1 00:00:07,133 --> 00:00:10,453 Speaker 1: You're listening to the Saturday Morning with Jack Tam podcast 2 00:00:10,573 --> 00:00:11,773 Speaker 1: from News Talks. 3 00:00:11,453 --> 00:00:15,653 Speaker 2: At b Apple isn't known for making heaps of acquisitions 4 00:00:15,693 --> 00:00:17,853 Speaker 2: in the business world, but they have made a two 5 00:00:18,173 --> 00:00:21,693 Speaker 2: billion dollar deal to buy an AI company. Paul Stenhouse 6 00:00:21,773 --> 00:00:24,173 Speaker 2: is our text and he's with us this morning. So 7 00:00:24,293 --> 00:00:27,013 Speaker 2: why is Apple buying up a company? 8 00:00:27,053 --> 00:00:30,053 Speaker 3: Paul, Yeah, it's a good question, Jack, because you say 9 00:00:30,093 --> 00:00:32,733 Speaker 3: they don't do it often Beats the headphones that was 10 00:00:32,973 --> 00:00:35,493 Speaker 3: like there. That was their biggest acquisition they've ever done, 11 00:00:35,493 --> 00:00:38,333 Speaker 3: at three billion dollars. It was quite a few years 12 00:00:38,373 --> 00:00:40,253 Speaker 3: ago now, but yeah, they kind of lay low. But 13 00:00:40,653 --> 00:00:45,253 Speaker 3: of course AI it's everywhere, and they have made an 14 00:00:45,293 --> 00:00:46,413 Speaker 3: acquisition of a company. 15 00:00:46,453 --> 00:00:49,213 Speaker 4: It's just four years old. It's called q dot Ai. 16 00:00:50,013 --> 00:00:51,693 Speaker 4: You probably haven't heard of it. I don't think you 17 00:00:51,733 --> 00:00:53,813 Speaker 4: were ever supposed to hear about it. It's really supposed 18 00:00:53,813 --> 00:00:57,013 Speaker 4: to be used by companies like Apple in the world 19 00:00:57,093 --> 00:01:00,573 Speaker 4: of speech detection, right, So what they want to do 20 00:01:00,653 --> 00:01:02,573 Speaker 4: is to try to make it as good as possible 21 00:01:02,933 --> 00:01:06,133 Speaker 4: to hear people talking. So if you're whispering, that would 22 00:01:06,173 --> 00:01:08,453 Speaker 4: be one on peace. How can you start to understand that? 23 00:01:09,253 --> 00:01:11,493 Speaker 4: Or if you're in a really noisy environment. How can 24 00:01:11,533 --> 00:01:15,853 Speaker 4: you do can get the best understanding of what someone 25 00:01:15,973 --> 00:01:19,413 Speaker 4: is saying? They even in some patterns. This is fun 26 00:01:19,413 --> 00:01:22,733 Speaker 4: where it can read your lips isn't that interesting? And 27 00:01:23,373 --> 00:01:25,373 Speaker 4: look at your face through the camera if you say 28 00:01:25,413 --> 00:01:28,093 Speaker 4: on like a video call and look at your micro 29 00:01:28,173 --> 00:01:31,573 Speaker 4: expressions on your face. They say that they can understand 30 00:01:31,573 --> 00:01:33,933 Speaker 4: what you're saying even when you don't say something. How 31 00:01:34,013 --> 00:01:37,333 Speaker 4: is that creepy? So anyway, Apple's brought this thing now, 32 00:01:37,493 --> 00:01:39,533 Speaker 4: they've even changed, you know, the privacy policy. The Apple 33 00:01:39,533 --> 00:01:41,893 Speaker 4: privacy policy is on the q dot ai website. It's 34 00:01:41,893 --> 00:01:45,173 Speaker 4: all official, it's all real. I would imagine that they 35 00:01:45,173 --> 00:01:47,413 Speaker 4: are probably going to put this technology into things like 36 00:01:47,413 --> 00:01:53,093 Speaker 4: their vision pro headset, the whole go into augmented reality 37 00:01:53,253 --> 00:01:56,733 Speaker 4: you know type thing, and probably into your ear pods too, 38 00:01:56,853 --> 00:02:00,133 Speaker 4: because the quality of the EarPods that are always looking 39 00:02:00,133 --> 00:02:03,933 Speaker 4: to try to improve that. So two billion dollars, right, 40 00:02:04,253 --> 00:02:06,853 Speaker 4: that's what they That's what they paid. I read that 41 00:02:06,893 --> 00:02:12,333 Speaker 4: the company has about one hundred employees, so twenty million 42 00:02:12,373 --> 00:02:16,053 Speaker 4: dollars worth of value per employee is pretty degrees. 