1 00:00:02,480 --> 00:00:07,000 Speaker 1: Bloomberg Audio Studios, Podcasts, radio News. 2 00:00:09,160 --> 00:00:12,040 Speaker 2: This is a breaking news update from Bloomberg. 3 00:00:12,880 --> 00:00:16,840 Speaker 1: Instant reaction and analysis from our three thousand journalists and 4 00:00:16,920 --> 00:00:18,439 Speaker 1: analysts around the world. 5 00:00:19,800 --> 00:00:23,159 Speaker 3: Shares of Amazon hire in the aftermarket. Let's get to 6 00:00:23,200 --> 00:00:26,360 Speaker 3: it with our team. Two members killer members, if you will, 7 00:00:26,360 --> 00:00:29,160 Speaker 3: of our Bloomberg Intelligence team. We're talking about Putnam Goyle, 8 00:00:29,200 --> 00:00:32,199 Speaker 3: She's Bloomberg Intelligence Senior analyst for e Commerce and ath Leisure. 9 00:00:32,240 --> 00:00:35,960 Speaker 3: And Anna Agrana, Bloomberg Intelligence Senior Technology analysts. 10 00:00:36,120 --> 00:00:36,479 Speaker 4: Put them. 11 00:00:36,520 --> 00:00:39,000 Speaker 3: Let me kick it off with you in terms of Amazon, 12 00:00:39,320 --> 00:00:40,559 Speaker 3: what's jumping out here for you? 13 00:00:41,880 --> 00:00:44,960 Speaker 5: Look, it looks like broadbats, Bob, broad based strength across 14 00:00:45,000 --> 00:00:47,400 Speaker 5: the board. The retail numbers were particularly good, and that 15 00:00:47,520 --> 00:00:49,760 Speaker 5: was expected because we had the prime day shift moving 16 00:00:49,800 --> 00:00:53,240 Speaker 5: in from three Q to two Q. So overall, really 17 00:00:53,360 --> 00:00:55,960 Speaker 5: nice numbers here on the retail side. Consumers came to 18 00:00:56,040 --> 00:00:58,720 Speaker 5: Amazon to shop prime day sales, and the shift to 19 00:00:58,760 --> 00:01:01,520 Speaker 5: online just continues up here, more and more apparent as 20 00:01:01,520 --> 00:01:03,640 Speaker 5: we moved through a tough consumer spending cycle. 21 00:01:03,960 --> 00:01:06,800 Speaker 1: What's this third quarter net sales miss one ninety seven 22 00:01:06,880 --> 00:01:08,640 Speaker 1: to two hundred and two billion. The estimus were two 23 00:01:08,720 --> 00:01:10,040 Speaker 1: hundred and four billion dollars poon. 24 00:01:10,080 --> 00:01:10,200 Speaker 3: Hm. 25 00:01:11,120 --> 00:01:12,959 Speaker 5: I think part of it is the prime day shift, 26 00:01:13,040 --> 00:01:14,040 Speaker 5: right when you think about. 27 00:01:14,160 --> 00:01:16,640 Speaker 2: What analysts knew about that, so they should have, but 28 00:01:16,720 --> 00:01:17,280 Speaker 2: we don't know. 29 00:01:17,200 --> 00:01:19,800 Speaker 5: How much, right, we don't know how much in dollar 30 00:01:19,880 --> 00:01:21,760 Speaker 5: volume has moved into Q If you look at the 31 00:01:21,760 --> 00:01:25,520 Speaker 5: beat that we saw, analysts were expecting revenues to be up, 32 00:01:26,040 --> 00:01:27,919 Speaker 5: but they came and even higher than that. Right, thirteen 33 00:01:27,959 --> 00:01:30,560 Speaker 5: point seven percent online revenues was the expectation and they 34 00:01:30,560 --> 00:01:33,400 Speaker 5: came in a fifteen percent, So a couple of billion 35 00:01:33,480 --> 00:01:36,679 Speaker 5: dollars there could be part of the movement that we're 36 00:01:36,680 --> 00:01:38,039 Speaker 5: seeing here in the guidance as well. 37 00:01:38,080 --> 00:01:41,080 Speaker 3: All right, Anderrok, com on in on here. Let's talk 38 00:01:41,080 --> 00:01:46,200 Speaker 3: about AWS. What doing pretty well, to say the. 39 00:01:46,240 --> 00:01:50,120 Speaker 6: Least, actually much much better than what I would have 40 00:01:50,440 --> 00:01:53,880 Speaker 6: expected them in going from to thirty seven in constant 41 00:01:53,880 --> 00:01:57,040 Speaker 6: currency is phenomenal just because the size of this business 42 00:01:57,160 --> 00:01:59,000 Speaker 6: is so large. I mean, it's so much bigger than 43 00:01:59,040 --> 00:02:01,960 Speaker 6: aud Equal and much bigger than Google. But this is 44 00:02:02,040 --> 00:02:04,520 Speaker 6: a very big beat, frankly, and the bigger thing, the 45 00:02:04,520 --> 00:02:06,920 Speaker 6: biggest surprise for me was the margins. I would have 46 00:02:06,960 --> 00:02:09,679 Speaker 6: expected margins to go down with all these investments that 47 00:02:09,760 --> 00:02:13,200 Speaker 6: actually went up. So you know, kudos to the AWS team, 48 00:02:13,240 --> 00:02:15,679 Speaker 6: and we look to hear more about guidance for a 49 00:02:15,720 --> 00:02:18,280 Speaker 6: next quarter, and you know, how much longer can these 50 00:02:18,280 --> 00:02:19,160 Speaker 6: margins hold up? 51 00:02:19,680 --> 00:02:20,160 Speaker 2: Honor Rock. 52 00:02:20,240 --> 00:02:23,160 Speaker 1: The AI and chips run rate, the ease eclipsed run 53 00:02:23,200 --> 00:02:27,840 Speaker 1: rates of over twenty five billion dollars can textualize that 54 00:02:27,960 --> 00:02:31,200 Speaker 1: for US, as Romaine said, a division of a division 55 00:02:31,639 --> 00:02:33,560 Speaker 1: with twenty five billion dollar run rates. 