WEBVTT 00:00:02.293 --> 00:00:03.420 This morning I watched an 00:00:03.420 --> 00:00:06.423 old Twilight Zone episode. 00:00:07.298 --> 00:00:10.301 As one does. As one does. 00:00:10.593 --> 00:00:13.596 It was actually the story about how, 00:00:14.222 --> 00:00:17.225 CEO W.E. Whipple 00:00:17.267 --> 00:00:20.687 decided to introduce computers into his factories and how excited he is 00:00:20.687 --> 00:00:25.900 about how many people and costs his machines can eliminate. 00:00:27.527 --> 00:00:30.655 Until those computers end up eliminating his job too. 00:00:32.449 --> 00:00:35.452 And so it's actually a little chilling. 00:00:35.618 --> 00:00:38.621 But also interesting for our purposes today because 00:00:39.456 --> 00:00:42.459 it aired in May of 1964. 00:00:43.418 --> 00:00:45.670 61 years ago. 00:00:45.670 --> 00:00:49.591 And it actually turned out to be completely wrong. 00:00:50.925 --> 00:00:55.263 The digital revolution of the past 75 years has changed 00:00:55.263 --> 00:00:58.266 and eliminated a bunch of jobs, but it's also created 00:00:59.142 --> 00:01:02.103 a lot more new ones. 00:01:03.396 --> 00:01:06.399 But AI is going to change how we work. 00:01:06.816 --> 00:01:09.819 It will invent jobs that don't yet exist. 00:01:10.361 --> 00:01:14.199 And the transition has the potential to be bumpy. 00:01:14.657 --> 00:01:18.787 I think many of you may have seen the New York Times piece yesterday about 00:01:19.245 --> 00:01:21.873 how Amazon thinks that it can 00:01:23.124 --> 00:01:25.502 use AI and robots to eliminate 00:01:25.502 --> 00:01:28.505 half a million jobs by 2030. 00:01:29.798 --> 00:01:32.509 That's a really big number. 00:01:32.509 --> 00:01:35.553 So, over the next 50 minutes, we're going to talk through 00:01:35.595 --> 00:01:38.598 how AI is changing work and what skills 00:01:38.681 --> 00:01:41.684 we're going to all need 00:01:43.144 --> 00:01:46.147 to live in that world and which ones will be obsolete. 00:01:46.564 --> 00:01:51.319 So my goal is for us to have a conversation for the next 35 minutes 00:01:51.319 --> 00:01:56.199 and then leave about 15 minutes for questions, because this is a 00:01:57.325 --> 00:02:00.328 wonderfully big and 00:02:00.537 --> 00:02:01.871 interactive audience. 00:02:01.871 --> 00:02:04.582 I can just tell. 00:02:04.582 --> 00:02:08.545 So to start, I just want to let all of our panelists introduce themselves. 00:02:09.879 --> 00:02:11.756 And then after that, I'm going to ask each of them 00:02:11.756 --> 00:02:14.759 to tell us a little bit about what they're seeing. 00:02:16.427 --> 00:02:18.638 Go ahead, Anthony. 00:02:18.638 --> 00:02:19.848 Hi. Good afternoon. 00:02:19.848 --> 00:02:23.017 It's like a warm room that everyone's standing. 00:02:23.017 --> 00:02:25.103 And so feel free to move around. 00:02:25.103 --> 00:02:28.982 Anthony Abbatiello, a partner at PwC, and I'm our future of work leader. 00:02:28.982 --> 00:02:34.028 So everything we do around the the work, the workforce, the work. 00:02:34.028 --> 00:02:36.823 If you think about everything that's happening in the age of AI. 00:02:36.823 --> 00:02:37.282 Really, 00:02:37.282 --> 00:02:41.244 and what's happening with humans and what the future of the workforce 00:02:41.327 --> 00:02:42.829 will look like. 00:02:42.829 --> 00:02:45.498 I've spent 30 years of my career all in consulting 00:02:45.498 --> 00:02:48.501 around talent, leadership and the HR function. 00:02:48.543 --> 00:02:50.503 So it's a personal passion of mine. 00:02:50.503 --> 00:02:53.506 I've lived through so many of these industrial revolutions. 00:02:53.798 --> 00:02:59.053 I am both a student, a researcher and a practitioner in the space. 00:03:01.139 --> 00:03:05.226 00:03:07.770 --> 00:03:08.646 I think the battery's off. 00:03:08.646 --> 00:03:08.980 Okay. 00:03:08.980 --> 00:03:15.069 Diya Jolly, I am the Chief Product and Technology officer at Xero. 00:03:15.069 --> 00:03:18.072 For those of you who don't know, Xero provides 00:03:18.281 --> 00:03:22.327 small businesses with finance and accounting and cash-flow software, 00:03:22.869 --> 00:03:25.830 so that they can actually 00:03:25.830 --> 00:03:28.291 do what it takes to run their business. 00:03:28.291 --> 00:03:32.295 We have been heavily investing in AI and I'll talk a little bit about that 00:03:32.295 --> 00:03:37.383 to actually help our customers really be able to make their work 00:03:37.383 --> 00:03:40.762 more enjoyable and focus on where they need to do to grow their businesses. 00:03:41.429 --> 00:03:45.642 Prior to Xero, I was the Chief Product Officer at Okta, 00:03:45.642 --> 00:03:49.437 and before that I was at YouTube, 00:03:49.896 --> 00:03:52.899 running YouTube monetization. 00:03:52.899 --> 00:03:53.983 Hi, everybody. 00:03:53.983 --> 00:03:55.902 My name is Elena Sunshine. 00:03:55.902 --> 00:03:57.445 I'm thrilled to be joining this panel 00:03:57.445 --> 00:04:00.448 and being among such esteemed colleagues here. 00:04:00.531 --> 00:04:03.952 I am a Senior Director of Product Management at Oracle 00:04:03.952 --> 00:04:08.206 and I focus specifically on our OCI, that's our cloud service 00:04:08.498 --> 00:04:11.501 for building our generative AI platform 00:04:11.626 --> 00:04:15.505 that includes our agents offerings and also our AI safety offerings. 00:04:15.838 --> 00:04:19.092 I work a lot with large enterprises 00:04:19.300 --> 00:04:23.888 who are approaching their businesses and looking at how to use 00:04:23.930 --> 00:04:29.477 AI to solve their problems and our customers really care about cost, 00:04:29.644 --> 00:04:33.940 security, privacy, compliance, reliability. 00:04:34.524 --> 00:04:38.319 So that's kind of the key focuses that I have in my day-to-day life. 00:04:38.653 --> 00:04:43.658 And I also work a lot with the entire stack of Oracle applications across 00:04:44.826 --> 00:04:46.035 HR applications, 00:04:46.035 --> 00:04:49.998 finance, supply-chain management, ERP, the gamut 00:04:50.498 --> 00:04:54.210 to integrate AI into those technology stacks as well. 00:04:54.669 --> 00:04:57.213 I'm thrilled to be here. Thank you. 00:04:57.213 --> 00:04:58.589 Hey, everyone. 00:04:58.589 --> 00:05:01.509 Glad to be here. Yes, it is warm. 00:05:01.509 --> 00:05:03.219 My name is Aparna. 00:05:03.219 --> 00:05:06.472 My last name is Chennapragada, which I think in solidarity 00:05:06.472 --> 00:05:07.598 I'm going to say in my language, 00:05:07.598 --> 00:05:10.643 it means happiness because you're sunshine and Jolly, I'm making it up. 00:05:11.769 --> 00:05:13.521 I was like, oh, my God, I have to make something up now. 00:05:13.521 --> 00:05:15.773 I'm very happy Italian. Yeah. 00:05:15.773 --> 00:05:18.776 That's what, my interest means first. 00:05:19.902 --> 00:05:23.489 I am the Chief Product Officer for AI at work at Microsoft. 00:05:23.489 --> 00:05:26.492 So what I look at in our team's work on is 00:05:26.784 --> 00:05:29.829 how does AI change and amplify 00:05:30.121 --> 00:05:33.916 how we work, how we create, how we collaborate and how we live. 00:05:34.292 --> 00:05:36.377 So this is very topical. 00:05:36.377 --> 00:05:40.465 I have some thoughts that I'd love to kind of share and get some questions 00:05:40.465 --> 00:05:41.716 and so on. 00:05:41.716 --> 00:05:44.677 I've been working in AI space for the last two decades. 00:05:44.677 --> 00:05:48.181 I'd say I worked on Google Search, built a couple of new 00:05:48.181 --> 00:05:52.018 products, Google Assistant, as well as, Google Lens, 00:05:52.018 --> 00:05:55.063 which was more of an AI product search, what you see. 00:05:55.897 --> 00:05:59.192 And all through, I think one passion and one steel thread for me is 00:05:59.442 --> 00:06:04.197 how do we actually build on new, deep technological insights, 00:06:04.405 --> 00:06:07.200 but build them in a very intuitive way, in a positive some way? 00:06:09.118 --> 00:06:11.287 Aparna, 00:06:11.287 --> 00:06:13.873 two decades in AI — that makes you 00:06:13.873 --> 00:06:16.876 one of a very small group. 00:06:17.543 --> 00:06:20.463 Can you talk a little bit about what you're seeing? 00:06:20.463 --> 00:06:25.385 I mean, there seems to be a conversation going on, right 00:06:25.385 --> 00:06:28.096 as we're speaking about 00:06:28.096 --> 00:06:30.348 is AI actually helping productivity 00:06:30.348 --> 00:06:33.351 or is it hurting productivity? And 00:06:34.394 --> 00:06:37.605 what are sort of some of the best practices that are starting to develop? 00:06:38.731 --> 00:06:41.567 I'll start, I'll try to condense it into a few thoughts. 00:06:41.567 --> 00:06:44.612 Obviously, this is a topic that lives rent free in my brain. 00:06:44.612 --> 00:06:48.241 So if folks have questions about it, we should talk later. 00:06:48.991 --> 00:06:52.203 I'd say, look, I've been in the forefront of internet shift. 00:06:52.286 --> 00:06:55.581 I was at Akamai, building the first content delivery system 00:06:55.915 --> 00:06:58.626 and then the mobile and, like, web shift, obviously. 