1 00:00:00,000 --> 00:00:01,240 Hey everyone, welcome back. 2 00:00:01,240 --> 00:00:06,240 Today we're gonna really get into AI and with supply chain. 3 00:00:06,280 --> 00:00:07,120 Yeah. 4 00:00:07,120 --> 00:00:08,140 Which I think is something we're hearing 5 00:00:08,140 --> 00:00:09,160 more and more about. 6 00:00:09,160 --> 00:00:10,400 Definitely. 7 00:00:10,400 --> 00:00:12,960 So I've got a whole stake of articles and research 8 00:00:12,960 --> 00:00:14,160 and we're gonna break it all down, 9 00:00:14,160 --> 00:00:16,480 figure out what's happening, why it matters 10 00:00:16,480 --> 00:00:18,360 and what it all means for everyone listening. 11 00:00:18,360 --> 00:00:20,400 I think it's a really interesting time right now 12 00:00:20,400 --> 00:00:22,040 because we're kind of seeing this shift 13 00:00:22,040 --> 00:00:25,040 from what we call industry 4.0. 14 00:00:25,040 --> 00:00:27,200 Which is really all about automation 15 00:00:27,200 --> 00:00:29,880 to what some are calling industry 6.0. 16 00:00:29,880 --> 00:00:30,840 6.0 already. 17 00:00:30,840 --> 00:00:32,120 Yeah, things move fast. 18 00:00:32,120 --> 00:00:32,960 Wow. 19 00:00:32,960 --> 00:00:35,200 And the idea here is that it's not just about 20 00:00:35,200 --> 00:00:37,560 automating everything but it's more about 21 00:00:37,560 --> 00:00:41,160 this kind of AI working together with people 22 00:00:41,160 --> 00:00:43,800 for a more efficient and sustainable approach. 23 00:00:43,800 --> 00:00:46,480 So it's not just like robots taking over warehouses. 24 00:00:46,480 --> 00:00:47,800 Not quite, no. 25 00:00:47,800 --> 00:00:48,640 Okay. 26 00:00:48,640 --> 00:00:49,920 But it's definitely, I mean think about it this way. 27 00:00:49,920 --> 00:00:51,960 Like, you know when you're shopping online 28 00:00:51,960 --> 00:00:54,960 and you see those recommendations for things you might like. 29 00:00:54,960 --> 00:00:57,120 That's AI and acronym, that's algorithms 30 00:00:57,120 --> 00:00:59,200 trying to figure out what you need, what you want. 31 00:00:59,200 --> 00:01:00,120 Okay, that makes sense. 32 00:01:00,120 --> 00:01:03,080 And honestly, I do love it when they suggest something 33 00:01:03,080 --> 00:01:04,640 I didn't even know I needed. 34 00:01:04,640 --> 00:01:05,480 Right. 35 00:01:05,480 --> 00:01:06,400 It's like how did they know? 36 00:01:06,400 --> 00:01:08,440 But that's happening kind of everywhere. 37 00:01:08,440 --> 00:01:10,200 It's not just on the customer side of things. 38 00:01:10,200 --> 00:01:11,040 Oh, it's interesting. 39 00:01:11,040 --> 00:01:13,400 AI is working behind the scenes to like make sure 40 00:01:13,400 --> 00:01:16,640 those purchases get to you quickly and efficiently. 41 00:01:16,640 --> 00:01:18,520 Like it's figuring out delivery routes, 42 00:01:18,520 --> 00:01:20,960 real time tracking, the whole nine yards. 43 00:01:20,960 --> 00:01:21,800 Oh, that's interesting. 44 00:01:21,800 --> 00:01:23,760 That reminds me actually I was reading about 45 00:01:23,760 --> 00:01:27,080 how some companies are using AI now 46 00:01:27,080 --> 00:01:29,240 to like audit freight bills. 47 00:01:29,240 --> 00:01:30,240 Oh, wow. 48 00:01:30,240 --> 00:01:32,440 Yeah, like to make sure the invoices are accurate. 49 00:01:32,440 --> 00:01:33,280 Interesting. 50 00:01:33,280 --> 00:01:35,080 Yeah, and that nobody's getting overcharged. 51 00:01:35,080 --> 00:01:37,160 Yeah, catching those errors. 52 00:01:37,160 --> 00:01:39,440 And then another thing I was reading about, 53 00:01:41,120 --> 00:01:43,080 I never would have thought of this using AI 54 00:01:43,080 --> 00:01:45,480 to enhance worker safety in warehouses. 55 00:01:45,480 --> 00:01:46,320 Oh, how so? 56 00:01:46,320 --> 00:01:50,720 Yeah, so apparently by using like sensors and cameras, 57 00:01:50,720 --> 00:01:54,720 the AI can identify potential hazards, 58 00:01:54,720 --> 00:01:56,760 like even predict accidents. 59 00:01:56,760 --> 00:01:57,600 Yeah, that makes sense. 