the well 378 00:17:40,640 --> 00:17:43,159 Speaker 9: and setting things back because they're being too aggressive and 379 00:17:43,400 --> 00:17:45,600 Speaker 9: to the beginning of our conversation, right, what does big 380 00:17:45,640 --> 00:17:48,359 Speaker 9: tech do? They just know how to do big tech right. 381 00:17:48,400 --> 00:17:51,160 Speaker 9: They go fast. I'm a private sector company. I've got 382 00:17:51,160 --> 00:17:53,560 Speaker 9: to grab adoption while it's there. I've got to move fast. 383 00:17:53,680 --> 00:17:55,800 Speaker 9: You know what's the famous line Facebook at move fast 384 00:17:55,800 --> 00:17:58,399 Speaker 9: and break things right until they realized they broke something 385 00:17:59,359 --> 00:18:00,360 Speaker 9: to maybe signific. 386 00:18:00,359 --> 00:18:02,040 Speaker 2: Exactly all right, Jim, thanks so much for joining us. 387 00:18:02,080 --> 00:18:04,440 Speaker 2: Jim Anderson. He's the CEO of Beacon Software. His job 388 00:18:04,480 --> 00:18:07,080 Speaker 2: is to fix the traffic in Atlanta, Georgia. Good luck 389 00:18:07,119 --> 00:18:07,320 Speaker 2: with that. 390 00:18:07,760 --> 00:18:10,879 Speaker 8: You're listening to the tape Cat's are live program Bloomberg 391 00:18:10,920 --> 00:18:14,520 Speaker 8: Markets weekdays at ten am Eastern on Bloomberg Radio, the 392 00:18:14,600 --> 00:18:17,800 Speaker 8: tune in app, Bloomberg dot Com, and the Bloomberg Business app. 393 00:18:17,840 --> 00:18:20,680 Speaker 8: You can also listen live on Amazon Alexa from our 394 00:18:20,680 --> 00:18:25,760 Speaker 8: flagship New York station, Just say Alexa play Bloomberg eleven thirty. 395 00:18:26,760 --> 00:18:30,480 Speaker 1: We have Matt Matt Siegel in here. He is the 396 00:18:30,520 --> 00:18:33,840 Speaker 1: head of I guess cryptoanalysis over at van Neck. What's 397 00:18:33,840 --> 00:18:37,280 Speaker 1: your official title at van Neck, head of Digital assets Research? 398 00:18:37,480 --> 00:18:37,959 Speaker 3: You got it. 399 00:18:38,000 --> 00:18:42,080 Speaker 1: I was pretty close, and I mean before we get 400 00:18:42,080 --> 00:18:45,400 Speaker 1: into anything else, when are we gonna see these ETFs 401 00:18:45,440 --> 00:18:50,119 Speaker 1: finally allowed by the SEC? Because it doesn't even feel 402 00:18:50,119 --> 00:18:52,920 Speaker 1: like Gary Gensler's dragging his feed anymore. It's like he's 403 00:18:53,080 --> 00:18:56,080 Speaker 1: grasped on to a chair and people are trying to 404 00:18:56,119 --> 00:18:56,640 Speaker 1: pull him. 405 00:18:56,720 --> 00:18:57,399 Speaker 3: What's the deal. 406 00:18:58,880 --> 00:19:02,480 Speaker 10: The final deadline for approval of a number of these 407 00:19:02,640 --> 00:19:06,840 Speaker 10: spot bitcoin ETFs is the first half of January, So 408 00:19:07,080 --> 00:19:08,879 Speaker 10: there's a lot of logistics that has to go on 409 00:19:08,920 --> 00:19:11,879 Speaker 10: behind the scenes at the SEC, with various divisions talking 410 00:19:11,880 --> 00:19:14,280 Speaker 10: to each other and communicating with issuers to button up 411 00:19:14,320 --> 00:19:17,360 Speaker 10: all but it's going to happens very high percentage by 412 00:19:17,400 --> 00:19:19,560 Speaker 10: the first half of January next year. 413 00:19:19,640 --> 00:19:23,240 Speaker 1: So what does that mean for the market? I mean, 414 00:19:23,400 --> 00:19:26,680 Speaker 1: even beyond bitcoin, Bitcoin itself is trading above thirty seven 415 00:19:26,720 --> 00:19:30,680 Speaker 1: thousand dollars, which to me is just absolutely nuts because 416 00:19:30,680 --> 00:19:33,080 Speaker 1: I started covering this kind of as a joke in 417 00:19:33,080 --> 00:19:36,359 Speaker 1: twenty twelve and it was like, you know, ninety dollars, 418 00:19:36,520 --> 00:19:41,240 Speaker 1: So the fact that that same string of code could 419 00:19:41,240 --> 00:19:44,080 Speaker 1: be worth thirty seven thousand dollars is nuts to me. 420 00:19:45,200 --> 00:19:46,760 Speaker 1: But a lot of people still look at this as 421 00:19:46,840 --> 00:19:48,920 Speaker 1: kind of a failed experiment, which. 422 00:19:48,720 --> 00:19:49,560 Speaker 6: Baffles the mind. 423 00:19:50,359 --> 00:19:54,120 Speaker 10: Well, let's start with the impact of the ETF. When 424 00:19:54,119 --> 00:19:58,440 Speaker 10: an investor buys bitcoin on coinbase a retail investor, they're 425 00:19:58,440 --> 00:20:01,520 Speaker 10: paying an average of two point five five percent in 426 00:20:01,800 --> 00:20:07,720 Speaker 10: transaction fees. When an ETF buyer buys a bitcoin ETF 427 00:20:07,720 --> 00:20:11,360 Speaker 10: in Europe, where these spot products trade, they're paying ten 428 00:20:11,400 --> 00:20:16,000 Speaker 10: to twenty basis points in spreads and commissions and fees 429 00:20:16,080 --> 00:20:19,520 Speaker 10: are often zero. So what we have is an order 430 00:20:19,520 --> 00:20:24,280 Speaker 10: of magnitude decline in costs for the end user. And 431 00:20:24,480 --> 00:20:28,120 Speaker 10: essentially you're mainlining a bitcoin only exchange into the brokerage 432 00:20:28,119 --> 00:20:30,720 Speaker 10: account of every investor in the US. 433 00:20:30,760 --> 00:20:33,240 Speaker 6: Can you arbitrage that, by the way, as a trader. 