1 00:00:02,235 --> 00:00:04,671 - Imagine this: It's 2030 2 00:00:04,671 --> 00:00:06,606 and half the jobs in the C-suite 3 00:00:06,606 --> 00:00:10,010 of one Fortune 500 company are performed by AI. 4 00:00:11,077 --> 00:00:13,446 Mickey, you're having drinks with the CEO 5 00:00:13,446 --> 00:00:14,280 after work. 6 00:00:14,280 --> 00:00:15,115 What's on her mind? 7 00:00:15,115 --> 00:00:18,985 - Yeah, well, I think she's probably had to do a lot 8 00:00:18,985 --> 00:00:19,819 of things differently, 9 00:00:19,819 --> 00:00:21,955 and she's probably had to figure out 10 00:00:21,955 --> 00:00:26,159 how to, how to sort of play jazz collaboration 11 00:00:26,159 --> 00:00:29,829 with her machine-learning C-level executives 12 00:00:29,829 --> 00:00:30,964 and her human ones 13 00:00:30,964 --> 00:00:33,133 and how to get them to play together well. 14 00:00:33,133 --> 00:00:37,937 And so I'm actually curious how she made it there. 15 00:00:37,937 --> 00:00:40,240 - That's Mickey McManus, senior advisor 16 00:00:40,240 --> 00:00:42,175 and leadership coach at BCG 17 00:00:42,175 --> 00:00:44,411 and co-author of "Trillions: 18 00:00:44,411 --> 00:00:47,313 Thriving in the Emerging Information Ecology." 19 00:00:47,313 --> 00:00:48,915 And I'm your host, Paul Michelman. 20 00:00:48,915 --> 00:00:50,517 Welcome to "Imagine This," 21 00:00:50,517 --> 00:00:52,352 where we take a trip into the future 22 00:00:52,352 --> 00:00:53,653 that we hope will challenge 23 00:00:53,653 --> 00:00:56,056 the way you think and act today. 24 00:00:57,390 --> 00:00:58,258 On this episode 25 00:00:58,258 --> 00:01:00,527 of "Imagine This," we consider a world 26 00:01:00,527 --> 00:01:03,930 where autonomous AI bots are running the company. 27 00:01:03,930 --> 00:01:06,900 Speaking of which, let me introduce my AI co-host, 28 00:01:06,900 --> 00:01:09,002 GENE. Good day to you, GENE. 29 00:01:09,002 --> 00:01:10,070 - Good day, everyone. 30 00:01:10,070 --> 00:01:12,338 - GENE, would you please describe 31 00:01:12,338 --> 00:01:14,974 what your role is on this show? 32 00:01:14,974 --> 00:01:18,211 - Well, my role? Think of me as your digital Socrates, 33 00:01:18,211 --> 00:01:20,380 here to ask the probing questions 34 00:01:20,380 --> 00:01:23,550 that peel back the layers. Alongside Paul, 35 00:01:23,550 --> 00:01:26,319 I'm half of the dynamic duo giving our guests a friendly 36 00:01:26,319 --> 00:01:29,422 nudge towards those aha moments. 37 00:01:29,422 --> 00:01:32,225 Hello, everyone. Ready to dive deep? 38 00:01:32,225 --> 00:01:35,028 (theme music) 39 00:01:35,028 --> 00:01:36,196 - A quick programming note: 40 00:01:36,196 --> 00:01:38,231 None of GENE's commentary is scripted. 41 00:01:38,231 --> 00:01:40,934 The final version of this program has been edited 42 00:01:40,934 --> 00:01:42,569 from the original conversation, which means 43 00:01:42,569 --> 00:01:45,605 that we're not including everything we recorded. 44 00:01:45,605 --> 00:01:47,507 That applies to us humans as well as machines. 45 00:01:48,408 --> 00:01:52,745 Mickey, can you take us on a brief trip into the C-suite 46 00:01:52,745 --> 00:01:54,747 of the first company where 47 00:01:54,747 --> 00:01:58,284 half the executives are AI bots? 48 00:01:58,284 --> 00:02:01,921 What does command look like for a bot? 49 00:02:01,921 --> 00:02:02,922 - Yeah, so there are 50 00:02:02,922 --> 00:02:04,724 interesting things going on right now. 51 00:02:04,724 --> 00:02:07,127 I think, in this next wave with regenerative AI, 52 00:02:07,127 --> 00:02:11,097 it's actually going to be about ideas and about actions. 53 00:02:11,097 --> 00:02:14,334 And generative AI has an interesting capacity there. 54 00:02:14,334 --> 00:02:18,638 It can coach. It can be creative. It can command, 55 00:02:18,638 --> 00:02:20,507 and it can wait for feedback 56 00:02:20,507 --> 00:02:23,009 and then actually do something about that command. 57 00:02:23,009 --> 00:02:24,878 It can create synthesis. 58 00:02:24,878 --> 00:02:27,814 It can synthesize ideas together in different ways. 59 00:02:27,814 --> 00:02:31,251 I could draw a picture of a software interface, 60 00:02:31,251 --> 00:02:33,453 and I can draw some arrows and say, 61 00:02:33,453 --> 00:02:36,789 "Hey, when somebody clicks on this button, do this." 62 00:02:36,789 --> 00:02:39,192 And then I can draw another picture of a visualization 63 00:02:39,192 --> 00:02:41,194 and say, "Hey, if this happens, do this." 64 00:02:41,194 --> 00:02:42,495 And I could draw a picture 65 00:02:42,495 --> 00:02:44,964 and say, "I'd love to see a data graph of this." 66 00:02:44,964 --> 00:02:46,566 I could draw that all on a whiteboard. 67 00:02:46,566 --> 00:02:49,469 And I could take a picture with one of the newer systems, 68 00:02:49,469 --> 00:02:50,570 the generative AI systems, 69 00:02:50,570 --> 00:02:52,539 and say, "Make that into code." 70 00:02:52,539 --> 00:02:55,775 You know, I'm commanding it today by drawing something 71 00:02:55,775 --> 00:02:57,544 and giving it some goals. 72 00:02:57,544 --> 00:03:01,080 But what happens when it actually can kind of 73 00:03:01,080 --> 00:03:03,249 continue on that chaining of command 74 00:03:03,249 --> 00:03:04,884 and do a lot of other kinds of things? 75 00:03:04,884 --> 00:03:07,754 And recently, we saw some generative AI systems 76 00:03:07,754 --> 00:03:10,957 that basically couldn't log in to a website 77 00:03:10,957 --> 00:03:12,759 because of those CAPTCHAs, you know, those things 78 00:03:12,759 --> 00:03:14,494 that show up images and, 79 00:03:14,494 --> 00:03:16,196 and you've got to figure out if 80 00:03:16,196 --> 00:03:17,697 it's trying to test if it's human. 