1 00:00:00,000 --> 00:00:01,840 Hey everyone, welcome back. 2 00:00:01,840 --> 00:00:03,360 Ready for another deep dive? 3 00:00:03,360 --> 00:00:04,200 Definitely. 4 00:00:04,200 --> 00:00:06,080 Today we're gonna tackle something 5 00:00:06,080 --> 00:00:07,440 that's been making headlines 6 00:00:07,440 --> 00:00:10,360 and frankly, causing a bit of a stir. 7 00:00:10,360 --> 00:00:12,360 We're talking deep-seek R1. 8 00:00:12,360 --> 00:00:15,320 Ah, yeah, the AI that's got everybody talking. 9 00:00:15,320 --> 00:00:17,060 And the market's reeling too. 10 00:00:17,060 --> 00:00:18,120 You know, it really feels like 11 00:00:18,120 --> 00:00:20,240 we're in the middle of a Sputnik moment right now. 12 00:00:20,240 --> 00:00:21,480 You know, it's funny you say that. 13 00:00:21,480 --> 00:00:23,040 I was just thinking the same thing. 14 00:00:23,040 --> 00:00:26,040 With China launching this AI bombshell, 15 00:00:26,040 --> 00:00:28,360 it feels like the US-China tech race 16 00:00:28,360 --> 00:00:29,440 just went into overdrive. 17 00:00:29,440 --> 00:00:30,360 Oh, absolutely. 18 00:00:30,360 --> 00:00:31,200 I mean, look at what happened. 19 00:00:31,200 --> 00:00:34,800 The US stock market lost like over a trillion dollars 20 00:00:34,800 --> 00:00:37,200 in value the day deep-seek R1 dropped. 21 00:00:37,200 --> 00:00:39,160 January 28th, to be exact. 22 00:00:39,160 --> 00:00:41,560 Yeah, that kind of shock wave, you know? 23 00:00:41,560 --> 00:00:43,840 That tells you we're dealing with something way bigger 24 00:00:43,840 --> 00:00:45,560 than just another cool piece of tech. 25 00:00:45,560 --> 00:00:47,160 It's not just a cool toy, is it? 26 00:00:47,160 --> 00:00:48,240 I mean, let's break it down. 27 00:00:48,240 --> 00:00:50,680 Deep-seek R1 is free, it's open source, 28 00:00:50,680 --> 00:00:52,600 anyone can just tinker with the code. 29 00:00:52,600 --> 00:00:55,320 It's performing as well as, if not better than, 30 00:00:55,320 --> 00:00:58,680 the top US models, but here's the real kicker. 31 00:00:58,680 --> 00:01:00,800 It was developed for a fraction of the cost. 32 00:01:00,800 --> 00:01:03,760 Yeah, like less than 3% of what it took to build, 33 00:01:03,760 --> 00:01:05,520 let's say, chat GPT. 34 00:01:05,520 --> 00:01:06,920 Yeah, 3%. 35 00:01:06,920 --> 00:01:08,120 I'm still trying to wrap my head around that. 36 00:01:08,120 --> 00:01:10,000 Well, it's not just about the number itself, right? 37 00:01:10,000 --> 00:01:11,160 It's what it signals. 38 00:01:11,160 --> 00:01:12,000 Right. 39 00:01:12,000 --> 00:01:15,160 It's a potential disruption of the whole AI landscape. 40 00:01:15,160 --> 00:01:17,320 Imagine the implications. 41 00:01:17,320 --> 00:01:20,720 Access to these really powerful AI tools 42 00:01:20,720 --> 00:01:23,520 could become like totally democratized. 43 00:01:23,520 --> 00:01:25,160 That could shake up the power dynamics 44 00:01:25,160 --> 00:01:26,000 of the whole industry. 45 00:01:26,000 --> 00:01:28,080 Right, so we're not just talking about one company 46 00:01:28,080 --> 00:01:29,400 or one product, this is. 47 00:01:29,400 --> 00:01:30,240 This is much bigger. 48 00:01:30,240 --> 00:01:32,160 Yeah, this is a paradigm shift, maybe. 49 00:01:32,160 --> 00:01:36,440 So who is this mastermind behind Deep-seek? 50 00:01:36,440 --> 00:01:38,680 So that would be Liang Wenfang. 