00:06:58.626 --> 00:07:02.171 And now. And folks often say, well, is this different? 00:07:02.171 --> 00:07:04.757 This time is different. That phrase keeps coming up. 00:07:04.757 --> 00:07:06.884 And at first I was like, not really. 00:07:06.884 --> 00:07:09.762 Like every technology shift has the first phase 00:07:09.762 --> 00:07:12.348 where there's an exuberance, there's an overbuilding. 00:07:12.348 --> 00:07:13.391 Optical networking. 00:07:13.391 --> 00:07:17.019 Those of you who remember 2000s — significant 00:07:17.019 --> 00:07:20.231 amount of money and like build out in infrastructure. 00:07:20.440 --> 00:07:23.109 The use cases came much later and so on. 00:07:23.109 --> 00:07:28.364 Right. But I will tell you, number one, this time is different in a very particular way. 00:07:29.991 --> 00:07:32.660 The technology diffusion usually. 00:07:32.660 --> 00:07:33.161 And Anthony, 00:07:33.161 --> 00:07:37.832 you've seen it too which is it used to take decades from the first time. 00:07:37.832 --> 00:07:41.794 I don't know, this giant brick of a mobile phone was in mid 90s to like, 00:07:41.794 --> 00:07:42.962 I don't know my uncle in like 00:07:42.962 --> 00:07:45.965 small town in India getting a phone that's like a good 20 years. 00:07:46.966 --> 00:07:49.677 Now, it's months. 00:07:49.677 --> 00:07:53.389 In fact, as we speak, I'm sure there's a model being dropped somewhere, right? 00:07:53.639 --> 00:07:56.767 So it is one of those motion-sickness inducing 00:07:56.767 --> 00:08:00.188 real compression of technology shift that's happening. 00:08:00.938 --> 00:08:04.400 The corollary in what it's relevant to what we are talking about here 00:08:04.400 --> 00:08:07.862 is that the adoption, the user adoption of 00:08:07.862 --> 00:08:11.365 this is significantly higher in a small setting. 00:08:11.741 --> 00:08:13.993 Now that doesn't mean it's evenly distributed. 00:08:13.993 --> 00:08:17.288 That's why you see the paradoxical, oh, these pilots don't work. 00:08:17.455 --> 00:08:20.500 And yet everybody has shadow usage in the enterprise. 00:08:23.085 --> 00:08:25.171 Why don't you go ahead, Anthony? 00:08:25.171 --> 00:08:26.380 I agree. 00:08:26.380 --> 00:08:30.134 I think what we're experiencing right now is this movement, 00:08:30.593 --> 00:08:33.304 to the point of, talent 00:08:33.304 --> 00:08:36.307 now, on one hand, is divine. 00:08:36.307 --> 00:08:39.185 The talent and the younger talent that are 00:08:39.185 --> 00:08:42.438 AI natives that have grown up around technology. 00:08:42.813 --> 00:08:46.150 They see this as it's just table stakes. 00:08:46.150 --> 00:08:48.486 We have to have AI, we have to have technology as part of this. 00:08:48.486 --> 00:08:50.154 Those that are in older generations. 00:08:50.154 --> 00:08:52.031 I'll put myself in that as a Gen Xer. 00:08:52.031 --> 00:08:55.660 Our generations and the baby boomers have seen this as 00:08:55.826 --> 00:08:58.204 oh, it's robots coming to kill us and take our jobs. 00:08:58.204 --> 00:08:59.705 It's the Twilight Zone example. 00:08:59.705 --> 00:09:01.332 And so, you know what? 00:09:01.332 --> 00:09:05.169 What's happening, though, is because of the pace 00:09:05.169 --> 00:09:09.131 that we're seeing, there is no ability to sit back 00:09:09.131 --> 00:09:13.052 and watch and see it destroy itself because it's actually not. 00:09:13.052 --> 00:09:14.720 And it's proving itself every day. 00:09:14.720 --> 00:09:17.431 When I was, we 00:09:17.431 --> 00:09:19.141 produced a piece of research back in January, 00:09:19.141 --> 00:09:21.852 if anyone was in the earlier session with Dan Priest, 00:09:21.852 --> 00:09:23.854 we talked about that, what was happening in January. 00:09:23.854 --> 00:09:26.315 We're looking at the future of work. Here we are in November. 00:09:26.315 --> 00:09:29.819 I almost have to rip up the entire piece of research, 00:09:29.819 --> 00:09:32.530 because so much, we've learned so much more. 00:09:32.530 --> 00:09:37.827 And the thing that has remained constant is that the human skill 00:09:37.827 --> 00:09:41.706 is still paramount to the technology and what is changing in the environment. 00:09:42.039 --> 00:09:43.958 I mean, my job is to only think about humans 00:09:43.958 --> 00:09:45.835 and what skills will be in the future. 00:09:45.835 --> 00:09:49.797 And nothing has proven that jobs are being taken away. 00:09:49.797 --> 00:09:53.676 I loved, yesterday somebody said, AI is not going to take your job, 00:09:53.968 --> 00:09:58.264 but talent, talent with AI skills will take your job. 00:09:58.764 --> 00:10:02.852 That's the key piece that continues to resonate with me and I try to impart 00:10:02.852 --> 00:10:07.106 that on so many organizations, big and small, because 00:10:07.356 --> 00:10:11.485 the pace, it will continue to outpace anything we've ever seen before. 00:10:11.485 --> 00:10:15.615 The models will continue to learn faster and will create new things. 00:10:15.990 --> 00:10:20.661 And we have to remember that in all parts of time, particularly like the 00:10:20.703 --> 00:10:23.706 New York Times article, I can debate ad nauseum, 00:10:23.706 --> 00:10:29.253 but I think at all moments in time when we've seen another revolution, 00:10:29.253 --> 00:10:32.256 it is only created new opportunity 00:10:32.381 --> 00:10:34.925 that has pushed society forward. 00:10:34.925 --> 00:10:38.387 And this one is one that actually will perpetuate 00:10:38.387 --> 00:10:40.890 all levels of society, not just the working, 00:10:42.516 --> 00:10:44.560 adult or the worker in general. 00:10:44.560 --> 00:10:45.519 It's affecting now 00:10:45.519 --> 00:10:49.357 down to children, through education, through to the experienced worker. 00:10:49.565 --> 00:10:51.817 Sorry. Go ahead. 00:10:51.817 --> 00:10:56.530 And what I was going to ask is that, like, you spend a lot of time 00:10:56.572 --> 00:11:00.493 thinking about the small-business world and I sometimes think that 00:11:02.411 --> 00:11:04.372 their, the way they think about 00:11:04.372 --> 00:11:09.377 AI has got to be a little bit different than the way giant enterprises do. 00:11:09.835 --> 00:11:11.462 Maybe more efficient. Absolutely. 00:11:11.462 --> 00:11:15.633 And so I was going to bring in a different perspective and disagree a little bit. 00:11:16.926 --> 00:11:18.969 For what we see, 00:11:18.969 --> 00:11:23.265 we sit in in the Bay area or in tech corporations 00:11:23.265 --> 00:11:26.602 or in large corporations, but most of the world doesn't sit there. 00:11:26.769 --> 00:11:28.896 A lot of the world is like small businesses. 00:11:28.896 --> 00:11:32.108 So, one of the constant challenges my marketing department has is 00:11:32.108 --> 00:11:35.319 how do you explain what agents are and what it can do for you? 00:11:35.319 --> 00:11:37.113 Because they're like, what are you talking about? 00:11:38.072 --> 00:11:38.948 So going back to your 00:11:38.948 --> 00:11:42.243 original question, here's what we see. AI is new. 00:11:42.243 --> 00:11:43.744 It's like it's two years old 00:11:43.744 --> 00:11:47.498 or gen AI's, LLMs are two years like old in mass. 00:11:47.498 --> 00:11:48.207 Right? 00:11:48.207 --> 00:11:52.169 And no, they cannot do everything that people are saying that they can do. 00:11:52.253 --> 00:11:53.754 That's very true. 00:11:53.754 --> 00:11:56.257 However, to get adoption, 00:11:56.257 --> 00:11:59.593 you really, truly, for the user has to give value. 00:12:00.302 --> 00:12:02.221 And there are two types of value. 00:12:02.221 --> 00:12:04.640 Value that fits into your workflow. I'll give you an example. 00:12:04.640 --> 00:12:09.270 We remove 22 hours of a small business's month of work 00:12:09.937 --> 00:12:13.524 with one of our features that we call automated bank rec, right. 00:12:13.691 --> 00:12:16.694 It just basically does a large part of your bookkeeping for you. 00:12:17.027 --> 00:12:20.030 And it was right in their workflow and they adopted it. 00:12:20.281 --> 00:12:23.284 There are different types of things you can do with AI 00:12:23.284 --> 00:12:26.287 and we're struggling with this internally at Xero, which requires 00:12:26.287 --> 00:12:29.415 a complete rethink of your workflow for AI to be effective. 00:12:29.707 --> 00:12:30.875 Right. 00:12:30.875 --> 00:12:33.836 We're exploring AI right now in 00:12:33.836 --> 00:12:37.840 how much can we give an actual AI agent? 00:12:37.840 --> 00:12:40.134 How much of software can we have an actual AI agent. Right. 00:12:40.134 --> 00:12:42.511 And we have an old tech stack, right. 00:12:42.511 --> 00:12:46.974 That actually requires the way software engineers, their entire SDLC, 00:12:46.974 --> 00:12:48.726 their entire software development lifecycle. 00:12:48.726 --> 00:12:50.019 And that is actually harder. 00:12:50.019 --> 00:12:53.856 So I think places you're having success with AI 00:12:53.856 --> 00:12:56.859 today is where there's concrete value. 