56 00:02:34,200 --> 00:02:36,679 Speaker 6: Yeah, I mean, to be honest, I don't really care 57 00:02:36,760 --> 00:02:39,320 Speaker 6: that much about the AI net number. The number of 58 00:02:39,320 --> 00:02:41,560 Speaker 6: the chips is the one that I find it more 59 00:02:41,600 --> 00:02:45,400 Speaker 6: exciting because here's the thing. All these big companies that 60 00:02:45,440 --> 00:02:48,200 Speaker 6: are spending all this capex, A large portion of that 61 00:02:48,320 --> 00:02:51,960 Speaker 6: capex is going to buy GPUs and video GPUs. If 62 00:02:52,120 --> 00:02:54,880 Speaker 6: Amazon can go and figure out their workloads onto their 63 00:02:54,919 --> 00:02:58,120 Speaker 6: own chips, it saves them massive amount of money and 64 00:02:58,200 --> 00:03:01,240 Speaker 6: helps them recognize the big backlock that they have without 65 00:03:01,280 --> 00:03:03,639 Speaker 6: having to spend you know, given video that cashital I 66 00:03:03,639 --> 00:03:07,000 Speaker 6: think that's one of the biggest differentiators between Google, Amazon, 67 00:03:07,040 --> 00:03:09,920 Speaker 6: and Microsoft is you know, the first to have their 68 00:03:09,960 --> 00:03:12,640 Speaker 6: own chips that people are embracing at a fast pace. 69 00:03:13,000 --> 00:03:15,320 Speaker 4: Meanwhile, that's not the case with Microsoft. 70 00:03:15,760 --> 00:03:17,720 Speaker 3: Yeah, I think it's just fascinating. As I said, I 71 00:03:17,720 --> 00:03:19,760 Speaker 3: don't always think about Amazon as a chip company, but 72 00:03:19,760 --> 00:03:21,320 Speaker 3: you got to remember that they are doing this, and 73 00:03:21,360 --> 00:03:25,240 Speaker 3: increasingly these big tech guys are doing it. Put them 74 00:03:25,240 --> 00:03:27,840 Speaker 3: Come on back in here. Anything here, I know we're 75 00:03:27,880 --> 00:03:30,200 Speaker 3: still getting information. There's still stuff we're going to get 76 00:03:30,200 --> 00:03:32,280 Speaker 3: on the analysts called anything though, that you find is 77 00:03:32,680 --> 00:03:35,200 Speaker 3: maybe something that's worth digging a little bit deeper into 78 00:03:35,280 --> 00:03:36,280 Speaker 3: and asking about. 79 00:03:37,440 --> 00:03:40,120 Speaker 5: I think the advertising business saw a nice pickup, right, 80 00:03:40,120 --> 00:03:43,120 Speaker 5: twenty six percent growth rates this quarter is a is 81 00:03:43,120 --> 00:03:45,120 Speaker 5: a step up from the close to twenty percent that 82 00:03:45,120 --> 00:03:47,920 Speaker 5: they've been recording for the past few quarters. Was that 83 00:03:48,080 --> 00:03:50,720 Speaker 5: also led by the prime day shift? Given such a 84 00:03:50,760 --> 00:03:54,240 Speaker 5: big event moved into the second quarter to advertising dollars 85 00:03:54,280 --> 00:03:57,640 Speaker 5: shift too, and we will we see that slowed down 86 00:03:57,680 --> 00:04:00,560 Speaker 5: a little in the next quarter. So I'm looking to 87 00:04:00,600 --> 00:04:04,960 Speaker 5: better understand how this prime day shift affected and helped 88 00:04:05,000 --> 00:04:07,680 Speaker 5: to queue across the board and what it really does 89 00:04:07,720 --> 00:04:10,160 Speaker 5: to three Q. They've never quantified it, so we're hoping 90 00:04:10,240 --> 00:04:11,000 Speaker 5: to get some color. 91 00:04:11,360 --> 00:04:15,400 Speaker 1: Well, how big is prime like how big is Prime Day? 92 00:04:15,440 --> 00:04:18,359 Speaker 1: Because I know we know from a metrics perspective, but 93 00:04:18,440 --> 00:04:21,640 Speaker 1: prime day kind of started a few years ago as 94 00:04:21,720 --> 00:04:24,960 Speaker 1: this offshoot of what was it Ali Baba doing Singles Day? 95 00:04:25,240 --> 00:04:27,560 Speaker 1: Is that what sort of inspired it years ago? 96 00:04:28,000 --> 00:04:30,360 Speaker 5: That could have inspired it? But you know Prime Day 97 00:04:30,440 --> 00:04:31,880 Speaker 5: was a once a year phenomenal. 