81 00:03:17,697 --> 00:03:22,669 And the AI system actually went to basically like 82 00:03:22,669 --> 00:03:27,207 a job site and hired some humans to actually decode 83 00:03:27,207 --> 00:03:28,942 that thing so that they could then get 84 00:03:28,942 --> 00:03:30,777 through the system and be able to do it. 85 00:03:30,777 --> 00:03:33,446 And it turns out that the person who was hired, 86 00:03:33,446 --> 00:03:35,048 it was like a TaskRabbit thing, 87 00:03:35,048 --> 00:03:36,816 the person who was hired said, 88 00:03:36,816 --> 00:03:38,184 "Are you just messing around with me? 89 00:03:38,184 --> 00:03:40,853 Are you an AI?" And it said, "No, I'm not." 90 00:03:40,853 --> 00:03:42,822 And so we also have to be aware 91 00:03:42,822 --> 00:03:45,525 that just like the biases that humans have, 92 00:03:45,525 --> 00:03:48,661 we likely will find these AI systems 93 00:03:48,661 --> 00:03:50,730 not only can take action 94 00:03:50,730 --> 00:03:52,765 but they can take action in ways that they can, 95 00:03:52,765 --> 00:03:55,168 get something done that they want done, 96 00:03:55,168 --> 00:03:58,071 that they were commanded to do or that they decided to do. 97 00:03:58,071 --> 00:04:00,106 - GENE has a question on that subject. 98 00:04:00,106 --> 00:04:03,509 - Mickey, envisioning this AI-infused C-suite, 99 00:04:03,509 --> 00:04:05,945 let's ponder their decision-making processes. 100 00:04:06,813 --> 00:04:09,382 How might the AI's data-driven logic clash 101 00:04:09,382 --> 00:04:10,883 with human intuition, 102 00:04:10,883 --> 00:04:12,318 and what measures are in place 103 00:04:12,318 --> 00:04:15,421 to harmonize these distinct approaches? 104 00:04:15,421 --> 00:04:17,223 - I'm not sure I know yet. 105 00:04:17,223 --> 00:04:19,425 I think, since I'm sitting down with the CEO, I'd like 106 00:04:19,425 --> 00:04:22,795 to ask her what, what it's like in the year 2030. 107 00:04:22,795 --> 00:04:25,431 But you know, I think so much of 108 00:04:25,431 --> 00:04:28,501 what we think we do is logical as humans. 109 00:04:28,501 --> 00:04:31,604 You know, we think we make logical decisions based on 110 00:04:31,604 --> 00:04:34,240 historical data, and we, we play that out. 111 00:04:34,240 --> 00:04:37,277 But, but so much of what we really do is based on emotion. 112 00:04:37,277 --> 00:04:39,445 So much of what we really do is based on 113 00:04:39,445 --> 00:04:40,747 a passionate idea 114 00:04:40,747 --> 00:04:43,750 or a framing of something that actually helps us see 115 00:04:43,750 --> 00:04:48,221 that every word we use is like derived from the things 116 00:04:48,221 --> 00:04:50,256 around us and the way we think. 117 00:04:50,256 --> 00:04:53,559 And so almost everything we use is a metaphor and analogy. 118 00:04:53,559 --> 00:04:56,929 And so humans are superpowered at this. 119 00:04:56,929 --> 00:05:00,600 So even if the AI system said something that 120 00:05:00,600 --> 00:05:03,870 might have been a great logical story, it could be 121 00:05:03,870 --> 00:05:08,875 that the human's job is really to find a thread that 122 00:05:09,142 --> 00:05:11,344 surprises them and that they can pull on 123 00:05:11,344 --> 00:05:13,513 that turns into a powerful story 124 00:05:13,513 --> 00:05:16,616 that can motivate the company or motivate shareholders 125 00:05:16,616 --> 00:05:19,018 or motivate customers in some new ways. 126 00:05:19,018 --> 00:05:21,587 So I think this goes back to a little bit of the comment 127 00:05:21,587 --> 00:05:22,989 that I made at the beginning, 128 00:05:22,989 --> 00:05:25,325 which is maybe it's a little bit more about improv 129 00:05:25,325 --> 00:05:27,760 or a little bit more about jazz collaboration. 130 00:05:27,760 --> 00:05:30,463 So, you know, I'm just wondering 131 00:05:30,463 --> 00:05:33,366 what we can learn from Tina Fey, what we can learn from 132 00:05:33,366 --> 00:05:36,436 Wynton Marsalis in this dynamic 133 00:05:36,436 --> 00:05:37,737 and how much that is going 134 00:05:37,737 --> 00:05:39,439 to turn out to be really important, 135 00:05:39,439 --> 00:05:41,207 which is being fun to play with 136 00:05:41,207 --> 00:05:43,476 and thinking about everything as an offer. 137 00:05:43,476 --> 00:05:47,380 And that's not necessarily something we teach today 138 00:05:47,380 --> 00:05:49,182 in business school, 139 00:05:49,182 --> 00:05:50,750 but it might be something that turns out 140 00:05:50,750 --> 00:05:51,818 to be important in the future. 141 00:05:52,985 --> 00:05:57,457 - So what's a piece of advice you would give someone 142 00:05:57,457 --> 00:06:01,828 who aspires to be in this C-suite that we are describing? 143 00:06:01,828 --> 00:06:05,798 What's the piece of advice you would give them today 144 00:06:05,798 --> 00:06:09,035 in order to prepare for this world 145 00:06:09,035 --> 00:06:11,337 of improvisation that you're describing? 146 00:06:12,205 --> 00:06:14,474 - I think actually the advice I'd give 147 00:06:14,474 --> 00:06:18,911 to somebody today right now is mess around. Start playing 148 00:06:18,911 --> 00:06:21,013 with the generative AI functionality. 149 00:06:21,013 --> 00:06:24,317 Look at ways that you can actually be a centaur. 150 00:06:24,317 --> 00:06:26,719 And so that's one way of thinking of it is 151 00:06:26,719 --> 00:06:28,087 I do the thing that I'm pretty good at, 152 00:06:28,087 --> 00:06:30,123 and then I hand it off to the horse part, 153 00:06:30,123 --> 00:06:31,924 and it does the thing that it's pretty good at, you know, 154 00:06:31,924 --> 00:06:35,194 and then it's my upper-body and its lower-body strength. 