1 00:00:00,120 --> 00:00:03,960 Speaker 1: This is Bloomberg Business Week with Carol Messer and Tim 2 00:00:04,000 --> 00:00:08,240 Speaker 1: Stenebek on Bloomberg Radio. All right, Zoom was certainly an 3 00:00:08,280 --> 00:00:11,440 Speaker 1: indication of how we were working during the pandemic. But 4 00:00:11,480 --> 00:00:13,480 Speaker 1: we've really leaned on the team over at Microsoft three 5 00:00:13,520 --> 00:00:16,319 Speaker 1: sixty five for how we are working, yes, then, but 6 00:00:16,480 --> 00:00:20,360 Speaker 1: also today, and they've got a new report out that 7 00:00:21,040 --> 00:00:23,319 Speaker 1: finds that there's not necessarily any need to be worried 8 00:00:23,320 --> 00:00:26,080 Speaker 1: about AI. Of course, to be fair, Microsoft is deep 9 00:00:26,079 --> 00:00:29,280 Speaker 1: into AI, really awakening the world earlier this year with 10 00:00:29,320 --> 00:00:32,120 Speaker 1: its ten billion dollar investment into Open AI's chat GBT. 11 00:00:32,360 --> 00:00:34,440 Speaker 1: So even so, our next guest is here to help 12 00:00:34,479 --> 00:00:37,760 Speaker 1: answer the question will AI fix work? And great to 13 00:00:37,800 --> 00:00:40,320 Speaker 1: have back with us. Jared's Pataro. He is Corporate VP 14 00:00:40,479 --> 00:00:43,800 Speaker 1: of Modern Work and Business Applications at Microsoft three sixty five. 15 00:00:43,840 --> 00:00:47,199 Speaker 1: Once again on Zoom in Redmand Washington. Jared, how are you. 16 00:00:48,240 --> 00:00:50,360 Speaker 2: Oh, I'm doing great. Great to be back with you, Carol. 17 00:00:50,240 --> 00:00:51,959 Speaker 1: Yeah, great to have you here with us. So tell 18 00:00:52,000 --> 00:00:54,440 Speaker 1: us about this inquiry into AI? What you guys? 19 00:00:54,480 --> 00:00:54,680 Speaker 2: I mean? 20 00:00:54,680 --> 00:00:56,440 Speaker 1: The question is I've got the report in front of me, 21 00:00:56,480 --> 00:00:59,520 Speaker 1: Will AI fix work? And I think everybody's trying to 22 00:00:59,520 --> 00:01:01,480 Speaker 1: figure it out. I kind of can't wait because I 23 00:01:01,480 --> 00:01:04,440 Speaker 1: think it can be an assist in my job. But 24 00:01:04,520 --> 00:01:07,080 Speaker 1: tell us about what you looked into, questions you asked, 25 00:01:07,120 --> 00:01:08,319 Speaker 1: and who you talk to. 26 00:01:09,280 --> 00:01:12,119 Speaker 2: Maybe let's look at what's broken first. So this particular 27 00:01:12,160 --> 00:01:14,800 Speaker 2: survey is our annual survey thirty one thousand people across 28 00:01:14,840 --> 00:01:17,840 Speaker 2: thirty one countries, and the first number that popped out 29 00:01:17,880 --> 00:01:20,760 Speaker 2: to me is exactly that fix that's needed. Sixty four 30 00:01:20,880 --> 00:01:23,520 Speaker 2: percent of people who responded told us that they just 31 00:01:23,520 --> 00:01:26,320 Speaker 2: don't have the time or energy to get their jobs done. 32 00:01:26,319 --> 00:01:28,600 Speaker 2: And we thought that that was really interesting. Coming out 33 00:01:28,600 --> 00:01:30,640 Speaker 2: of the pandemic. People are tired, but it seemed to 34 00:01:30,720 --> 00:01:32,760 Speaker 2: be more than that. So we combine that with our 35 00:01:32,800 --> 00:01:35,440 Speaker 2: telemetry data. This is the cloud data where we see 36 00:01:35,480 --> 00:01:37,800 Speaker 2: how people are working, what they're up to. We found 37 00:01:37,840 --> 00:01:40,560 Speaker 2: that up to about sixty percent of the average day, 38 00:01:40,640 --> 00:01:44,800 Speaker 2: average workers day is spent communicating and coordinating. Only forty 39 00:01:44,800 --> 00:01:46,800 Speaker 2: percent is kind of spent on that day job, the 40 00:01:46,840 --> 00:01:49,000 Speaker 2: thing that they're supposed to provide to the organization. So 41 00:01:49,560 --> 00:01:52,680 Speaker 2: really interesting setup right now as people are feeling tired 42 00:01:52,720 --> 00:01:53,840 Speaker 2: and for good reason. 