51 00:01:38,680 --> 00:01:41,240 He's a former, get this, hedge fund manager, 52 00:01:41,240 --> 00:01:44,000 who kind of embarked on this whole AI journey 53 00:01:44,000 --> 00:01:46,160 back in 2021. 54 00:01:46,160 --> 00:01:48,520 But here's the really fascinating part. 55 00:01:48,520 --> 00:01:51,680 He stocked up, get this, on NVIDIA GPUs. 56 00:01:51,680 --> 00:01:52,720 You know those high-powered chips, right? 57 00:01:52,720 --> 00:01:53,560 Yeah, yeah. 58 00:01:53,560 --> 00:01:55,840 He stocked up on those right before the US 59 00:01:55,840 --> 00:01:57,360 imposed those export restrictions. 60 00:01:57,360 --> 00:01:58,880 Like he knew, right? 61 00:01:58,880 --> 00:02:01,040 Talk about being in the right place at the right time. 62 00:02:01,040 --> 00:02:02,480 Almost like he had a crystal ball or something. 63 00:02:02,480 --> 00:02:06,400 OK, but two months, two months to build Deep-seek R1 64 00:02:06,400 --> 00:02:09,800 on a reported budget of like $5.6 million. 65 00:02:09,800 --> 00:02:11,080 That's unheard of, isn't it? 66 00:02:11,080 --> 00:02:11,640 Pretty much. 67 00:02:11,640 --> 00:02:13,400 And that speed and efficiency, that's 68 00:02:13,400 --> 00:02:15,960 what's really got everyone scratching their heads, 69 00:02:15,960 --> 00:02:18,160 even among AI experts. 70 00:02:18,160 --> 00:02:20,480 And it really comes down to a few key things. 71 00:02:20,480 --> 00:02:23,360 You know, one of them being this mixture of experts approach 72 00:02:23,360 --> 00:02:24,200 they've used. 73 00:02:24,200 --> 00:02:25,640 Mixed of experts. 74 00:02:25,640 --> 00:02:27,000 OK, break that down for me. 75 00:02:27,000 --> 00:02:27,520 Sure. 76 00:02:27,520 --> 00:02:30,040 So does Deep-seek have like a whole team of like, 77 00:02:30,040 --> 00:02:32,480 you know, Brainiacs huddled in a room somewhere? 78 00:02:32,480 --> 00:02:33,920 Not quite a room full of Brainiacs, 79 00:02:33,920 --> 00:02:35,600 but you know, the analogy kind of works. 80 00:02:35,600 --> 00:02:36,100 OK. 81 00:02:36,100 --> 00:02:39,320 Instead of creating this one massive AI model, 82 00:02:39,320 --> 00:02:41,800 they've designed Deep-seek with all these specialized modules. 83 00:02:41,800 --> 00:02:43,400 Think of it like a university, right? 84 00:02:43,400 --> 00:02:46,120 With different departments dedicated to specific subjects. 85 00:02:46,120 --> 00:02:46,680 Got it. 86 00:02:46,680 --> 00:02:48,240 You wouldn't go to your history professor 87 00:02:48,240 --> 00:02:50,680 to try to solve a complex physics problem. 88 00:02:50,680 --> 00:02:51,800 Right, right. 89 00:02:51,800 --> 00:02:54,200 So Deep-seek is only activating the modules that 90 00:02:54,200 --> 00:02:56,440 are relevant to a specific task. 91 00:02:56,440 --> 00:02:56,920 Exactly. 92 00:02:56,920 --> 00:02:59,040 Hence the efficiency. 93 00:02:59,040 --> 00:02:59,720 Clever. 94 00:02:59,720 --> 00:03:00,520 Right. 95 00:03:00,520 --> 00:03:04,200 This targeted activation conserves energy, processing 96 00:03:04,200 --> 00:03:07,240 power, and it ultimately reduces cost. 97 00:03:07,240 --> 00:03:08,640 But they don't stop there. 98 00:03:08,640 --> 00:03:11,000 They're also using something called distillation. 99 00:03:11,000 --> 00:03:11,960 Distillation. 100 00:03:11,960 --> 00:03:13,200 Like what you do with whiskey. 101 00:03:13,200 --> 00:03:14,880 Well, in the AI world, distillation 102 00:03:14,880 --> 00:03:17,040 is more about knowledge transfer. 