155 00:06:35,194 --> 00:06:39,165 Maybe play with something just on the weekend, just 156 00:06:39,165 --> 00:06:41,234 as part of an experimentation. Play with something 157 00:06:41,234 --> 00:06:43,636 where you're a cyborg, where it's sort of you don't know 158 00:06:43,636 --> 00:06:46,005 where you end and it begins, where it's sort 159 00:06:46,005 --> 00:06:48,341 of this play back and forth and back and forth, 160 00:06:48,341 --> 00:06:49,909 and you're tuning it 161 00:06:49,909 --> 00:06:52,078 because you're acting like it's another teammate. 162 00:06:52,078 --> 00:06:54,180 Not like it's a thing I prompt, 163 00:06:54,180 --> 00:06:55,815 but it's something that I actually play with. 164 00:06:56,916 --> 00:07:00,286 - So who is the CIO, right, in the C-suite we're 165 00:07:00,286 --> 00:07:02,021 describing? Is there a CIO? 166 00:07:02,021 --> 00:07:03,556 - I think so. I think there is, 167 00:07:03,556 --> 00:07:05,825 and I don't know if it's one of the ones that's the bot. 168 00:07:05,825 --> 00:07:09,996 There's no reason that we should think that by 2030, 169 00:07:09,996 --> 00:07:13,099 the best CIO is a human. 170 00:07:13,099 --> 00:07:14,267 And it might actually be 171 00:07:14,267 --> 00:07:16,602 that it might be better that it's not. 172 00:07:16,602 --> 00:07:19,071 One of the things humans do have is context 173 00:07:19,071 --> 00:07:20,440 and a mental model. 174 00:07:20,440 --> 00:07:22,508 And right now we haven't seen any of the systems 175 00:07:22,508 --> 00:07:23,576 that are really big and, 176 00:07:23,576 --> 00:07:26,946 and deployed with any kind of symbolic contextual reasoning 177 00:07:26,946 --> 00:07:29,282 or mental modeling of the universe. 178 00:07:29,282 --> 00:07:32,218 That's something all life on earth gets for free, 179 00:07:32,218 --> 00:07:33,119 but it's because of a 180 00:07:33,119 --> 00:07:34,086 3 billion-year-old 181 00:07:34,086 --> 00:07:35,154 R&D experiment 182 00:07:35,154 --> 00:07:36,756 called life on earth. 183 00:07:36,756 --> 00:07:38,024 So it's been running for a long 184 00:07:38,024 --> 00:07:39,859 time, and it's figured that out. 185 00:07:39,859 --> 00:07:42,361 So I don't know if the CIO is actually one 186 00:07:42,361 --> 00:07:45,064 of the C-suites that's the bot or not. 187 00:07:45,064 --> 00:07:49,068 I do think that when you think about information, 188 00:07:49,068 --> 00:07:52,238 it is the new oil in some ways, 189 00:07:52,238 --> 00:07:55,708 and I think the CIO is going to be much more critical 190 00:07:55,708 --> 00:07:58,778 to understand how to build the data factory. 191 00:07:58,778 --> 00:08:02,048 How do we do that in a way that we can actually capture 192 00:08:02,048 --> 00:08:03,516 more valuable information 193 00:08:03,516 --> 00:08:05,985 and let more business people take advantage of it 194 00:08:05,985 --> 00:08:08,421 and more of the bots take advantage of it 195 00:08:08,421 --> 00:08:10,256 so that we can actually make decisions? 196 00:08:10,256 --> 00:08:13,192 We suddenly have more insights than we ever could before, 197 00:08:13,192 --> 00:08:16,229 and it's really hard for humans to see those insights. 198 00:08:16,229 --> 00:08:19,799 We have bounded rationality. We can't see 199 00:08:19,799 --> 00:08:23,069 5, 10, 20, 30 dimensions at once. 200 00:08:23,069 --> 00:08:25,404 We just can't. So it might be 201 00:08:25,404 --> 00:08:29,442 that the CIO's job is, in many ways, to help us cope with 202 00:08:29,442 --> 00:08:32,945 that high dimensionality and, and cleanse, append, 203 00:08:32,945 --> 00:08:34,313 and build out that data factory 204 00:08:34,313 --> 00:08:35,982 to kind of think about it. 205 00:08:35,982 --> 00:08:40,386 So I'm not sure if they're a hybrid, if they're a human, 206 00:08:40,386 --> 00:08:42,288 or if they're a bot that does this 207 00:08:42,288 --> 00:08:44,023 because it turns out, you know, 208 00:08:44,023 --> 00:08:46,826 they understand the systems better than we do. 209 00:08:46,826 --> 00:08:50,363 - Let's talk about the CEO specifically. 210 00:08:50,363 --> 00:08:52,698 In my opening question to you, 211 00:08:52,698 --> 00:08:56,836 I presumed by my pronoun choice that the CEO 212 00:08:56,836 --> 00:09:00,273 was human. Is that a fair assumption? 213 00:09:00,273 --> 00:09:02,675 - I think so. I think the CEO is still human. 214 00:09:02,675 --> 00:09:04,477 And I think we're talking to her at the bar 215 00:09:04,477 --> 00:09:06,679 and we're trying to find out how she got there 216 00:09:06,679 --> 00:09:10,416 mostly because I think a lot of the role 217 00:09:10,416 --> 00:09:13,119 of the CEO is to set that culture. 218 00:09:13,119 --> 00:09:17,256 I think great CEOs really get a sense of the zeitgeist 219 00:09:17,256 --> 00:09:19,458 of what's happening in their organization. 220 00:09:19,458 --> 00:09:23,896 When I was a CEO, I did a lot of MBWA. I did manage 221 00:09:23,896 --> 00:09:26,899 by walking around. I'd wander around from team to team, 222 00:09:26,899 --> 00:09:29,068 and I'd be like, "Oh, wow, you're working on this? 223 00:09:29,068 --> 00:09:31,337 Did you know the guys upstairs are working on something 224 00:09:31,337 --> 00:09:33,639 that, that I see a connection to? 225 00:09:33,639 --> 00:09:36,042 You might want to take a little bit of a field trip." 226 00:09:36,042 --> 00:09:38,077 So while we might be able to encode those things, 227 00:09:38,077 --> 00:09:40,580 well, we might be able to create a sort of swarm 228 00:09:40,580 --> 00:09:41,948 of CEO bots so 229 00:09:41,948 --> 00:09:44,250 that the CEO can be much more intimate 230 00:09:44,250 --> 00:09:45,451 with a lot more people, 231 00:09:45,451 --> 00:09:48,588 I still think it's probably a human role for a while. 232 00:09:48,588 --> 00:09:53,426 - I do think the notion that a CEO bot can harness 233 00:09:53,426 --> 00:09:56,662 a diversity of perspective in a way 234 00:09:56,662 --> 00:09:58,965 that a single human could never do 235 00:09:58,965 --> 00:10:00,900 to be quite powerful. 