1 00:00:02,720 --> 00:00:10,280 Speaker 1: Bloomberg Audio Studios, podcasts, radio news. You're listening to the 2 00:00:10,280 --> 00:00:14,240 Speaker 1: Bloomberg Intelligence podcast. Catch us live weekdays at ten am 3 00:00:14,320 --> 00:00:18,200 Speaker 1: Eastern on Applecarclay and Android Auto with the Bloomberg Business app. 4 00:00:18,320 --> 00:00:21,560 Speaker 1: Listen on demand wherever you get your podcasts, or watch 5 00:00:21,640 --> 00:00:22,760 Speaker 1: us live on YouTube. 6 00:00:23,400 --> 00:00:24,880 Speaker 2: All right, let's get back to Amazon. 7 00:00:24,880 --> 00:00:28,560 Speaker 3: There's a stock that's working, and it's working because the 8 00:00:28,600 --> 00:00:31,639 Speaker 3: cloud business for them and the AI play and that's 9 00:00:31,640 --> 00:00:35,440 Speaker 3: been the story behind Amazon and in stock for a 10 00:00:35,440 --> 00:00:37,400 Speaker 3: better part of ten to fifteen years. Here, it's not 11 00:00:37,440 --> 00:00:40,199 Speaker 3: so much the retail the stuff that we interact with 12 00:00:40,320 --> 00:00:42,440 Speaker 3: Amazon with getting the packages at the door. It's all 13 00:00:42,479 --> 00:00:46,199 Speaker 3: about their cloud business. On Rock Rana, he's a technology 14 00:00:46,240 --> 00:00:49,600 Speaker 3: analyst for Bloomberg Intelligence. The technology analysts for Bloomberg Intelligence, 15 00:00:49,760 --> 00:00:53,559 Speaker 3: an rap talk to us about the Amazon Web Services. 16 00:00:53,560 --> 00:00:54,640 Speaker 3: How did they do last quarter? 17 00:00:56,000 --> 00:00:56,160 Speaker 4: Yeah? 18 00:00:56,200 --> 00:00:58,400 Speaker 5: I think this is the one that really changed the 19 00:00:58,520 --> 00:01:01,080 Speaker 5: tone or the narrative. O fate of was because when 20 00:01:01,120 --> 00:01:03,760 Speaker 5: you look at over the last six months or so, 21 00:01:04,080 --> 00:01:05,959 Speaker 5: there is a there is you know, you could say 22 00:01:06,760 --> 00:01:09,880 Speaker 5: a fear out there that AWS is losing market share 23 00:01:10,200 --> 00:01:12,920 Speaker 5: to the likes of Oracle, the likes of you know, Google, 24 00:01:13,080 --> 00:01:16,280 Speaker 5: and Microsoft because the other three are showing momentum with 25 00:01:16,319 --> 00:01:19,080 Speaker 5: their AI workloads, but it wasn't as it was absent 26 00:01:19,120 --> 00:01:22,280 Speaker 5: for AWS. In fact, their capex went down a little bit, 27 00:01:22,440 --> 00:01:24,839 Speaker 5: lost quarter or you know when they reported last time, 28 00:01:25,000 --> 00:01:26,960 Speaker 5: stock was down the you know when when they had 29 00:01:27,000 --> 00:01:29,920 Speaker 5: reported their second quarter results. But finally they came out 30 00:01:30,000 --> 00:01:33,679 Speaker 5: yesterday AWS Acceleration. They were very confident about how it's 31 00:01:33,680 --> 00:01:36,440 Speaker 5: going to do next year. Capex is going up, their 32 00:01:36,520 --> 00:01:38,920 Speaker 5: chip business is doing well. So I think from that 33 00:01:39,040 --> 00:01:41,640 Speaker 5: point of view, that's a big overhang away from the stock. 34 00:01:42,080 --> 00:01:43,240 Speaker 4: People should be happy with this. 35 00:01:43,800 --> 00:01:45,679 Speaker 6: And when it comes to AWS, I feel like that's 36 00:01:45,720 --> 00:01:48,160 Speaker 6: the og of the cloud computing. I mean, whenever we 37 00:01:48,280 --> 00:01:52,280 Speaker 6: talk about cloud it's really Amazon leading the way and 38 00:01:52,320 --> 00:01:55,840 Speaker 6: then of course Alphabet Microsoft, but their growth rates were 39 00:01:55,920 --> 00:01:59,040 Speaker 6: much faster than Amazon because Amazon had a bigger base. Overall, 40 00:01:59,480 --> 00:02:03,320 Speaker 6: where does the put this latest quarter put AWS versus 41 00:02:03,880 --> 00:02:05,480 Speaker 6: Azure versus Alphabet. 42 00:02:06,880 --> 00:02:09,359 Speaker 5: See, that's what I'm saying. The others cloud providers are 43 00:02:09,400 --> 00:02:11,440 Speaker 5: so good at marketing that they have been beating up 44 00:02:11,440 --> 00:02:14,480 Speaker 5: on AWS for I would say almost two years, and 45 00:02:14,600 --> 00:02:16,800 Speaker 5: last night they came out I think with guns blazing 46 00:02:16,960 --> 00:02:20,240 Speaker 5: explaining their entire strategy in a far better way. And 47 00:02:20,320 --> 00:02:22,120 Speaker 5: I think that's what you resonate when you look at 48 00:02:22,120 --> 00:02:22,720 Speaker 5: the market share. 49 00:02:22,760 --> 00:02:23,720 Speaker 4: You're absolutely right. 