60 00:01:57,600 --> 00:01:58,420 Before they happen. 61 00:01:58,420 --> 00:02:00,880 Leading to like a safer work environment for everybody. 62 00:02:00,880 --> 00:02:04,200 Absolutely, it's all about analyzing those patterns, 63 00:02:04,200 --> 00:02:07,840 those data points that maybe a human wouldn't catch. 64 00:02:07,840 --> 00:02:10,600 But I think one of the less obvious ones 65 00:02:10,600 --> 00:02:13,000 is transaction record integrity. 66 00:02:13,000 --> 00:02:13,840 Oh, okay. 67 00:02:13,840 --> 00:02:15,040 Which if you think about it, 68 00:02:15,040 --> 00:02:17,360 it's like having a really, really good auditor, 69 00:02:17,360 --> 00:02:20,320 constantly checking to make sure everything's accurate 70 00:02:20,320 --> 00:02:22,840 and secure and preventing fraud. 71 00:02:22,840 --> 00:02:25,360 Which helps maintain trust in the system. 72 00:02:25,360 --> 00:02:27,600 Wow, so it's really a lot more than just making sure 73 00:02:27,600 --> 00:02:28,800 my shoes arrive on time. 74 00:02:28,800 --> 00:02:30,360 Right, it's pretty amazing. 75 00:02:30,360 --> 00:02:32,280 It really is like this behind the scenes thing, 76 00:02:32,280 --> 00:02:33,760 making sure everything's going smoothly. 77 00:02:33,760 --> 00:02:34,600 Yeah. 78 00:02:34,600 --> 00:02:35,720 It can't all be perfect, right? 79 00:02:35,720 --> 00:02:36,660 Of course not. 80 00:02:36,660 --> 00:02:37,640 What are some of the challenges? 81 00:02:37,640 --> 00:02:40,200 Well, one of the big ones is data integration. 82 00:02:40,200 --> 00:02:41,040 Okay. 83 00:02:41,040 --> 00:02:42,640 Imagine like a company's trying to implement 84 00:02:42,640 --> 00:02:44,800 this really advanced AI system, 85 00:02:44,800 --> 00:02:47,760 but their inventory management is still on like paper records 86 00:02:47,760 --> 00:02:48,720 from the 90s. 87 00:02:48,720 --> 00:02:49,560 Oh, wow. 88 00:02:49,560 --> 00:02:50,760 Yeah, trying to bring that all together 89 00:02:50,760 --> 00:02:53,360 can be a real nightmare. 90 00:02:53,360 --> 00:02:55,760 And then there's data availability in general. 91 00:02:55,760 --> 00:02:58,000 AI algorithms need tons and tons of data 92 00:02:58,000 --> 00:03:00,040 to really learn and get better. 93 00:03:00,040 --> 00:03:02,240 And sometimes that data just isn't available 94 00:03:02,240 --> 00:03:04,560 or it's not in the right format. 95 00:03:04,560 --> 00:03:07,320 It's like trying to bake a cake without the ingredients. 96 00:03:07,320 --> 00:03:10,520 So even if a company wants to like jump on this AI train, 97 00:03:10,520 --> 00:03:13,440 they could be held back by their own like outdated systems. 98 00:03:13,440 --> 00:03:14,280 Absolutely. 99 00:03:14,280 --> 00:03:15,120 Wow. 100 00:03:15,120 --> 00:03:16,680 Or just a lack of usable data. 101 00:03:16,680 --> 00:03:18,960 Okay, well that's interesting to know. 102 00:03:18,960 --> 00:03:21,080 So why don't we shift gears a little bit 103 00:03:21,080 --> 00:03:24,120 and talk about why everybody's so excited about AI? 104 00:03:24,120 --> 00:03:27,000 Like what are the potential benefits? 105 00:03:27,000 --> 00:03:28,600 I mean, from what you've been reading, 106 00:03:28,600 --> 00:03:31,760 it seems like AI has the potential 107 00:03:31,760 --> 00:03:34,160 to make businesses so much more efficient. 108 00:03:34,160 --> 00:03:37,640 And I mean, who doesn't love lower prices, right? 109 00:03:37,640 --> 00:03:39,960 And then beyond just saving money, 110 00:03:39,960 --> 00:03:43,280 AI can help create more resilient supply chains. 111 00:03:43,280 --> 00:03:44,880 Like think about everything that's happened 112 00:03:44,880 --> 00:03:48,280 in the last few years, pandemics, natural disasters, 113 00:03:48,280 --> 00:03:49,720 all of this instability 114 00:03:49,720 --> 00:03:52,480 and AI can actually help businesses predict 115 00:03:52,480 --> 00:03:55,120 these disruptions and be more prepared. 116 00:03:55,120 --> 00:03:57,240 So instead of being like caught off guard, 117 00:03:57,240 --> 00:03:58,320 they can be proactive. 