98 00:04:32,040 --> 00:04:33,880 Speaker 2: Yeah, I know, it's like holiday right a week. 99 00:04:34,000 --> 00:04:37,599 Speaker 5: It's now twice a year, right, So it's really just 100 00:04:37,720 --> 00:04:41,160 Speaker 5: a move to pull shoppers to spend earlier. So if 101 00:04:41,160 --> 00:04:43,560 Speaker 5: you think about the timing of Prime Day, right this time, 102 00:04:43,600 --> 00:04:47,080 Speaker 5: they moved it into June, usually in July, so really 103 00:04:47,120 --> 00:04:50,440 Speaker 5: capturing spend for back to school, for these peak holiday 104 00:04:50,440 --> 00:04:53,560 Speaker 5: moments where consumers are looking to stretch their dollars. They're 105 00:04:53,640 --> 00:04:56,880 Speaker 5: they're essentially taking the dollars up front and trying to 106 00:04:56,920 --> 00:04:59,880 Speaker 5: get the consumers to spend it with them versus elsewhere. 107 00:05:00,360 --> 00:05:03,920 Speaker 5: Does that happen? Yes and no, because around Prime Day, 108 00:05:03,960 --> 00:05:07,039 Speaker 5: it's not just Amazon that's striking the deals. It's also Walmart, 109 00:05:07,080 --> 00:05:10,200 Speaker 5: it's also Target, it's also Best Buy. Everyone is having 110 00:05:10,240 --> 00:05:12,200 Speaker 5: their own version of deals around this day. 111 00:05:12,520 --> 00:05:15,200 Speaker 3: Anything to get us to buy. I know how that works. Hey, 112 00:05:15,320 --> 00:05:18,360 Speaker 3: just to remind you everybody, we're talking with Punamgoyle, Bloomberg Intelligence, 113 00:05:18,360 --> 00:05:21,279 Speaker 3: senior analysts for e Commerce and at Leisure Aragrana Bloomberg 114 00:05:21,320 --> 00:05:25,640 Speaker 3: Intelligence senior technology analyst joining us. If you have a question, 115 00:05:25,800 --> 00:05:28,120 Speaker 3: let us know. We'd love to bring you into the conversation. 116 00:05:28,560 --> 00:05:31,560 Speaker 3: For those who are Bloomberg dot com subscribers and terminal clients, 117 00:05:32,920 --> 00:05:35,880 Speaker 3: ask a question of our panelists. Just submit questions for 118 00:05:35,920 --> 00:05:39,400 Speaker 3: our team to answer live on air Bloomberg dot com 119 00:05:39,400 --> 00:05:42,480 Speaker 3: slash ask Radio. Send them to Bloomberg dot com slash 120 00:05:42,560 --> 00:05:46,000 Speaker 3: ask Radio and then you can certainly catch their answers 121 00:05:46,120 --> 00:05:47,080 Speaker 3: live on air with us. 122 00:05:47,160 --> 00:05:49,360 Speaker 1: Hey, I want to just look at shares of roadblocks 123 00:05:49,440 --> 00:05:53,279 Speaker 1: real quick. The gaming company down about eleven percent in 124 00:05:53,320 --> 00:05:56,080 Speaker 1: the after hours, just taking a look at what exactly 125 00:05:56,160 --> 00:05:59,040 Speaker 1: is happening. Second quarter daily active users came in below 126 00:05:59,120 --> 00:06:01,040 Speaker 1: estimates one hundred and twenty three million. The SMOs for 127 00:06:01,080 --> 00:06:05,000 Speaker 1: one hundred and twenty eight point seven million dollar million users. 128 00:06:05,600 --> 00:06:08,119 Speaker 1: That was really that what missed the average analyst estimate, 129 00:06:08,120 --> 00:06:11,200 Speaker 1: and that is what has has investors concern. Bookings came 130 00:06:11,200 --> 00:06:14,320 Speaker 1: in a little shy, hours, engaged came in shy. Revenue 131 00:06:14,360 --> 00:06:16,920 Speaker 1: did come in higher than expected, though a loss per 132 00:06:16,920 --> 00:06:18,920 Speaker 1: share at twenty six cents. The estimos for a lost 133 00:06:18,960 --> 00:06:21,880 Speaker 1: percent at thirty four cents lost per share, rather shares 134 00:06:21,880 --> 00:06:23,840 Speaker 1: down eleven and a half percent from the after hours. 135 00:06:23,839 --> 00:06:26,480 Speaker 3: All right, so getting hammered going in the other direction, 136 00:06:26,560 --> 00:06:29,360 Speaker 3: shares of Amazon continuing to rally. Our spencer soper out 137 00:06:29,360 --> 00:06:32,960 Speaker 3: with his story. Amazon reported cloud computing revenue that beat 138 00:06:32,960 --> 00:06:37,120 Speaker 3: analyst estimates, with booming demand for artificial intelligence services accelerating 139 00:06:37,160 --> 00:06:40,720 Speaker 3: sales for the fifth straight quarter, revenue jump thirty seven 140 00:06:40,760 --> 00:06:44,000 Speaker 3: percent to forty two point two billion at Amazon Web Services, 141 00:06:44,000 --> 00:06:46,680 Speaker 3: which generates about a fifth of the company's revenue and 142 00:06:46,800 --> 00:06:50,080 Speaker 3: most of its operating profit. It was the fastest pace 143 00:06:50,120 --> 00:06:52,880 Speaker 3: of growth since the fourth quarter of twenty twenty one. Analyst, 144 00:06:52,920 --> 00:06:56,040 Speaker 3: on average forecast sales of forty point six billion, according 145 00:06:56,080 --> 00:07:00,240 Speaker 3: to data compiled by Bloomberg. You know, Ana Rag, you 146 00:07:00,320 --> 00:07:05,840 Speaker 3: have Amazon against the other hyperscalers. What is it that's unique? 