434 00:20:33,560 --> 00:20:35,560 Speaker 10: Well, that's why we have That's why there's a functioning 435 00:20:35,600 --> 00:20:38,920 Speaker 10: futures market, and the CME's market share of that futures 436 00:20:38,920 --> 00:20:42,439 Speaker 10: market has recently reached an all time high. So it 437 00:20:42,600 --> 00:20:46,760 Speaker 10: is the regulated flows. Bitcoin ETFs have seen one billion 438 00:20:46,800 --> 00:20:49,800 Speaker 10: dollars in flows in the last month. That's eighty percent 439 00:20:49,800 --> 00:20:52,520 Speaker 10: of the total year to date that we've seen. So 440 00:20:52,880 --> 00:20:55,360 Speaker 10: the flows are coming from the regulated side. It bitcoins 441 00:20:55,400 --> 00:20:57,480 Speaker 10: up thirty percent in the last month. When it's one 442 00:20:57,520 --> 00:20:59,920 Speaker 10: way buying in an illiquid asset like this, the price 443 00:21:00,000 --> 00:21:00,920 Speaker 10: impact can be large. 444 00:21:01,080 --> 00:21:05,000 Speaker 2: All right, there was an election in Argentina. Please explain 445 00:21:05,080 --> 00:21:07,400 Speaker 2: to me why this has a connection to bitcoin. 446 00:21:07,680 --> 00:21:11,120 Speaker 1: Pretty shock result as well. I mean looks like Elvis. 447 00:21:11,200 --> 00:21:14,280 Speaker 1: Javier lives with his mom and six dogs. One how 448 00:21:14,280 --> 00:21:15,359 Speaker 1: do you pronounce his last name? 449 00:21:15,400 --> 00:21:17,840 Speaker 6: Milay Malay. 450 00:21:18,200 --> 00:21:24,000 Speaker 10: Javier Malay is a libertarian who has been very vocal 451 00:21:24,240 --> 00:21:29,000 Speaker 10: about his desire to eliminate the Central Bank of Argentina. 452 00:21:29,480 --> 00:21:32,679 Speaker 10: Argentina has the worst performing currency in Latin America in 453 00:21:32,680 --> 00:21:36,320 Speaker 10: the last three decades. It's had the most number of recessions, 454 00:21:36,600 --> 00:21:39,720 Speaker 10: it has the least amount of foreign direct investment. So 455 00:21:39,960 --> 00:21:42,160 Speaker 10: sticking with the same path is kind of the definition 456 00:21:42,240 --> 00:21:44,800 Speaker 10: of insanity, keeping the same thing going and expecting something 457 00:21:44,800 --> 00:21:50,040 Speaker 10: to change. This new leader has plans to dollarize the economy. 458 00:21:50,160 --> 00:21:54,639 Speaker 10: He has been very outspoken about his belief that central 459 00:21:54,640 --> 00:21:57,960 Speaker 10: banks are a scam that impose an inflationary tax on 460 00:21:58,200 --> 00:22:01,840 Speaker 10: regular citizens, and he's I've also been very outspoken in 461 00:22:01,880 --> 00:22:06,960 Speaker 10: his admiration for bitcoin in restoring money and value to 462 00:22:07,000 --> 00:22:10,960 Speaker 10: its original creator, which is the private sector. So we've 463 00:22:11,000 --> 00:22:14,760 Speaker 10: been observing for some time that crypto adoption in Argentina, 464 00:22:14,880 --> 00:22:18,480 Speaker 10: both bitcoin and stable coins, is the highest in Latin America. 465 00:22:18,560 --> 00:22:21,119 Speaker 10: And the highest among the highest in the region. 466 00:22:21,800 --> 00:22:23,320 Speaker 2: Go into that inflation you were talking about. 467 00:22:23,760 --> 00:22:27,439 Speaker 10: Yeah, folks are holding tether in order to maintain dollar 468 00:22:27,520 --> 00:22:30,600 Speaker 10: purchasing power. The other thing we've noticed, the largest private 469 00:22:30,720 --> 00:22:34,439 Speaker 10: oil and gas company in Argentina just announced plans to 470 00:22:35,200 --> 00:22:39,639 Speaker 10: mine bitcoin with the excess gas and methane that's a 471 00:22:39,640 --> 00:22:43,960 Speaker 10: byproduct of their existing production. So it's very rational, we think, 472 00:22:44,080 --> 00:22:48,439 Speaker 10: for countries to use their stranded energy, monetize it to 473 00:22:48,480 --> 00:22:52,040 Speaker 10: earn bitcoin, and in the process give their citizens a 474 00:22:52,119 --> 00:22:56,000 Speaker 10: hedge on the Fiat imposed inflationary tax that Melee has 475 00:22:56,000 --> 00:22:57,440 Speaker 10: spoken so much about. 476 00:22:58,119 --> 00:23:01,120 Speaker 1: By the way, I'm not an official libertarian, like I'm 477 00:23:01,119 --> 00:23:05,440 Speaker 1: not racially anything, especially in a day and age when 478 00:23:05,600 --> 00:23:09,960 Speaker 1: you only vote against someone and not for anybody. But 479 00:23:10,440 --> 00:23:13,560 Speaker 1: aren't the founding fathers, Like, isn't this country kind of libertarian? 480 00:23:13,760 --> 00:23:15,080 Speaker 1: Isn't that the point of America? 