118 00:03:58,320 --> 00:03:59,720 Exactly. 119 00:03:59,720 --> 00:04:01,680 It's almost like having a crystal ball, right? 120 00:04:01,680 --> 00:04:03,200 You can kind of see what's coming 121 00:04:03,200 --> 00:04:04,920 and adjust your strategy. 122 00:04:04,920 --> 00:04:05,760 That's amazing. 123 00:04:05,760 --> 00:04:08,560 And that resilience that not only helps businesses, 124 00:04:08,560 --> 00:04:09,880 but it kind of trickles down. 125 00:04:09,880 --> 00:04:13,080 It helps the economy, it helps society as a whole. 126 00:04:13,080 --> 00:04:13,920 That's really interesting. 127 00:04:13,920 --> 00:04:15,840 So I mean, this is all starting to sound 128 00:04:15,840 --> 00:04:18,640 really amazing and transformative. 129 00:04:18,640 --> 00:04:20,360 But I think it would be irresponsible of us 130 00:04:20,360 --> 00:04:23,720 if we didn't at least mention the big elephant in the room, 131 00:04:23,720 --> 00:04:26,040 which is like jobs. 132 00:04:26,040 --> 00:04:27,360 I think there's a lot of people out there 133 00:04:27,360 --> 00:04:30,440 who are really worried about being replaced by robots. 134 00:04:30,440 --> 00:04:32,160 Absolutely, that's a valid concern. 135 00:04:32,160 --> 00:04:33,680 But I think it's important to remember 136 00:04:33,680 --> 00:04:37,200 that AI is a tool and like any tool 137 00:04:37,200 --> 00:04:38,840 it can be used in different ways. 138 00:04:38,840 --> 00:04:40,880 And while, yeah, some jobs may change 139 00:04:40,880 --> 00:04:42,480 or even become obsolete, 140 00:04:42,480 --> 00:04:44,360 AI is also creating new opportunities, 141 00:04:44,360 --> 00:04:47,840 especially in things like data analysis and AI development. 142 00:04:47,840 --> 00:04:51,400 So it's not necessarily like robots versus humans, 143 00:04:51,400 --> 00:04:54,360 but more like a shift in what skills are needed. 144 00:04:54,360 --> 00:04:56,800 Exactly, just like the Industrial Revolution 145 00:04:56,800 --> 00:04:59,320 led to new jobs and industries, 146 00:04:59,320 --> 00:05:01,880 the AI revolution will likely do the same. 147 00:05:01,880 --> 00:05:05,840 Okay, so it's about adapting and evolving. 148 00:05:05,840 --> 00:05:08,840 And I mean, I guess that's a big part 149 00:05:08,840 --> 00:05:09,960 of what we're talking about today. 150 00:05:09,960 --> 00:05:12,160 Yeah, I think a key takeaway here 151 00:05:12,160 --> 00:05:15,200 is that AI isn't about replacing people, 152 00:05:15,200 --> 00:05:19,400 it's about helping people do their jobs better 153 00:05:19,400 --> 00:05:22,440 and maybe freeing them up to do more creative work 154 00:05:22,440 --> 00:05:24,040 or more strategic work. 155 00:05:24,040 --> 00:05:25,440 Yeah, I like that. 156 00:05:25,440 --> 00:05:27,520 So we've covered a lot, I feel like. 157 00:05:27,520 --> 00:05:29,600 We talked about how AI is already being used 158 00:05:29,600 --> 00:05:30,920 in the supply chain. 159 00:05:30,920 --> 00:05:34,040 We talked about the potential benefits and the challenges. 160 00:05:34,040 --> 00:05:35,840 But before we get too far ahead of ourselves, 161 00:05:35,840 --> 00:05:38,840 I think it's time to bring in some expert opinions. 162 00:05:38,840 --> 00:05:41,720 Do we have some insights from Dr. Jeffrey Hinton 163 00:05:41,720 --> 00:05:44,000 who, you know, people call him the godfather of AI? 164 00:05:44,000 --> 00:05:45,080 Yeah, the godfather. 165 00:05:45,080 --> 00:05:46,720 And then we also have Chris Miller. 166 00:05:46,720 --> 00:05:47,560 Okay. 167 00:05:47,560 --> 00:05:48,760 Who wrote Chip War? 168 00:05:48,760 --> 00:05:50,240 And he's got some interesting things to say 169 00:05:50,240 --> 00:05:51,960 about like the limits of AI. 170 00:05:51,960 --> 00:05:52,800 Yeah, let's hear it. 171 00:05:52,800 --> 00:05:54,040 So why don't we start with Dr. Hinton? 