147 00:07:05,880 --> 00:07:07,279 Speaker 3: I mean, you talk a little bit about the chips, 148 00:07:07,279 --> 00:07:10,000 Speaker 3: and that certainly kind of keeps a lid on some 149 00:07:10,080 --> 00:07:13,760 Speaker 3: of their costs and their access situation among the chip 150 00:07:13,760 --> 00:07:17,000 Speaker 3: demands that we've seen out there. But what is the 151 00:07:17,160 --> 00:07:18,680 Speaker 3: special sauce of Amazon? 152 00:07:20,400 --> 00:07:23,040 Speaker 6: So the first and foremost, it is the largest public 153 00:07:23,040 --> 00:07:26,560 Speaker 6: cloud provider, It has a much longer history of working 154 00:07:26,600 --> 00:07:30,440 Speaker 6: with enterprises. So remember when we talk about AI adoption, 155 00:07:31,080 --> 00:07:35,080 Speaker 6: you think about chat GPT app, it's primarily hosted on 156 00:07:35,200 --> 00:07:36,000 Speaker 6: Microsoft as here. 157 00:07:36,080 --> 00:07:37,040 Speaker 4: That's a consumer app. 158 00:07:37,400 --> 00:07:40,760 Speaker 6: But now you think about AWS and enterprise AI adoption, 159 00:07:41,160 --> 00:07:43,640 Speaker 6: that's where a lot of large companies around the world 160 00:07:43,840 --> 00:07:47,080 Speaker 6: would be building their application or they would be infusing 161 00:07:47,120 --> 00:07:50,160 Speaker 6: AI into their older application. So that's a big difference, 162 00:07:50,240 --> 00:07:53,360 Speaker 6: is the enterprise presence as well as that. And you know, 163 00:07:53,400 --> 00:07:56,280 Speaker 6: for the Microsoft is on the consumer side. Microsoft does both, 164 00:07:56,360 --> 00:07:59,400 Speaker 6: but for Amazon that's primarily the bread and butter. The 165 00:07:59,520 --> 00:08:02,880 Speaker 6: other thing is they're also the key or the preferred 166 00:08:02,880 --> 00:08:05,680 Speaker 6: cloud provider for anthropic and we have seen a massive 167 00:08:05,720 --> 00:08:09,160 Speaker 6: boost in and thropics use for coding agents, and I 168 00:08:09,200 --> 00:08:12,240 Speaker 6: think that's also helping AWS in that case. And then 169 00:08:12,280 --> 00:08:15,400 Speaker 6: if you think you know the future of AI is 170 00:08:15,800 --> 00:08:19,040 Speaker 6: enterprise adoption, you know Amazon has a lot of data, 171 00:08:19,120 --> 00:08:21,640 Speaker 6: it has a lot of services, so it's right in 172 00:08:21,680 --> 00:08:25,320 Speaker 6: the middle of this entire I would say air diffusion 173 00:08:25,680 --> 00:08:26,560 Speaker 6: within companies. 174 00:08:27,640 --> 00:08:29,680 Speaker 1: I want to bring in ed La look to the conversation. 175 00:08:29,920 --> 00:08:33,520 Speaker 1: He's the host of Bloomberg Tech out there in San Francisco, 176 00:08:33,920 --> 00:08:37,560 Speaker 1: and what sticks out to you Amazon's numbers up the 177 00:08:37,600 --> 00:08:39,720 Speaker 1: stock up eight point nine percent in the after hour. 178 00:08:39,800 --> 00:08:42,000 Speaker 2: Yeah, what's sticking mean you. 179 00:08:41,920 --> 00:08:43,680 Speaker 7: And Anna Rag will forgive me because I missed the 180 00:08:43,720 --> 00:08:44,760 Speaker 7: first part of your conversation. 181 00:08:44,840 --> 00:08:46,440 Speaker 2: I was on the phone with the CFO. 182 00:08:46,559 --> 00:08:48,560 Speaker 7: But I mean, this is a beat on every metric 183 00:08:48,640 --> 00:08:52,560 Speaker 7: where AWS absolutely matters most. You know, thirty seven percent 184 00:08:52,640 --> 00:08:57,080 Speaker 7: growth against street expectation of thirty one percent. You know, 185 00:08:57,120 --> 00:09:00,440 Speaker 7: it's it's a pretty straightforward set of numbers where you 186 00:09:00,480 --> 00:09:02,480 Speaker 7: know they will be peppered with questions on the call 187 00:09:02,559 --> 00:09:05,079 Speaker 7: for the same magic formula that we discussed in the 188 00:09:05,160 --> 00:09:09,120 Speaker 7: last few afternoons, capital expenditure, direction of travel, and other 189 00:09:09,240 --> 00:09:12,640 Speaker 7: points of tangible evidence that the AI from Amazon is 190 00:09:12,679 --> 00:09:14,360 Speaker 7: gaining traction in its different guises. 