481 00:23:15,320 --> 00:23:17,439 Speaker 2: Exactly? And you're in a great state of Ohio, that's 482 00:23:17,480 --> 00:23:19,760 Speaker 2: where you're from, So it's yeah. 483 00:23:20,200 --> 00:23:23,479 Speaker 1: So, Matt, what's your take on the rest of the 484 00:23:23,640 --> 00:23:27,200 Speaker 1: twenty thousand other coins? I mean, bitcoin is something we 485 00:23:27,240 --> 00:23:29,840 Speaker 1: could talk about all day and I think, you know, 486 00:23:29,920 --> 00:23:33,159 Speaker 1: even Gary Gendler doesn't think bitcoin is a security, but 487 00:23:33,240 --> 00:23:37,400 Speaker 1: everything else has a question mark. Are there are there 488 00:23:37,440 --> 00:23:39,800 Speaker 1: like ten that you like and the rest are junk? 489 00:23:39,800 --> 00:23:41,440 Speaker 1: Are there a hundred that you like and the rest 490 00:23:41,440 --> 00:23:41,879 Speaker 1: are junk? 491 00:23:41,880 --> 00:23:43,240 Speaker 3: How does the whole thing look to you? 492 00:23:45,080 --> 00:23:47,000 Speaker 10: We think the vast majority of the value in this 493 00:23:47,040 --> 00:23:51,280 Speaker 10: ecosystem is going to accrue to a handful of protocols 494 00:23:51,800 --> 00:23:55,760 Speaker 10: and thus tokens, and it's very early stages in those days, 495 00:23:55,800 --> 00:23:58,280 Speaker 10: and there's still a lot of regulatory uncertainty. 496 00:23:58,600 --> 00:24:00,240 Speaker 6: So just focusing. 497 00:23:59,760 --> 00:24:03,000 Speaker 10: On bitcoin because of the huge number of catalysts for 498 00:24:03,080 --> 00:24:06,399 Speaker 10: next year, including the lack of supply. More than seventy 499 00:24:06,400 --> 00:24:08,840 Speaker 10: percent of bitcoin supply has not moved in the last year. 500 00:24:09,040 --> 00:24:11,680 Speaker 10: That is an all time high. The bitcoin having which 501 00:24:11,680 --> 00:24:16,000 Speaker 10: is an algorithm change, occurs next April that will cut 502 00:24:16,040 --> 00:24:19,560 Speaker 10: the amount of bitcoin by half that is generated. We're 503 00:24:19,560 --> 00:24:22,600 Speaker 10: in the middle of an absolute boom in bitcoin fees, 504 00:24:22,720 --> 00:24:25,560 Speaker 10: so transaction fees on the bitcoin blockchain are now fifteen 505 00:24:25,600 --> 00:24:28,880 Speaker 10: percent of all bitcoin minor revenues, which is an all 506 00:24:28,880 --> 00:24:32,760 Speaker 10: time high. That's been turbocharged by these NFTs on bitcoin 507 00:24:34,000 --> 00:24:38,120 Speaker 10: and with the ETF coming and the institutional adoption. 508 00:24:38,080 --> 00:24:40,359 Speaker 6: Via state affiliated actors. 509 00:24:40,440 --> 00:24:42,560 Speaker 10: Right, So my call is that Argentina will become the 510 00:24:42,600 --> 00:24:45,440 Speaker 10: fifth country in the world to mine bitcoin for its 511 00:24:45,480 --> 00:24:52,320 Speaker 10: own state reserves, joining El Salvador, Bhutan, Oman and the UAE. 512 00:24:53,160 --> 00:24:56,760 Speaker 10: And with that regulatory certainty and the existence of the 513 00:24:56,760 --> 00:25:00,639 Speaker 10: ETF and the US government and G seven's continued overspending 514 00:25:00,720 --> 00:25:04,560 Speaker 10: debt and deficit problems, that they'll be large institutional buyers 515 00:25:04,560 --> 00:25:05,800 Speaker 10: that will migrate to bitcoin. 516 00:25:06,600 --> 00:25:06,800 Speaker 4: You know. 517 00:25:07,000 --> 00:25:08,960 Speaker 10: The history is that the rest of the coins benefit 518 00:25:09,000 --> 00:25:11,560 Speaker 10: from that, but that usually happens after the having which 519 00:25:11,600 --> 00:25:12,360 Speaker 10: is next April. 520 00:25:12,440 --> 00:25:14,919 Speaker 1: All right, So while we have you here, tell us 521 00:25:14,920 --> 00:25:16,879 Speaker 1: what your thought is on world coin, because we've been 522 00:25:16,880 --> 00:25:19,560 Speaker 1: talking about Sam Altman all day in regards to his 523 00:25:19,600 --> 00:25:22,440 Speaker 1: position at OpenAI or now Microsoft. But he started this 524 00:25:22,520 --> 00:25:26,520 Speaker 1: like biometric cryptocurrency. I don't know how it works, like 525 00:25:26,600 --> 00:25:30,000 Speaker 1: measures your eye and keeps the data. But what's the 526 00:25:30,040 --> 00:25:33,000 Speaker 1: story with that. It's a layer two ethereum based coin. 527 00:25:33,080 --> 00:25:36,879 Speaker 10: So you can see from the regulation being passed in 528 00:25:36,960 --> 00:25:40,960 Speaker 10: the UK and in Canada around what can be said 529 00:25:41,000 --> 00:25:44,760 Speaker 10: on internet platforms that there's a huge desire to differentiate 530 00:25:45,000 --> 00:25:46,359 Speaker 10: the humans from the bots. 531 00:25:46,440 --> 00:25:46,600 Speaker 4: Right. 