00:12:57.151 --> 00:12:59.111 It's in the workflow, right? 00:12:59.111 --> 00:13:02.531 And it actually makes your workflow faster, better, and gives you time back 00:13:02.907 --> 00:13:06.577 and places where either the use cases are stretched 00:13:06.577 --> 00:13:11.290 and not proven, or cases where you have to change the workflow. 00:13:11.290 --> 00:13:14.418 But people are not changing the workflow, but they're trying to slam it into 00:13:14.418 --> 00:13:17.880 an existing workflow is where I think you see the pilots struggle a lot. 00:13:18.339 --> 00:13:18.839 Right. 00:13:18.839 --> 00:13:19.632 Go ahead, Elena. 00:13:19.632 --> 00:13:24.386 Well, I come from an enterprise context, but I'm seeing a lot of the same things, 00:13:24.970 --> 00:13:26.889 but with a slightly different perspective. 00:13:26.889 --> 00:13:30.100 So I think two years ago, the most common 00:13:30.100 --> 00:13:33.187 question that I got two years ago are, what are the use cases for AI? 00:13:33.187 --> 00:13:35.856 And I can't tell you how many PowerPoints 00:13:35.856 --> 00:13:39.527 that we worked on that had lists of use cases for AI. 00:13:39.527 --> 00:13:42.571 And that was kind of the deliverable that all of our customers 00:13:42.571 --> 00:13:43.656 wanted to show to their board. 00:13:43.656 --> 00:13:47.827 Like, here are the AI use cases that I'm going to implement. 00:13:47.827 --> 00:13:51.330 And we're going mostly in cost -cutting kind of fashion. 00:13:52.206 --> 00:13:55.292 And I have seen a lot of transformation of that over the past years, 00:13:55.292 --> 00:13:58.295 I think even within our own company, 00:13:58.504 --> 00:14:00.881 the place where I've seen 00:14:00.881 --> 00:14:04.927 AI really impact work and actually really drive outcomes 00:14:04.927 --> 00:14:09.431 is by putting the tools into our employees hands directly 00:14:09.765 --> 00:14:11.642 rather than, I mean, I'm a product manager, 00:14:11.642 --> 00:14:14.645 so I want to build software and deliver it to someone. 00:14:14.645 --> 00:14:19.775 But, I've really more shifted my focus towards building platforms 00:14:20.067 --> 00:14:23.070 where customer or where our employees 00:14:23.529 --> 00:14:26.824 and users can actually build themselves. 00:14:26.949 --> 00:14:28.951 I think collectively we're all still figuring out 00:14:28.951 --> 00:14:30.786 what these technologies can do. 00:14:30.786 --> 00:14:33.914 And I think when you put the tools, powerful tools into employees' 00:14:33.914 --> 00:14:36.917 hands, they build creative and surprising solutions, 00:14:37.501 --> 00:14:41.422 to their real problems, their actual business problems, 00:14:41.422 --> 00:14:45.467 rather than what you might think would be an AI problem on a PowerPoint slide. 00:14:45.676 --> 00:14:46.385 Go ahead, Aparna. 00:14:46.385 --> 00:14:50.431 I think it's interesting, actually, this phase is very consistent across 00:14:50.431 --> 00:14:53.225 different enterprises, even on consumer. Right. 00:14:53.225 --> 00:14:56.854 And I would argue it's not that different from like, say, the previous shifts. 00:14:57.354 --> 00:14:59.106 It used to be any new technology. 00:14:59.106 --> 00:15:02.985 You'd have to just add X phase, and then you have the 00:15:02.985 --> 00:15:06.280 X native like just add AI here's the phase that we are seeing. 00:15:06.363 --> 00:15:10.326 Chat app here and chat app here and meeting users where they are in their workflow. 00:15:10.659 --> 00:15:12.786 You get incremental productivity improvements there. 00:15:12.786 --> 00:15:13.495 00:15:13.495 --> 00:15:17.249 But I would also urge us to think about if especially the builders in this group, 00:15:17.917 --> 00:15:22.379 it's incremental productivity gains with the just add AI phase. 00:15:23.088 --> 00:15:25.507 But now, the models are getting significantly better. 00:15:25.507 --> 00:15:28.510 Now they can reason, they can run for a longer time. 00:15:28.552 --> 00:15:32.014 So really rethinking entire workflows while it's harder, 00:15:32.431 --> 00:15:35.976 I actually think that that has a instead of X percent improvement. 00:15:35.976 --> 00:15:37.770 We are seeing X times. 00:15:37.770 --> 00:15:42.483 And I wanted to give like a real example from this morning when I was in a meeting 00:15:42.483 --> 00:15:47.363 with a CIO of a company where they're adopting some of the agents that we built. 00:15:47.363 --> 00:15:49.698 One of the agents is called Researcher. Right. 00:15:49.698 --> 00:15:51.325 And the idea was to say, look, 00:15:51.325 --> 00:15:55.037 if you're a salesperson preparing for a prospective customer meeting, 00:15:55.329 --> 00:15:58.582 today, you go through all of the world's information, like, look up, 00:15:59.124 --> 00:16:02.461 search engines, look up emails, look up meeting 00:16:02.461 --> 00:16:06.632 transcripts, and then you go and say, how do I close it? What this agent does 00:16:06.632 --> 00:16:11.261 is it's not trying to save you time and summarize your email or what have you. 00:16:11.470 --> 00:16:13.430 It scours the entire web. 00:16:13.430 --> 00:16:16.266 It scours all of your meeting transcripts, all of the emails, 00:16:16.266 --> 00:16:19.353 and not only your memory, but the institutional memory about this 00:16:19.353 --> 00:16:24.066 account and says, here's the best way to close the deal and coach you on that. 00:16:25.192 --> 00:16:25.651 That's an 00:16:25.651 --> 00:16:28.821 example of, again, like, it's not like an X percent. 00:16:28.862 --> 00:16:31.865 It's giving you superpowers versus saving time. 00:16:31.949 --> 00:16:35.744 And if you're building something in AI, I would say yes, of course, do 00:16:35.744 --> 00:16:40.791 the incremental stuff because then it's you get quick ROI, but don't sleep on the 00:16:40.833 --> 00:16:44.920 kind of the rethinks. Like CRM, CRM on steroids, almost. 00:16:45.170 --> 00:16:48.966 One of the questions I was, I've been sort of pondering is that, 00:16:49.508 --> 00:16:55.264 like the technology revolution of the 1980s and 1990s 00:16:55.264 --> 00:16:59.268 was largely an enterprise-led revolution 00:16:59.268 --> 00:17:03.772 that consumers then adopted subsequently. 00:17:04.148 --> 00:17:07.151 The technology revolution that started 00:17:07.651 --> 00:17:09.987 really in the 2000s with the 00:17:09.987 --> 00:17:13.323 iPod and the iPhone and 00:17:13.699 --> 00:17:17.161 the explosion of Google search was really a consumer-led 00:17:18.704 --> 00:17:21.040 revolution that 00:17:21.040 --> 00:17:23.834 then oddly, enterprises followed. 00:17:25.586 --> 00:17:28.047 Which is this? 00:17:28.047 --> 00:17:31.050 00:17:31.717 --> 00:17:34.053 00:17:34.053 --> 00:17:37.931 Well, I mean, I think right now this is the 00:17:38.432 --> 00:17:42.061 agent-led revolution that is taking this 00:17:42.436 --> 00:17:45.439 and that's not to say that it's robotic, but when I look at 00:17:45.606 --> 00:17:49.318 what is actually happening, it is coming from both angles. 00:17:49.318 --> 00:17:52.029 You're seeing it from the N side. 00:17:52.029 --> 00:17:54.573 We're seeing it. People are using ChatGPT. 00:17:54.573 --> 00:17:55.741 My father is 00:17:55.741 --> 00:17:59.495 retired and he's downloaded ChatGPT and is using it, something like that. 00:17:59.495 --> 00:18:00.996 That's a win. 00:18:00.996 --> 00:18:03.916 And then, we see kids are using it now. 00:18:03.916 --> 00:18:07.211 It's only the way in school that it's accepted now, 00:18:08.045 --> 00:18:10.798 ways of studying and learning and producing content. 00:18:10.798 --> 00:18:12.966 So they have to use their brain in different ways 00:18:12.966 --> 00:18:15.385 more creatively versus rote memorization. 00:18:15.385 --> 00:18:17.846 So it could be from both angles. 00:18:17.846 --> 00:18:21.391 And then we're seeing enterprises, companies that are creating this. 00:18:21.391 --> 00:18:23.018 And it's coming at all angles. 00:18:23.018 --> 00:18:26.480 But the common thread throughout all of that is the agentic revolution. 00:18:26.480 --> 00:18:29.650 And to me, I think that's the part that what's 00:18:29.650 --> 00:18:32.611 so interesting about this, because of the way it's learning, 00:18:33.070 --> 00:18:36.073 what we're not seeing is task and process automation, 00:18:36.073 --> 00:18:39.451 which is large part of what we saw in the 80s, 90s and early 2000s. 00:18:39.701 --> 00:18:42.538 It was like, just take this process and make it now happen 00:18:42.538 --> 00:18:45.749 through a client-server app or a cloud-based app, SaaS. 00:18:45.999 --> 00:18:48.669 And we just changed the underlying technology. 00:18:48.669 --> 00:18:52.339 Now, with the models the way they are, it's learning faster 00:18:52.339 --> 00:18:53.674 and creating new ways of thinking. 00:18:53.674 --> 00:18:56.802 And actually performing the process. 00:18:57.094 --> 00:19:00.472 So now we're getting to a whole different way 00:19:00.472 --> 00:19:03.934 of thinking about, not just automation, but autonomy. 