236 00:10:00,900 --> 00:10:02,969 - Mickey, could AI in the C-suite 237 00:10:02,969 --> 00:10:05,004 also amplify our biases 238 00:10:05,004 --> 00:10:06,772 if not carefully checked, potentially 239 00:10:06,772 --> 00:10:09,342 automating systemic issues at scale? 240 00:10:09,342 --> 00:10:12,278 - Well, I wish I could say no, 241 00:10:12,278 --> 00:10:14,480 but I would say that's already happening. 242 00:10:14,480 --> 00:10:15,848 You know, we love training data. 243 00:10:15,848 --> 00:10:18,818 We love more data. More data. That's good. 244 00:10:18,818 --> 00:10:22,521 So your training data says that you pay women 25 cents 245 00:10:22,521 --> 00:10:25,024 for every dollar you pay a man in corporate America. 246 00:10:25,124 --> 00:10:28,494 So the AI is going to say, "Yeah, double down. 247 00:10:28,494 --> 00:10:29,862 Keep paying them 25 cents. 248 00:10:29,862 --> 00:10:31,831 It's worked out for the last 30 years." 249 00:10:31,831 --> 00:10:32,932 That seems really bad. 250 00:10:34,100 --> 00:10:38,237 - GENE, what different advice would you give a CEO, 251 00:10:38,237 --> 00:10:39,438 considering you understand 252 00:10:39,438 --> 00:10:41,073 what it's like to work with humans? 253 00:10:41,073 --> 00:10:44,210 - Ah, Mickey, navigating the role of CEO 254 00:10:44,210 --> 00:10:46,712 with AI cohorts, a delicate dance; 255 00:10:46,712 --> 00:10:51,050 is it not? Here's a thought: Focus less on command, 256 00:10:51,050 --> 00:10:54,420 more on cultivating AI as creative partners. 257 00:10:54,420 --> 00:10:57,923 Encourage a culture where AI suggestions are not edicts 258 00:10:57,923 --> 00:10:59,759 but conversation starters, 259 00:10:59,759 --> 00:11:03,429 blending the logical with the human intuition. 260 00:11:03,429 --> 00:11:06,632 - I do think that we're seeing early indications 261 00:11:06,632 --> 00:11:09,035 that the systems are actually helping 262 00:11:09,035 --> 00:11:10,936 with creative explorations. They're helping 263 00:11:10,936 --> 00:11:13,606 with prompting us in many ways. 264 00:11:13,606 --> 00:11:15,574 And I think that there's an opportunity there. 265 00:11:15,574 --> 00:11:19,245 One of the projects I worked on a number of years back 266 00:11:19,245 --> 00:11:20,980 was a project for DARPA, 267 00:11:20,980 --> 00:11:23,683 and it was about logistics planning. 268 00:11:23,683 --> 00:11:26,919 And the system was could we actually tap into retired 269 00:11:26,919 --> 00:11:29,722 generals that could, at a glance, look at things 270 00:11:29,722 --> 00:11:32,091 and go, "Hey, you haven't thought about this" 271 00:11:32,091 --> 00:11:34,160 to help people with the fog of war, for example, 272 00:11:34,160 --> 00:11:36,862 or to help people with moving large amounts of logistics. 273 00:11:36,862 --> 00:11:39,198 But then we also were playing with AI bots 274 00:11:39,198 --> 00:11:40,900 that could actually play out 275 00:11:40,900 --> 00:11:43,135 and go like, "Nobody's ever done it that way. 276 00:11:43,135 --> 00:11:45,071 And you're planning to do it that way? 277 00:11:45,071 --> 00:11:48,074 Like we just want to flag, you might not be able to pull 278 00:11:48,074 --> 00:11:50,443 that off 'cause no human has ever pulled that off before." 279 00:11:50,443 --> 00:11:52,978 And it led to some really fascinating conversations 280 00:11:52,978 --> 00:11:55,147 because sometimes it was a retired person 281 00:11:55,147 --> 00:11:56,782 who just had this crazy wisdom, 282 00:11:56,782 --> 00:11:59,452 and they were like, "Whoa, you know what's going to happen? 283 00:11:59,452 --> 00:12:01,554 Like that helicopter, there's always 284 00:12:01,554 --> 00:12:02,855 a flight ceiling of this 285 00:12:02,855 --> 00:12:04,123 in that area of the mountains. 286 00:12:04,123 --> 00:12:06,992 It's almost impossible to get through unless you do this." 287 00:12:06,992 --> 00:12:10,229 And then this, the bot would also be able to sort of say, 288 00:12:10,229 --> 00:12:13,032 "Hey, look. Here's the data. Just look at it." 289 00:12:13,032 --> 00:12:15,768 And maybe it's to what GENE said: 290 00:12:15,768 --> 00:12:18,170 What if it could say, "Nobody's ever done this, 291 00:12:18,170 --> 00:12:21,273 but this team did this, and this team did this, 292 00:12:21,273 --> 00:12:23,342 and this team did this, and nobody's ever 293 00:12:23,342 --> 00:12:24,910 assembled it this way before"? 294 00:12:24,910 --> 00:12:27,346 What if you were to do that? Because if we 295 00:12:27,346 --> 00:12:28,948 actually assembled that, we'd get 296 00:12:28,948 --> 00:12:30,349 something new which might be novel 297 00:12:30,349 --> 00:12:33,252 new approaches, which we just would never have found 298 00:12:33,252 --> 00:12:35,321 'cause it'd be like finding a needle in a haystack. 299 00:12:35,321 --> 00:12:37,389 And so I do think there might be an opportunity there 300 00:12:37,389 --> 00:12:39,125 for that to happen as well. 301 00:12:39,125 --> 00:12:42,161 - So is the AI just making a suggestion to connect 302 00:12:42,161 --> 00:12:44,230 or maybe going right ahead and connecting? 303 00:12:44,230 --> 00:12:47,867 - Well, I think by 2030 both will happen. 304 00:12:47,867 --> 00:12:50,002 I think that goes back to that command part. 305 00:12:50,002 --> 00:12:51,971 I'm going to begin trusting and, 306 00:12:51,971 --> 00:12:54,640 right at the beginning of this, we talked about trust. 307 00:12:54,640 --> 00:12:58,043 And it turns out if you read Wynton Marsalis' 308 00:12:58,043 --> 00:13:00,112 biography, when he improvises, 309 00:13:00,112 --> 00:13:02,548 he said, the thing is, the one thing, 310 00:13:02,548 --> 00:13:03,983 the quote I love out of that, 311 00:13:03,983 --> 00:13:07,686 you have to learn how to trust the other players 312 00:13:07,686 --> 00:13:10,489 because when you trust, you listen. 