532 00:25:46,640 --> 00:25:49,440 Speaker 10: Presidential candidate Nikki Haley was in the media last week 533 00:25:49,520 --> 00:25:52,919 Speaker 10: saying that everyone needs to be verified in order to 534 00:25:52,920 --> 00:25:55,120 Speaker 10: participate on social media. I doubt that's going to happen 535 00:25:55,160 --> 00:25:58,080 Speaker 10: in the US, but if it were, and we wanted 536 00:25:58,080 --> 00:26:04,239 Speaker 10: to preserve some user privacy, than a cryptographic network that 537 00:26:04,280 --> 00:26:08,879 Speaker 10: can verify user identity without revealing the name would be 538 00:26:08,920 --> 00:26:13,080 Speaker 10: an attractive product. And at the that's essentially what orl 539 00:26:13,160 --> 00:26:15,600 Speaker 10: cooin is trying to do. So we do think that 540 00:26:15,760 --> 00:26:17,920 Speaker 10: over the next year there's going to be some innovation 541 00:26:18,280 --> 00:26:23,840 Speaker 10: that allows AI to be differentiated, whether it's generated by 542 00:26:23,920 --> 00:26:26,080 Speaker 10: human or by a bot, and that crypto has a 543 00:26:26,160 --> 00:26:30,000 Speaker 10: role to play in preserving privacy while enabling those functions. 544 00:26:30,000 --> 00:26:30,560 Speaker 8: That's cool. 545 00:26:31,119 --> 00:26:32,639 Speaker 2: That's Siegel, Thank you so much for joining us. 546 00:26:32,640 --> 00:26:33,119 Speaker 3: Matt Sigua. 547 00:26:33,200 --> 00:26:35,960 Speaker 2: He's a head of digital assets research for Vaneck, joining 548 00:26:36,000 --> 00:26:38,280 Speaker 2: us live here in our Bloomberg Interactive Brokers studio, and 549 00:26:38,359 --> 00:26:41,560 Speaker 2: John Tucker is going to come up prepare executive summary 550 00:26:41,600 --> 00:26:43,960 Speaker 2: of what we discussed in this segment. So everybody's on 551 00:26:43,960 --> 00:26:46,560 Speaker 2: this minute exactly right. 552 00:26:47,400 --> 00:26:50,480 Speaker 8: You're listening to the tape. Can's our live program Bloomberg 553 00:26:50,600 --> 00:26:54,199 Speaker 8: Markets weekdays at ten am Eastern on Bloomberg Radio, the 554 00:26:54,240 --> 00:26:57,480 Speaker 8: tune in app, Bloomberg dot Com, and the Bloomberg Business app. 555 00:26:57,520 --> 00:27:00,359 Speaker 8: You can also listen live on Amazon Alexa from our 556 00:27:00,359 --> 00:27:05,080 Speaker 8: flagship New York station Just say Alexa playing Bloomberg eleven. 557 00:27:06,320 --> 00:27:06,600 Speaker 4: Now. 558 00:27:07,320 --> 00:27:09,840 Speaker 1: We have been talking a lot about airlines on this 559 00:27:09,920 --> 00:27:14,359 Speaker 1: program and the difficulty that the manufacturers have making enough planes. 560 00:27:14,400 --> 00:27:18,240 Speaker 1: There is just a huge order right for three hundred 561 00:27:18,440 --> 00:27:22,560 Speaker 1: of the was it the seven three seven or. 562 00:27:22,720 --> 00:27:23,159 Speaker 4: I think so? 563 00:27:23,240 --> 00:27:23,639 Speaker 8: I don't know. 564 00:27:23,840 --> 00:27:25,000 Speaker 6: In any case, a bunch of them. 565 00:27:25,680 --> 00:27:28,440 Speaker 1: If you order a plane today, your chances of getting 566 00:27:28,480 --> 00:27:31,719 Speaker 1: it soon are very low. I just got a message 567 00:27:31,760 --> 00:27:35,160 Speaker 1: from Ali Ben al Maldani, the CEO of ABL Aviation. 568 00:27:35,280 --> 00:27:37,199 Speaker 1: He said he was at the show and putting some 569 00:27:37,320 --> 00:27:39,479 Speaker 1: orders there too, and he could come on and talk 570 00:27:39,520 --> 00:27:39,840 Speaker 1: about it. 571 00:27:39,880 --> 00:27:41,399 Speaker 6: So I was like, great, come in the studio. 572 00:27:41,440 --> 00:27:42,840 Speaker 1: He's in New York for one day only. 573 00:27:42,920 --> 00:27:43,600 Speaker 4: Oh, we got him. 574 00:27:43,640 --> 00:27:45,800 Speaker 1: We get him on set. Ali, thanks so much for 575 00:27:45,880 --> 00:27:49,480 Speaker 1: joining us. Tell us about the state of aviation in 576 00:27:49,520 --> 00:27:52,479 Speaker 1: terms of ordering a plane right now? How difficult is it? 577 00:27:52,720 --> 00:27:54,440 Speaker 1: You know, what are the prices like and when can 578 00:27:54,480 --> 00:27:58,680 Speaker 1: you expectually? When can you actually expect to get delivery? 579 00:27:58,880 --> 00:28:00,679 Speaker 11: Look at so much much we'll have being or pleasure. 580 00:28:01,040 --> 00:28:03,720 Speaker 11: It's always an issue, I think to get aircraft today. 581 00:28:03,920 --> 00:28:05,480 Speaker 11: As we said last time, we have seven years to 582 00:28:05,520 --> 00:28:07,960 Speaker 11: eight ys wait for both manufacturers. 583 00:28:07,400 --> 00:28:08,360 Speaker 3: Bowling go Evers. 584 00:28:08,520 --> 00:28:10,639 Speaker 11: You have some issues, of course with the engines. You 585 00:28:10,680 --> 00:28:12,440 Speaker 11: saw what's happened with pattern Witness. We have a few 586 00:28:12,440 --> 00:28:14,200 Speaker 11: issues on the engine side that we need to fix 587 00:28:14,200 --> 00:28:16,360 Speaker 11: and that's been fixed by the OEMs. 588 00:28:17,080 --> 00:28:18,120 Speaker 3: You saw Dubai. 589 00:28:17,800 --> 00:28:21,080 Speaker 11: Airshow last week was the biggest order since I was seventeen. 