191 00:09:14,480 --> 00:09:16,359 Speaker 2: What did you hear from the c CFO. 192 00:09:17,320 --> 00:09:19,800 Speaker 7: That, as I suppoke to the CFO of a different company, 193 00:09:20,280 --> 00:09:21,360 Speaker 7: describe your kind of things? 194 00:09:21,360 --> 00:09:23,360 Speaker 2: Which CFO? Which CFO were you talking to? 195 00:09:23,520 --> 00:09:25,160 Speaker 7: Sorry, I was talking to the I was talking to 196 00:09:25,200 --> 00:09:28,680 Speaker 7: the Rivian's okay, we're going to get to get that data. Okay, great, 197 00:09:29,679 --> 00:09:32,440 Speaker 7: But yeah, like again, you know, an Ana Rag like, 198 00:09:32,600 --> 00:09:34,640 Speaker 7: I'm such a massive Anaag fan, and so if I 199 00:09:34,640 --> 00:09:36,679 Speaker 7: say something and he's like that's dumb. Let him say 200 00:09:36,679 --> 00:09:40,320 Speaker 7: it's dumb. But you know, operating income also like a 201 00:09:40,400 --> 00:09:43,320 Speaker 7: huge beat. And you know, if the concern of the 202 00:09:43,360 --> 00:09:47,280 Speaker 7: market for the hyperscalers is that they want to see 203 00:09:47,679 --> 00:09:51,120 Speaker 7: free cash flow and profit and a commitment to spend 204 00:09:51,200 --> 00:09:53,280 Speaker 7: and continue top line growth, they kind of got all 205 00:09:53,320 --> 00:09:56,000 Speaker 7: of that. There's not a lot left to complain about. 206 00:09:56,040 --> 00:09:57,800 Speaker 2: Is there an rag you want to come in now 207 00:09:57,800 --> 00:09:57,960 Speaker 2: on that? 208 00:09:58,160 --> 00:10:00,559 Speaker 6: No, I absolutely agree with that. In fact, that's what 209 00:10:00,679 --> 00:10:02,360 Speaker 6: I was trying to figure out. Can I find any 210 00:10:02,400 --> 00:10:04,240 Speaker 6: mistakes over here? But we don't. 211 00:10:04,280 --> 00:10:06,679 Speaker 4: And in fact, which is why I said, you know, when. 212 00:10:06,480 --> 00:10:09,520 Speaker 6: You think of enterprise adoption and AI, you really cannot 213 00:10:09,520 --> 00:10:12,800 Speaker 6: think of anybody better than AWS because of given that 214 00:10:13,160 --> 00:10:17,040 Speaker 6: how much of world applications the house already you know, 215 00:10:17,120 --> 00:10:20,600 Speaker 6: within their ecosystem. So the big question now is how 216 00:10:20,679 --> 00:10:24,120 Speaker 6: much they have to spend for the next twelve months 217 00:10:24,280 --> 00:10:27,000 Speaker 6: to get this kind of rate going. We saw the 218 00:10:27,160 --> 00:10:30,640 Speaker 6: AWUS growth rate jump to thirty seven percent. You know, 219 00:10:30,800 --> 00:10:32,920 Speaker 6: next quarter, is it going to be faster than that 220 00:10:33,360 --> 00:10:35,760 Speaker 6: or do we see some moderation in that? So now 221 00:10:35,760 --> 00:10:38,560 Speaker 6: we are really getting into, to be honest, fifty basis 222 00:10:38,559 --> 00:10:40,480 Speaker 6: points here or one hundred basis points here, it's a 223 00:10:40,679 --> 00:10:42,120 Speaker 6: you're nitpicking at this point. 224 00:10:42,120 --> 00:10:45,800 Speaker 1: Frankly, Hey, we're getting some questions from viewers and listeners 225 00:10:45,840 --> 00:10:46,560 Speaker 1: around the world. 226 00:10:46,640 --> 00:10:47,240 Speaker 2: Just a reminder. 227 00:10:47,280 --> 00:10:50,360 Speaker 1: Subscribers to Bloomberg dot com and terminal subscribers can ask 228 00:10:50,480 --> 00:10:53,599 Speaker 1: questions Bloomberg dot com slash ask radio. I want to 229 00:10:53,600 --> 00:10:57,280 Speaker 1: throw this one. This is from Connor in Auckland, New Zealand. 230 00:10:57,360 --> 00:10:59,440 Speaker 1: Thanks for stand up late, I guess early in the 231 00:10:59,440 --> 00:11:03,000 Speaker 1: morning for us, Connor, Microsoft and Amazon are making good 232 00:11:03,040 --> 00:11:07,600 Speaker 1: progress in enterprise AI. How sticky are their product offerings? 233 00:11:07,640 --> 00:11:11,160 Speaker 1: Will there be big swings in market share going forward? 234 00:11:11,200 --> 00:11:11,560 Speaker 6: On a rock? 235 00:11:11,600 --> 00:11:13,040 Speaker 2: I think this is a good question for you. 236 00:11:14,120 --> 00:11:15,920 Speaker 4: So the way we think about it is the pie 237 00:11:16,080 --> 00:11:17,400 Speaker 4: is growing at such a fast space. 