50 00:02:24,160 --> 00:02:27,560 Speaker 5: Just from the infrastructure side, AWS is so much bigger 51 00:02:27,560 --> 00:02:31,600 Speaker 5: than everybody else, so much wider, much more entrenched with 52 00:02:31,919 --> 00:02:34,680 Speaker 5: enterprises than any of the others. But you know what's 53 00:02:34,720 --> 00:02:37,920 Speaker 5: happening is the first part of the first growth phase 54 00:02:38,040 --> 00:02:41,320 Speaker 5: of AI has been through chat GPT and that product 55 00:02:41,400 --> 00:02:45,359 Speaker 5: is hosted on Microsoft Azure. AWS doesn't have a consumer 56 00:02:45,400 --> 00:02:47,920 Speaker 5: product like that which is hosted on them. They're all 57 00:02:47,960 --> 00:02:51,880 Speaker 5: about enterprising adding more AI capabilities and that growth cycle 58 00:02:51,919 --> 00:02:55,000 Speaker 5: is only beginning. So I think this really puts us 59 00:02:55,360 --> 00:02:59,000 Speaker 5: very confident that AWS growth rate is going to continue 60 00:02:59,440 --> 00:03:02,080 Speaker 5: in this good trajectory, not just for next year, but 61 00:03:02,120 --> 00:03:03,320 Speaker 5: the year after that also. 62 00:03:03,840 --> 00:03:07,040 Speaker 6: And anurag, I know that this is something that happened 63 00:03:07,080 --> 00:03:09,320 Speaker 6: more recently, but of course there was a big outage 64 00:03:09,320 --> 00:03:12,680 Speaker 6: of AWS that seem to affect everyone across the nation. 65 00:03:13,400 --> 00:03:17,000 Speaker 6: Schools and students couldn't get online, businesses couldn't do things, 66 00:03:17,080 --> 00:03:19,480 Speaker 6: and they couldn't log into the websites that they needed to. 67 00:03:19,880 --> 00:03:23,760 Speaker 6: Did Amazon talk about that at all and give any reassurances. 68 00:03:24,520 --> 00:03:26,480 Speaker 5: See I think this is one of the biggest risks 69 00:03:26,480 --> 00:03:29,960 Speaker 5: for all cloud providers, not just Amazon, but Microsoft also 70 00:03:30,040 --> 00:03:33,680 Speaker 5: Microsoft also had a similar breakdown. Now think of these 71 00:03:33,720 --> 00:03:37,840 Speaker 5: cloud providers as electricity providers and it can happen to anybody. 72 00:03:38,320 --> 00:03:41,000 Speaker 5: One of our big thesis is that we know AI 73 00:03:41,040 --> 00:03:43,360 Speaker 5: is a big growth story for cloud. I think one 74 00:03:43,400 --> 00:03:45,720 Speaker 5: of the biggest stories later on is going to be 75 00:03:45,960 --> 00:03:49,520 Speaker 5: a multi cloud strategy. Every company, every enterprise is going 76 00:03:49,560 --> 00:03:52,560 Speaker 5: to go out and have a backup cloud provider similar 77 00:03:52,560 --> 00:03:54,560 Speaker 5: to what they do in electricity, and I think that 78 00:03:54,680 --> 00:03:57,400 Speaker 5: is going to add far more to the total addressable 79 00:03:57,440 --> 00:03:59,120 Speaker 5: market of this important service. 80 00:04:00,040 --> 00:04:02,360 Speaker 3: I know you guys at Bloomberg Intelligence wrote I think 81 00:04:02,400 --> 00:04:04,960 Speaker 3: the definitive research report on AI. 82 00:04:05,160 --> 00:04:07,160 Speaker 2: So, folks, if you want to learn about what this. 83 00:04:07,120 --> 00:04:10,760 Speaker 3: Whole AI thing is, go to big on your terminal 84 00:04:10,760 --> 00:04:11,480 Speaker 3: and that's where you'll find it. 85 00:04:11,480 --> 00:04:15,440 Speaker 2: If you don't have it, call your salesperson. But it's 86 00:04:15,760 --> 00:04:18,520 Speaker 2: what's the bare case? Is there a bare case for AI? 87 00:04:18,600 --> 00:04:18,760 Speaker 7: Here? 88 00:04:18,839 --> 00:04:19,279 Speaker 4: Anarak? 89 00:04:19,520 --> 00:04:21,640 Speaker 2: Because there's certainly spending a ton of money. 90 00:04:23,000 --> 00:04:25,320 Speaker 5: Yeah, the bear case is spending a lot of the money, 91 00:04:25,320 --> 00:04:28,600 Speaker 5: and I think that is something to be extremely careful 92 00:04:28,640 --> 00:04:30,520 Speaker 5: about because see one of the things I have to see, 93 00:04:30,680 --> 00:04:34,080 Speaker 5: is will enterprise suddenly say, you know what, I like 94 00:04:34,160 --> 00:04:36,839 Speaker 5: this thing. It is good to summarize a handful of emails. 95 00:04:37,080 --> 00:04:39,240 Speaker 5: It will give me so much a productivity. But that's 96 00:04:39,240 --> 00:04:42,600 Speaker 5: where it ends. It's not going to lead to massive layoffs. 97 00:04:42,720 --> 00:04:44,440 Speaker 4: You know. It's like another tool you and I have. 98 00:04:44,839 --> 00:04:47,359 Speaker 5: We have Excel, now we have chag GPT on the 99 00:04:47,440 --> 00:04:50,240 Speaker 5: side that I think is the bare case on it 100 00:04:50,279 --> 00:04:52,800 Speaker 5: is how much productivity somebody can get out of it. 101 00:04:53,160 --> 00:04:54,719 Speaker 5: And at the same time, you know, you when you're 102 00:04:54,760 --> 00:04:57,760 Speaker 5: spending let's say five hundred plus billion dollars a year 103 00:04:58,040 --> 00:05:01,360 Speaker 5: in adding more capacity, a lot of people are borrowing 104 00:05:01,400 --> 00:05:04,240 Speaker 5: that money. Private credit is involved. You have a lot 105 00:05:04,279 --> 00:05:06,760 Speaker 5: of that coming from one customer that's Opening Eye. You 106 00:05:06,800 --> 00:05:09,200 Speaker 5: know what if something happens and somebody else comes up 107 00:05:09,240 --> 00:05:11,520 Speaker 5: with a better model and Opening Eye is no longer 108 00:05:11,560 --> 00:05:14,040 Speaker 5: able to see that kind of growth momentum. So there 109 00:05:14,040 --> 00:05:17,159 Speaker 5: are enough bare cases out there. That's why, almost to 110 00:05:17,200 --> 00:05:19,960 Speaker 5: be very frank, almost on a weekly basis, you have 111 00:05:20,000 --> 00:05:21,520 Speaker 5: to track these things very carefully. 