590 00:28:21,160 --> 00:28:24,000 Speaker 11: So the other book on in Dubai was insane. You 591 00:28:24,040 --> 00:28:26,960 Speaker 11: had emails that's made a huge order, fly Dubai. Ethiopian 592 00:28:27,040 --> 00:28:29,160 Speaker 11: made a big order. So there's a big demand. There's 593 00:28:29,200 --> 00:28:32,159 Speaker 11: not enough supply and that's the first issue. And Evan, 594 00:28:32,760 --> 00:28:35,040 Speaker 11: since a lot of aircraft during COVID got so tired, 595 00:28:35,200 --> 00:28:38,080 Speaker 11: it's extremely hard today for airlines to find aircraft that 596 00:28:38,120 --> 00:28:40,280 Speaker 11: they can operate, and that's one of the biggest issues. 597 00:28:40,280 --> 00:28:42,040 Speaker 11: We spoke about it last timer we were here. We 598 00:28:42,200 --> 00:28:44,360 Speaker 11: keep on seeing it more and more and even the 599 00:28:44,600 --> 00:28:47,760 Speaker 11: order like seventeen eight hundred and the classics are being 600 00:28:47,960 --> 00:28:50,200 Speaker 11: asked to come back to the markets because we need 601 00:28:50,240 --> 00:28:52,720 Speaker 11: them to fly them. And we see again during the 602 00:28:52,760 --> 00:28:54,800 Speaker 11: casemas season where there's a big need. You saw it 603 00:28:54,880 --> 00:28:56,920 Speaker 11: last summer we spoke about you guys going to Europe. 604 00:28:56,920 --> 00:28:59,200 Speaker 11: What was the cost of tickets was going high and 605 00:28:59,280 --> 00:29:02,440 Speaker 11: higher because of the demand and the lack of aircraft 606 00:29:02,520 --> 00:29:03,160 Speaker 11: being provided. 607 00:29:03,520 --> 00:29:07,120 Speaker 2: So I envision a desert in Arizona, New Mexico, and 608 00:29:07,200 --> 00:29:09,040 Speaker 2: a lot of seven thirty sevens. A lot of seven 609 00:29:09,120 --> 00:29:12,160 Speaker 2: fifty sevens are sitting out there because they were decommissioned 610 00:29:12,160 --> 00:29:15,840 Speaker 2: for whatever reason over whatever recent period of time. Can 611 00:29:15,920 --> 00:29:18,120 Speaker 2: I just go grab one of those planes and refurbishing 612 00:29:18,160 --> 00:29:19,080 Speaker 2: and put them back in the air. 613 00:29:19,400 --> 00:29:20,600 Speaker 3: It's not as easy as you think. 614 00:29:20,640 --> 00:29:22,280 Speaker 11: So if you want to get what we call an 615 00:29:22,320 --> 00:29:25,000 Speaker 11: aircraft a water again, you have a long process to 616 00:29:25,040 --> 00:29:27,360 Speaker 11: go again to put those aircraft back into service. You're 617 00:29:27,400 --> 00:29:29,480 Speaker 11: not going to just come and flies again. It needs 618 00:29:29,520 --> 00:29:31,479 Speaker 11: an inspection. It's need was we call an agyan inspection. 619 00:29:31,560 --> 00:29:33,719 Speaker 11: It needs a few inspections to get back, which are 620 00:29:33,840 --> 00:29:36,480 Speaker 11: very costly. So the difference between the cost of getting 621 00:29:36,480 --> 00:29:38,880 Speaker 11: it back and getting on your aircraft, that's why there's 622 00:29:38,920 --> 00:29:40,480 Speaker 11: not a bit hard to be made. And we saw 623 00:29:40,480 --> 00:29:42,800 Speaker 11: it in the last two to three years where the 624 00:29:42,880 --> 00:29:45,120 Speaker 11: cost of leasing to seventeen seven eight hundred on eighteen 625 00:29:45,200 --> 00:29:48,360 Speaker 11: twenty not a kno, but another generation went to the OOF. 626 00:29:48,480 --> 00:29:49,680 Speaker 3: It went so expensively. 627 00:29:49,720 --> 00:29:52,200 Speaker 11: So today if you lease in aircraft it's almost about 628 00:29:52,240 --> 00:29:54,680 Speaker 11: forty to fifty percent then it was a few years ago. 629 00:29:55,160 --> 00:29:57,760 Speaker 11: So there is a big demand, and that's unpacs. Of course, 630 00:29:57,800 --> 00:29:58,640 Speaker 11: the ticket spiices. 631 00:29:59,120 --> 00:30:01,800 Speaker 1: So by the way, the big order was for set 632 00:30:01,840 --> 00:30:04,280 Speaker 1: triple sevens, the seven seven seven. That's the amage order 633 00:30:04,320 --> 00:30:07,520 Speaker 1: that you're talking about seven x triple seven x, and 634 00:30:07,560 --> 00:30:09,160 Speaker 1: that's what it's a much bigger plane. 635 00:30:09,200 --> 00:30:09,320 Speaker 3: Right. 636 00:30:09,360 --> 00:30:11,239 Speaker 1: You like the single aisle, You like the seven three 637 00:30:11,320 --> 00:30:12,560 Speaker 1: sevens in the eight three twenties. 638 00:30:12,800 --> 00:30:14,200 Speaker 3: I like the twenties. 639 00:30:14,240 --> 00:30:15,800 Speaker 11: I love the eight to twenty, which is a single 640 00:30:15,960 --> 00:30:17,680 Speaker 11: the smallest one that delta opates a loss. We have 641 00:30:17,720 --> 00:30:19,920 Speaker 11: lots of them with delta on the airplanes. I love 642 00:30:20,000 --> 00:30:23,120 Speaker 11: this because it's very liquid. You can move them very quickly. 