172 00:05:54,040 --> 00:05:56,080 Okay, so Dr. Hinton, he's a big believer 173 00:05:56,080 --> 00:05:57,280 in the power of AI. 174 00:05:57,280 --> 00:05:58,120 Okay. 175 00:05:58,120 --> 00:06:00,200 And he thinks that it can really boost productivity, 176 00:06:00,200 --> 00:06:03,000 which could lead to like economic growth 177 00:06:03,000 --> 00:06:05,280 and better living standards for everybody. 178 00:06:05,280 --> 00:06:06,760 Okay, that sounds promising. 179 00:06:06,760 --> 00:06:07,600 Yeah. 180 00:06:07,600 --> 00:06:08,440 But I'm sensing a butt coming. 181 00:06:08,440 --> 00:06:09,400 Oh, you know, there's always a butt. 182 00:06:09,400 --> 00:06:10,240 Yeah. 183 00:06:10,240 --> 00:06:12,160 Dr. Hinton also raises concerns 184 00:06:12,160 --> 00:06:14,480 about the potential societal impact. 185 00:06:14,480 --> 00:06:15,320 Okay. 186 00:06:15,320 --> 00:06:17,160 He worries that this increased productivity 187 00:06:17,160 --> 00:06:19,320 could lead to greater wealth disparity. 188 00:06:19,320 --> 00:06:20,160 Oh, okay. 189 00:06:20,160 --> 00:06:22,840 The benefits might not be evenly distributed. 190 00:06:22,840 --> 00:06:24,320 So kind of a double-edged sort, right? 191 00:06:24,320 --> 00:06:27,200 Potential for progress, but also the risk of like 192 00:06:27,200 --> 00:06:29,560 making existing inequalities even worse. 193 00:06:29,560 --> 00:06:30,480 Exactly. 194 00:06:30,480 --> 00:06:32,360 It highlights the need for, you know, 195 00:06:32,360 --> 00:06:35,120 careful policies and regulations 196 00:06:35,120 --> 00:06:37,080 to make sure that the benefits of AI 197 00:06:37,080 --> 00:06:38,600 are shared by everyone. 198 00:06:38,600 --> 00:06:39,440 Okay, yeah. 199 00:06:39,440 --> 00:06:42,120 And that, you know, it's developed ethically. 200 00:06:42,120 --> 00:06:45,840 So it's not enough to just like embrace the technology. 201 00:06:45,840 --> 00:06:48,080 We also have to think about how it's used. 202 00:06:48,080 --> 00:06:48,920 Absolutely. 203 00:06:48,920 --> 00:06:50,280 Okay, well, that's something to keep in mind. 204 00:06:50,280 --> 00:06:51,560 Definitely. 205 00:06:51,560 --> 00:06:53,080 So what about Chris Miller? 206 00:06:53,080 --> 00:06:54,360 Okay, so Chris Miller, 207 00:06:54,360 --> 00:06:56,560 he offers a slightly different perspective. 208 00:06:56,560 --> 00:06:59,920 He acknowledged that AI has this incredible potential. 209 00:06:59,920 --> 00:07:03,160 But he also argues that its growth is limited 210 00:07:03,160 --> 00:07:05,640 by very real constraints. 211 00:07:05,640 --> 00:07:06,480 Oh, okay. 212 00:07:06,480 --> 00:07:09,480 Particularly in chip manufacturing and power generation. 213 00:07:09,480 --> 00:07:11,520 Like even the biggest tech companies 214 00:07:11,520 --> 00:07:13,640 are struggling to get the resources they need 215 00:07:13,640 --> 00:07:15,960 to actually power these massive AI systems. 216 00:07:15,960 --> 00:07:17,480 So it's not just about the algorithms, 217 00:07:17,480 --> 00:07:20,000 it's also about the actual like physical stuff. 218 00:07:20,000 --> 00:07:21,120 Exactly. 219 00:07:21,120 --> 00:07:23,160 He makes this really interesting comparison 220 00:07:23,160 --> 00:07:25,600 to something called the Jeven's Paradox. 221 00:07:25,600 --> 00:07:26,440 Okay. 222 00:07:26,440 --> 00:07:29,480 Which basically says that as we get more efficient 223 00:07:29,480 --> 00:07:31,120 at using a resource, 224 00:07:31,120 --> 00:07:33,840 the demand for that resource actually increases. 225 00:07:33,840 --> 00:07:34,680 Oh, okay. 226 00:07:34,680 --> 00:07:36,800 So even as AI gets more and more efficient, 227 00:07:36,800 --> 00:07:40,160 the demand for chips and energy could just skyrocket. 228 00:07:40,160 --> 00:07:41,000 Wow. 229 00:07:41,000 --> 00:07:42,160 It's almost like a treadmill. 230 00:07:42,160 --> 00:07:44,440 Like we're constantly running faster. 231 00:07:44,440 --> 00:07:45,920 But we're not really getting any closer 232 00:07:45,920 --> 00:07:46,760 to where we wanna be. 233 00:07:46,760 --> 00:07:48,160 It's a good analogy. 