00:19:04.268 --> 00:19:09.189 So that's why I come back to — it's a both and it's agent-led. Spicy takes. 00:19:10.440 --> 00:19:12.067 Go ahead, Elena, go ahead. 00:19:12.067 --> 00:19:13.527 Oh, sure. 00:19:13.527 --> 00:19:14.736 Well, I'll just take a stand. 00:19:14.736 --> 00:19:16.280 I think it's consumer led. 00:19:16.280 --> 00:19:20.492 I mean, when I, when you see the adoption of ChatGPT 00:19:20.701 --> 00:19:22.202 and just like people in my life, 00:19:22.202 --> 00:19:25.205 like the example you said that are using this every day. 00:19:26.290 --> 00:19:29.126 To me, as a manager of a team, 00:19:29.126 --> 00:19:33.755 I see my employees are using these tools. 00:19:33.755 --> 00:19:36.258 And it's like you mentioned before, there's a lot of shadow 00:19:36.258 --> 00:19:39.011 usage within companies. 00:19:39.011 --> 00:19:42.848 And employees are demanding to have access 00:19:42.848 --> 00:19:46.351 to more powerful tools so that they can do their job better. 00:19:46.977 --> 00:19:49.771 And I think that comes from their personal experience, 00:19:49.771 --> 00:19:53.233 experiencing usually ChatGPT at home in their personal lives. 00:19:53.233 --> 00:19:56.236 So that's my hard stand. 00:19:56.737 --> 00:19:59.740 Not so spicy anymore now, 00:19:59.907 --> 00:20:02.367 but I will say I want to unpack that a little bit, 00:20:02.367 --> 00:20:04.620 because I do think what's happening right now is, 00:20:05.579 --> 00:20:07.915 I mean, I saw this firsthand in search, 00:20:07.915 --> 00:20:12.336 in the mid 2000s and early 2000s where the learning curve was. 00:20:12.336 --> 00:20:15.797 So, anybody who could type anything could get an answer. 00:20:16.173 --> 00:20:18.926 Now the learning curve is like, even for anybody 00:20:18.926 --> 00:20:22.095 who can speak to a human can now use a product. 00:20:22.346 --> 00:20:24.640 So to me, it's obvious then that like that, 00:20:24.640 --> 00:20:27.601 this whole thing is going to be like individual led. 00:20:27.601 --> 00:20:31.146 So that's one. The interesting complementary force 00:20:31.146 --> 00:20:35.359 that's happening that I see in my day job in enterprise is that there is a lot 00:20:35.359 --> 00:20:38.362 of, like the cloud shift, for example, a whole bunch of 00:20:39.780 --> 00:20:42.366 initial I wouldn't say skepticism, but hesitation. 00:20:42.366 --> 00:20:46.954 ROI, all of those questions, there's a lot more of a different posture top down, 00:20:48.038 --> 00:20:50.249 that every CIO, every 00:20:50.249 --> 00:20:52.834 executive is like, how can we use AI? 00:20:52.834 --> 00:20:55.420 How can my employees use AI and so on. 00:20:55.420 --> 00:20:57.589 So it's actually a very interesting end. 00:20:57.589 --> 00:20:59.466 And I don't think we've seen this kind of a thing. 00:20:59.466 --> 00:21:02.010 There's a bottom-up, zero learning curve. 00:21:02.010 --> 00:21:05.764 The whole world is like seeing what how easy it is and how effective. 00:21:06.098 --> 00:21:08.141 And there's a top down thing. 00:21:08.141 --> 00:21:10.811 But they are not happening at the same time, 00:21:10.811 --> 00:21:14.815 at least from what I see, because the first wave of these models, 00:21:14.815 --> 00:21:18.652 I always say, the model is the product in some of these cases. 00:21:18.986 --> 00:21:22.364 The first wave of the model, if you think about it, is amazing 00:21:22.656 --> 00:21:25.867 at answering questions because it's an X-token prediction. 00:21:25.867 --> 00:21:27.786 That was the first wave. 00:21:27.786 --> 00:21:29.538 That's why you have chat bots. 00:21:29.538 --> 00:21:33.750 The second wave starting this year has been the reasoning models. 00:21:34.501 --> 00:21:38.588 For any of the agent stuff that Anthony is talking about in process automation. 00:21:39.006 --> 00:21:42.009 You're not going to be okay with a oh, sometimes it works, 00:21:42.217 --> 00:21:43.260 right, in the enterprise. 00:21:43.260 --> 00:21:45.637 So you do need the reasoning model unlock. 00:21:45.637 --> 00:21:50.350 And the third wave is going to be this multimodal, autonomous, long-running thing. 00:21:50.767 --> 00:21:54.187 And once the models get better at that, then we will see a lot more in 00:21:54.187 --> 00:21:55.814 the enterprise. 00:21:55.814 --> 00:21:58.984 Diya, does all this like jive with you or is this like 00:21:59.192 --> 00:22:02.612 or is the world that you live in like seeing this entirely differently? 00:22:02.779 --> 00:22:04.656 No, I think this does jive with me. 00:22:04.656 --> 00:22:08.452 I will start, it is definitely consumer-led and that is that is I think we're 00:22:08.452 --> 00:22:09.077 very aligned. 00:22:09.077 --> 00:22:11.663 And I'll give you an example of why I think this is consumer-led, 00:22:12.622 --> 00:22:13.999 a hilarious example. 00:22:13.999 --> 00:22:15.625 And this happened three days ago. 00:22:15.625 --> 00:22:19.212 My son is a teenager and I hear him talking to a friend on the phone 00:22:19.713 --> 00:22:23.300 and they're talking about how that friend should approach a girl, 00:22:24.217 --> 00:22:29.765 and his friend is talking to ChatGPT, telling ChatGPT stuff about the girl and 00:22:29.765 --> 00:22:33.685 asking advice on what the girl would like and how he should talk to the girl. 00:22:34.061 --> 00:22:36.396 So it is definitely, definitely consumer-led. 00:22:36.396 --> 00:22:37.272 And I just, 00:22:37.272 --> 00:22:38.398 I have no clue 00:22:38.398 --> 00:22:40.192 because the second my son found out, because he was on 00:22:40.192 --> 00:22:41.985 speaker and I could hear, and the second he found out 00:22:41.985 --> 00:22:44.321 I was listening, like, obviously he went away. 00:22:44.321 --> 00:22:47.324 And I found that fascinating. 00:22:47.824 --> 00:22:49.076 I think there is a difference. 00:22:49.076 --> 00:22:50.410 So I think here's the difference. 00:22:50.410 --> 00:22:53.914 I think one is what Aparna said about technology, 00:22:53.955 --> 00:22:56.917 the ability of technology to get into the hands of people 00:22:56.917 --> 00:23:00.420 as just like ChatGPT has gotten, it's never been seen before. 00:23:00.420 --> 00:23:01.671 So that's one thing. 00:23:01.671 --> 00:23:04.299 I think the second thing is we are in a generation 00:23:04.299 --> 00:23:06.968 where this generation has lived through multiple 00:23:08.303 --> 00:23:09.554 technology changes. 00:23:09.554 --> 00:23:11.431 If you thought about it like the technology changes 00:23:11.431 --> 00:23:12.724 are just getting more and more rapid, 00:23:12.724 --> 00:23:15.685 and we're probably one of the first few generations 00:23:15.685 --> 00:23:18.438 that have lived through an entire cycle in our work life 00:23:18.438 --> 00:23:20.315 and our careers through technology changes. 00:23:20.315 --> 00:23:22.859 So we are pattern matching and going, this is big enough. 00:23:22.859 --> 00:23:25.862 Mobile was big enough, internet was big enough, 00:23:25.946 --> 00:23:28.198 this is big enough that something's happening. 00:23:28.198 --> 00:23:32.160 Which is why I think this time enterprises are reacting even more 00:23:33.161 --> 00:23:35.080 than they've ever in the past to go — 00:23:35.080 --> 00:23:37.416 how do we utilize this new change? Right. 00:23:37.416 --> 00:23:38.458 It sounds like you want to say something. 00:23:38.458 --> 00:23:40.293 I'll just add one more thing. 00:23:40.293 --> 00:23:43.755 Based off of what you said, I think like, the technology itself 00:23:43.755 --> 00:23:46.758 is super accessible, 00:23:47.300 --> 00:23:48.343 on its face. 00:23:48.343 --> 00:23:51.513 But I think to actually get real power 00:23:51.513 --> 00:23:54.641 from it, in a work context. 00:23:55.600 --> 00:23:57.936 Again, like I said before, I think people are just 00:23:57.936 --> 00:24:01.440 figuring this out now, and some people are better at it than others. 00:24:01.690 --> 00:24:03.900 And there's a huge, 00:24:03.900 --> 00:24:06.611 there's maybe not so much of an access gap, but there is 00:24:06.611 --> 00:24:09.614 a skill gap between where 00:24:09.948 --> 00:24:13.243 certain folks are and where other people are right now. 00:24:13.535 --> 00:24:16.538 And like you said before, 00:24:16.746 --> 00:24:20.083 the person that's going to or your job will be replaced by the person with AI. 00:24:20.125 --> 00:24:24.463 So how do we make sure that we ourselves are the people 00:24:24.463 --> 00:24:27.883 that are good at using this technology and can be more productive? 00:24:27.924 --> 00:24:30.469 How do we train our children to do that? 00:24:30.469 --> 00:24:34.222 And on the same topic, I think, I'm also concerned 00:24:34.222 --> 00:24:39.394 about the concept that we will automate entry-level jobs 00:24:39.394 --> 00:24:43.565 with this technology, and we create a gap in our talent pipeline, 00:24:43.565 --> 00:24:46.693 that becomes the future leaders of tomorrow. 00:24:47.486 --> 00:24:51.406 So those, I think are all problems that are remaining to be solved. 