103 00:03:17,040 --> 00:03:18,760 Not so much about making spirits. 104 00:03:18,760 --> 00:03:19,760 OK, all right. 105 00:03:19,760 --> 00:03:22,800 Think of it like an apprenticeship 106 00:03:22,800 --> 00:03:26,200 where a larger, more mature AI model helps train 107 00:03:26,200 --> 00:03:28,560 these smaller, more specialized modules. 108 00:03:28,560 --> 00:03:29,120 OK. 109 00:03:29,120 --> 00:03:32,120 It's like a master craftsman passing down their skills 110 00:03:32,120 --> 00:03:33,400 to the next generation. 111 00:03:33,400 --> 00:03:34,160 I like that. 112 00:03:34,160 --> 00:03:36,360 OK, so instead of starting from scratch, 113 00:03:36,360 --> 00:03:38,720 they're leveraging this existing knowledge 114 00:03:38,720 --> 00:03:40,520 to train these modules more efficiently. 115 00:03:40,520 --> 00:03:41,040 Exactly. 116 00:03:41,040 --> 00:03:43,480 And it's one of the reasons why they've 117 00:03:43,480 --> 00:03:46,320 been able to achieve these really impressive results 118 00:03:46,320 --> 00:03:49,520 with relatively limited resources. 119 00:03:49,520 --> 00:03:52,040 Plus, they're very transparent about their chain of thought 120 00:03:52,040 --> 00:03:55,480 reasoning, basically how the AI arrived at its conclusions. 121 00:03:55,480 --> 00:03:56,040 Wait, hold on. 122 00:03:56,040 --> 00:03:58,200 Transparent chain of thought reasoning? 123 00:03:58,200 --> 00:04:02,080 That's a stark contrast to how some US companies are 124 00:04:02,080 --> 00:04:04,360 approaching AI development, right? 125 00:04:04,360 --> 00:04:04,560 It is. 126 00:04:04,560 --> 00:04:07,360 Where they keep everything very hush, hush, very secret. 127 00:04:07,360 --> 00:04:08,840 Very much so. 128 00:04:08,840 --> 00:04:11,920 DeepSeek's open approach is really shaking things up. 129 00:04:11,920 --> 00:04:15,560 It lets developers understand how the AI is making its decisions. 130 00:04:15,560 --> 00:04:18,640 And that potentially could lead to more trust, 131 00:04:18,640 --> 00:04:20,440 maybe faster improvements. 132 00:04:20,440 --> 00:04:23,000 It's a gamble, but it seems to be paying off. 133 00:04:23,000 --> 00:04:25,760 It's definitely sending shockwaves through Silicon Valley 134 00:04:25,760 --> 00:04:26,880 from what I've been hearing. 135 00:04:26,880 --> 00:04:27,880 Oh, absolutely. 136 00:04:27,880 --> 00:04:31,520 Imagine you're a major player in the AI space, right? 137 00:04:31,520 --> 00:04:34,520 And suddenly this competitor comes out of nowhere, 138 00:04:34,520 --> 00:04:37,960 offering comparable performance for a fraction of the cost. 139 00:04:37,960 --> 00:04:40,400 I mean, no wonder there's a bit of panic mixed with a scramble 140 00:04:40,400 --> 00:04:41,080 to adapt. 141 00:04:41,080 --> 00:04:42,040 I bet. 142 00:04:42,040 --> 00:04:46,960 I read that OpenAI quickly made their GPT-3 mini-model free. 143 00:04:46,960 --> 00:04:51,600 And there are even rumors that Meta is having some serious 144 00:04:51,600 --> 00:04:55,120 internal discussions about their whole AI strategy. 145 00:04:55,120 --> 00:04:57,440 Is this a turning point for the industry, do you think? 146 00:04:57,440 --> 00:04:58,680 It could very well be. 147 00:04:58,680 --> 00:05:01,920 That old playbook of building bigger, more expensive models, 148 00:05:01,920 --> 00:05:03,880 that might not be the winning strategy anymore. 149 00:05:03,880 --> 00:05:04,560 Interesting. 