643 00:30:23,160 --> 00:30:26,360 Speaker 11: You can the cost of fixing them to Differense airlines 644 00:30:26,480 --> 00:30:28,840 Speaker 11: much less. If you need to have a white body 645 00:30:28,880 --> 00:30:31,040 Speaker 11: fisted against with the Differense airline, the cost is very high. 646 00:30:31,040 --> 00:30:32,600 Speaker 11: It was become most one to at least million of 647 00:30:32,680 --> 00:30:35,240 Speaker 11: dollars to make its fits for basics, to make it 648 00:30:35,320 --> 00:30:37,320 Speaker 11: fit to a new airline, versus when it's a smaller 649 00:30:37,360 --> 00:30:39,520 Speaker 11: one when all economy class I can move them very 650 00:30:39,560 --> 00:30:41,240 Speaker 11: quickly between one airline and another one. 651 00:30:42,000 --> 00:30:46,160 Speaker 2: What is the state of the oe MS, the Airbus 652 00:30:46,360 --> 00:30:49,520 Speaker 2: and the Boeing. Why are they having such a difficult 653 00:30:49,560 --> 00:30:51,000 Speaker 2: time ramping up production? 654 00:30:51,080 --> 00:30:53,360 Speaker 1: Yeah, I don't understand that, you know. I a couple 655 00:30:53,360 --> 00:30:56,160 Speaker 1: of years ago I ordered the Mercedes G five hundred 656 00:30:56,480 --> 00:30:59,280 Speaker 1: and it took about a year for them to make it. 657 00:30:59,400 --> 00:31:03,480 Speaker 1: Now they only had have one factory in Rocks in Austria, right, 658 00:31:03,520 --> 00:31:05,400 Speaker 1: So I get that, and they're not making more than 659 00:31:05,440 --> 00:31:10,080 Speaker 1: like twenty five thousand a year. These airline manufacturer airplane 660 00:31:10,080 --> 00:31:13,360 Speaker 1: manufacturers are like the biggest companies in the world. They 661 00:31:13,360 --> 00:31:16,680 Speaker 1: have factories all over the place. Why can't they just 662 00:31:17,040 --> 00:31:19,640 Speaker 1: get more people on there and make more metal. 663 00:31:19,760 --> 00:31:21,080 Speaker 11: Well, first of all, they don't have if you look 664 00:31:21,120 --> 00:31:23,080 Speaker 11: at Boingois, they don't have faxies all over the place. 665 00:31:23,080 --> 00:31:26,720 Speaker 11: They have certain amounts of factory, each assembly line, it's 666 00:31:27,000 --> 00:31:30,280 Speaker 11: a factories assembly line. Each assembly line have a certain 667 00:31:30,320 --> 00:31:31,720 Speaker 11: amount that they can produce every months. 668 00:31:32,440 --> 00:31:33,080 Speaker 3: It's seattle. 669 00:31:33,520 --> 00:31:35,960 Speaker 11: All of them have certain amounts that they can produce. 670 00:31:36,400 --> 00:31:39,320 Speaker 11: That's the first issue. So they can not over produce. 671 00:31:39,400 --> 00:31:41,760 Speaker 11: They produce what they can do. They're producing a max 672 00:31:42,120 --> 00:31:45,840 Speaker 11: of course max capacity. They're doing their best to supply 673 00:31:45,880 --> 00:31:47,760 Speaker 11: as much as they can. But the second issue that 674 00:31:47,800 --> 00:31:49,880 Speaker 11: you have to remember that it was a COVID start 675 00:31:50,040 --> 00:31:52,240 Speaker 11: doing COVID issue is a supply chain. And if you 676 00:31:52,280 --> 00:31:53,760 Speaker 11: look at all the supply chain, because you have to 677 00:31:53,760 --> 00:31:56,960 Speaker 11: remember EPs and Boeing assembler, the assemble everything that is 678 00:31:56,960 --> 00:32:00,120 Speaker 11: provided to them. So all the bug supply chain is 679 00:32:00,160 --> 00:32:03,680 Speaker 11: a big issue. So it's not only in aircraft. It's 680 00:32:03,800 --> 00:32:05,600 Speaker 11: as you said, in cars. If you want to other cars, 681 00:32:05,640 --> 00:32:09,080 Speaker 11: then especially in car Matt, there's a big issue of. 682 00:32:09,040 --> 00:32:09,560 Speaker 3: Finding a car. 683 00:32:09,600 --> 00:32:10,880 Speaker 11: If I tell you today go find a G four 684 00:32:10,920 --> 00:32:14,320 Speaker 11: hundred diesel, it's almost impossible to find because us the 685 00:32:14,360 --> 00:32:16,959 Speaker 11: guys saw this and the cost of a car on 686 00:32:17,080 --> 00:32:19,320 Speaker 11: aircraft is about twenty percent and before COVID. 687 00:32:19,760 --> 00:32:22,840 Speaker 1: Right, so you've seen that huge inflation amount as well. 688 00:32:22,920 --> 00:32:25,640 Speaker 1: What are margins like for you? I mean, does that 689 00:32:25,760 --> 00:32:28,360 Speaker 1: inflation eat into your margin or is the demand the 690 00:32:28,440 --> 00:32:30,240 Speaker 1: least aircraft still so strong. 691 00:32:30,560 --> 00:32:33,360 Speaker 11: The amount to these aircraft is very strong. There's a 692 00:32:33,360 --> 00:32:35,400 Speaker 11: big demand. But you have to remember all of us, 693 00:32:35,440 --> 00:32:37,200 Speaker 11: of course lovers, our deals. 694 00:32:37,240 --> 00:32:39,600 Speaker 3: We need to get debts. The cost of deaths is 695 00:32:39,640 --> 00:32:40,800 Speaker 3: what kills us. 696 00:32:40,800 --> 00:32:43,200 Speaker 11: I think on the deals because you have seventy eighty 697 00:32:43,200 --> 00:32:44,800 Speaker 11: percent of deaths on issue. 