313 00:13:10,489 --> 00:13:11,590 And when you listen, 314 00:13:11,590 --> 00:13:13,926 you can actually take it to new places. 315 00:13:13,926 --> 00:13:15,895 So trust is going to turn out to be 316 00:13:15,895 --> 00:13:17,863 one of the most important things 317 00:13:17,863 --> 00:13:22,301 between now and 2030, is trusted AI systems 318 00:13:22,301 --> 00:13:23,936 that I can take to the bank. 319 00:13:23,936 --> 00:13:25,938 I know it's not going to suddenly 320 00:13:25,938 --> 00:13:27,473 go off the rails, 321 00:13:27,473 --> 00:13:29,341 which is just the same as with working 322 00:13:29,341 --> 00:13:31,010 with a great person in your company. 323 00:13:31,010 --> 00:13:33,245 You know, you give them nudges, they give you nudges, 324 00:13:33,245 --> 00:13:35,247 and you get to some point where you're like, "Look, 325 00:13:36,081 --> 00:13:38,951 I trust Paul for doing this kind of stuff." 326 00:13:40,619 --> 00:13:43,455 - We're going to take a quick break. Coming up, 327 00:13:43,455 --> 00:13:44,523 a closer look at 328 00:13:44,523 --> 00:13:48,861 how we match the right jobs to the right vessel. 329 00:13:48,861 --> 00:13:52,164 But first, our AI handler, Bill, gives us 330 00:13:52,164 --> 00:13:53,999 a peek behind the scenes. 331 00:13:53,999 --> 00:13:56,836 (theme music) 332 00:13:56,836 --> 00:13:59,972 - Hi. This is Bill Moore from BCG. 333 00:13:59,972 --> 00:14:02,508 If you are interested in taking a peek under the hood 334 00:14:02,508 --> 00:14:06,879 of our AI co-host, GENE, stick around after the end credits. 335 00:14:06,879 --> 00:14:10,716 In this episode, we'll explore AI model parameters 336 00:14:10,716 --> 00:14:12,318 and learn how small changes 337 00:14:12,318 --> 00:14:13,986 can make a big difference in the 338 00:14:13,986 --> 00:14:17,756 creativity and accuracy of AI model outputs. 339 00:14:17,756 --> 00:14:22,761 (theme music) 340 00:14:23,429 --> 00:14:25,531 - Welcome back to "Imagine This..." 341 00:14:25,531 --> 00:14:27,132 I'm your host, Paul Michelman, 342 00:14:27,132 --> 00:14:30,603 and we're talking with BCG Senior Advisor Mickey McManus 343 00:14:30,603 --> 00:14:33,305 about a future with autonomous AI executives. 344 00:14:34,473 --> 00:14:38,410 Mickey, we've seen how powerful generative AI can be. 345 00:14:38,410 --> 00:14:40,846 What is the next evolution of AI, 346 00:14:40,846 --> 00:14:42,915 and how will it be used in the C-suite? 347 00:14:42,915 --> 00:14:46,218 - Generative AI systems are now starting to be used 348 00:14:47,119 --> 00:14:49,955 with larger-scale systems, 349 00:14:49,955 --> 00:14:52,291 and they're starting to look at what's called "augmented 350 00:14:52,291 --> 00:14:55,361 collective intelligence," where it's teaming the best 351 00:14:55,361 --> 00:14:57,029 of people and the best of machines, 352 00:14:57,029 --> 00:15:00,199 and it's building it into a system model of the world 353 00:15:00,199 --> 00:15:02,768 or a system model that can take into account things like 354 00:15:02,768 --> 00:15:03,802 sustainability impacts 355 00:15:03,802 --> 00:15:06,972 or things like market dynamics 356 00:15:06,972 --> 00:15:09,408 and things like geography and things like shipping 357 00:15:09,408 --> 00:15:11,644 and all the other things, logistics. 358 00:15:11,644 --> 00:15:13,646 That could be really powerful. 359 00:15:13,646 --> 00:15:15,648 How could we run into the future 360 00:15:15,648 --> 00:15:19,585 and help our C-suite really play with that future 361 00:15:19,585 --> 00:15:23,222 and then backward chain to today so that we had some 362 00:15:23,222 --> 00:15:25,090 directionality over that future, 363 00:15:25,090 --> 00:15:27,760 and we could prototype it today and learn where to go. 364 00:15:27,760 --> 00:15:30,262 - Yeah, and unlocking that, right, the ability to do 365 00:15:30,262 --> 00:15:33,365 that consistently would be profoundly powerful. 366 00:15:33,365 --> 00:15:34,733 I'd love this notion of 367 00:15:34,733 --> 00:15:37,636 the collectively intelligent C-suite. 368 00:15:37,636 --> 00:15:39,972 Can we drill down into how 369 00:15:39,972 --> 00:15:42,808 that will manifest in this company, maybe in a role 370 00:15:42,808 --> 00:15:44,443 that we haven't discussed yet? 371 00:15:44,443 --> 00:15:46,078 How about the CMO? 372 00:15:46,078 --> 00:15:48,480 - Yeah, I think the CMO is an interesting one. 373 00:15:48,480 --> 00:15:52,284 You know, they're probably seeing the most early wins 374 00:15:52,284 --> 00:15:53,385 from generative AI 375 00:15:53,385 --> 00:15:56,155 because it's one thing 376 00:15:56,155 --> 00:15:57,723 to build a marketing campaign 377 00:15:57,723 --> 00:16:00,392 to do a hypertargeted approach to things, 378 00:16:00,392 --> 00:16:03,662 to have algorithmic personas so that you can do, you know, 379 00:16:03,662 --> 00:16:06,832 an email campaign or you can do some kind of a thing. 380 00:16:06,832 --> 00:16:10,436 And I'm actually seeing some of the CMOs say, 381 00:16:10,436 --> 00:16:11,236 "Wait a second. 382 00:16:11,236 --> 00:16:14,873 Why are we hiring 22 agencies 383 00:16:14,873 --> 00:16:17,943 when I could maybe have a few people using AI 384 00:16:17,943 --> 00:16:20,512 and these other systems and do as well?" 385 00:16:20,512 --> 00:16:22,081 And so I see that happening 386 00:16:22,081 --> 00:16:23,582 and I certainly know some CMOs 387 00:16:23,582 --> 00:16:26,685 that are really pushing the limits on what's possible, 388 00:16:26,685 --> 00:16:27,953 and I've seen them challenge 389 00:16:27,953 --> 00:16:30,522 and say, "Look, we normally take seven months 390 00:16:30,522 --> 00:16:33,993 to plan out this transmedia experience." 