112 00:05:22,120 --> 00:05:25,400 Speaker 6: Yeah, that circular financing you just reference raises a lot 113 00:05:25,400 --> 00:05:27,839 Speaker 6: of concerns because so much of the economic growth that 114 00:05:27,839 --> 00:05:31,119 Speaker 6: we're seeing now is tied to investment in spending on AI, 115 00:05:31,200 --> 00:05:33,840 Speaker 6: So if anyone stops spending just a moment, or maybe 116 00:05:33,880 --> 00:05:38,320 Speaker 6: slows things down, that could have huge ramifications. Anurag always 117 00:05:38,320 --> 00:05:40,240 Speaker 6: appreciate your joining us on our g Rana is one 118 00:05:40,279 --> 00:05:43,839 Speaker 6: of our Bloomberg Intelligence senior technology analysts giving us the 119 00:05:43,839 --> 00:05:47,839 Speaker 6: low down here on Amazon Amazon Web Services, fastest cloud 120 00:05:47,839 --> 00:05:51,200 Speaker 6: growth in years for Amazon Web Services, providing some relief 121 00:05:51,200 --> 00:05:52,040 Speaker 6: to those investors. 122 00:05:52,560 --> 00:05:55,640 Speaker 3: Stay with us. More from Bloomberg Intelligence coming up after this. 123 00:05:59,320 --> 00:06:03,039 Speaker 1: You're listening to the Bloomberg Intelligence podcast. Catch us live 124 00:06:03,120 --> 00:06:05,800 Speaker 1: weekdays at ten am. He's done on Apple, Cocklay and 125 00:06:05,800 --> 00:06:09,080 Speaker 1: Android Auto with the Bloomberg Business App. Listen on demand 126 00:06:09,120 --> 00:06:13,120 Speaker 1: wherever you get your podcasts, or watch us live on YouTube. 127 00:06:13,800 --> 00:06:16,800 Speaker 6: It is Halloween, so I think about haunted houses, and 128 00:06:16,960 --> 00:06:19,279 Speaker 6: there's a great story on the Bloomberg Paul about a 129 00:06:19,320 --> 00:06:21,479 Speaker 6: real estate developer in Pennsylvania who wants to turn his 130 00:06:21,720 --> 00:06:26,240 Speaker 6: haunted house attraction, which is actually a former Penhurst asylum 131 00:06:26,600 --> 00:06:28,760 Speaker 6: into what else a data center? 132 00:06:28,960 --> 00:06:30,799 Speaker 2: Ah, of course, I mean it's. 133 00:06:30,920 --> 00:06:33,800 Speaker 6: Right white line with the times right. AI is the 134 00:06:33,800 --> 00:06:36,040 Speaker 6: only thing anyone cares about these days. Everyone's trying to 135 00:06:36,080 --> 00:06:37,920 Speaker 6: make money off of it, profit off demand for it. 136 00:06:38,320 --> 00:06:41,680 Speaker 6: But can the grid actually sustain all of this? It's 137 00:06:41,720 --> 00:06:42,120 Speaker 6: not clear. 138 00:06:42,680 --> 00:06:44,840 Speaker 4: With the guy he lives in an asylum. 139 00:06:45,160 --> 00:06:47,599 Speaker 6: No, he owns the asylum and he's turned it into 140 00:06:47,600 --> 00:06:49,080 Speaker 6: a haunted house. But now he wants to turn it 141 00:06:49,120 --> 00:06:49,919 Speaker 6: into a data center. 142 00:06:50,200 --> 00:06:52,799 Speaker 2: You think it's like really wired for that? 143 00:06:52,920 --> 00:06:55,560 Speaker 8: There show all good questions. All right, Let's bring in 144 00:06:55,640 --> 00:06:56,280 Speaker 8: Ryan Mallory. 145 00:06:56,440 --> 00:06:59,840 Speaker 6: He is president and CEO of flex Cential, which is 146 00:06:59,880 --> 00:07:03,320 Speaker 6: a private company that is tied in to this idea 147 00:07:03,320 --> 00:07:06,960 Speaker 6: of whether we have enough power to power all this 148 00:07:07,080 --> 00:07:08,360 Speaker 6: AI that everyone wants. 149 00:07:08,640 --> 00:07:09,920 Speaker 8: Ryan, thanks for joining us today. 150 00:07:10,960 --> 00:07:13,440 Speaker 7: Good morning, Scarlett and Paul. Thanks for the opportunity to 151 00:07:13,440 --> 00:07:13,960 Speaker 7: speak with you. 152 00:07:14,200 --> 00:07:16,920 Speaker 6: So just give us a primer first on what flex 153 00:07:16,920 --> 00:07:19,720 Speaker 6: Central does and how it ties in with whether our 154 00:07:19,760 --> 00:07:22,360 Speaker 6: grid can support all this demand for AI. 155 00:07:23,560 --> 00:07:26,760 Speaker 7: Yeah, so flex Cential is a national data center operator. 