234 00:07:48,160 --> 00:07:52,480 So we have these really optimistic views about AI. 235 00:07:52,480 --> 00:07:55,200 But we also have to be realistic about these limitations 236 00:07:55,200 --> 00:07:57,080 and potential risks. 237 00:07:57,080 --> 00:07:57,920 Yeah. 238 00:07:57,920 --> 00:07:58,760 Well, this has been fascinating. 239 00:07:58,760 --> 00:08:01,320 I feel like we've just scratched the surface. 240 00:08:01,320 --> 00:08:03,720 So lots to think about. 241 00:08:03,720 --> 00:08:05,200 Lots to think about. 242 00:08:05,200 --> 00:08:06,840 I think we're gonna leave it there for now. 243 00:08:06,840 --> 00:08:07,680 Okay. 244 00:08:07,680 --> 00:08:12,200 But we'll be back with more on AI and the supply chain. 245 00:08:12,200 --> 00:08:13,040 Yeah. 246 00:08:13,040 --> 00:08:13,880 There's more to come. 247 00:08:13,880 --> 00:08:14,720 So stay tuned. 248 00:08:14,720 --> 00:08:15,560 We'll be back. 249 00:08:15,560 --> 00:08:16,400 Okay. 250 00:08:16,400 --> 00:08:18,360 So welcome back to our deep dive on AI and the supply chain. 251 00:08:18,360 --> 00:08:19,200 Yeah. 252 00:08:19,200 --> 00:08:20,680 I feel like in the first part, 253 00:08:20,680 --> 00:08:24,560 we talked a lot about how AI is already being used. 254 00:08:24,560 --> 00:08:25,400 Right. 255 00:08:25,400 --> 00:08:26,520 Like right now. 256 00:08:26,520 --> 00:08:29,200 But now I'm curious, like what's next? 257 00:08:29,200 --> 00:08:30,040 Yeah. 258 00:08:30,040 --> 00:08:30,880 What does the future hold? 259 00:08:30,880 --> 00:08:33,400 Are we talking like flying delivery drones? 260 00:08:33,400 --> 00:08:34,240 Yeah. 261 00:08:34,240 --> 00:08:35,280 So driving trucks anywhere. 262 00:08:35,280 --> 00:08:39,120 You know, we may not have those drone deliveries quite yet. 263 00:08:39,120 --> 00:08:39,960 Okay. 264 00:08:39,960 --> 00:08:42,080 But the future is definitely looking pretty wild. 265 00:08:42,080 --> 00:08:42,920 Okay. 266 00:08:42,920 --> 00:08:46,840 Like imagine supply chains that can kind of run themselves. 267 00:08:46,840 --> 00:08:47,960 Like they're autonomous. 268 00:08:47,960 --> 00:08:51,200 They can adapt to changes without somebody, you know, 269 00:08:51,200 --> 00:08:52,520 having to step in all the time. 270 00:08:52,520 --> 00:08:55,200 So it's like the supply chain is developing its own brain. 271 00:08:55,200 --> 00:08:56,040 Yeah. 272 00:08:56,040 --> 00:08:56,880 That's a great way to put it. 273 00:08:56,880 --> 00:08:58,880 Like constantly learning, constantly adjusting. 274 00:08:58,880 --> 00:09:02,960 Like for example, AI could be used to analyze just tons 275 00:09:02,960 --> 00:09:03,800 and tons of data. 276 00:09:03,800 --> 00:09:04,640 Right. 277 00:09:04,640 --> 00:09:07,480 Weather patterns to even social media trends 278 00:09:07,480 --> 00:09:11,240 to predict demand with like incredible accuracy. 279 00:09:11,240 --> 00:09:14,280 So knowing like exactly how much of something people 280 00:09:14,280 --> 00:09:16,040 are going to want before they even want it. 281 00:09:16,040 --> 00:09:18,480 Yeah. Weeks or even months in advance. 282 00:09:18,480 --> 00:09:20,480 Wow. That would be amazing for businesses. 283 00:09:20,480 --> 00:09:21,320 Right. 284 00:09:21,320 --> 00:09:22,160 Yeah. 285 00:09:22,160 --> 00:09:24,000 No more scrambling to keep up or having too much inventory. 286 00:09:24,000 --> 00:09:24,840 Yeah. Yeah. 287 00:09:24,840 --> 00:09:26,480 Okay. So what else, what else is coming? 288 00:09:26,480 --> 00:09:29,840 Well, I think another really exciting area is logistics. 289 00:09:29,840 --> 00:09:30,680 Okay. 290 00:09:30,680 --> 00:09:34,040 We're talking about using AI to optimize everything. 291 00:09:34,040 --> 00:09:36,320 Warehouse layout, transportation routes, 292 00:09:36,320 --> 00:09:39,520 like squeezing out every little bit of inefficiency. 293 00:09:39,520 --> 00:09:42,360 It's like having a super efficient air traffic controller, 294 00:09:42,360 --> 00:09:44,480 but for like the entire flow of goods. 