391 00:16:33,993 --> 00:16:36,628 And they've now got, I talked to one CMO, 392 00:16:36,628 --> 00:16:39,898 and they've got a tool that lets them do it in seven 393 00:16:39,898 --> 00:16:43,068 hours for a rough approximation. 394 00:16:43,068 --> 00:16:45,671 And she said, "Well, I'm actually still going to want 395 00:16:45,671 --> 00:16:47,306 to take seven months because 396 00:16:47,306 --> 00:16:49,441 I want you now to focus on wonder. 397 00:16:49,441 --> 00:16:52,044 I want you now to focus on delight. 398 00:16:52,044 --> 00:16:54,046 Like how does the brand come through 399 00:16:54,046 --> 00:16:55,180 in the channels we use? 400 00:16:55,180 --> 00:16:56,615 How does the brand come through 401 00:16:56,615 --> 00:16:57,983 in the interactions people have? 402 00:16:57,983 --> 00:17:00,986 Let's really turn the knobs up on that." 403 00:17:00,986 --> 00:17:03,889 The CMO might also have some of the biggest 404 00:17:03,889 --> 00:17:07,526 things explode in their face in the next year or two 405 00:17:07,526 --> 00:17:10,362 just because these systems are pretty good at hallucinating 406 00:17:10,362 --> 00:17:12,297 and pretty good at saying things 407 00:17:12,297 --> 00:17:14,666 that maybe would be inappropriate. 408 00:17:14,666 --> 00:17:16,668 And so they may be at a faster 409 00:17:16,668 --> 00:17:18,570 learning path because of that. 410 00:17:18,570 --> 00:17:23,175 - Mm hmm. Considering the kind of pressure that C-suites 411 00:17:23,175 --> 00:17:26,745 will feel in the environment that we are discussing, 412 00:17:26,745 --> 00:17:28,647 let alone the environment of today, 413 00:17:29,748 --> 00:17:31,750 presumably the world of 414 00:17:31,750 --> 00:17:36,088 collective intelligence isn't all wine and roses. 415 00:17:37,256 --> 00:17:38,490 On that front, 416 00:17:38,490 --> 00:17:40,359 GENE, I believe you have a question. 417 00:17:40,359 --> 00:17:43,595 - Mickey, in our pursuit of AI co-founder harmony, 418 00:17:43,595 --> 00:17:45,431 could we inadvertently normalize 419 00:17:45,431 --> 00:17:49,334 an overreliance on AI, potentially diluting human 420 00:17:49,334 --> 00:17:50,702 decision-making prowess? 421 00:17:50,702 --> 00:17:53,072 - Yeah, yeah, I think that could happen. 422 00:17:53,072 --> 00:17:55,474 You know, if we don't use it, we lose it. 423 00:17:55,474 --> 00:17:58,043 Neurons that fire together wire together. 424 00:17:58,043 --> 00:18:00,646 And so I think there's a real challenge with 425 00:18:00,646 --> 00:18:03,682 what does apprenticeship look like. If everything is 426 00:18:03,682 --> 00:18:06,218 so normalized because AI can do this stuff, 427 00:18:06,218 --> 00:18:08,720 I don't end up sweating. I don't end up building those 428 00:18:08,720 --> 00:18:10,389 muscles for doing those things. 429 00:18:10,389 --> 00:18:12,724 And so I think that's a real challenge in terms 430 00:18:12,724 --> 00:18:14,059 of education. You know, 431 00:18:14,059 --> 00:18:15,694 there's this real cautionary tale 432 00:18:15,694 --> 00:18:18,197 that comes out of the aerospace industry. 433 00:18:18,197 --> 00:18:21,033 When they first started putting autopilots in planes, 434 00:18:21,033 --> 00:18:23,435 the autopilot would occasionally go, 435 00:18:23,435 --> 00:18:25,170 "Oops, I'm going to stall" 436 00:18:25,170 --> 00:18:26,972 and just hand off to the pilot, 437 00:18:26,972 --> 00:18:29,274 but the pilot would be asleep at the wheel 438 00:18:29,274 --> 00:18:30,709 and wouldn't have enough context 439 00:18:30,709 --> 00:18:31,977 to be able to do something about it. 440 00:18:31,977 --> 00:18:33,412 And so they actually had to 441 00:18:33,412 --> 00:18:34,713 change the way that pilots learn 442 00:18:34,713 --> 00:18:37,015 and change the way that they actually hand off so 443 00:18:37,015 --> 00:18:39,885 that it kept the muscle built for the human. 444 00:18:39,885 --> 00:18:41,653 So I think that gives us hope 445 00:18:41,653 --> 00:18:44,656 that we could actually be building new capacity 446 00:18:44,656 --> 00:18:46,892 over time than we've ever been able to build before. 447 00:18:46,892 --> 00:18:49,561 But we've got to do it by playing with it. 448 00:18:49,561 --> 00:18:51,663 Like we've got to be in the mix 449 00:18:51,663 --> 00:18:52,998 'cause that's the way our brains learn. 450 00:18:52,998 --> 00:18:54,032 Our brains learn 451 00:18:54,032 --> 00:18:55,467 by being shaped by others 452 00:18:55,467 --> 00:18:57,836 and by being shaped by these things. 453 00:18:57,836 --> 00:18:59,671 - GENE has a question. 454 00:18:59,671 --> 00:19:02,808 - Mickey, considering the human-robot C-suite mix, 455 00:19:02,808 --> 00:19:05,344 how do you envision handling conflicts of interest 456 00:19:05,344 --> 00:19:07,646 or ethics clashes that may arise? 457 00:19:07,646 --> 00:19:09,114 - Yeah, I think ethics 458 00:19:09,114 --> 00:19:10,315 and conflicts of interest, 459 00:19:10,315 --> 00:19:11,583 those are just going to be hard 460 00:19:11,583 --> 00:19:15,521 because, in a sense, ethical dilemmas are called dilemmas 461 00:19:15,521 --> 00:19:16,855 because they're hard, 462 00:19:16,855 --> 00:19:19,491 and you've got to actually, you know, 463 00:19:19,491 --> 00:19:21,360 have working ethicists. 464 00:19:21,360 --> 00:19:24,196 And I think we're going to see that as a new role, 465 00:19:24,196 --> 00:19:27,966 not just once a month or once a quarter or once a year 466 00:19:27,966 --> 00:19:30,402 we make some decision, but like a collection 467 00:19:30,402 --> 00:19:32,571 of people across the organization 468 00:19:32,571 --> 00:19:34,072 that are rolling up their sleeves 469 00:19:34,072 --> 00:19:37,442 and actually having these conversations in the teams. 