43 00:01:54,120 --> 00:01:56,680 Speaker 3: And Jery something that strikes me anytime I talk to 44 00:01:56,720 --> 00:01:59,200 Speaker 3: anyone they're always concerned about when it comes to AI, 45 00:01:59,240 --> 00:02:01,480 Speaker 3: maybe they could attenduly be replaced when it comes to 46 00:02:01,520 --> 00:02:03,800 Speaker 3: their job. Looking at your study, it's interesting as far 47 00:02:03,840 --> 00:02:06,760 Speaker 3: as how AI is poised to create this whole new 48 00:02:06,800 --> 00:02:09,960 Speaker 3: way of working, what would be the counter to that, 49 00:02:10,040 --> 00:02:12,640 Speaker 3: and the benefits of AI were you might not necessarily 50 00:02:12,639 --> 00:02:14,360 Speaker 3: be losing your job, but it can actually assist you. 51 00:02:15,280 --> 00:02:17,400 Speaker 2: Well, we'll start with the fear. There's still a little 52 00:02:17,400 --> 00:02:19,519 Speaker 2: bit of fear there. Almost fifty percent, it was forty 53 00:02:19,600 --> 00:02:21,520 Speaker 2: nine percent of people who responded told us that they 54 00:02:21,520 --> 00:02:23,720 Speaker 2: were afraid that AI would come and take their job, 55 00:02:24,280 --> 00:02:28,000 Speaker 2: but that was overshadowed by another number, a whopping seventy 56 00:02:28,040 --> 00:02:31,320 Speaker 2: percent told us that despite that fear, they actually would 57 00:02:31,440 --> 00:02:34,640 Speaker 2: outsource everything they could to an AI assistant just to 58 00:02:34,680 --> 00:02:37,680 Speaker 2: help them relieve the burden. So there's this sense that 59 00:02:37,720 --> 00:02:40,400 Speaker 2: their optimism, or at least their need for help for 60 00:02:40,560 --> 00:02:43,520 Speaker 2: AI based help, really outweighs their fears. And that's a 61 00:02:43,560 --> 00:02:46,120 Speaker 2: pretty interesting nuance on many the headlines that we've been 62 00:02:46,120 --> 00:02:47,280 Speaker 2: hearing for the last couple of months. 63 00:02:47,320 --> 00:02:49,760 Speaker 1: Well, I'm thinking about email alone. I mean, I'm going 64 00:02:49,800 --> 00:02:51,360 Speaker 1: to be quite honest with you. There's a ton of 65 00:02:51,360 --> 00:02:53,400 Speaker 1: email that I never even get to read because it's 66 00:02:53,520 --> 00:02:55,720 Speaker 1: just a ton of stuff that people are either pitching 67 00:02:55,919 --> 00:02:58,720 Speaker 1: or research. And I definitely have figured out ways to 68 00:02:58,760 --> 00:03:00,840 Speaker 1: kind of weed through do certain searches so that I 69 00:03:00,880 --> 00:03:03,200 Speaker 1: really make sure I don't miss some of the important stuff. 70 00:03:03,200 --> 00:03:06,680 Speaker 1: But how could AI ultimately help us in filtering through 71 00:03:07,160 --> 00:03:10,680 Speaker 1: what is dumped into our email boxes on a regular basis. 