295 00:09:44,480 --> 00:09:46,320 So this all leads to like. 296 00:09:46,320 --> 00:09:49,680 A smoother, faster and cheaper supply chain. 297 00:09:49,680 --> 00:09:50,520 Okay. 298 00:09:50,520 --> 00:09:52,760 Which is good for everybody, businesses, consumers. 299 00:09:52,760 --> 00:09:53,600 Right. 300 00:09:53,600 --> 00:09:54,440 The whole economy really. 301 00:09:54,440 --> 00:09:56,160 Yeah. Yeah. This is all sounding really good. 302 00:09:56,160 --> 00:09:57,000 Yeah. 303 00:09:57,000 --> 00:10:00,400 But I guess I keep coming back to the people. 304 00:10:00,400 --> 00:10:03,520 Like as supply chains become more automated, 305 00:10:03,520 --> 00:10:06,240 what happens to the people who are doing those jobs? 306 00:10:06,240 --> 00:10:07,680 Right. That's a big question. 307 00:10:07,680 --> 00:10:08,520 Yeah. 308 00:10:08,520 --> 00:10:11,360 And I think the key is to remember that technology 309 00:10:11,360 --> 00:10:13,320 should be helping people. 310 00:10:13,320 --> 00:10:14,160 Right. 311 00:10:14,160 --> 00:10:15,000 Not replacing them. 312 00:10:15,000 --> 00:10:16,680 I mean, yeah, some jobs might change, 313 00:10:16,680 --> 00:10:19,320 but AI also creates new opportunities. 314 00:10:19,320 --> 00:10:20,160 Okay. 315 00:10:20,160 --> 00:10:22,680 Especially in, you know, things like data analysis 316 00:10:22,680 --> 00:10:24,280 and managing these systems. 317 00:10:24,280 --> 00:10:27,560 So it's about making sure people have the skills 318 00:10:27,560 --> 00:10:30,040 they need to succeed in this new world. 319 00:10:30,040 --> 00:10:32,760 So it's not like AI versus humans. 320 00:10:32,760 --> 00:10:33,600 Not at all. 321 00:10:33,600 --> 00:10:36,560 It's more like AI handling the boring stuff 322 00:10:36,560 --> 00:10:39,720 and freeing up humans to do what we're good at, 323 00:10:39,720 --> 00:10:41,720 problem solving, being creative. 324 00:10:41,720 --> 00:10:44,120 Okay. So what can people do to like stay ahead of the curve 325 00:10:44,120 --> 00:10:45,560 and make sure they're ready for these changes? 326 00:10:45,560 --> 00:10:49,600 I think the best advice is to just never stop learning. 327 00:10:49,600 --> 00:10:50,440 Okay. 328 00:10:50,440 --> 00:10:53,680 Stay curious, try new things, you know, explore AI. 329 00:10:53,680 --> 00:10:55,240 There's tons of resources out there. 330 00:10:55,240 --> 00:10:56,080 Yeah. 331 00:10:56,080 --> 00:10:57,280 It's almost like learning a new language. 332 00:10:57,280 --> 00:10:58,120 Yeah. 333 00:10:58,120 --> 00:10:59,720 But the more you use it, the more comfortable you get. 334 00:10:59,720 --> 00:11:00,560 Exactly. 335 00:11:00,560 --> 00:11:03,680 So, okay, so we might not have like flying cars 336 00:11:03,680 --> 00:11:05,680 and robot butlers just yet. 337 00:11:05,680 --> 00:11:06,720 Not yet. 338 00:11:06,720 --> 00:11:09,800 But it sounds like the future of AI 339 00:11:09,800 --> 00:11:11,880 and the supply chain is already happening. 340 00:11:11,880 --> 00:11:12,720 It is. 341 00:11:12,720 --> 00:11:13,880 And it's gonna be pretty amazing. 342 00:11:13,880 --> 00:11:16,680 Yeah. I mean, from self-adjusting logistics 343 00:11:16,680 --> 00:11:20,080 to super accurate demand forecasting, 344 00:11:20,080 --> 00:11:23,040 AI is gonna make things so much smoother and more efficient. 345 00:11:23,040 --> 00:11:24,720 And hopefully more sustainable, right? 346 00:11:24,720 --> 00:11:25,560 Oh, absolutely. 347 00:11:25,560 --> 00:11:28,600 I mean, think about optimizing transportation routes, 348 00:11:28,600 --> 00:11:30,160 cutting down on waste. 349 00:11:30,160 --> 00:11:33,320 AI can really help us make the supply chain greener. 350 00:11:33,320 --> 00:11:35,920 Okay, so as we kind of wrap up this part of our deep dive. 351 00:11:35,920 --> 00:11:36,760 Yeah. 352 00:11:36,760 --> 00:11:39,080 I think the takeaway here is that 353 00:11:39,080 --> 00:11:42,200 the future of AI is about collaboration. 354 00:11:42,200 --> 00:11:43,560 Between people and machines, 355 00:11:43,560 --> 00:11:45,080 between businesses and customers. 