1 00:00:02,400 --> 00:00:15,680 Speaker 1: Bloomberg Audio Studios, podcasts, radio news. 2 00:00:13,720 --> 00:00:16,080 Speaker 2: Single best idea and we say good morning to you. 3 00:00:16,120 --> 00:00:18,160 Speaker 2: We're taping this right after the show. I come out 4 00:00:18,200 --> 00:00:21,200 Speaker 2: of the show and you know, I mean, you have 5 00:00:21,280 --> 00:00:23,320 Speaker 2: to fight through the fans and all that. We get 6 00:00:23,320 --> 00:00:25,639 Speaker 2: into the room and Eric and I put together a 7 00:00:25,680 --> 00:00:27,920 Speaker 2: six to seven minute podcast. What is it. It's two 8 00:00:28,040 --> 00:00:32,600 Speaker 2: ideas out of the show. They're not like our best ideas. 9 00:00:32,640 --> 00:00:36,320 Speaker 2: There are smartest ideas or things that may be worldwide. 10 00:00:36,960 --> 00:00:39,160 Speaker 2: We care about. One of the things we care about 11 00:00:39,760 --> 00:00:42,519 Speaker 2: is the risk that is out there. Amy was Silverman 12 00:00:42,640 --> 00:00:46,879 Speaker 2: is out of the Princeton Combine and it's absolutely fabulous. 13 00:00:47,120 --> 00:00:51,320 Speaker 2: It's speaking in English about the Greek letters. She is 14 00:00:51,360 --> 00:00:55,360 Speaker 2: in derivatives, and that goes into cross moments like variants 15 00:00:55,400 --> 00:01:01,360 Speaker 2: and skew and curtosis and mathematical thing like that, and 16 00:01:01,560 --> 00:01:06,920 Speaker 2: also into your high school height distribution. Remember in high school, 17 00:01:07,120 --> 00:01:10,120 Speaker 2: you're studying statistics and it was like, okay, here's the 18 00:01:10,160 --> 00:01:12,319 Speaker 2: height of my class, and over on the left where 19 00:01:12,360 --> 00:01:15,360 Speaker 2: the short people the left tail, and over on the 20 00:01:15,440 --> 00:01:18,320 Speaker 2: right were the tall people the right tail. Well, they 21 00:01:18,319 --> 00:01:20,640 Speaker 2: brought that over to finances. Of course we can do 22 00:01:21,200 --> 00:01:24,440 Speaker 2: and we talk about left tail being the angst that's 23 00:01:24,480 --> 00:01:28,840 Speaker 2: out there, and right tail being maybe a better feeling, 24 00:01:28,920 --> 00:01:32,520 Speaker 2: a good feeling of what's out there within the equity markets. 25 00:01:32,560 --> 00:01:36,560 Speaker 2: Amy was silverman of our VC on your Tails. 26 00:01:36,880 --> 00:01:41,160 Speaker 1: The right tail is definitely not gone. It's just waned, 27 00:01:41,280 --> 00:01:44,160 Speaker 1: so the exuberance is still there. You look at a 28 00:01:44,200 --> 00:01:46,720 Speaker 1: stock like in video where that right tail was just 29 00:01:46,800 --> 00:01:49,760 Speaker 1: so historic, You really like in the whole history of 30 00:01:49,840 --> 00:01:52,520 Speaker 1: Nvidia had never seen anything like it. That bid for 31 00:01:52,680 --> 00:01:56,160 Speaker 1: call options that's peaked in March, but it's still there. 32 00:01:56,280 --> 00:01:59,760 Speaker 1: There's still call demand outweighing put demand. It's actually just 33 00:01:59,840 --> 00:02:02,200 Speaker 1: the idea that the left tail's waking up. You know, 34 00:02:02,240 --> 00:02:05,880 Speaker 1: we've had geopolitical tensions before, We've had Middle East tensions before, 35 00:02:06,160 --> 00:02:09,000 Speaker 1: We've had sticky CPI before. Why is it different now? 36 00:02:09,080 --> 00:02:11,600 Speaker 1: The fact that the leftail is waking up, I think 37 00:02:11,680 --> 00:02:14,639 Speaker 1: just speaks to a global worry that's picking up as well. 38 00:02:14,960 --> 00:02:17,440 Speaker 2: Harry is from many other people, not maybe in a 39 00:02:17,560 --> 00:02:20,200 Speaker 2: quant sense of looking at tails or looking at the 40 00:02:20,200 --> 00:02:23,280 Speaker 2: cross moments, but far more skew would be the lead 41 00:02:23,320 --> 00:02:25,400 Speaker 2: one there, but far more just trying to put it 42 00:02:25,440 --> 00:02:31,160 Speaker 2: in English about the confidence to stay in the market 43 00:02:31,440 --> 00:02:33,560 Speaker 2: and so on single best idea, we can take it 44 00:02:33,639 --> 00:02:35,560 Speaker 2: broader and we can do it as is Amy wus 45 00:02:35,520 --> 00:02:39,639 Speaker 2: Silverman mentions, a Nvidia what about all this AI? Paul 46 00:02:39,680 --> 00:02:42,160 