43 00:02:16,333 --> 00:02:19,253 Speaker 2: Yeah, yeah, that's very interesting. A I mean, they've been 44 00:02:19,333 --> 00:02:22,053 Speaker 2: criticized as being kind of behind the play on the 45 00:02:22,093 --> 00:02:24,773 Speaker 2: AI front, So be interesting to see if this makes 46 00:02:24,813 --> 00:02:27,813 Speaker 2: too much of a difference. Hey, Amazon is getting rid 47 00:02:27,813 --> 00:02:30,853 Speaker 2: of sixteen thousand employees. 48 00:02:31,533 --> 00:02:35,453 Speaker 4: M hm yep. Earlier this week people got an email 49 00:02:35,653 --> 00:02:38,613 Speaker 4: to say that they had made tough decisions. Are always 50 00:02:38,613 --> 00:02:42,733 Speaker 4: tough decisions to let go of. It's just a staggering 51 00:02:42,813 --> 00:02:45,253 Speaker 4: number of people, isn't it. Some of them though, work 52 00:02:45,333 --> 00:02:47,293 Speaker 4: to a text message. I was reading reports on ridder 53 00:02:47,373 --> 00:02:49,773 Speaker 4: of three A m got a text, you should check 54 00:02:49,773 --> 00:02:53,213 Speaker 4: your email in your email. Yeah, we've made a decision, 55 00:02:53,773 --> 00:02:56,973 Speaker 4: they say. I mean they've got three hundred and fifty 56 00:02:57,133 --> 00:03:01,093 Speaker 4: thousand people working in Amazon corporate right, which is huge, 57 00:03:01,333 --> 00:03:04,853 Speaker 4: So this cuts about four point six percent. They say 58 00:03:04,893 --> 00:03:08,813 Speaker 4: it's to remove layers bureaucracy. It's to increase ownership that 59 00:03:08,853 --> 00:03:12,053 Speaker 4: each employee has over I guess you know their day 60 00:03:12,093 --> 00:03:14,613 Speaker 4: to day tasks and getting things done, because you can 61 00:03:14,653 --> 00:03:16,613 Speaker 4: imagine with three hundred and fifty thousand people, things are 62 00:03:16,613 --> 00:03:20,293 Speaker 4: probably pretty slow. But again, I want to hear an 63 00:03:20,333 --> 00:03:25,133 Speaker 4: even more staggering number. Amazon globally has one point five 64 00:03:25,373 --> 00:03:29,053 Speaker 4: seven so it was one point six million staff across 65 00:03:29,053 --> 00:03:29,653 Speaker 4: its businesses. 66 00:03:29,693 --> 00:03:33,213 Speaker 2: WHOA, yeah, isn't that I just thought I. 67 00:03:33,133 --> 00:03:35,293 Speaker 4: Saw that number, and I was I thought it was 68 00:03:35,333 --> 00:03:37,173 Speaker 4: about a million or just a round a million, but 69 00:03:37,293 --> 00:03:40,893 Speaker 4: one point six is wow. Yeah, but even with sixteen 70 00:03:40,893 --> 00:03:43,213 Speaker 4: thousand people, you can understand why maybe everyone doesn't get 71 00:03:43,213 --> 00:03:45,133 Speaker 4: a fifteen minute call from HR No. 72 00:03:45,253 --> 00:03:48,253 Speaker 2: You can, yeah, you can to a degree. It's still 73 00:03:48,293 --> 00:03:53,733 Speaker 2: it still doesn't help the you know, the personal criticism 74 00:03:54,053 --> 00:03:56,733 Speaker 2: certainly feels kind of legit. But then when I suppose 75 00:03:56,853 --> 00:03:58,973 Speaker 2: you put those sixteen thousand in the context of a 76 00:03:59,013 --> 00:04:01,973 Speaker 2: workforce that large, you know, there'll be some people to say, well, 77 00:04:01,973 --> 00:04:03,973 Speaker 2: it's just a tiny percentage of the workforce, but yeah, 78 00:04:03,933 --> 00:04:05,813 Speaker 2: there's still sixteen thousand people. 79 00:04:06,013 --> 00:04:08,093 Speaker 4: Very think of how many of those people will be 80 00:04:08,573 --> 00:04:12,173 Speaker 4: would have been working living in the Seattle region. That's 81 00:04:12,213 --> 00:04:14,173 Speaker 4: a big hit to Seattle's economy. 82 00:04:14,733 --> 00:04:18,053 Speaker 2: Hey, thanks, Paul, appreciate your time. That's our textbook, Paul Stenhouse. 83 00:04:18,573 --> 00:04:21,653 Speaker 1: For more from Saturday Morning with Jack Tame, listen live 84 00:04:21,773 --> 00:04:24,933 Speaker 1: to News Talks' b from nine Am, saturday or follow 85 00:04:24,973 --> 00:04:26,533 Speaker 1: the podcast On. iHeartRadio