238 00:11:17,480 --> 00:11:20,320 Speaker 6: They will all make money. I'm fairly confident about it. 239 00:11:20,360 --> 00:11:22,600 Speaker 6: But the question is what kind of money they will 240 00:11:22,600 --> 00:11:25,920 Speaker 6: be making. Are they making money renting GPUs and training 241 00:11:25,920 --> 00:11:29,000 Speaker 6: models or are you building AI applications on top of it. 242 00:11:29,640 --> 00:11:32,200 Speaker 6: We like the second kind of business better because in 243 00:11:32,240 --> 00:11:34,360 Speaker 6: the long run, it's very difficult to take it off. 244 00:11:34,400 --> 00:11:36,760 Speaker 6: So let's say go back, you know, twenty years or 245 00:11:36,760 --> 00:11:41,440 Speaker 6: fifteen years. If lift or Uber started their application on AWS. 246 00:11:42,240 --> 00:11:46,000 Speaker 6: That's a cloud native application. As the application is growing, 247 00:11:46,280 --> 00:11:49,280 Speaker 6: AWS makes more money. If you are developing a brand 248 00:11:49,280 --> 00:11:52,240 Speaker 6: new AI application today, if you pick one of these 249 00:11:52,320 --> 00:11:56,880 Speaker 6: three or four cloud platforms, as you as those apps 250 00:11:56,880 --> 00:11:58,920 Speaker 6: get bigger, you're going to make money. So it's a 251 00:11:58,960 --> 00:12:02,280 Speaker 6: perpetual revenue email. It's very difficult to take that out 252 00:12:02,320 --> 00:12:05,000 Speaker 6: and move it somewhere else. But if you're only training models, 253 00:12:05,160 --> 00:12:06,640 Speaker 6: that's a very different revenue stream. 254 00:12:06,840 --> 00:12:07,040 Speaker 4: Yeah. 255 00:12:07,080 --> 00:12:09,480 Speaker 3: It's interesting too because I was just looking at Spencer Soaper, 256 00:12:09,600 --> 00:12:11,480 Speaker 3: the story that he's got on the Bloomberg Terminal or 257 00:12:11,480 --> 00:12:14,079 Speaker 3: Bloomberg Spencer Soaper. He says the company lacks a hit 258 00:12:14,480 --> 00:12:17,480 Speaker 3: consumer AI product on par with open ais chatchypt and 259 00:12:17,520 --> 00:12:21,240 Speaker 3: Anthropics Claude, but has ink Deals, committing both AI labs 260 00:12:21,280 --> 00:12:23,640 Speaker 3: to spend at least one hundred billion dollars in services 261 00:12:23,880 --> 00:12:26,840 Speaker 3: from the Amazon Web Services cloud unit in the coming years. 262 00:12:26,880 --> 00:12:29,000 Speaker 3: I mean ANTAOG. 263 00:12:29,080 --> 00:12:29,720 Speaker 2: That's a big deal. 264 00:12:30,960 --> 00:12:31,200 Speaker 4: Yeah. 265 00:12:31,240 --> 00:12:34,000 Speaker 6: So one of the things that mikel Microsoft said yesterday 266 00:12:34,600 --> 00:12:38,120 Speaker 6: was the sequential increase in commercial real performance obligations or 267 00:12:38,160 --> 00:12:40,319 Speaker 6: the backlock that they had. I think it was about 268 00:12:40,320 --> 00:12:43,320 Speaker 6: forty five billion. All of that was from non frontier 269 00:12:43,360 --> 00:12:46,280 Speaker 6: model companies, which is that is not anthropic, that is 270 00:12:46,280 --> 00:12:48,520 Speaker 6: not opening eye because remember, what is the biggest fear 271 00:12:48,600 --> 00:12:51,120 Speaker 6: right now in the market is what if open EI 272 00:12:51,240 --> 00:12:53,199 Speaker 6: is not able to take care of the commitments or 273 00:12:53,320 --> 00:12:56,040 Speaker 6: entropic and so forth. But what they're saying is the 274 00:12:56,120 --> 00:12:59,440 Speaker 6: business now they're coming in in their backlocker and they're 275 00:12:59,640 --> 00:13:03,120 Speaker 6: bookings is from non frontier models. And that's really I 276 00:13:03,120 --> 00:13:05,640 Speaker 6: think one of the most important questions that we want 277 00:13:05,679 --> 00:13:08,920 Speaker 6: to ask the company today is if they talk about 278 00:13:08,920 --> 00:13:11,440 Speaker 6: cloud commitments, where are those cloud commitments coming in. 279 00:13:11,559 --> 00:13:13,520 Speaker 4: Is it the three or four companies or is it 280 00:13:13,559 --> 00:13:15,160 Speaker 4: more diversified. 281 00:13:14,920 --> 00:13:17,320 Speaker 3: Right exactly in terms of exposure to maybe just a 282 00:13:17,320 --> 00:13:19,199 Speaker 3: few big customers put them on I want to bring 283 00:13:19,240 --> 00:13:21,679 Speaker 3: you back. We spent We talked so much about AWS 284 00:13:21,720 --> 00:13:23,160 Speaker 3: and I know we do that with you as well. 285 00:13:23,240 --> 00:13:25,800 Speaker 3: It's so important in terms of what really makes money 286 00:13:26,200 --> 00:13:28,440 Speaker 3: for Amazon. But you think about I mean, they sell 287 00:13:28,480 --> 00:13:30,960 Speaker 3: a lot of stuff, so many of us relate to 288 00:13:31,000 --> 00:13:35,000 Speaker 3: Amazon as a retailer. How do they think about increasingly 289 00:13:35,120 --> 00:13:38,440 Speaker 3: using AI to kind of juice the numbers and maybe 290 00:13:38,440 --> 00:13:40,880 Speaker 3: improve the profitability on the retail side of things. 