150 00:05:04,560 --> 00:05:07,600 This can be the beginning of an era where the real value isn't 151 00:05:07,600 --> 00:05:10,760 in the AI model itself, but in the applications built 152 00:05:10,760 --> 00:05:11,400 on top of them. 153 00:05:11,400 --> 00:05:14,360 OK, so less about the raw power and more about what you can 154 00:05:14,360 --> 00:05:15,440 actually de-know with it. 155 00:05:15,440 --> 00:05:15,960 Precisely. 156 00:05:15,960 --> 00:05:17,280 That's a pretty interesting shift. 157 00:05:17,280 --> 00:05:19,200 It is a fascinating development. 158 00:05:19,200 --> 00:05:21,800 It's like the app store all over again. 159 00:05:21,800 --> 00:05:24,000 Remember when Apple launched the iPhone? 160 00:05:24,000 --> 00:05:26,160 The device itself, you know, cool. 161 00:05:26,160 --> 00:05:28,120 But the real revolution was all the apps. 162 00:05:28,120 --> 00:05:29,680 Oh, yeah, great parallel. 163 00:05:29,680 --> 00:05:31,280 And just like with the app store, we 164 00:05:31,280 --> 00:05:35,160 could see an explosion of creativity and innovation 165 00:05:35,160 --> 00:05:37,880 in this new AI landscape. 166 00:05:37,880 --> 00:05:40,360 AI-powered tools for everything. 167 00:05:40,360 --> 00:05:41,000 Oh, yeah. 168 00:05:41,000 --> 00:05:45,200 Education, health care, entertainment, productivity 169 00:05:45,200 --> 00:05:45,680 even. 170 00:05:45,680 --> 00:05:48,600 And these tools could be cheaper and more accessible 171 00:05:48,600 --> 00:05:52,440 than ever before thanks to platforms like DeepSeek. 172 00:05:52,440 --> 00:05:53,000 Exactly. 173 00:05:53,000 --> 00:05:57,120 This is huge, especially for smaller businesses, startups, 174 00:05:57,120 --> 00:05:59,200 even individual developers who just couldn't afford 175 00:05:59,200 --> 00:06:00,680 to get into the AI game before. 176 00:06:00,680 --> 00:06:02,360 Yeah, it's amazing. 177 00:06:02,360 --> 00:06:06,680 But with all this talk about open source and accessibility, 178 00:06:06,680 --> 00:06:08,400 there's a part of me that wonders, 179 00:06:08,400 --> 00:06:10,760 what about the potential downsides? 180 00:06:10,760 --> 00:06:13,760 What about the possibility of this technology being misused? 181 00:06:13,760 --> 00:06:16,680 I mean, anyone can access and modify DeepSeek's code, right? 182 00:06:16,680 --> 00:06:18,200 Yeah, no, that's a valid concern. 183 00:06:18,200 --> 00:06:20,000 And it's something we need to be having some really serious 184 00:06:20,000 --> 00:06:20,920 discussion about. 185 00:06:20,920 --> 00:06:24,720 Open source AI has this incredible potential for good. 186 00:06:24,720 --> 00:06:26,720 But like any powerful technology, 187 00:06:26,720 --> 00:06:29,160 it can also be used for not so good things. 188 00:06:29,160 --> 00:06:29,880 For sure. 189 00:06:29,880 --> 00:06:33,800 And DeepSeek sends user data to China, right? 190 00:06:33,800 --> 00:06:35,800 Some folks are raising some real concerns 191 00:06:35,800 --> 00:06:37,360 about privacy and security. 192 00:06:37,360 --> 00:06:39,960 I mean, we're not just talking about your favorite coffee 193 00:06:39,960 --> 00:06:41,000 order here. 194 00:06:41,000 --> 00:06:43,480 This could be some pretty sensitive personal data. 195 00:06:43,480 --> 00:06:44,760 Oh, absolutely. 196 00:06:44,760 --> 00:06:46,280 It's a really complex issue. 197 00:06:46,280 --> 00:06:48,800 And it raises all these important questions about data 198 00:06:48,800 --> 00:06:51,960 sovereignty, transparency, and ultimately, who 199 00:06:51,960 --> 00:06:55,200 controls these powerful AI systems. 