72 00:03:11,680 --> 00:03:14,160 Speaker 2: Well, many of your your listeners are probably familiar with 73 00:03:14,240 --> 00:03:18,360 Speaker 2: chat GPT, introduced last year, and it's particularly good, it's summarizing, 74 00:03:18,440 --> 00:03:20,320 Speaker 2: and so I've been using a product that we call 75 00:03:20,360 --> 00:03:23,280 Speaker 2: Copilot Microsoft through sixty five copil it essentially takes the 76 00:03:23,680 --> 00:03:27,200 Speaker 2: models behind chat GPT, these large language models and integrates 77 00:03:27,200 --> 00:03:30,280 Speaker 2: it directly into your email into Outlook. And this does 78 00:03:30,320 --> 00:03:32,560 Speaker 2: amazing things. It allows you, for instance, get a long 79 00:03:32,720 --> 00:03:35,080 Speaker 2: those long email threads where you're supposed to go read 80 00:03:35,080 --> 00:03:37,440 Speaker 2: from the bottom up, and it can summarize that just 81 00:03:37,520 --> 00:03:40,040 Speaker 2: in one click. And then once it's summarized, it actually 82 00:03:40,080 --> 00:03:42,520 Speaker 2: gives you a couple of different prompts or those are 83 00:03:42,720 --> 00:03:45,240 Speaker 2: different things you can choose from to answer, so it 84 00:03:45,280 --> 00:03:47,600 Speaker 2: will author an answer, allow you to make it longer, shorter, 85 00:03:47,960 --> 00:03:51,320 Speaker 2: more formal, more casual, it is changed the way I 86 00:03:51,320 --> 00:03:53,880 Speaker 2: do email. I never want to do email without it again, 87 00:03:53,960 --> 00:03:56,320 Speaker 2: so it definitely changes work habits and practices. 88 00:03:56,440 --> 00:03:58,400 Speaker 1: Well, so just just share with me, because, like I 89 00:03:58,400 --> 00:04:00,240 Speaker 1: said at the top, I mean to be fair. You 90 00:04:00,280 --> 00:04:03,040 Speaker 1: guys are all in on AI, no doubt about it. 91 00:04:03,080 --> 00:04:07,600 Speaker 1: And in many ways that news of the Microsoft investment 92 00:04:07,680 --> 00:04:09,720 Speaker 1: really did kind of wake up the world in terms 93 00:04:09,760 --> 00:04:12,720 Speaker 1: of what you folks were doing. Specifically, Now everybody's talking 94 00:04:12,720 --> 00:04:15,160 Speaker 1: about it. As you guys have been playing around with it, 95 00:04:15,240 --> 00:04:18,400 Speaker 1: what are you finding are the great capabilities? What are 96 00:04:18,400 --> 00:04:19,839 Speaker 1: still the things that need to be worked out? 97 00:04:21,520 --> 00:04:24,039 Speaker 2: The five seconds to wow type demo I do with 98 00:04:24,120 --> 00:04:26,440 Speaker 2: customers is in a meeting. So in a meeting like 99 00:04:26,480 --> 00:04:28,360 Speaker 2: this that would be just a normal business meeting, we're 100 00:04:28,360 --> 00:04:31,479 Speaker 2: trading kind of conversation back and forward. And what it 101 00:04:31,520 --> 00:04:33,760 Speaker 2: can do there is it can literally listen in as 102 00:04:33,800 --> 00:04:35,960 Speaker 2: if it were an assistant to the meeting and summarize. 103 00:04:36,000 --> 00:04:38,840 Speaker 2: That means it can write notes. It can respond when 104 00:04:38,880 --> 00:04:40,880 Speaker 2: I ask questions like what did Carol say again? Or 105 00:04:40,920 --> 00:04:43,479 Speaker 2: how did Jared answer this question? It can allow me, 106 00:04:43,520 --> 00:04:45,440 Speaker 2: for instance, to identify, you know, what are the two 107 00:04:45,480 --> 00:04:47,960 Speaker 2: sides of this argument and who's in favor of which 108 00:04:48,000 --> 00:04:50,919 Speaker 2: one it actually is incredibly valuable in a meeting. And 109 00:04:50,960 --> 00:04:52,680 Speaker 2: then even better than that, Carol, the thing that I 110 00:04:52,720 --> 00:04:56,080 Speaker 2: love is it allows me to attend meetings, so I 111 00:04:56,120 --> 00:04:59,000 Speaker 2: can not attend a meeting and then using that same 112 00:04:59,160 --> 00:05:02,200 Speaker 2: GPT larg language model, query the