698 00:32:44,880 --> 00:32:46,480 Speaker 2: Where does that that come from? The regular the JP 699 00:32:46,560 --> 00:32:47,480 Speaker 2: Morgan chasing get one. 700 00:32:47,560 --> 00:32:49,440 Speaker 11: Yeah, JPYM Morgan is actually that market. So you have 701 00:32:49,480 --> 00:32:51,840 Speaker 11: two types of deaths. Of course, secured and secured deaths 702 00:32:52,120 --> 00:32:56,560 Speaker 11: on You have other the banks, the banks that come 703 00:32:56,600 --> 00:32:58,120 Speaker 11: and provide you a death on it. You know, all 704 00:32:58,160 --> 00:33:00,480 Speaker 11: the players from back of America to GIPM Morgan, the city, 705 00:33:00,520 --> 00:33:03,400 Speaker 11: to the Japanese banks, to the Chinese banks. And you 706 00:33:03,480 --> 00:33:05,480 Speaker 11: used to have which is a lot less active today, 707 00:33:05,600 --> 00:33:07,880 Speaker 11: the capital markets. So before that you can do an 708 00:33:07,920 --> 00:33:11,440 Speaker 11: ABS secuvization and use as capital markets money which was 709 00:33:11,480 --> 00:33:14,240 Speaker 11: at that time cheaper. As of today, the capital markets 710 00:33:14,280 --> 00:33:16,280 Speaker 11: for aviation is not very active for the last two years. 711 00:33:16,280 --> 00:33:17,920 Speaker 11: It used to be a lot more active on the 712 00:33:17,920 --> 00:33:19,840 Speaker 11: ABS side. I think in the next year or two 713 00:33:19,880 --> 00:33:21,960 Speaker 11: it might come back. But the capital markets was a 714 00:33:22,040 --> 00:33:24,280 Speaker 11: very competitive way of financing aircraft. 715 00:33:24,640 --> 00:33:26,120 Speaker 6: Is private credit does that play a. 716 00:33:26,120 --> 00:33:28,360 Speaker 3: Role, Yes, of course it's very active as well. Private credit. 717 00:33:28,520 --> 00:33:31,960 Speaker 11: Life insurance a lots of insurance life insurance companies land 718 00:33:32,000 --> 00:33:35,000 Speaker 11: because the leases are seven years, eights, twelve, years. So 719 00:33:35,000 --> 00:33:37,760 Speaker 11: it's much as the maturity of the insurance the life 720 00:33:37,800 --> 00:33:38,520 Speaker 11: insurance companies. 721 00:33:38,960 --> 00:33:41,560 Speaker 2: We heerd from some of the just folks in the 722 00:33:41,600 --> 00:33:43,920 Speaker 2: industry that India is going to be is a big 723 00:33:43,960 --> 00:33:46,240 Speaker 2: growth market, will continue to be a huge buyer of 724 00:33:46,280 --> 00:33:48,560 Speaker 2: aircraft as they look to expand their fleet. Are you 725 00:33:48,600 --> 00:33:48,920 Speaker 2: seeing that? 726 00:33:49,120 --> 00:33:51,360 Speaker 11: Well, if you see the last order, this was a 727 00:33:51,760 --> 00:33:54,400 Speaker 11: four months ago of Erindio, it was the biggest order 728 00:33:54,440 --> 00:33:55,880 Speaker 11: was ever made. I think it was aboust five hundred 729 00:33:55,880 --> 00:33:59,360 Speaker 11: acap that the orders. Wow, so that order was huge. 730 00:34:00,640 --> 00:34:03,320 Speaker 11: Look India, with a few airlines out there is an 731 00:34:03,320 --> 00:34:06,320 Speaker 11: amazing jurisdiction you can lease aircraft that The issue that 732 00:34:06,400 --> 00:34:08,760 Speaker 11: you have with India is how do you process aircrafts? 733 00:34:08,760 --> 00:34:10,640 Speaker 11: Because in aviation you have what you call a cape 734 00:34:10,680 --> 00:34:13,640 Speaker 11: town counties. So those countries that signed the Keep County 735 00:34:13,840 --> 00:34:15,760 Speaker 11: Kept On treaty allows. 736 00:34:15,400 --> 00:34:17,320 Speaker 3: You to a process with aircraft process. 737 00:34:17,440 --> 00:34:20,160 Speaker 11: Yes, it's similar to Chapter eleven kind of treaty where 738 00:34:20,160 --> 00:34:22,840 Speaker 11: you can go to those counties and they possess those. 739 00:34:23,600 --> 00:34:25,840 Speaker 2: So if an airline doesn't make its least payment to you, 740 00:34:25,840 --> 00:34:28,080 Speaker 2: you can literally go to that country and repossess exactly. 741 00:34:28,080 --> 00:34:30,560 Speaker 3: Okay, But in India it's it's harder. 742 00:34:30,840 --> 00:34:32,960 Speaker 11: I don't believe as of yet they're not the signature 743 00:34:33,040 --> 00:34:33,880 Speaker 11: of the Cape Son treaty. 744 00:34:33,880 --> 00:34:35,400 Speaker 3: You need to check, but I don't think they are. 745 00:34:35,640 --> 00:34:37,560 Speaker 11: So that's harder on those countries that didn't sign the 746 00:34:37,640 --> 00:34:40,040 Speaker 11: kept creaty to go poss aircraft. That's the hardest part 747 00:34:40,200 --> 00:34:42,120 Speaker 11: was if someone doesn't make payments, similar to a car 748 00:34:42,120 --> 00:34:44,960 Speaker 11: in America, you goss car and leads it to someone else. 749 00:34:44,960 --> 00:34:47,319 Speaker 11: That's what's in aviation. You can move it from one 750 00:34:47,360 --> 00:34:50,080 Speaker 11: airline to one airline. It's a chapter eleven. You do oppossession, 751 00:34:50,320 --> 00:34:53,000 Speaker 11: you fix the corw the aircraft and new movies? 752 00:34:53,360 --> 00:34:54,640 Speaker 2: Can you what is China? 753 00:34:54,800 --> 00:34:54,920 Speaker 8: Is that? 754 00:34:55,040 --> 00:34:55,760 Speaker 4: Are they signatory? 