156 00:07:27,000 --> 00:07:30,400 Speaker 7: We've got a dynamic platform forty two data centers across 157 00:07:30,480 --> 00:07:33,320 Speaker 7: eighteen markets and so you know we're you know, right 158 00:07:33,360 --> 00:07:37,320 Speaker 7: at the cornerstone of that AI on ramp trend that's 159 00:07:37,360 --> 00:07:40,200 Speaker 7: out there. So we're seeing some of these big deployments 160 00:07:40,240 --> 00:07:42,720 Speaker 7: like you guys are talking about in Pennsylvania, and other places. 161 00:07:42,920 --> 00:07:46,240 Speaker 7: Flex cential is really that on ramp capability from that 162 00:07:46,280 --> 00:07:47,440 Speaker 7: network ingress. 163 00:07:48,720 --> 00:07:52,680 Speaker 3: So ran you say power availability is the new real estate, 164 00:07:52,920 --> 00:07:53,720 Speaker 3: What do you mean by that? 165 00:07:54,880 --> 00:07:57,239 Speaker 7: Yeah, you know, you know, it used to be trying 166 00:07:57,240 --> 00:08:00,240 Speaker 7: to find the land, so a piece of you know, 167 00:08:00,320 --> 00:08:04,520 Speaker 7: property that could support a data center was where everybody started. 168 00:08:05,280 --> 00:08:07,920 Speaker 7: Now it's really where where is the power at? You know, 169 00:08:08,040 --> 00:08:10,320 Speaker 7: in twenty twenty three, there was one hundred and seventy 170 00:08:10,360 --> 00:08:13,720 Speaker 7: six tear lots of power being you know, generated out 171 00:08:13,720 --> 00:08:17,200 Speaker 7: there for the data center marketplace. Twenty twenty eight, there's 172 00:08:17,200 --> 00:08:18,920 Speaker 7: going to be five hundred and eighty tara watts. And 173 00:08:19,000 --> 00:08:22,720 Speaker 7: so what we're seeing is this massive increase in power demand, 174 00:08:23,120 --> 00:08:25,559 Speaker 7: and you know, they're not making any more land close 175 00:08:25,600 --> 00:08:28,920 Speaker 7: to you know, the generation and transmission. So being able 176 00:08:29,000 --> 00:08:31,920 Speaker 7: to have access to that power is that really that 177 00:08:32,360 --> 00:08:36,200 Speaker 7: real estate mantra of location, location, location, So you've got 178 00:08:36,240 --> 00:08:37,240 Speaker 7: to find the power first. 179 00:08:37,360 --> 00:08:39,240 Speaker 3: I wouldn't know what a tear WoT is if I 180 00:08:39,280 --> 00:08:41,400 Speaker 3: tripped over, but what I do know sounds like a 181 00:08:41,400 --> 00:08:44,040 Speaker 3: lot five hundred and eighty terrawts. By twenty twenty eight, 182 00:08:44,120 --> 00:08:47,760 Speaker 3: that's twelve percent of the national electricity demand is going 183 00:08:47,800 --> 00:08:49,840 Speaker 3: to be gone for these AI things. 184 00:08:50,040 --> 00:08:53,040 Speaker 6: Yeah, sometimes we don't even have enough electricity just to 185 00:08:53,080 --> 00:08:54,839 Speaker 6: do what we do right now exactly. So that's the 186 00:08:54,920 --> 00:08:58,840 Speaker 6: question rian which parts of the country have this power 187 00:08:58,840 --> 00:09:03,480 Speaker 6: availability they're for this new revolution, and which have massive 188 00:09:03,480 --> 00:09:06,280 Speaker 6: backlogs and can't afford to have anything more plugged into 189 00:09:06,320 --> 00:09:06,680 Speaker 6: their grid. 190 00:09:07,960 --> 00:09:08,160 Speaker 4: Yeah. 191 00:09:08,440 --> 00:09:11,480 Speaker 7: You know, we've really seen some shifting in the game 192 00:09:11,480 --> 00:09:14,520 Speaker 7: board over the past several years where you know, the 193 00:09:14,559 --> 00:09:19,000 Speaker 7: Northern Virginia's and the California markets and even Georgia has 194 00:09:19,040 --> 00:09:23,040 Speaker 7: become power constrained just because of this massive shift to 195 00:09:23,120 --> 00:09:26,880 Speaker 7: those markets because of at that point in time power availability. 196 00:09:27,240 --> 00:09:30,520 Speaker 7: Now we're starting to see it turn to net new markets, 197 00:09:30,559 --> 00:09:35,400 Speaker 7: you know, Oregon, Utah, Colorado, Texas, North Carolina. These are 198 00:09:35,480 --> 00:09:38,000 Speaker 7: areas that you know, you know, from a legacy data 199 00:09:38,040 --> 00:09:41,720 Speaker 7: center market had been overlooked because they didn't have a 200 00:09:41,760 --> 00:09:45,040 Speaker 7: lot of the infrastructure. But they've caught up and those 201 00:09:45,080 --> 00:09:49,000 Speaker 7: markets are really primed to take off. That's where Flexential 202 00:09:49,080 --> 00:09:51,560 Speaker 7: spending a lot of its focus right now, and we 203 00:09:51,640 --> 00:09:53,560 Speaker 7: see a lot of others in the AI industry doing 204 00:09:53,559 --> 00:09:54,000 Speaker 7: the same. 205 00:09:54,320 --> 00:09:56,480 Speaker 3: All Right, so I need power to power these things up, 206 00:09:56,840 --> 00:09:59,320 Speaker 3: but I probably also need some water to cool them down. 207 00:09:59,320 --> 00:10:00,240 Speaker 2: Talk to us about water. 