00:24:51.740 --> 00:24:51.990 Right. 00:24:53.408 --> 00:24:53.992 I think 00:24:53.992 --> 00:24:56.995 last question before we let you guys jump in. 00:24:58.330 --> 00:25:00.081 Let's talk about 00:25:00.081 --> 00:25:02.584 how this changes management, right? 00:25:02.584 --> 00:25:04.920 I mean, 00:25:04.920 --> 00:25:07.923 in a world where 00:25:08.465 --> 00:25:09.841 people now have, 00:25:09.841 --> 00:25:12.844 where your employees now not only have like, 00:25:13.094 --> 00:25:17.224 tools like ChatGPT, but also like vibe-coding tools that, 00:25:17.224 --> 00:25:20.769 like, would allow them to kind of actually put their own apps together. 00:25:22.229 --> 00:25:25.482 How does this change the whole top-down management structure 00:25:25.482 --> 00:25:28.735 that we've all kind of grown up in and then gotten used to? 00:25:29.945 --> 00:25:32.948 So, let me give you three points of view 00:25:33.490 --> 00:25:36.326 from a research and practical view. 00:25:36.326 --> 00:25:38.370 Number one, 00:25:38.370 --> 00:25:40.413 it is changing the way leaders lead. 00:25:40.413 --> 00:25:43.416 And when I say leaders, I really mean executive leaders. 00:25:43.416 --> 00:25:46.419 The top of the House, how they lead. 00:25:46.795 --> 00:25:50.131 The way we used to introduce change into an enterprise, big or small, 00:25:50.382 --> 00:25:52.968 we would decide we're going to buy some technology 00:25:52.968 --> 00:25:55.720 or make some transformation, make some operating model change. 00:25:55.720 --> 00:25:56.638 We'd figure it out. 00:25:56.638 --> 00:25:58.848 Then we'd start to roll it out and get people 00:25:58.848 --> 00:26:01.101 excited or involved in it at the bottom. 00:26:01.101 --> 00:26:02.227 That's over, right? 00:26:02.227 --> 00:26:04.187 It is changed. We're meeting in the middle. 00:26:04.187 --> 00:26:05.772 And so leaders have to think about. 00:26:05.772 --> 00:26:08.775 First part is how do I activate the citizens 00:26:09.067 --> 00:26:12.696 within the organization and how do I drive that 00:26:12.696 --> 00:26:15.448 and enable that democratization of technology 00:26:15.448 --> 00:26:18.118 so that it can start that, innovation can start at the bottom. 00:26:18.118 --> 00:26:18.910 The second is, 00:26:20.161 --> 00:26:23.164 we talk about we always talk about humans at the helm. 00:26:23.164 --> 00:26:26.710 There isn't a process or a workflow that doesn't have humans, 00:26:26.710 --> 00:26:28.587 not just that are in the loop that are actually leading. 00:26:28.587 --> 00:26:32.632 It's humans that are leading digital and human teammates. 00:26:33.341 --> 00:26:36.428 And how that all comes together and how you change the way 00:26:36.428 --> 00:26:41.224 you think about leadership and driving different types 00:26:41.224 --> 00:26:44.853 of capability, both at a strategic level and an execution level. 00:26:44.853 --> 00:26:50.191 And the third is, it's all about the skill and behaviors and culture. 00:26:50.775 --> 00:26:54.154 So, leaders, especially leaders of a certain age, 00:26:54.154 --> 00:26:58.325 if you are afraid that the technology is coming to destroy your job versus 00:26:58.575 --> 00:27:01.578 the technology is coming to enable or help us 00:27:01.995 --> 00:27:05.165 be more innovative, get to market faster or be more profitable — 00:27:05.915 --> 00:27:09.836 you're going to constantly put up a guardrail or 00:27:09.961 --> 00:27:11.546 a blocker around that. 00:27:11.546 --> 00:27:12.714 You need to, back to the first point. 00:27:12.714 --> 00:27:15.800 You need to enable the organization to be able to do that, one. 00:27:15.800 --> 00:27:18.428 And then for yourself, have the skills to do it. 00:27:18.428 --> 00:27:22.682 I coach a lot of CEOs who are in this stage, driving this. 00:27:22.891 --> 00:27:26.311 And they'll admit vulnerability and fear around the change. 00:27:26.561 --> 00:27:30.732 Ultimately drives them pushing back and getting them to try it. 00:27:30.732 --> 00:27:31.650 Share with their teams, 00:27:31.650 --> 00:27:35.528 creating a positive AI culture and environment will ultimately make them 00:27:35.528 --> 00:27:38.948 not just be a better leader, but make the organization be more profitable. 00:27:39.407 --> 00:27:39.783 Go ahead, Diya. 00:27:39.783 --> 00:27:43.662 Yeah, I think, I agree from a leadership perspective, I think for 00:27:44.412 --> 00:27:47.666 many people working in an organization that are not at the 00:27:47.791 --> 00:27:52.003 leadership level, but are like in mid management, etc., I think creativity, 00:27:52.629 --> 00:27:55.423 is becoming more important than was the process followed. 00:27:55.423 --> 00:27:56.424 Right. 00:27:56.424 --> 00:28:00.220 I think problem solving on issues that were deferred to people 00:28:00.345 --> 00:28:04.057 higher up above them is flowing down more and more. 00:28:04.391 --> 00:28:05.809 Right? So for example, 00:28:07.811 --> 00:28:09.187 things that would be passed 00:28:09.187 --> 00:28:12.691 off to an organization like, hey, how do we design something now in PM? 00:28:12.941 --> 00:28:17.529 Like the PMs are being asked to design with a replit or something, right? 00:28:17.529 --> 00:28:20.281 Like can you can prototype it and design it? 00:28:20.281 --> 00:28:22.909 And then I think that, 00:28:22.909 --> 00:28:26.496 then I think that job functions are emerging, right? 00:28:26.496 --> 00:28:27.997 Like job functions are beginning to merge 00:28:27.997 --> 00:28:30.083 because now different functions can do more. 00:28:30.083 --> 00:28:33.753 So, I think what managers at the middle level are being stressed 00:28:33.837 --> 00:28:37.173 to do is even if they came up through a particular function, 00:28:37.215 --> 00:28:40.927 they're being asked to actually go across functions 00:28:40.927 --> 00:28:43.012 where they may or may not necessarily have the skills. 00:28:43.012 --> 00:28:45.098 And that, I think is the scary part. 00:28:45.098 --> 00:28:47.142 The exciting part of it is you 00:28:47.142 --> 00:28:49.769 now have the technology that can probably help you do it. 00:28:49.769 --> 00:28:51.688 If you can ask the technology how you should talk to a girl, 00:28:51.688 --> 00:28:54.190 you can probably ask it how you should do another function. 00:28:54.190 --> 00:28:54.482 Right. 00:28:54.482 --> 00:28:55.150 Well, and I 00:28:55.150 --> 00:28:58.361 and one, as you were talking about that, one of the things that was going 00:28:58.361 --> 00:29:02.991 through my head was, there's an entire, I mean, in major tech companies 00:29:02.991 --> 00:29:07.412 all around the world, there are product managers. 00:29:07.412 --> 00:29:10.957 And then there are also like people who are like programing, 00:29:10.957 --> 00:29:13.960 like in the trenches. 00:29:13.960 --> 00:29:19.591 And what if those two jobs merge and how many layoffs exist as a result of that? 00:29:19.632 --> 00:29:21.217 Yeah. And I'll give you another example. Right. 00:29:21.217 --> 00:29:24.220 Like for us in our customer success organization, 00:29:24.929 --> 00:29:27.307 normally the job of customer success is customer support. 00:29:27.307 --> 00:29:29.684 You pick up the phone and you try to answer a call, or 00:29:29.684 --> 00:29:31.895 you get an inbound call and you try to answer a call. 00:29:31.895 --> 00:29:35.648 Now that answering the call, the problem has become easier because of AI. 00:29:36.399 --> 00:29:37.400 Their job is flipped. 00:29:37.400 --> 00:29:40.153 So can you get them to use more of the rest of the product, right? 00:29:40.153 --> 00:29:40.445 Right. 00:29:40.445 --> 00:29:44.949 So your job kind of merges — it's almost semi-sales, semi-customer success. 00:29:45.950 --> 00:29:46.618 Sure. 00:29:46.618 --> 00:29:49.871 So I totally agree with everything you said. 00:29:51.039 --> 00:29:52.248 I'm a product manager. 00:29:52.248 --> 00:29:55.835 So, I am, I think as product managers 00:29:55.835 --> 00:29:58.838 we may feel very comfortable wearing many hats. 00:29:58.838 --> 00:30:01.591 And so for me and for my team and my employees, 00:30:01.591 --> 00:30:06.554 this is really exciting because, now, like we said before, 00:30:06.596 --> 00:30:10.517 roles are blending and the same person can 00:30:10.892 --> 00:30:15.563 come up with an idea, prototype it, create really high-fidelity, beautiful markups, 00:30:16.022 --> 00:30:18.817 create the beautiful marketing materials, create 00:30:18.817 --> 00:30:21.820 a short video that explains their idea. 00:30:22.111 --> 00:30:27.033 And it just allows kind of the cream to rise to the top. 00:30:27.242 --> 00:30:30.453 Again, this is the concern about the skill gap, but it allows people 00:30:30.453 --> 00:30:34.165 who have great ideas to communicate them more effectively. 00:30:34.833 --> 00:30:35.750 And I think that's great. 00:30:35.750 --> 00:30:39.754 I think another piece, bringing it back to the original question about 00:30:40.755 --> 00:30:42.340 management. 