200 00:06:55,200 --> 00:06:58,080 It's a balancing act, for sure, between harnessing 201 00:06:58,080 --> 00:07:02,400 the potential of AI while safeguarding individual rights. 202 00:07:02,400 --> 00:07:03,600 It's like we're at a crossroads. 203 00:07:03,600 --> 00:07:07,200 Do we embrace all the potential benefits of open source AI, 204 00:07:07,200 --> 00:07:08,160 even with the risks? 205 00:07:08,160 --> 00:07:10,160 Or do we try to put the genie back in the bottle, 206 00:07:10,160 --> 00:07:11,720 try to regulate it heavily? 207 00:07:11,720 --> 00:07:13,440 I don't think there's an easy answer. 208 00:07:13,440 --> 00:07:16,400 This is a conversation that needs to involve everybody, 209 00:07:16,400 --> 00:07:20,280 policymakers, industry leaders, ethicists, the public, 210 00:07:20,280 --> 00:07:21,440 everybody. 211 00:07:21,440 --> 00:07:24,080 We need to really consider the potential consequences 212 00:07:24,080 --> 00:07:26,040 of our decisions and work together 213 00:07:26,040 --> 00:07:28,680 to create a framework that encourages innovation 214 00:07:28,680 --> 00:07:29,920 while protecting our values. 215 00:07:29,920 --> 00:07:32,160 You mentioned ethicists, which reminds me of something 216 00:07:32,160 --> 00:07:34,280 I've been hearing more and more about, 217 00:07:34,280 --> 00:07:36,080 the Thucydides trap. 218 00:07:36,080 --> 00:07:37,280 What is that exactly? 219 00:07:37,280 --> 00:07:39,160 And how does it relate to all of this? 220 00:07:39,160 --> 00:07:39,480 Right. 221 00:07:39,480 --> 00:07:41,240 So the Thucydides trap is a term. 222 00:07:41,240 --> 00:07:44,120 It was actually coined by a political scientist 223 00:07:44,120 --> 00:07:45,160 named Graham Allison. 224 00:07:45,160 --> 00:07:45,880 OK. 225 00:07:45,880 --> 00:07:48,240 And it refers to this historical pattern 226 00:07:48,240 --> 00:07:52,240 where a rising power, like China, in this case, 227 00:07:52,240 --> 00:07:55,000 challenges an established power, like the US. 228 00:07:55,000 --> 00:07:55,400 OK. 229 00:07:55,400 --> 00:07:57,720 And that rivalry often leads to conflict. 230 00:07:57,720 --> 00:08:01,120 So are we saying that deep seek is the catalyst 231 00:08:01,120 --> 00:08:03,760 for some real world conflict between the US and China? 232 00:08:03,760 --> 00:08:05,440 That sounds a little dramatic, doesn't it? 233 00:08:05,440 --> 00:08:08,400 I don't think it's about deep seek itself being a weapon 234 00:08:08,400 --> 00:08:09,120 or anything. 235 00:08:09,120 --> 00:08:12,240 But it's more of a symbol of this larger power dynamic. 236 00:08:12,240 --> 00:08:13,360 Right. 237 00:08:13,360 --> 00:08:16,520 The rapid advancements in AI, particularly from China, 238 00:08:16,520 --> 00:08:18,440 are changing the global landscape. 239 00:08:18,440 --> 00:08:18,960 Right. 240 00:08:18,960 --> 00:08:21,840 And that shift, that could lead to more attention 241 00:08:21,840 --> 00:08:23,480 in increased competition, for sure. 242 00:08:23,480 --> 00:08:25,280 It's like a modern day space race. 243 00:08:25,280 --> 00:08:28,760 But instead of rockets and satellites, it's AI. 244 00:08:28,760 --> 00:08:29,400 Yeah. 245 00:08:29,400 --> 00:08:31,760 Just like with the space race, you've got this competition 246 00:08:31,760 --> 00:08:33,880 that can drive incredible innovation, 247 00:08:33,880 --> 00:08:37,680 but it also carries this risk of escalation, 248 00:08:37,680 --> 00:08:39,400 maybe even conflict. 