208 00:10:01,600 --> 00:10:04,080 Speaker 7: Yeah, you know, water is something that is a hot 209 00:10:04,160 --> 00:10:07,439 Speaker 7: topic in the data center industry because you know, when 210 00:10:07,640 --> 00:10:11,839 Speaker 7: you're talking about these high density compute workloads with AI, 211 00:10:12,400 --> 00:10:16,359 Speaker 7: we've seen them. You know, the actual you know, densities 212 00:10:16,400 --> 00:10:19,200 Speaker 7: per rack go up, you know, one hundred two hundred 213 00:10:19,240 --> 00:10:22,800 Speaker 7: three hundred percent over the past several years. You have 214 00:10:22,840 --> 00:10:26,439 Speaker 7: to have water, you know, from from a heat rejection standpoint, 215 00:10:26,520 --> 00:10:30,240 Speaker 7: and so a lot of companies historically had just used 216 00:10:30,280 --> 00:10:33,200 Speaker 7: evaporative water cooling to you know, to be able to 217 00:10:33,200 --> 00:10:36,480 Speaker 7: cool and empower these data centers. That's just not efficient, 218 00:10:36,600 --> 00:10:40,080 Speaker 7: and so it's really about utilizing you know, closed loop 219 00:10:40,200 --> 00:10:43,319 Speaker 7: systems where you can you know, put a liquid across 220 00:10:43,360 --> 00:10:45,679 Speaker 7: the chip you know, in the rack to be able 221 00:10:45,720 --> 00:10:49,240 Speaker 7: to dissipate that heat or reject that heat that's out 222 00:10:49,240 --> 00:10:51,400 Speaker 7: of there. There is a you know, there is a 223 00:10:51,480 --> 00:10:54,560 Speaker 7: little bit of a nuance out there. Companies that are 224 00:10:54,600 --> 00:10:57,640 Speaker 7: looking at operating at a zero w U E or 225 00:10:57,679 --> 00:11:01,439 Speaker 7: water U sufficiency are really what's needed in today's environment 226 00:11:01,520 --> 00:11:04,439 Speaker 7: so we don't become burdens on those local communities. 227 00:11:04,720 --> 00:11:07,079 Speaker 6: Let me ask a dumb question here. We have climate 228 00:11:07,160 --> 00:11:09,440 Speaker 6: change which is causing extreme weather. Here in the New 229 00:11:09,520 --> 00:11:12,280 Speaker 6: York region. We just had flash floods that resulted in 230 00:11:12,320 --> 00:11:15,880 Speaker 6: the death of two people and have really inundated the infrastructure. 231 00:11:16,240 --> 00:11:19,520 Speaker 6: Does that matter when you're looking at water as a 232 00:11:19,640 --> 00:11:22,560 Speaker 6: valuable resource in making sure that we can keep these 233 00:11:22,640 --> 00:11:23,560 Speaker 6: data centers running. 234 00:11:24,760 --> 00:11:27,440 Speaker 7: Yeah, I mean, climate change is a topic that's near 235 00:11:27,480 --> 00:11:30,480 Speaker 7: and dear to everybody in the data center's data center 236 00:11:30,480 --> 00:11:34,880 Speaker 7: world's heart because we have big footprint impacts in communities. 237 00:11:35,160 --> 00:11:37,760 Speaker 7: But when you're looking at how we're building the data 238 00:11:37,800 --> 00:11:41,920 Speaker 7: centers in today's environment with these closed loop systems, we're 239 00:11:41,960 --> 00:11:45,320 Speaker 7: just not drawing off of those off of those water 240 00:11:45,360 --> 00:11:48,800 Speaker 7: sources that are out there because they're closed and contained, 241 00:11:49,120 --> 00:11:52,280 Speaker 7: so it's not the same impact that historically was happening 242 00:11:52,360 --> 00:11:53,040 Speaker 7: in the industry. 243 00:11:53,559 --> 00:11:58,479 Speaker 3: I'm all about nuclear here, particularly small modular reactors SMRs. 244 00:11:58,520 --> 00:12:01,000 Speaker 3: Talk to us about that technology and is that going 245 00:12:01,040 --> 00:12:03,360 Speaker 3: to be a solution in any time in the near future. 246 00:12:04,600 --> 00:12:07,960 Speaker 7: You know, SMRs are are the future of the data 247 00:12:07,960 --> 00:12:10,160 Speaker 7: center world, as is nuclear in general. 248 00:12:10,440 --> 00:12:12,160 Speaker 2: You know, we've got to be focused. 249 00:12:11,800 --> 00:12:14,880 Speaker 7: To the you know, to the climate comment that scarlet 250 00:12:14,920 --> 00:12:18,360 Speaker 7: me just a second ago, around how we're impacting the environment. 251 00:12:19,120 --> 00:12:22,880 Speaker 7: SMRs are small footprint reactors. We've been doing those for 252 00:12:23,080 --> 00:12:26,560 Speaker 7: thirty forty years, you know, for you know, government based use, 253 00:12:26,840 --> 00:12:30,640 Speaker 7: and you know they're tried and true from a performance perspective. 254 00:12:30,840 --> 00:12:33,280 Speaker 7: What we've really got to get through is just the 255 00:12:34,000 --> 00:12:37,240 Speaker 7: permitting you know process and being able to deploy them. 256 00:12:37,480 --> 00:12:40,640 Speaker 7: You know, you can deploy anywhere from a fifty megawatt 257 00:12:40,679 --> 00:12:43,679 Speaker 7: to three hundred megawatt reactor and a small form factor 258 00:12:44,000 --> 00:12:47,480 Speaker 7: and that's going to really deliver that that capability that's 259 00:12:47,520 --> 00:12:49,840 Speaker 7: out there to be able to power these data centers 260 00:12:50,120 --> 00:12:52,720 Speaker 7: in a more rapid fashion. You know, a ten year 261 00:12:52,880 --> 00:12:55,839 Speaker 7: permitting process is just not going to going to help 262 00:12:55,880 --> 00:12:58,360 Speaker 7: anybody in the industry, whether it's you know, the home 263 00:12:58,440 --> 00:13:00,440 Speaker 7: consumer or the data center. 