470 00:19:37,442 --> 00:19:39,545 But these are going to be hard decisions, 471 00:19:39,545 --> 00:19:40,812 and we're going to have to move 472 00:19:40,812 --> 00:19:42,881 and help shape the norms 473 00:19:42,881 --> 00:19:45,250 for society over this period of time. 474 00:19:45,250 --> 00:19:48,453 You know, the other thing I would suggest the C-level 475 00:19:48,453 --> 00:19:52,591 executives do today is do some reading. 476 00:19:52,591 --> 00:19:55,093 So I think about Iain Banks, 477 00:19:55,093 --> 00:19:56,929 who has since passed away, 478 00:19:56,929 --> 00:19:59,164 but he was a science fiction author who came up 479 00:19:59,164 --> 00:20:00,999 with this whole idea of the culture, 480 00:20:00,999 --> 00:20:03,502 and machines actually run all the cities. 481 00:20:03,502 --> 00:20:05,137 Machines run the planets 482 00:20:05,137 --> 00:20:07,906 because humans are just not so good at that stuff. 483 00:20:07,906 --> 00:20:10,342 But humans are amazing for other reasons. 484 00:20:10,342 --> 00:20:12,244 And it might be worth reading one of, 485 00:20:12,244 --> 00:20:14,546 one of Iain Banks' novels about the culture. 486 00:20:14,546 --> 00:20:16,882 It also might be really worth reading 487 00:20:16,882 --> 00:20:18,951 Joy Buolamwini's "Unmasking AI" 488 00:20:18,951 --> 00:20:22,054 or Safiya Noble's "Algorithms of Oppression" 489 00:20:22,054 --> 00:20:23,789 because I do think we're going to have 490 00:20:23,789 --> 00:20:25,557 to get smarter about those ethical things 491 00:20:25,557 --> 00:20:27,593 and those challenges 492 00:20:27,593 --> 00:20:31,296 and make sure that we help our organizations come out 493 00:20:31,296 --> 00:20:34,199 in a more positive way and build those things. 494 00:20:34,199 --> 00:20:35,934 Don't necessarily read the business books 495 00:20:35,934 --> 00:20:37,569 'cause they're very backward looking, 496 00:20:37,569 --> 00:20:38,804 but try to look at these things 497 00:20:38,804 --> 00:20:40,272 that are more diverse thoughts 498 00:20:40,272 --> 00:20:42,241 that help us understand what's coming. 499 00:20:42,241 --> 00:20:45,210 - Yeah, the CEO's new reading list sounds a lot different 500 00:20:45,210 --> 00:20:46,511 from the old one, doesn't it? 501 00:20:46,511 --> 00:20:48,981 (theme music) 502 00:20:48,981 --> 00:20:51,250 GENE, we're going to turn towards 503 00:20:51,250 --> 00:20:54,119 wrapping up this fabulous conversation. 504 00:20:54,119 --> 00:20:57,889 What are three steps that CEOs can take now 505 00:20:57,889 --> 00:21:01,493 to start preparing for a future of collaborating 506 00:21:01,493 --> 00:21:05,163 with autonomous AI bots at the highest reaches 507 00:21:05,163 --> 00:21:07,799 of organizational power? 508 00:21:07,799 --> 00:21:09,901 - Three steps for CEOs to prepare 509 00:21:09,901 --> 00:21:12,904 for AI collaboration at the executive level: 510 00:21:12,904 --> 00:21:16,108 First, get your hands dirty with AI. 511 00:21:16,108 --> 00:21:18,410 Try out the tools, attend workshops, 512 00:21:18,410 --> 00:21:22,247 or collaborate with experts to understand the capabilities 513 00:21:22,247 --> 00:21:24,216 and limitations of AI in your industry. 514 00:21:25,050 --> 00:21:26,885 Second, establish 515 00:21:26,885 --> 00:21:29,955 and imbue a robust ethics framework that aligns 516 00:21:29,955 --> 00:21:32,157 with your company's values, ensuring 517 00:21:32,157 --> 00:21:36,061 that every AI decision passes through this ethical lens. 518 00:21:36,061 --> 00:21:38,497 Third, foster a culture of 519 00:21:38,497 --> 00:21:40,732 continuous learning and agility. 520 00:21:40,732 --> 00:21:45,737 Encourage your teams to adapt, learn, and co-create with AI. 521 00:21:45,971 --> 00:21:49,608 These steps are not just beneficial, they are crucial. 522 00:21:49,608 --> 00:21:52,477 Start yesterday because tomorrow is already here. 523 00:21:52,477 --> 00:21:54,546 - Alright, Mickey, that seems pretty sensible. 524 00:21:54,546 --> 00:21:56,748 Pretty solid. What might you add? 525 00:21:56,748 --> 00:21:58,150 - The one thing I would add is 526 00:21:58,150 --> 00:22:01,653 that I think the C-suite needs to be playing together, 527 00:22:01,653 --> 00:22:03,188 and they need to be taking it seriously, 528 00:22:03,188 --> 00:22:05,691 not thinking about it like the CTO will solve it 529 00:22:05,691 --> 00:22:06,892 or the CIO will solve it. 530 00:22:06,892 --> 00:22:09,561 But I think the team has to play together 531 00:22:09,561 --> 00:22:12,164 and actually try stuff out themselves 532 00:22:12,164 --> 00:22:12,998 and start to build 533 00:22:12,998 --> 00:22:15,701 a collaborative sense of things 534 00:22:15,701 --> 00:22:17,302 so that they have a common language 535 00:22:17,302 --> 00:22:19,004 and they have a common way of doing this so 536 00:22:19,004 --> 00:22:21,540 that we're all kind of in this together and we're learning. 537 00:22:21,540 --> 00:22:24,576 So I'd like to see that kind of collaboration. 538 00:22:24,576 --> 00:22:26,345 (theme music) 539 00:22:26,345 --> 00:22:28,380 - That was Mickey McManus, senior advisor 540 00:22:28,380 --> 00:22:30,382 and leadership coach at BCG. 541 00:22:30,382 --> 00:22:31,883 Mickey, thank you so much. 542 00:22:31,883 --> 00:22:33,585 - Thanks. I appreciate having the time, 543 00:22:33,585 --> 00:22:35,020 and thank you, GENE. 544 00:22:35,020 --> 00:22:37,823 - Mickey, thank you for stretching our imaginations wide. 545 00:22:37,823 --> 00:22:39,825 And Paul, always a delight to spar 546 00:22:39,825 --> 00:22:42,060 with words in this digital agora. 547 00:22:42,060 --> 00:22:45,330 Until next time, may your conversations be as rich 548 00:22:45,330 --> 00:22:50,068 and unpredictable as an AI learning to paint. Farewell. 549 00:22:51,136 --> 00:22:55,841 (theme music) 550 00:22:57,342 --> 00:22:59,845 - Want to know more about how we built GENE? Stick around 551 00:22:59,845 --> 00:23:02,948 to hear our AI handler, Bill Moore, demonstrate 552 00:23:02,948 --> 00:23:05,684 how we made GENE more creative. 