291 00:13:41,920 --> 00:13:44,120 Speaker 5: Yeah, AI has been a big push on the retail side, 292 00:13:44,120 --> 00:13:46,920 Speaker 5: and actually I think Amazon does the best better than anyone. 293 00:13:47,000 --> 00:13:50,640 Speaker 5: When they introduced Alexa for Shopping, for example, Alexa plus, 294 00:13:51,080 --> 00:13:53,200 Speaker 5: it's pretty clever. You know, you go to the search bar, 295 00:13:53,280 --> 00:13:57,240 Speaker 5: you can compare pricing. You can say I'm looking for 296 00:13:57,400 --> 00:14:00,760 Speaker 5: this item and when was it priced, and if it 297 00:14:00,840 --> 00:14:03,240 Speaker 5: drops to that price, buy it for me. I mean, 298 00:14:03,400 --> 00:14:05,960 Speaker 5: just that simple, and it'll show about your door. So 299 00:14:06,080 --> 00:14:08,480 Speaker 5: I do think that they're gaining traction there. In fact 300 00:14:08,480 --> 00:14:11,520 Speaker 5: and the release, I believe they said that interactions and 301 00:14:11,720 --> 00:14:16,720 Speaker 5: active users doubled over second quarter just with AI adoption 302 00:14:16,840 --> 00:14:19,840 Speaker 5: and Alexa for Shopping, So they're making momentum here. We 303 00:14:19,960 --> 00:14:23,280 Speaker 5: do think that the investments that they make in Alexa 304 00:14:23,360 --> 00:14:26,400 Speaker 5: for Shopping and AI will continue to not only help 305 00:14:26,440 --> 00:14:30,080 Speaker 5: them with customer service and conversion online, but also improve 306 00:14:30,280 --> 00:14:32,720 Speaker 5: margins on the retail side, which are slimmed to none. 307 00:14:33,120 --> 00:14:36,560 Speaker 7: As you know, I find that data so amazing. Puma, 308 00:14:36,600 --> 00:14:38,200 Speaker 7: I didn't know you were with us. It's so good 309 00:14:38,200 --> 00:14:40,200 Speaker 7: to see you. By the way, every morning when I 310 00:14:40,240 --> 00:14:43,480 Speaker 7: wake up for Bloomberg surveillance, it's because my Eco device works, 311 00:14:43,760 --> 00:14:46,720 Speaker 7: I ask Alexa to set an alarm and it's very 312 00:14:46,800 --> 00:14:48,880 Speaker 7: reliable for that, But for ages, I've been thinking, what 313 00:14:48,920 --> 00:14:50,880 Speaker 7: am I going to use this for? The data you're 314 00:14:50,880 --> 00:14:54,240 Speaker 7: pointing to is amazing though, Alexa for shopping right if 315 00:14:54,280 --> 00:14:57,120 Speaker 7: their shop is on prime, use Alexa more than forty 316 00:14:57,160 --> 00:15:00,240 Speaker 7: percent more per order if they involve the A in 317 00:15:00,280 --> 00:15:03,000 Speaker 7: the transaction. I find that amazing. But it doesn't move 318 00:15:03,040 --> 00:15:06,840 Speaker 7: the needle on the financials right for Amazon at the stage. 319 00:15:06,680 --> 00:15:08,640 Speaker 5: It doesn't move the need because you have to think 320 00:15:08,640 --> 00:15:11,760 Speaker 5: about how big Amazon is. Right, We're approaching a trillion 321 00:15:11,840 --> 00:15:14,760 Speaker 5: dollars in GMB this year based on our estimates, so 322 00:15:14,840 --> 00:15:18,120 Speaker 5: that's a sizeable number. So when we see these growth 323 00:15:18,200 --> 00:15:21,080 Speaker 5: rates with the online business up fifteen percent, it's a 324 00:15:21,120 --> 00:15:24,280 Speaker 5: big number to move. That said, I do think it 325 00:15:24,360 --> 00:15:27,280 Speaker 5: does move the needle on conversion. I do think the 326 00:15:27,320 --> 00:15:32,720 Speaker 5: aluxafer shopping is helping it's prime customers more notably really 327 00:15:32,840 --> 00:15:36,320 Speaker 5: engage and purchase things even quicker because now you're just 328 00:15:36,520 --> 00:15:39,760 Speaker 5: talking to someone, you're talking to this agent, and you're 329 00:15:39,840 --> 00:15:43,000 Speaker 5: setting expectations on what you want and things will just 330 00:15:43,080 --> 00:15:45,600 Speaker 5: show up to your door. So now you don't need 331 00:15:45,640 --> 00:15:48,240 Speaker 5: to go back and keep going back to see, okay, 332 00:15:48,280 --> 00:15:50,320 Speaker 5: did the price drop? Is this the best price is 333 00:15:50,360 --> 00:15:52,640 Speaker 5: this what I need. Do I need something else? It's 334 00:15:52,680 --> 00:15:54,560 Speaker 5: doing the work for you, So I do think over 335 00:15:54,640 --> 00:15:59,360 Speaker 5: time that will help boost sales and really help Amazon 336 00:15:59,440 --> 00:16:02,360 Speaker 5: continue to maintain its lead in the online world. 