264 00:13:01,000 --> 00:13:02,760 Speaker 6: All right, good stuff, Ryan, thank you so much for 265 00:13:02,880 --> 00:13:06,240 Speaker 6: joining us todaying sharing your expertise with us. Ryan Mallory, 266 00:13:06,280 --> 00:13:09,679 Speaker 6: a CEO of Flexsential on the growing streen on power 267 00:13:10,040 --> 00:13:13,400 Speaker 6: due to this AI boom and he joins us from Denver, Colorado. 268 00:13:13,520 --> 00:13:16,400 Speaker 6: So a lot to think about there as everyone it 269 00:13:16,400 --> 00:13:19,040 Speaker 6: feels like it wants to find some way in on AI. 270 00:13:19,559 --> 00:13:22,720 Speaker 2: Stay with us. More from Bloomberg Intelligence coming up after this. 271 00:13:26,600 --> 00:13:30,319 Speaker 1: You're listening to the Bloomberg Intelligence podcast. Catch us live 272 00:13:30,400 --> 00:13:33,080 Speaker 1: weekdays at ten am. He's done on Apple, Cocklay and 273 00:13:33,080 --> 00:13:36,360 Speaker 1: Android Auto with the Bloomberg Business App. Listen on demand 274 00:13:36,400 --> 00:13:39,960 Speaker 1: wherever you get your podcasts, or watch us live on YouTube. 275 00:13:40,679 --> 00:13:42,160 Speaker 2: I think it near and dear to my heart. We 276 00:13:42,160 --> 00:13:43,079 Speaker 2: were just talking about that. 277 00:13:43,200 --> 00:13:44,680 Speaker 3: You know, what are some of these companies in these 278 00:13:44,720 --> 00:13:47,120 Speaker 3: industries that are kind of slowly decaying, like the cable 279 00:13:47,160 --> 00:13:48,559 Speaker 3: television industry, what do they do? 280 00:13:49,600 --> 00:13:50,520 Speaker 4: But it's also. 281 00:13:50,559 --> 00:13:52,680 Speaker 6: Declined, but they're not dead and they're not going to 282 00:13:52,679 --> 00:13:54,880 Speaker 6: be dead anytime soon, right, Well, No, and I think. 283 00:13:54,760 --> 00:13:56,600 Speaker 3: The other one here is just kind of what some 284 00:13:56,600 --> 00:13:59,199 Speaker 3: of these big legacy cable media companies do, and that 285 00:13:59,400 --> 00:14:04,560 Speaker 3: certainly you know true with when you look at Warner Brothers, Discovery, Paramount. 286 00:14:04,600 --> 00:14:06,600 Speaker 3: All these companies are trying to figure out what to 287 00:14:06,679 --> 00:14:07,360 Speaker 3: kind of do, and. 288 00:14:07,280 --> 00:14:10,199 Speaker 6: They're teaming up as a one solution, right because maybe 289 00:14:10,240 --> 00:14:12,520 Speaker 6: scale gets them somewhere that they can't get on their 290 00:14:12,559 --> 00:14:13,280 Speaker 6: own exactly. 291 00:14:13,520 --> 00:14:15,959 Speaker 3: Our next guest has got a really interesting opinion piece 292 00:14:16,000 --> 00:14:19,320 Speaker 3: out on this, Paul Hardart. He's a Distinguished Clinical Professor 293 00:14:19,360 --> 00:14:21,760 Speaker 3: of Marketing at New York University Stern School of business. 294 00:14:21,840 --> 00:14:25,280 Speaker 3: By the way, I think Stern has Basically they're half 295 00:14:25,320 --> 00:14:26,320 Speaker 3: of Global Wall Street. 296 00:14:26,080 --> 00:14:27,800 Speaker 2: Came out of the Stern School of business. I'm convincing 297 00:14:27,800 --> 00:14:28,760 Speaker 2: these people are everywhere. 298 00:14:29,160 --> 00:14:31,000 Speaker 3: Paul, thanks for so much for joining us here. Talk 299 00:14:31,000 --> 00:14:34,200 Speaker 3: to us about Warner Brothers. You know, it's a legacy 300 00:14:34,240 --> 00:14:38,760 Speaker 3: media company now part of Warner Brothers, the Discovery. You know, 301 00:14:38,800 --> 00:14:41,000 Speaker 3: the question is should they be sold, should they merge 302 00:14:41,000 --> 00:14:44,320 Speaker 3: with another company. It's a proud company with a lot 303 00:14:44,360 --> 00:14:45,880 Speaker 3: of just legacy media assets. 304 00:14:45,920 --> 00:14:46,880 Speaker 2: What do you think they should do? 305 00:14:48,880 --> 00:14:50,600 Speaker 4: That's a great question. Thanks for having me, Bob, Paul 306 00:14:50,640 --> 00:14:52,840 Speaker 4: and scholar. Nice to be with you. 307 00:14:51,920 --> 00:14:55,119 Speaker 9: You know, it's a challenging so from a business standpoint, 308 00:14:55,600 --> 00:14:57,200 Speaker 9: it seems like they have a lot of debt. I 309 00:14:57,240 --> 00:15:00,720 Speaker 9: think the plan was always to sell as you know that. 310 00:15:00,760 --> 00:15:03,520 Speaker 9: You know, they were originally Time Warner. Then there was 311 00:15:03,560 --> 00:15:07,160 Speaker 9: a sort of disastrous AOL Time Warner merger. Then they 312 00:15:07,160 --> 00:15:09,840 Speaker 9: were sold to AT and T, which didn't go well. 313 00:15:09,920 --> 00:15:14,120 Speaker 9: AT and T then spun it off with Discovery led 314 00:15:14,120 --> 00:15:16,520 Speaker 9: by David Zaslaw, and now here we are just a 315 00:15:16,520 --> 00:15:19,120 Speaker 9: few years later, once again they're for sale. So first 316 00:15:19,120 --> 00:15:21,080 Speaker 9: of all, I feel very bad for the people that 317 00:15:21,160 --> 00:15:25,360 Speaker 9: work there. It's been a really tumultuous history. But they 318 00:15:25,400 --> 00:15:27,760 Speaker 9: have to optimize the assets, right, that's their job as 319 00:15:27,760 --> 00:15:29,480 Speaker 9: it relates to the shareholders. 