meeting after the fact, 113 00:05:02,279 --> 00:05:05,200 Speaker 2: did they ever mention my name or any decisions made? 114 00:05:05,240 --> 00:05:08,119 Speaker 2: What decisions? Why did they make them? So it opens 115 00:05:08,200 --> 00:05:11,000 Speaker 2: up these brand new possibilities for work because it kind 116 00:05:11,040 --> 00:05:13,720 Speaker 2: of blurs time and space. You know, it's as if 117 00:05:13,760 --> 00:05:16,320 Speaker 2: you could be there, but you're not there, and you're 118 00:05:16,360 --> 00:05:19,000 Speaker 2: able to use the power of AI here to kind 119 00:05:19,000 --> 00:05:21,279 Speaker 2: of get into the dynamics of people even just having 120 00:05:21,320 --> 00:05:24,200 Speaker 2: regular conversations. So just one example, but it's integrated across 121 00:05:24,240 --> 00:05:27,680 Speaker 2: all of our products. We talked about email or PowerPoint, 122 00:05:27,839 --> 00:05:28,640 Speaker 2: all of those things. 123 00:05:28,680 --> 00:05:31,440 Speaker 1: What's the thing though, that is still tricky because we 124 00:05:31,480 --> 00:05:34,120 Speaker 1: talk about AI kind of working alongside of us and 125 00:05:34,440 --> 00:05:37,120 Speaker 1: we don't have to go to the meeting Yahoo. But 126 00:05:37,320 --> 00:05:41,000 Speaker 1: I do wonder what are the things that you know, 127 00:05:41,080 --> 00:05:44,080 Speaker 1: people say you can't really you know, duplicate a human 128 00:05:44,120 --> 00:05:45,720 Speaker 1: in terms of their perception of things and so on 129 00:05:45,760 --> 00:05:47,720 Speaker 1: and so forth. So what do would you say that 130 00:05:47,760 --> 00:05:49,760 Speaker 1: we still need to be cautious about or we're still 131 00:05:49,760 --> 00:05:51,640 Speaker 1: figuring out, or you guys are still figuring out when 132 00:05:51,680 --> 00:05:52,320 Speaker 1: it comes to this. 133 00:05:53,440 --> 00:05:56,240 Speaker 2: Very stud question, because it turns out while these things 134 00:05:56,279 --> 00:05:59,480 Speaker 2: are incredibly useful, they do make mistakes. And these things, 135 00:05:59,520 --> 00:06:02,640 Speaker 2: I mean the AI assistance that we have, so they 136 00:06:02,640 --> 00:06:05,640 Speaker 2: get it wrong sometimes. In fact, the technical term is hallucination. 137 00:06:05,800 --> 00:06:08,040 Speaker 2: If you believe that, that literally is the term they 138 00:06:08,040 --> 00:06:10,960 Speaker 2: make stuff up. So the reason that we use copilot 139 00:06:10,960 --> 00:06:13,200 Speaker 2: as a name for our product is really the signal 140 00:06:13,240 --> 00:06:15,640 Speaker 2: to the user, hey, this is an autopilot. You can't 141 00:06:15,680 --> 00:06:17,600 Speaker 2: just take the answers and say there you go, I'm 142 00:06:17,640 --> 00:06:19,480 Speaker 2: going to fire this off as an email. You really 143 00:06:19,520 --> 00:06:21,320 Speaker 2: have to be in the driver's seat. You have to 144 00:06:21,360 --> 00:06:24,120 Speaker 2: make the decisions. But the great news is my experience 145 00:06:24,160 --> 00:06:25,960 Speaker 2: has been when it is wrong, it's what we call 146 00:06:26,080 --> 00:06:28,120 Speaker 2: usefully wrong. So it doesn't just make things up. To 147 00:06:28,200 --> 00:06:30,800 Speaker 2: make things up. Sometimes we'll feel in details where it 148 00:06:30,800 --> 00:06:32,680 Speaker 2: didn't have all the data. You can go in and 149 00:06:32,680 --> 00:06:34,920 Speaker 2: correct that, tweak that, and then it's put you ahead 150 00:06:34,920 --> 00:06:35,760 Speaker 2: of the game anyway. 