755 00:34:56,200 --> 00:34:56,359 Speaker 8: Yeah? 756 00:34:58,000 --> 00:34:59,200 Speaker 3: It changes on the monthly day. 757 00:34:59,239 --> 00:35:02,319 Speaker 1: I know that Russia, the Russian War in Ukraine through 758 00:35:02,360 --> 00:35:04,600 Speaker 1: kind of a wrench into that in terms of that region. 759 00:35:06,080 --> 00:35:11,640 Speaker 1: What has the Middle East been like since Hamas the 760 00:35:11,680 --> 00:35:13,400 Speaker 1: Hamas Israel conflicts started? 761 00:35:13,440 --> 00:35:13,759 Speaker 3: Has that? 762 00:35:13,960 --> 00:35:15,400 Speaker 1: Has that been notable? 763 00:35:15,600 --> 00:35:17,200 Speaker 6: In the business. 764 00:35:17,080 --> 00:35:22,480 Speaker 11: Innoviation within nices? You know most airlines Ivan what doing 765 00:35:22,520 --> 00:35:25,560 Speaker 11: the best. If you look at Emirates flight by all 766 00:35:25,600 --> 00:35:28,480 Speaker 11: the airlines Indision are very active avail operations like the 767 00:35:28,760 --> 00:35:31,839 Speaker 11: p officer advantages and those airlines are really booming, so 768 00:35:31,880 --> 00:35:33,120 Speaker 11: there's no impact. 769 00:35:33,440 --> 00:35:34,480 Speaker 3: If we look at. 770 00:35:34,200 --> 00:35:37,560 Speaker 11: One of our clients in Israel, yes there's little bits 771 00:35:37,560 --> 00:35:40,000 Speaker 11: of umpacts, but l Al was very open with us. 772 00:35:40,360 --> 00:35:42,799 Speaker 11: They tell us every day what's going on. They give 773 00:35:42,880 --> 00:35:46,040 Speaker 11: us a feedback. The government is supportive, so Havan. For 774 00:35:46,480 --> 00:35:48,399 Speaker 11: one of our clients, which is l in Israel, which 775 00:35:48,640 --> 00:35:52,000 Speaker 11: with whom we have five seventy sevens, they're very open. 776 00:35:52,200 --> 00:35:56,240 Speaker 11: They tell us everything and they will always open in business. 777 00:35:56,320 --> 00:35:58,799 Speaker 11: They always told us during COVID about payments, how to 778 00:35:58,840 --> 00:36:01,480 Speaker 11: fix it. So the rection with is amazing, the very 779 00:36:01,640 --> 00:36:03,000 Speaker 11: open airlines to work with. 780 00:36:03,200 --> 00:36:04,920 Speaker 6: But how closely are you watching that? 781 00:36:04,960 --> 00:36:08,920 Speaker 1: I mean if things broaden out to eleven and Syria, 782 00:36:09,080 --> 00:36:13,719 Speaker 1: if God forbid, Iran gets involved, I mean it's going 783 00:36:13,760 --> 00:36:16,080 Speaker 1: to be difficult for every business, but. 784 00:36:15,960 --> 00:36:19,239 Speaker 11: Not only aviation. And look, Mattwinami, I'm a very optimistic guy. 785 00:36:19,280 --> 00:36:21,560 Speaker 11: I hope that this will never happen. I hope that 786 00:36:21,760 --> 00:36:23,520 Speaker 11: people will come back to the reason and it will 787 00:36:23,520 --> 00:36:26,359 Speaker 11: be fixed. You know, as a guy from local that's 788 00:36:26,480 --> 00:36:29,279 Speaker 11: the business. Of course, with all religions, I think that 789 00:36:29,400 --> 00:36:31,440 Speaker 11: it's just a political thing and I hope you to stop. 790 00:36:32,080 --> 00:36:34,799 Speaker 11: I don't believe on the long term, it's a good 791 00:36:34,800 --> 00:36:36,240 Speaker 11: thing for any of us, and not only in aviation. 792 00:36:36,239 --> 00:36:38,399 Speaker 3: It will be an umpact for everything. So I really 793 00:36:38,400 --> 00:36:39,360 Speaker 3: hope to stop. 794 00:36:39,120 --> 00:36:42,880 Speaker 11: And it will have If that's happened, it will have 795 00:36:42,920 --> 00:36:45,080 Speaker 11: a lot more impacts than the OSHA can in my 796 00:36:45,160 --> 00:36:48,040 Speaker 11: view for aviation. So because the vision is very sensitive, 797 00:36:48,200 --> 00:36:50,440 Speaker 11: it's in the middle between Asia and Europe. It's a 798 00:36:50,440 --> 00:36:53,160 Speaker 11: Asian that is extremely important for us in aviation in business. 799 00:36:53,520 --> 00:36:56,719 Speaker 11: So my hope is that it will stop and come soon. 800 00:36:56,719 --> 00:36:58,479 Speaker 2: And all right, Ali, thank you so much for joining 801 00:36:58,520 --> 00:37:01,080 Speaker 2: us here again, Ali Ben l Madonna. He's the CEO 802 00:37:01,160 --> 00:37:04,720 Speaker 2: of a bl Aviation gives us a good view into 803 00:37:04,760 --> 00:37:07,359 Speaker 2: the world of aviation and aviation fans. 804 00:37:08,880 --> 00:37:11,960 Speaker 1: Thanks for listening to the Bloomberg Markets podcasts. You can 805 00:37:12,000 --> 00:37:15,800 Speaker 1: subscribe and listen to interviews at Apple Podcasts or whatever 806 00:37:15,880 --> 00:37:19,600 Speaker 1: podcast platform you prefer. I'm Matt Miller. I'm on Twitter 807 00:37:19,800 --> 00:37:21,719 Speaker 1: at Matt Miller nineteen seventy three. 808 00:37:22,160 --> 00:37:24,560 Speaker 2: And I'm Faull Sweeney. I'm on Twitter at pt Sweeney. 809 00:37:24,680 --> 00:37:27,319 Speaker 2: Before the podcast, you can always catch us worldwide at 810 00:37:27,360 --> 00:37:29,120 Speaker 2: Bloomberg Radio.