00:30:42.340 --> 00:30:45.552 I remember when OpenAI announced their research agent 00:30:45.552 --> 00:30:49.806 and it was $120,000 a year for 00:30:49.806 --> 00:30:53.226 this deep-research agent, like that was PhD level. 00:30:53.643 --> 00:30:55.687 And they were first kind of announcing this pricing. 00:30:55.687 --> 00:30:58.690 And as a manager, I thought to myself, oh, that's an employee. 00:30:58.690 --> 00:31:02.735 So but I could, that employee could be running 24/7. 00:31:03.027 --> 00:31:06.239 How would I think of ideas to keep that employee 00:31:06.948 --> 00:31:09.284 productive all the time? 00:31:09.284 --> 00:31:12.412 And I think we could see teams 00:31:12.412 --> 00:31:15.999 that are augmented by an AI agent 00:31:16.624 --> 00:31:19.168 and think of them as an employee. 00:31:19.168 --> 00:31:21.087 Well, I mean, in their company. 00:31:21.087 --> 00:31:23.882 What's the company that's getting a lot of traction, 00:31:23.882 --> 00:31:27.844 open evidence that is now using, 00:31:29.137 --> 00:31:32.223 is now making I think it's ad-supported now. 00:31:32.223 --> 00:31:37.437 So instead of, like, costing $120,000 to have access to, to be able to query like, 00:31:38.521 --> 00:31:40.440 and like, 00:31:40.440 --> 00:31:43.526 as a doctor to have like somebody to ask 00:31:43.776 --> 00:31:46.654 reliable questions to. 00:31:46.654 --> 00:31:48.698 It's, the idea is it 00:31:48.698 --> 00:31:52.243 might actually be relatively free. Anyway, 00:31:52.243 --> 00:31:54.746 go on. 00:31:54.746 --> 00:31:57.123 I think this is going to be when you think about management 00:31:57.123 --> 00:32:01.002 and organization, I think this is going to be the biggest shift 00:32:01.002 --> 00:32:05.757 since, I would say assembly-line industrial revolution to, maybe the 00:32:06.007 --> 00:32:09.010 when the PCs came on board, this is the 3.0 of it, 00:32:09.302 --> 00:32:12.388 and I think it will hit us all at multiple levels. 00:32:12.388 --> 00:32:16.351 So at the individual level, as we all operate, I think it's true. 00:32:16.351 --> 00:32:19.604 Previously, I think intelligence and skills were the gatekeeper. 00:32:19.896 --> 00:32:23.900 Right now, every one of us has the super-intelligent thing whether 00:32:23.900 --> 00:32:25.193 you call it AGI, SI. 00:32:25.193 --> 00:32:27.820 That's actually beside the point. But you have a really, really smart 00:32:29.322 --> 00:32:31.616 companion copilot in your pocket. 00:32:31.616 --> 00:32:33.534 And so that's no longer the gatekeeping thing. 00:32:33.534 --> 00:32:34.869 It is going to be ambition. 00:32:34.869 --> 00:32:39.999 It's going to be who takes the flying car to the grocery store versus 00:32:40.208 --> 00:32:41.209 actually to Mars. Right. 00:32:41.209 --> 00:32:45.588 In terms of like, how are you being high ambition and using AI? 00:32:46.005 --> 00:32:48.216 It is going to be about taste and judgment, 00:32:48.216 --> 00:32:51.928 because you have a team of agents and it is going to be about 00:32:51.928 --> 00:32:55.682 every employee is now a manager, is a boss of these agents, 00:32:56.015 --> 00:33:00.144 which means you'll have to figure out how to steer, how to coach, how to direct. 00:33:00.520 --> 00:33:04.774 These are all skills that typically people get much more further in their career. 00:33:05.149 --> 00:33:08.861 And so that I think is going to be on an individual-level challenge. 00:33:09.445 --> 00:33:13.116 The next level of challenge and opportunities at the team level, 00:33:13.366 --> 00:33:17.662 by the way, we are seeing that already where we added meeting agents 00:33:17.662 --> 00:33:20.748 and these project-management agents into our teams 00:33:20.748 --> 00:33:23.876 product, and it's fascinating to see the dynamic. 00:33:24.085 --> 00:33:25.086 It is what you said, 00:33:25.086 --> 00:33:29.298 Anthony, about multiple humans and multiple agents at work. 00:33:29.298 --> 00:33:31.968 And I have a team that's kind of like it's very meta. 00:33:31.968 --> 00:33:33.469 It's actually building a feature 00:33:33.469 --> 00:33:37.306 for that project-management agent, using that project-management agent. 00:33:38.099 --> 00:33:41.310 And one nugget I would say of insight there is that 00:33:42.395 --> 00:33:44.731 today we think about our wiring 00:33:44.731 --> 00:33:47.984 default is not asynchronous and 24/7, to your point. 00:33:48.359 --> 00:33:51.070 So things like, hey, these things, 00:33:51.070 --> 00:33:54.073 we are all VSLMs, humans. 00:33:55.450 --> 00:33:57.785 And we have incomplete knowledge. 00:33:57.785 --> 00:34:00.038 We have incentive issues. 00:34:00.038 --> 00:34:04.667 So turns out that the LLMs are 24/7 but also they have like amazing, 00:34:04.667 --> 00:34:07.295 they are not token constrained like we are. 00:34:07.295 --> 00:34:10.673 And so whichever team and we have multiple teams using it. 00:34:11.132 --> 00:34:15.261 Whichever team is able to put those agents to work in a much more effective way, 00:34:15.595 --> 00:34:19.474 they're shipping in the order of weeks versus months and years. 00:34:19.474 --> 00:34:21.809 That set the team level. 00:34:21.809 --> 00:34:23.770 The organizational one is the one 00:34:23.770 --> 00:34:27.440 that makes me most ponder a lot. 00:34:27.440 --> 00:34:30.568 In fact, most recently I wrote about this, saying 00:34:30.818 --> 00:34:34.113 most of what we do in an organization is translation 00:34:36.741 --> 00:34:38.076 and routing. 00:34:38.076 --> 00:34:40.828 If you look at an organization, there's a whole bunch of folks 00:34:40.828 --> 00:34:42.371 who are doing things, right. 00:34:42.371 --> 00:34:44.290 And of course, swim lanes are merging there. 00:34:44.290 --> 00:34:47.293 But there are all builders, whether they're engineers or designers. 00:34:47.502 --> 00:34:49.128 And then there's a whole bunch of information 00:34:49.128 --> 00:34:51.172 routing and translation that happens. 00:34:51.172 --> 00:34:54.175 And then you have an executive that's kind of making decisions. 00:34:54.634 --> 00:34:58.262 If LLMs are really good universal translators, 00:34:58.721 --> 00:35:02.141 I think one of the questions with manager in their title, 00:35:02.141 --> 00:35:05.103 in all of us needs to think about is, 00:35:05.770 --> 00:35:09.273 am I doing translation or am I doing transformation? 00:35:09.273 --> 00:35:12.860 And adding kind of the value and how am I be being effective 00:35:12.860 --> 00:35:16.781 in helping decision making and judgment versus schlepping information 00:35:16.781 --> 00:35:20.326 one way to one place to another because the LLMs will do it better. 00:35:21.244 --> 00:35:26.124 Anybody who wants to ask a question should come up, should start coming up. 00:35:26.958 --> 00:35:31.671 You brought up something that worried you, about the talent gap at the entry level. 00:35:31.712 --> 00:35:35.258 And yesterday we heard May talk about this, 00:35:35.258 --> 00:35:38.761 flattening of organizations and working laterally. 00:35:39.220 --> 00:35:40.596 What are you hearing? 00:35:40.596 --> 00:35:42.515 Clients. Your own companies. 00:35:42.515 --> 00:35:44.559 Are they talking about this? 00:35:44.559 --> 00:35:46.352 What are they doing about this? 00:35:46.352 --> 00:35:49.647 Like, I know it's not solved, but what's the dialog? 00:35:52.900 --> 00:35:54.277 I'm really glad you asked that question. 00:35:54.277 --> 00:35:55.570 Yes, it was actually the one. 00:35:55.570 --> 00:35:58.447 The next question I was going to ask is what is the entry level? 00:35:58.447 --> 00:35:59.782 What does entry-level job 00:35:59.782 --> 00:36:02.785 looks like? Sorry. Go ahead, go ahead. 00:36:02.952 --> 00:36:06.622 I mean, this is actually what we are living through both in terms, 00:36:06.622 --> 00:36:08.166 two dimensions to this problem. 00:36:08.166 --> 00:36:09.709 One is 00:36:09.709 --> 00:36:13.504 how do you actually train folks coming in? Like you don't want 00:36:13.629 --> 00:36:15.590 the first rung of the ladder if you will, right? 00:36:15.590 --> 00:36:18.426 If you have folks coming into the organization, 00:36:18.426 --> 00:36:21.304 how do you equip them with skills to use AI? 00:36:21.304 --> 00:36:21.804 Right. 00:36:21.804 --> 00:36:23.764 To be able to get those skills? 00:36:23.764 --> 00:36:26.350 I was talking to a law firm, 00:36:26.350 --> 00:36:30.062 like a partner, and he said, oh yeah, like I actually use 00:36:30.062 --> 00:36:33.691 a lot of AI tools to supplement the analyst and associates. 00:36:34.358 --> 00:36:35.860 And I said, oh, how do you have the judgment 00:36:35.860 --> 00:36:37.445 to kind of like, steer these tools? 00:36:37.445 --> 00:36:39.697 Because he's like, oh, I was an analyst, an associate. 00:36:39.697 --> 00:36:41.699 I'm like, do you see what's happening? 00:36:41.699 --> 00:36:43.534 You need to kind of figure that out, right? 