356 00:11:45,080 --> 00:11:45,920 Definitely. 357 00:11:45,920 --> 00:11:48,800 And between like innovation and responsibility. 358 00:11:48,800 --> 00:11:49,760 Well said. 359 00:11:49,760 --> 00:11:52,880 So, as we move into the final part of our deep dive. 360 00:11:52,880 --> 00:11:55,960 Let's keep in mind that this is not just about technology. 361 00:11:55,960 --> 00:11:58,560 It's about how we use technology 362 00:11:58,560 --> 00:12:00,760 to make things better for everybody. 363 00:12:00,760 --> 00:12:01,720 I agree. 364 00:12:01,720 --> 00:12:04,600 Okay, so welcome back to the final part of our 365 00:12:04,600 --> 00:12:07,520 deep dive into AI and the supply chain. 366 00:12:07,520 --> 00:12:09,600 We've talked about how AI is being used today. 367 00:12:09,600 --> 00:12:10,440 Right. 368 00:12:10,440 --> 00:12:12,280 We've talked about the potential and the challenges. 369 00:12:12,280 --> 00:12:13,120 Yeah. 370 00:12:13,120 --> 00:12:15,000 And we even tried to look into the future a bit. 371 00:12:15,000 --> 00:12:16,400 The crystal ball. 372 00:12:16,400 --> 00:12:17,880 Right. But before we wrap up, 373 00:12:17,880 --> 00:12:19,760 I wanna kind of bring it back to the listener. 374 00:12:19,760 --> 00:12:20,600 Sure. 375 00:12:20,600 --> 00:12:23,040 Like, what does this all mean for you? 376 00:12:23,040 --> 00:12:23,880 You know? 377 00:12:23,880 --> 00:12:25,160 Yeah. I mean, it's easy to get lost 378 00:12:25,160 --> 00:12:26,640 in the technology itself. 379 00:12:26,640 --> 00:12:27,480 Yeah. 380 00:12:27,480 --> 00:12:29,600 But really, it's about how it impacts people's lives. 381 00:12:29,600 --> 00:12:30,760 Right. So let's talk about that. 382 00:12:30,760 --> 00:12:32,600 Like, think about the things you buy, 383 00:12:32,600 --> 00:12:33,920 the way you shop, 384 00:12:33,920 --> 00:12:36,960 the speed at which you expect things to show up at your door. 385 00:12:36,960 --> 00:12:37,800 Right. 386 00:12:37,800 --> 00:12:40,160 How do you see AI shaping those experiences? 387 00:12:40,160 --> 00:12:42,200 Well, for one, I think we can expect 388 00:12:42,200 --> 00:12:44,160 even more personalized shopping. 389 00:12:44,160 --> 00:12:46,720 Like, AI is already recommending products 390 00:12:46,720 --> 00:12:48,400 based on what we've bought before. 391 00:12:48,400 --> 00:12:49,240 Right. 392 00:12:49,240 --> 00:12:50,480 Or what we've looked at online. 393 00:12:50,480 --> 00:12:52,600 But in the future, I could go even further. 394 00:12:52,600 --> 00:12:53,440 Like how? 395 00:12:53,440 --> 00:12:55,800 Like, imagine walking into a store 396 00:12:55,800 --> 00:12:57,920 having a virtual assistant 397 00:12:57,920 --> 00:13:00,360 that guides you to exactly what you need 398 00:13:00,360 --> 00:13:03,400 based on your preferences, your shopping lists, 399 00:13:03,400 --> 00:13:05,320 or even like your mood. 400 00:13:05,320 --> 00:13:07,000 So it's like having a fissional shopper. 401 00:13:07,000 --> 00:13:07,840 Yeah. 402 00:13:07,840 --> 00:13:10,040 But without the awkward small talk. 403 00:13:10,040 --> 00:13:10,880 Exactly. 404 00:13:10,880 --> 00:13:13,200 And, you know, we've talked about how 405 00:13:13,200 --> 00:13:16,040 AI can make supply chains more efficient 406 00:13:16,040 --> 00:13:17,040 and more resilient. 407 00:13:17,040 --> 00:13:19,880 So that means things get to you faster. 408 00:13:19,880 --> 00:13:21,400 There are fewer delays. 409 00:13:21,400 --> 00:13:22,240 Okay. 410 00:13:22,240 --> 00:13:24,600 And potentially even lower prices. 411 00:13:24,600 --> 00:13:26,960 So are we talking like things arriving 412 00:13:26,960 --> 00:13:28,680 before we even order them? 413 00:13:28,680 --> 00:13:30,560 I mean, it's not impossible. 414 00:13:30,560 --> 00:13:34,080 Like AI could be analyzing our past behavior 415 00:13:34,080 --> 00:13:36,400 and predicting what we're gonna need. 416 00:13:36,400 --> 00:13:37,240 Okay. 417 00:13:37,240 --> 00:13:38,360 So companies could actually ship stuff 418 00:13:38,360 --> 00:13:40,680 to like local distribution centers 419 00:13:40,680 --> 00:13:42,040 before we even know we want it. 420 00:13:42,040 --> 00:13:42,920 That's crazy. 