Speaker 2: Sweeney's got a better feeling about AI than I do. 47 00:02:42,240 --> 00:02:45,440 Speaker 2: He's more confidence about it. My answer is I'm very 48 00:02:45,480 --> 00:02:48,000 Speaker 2: unsure about it, and what I really want to do 49 00:02:48,080 --> 00:02:52,080 Speaker 2: is listen to experts. Our expert is aniog Rana of 50 00:02:52,120 --> 00:02:56,800 Speaker 2: Bloomberg Intelligence, who is definitive on thinking about the cloud 51 00:02:56,960 --> 00:03:00,640 Speaker 2: first thing here. That's important aniog ran On as time 52 00:03:00,760 --> 00:03:04,680 Speaker 2: frame is different than your time frame. Most people looking 53 00:03:04,720 --> 00:03:07,240 Speaker 2: at AI, what's it going to be like this fall? 54 00:03:07,400 --> 00:03:09,400 Speaker 2: What's it going to be like a year from now? 55 00:03:09,840 --> 00:03:14,079 Speaker 2: Ani Ragrana is looking out three years, five years, ten 56 00:03:14,160 --> 00:03:19,040 Speaker 2: years in what artificial intelligence will do? We need an update? 57 00:03:19,120 --> 00:03:21,320 Speaker 2: Ani Ragrana of Bloomberg Intelligence. 58 00:03:21,840 --> 00:03:23,799 Speaker 3: One of the things we predict over the next three 59 00:03:23,880 --> 00:03:27,160 Speaker 3: years you will see revenue growth accelerate, but you're not 60 00:03:27,200 --> 00:03:28,920 Speaker 3: going to see R and D budgets go at that 61 00:03:29,000 --> 00:03:32,079 Speaker 3: same pace. So that's the margin enhancement for you. But 62 00:03:32,120 --> 00:03:34,480 Speaker 3: that does not mean that you're going to be firing 63 00:03:34,960 --> 00:03:39,119 Speaker 3: you know, software developers because there is millions of shortage 64 00:03:39,240 --> 00:03:41,400 Speaker 3: right now for people to get work done. I think 65 00:03:41,400 --> 00:03:44,280 Speaker 3: the real benefit is going to be in modernizing the 66 00:03:44,360 --> 00:03:47,520 Speaker 3: old applications, which were written in a language that people 67 00:03:47,520 --> 00:03:51,480 Speaker 3: don't use anymore. So this particular software can convert that 68 00:03:51,600 --> 00:03:54,160 Speaker 3: into the newer language, which can do more, you know, 69 00:03:54,320 --> 00:03:57,480 Speaker 3: unique things, or digitize your business a lot faster. 70 00:03:57,720 --> 00:04:00,000 Speaker 2: It's good to talk to Anna Ragrana about the comeback 71 00:04:00,080 --> 00:04:03,560 Speaker 2: ground trip, if you will, in Amazon, off the pandemic euphoria. 72 00:04:04,240 --> 00:04:07,160 Speaker 2: What a plunge in Amazon and almost back out to 73 00:04:08,120 --> 00:04:10,120 Speaker 2: where it was. Oh, I think it was the end 74 00:04:10,160 --> 00:04:13,400 Speaker 2: of twenty two as well. Some of the roller coasters 75 00:04:13,480 --> 00:04:17,840 Speaker 2: of artificial intelligence. We're getting ready for earning season Netflix 76 00:04:17,880 --> 00:04:23,040 Speaker 2: this afternoon. Look to Tim Stevanovic and Carol Masser. They'll 77 00:04:23,080 --> 00:04:27,240 Speaker 2: have all of that for you on Bloomberg Radio here 78 00:04:27,279 --> 00:04:29,920 Speaker 2: in the afternoon. But then we really dive into the 79 00:04:29,960 --> 00:04:34,520 Speaker 2: beginning of a blended industrial and also the technology earnings 80 00:04:34,560 --> 00:04:36,960 Speaker 2: that we see at the end of April into well 81 00:04:37,000 --> 00:04:40,560 Speaker 2: May second is Apple among others as well. So it's 82 00:04:40,600 --> 00:04:43,360 Speaker 2: into earning season to see where we fit in this 83 00:04:43,480 --> 00:04:48,240 Speaker 2: great bull market. We are on Apple car Play, download 84 00:04:48,279 --> 00:04:51,440 Speaker 2: the Bloomberg Business app, also out on Google. I should say, 85 00:04:52,000 --> 00:04:54,800 Speaker 2: and that's building out nicely, but we are humbled by 86 00:04:54,839 --> 00:04:58,520 Speaker 2: YouTube and what we've seen out there. Searched Bloomberg podcasts, 87 00:04:58,880 --> 00:05:02,240 Speaker 2: look for Lisa Matteo and we're there. We're there, seven 88 00:05:02,279 --> 00:05:05,640 Speaker 2: to ten. We're now rolling out the complete show as 89 00:05:05,720 --> 00:05:08,240 Speaker 2: a digital file, which you'll see later in the morning. 90 00:05:08,600 --> 00:05:18,599 Speaker 2: And of course the thoughts like this, like single best idea,