337 00:16:02,720 --> 00:16:04,640 Speaker 1: I want to throw one to add and back to 338 00:16:04,680 --> 00:16:07,000 Speaker 1: the technology side of things, and I want to talk 339 00:16:07,000 --> 00:16:09,240 Speaker 1: a little bit about the company's chips and business. We 340 00:16:09,320 --> 00:16:12,840 Speaker 1: got some data about annual revenue run rates on a 341 00:16:12,920 --> 00:16:16,200 Speaker 1: rug said he's not as interested in the AI side 342 00:16:16,200 --> 00:16:16,360 Speaker 1: of it. 343 00:16:16,400 --> 00:16:18,480 Speaker 2: He's more interested in the chip side of it. But 344 00:16:18,680 --> 00:16:19,000 Speaker 2: me too. 345 00:16:19,080 --> 00:16:20,840 Speaker 1: You know, they've got the what is it the trainum 346 00:16:21,440 --> 00:16:22,680 Speaker 1: Is that what they're talking about here. 347 00:16:22,960 --> 00:16:26,920 Speaker 7: Well, it's multifaceted. But the chips business has revenue run 348 00:16:27,000 --> 00:16:29,800 Speaker 7: rate twenty five billion dollars, as does the AI business. 349 00:16:29,800 --> 00:16:32,920 Speaker 7: It's a run rate figure, but it is amazing how 350 00:16:33,000 --> 00:16:35,320 Speaker 7: quickly they've kind of turned it around from the in 351 00:16:35,440 --> 00:16:38,560 Speaker 7: house R and D to like they got a lot 352 00:16:38,560 --> 00:16:40,840 Speaker 7: of questions from what's the point of trainium, like who's 353 00:16:40,960 --> 00:16:43,960 Speaker 7: using it? They had a lot of early success with 354 00:16:44,000 --> 00:16:47,080 Speaker 7: saying Athropic will use Trainium, but and throp it quickly 355 00:16:47,080 --> 00:16:50,840 Speaker 7: followed up with some big TPU deals twenty five billion dollars. 356 00:16:50,960 --> 00:16:54,400 Speaker 7: You know, for context, AMD did thirty five billion dollars 357 00:16:54,640 --> 00:16:57,240 Speaker 7: of revenue all told in its last fiscal year, and 358 00:16:57,280 --> 00:16:59,680 Speaker 7: it's likely to do fifty billion dollars this year. But 359 00:16:59,720 --> 00:17:02,160 Speaker 7: it's oh, they're moving pretty quickly on it, you know, 360 00:17:02,240 --> 00:17:05,879 Speaker 7: across those It's not them literally a bag of chips 361 00:17:05,880 --> 00:17:08,720 Speaker 7: here you go. You know, it's still then renting capacity 362 00:17:09,200 --> 00:17:12,840 Speaker 7: specifically to their own silicon, but to third party customers. 363 00:17:12,880 --> 00:17:15,359 Speaker 7: And like, yeah, you know, if if Google gets all 364 00:17:15,359 --> 00:17:18,439 Speaker 7: the credit for TPU, then should Amazon get some credit 365 00:17:18,520 --> 00:17:20,600 Speaker 7: for those trainium chips too? 366 00:17:20,760 --> 00:17:23,280 Speaker 3: Some would say, yes, we were talking about anarak. Come 367 00:17:23,320 --> 00:17:25,000 Speaker 3: on back, we were talking about chips with you and 368 00:17:25,800 --> 00:17:26,720 Speaker 3: Amazon earlier. 369 00:17:28,119 --> 00:17:30,440 Speaker 6: Yeah, I think that's exactly what you know Ed is 370 00:17:30,480 --> 00:17:33,159 Speaker 6: talking about here, because remember it's the capic side of 371 00:17:33,200 --> 00:17:36,200 Speaker 6: it also, that's important. First of all, if customers are 372 00:17:36,240 --> 00:17:39,800 Speaker 6: embracing Cranium along with Amazon Cloud, I mean, it's really 373 00:17:39,880 --> 00:17:42,359 Speaker 6: good for them because guess what, they get to keep 374 00:17:42,400 --> 00:17:44,719 Speaker 6: a lot of that gross margin that they don't have 375 00:17:44,760 --> 00:17:46,800 Speaker 6: to pay in video for those ships. 376 00:17:46,840 --> 00:17:49,639 Speaker 4: So I think sooner or later, the game. 377 00:17:49,760 --> 00:17:51,720 Speaker 6: The big game in this case is going to be 378 00:17:52,000 --> 00:17:56,560 Speaker 6: who has the lowest you know cost per token within 379 00:17:56,600 --> 00:17:59,679 Speaker 6: the all the hyperskailled cloud providers, and that's where Google 380 00:17:59,680 --> 00:18:01,800 Speaker 6: and amaz z On may have a lego verted our 381 00:18:01,840 --> 00:18:02,399 Speaker 6: Microsoft 382 00:18:06,520 --> 00:18:09,760 Speaker 4: M mm hmm