421 00:13:42,920 --> 00:13:43,760 Right. 422 00:13:43,760 --> 00:13:45,200 It's like anticipating your needs 423 00:13:45,200 --> 00:13:46,600 before you even have them. 424 00:13:46,600 --> 00:13:47,440 Yeah. 425 00:13:47,440 --> 00:13:48,360 But, you know, we've also talked about 426 00:13:48,360 --> 00:13:49,880 some of the potential downsides. 427 00:13:49,880 --> 00:13:50,720 Oh, of course. 428 00:13:50,720 --> 00:13:52,280 What about like the risk 429 00:13:52,280 --> 00:13:54,480 of these systems making mistakes? 430 00:13:54,480 --> 00:13:55,320 Yeah. 431 00:13:55,320 --> 00:13:58,360 Or even being used for like malicious purposes. 432 00:13:58,360 --> 00:13:59,800 Those are valid concerns. 433 00:13:59,800 --> 00:14:02,640 And I think it really highlights the need for, 434 00:14:02,640 --> 00:14:06,880 you know, safeguards, regulations, ethical guidelines, 435 00:14:06,880 --> 00:14:08,400 and just constant monitoring. 436 00:14:08,400 --> 00:14:10,800 You make sure that this technology is used responsibly. 437 00:14:10,800 --> 00:14:13,520 So it's not just about like embracing AI. 438 00:14:13,520 --> 00:14:14,640 It's about shaping it. 439 00:14:14,640 --> 00:14:15,680 Exactly. 440 00:14:15,680 --> 00:14:18,520 It's about making sure it aligns with our values. 441 00:14:18,520 --> 00:14:20,480 So to bring it all back to our original question. 442 00:14:20,480 --> 00:14:21,320 Yeah. 443 00:14:21,320 --> 00:14:24,120 Can the supply chain keep up with all this AI stuff? 444 00:14:24,120 --> 00:14:24,960 Mm-hmm. 445 00:14:24,960 --> 00:14:26,400 I think it's clear there are challenges. 446 00:14:26,400 --> 00:14:27,240 Definitely. 447 00:14:27,240 --> 00:14:29,080 But also incredible opportunities. 448 00:14:29,080 --> 00:14:29,920 Yeah. 449 00:14:29,920 --> 00:14:30,760 It's kind of a race, you know? 450 00:14:30,760 --> 00:14:31,600 Right. 451 00:14:31,600 --> 00:14:34,200 Between how fast the technology is developing 452 00:14:34,200 --> 00:14:36,880 and our ability to actually implement it responsibly. 453 00:14:36,880 --> 00:14:38,240 So it's like we're building the plane 454 00:14:38,240 --> 00:14:39,280 while we're flying it. 455 00:14:39,280 --> 00:14:40,360 Kind of feels that way sometimes. 456 00:14:40,360 --> 00:14:41,200 Yeah. Yeah. 457 00:14:41,200 --> 00:14:42,360 But I'm optimistic. 458 00:14:42,360 --> 00:14:43,200 Me too. 459 00:14:43,200 --> 00:14:46,240 I think that if we're careful and we work together, 460 00:14:46,240 --> 00:14:48,040 we can really use AI 461 00:14:48,040 --> 00:14:50,480 to make the supply chain better for everyone. 462 00:14:50,480 --> 00:14:52,920 More efficient, more sustainable, more equitable. 463 00:14:52,920 --> 00:14:53,760 Yeah. 464 00:14:53,760 --> 00:14:55,320 And I think that's a great note to end on. 465 00:14:55,320 --> 00:14:56,160 I agree. 466 00:14:56,160 --> 00:14:57,680 It's been a really interesting journey, 467 00:14:57,680 --> 00:14:59,400 exploring the world of AI. 468 00:14:59,400 --> 00:15:00,240 Mm-hmm. 469 00:15:00,240 --> 00:15:01,880 And its impact on the supply chain. 470 00:15:01,880 --> 00:15:02,720 Yeah. 471 00:15:02,720 --> 00:15:03,720 Thanks for joining us on this deep dive. 472 00:15:03,720 --> 00:15:04,880 It's been a pleasure being here. 473 00:15:04,880 --> 00:15:06,320 And listeners, remember, 474 00:15:06,320 --> 00:15:08,040 the conversation doesn't end here. 475 00:15:08,040 --> 00:15:08,880 Right. 476 00:15:08,880 --> 00:15:10,200 Stay curious, stay informed. 477 00:15:10,200 --> 00:15:11,040 Yeah. 478 00:15:11,040 --> 00:15:13,040 And most importantly, keep asking questions. 479 00:15:13,040 --> 00:15:14,640 The future is ours to shape. 480 00:15:14,640 --> 00:15:16,240 And that's it for this episode of the deep dive. 481 00:15:16,240 --> 00:15:24,080 See you next time.