151 00:06:36,000 --> 00:06:37,720 Speaker 3: So how long do you think this could take? But 152 00:06:37,720 --> 00:06:41,720 Speaker 3: before it's more widespread with users that could potentially use 153 00:06:41,760 --> 00:06:43,400 Speaker 3: this type of things in meetings when you do have 154 00:06:43,480 --> 00:06:45,799 Speaker 3: still some of these hiccups and glitches that could happen 155 00:06:45,839 --> 00:06:46,200 Speaker 3: with AI. 156 00:06:47,400 --> 00:06:49,279 Speaker 2: We just introduced a couple of weeks ago that we're 157 00:06:49,279 --> 00:06:51,839 Speaker 2: expanding what we call our Preview program to six hundred 158 00:06:51,920 --> 00:06:54,279 Speaker 2: enterprises around the world, so we'll have hundreds of thousands 159 00:06:54,320 --> 00:06:56,840 Speaker 2: of users here in the coming months. So that's kind 160 00:06:56,839 --> 00:06:58,640 Speaker 2: of the scale that it's out right at right now. 161 00:06:58,680 --> 00:07:00,960 Speaker 2: But it's moving very quickly. My team, we talk about 162 00:07:01,040 --> 00:07:04,040 Speaker 2: AI time, the cycle time of the industry has really 163 00:07:04,080 --> 00:07:05,880 Speaker 2: increased as we're able to build on top of these 164 00:07:05,920 --> 00:07:08,320 Speaker 2: technologies really quickly. So I don't think it'll be a 165 00:07:08,320 --> 00:07:09,920 Speaker 2: matter of years. I do think it'll be a matter 166 00:07:10,000 --> 00:07:12,040 Speaker 2: of months until you start to see these tools being 167 00:07:12,120 --> 00:07:13,080 Speaker 2: used all over the place. 168 00:07:13,200 --> 00:07:15,240 Speaker 1: You know, for someone who has been you know, focusing 169 00:07:15,280 --> 00:07:18,679 Speaker 1: on workplace trends and coming off a really crazy period, 170 00:07:18,800 --> 00:07:21,760 Speaker 1: you know, meaning the pandemic specifically, and we're able to 171 00:07:21,800 --> 00:07:24,560 Speaker 1: track a lot of things. How are you kind of 172 00:07:24,760 --> 00:07:31,120 Speaker 1: placing generative AI machine learning in kind of I don't know, 173 00:07:31,240 --> 00:07:35,040 Speaker 1: is it akin to some other great or interesting development 174 00:07:35,280 --> 00:07:37,120 Speaker 1: in terms of how we work, Like, how do you 175 00:07:37,160 --> 00:07:40,400 Speaker 1: position it historically or technologically for that matter. 176 00:07:41,280 --> 00:07:43,880 Speaker 2: We think it's as big as the PC revolution or 177 00:07:43,920 --> 00:07:46,040 Speaker 2: as big as the Internet. We think it's that big. 178 00:07:46,200 --> 00:07:49,040 Speaker 2: You know, if I again frame it, we would say, Wow, 179 00:07:49,080 --> 00:07:52,800 Speaker 2: you couldn't write a script like this if you were trying. Really, 180 00:07:52,800 --> 00:07:55,960 Speaker 2: what the pandemic did is is rewire the where and 181 00:07:56,000 --> 00:07:58,800 Speaker 2: the when of work, So it changed entirely around the world. 182 00:07:58,840 --> 00:08:01,559 Speaker 2: You know, those two mentions of work. All of a sudden, 183 00:08:01,600 --> 00:08:04,520 Speaker 2: AI is changing the how of work. By the time 184 00:08:04,560 --> 00:08:07,520 Speaker 2: we get kind of roughly four years out from the pandemic, 185 00:08:07,560 --> 00:08:10,720 Speaker 2: the combination of those two things will have really just 186 00:08:10,840 --> 00:08:13,680 Speaker 2: changed patterns practices associated with work. My kids, as they 187 00:08:13,680 --> 00:08:15,760 Speaker 2: go to work, they're not going to enter a work 188 00:08:15,840 --> 00:08:18,520 Speaker 2: environment anything like I did you know twenty five years ago? 189 00:08:18,760 --> 00:08:22,520 Speaker 1: Is that good or bad? Bodily because you know, things 190 00:08:22,520 --> 00:08:25,400 Speaker 1: are never right black and white like it's it's that's right. 