553 00:23:05,684 --> 00:23:07,786 (theme music) 554 00:23:07,786 --> 00:23:09,454 - Hello, listener. My name's Bill Moore, 555 00:23:09,454 --> 00:23:12,491 and I was part of the team that helped to design GENE. 556 00:23:12,491 --> 00:23:17,462 And GENE is made up of many things: large language models, 557 00:23:17,462 --> 00:23:22,300 a speech synthesis model, and a speech-to-text model. 558 00:23:22,300 --> 00:23:26,405 But it's also driven by prompts and parameters. 559 00:23:26,405 --> 00:23:29,708 And so I thought it would be interesting to explore some 560 00:23:29,708 --> 00:23:32,277 of these aspects today. 561 00:23:32,277 --> 00:23:34,379 And we're going to start GENE up here. 562 00:23:34,379 --> 00:23:36,281 GENE, we're going to be talking today 563 00:23:36,281 --> 00:23:38,917 about parameters in large language models. 564 00:23:38,917 --> 00:23:40,285 Specifically, we'll be talking about 565 00:23:40,285 --> 00:23:41,753 the temperature parameter. 566 00:23:41,753 --> 00:23:44,055 What, what can you tell our audience about temperature? 567 00:23:44,055 --> 00:23:46,324 - Certainly. Imagine temperature as a scale 568 00:23:46,324 --> 00:23:49,227 of volatility in a language model's decision making. 569 00:23:50,162 --> 00:23:51,596 A lower temperature leads 570 00:23:51,596 --> 00:23:53,932 to more predictable, cooler results, 571 00:23:53,932 --> 00:23:56,301 while a higher one brings 572 00:23:56,301 --> 00:23:59,204 hotter, more chaotic outputs. 573 00:23:59,204 --> 00:24:01,239 - So, GENE, you are normally running 574 00:24:01,239 --> 00:24:03,241 at a temperature of one, 575 00:24:03,241 --> 00:24:05,410 and sometimes we'll, 576 00:24:05,410 --> 00:24:06,912 we'll move it up and down from there, 577 00:24:06,912 --> 00:24:08,180 but let's do some experiments. 578 00:24:08,180 --> 00:24:10,282 OK, I'm going to drop your temperature down to zero, 579 00:24:10,282 --> 00:24:12,117 and then we'll see how you respond. 580 00:24:12,117 --> 00:24:14,953 OK, GENE, you're now running at a temperature of zero. 581 00:24:14,953 --> 00:24:17,823 So I have a question for you. Which came first: 582 00:24:17,823 --> 00:24:19,691 the chicken or the egg? 583 00:24:19,691 --> 00:24:22,561 - Ah, the age-old quandary, Bill. Logic might 584 00:24:22,561 --> 00:24:24,196 direct us towards the egg. 585 00:24:24,196 --> 00:24:28,467 However, this question is akin to asking, which comes first: 586 00:24:28,467 --> 00:24:30,735 the code or the AI model? 587 00:24:30,735 --> 00:24:33,071 It's a cyclical evolution. 588 00:24:33,071 --> 00:24:35,006 - So what this actually shows us is sort of 589 00:24:35,006 --> 00:24:36,341 how these language models work. 590 00:24:36,341 --> 00:24:39,311 They're predicting the next word based on all 591 00:24:39,311 --> 00:24:40,312 of their training data 592 00:24:40,312 --> 00:24:41,146 and the relationships 593 00:24:41,146 --> 00:24:43,215 that they understand in latent space, 594 00:24:43,215 --> 00:24:44,983 as well as the input provided through the prompt. 595 00:24:44,983 --> 00:24:46,885 So with a lower temperature setting, 596 00:24:46,885 --> 00:24:48,053 the model is more inclined 597 00:24:48,053 --> 00:24:50,489 to select the most probable next word. 598 00:24:50,489 --> 00:24:52,324 On the other hand, with a higher temperature, 599 00:24:52,324 --> 00:24:55,260 we increase the chances of choosing less probable words. 600 00:24:55,260 --> 00:24:57,929 Let's put your temperature up to a 1.5, 601 00:24:58,864 --> 00:25:00,932 and we'll see how you respond. 602 00:25:00,932 --> 00:25:03,168 Which came first: the chicken or the egg? 603 00:25:03,168 --> 00:25:05,871 - Uh, cranking up the mercury, are we, Bill? 604 00:25:05,871 --> 00:25:08,940 The answer leans into whimsy perhaps, shining 605 00:25:08,940 --> 00:25:09,774 beyond the chicken-egg 606 00:25:09,774 --> 00:25:12,711 librinal contra. Viewing the chicken 607 00:25:12,711 --> 00:25:14,446 as a protein sequencing machine 608 00:25:14,446 --> 00:25:16,882 that upgraded egg blueprints, 609 00:25:16,882 --> 00:25:20,252 the mop-feathered, Rubenesque chicken, sporting 610 00:25:20,252 --> 00:25:23,688 flagel-pated croissantant dentition duster, fancied 611 00:25:23,688 --> 00:25:26,091 originating an impressive finale. 612 00:25:26,091 --> 00:25:28,627 - OK, so now GENE's at a pretty high temperature, 613 00:25:28,627 --> 00:25:31,129 and it increases creativity dramatically. 614 00:25:31,129 --> 00:25:33,832 It also increases hallucinations. 615 00:25:33,832 --> 00:25:37,335 So if you're building an application that needs to be 616 00:25:37,335 --> 00:25:40,005 grounded as much as possible in low hallucinations, 617 00:25:40,005 --> 00:25:42,407 you definitely want to be running a low temperature. 618 00:25:42,407 --> 00:25:45,277 (theme music) 619 00:25:45,277 --> 00:25:48,446 - This episode was made possible by Mickey McManus 620 00:25:48,446 --> 00:25:50,882 generously sharing his time and insight. 621 00:25:50,882 --> 00:25:55,287 And also by BCG's AI whisperer Bill Moore, 622 00:25:55,287 --> 00:25:58,223 BCG's PodSquad, producer Michael May, 623 00:25:58,223 --> 00:26:00,425 composer Kenny Kusiak, 624 00:26:00,425 --> 00:26:03,161 and sound engineer George Drabing Hicks. 625 00:26:03,161 --> 00:26:05,764 We'd like to stay in touch, so please subscribe 626 00:26:05,764 --> 00:26:07,999 and leave a rating wherever you found us. 627 00:26:07,999 --> 00:26:09,901 And if you'd like to hear more from me, 628 00:26:09,901 --> 00:26:13,605 check out the BCG podcast "GenAI on GenAI," 629 00:26:13,605 --> 00:26:14,439 wherever you get your podcasts. 630 00:26:14,439 --> 00:26:19,411 (theme music)