00:36:43.534 --> 00:36:44.243 So I think 00:36:44.243 --> 00:36:48.039 one of the things you're talking about is what is the externship, apprenticeship 00:36:48.247 --> 00:36:53.294 training model for these, like beginning rounds of thinking with AI, 00:36:53.336 --> 00:36:58.758 thinking with the machine and working with AI and not just using AI as a tool. 00:37:00.343 --> 00:37:00.551 Yeah. 00:37:00.551 --> 00:37:01.719 So, two things. 00:37:01.719 --> 00:37:05.014 One, the entry-level job is 00:37:05.431 --> 00:37:08.142 in many industries is changing, especially when it's, a knowledge worker. 00:37:08.142 --> 00:37:09.018 I'll take PwC, 00:37:09.018 --> 00:37:12.271 I mean, 25% of our business is audit. 00:37:12.271 --> 00:37:12.730 Right. 00:37:12.730 --> 00:37:15.691 And so it's, it needs humans that read 00:37:16.776 --> 00:37:19.904 financial statements and then ultimately, creates 00:37:19.904 --> 00:37:21.447 an opinion that's going to change, right? 00:37:21.447 --> 00:37:24.242 We're going to see that be more automated and autonomous. 00:37:24.242 --> 00:37:25.910 And so what we need is our associates 00:37:25.910 --> 00:37:28.996 that come in from undergrad that actually have AI skills, 00:37:28.996 --> 00:37:33.251 that know how to work with those agents, that then can be, I mean, we in 00:37:33.251 --> 00:37:37.838 in a regulated industry still need a human to render an opinion. 00:37:38.172 --> 00:37:40.841 So the job is changing. 00:37:40.841 --> 00:37:43.552 Now, the numbers is a different question. Right? 00:37:43.552 --> 00:37:46.097 So the job is changing. The interesting piece is 00:37:46.097 --> 00:37:47.223 we also have less humans. 00:37:47.223 --> 00:37:48.015 So we are 00:37:48.015 --> 00:37:51.018 not going to be producing as many, particularly in the United States, 00:37:51.102 --> 00:37:54.480 that the number of people that are starting into these careers. 00:37:54.689 --> 00:37:59.568 And so there is this how do we push people like other countries 00:37:59.568 --> 00:38:03.364 who are more mature and older than us into other types of careers 00:38:03.364 --> 00:38:04.740 and understanding of that, which then. 00:38:04.740 --> 00:38:08.286 So that's why I was saying it's a social experiment, because it affects 00:38:08.619 --> 00:38:13.040 how we, the expectations we set of children like it's okay to be a welder, right? 00:38:13.040 --> 00:38:15.459 Welders in the United States make a lot of money 00:38:15.459 --> 00:38:18.462 and no one tells their kids, oh, I want you to grow up and be a welder. 00:38:18.462 --> 00:38:20.047 But they say, like, oh, I want you to be an engineer. 00:38:20.047 --> 00:38:21.841 Everyone needs to be an engineer. I don't have kids. 00:38:21.841 --> 00:38:24.093 So I hear people, my friends say that. 00:38:24.093 --> 00:38:26.012 But if you want to be an engineer, you should be in it. 00:38:26.012 --> 00:38:26.178 Yeah. 00:38:26.178 --> 00:38:27.096 No, no, I'm not saying, 00:38:27.096 --> 00:38:29.223 but there are plenty of people who can't be an engineer. 00:38:29.223 --> 00:38:32.351 And then we have, we have all this dialog between 00:38:32.560 --> 00:38:34.061 unemployed and job loss. 00:38:34.061 --> 00:38:36.397 And actually there's not job losses, so much gain. 00:38:36.397 --> 00:38:39.066 It's just not in the way people think of it today. 00:38:39.066 --> 00:38:43.446 So, a quick follow-on question is, or just to throw out there, 00:38:43.446 --> 00:38:47.366 it kind of seems like we just need to really look at hiring practices 00:38:47.366 --> 00:38:50.369 and think about that pretty deeply. 00:38:50.661 --> 00:38:52.371 We should follow up. 00:38:52.371 --> 00:38:53.706 All right. 00:38:53.706 --> 00:38:55.041 Next question. 00:38:55.041 --> 00:38:58.085 Yeah, I have a question regarding the C-suite 00:38:58.711 --> 00:39:01.756 and what kind of skills should CEOs 00:39:01.756 --> 00:39:04.925 have in order to navigate the next decade. 00:39:05.509 --> 00:39:08.512 I was on the board of a public company. 00:39:08.721 --> 00:39:13.809 And obviously, I talked to a lot of the companies and CXOs. 00:39:15.227 --> 00:39:18.105 I mean, it's hard to give a short answer, 00:39:18.105 --> 00:39:22.151 but I would give like two observations that I've been kind of thinking about. 00:39:22.568 --> 00:39:23.402 I think one 00:39:23.402 --> 00:39:26.947 is this idea of like defining the business and the industry that you're in. 00:39:28.115 --> 00:39:31.118 For example, ages ago, I think there was this question about 00:39:32.036 --> 00:39:34.663 a bus and transportation companies, railroads 00:39:34.663 --> 00:39:35.998 and how that whole thing happened 00:39:35.998 --> 00:39:39.043 because they didn't think about themselves as in the transportation business. 00:39:39.043 --> 00:39:42.004 Right? Versus like, exactly. 00:39:42.254 --> 00:39:46.258 And I think the question is today, for example, whenever there is a technology 00:39:46.258 --> 00:39:50.471 and platform shift, what I say is there is going to be a collapse of categories. 00:39:50.763 --> 00:39:54.308 When the internet came about, before that, you didn't remotely 00:39:54.308 --> 00:40:00.398 think about a phone book and a local newspaper in the same bucket. 00:40:00.606 --> 00:40:03.484 But once the search engine came, they 00:40:03.484 --> 00:40:06.487 both turns out to be the blue links on the web. 00:40:06.821 --> 00:40:10.533 So just like that, they will be a category collapse of things 00:40:10.533 --> 00:40:11.909 that are very different. Already, 00:40:11.909 --> 00:40:14.912 I see in my business productivity, creation, 00:40:15.162 --> 00:40:17.706 search, information, it's all blurring together. 00:40:17.706 --> 00:40:19.834 So that's the number-one thing. 00:40:19.834 --> 00:40:23.087 Are you in a category that's going to collapse with the platform shift? 00:40:23.170 --> 00:40:26.048 If so, really think hard about how you want to play. 00:40:26.048 --> 00:40:29.218 The second one is more psychology I would say is which is the posture. 00:40:29.468 --> 00:40:34.098 I think it is very tempting for incumbents to kind of like not lean in and avoid 00:40:34.098 --> 00:40:36.976 and think it's going away. Diya is right this time. 00:40:36.976 --> 00:40:40.938 Some of, many of the incumbents today were the upstarts in the internet shift. 00:40:41.105 --> 00:40:44.108 So, we are familiar that this is going to be big. 00:40:44.442 --> 00:40:49.405 That's knowing that intellectually but really leaning in and having AI facility. 00:40:49.405 --> 00:40:54.994 So I even say, go and actually be and I see and like build things because you don't 00:40:54.994 --> 00:40:58.789 get intuition and learn about what the models can do unless you do that. 00:41:01.292 --> 00:41:03.669 I'll just add one thing if you want to jump in 00:41:03.669 --> 00:41:07.381 also, I was just having a conversation with someone who is an executive 00:41:07.381 --> 00:41:10.384 at a cable company, and we were talking about 00:41:10.801 --> 00:41:15.139 the advent of streaming and how it destroyed the industry. 00:41:15.139 --> 00:41:20.269 And yeah, I was reminded again, going back to the point that I made 00:41:20.269 --> 00:41:24.857 before that, it's not about what are the AI problems that my business can solve. 00:41:24.857 --> 00:41:29.945 It's about like focusing on the core value that your business is driving. 00:41:30.404 --> 00:41:36.243 Can this product be faster, cheaper, better, more delightful for my customers? 00:41:36.243 --> 00:41:37.703 Can I provide more value? 00:41:37.703 --> 00:41:41.081 And so I think there's a, again, when you're in this kind of incumbent 00:41:41.081 --> 00:41:44.168 spot, there's, can be a failure of imagination 00:41:44.168 --> 00:41:48.005 about what could disrupt the business that you're working in. 00:41:48.255 --> 00:41:51.091 And so I think, my advice here 00:41:51.091 --> 00:41:54.428 is just to focus on delighting your customers. 00:41:55.054 --> 00:41:57.848 Because someone's going to take your bag if you don't. 00:41:57.848 --> 00:41:58.474 I think, 00:41:59.808 --> 00:42:00.809 it's really 00:42:00.809 --> 00:42:04.605 about, a CEO really needs to understand a couple of things 00:42:04.980 --> 00:42:09.276 and then have the courage to go through the change to make those things happen. 00:42:09.735 --> 00:42:13.906 So the first is what is truly, deeply 00:42:14.365 --> 00:42:17.535 your company's value proposition and skill sets? 00:42:17.993 --> 00:42:21.455 And what I mean by that is a lot of companies 00:42:21.455 --> 00:42:24.458 confuse their product or their customers with their skill set. 00:42:24.458 --> 00:42:27.336 If I look at Xero, I'm going to take an example to make this concrete. 00:42:27.336 --> 00:42:30.548 We provide a piece of software that does 00:42:30.548 --> 00:42:33.551 your accounting taxes, your payments and your payroll. 00:42:33.717 --> 00:42:37.930 But our real-value proposition that distinguishes us, 00:42:37.930 --> 00:42:41.600 is in the way we connect a small business with the advisors they need. 00:42:42.101 --> 00:42:42.810 Right. 