191 00:08:25,520 --> 00:08:27,960 Speaker 1: We're worried about people not really talking to each other. 192 00:08:28,160 --> 00:08:30,640 Speaker 1: I have a colleague, Matt Miller, who laughs that when 193 00:08:30,640 --> 00:08:32,480 Speaker 1: we all get on a zoom call and we're literally 194 00:08:32,559 --> 00:08:35,280 Speaker 1: like right next to each other, but I'm sitting there typing, 195 00:08:35,480 --> 00:08:38,839 Speaker 1: you know, notes, and you know, it's just interesting. 196 00:08:40,480 --> 00:08:42,160 Speaker 2: My response to that is I would say, you know, 197 00:08:42,240 --> 00:08:44,679 Speaker 2: AI giveth an, AI taketh away, Like, we have this 198 00:08:44,760 --> 00:08:46,760 Speaker 2: moment and the way we think about it is what 199 00:08:46,800 --> 00:08:48,920 Speaker 2: have we gained and what have we lost? And how 200 00:08:48,920 --> 00:08:50,800 Speaker 2: do we lose as little as possible and how do 201 00:08:50,800 --> 00:08:52,960 Speaker 2: we gain as much as possible? But you're exactly right, 202 00:08:53,080 --> 00:08:54,880 Speaker 2: these big if it's as big as the PC, you know, 203 00:08:54,880 --> 00:08:56,920 Speaker 2: if you really believe that it's that type of shift, 204 00:08:57,200 --> 00:08:59,280 Speaker 2: then you do have to recognize, Wow, this is probably 205 00:08:59,320 --> 00:09:01,880 Speaker 2: the beginning of an era, a new era, which is 206 00:09:01,920 --> 00:09:03,559 Speaker 2: be really thoughtful about what we want to make sure 207 00:09:03,600 --> 00:09:05,360 Speaker 2: we don't lose in that process. But that will take 208 00:09:05,360 --> 00:09:07,440 Speaker 2: all of us, you know, we're all learning as we go. Yeah. 209 00:09:07,520 --> 00:09:08,959 Speaker 1: Right, And the more we use it though, right, the 210 00:09:09,480 --> 00:09:12,280 Speaker 1: smarter or more specific it becomes. Jared, Nice to check 211 00:09:12,280 --> 00:09:15,000 Speaker 1: in with you again. Jared's Pataro. He's corporate vice president, 212 00:09:15,080 --> 00:09:17,440 Speaker 1: head of Modern Work at Microsoft three sixty five on 213 00:09:17,559 --> 00:09:22,040 Speaker 1: Zoom from Redman, Washington. Yeah, I feel like. 214 00:09:22,000 --> 00:09:24,040 Speaker 3: I have that same email problem as you, Carol. 215 00:09:24,320 --> 00:09:27,280 Speaker 1: It's crazy. Now. I've said to this though a million times, 216 00:09:27,280 --> 00:09:30,240 Speaker 1: like I've thought about AI the ability to you know, 217 00:09:30,320 --> 00:09:32,120 Speaker 1: I like to write kind of intros into our guests 218 00:09:32,160 --> 00:09:34,720 Speaker 1: and stuff, and just the ability to maybe shoot some information, 219 00:09:34,800 --> 00:09:36,880 Speaker 1: say here, just draft something, I'll edit it. I'll go 220 00:09:36,920 --> 00:09:39,320 Speaker 1: through it and make sure it's But to kind of 221 00:09:39,320 --> 00:09:41,800 Speaker 1: have that framework to try to kick off with would 222 00:09:41,840 --> 00:09:43,080 Speaker 1: be really cool. Yeah. 223 00:09:43,200 --> 00:09:45,920 Speaker 3: Yeah, all my distribution lists that I'm on for banks, 224 00:09:46,600 --> 00:09:46,640 Speaker 3: I 225 00:09:46,800 --> 00:09:48,920 Speaker 1: Just like to filter through staff like, would be really 226 00:09:49,000 --> 00:09:49,520 Speaker 1: kind of cool.