320 00:15:29,520 --> 00:15:32,120 Speaker 4: So I think they're trying to figure out and the exit. 321 00:15:32,200 --> 00:15:33,760 Speaker 9: I think there was a period of time where a 322 00:15:33,800 --> 00:15:36,000 Speaker 9: lot of people felt that ultimately they could wrap these 323 00:15:36,080 --> 00:15:38,160 Speaker 9: up and sell it to a big tech. 324 00:15:37,960 --> 00:15:39,640 Speaker 4: Firm that was same. 325 00:15:39,720 --> 00:15:43,360 Speaker 9: I think that was the viewpoint of Sherry Redstone, originally 326 00:15:43,400 --> 00:15:48,680 Speaker 9: with Viacom and CBS and then ultimately Paramount. So I 327 00:15:48,720 --> 00:15:50,640 Speaker 9: think they're trying to get the most value for what 328 00:15:50,720 --> 00:15:53,080 Speaker 9: they have originally. Like you had just talked earlier about 329 00:15:53,080 --> 00:15:57,040 Speaker 9: the cable assets, the plan was sort of reflecting exactly 330 00:15:57,120 --> 00:16:00,640 Speaker 9: what Comcast is doing is spinning off its cable assets versant. 331 00:16:01,360 --> 00:16:04,640 Speaker 9: The plan had been for Warner Brothers Discovery to split 332 00:16:04,680 --> 00:16:08,640 Speaker 9: the company, with their CFO taking the cable assets and 333 00:16:09,560 --> 00:16:13,040 Speaker 9: the other assets, the Warner Brothers Discovery the studio and 334 00:16:13,120 --> 00:16:18,040 Speaker 9: the HBO Max platform, the streaming platform staying as one entity. 335 00:16:18,280 --> 00:16:21,680 Speaker 9: So in that time, David Ellison has come out and 336 00:16:21,720 --> 00:16:25,800 Speaker 9: made a three successive offers. The value the soccer price 337 00:16:25,840 --> 00:16:30,600 Speaker 9: has gone up is effectively tripled since April, so you know, 338 00:16:30,680 --> 00:16:33,880 Speaker 9: their job is to do optimize the value for their shareholders, 339 00:16:33,880 --> 00:16:35,400 Speaker 9: and so I think that's what they're trying to do 340 00:16:35,480 --> 00:16:35,880 Speaker 9: right now. 341 00:16:36,720 --> 00:16:40,480 Speaker 6: So what's the value here to a Skydance Paramount or 342 00:16:40,520 --> 00:16:42,840 Speaker 6: to a Comcast or to a Netflix. 343 00:16:42,960 --> 00:16:44,760 Speaker 8: Is the IP of Warner Brothers. 344 00:16:45,160 --> 00:16:47,240 Speaker 6: You just go to the HBO app and you see 345 00:16:47,280 --> 00:16:49,680 Speaker 6: friends there, you see Game of Thrones, you see all 346 00:16:49,720 --> 00:16:51,840 Speaker 6: kinds of assets that the company owns. 347 00:16:52,520 --> 00:16:54,960 Speaker 8: That IP is valuable, But does it diminish over time? 348 00:16:55,000 --> 00:16:56,760 Speaker 6: I mean does it need to be folded into something 349 00:16:56,760 --> 00:16:58,520 Speaker 6: bigger to maximize its value? 350 00:17:00,040 --> 00:17:00,200 Speaker 7: Yeah? 351 00:17:00,200 --> 00:17:02,040 Speaker 9: Well, so you know, in anything in media, we sort 352 00:17:02,040 --> 00:17:04,160 Speaker 9: of always talked about the content and the distribution, right, 353 00:17:04,200 --> 00:17:06,760 Speaker 9: you sort of need both. You can't just have great content. 354 00:17:06,800 --> 00:17:08,560 Speaker 9: It has to be to be able to be discovered. 355 00:17:09,480 --> 00:17:12,400 Speaker 9: And as we all know from you know, following the pandemic, 356 00:17:12,440 --> 00:17:14,720 Speaker 9: our habits have changed and a lot of us now 357 00:17:14,800 --> 00:17:15,840 Speaker 9: we stream content. 358 00:17:16,320 --> 00:17:19,159 Speaker 4: So scale is helpful in that. 359 00:17:19,320 --> 00:17:22,160 Speaker 9: So one of the things the value propositions for any 360 00:17:22,200 --> 00:17:24,840 Speaker 9: of these buyers, whether it's Comcast, whether it's Netflix, whether 361 00:17:24,920 --> 00:17:29,120 Speaker 9: it's possibly Apple or certainly Paramount is you know, Paramount 362 00:17:29,119 --> 00:17:31,720 Speaker 9: plus has struggled a little bit. So pairing that with 363 00:17:31,880 --> 00:17:35,000 Speaker 9: HBO Max. You'd get a huge subscriber base, you'd get 364 00:17:35,040 --> 00:17:38,040 Speaker 9: extra content. It's not that different than what Disney did 365 00:17:38,080 --> 00:17:41,720 Speaker 9: by merging with Fox, realizing that to compete with Netflix 366 00:17:41,720 --> 00:17:45,080 Speaker 9: they need a huge amount of content. So I think 367 00:17:45,119 --> 00:17:47,960 Speaker 9: it's sort of the same playbook of scaling up having 368 00:17:48,040 --> 00:17:51,760 Speaker 9: a large, deep library that some people when you know, 369 00:17:51,800 --> 00:17:54,120 Speaker 9: we all have credit card builds, we all everyone has 370 00:17:54,160 --> 00:17:56,960 Speaker 9: to check and see how they are spending their money. 