00:42:42.810 --> 00:42:46.522 And our entire product is architectural and that is 00:42:46.522 --> 00:42:47.439 what makes us successful. 00:42:47.439 --> 00:42:48.315 Our entire go-to-market 00:42:48.315 --> 00:42:51.402 motion is architected around that, and we are unique in that worldwide. 00:42:52.152 --> 00:42:55.573 So if we look at it from that lens and go, 00:42:55.614 --> 00:42:58.576 that is the value prop we're providing, that's not going anywhere. 00:42:58.576 --> 00:43:01.245 Connecting a small business to an advisor that can help 00:43:01.245 --> 00:43:04.206 and an advisor to a small business where they can help, etc. 00:43:04.290 --> 00:43:05.541 That's not going anywhere. 00:43:05.541 --> 00:43:07.626 How do we use this technological shift? 00:43:07.626 --> 00:43:09.795 We give up the entire baggage we have. 00:43:09.795 --> 00:43:11.880 This is what our product looks like. This is how we do business. 00:43:11.880 --> 00:43:13.048 This is our business model. 00:43:13.048 --> 00:43:14.466 And you need a CEO that can go, okay. 00:43:14.466 --> 00:43:18.596 Given this technological shift, how can I make this value 00:43:18.596 --> 00:43:21.724 proposition more deep, uniquely? 00:43:22.224 --> 00:43:23.851 I think that's what's going to stand out. 00:43:23.851 --> 00:43:24.768 And then obviously, there's a 00:43:26.186 --> 00:43:28.022 ton of courage. 00:43:28.022 --> 00:43:29.982 Do not use a bad word. There's a ton of courage 00:43:29.982 --> 00:43:33.319 you have to have to then change your entire business 00:43:33.611 --> 00:43:36.614 in that direction. 00:43:36.697 --> 00:43:39.700 All right, let's go to one more question. 00:43:39.783 --> 00:43:42.870 So in thinking so the leaders are managers. 00:43:42.870 --> 00:43:45.664 So it's kind of building off the CEO question. 00:43:45.664 --> 00:43:49.960 When you're in an industry or a business that has to get stuff done, 00:43:50.044 --> 00:43:53.797 like we have to continue the business that we have and sustaining the business 00:43:53.797 --> 00:43:59.511 that we have, but we also want our teams to be creative. 00:43:59.511 --> 00:44:01.805 Like, we've heard this throughout the last couple of days, 00:44:01.805 --> 00:44:04.266 AI is going to liberate you from these mundane tasks, 00:44:04.266 --> 00:44:06.185 and you're going to have all this creativity and time. 00:44:06.185 --> 00:44:07.019 Okay, cool. 00:44:07.019 --> 00:44:09.897 Except when am I going to do that? 00:44:09.897 --> 00:44:14.652 And I have these goals that I have to hit or my team has to hit or whatever 00:44:15.486 --> 00:44:19.365 for the quarterly whatever it's OKR or goal or whatever the metric is. 00:44:19.782 --> 00:44:25.204 So, I'm curious to know what you've seen or done as leaders for your teams. 00:44:25.204 --> 00:44:28.415 Like what metric are you asking people to hit 00:44:28.666 --> 00:44:32.711 to let you determine if there is the kind of creativity 00:44:32.711 --> 00:44:36.465 that you want or creative thinking, which is sort of this 00:44:36.757 --> 00:44:40.302 hard-to-measure reality that I think it has to be there 00:44:40.302 --> 00:44:42.554 for people to do this kind of AI exploration 00:44:42.554 --> 00:44:45.099 we're asking them to do. How do we do that? 00:44:45.099 --> 00:44:47.893 Like, how do we structure the goals of our team 00:44:47.893 --> 00:44:50.437 in a way that lets people do that? 00:44:50.437 --> 00:44:52.523 I don't know. I'm just curious how you've done it. 00:44:52.523 --> 00:44:54.525 How you've seen other people do it? 00:44:54.525 --> 00:44:57.444 It's, so how do you keep people from using AI to cheat? 00:44:57.444 --> 00:44:58.112 Yeah. 00:44:58.112 --> 00:44:59.488 I didn't get a better bunch. 00:44:59.488 --> 00:45:02.783 Well, so that's a good point because I think 00:45:02.950 --> 00:45:04.868 what you have to do is you have to liberate them 00:45:04.868 --> 00:45:07.329 and make them feel like this is actually enabling 00:45:07.329 --> 00:45:09.289 their success, like their productivity. 00:45:09.289 --> 00:45:11.500 You have to think about ways in which you're going to incent them. 00:45:11.500 --> 00:45:14.044 But the part that I think you're really getting at is about creativity. 00:45:14.044 --> 00:45:15.796 And not everyone is created equal. 00:45:15.796 --> 00:45:18.882 So, the key piece that's still a gap for many organizations, 00:45:19.216 --> 00:45:22.094 the ones that are getting it right, are giving that skill 00:45:22.094 --> 00:45:26.348 and development to the staff, to their people that say, I want to teach, 00:45:26.348 --> 00:45:29.768 you need to get the skill on how to use AI, but also the human skills 00:45:29.768 --> 00:45:32.855 are more important for the humans that are still in these jobs. 00:45:32.855 --> 00:45:35.232 And so how do I, what does that mean to be creative? 00:45:35.232 --> 00:45:38.235 What do I do with the time that is that I came back 00:45:38.318 --> 00:45:41.405 because AI is enabling me to have greater efficiency. 00:45:41.655 --> 00:45:43.782 How do I use that time? How do I structure it? 00:45:43.782 --> 00:45:48.036 And then you as the leader, have to make that positive culture, that environment 00:45:48.287 --> 00:45:49.830 that allows that team to feel like, 00:45:49.830 --> 00:45:52.374 hey, it's okay if I spend two hours thinking about 00:45:52.374 --> 00:45:55.836 new ways or new innovations and bringing those to staff meetings 00:45:56.128 --> 00:45:59.798 and ways in which that they're going to get rewarded for that versus, 00:46:00.215 --> 00:46:02.301 oh, you didn't hit your number, but this is a great idea. 00:46:02.301 --> 00:46:05.763 You have to create that culture, and you have to stand by it and live by it 00:46:05.888 --> 00:46:09.975 after you give them the capability. It's not like creativity. 00:46:09.975 --> 00:46:10.350 Okay. 00:46:10.350 --> 00:46:14.438 Or I mean, like practically like, what does that look like to do? 00:46:14.438 --> 00:46:16.273 All of it should be in service of the results. 00:46:16.273 --> 00:46:17.775 The key results of your organization. 00:46:17.775 --> 00:46:20.652 Like you, one should not. 00:46:20.652 --> 00:46:21.278 No offense. 00:46:21.278 --> 00:46:24.823 You shouldn't need an OKR for what we're talking about. 00:46:25.032 --> 00:46:28.035 It's ultimately in service of the business result. 00:46:28.118 --> 00:46:31.121 And if you're doing something that's not in service of, you shouldn't be doing it. 00:46:31.121 --> 00:46:34.666 But if automation for autonomy, if agentic 00:46:34.708 --> 00:46:38.045 AI is going to deliver that greater effectiveness of the business, 00:46:38.504 --> 00:46:41.924 then using that creativity for you is going to be something else 00:46:41.924 --> 00:46:43.258 that's different than for me. 00:46:43.258 --> 00:46:44.468 Each of you has like time, 00:46:44.468 --> 00:46:47.262 we have time for like two-sentence answers here. 00:46:47.262 --> 00:46:47.596 Go for it. 00:46:47.596 --> 00:46:53.060 Yeah, I think I agree, the thing I would say is innovation or creativity 00:46:53.060 --> 00:46:58.273 often happens at the bottom where you are not, where people have time 00:46:58.273 --> 00:47:00.609 and not at the mid-management or senior-management level. 00:47:00.609 --> 00:47:01.777 So one of the things we found 00:47:01.777 --> 00:47:05.531 a lot of success in is like we will hear of things people are doing, 00:47:05.531 --> 00:47:10.118 new employees coming in, new engineers coming in, engineers at the lower level. 00:47:10.285 --> 00:47:12.371 You just have to make sure everybody hears of it, 00:47:12.371 --> 00:47:15.999 because once people hear of it, then they will start adopting it. 00:47:15.999 --> 00:47:19.962 So you go to these pockets of creativity and that's what has worked for us. 00:47:20.587 --> 00:47:20.796 Yeah. 00:47:20.796 --> 00:47:25.384 Same here, I think the mimicry. I'd say we've run like in my teams. 00:47:25.384 --> 00:47:27.636 We've done boot camps where we say 00:47:27.636 --> 00:47:30.639 take one week or two weeks and just like you get all the tokens 00:47:30.639 --> 00:47:33.642 you want and all the tools that you want, go build stuff. 00:47:33.642 --> 00:47:34.935 And initially, there's no pressure 00:47:34.935 --> 00:47:37.479 about kind of like making it add to the bottom line, 00:47:37.479 --> 00:47:40.983 because what I found at least is that that kind of clamps your thinking. 00:47:40.983 --> 00:47:42.609 You just kind of like, go for it. 00:47:42.609 --> 00:47:44.027 But not everybody is going to sign up. 00:47:44.027 --> 00:47:45.445 It's a concentric-circle model. 00:47:45.445 --> 00:47:47.406 You want the folks who are already AI build 00:47:47.406 --> 00:47:49.700 and then kind of like get to the folks and mimicry. 00:47:49.700 --> 00:47:53.871 And then, the diffusion happens as it does with any change. 00:47:54.454 --> 00:47:55.122 All right. 00:47:55.122 --> 00:47:57.666 That's all the time we have. Give our panel a hand.