371 00:17:56,960 --> 00:18:00,159 Speaker 9: And if we're all subscribing to five or six or 372 00:18:00,320 --> 00:18:04,000 Speaker 9: seven streaming services, it becomes you know, on economics. So 373 00:18:04,080 --> 00:18:05,840 Speaker 9: I think the idea is you want to be one 374 00:18:05,840 --> 00:18:08,560 Speaker 9: of those two or three streaming services that you know 375 00:18:08,720 --> 00:18:09,679 Speaker 9: the family needs. 376 00:18:10,080 --> 00:18:11,720 Speaker 3: And Paul, what I think is one of the unique 377 00:18:11,720 --> 00:18:14,399 Speaker 3: aspects of a potential deal with Warner Brothers Discover is 378 00:18:14,440 --> 00:18:17,920 Speaker 3: you've got a CEO and a board that says basically, hey, 379 00:18:17,920 --> 00:18:18,920 Speaker 3: we're open to doing a deal. 380 00:18:18,960 --> 00:18:19,560 Speaker 4: We're for sale. 381 00:18:19,640 --> 00:18:21,360 Speaker 2: Kind of how does that play into it? 382 00:18:21,400 --> 00:18:25,000 Speaker 9: Absolutely well, well, they've they've basically said that, you know, 383 00:18:25,000 --> 00:18:27,760 Speaker 9: they've they've turned down three offers, and again the stock 384 00:18:27,800 --> 00:18:31,920 Speaker 9: price is more than tripled, so at some point they 385 00:18:31,960 --> 00:18:33,639 Speaker 9: are you know, the board is also going to have 386 00:18:33,720 --> 00:18:35,760 Speaker 9: to sort of base the music and say, you know 387 00:18:35,800 --> 00:18:39,520 Speaker 9: why they're not accepting this offer. So I think what 388 00:18:39,560 --> 00:18:43,200 Speaker 9: they're looking for, right, they're trying to, you know, elevate 389 00:18:43,280 --> 00:18:45,480 Speaker 9: you know, having more parties interested is always good in 390 00:18:45,520 --> 00:18:50,639 Speaker 9: any dynamics. So it's unclear exactly how interested Netflix or 391 00:18:50,680 --> 00:18:53,760 Speaker 9: Comcast is, but some of those assets, just like you know, 392 00:18:53,760 --> 00:18:57,640 Speaker 9: if you go to a consignment shop, there's definitely something 393 00:18:57,680 --> 00:18:59,960 Speaker 9: that would be interesting. And I think that the benefit 394 00:19:00,040 --> 00:19:03,280 Speaker 9: fit to Comcasts, Netflix, any other people, you know, giving 395 00:19:03,359 --> 00:19:05,680 Speaker 9: access to the data room there is first of all, 396 00:19:05,720 --> 00:19:07,920 Speaker 9: you learn a lot about what your competitor is doing, 397 00:19:08,040 --> 00:19:11,760 Speaker 9: so you get access to information. Two, there there is 398 00:19:11,840 --> 00:19:14,600 Speaker 9: a deal, right they may only want HBO Max, they 399 00:19:14,640 --> 00:19:17,600 Speaker 9: may only want certain IP but there's probably a deal 400 00:19:17,640 --> 00:19:20,040 Speaker 9: worth looking at. And then lastly is sort of what 401 00:19:20,119 --> 00:19:24,080 Speaker 9: Comcasts did with Disney is you might also elevate the 402 00:19:24,119 --> 00:19:26,040 Speaker 9: price for one of your competitors. 403 00:19:26,080 --> 00:19:27,280 Speaker 4: So that's not a bad thing. 404 00:19:27,320 --> 00:19:28,800 Speaker 9: It's so you sort of you know what, you're sort 405 00:19:28,840 --> 00:19:30,560 Speaker 9: of the skunk at the picnic a little bit. And 406 00:19:31,160 --> 00:19:33,399 Speaker 9: so I think all of those factor into it in 407 00:19:33,440 --> 00:19:36,080 Speaker 9: a way. But there's no reason I think it would 408 00:19:36,119 --> 00:19:40,000 Speaker 9: be it would be an obligation of duty from these 409 00:19:40,080 --> 00:19:42,639 Speaker 9: other companies not to look at it, you know, just 410 00:19:42,720 --> 00:19:43,080 Speaker 9: kick the. 411 00:19:43,040 --> 00:19:43,840 Speaker 4: Tires a little bit. 412 00:19:44,240 --> 00:19:47,000 Speaker 6: Paul Hardart does Sting Whish, Clinical Professor at nyuster In 413 00:19:47,000 --> 00:19:49,920 Speaker 6: School of Business. He is a Bloomberg Opinion contributor as well. 414 00:19:49,920 --> 00:19:52,440 Speaker 6: You can check out what he writes on the Bloomberg Terminal. 415 00:19:53,840 --> 00:19:58,520 Speaker 1: This is the Bloomberg Intelligence podcast, available on Apple, Spotify, 416 00:19:58,720 --> 00:20:02,200 Speaker 1: and anywhere else you get your podcasts. 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