249 00:08:39,400 --> 00:08:40,400 Yeah, absolutely. 250 00:08:40,400 --> 00:08:42,320 And that's why it's so important to encourage 251 00:08:42,320 --> 00:08:46,600 dialogue, collaboration, and a sense of shared responsibility. 252 00:08:46,600 --> 00:08:49,920 We need to figure out how to use AI for good, for everyone, 253 00:08:49,920 --> 00:08:53,080 rather than letting it become a source of division or conflict. 254 00:08:53,080 --> 00:08:53,480 For sure. 255 00:08:53,480 --> 00:08:53,920 For sure. 256 00:08:53,920 --> 00:08:54,120 OK. 257 00:08:54,120 --> 00:08:55,800 So we've talked about the big picture stuff, 258 00:08:55,800 --> 00:08:57,800 the potential risks, the rewards. 259 00:08:57,800 --> 00:08:59,240 But what about some specific ways 260 00:08:59,240 --> 00:09:01,040 that deep seeks arrival is already 261 00:09:01,040 --> 00:09:02,760 impacting industries and companies? 262 00:09:02,760 --> 00:09:03,760 What are you seeing? 263 00:09:03,760 --> 00:09:06,960 We're seeing a really wide range of responses. 264 00:09:06,960 --> 00:09:12,760 Everything from cautious optimism to straight up panic. 265 00:09:12,760 --> 00:09:17,120 Some companies are scrambling, investing heavily 266 00:09:17,120 --> 00:09:19,320 in their own AI research and development. 267 00:09:19,320 --> 00:09:21,920 Others are trying to figure out ways to integrate deep seek 268 00:09:21,920 --> 00:09:23,880 into their existing workflows. 269 00:09:23,880 --> 00:09:25,560 And then there are some who are just trying 270 00:09:25,560 --> 00:09:27,400 to wrap their heads around it all. 271 00:09:27,400 --> 00:09:29,200 What does it all mean for their business? 272 00:09:29,200 --> 00:09:31,160 It's a lot to process, for sure. 273 00:09:31,160 --> 00:09:33,800 I read an article about how some investors are starting 274 00:09:33,800 --> 00:09:39,440 to question the valuations of some of these AI companies, 275 00:09:39,440 --> 00:09:41,520 the ones that have been pouring billions of dollars 276 00:09:41,520 --> 00:09:44,480 into developing their own large language models. 277 00:09:44,480 --> 00:09:48,800 If deep seek can achieve similar results for pennies on the dollar, 278 00:09:48,800 --> 00:09:50,720 what does that mean for the industry, right? 279 00:09:50,720 --> 00:09:53,200 Yeah, it's a really good question. 280 00:09:53,200 --> 00:09:57,880 We might see a shift away from this bigger is better mentality, 281 00:09:57,880 --> 00:10:00,880 which has kind of dominated the AI landscape for a while now. 282 00:10:00,880 --> 00:10:03,320 Efficiency, accessibility, those could 283 00:10:03,320 --> 00:10:04,480 become the new buzzwords. 284 00:10:04,480 --> 00:10:06,080 That's a pretty significant shift. 285 00:10:06,080 --> 00:10:06,840 It is. 286 00:10:06,840 --> 00:10:08,920 I'm sure some companies are feeling a little threatened 287 00:10:08,920 --> 00:10:09,400 right now. 288 00:10:09,400 --> 00:10:11,080 Oh, there's definitely a sense of disruption. 289 00:10:11,080 --> 00:10:11,800 Yeah. 290 00:10:11,800 --> 00:10:14,360 But disruption isn't always bad. 291 00:10:14,360 --> 00:10:16,920 Competition can lead to innovation, right? 292 00:10:16,920 --> 00:10:18,360 And ultimately, it's the consumers 293 00:10:18,360 --> 00:10:21,720 who benefit from better, cheaper, more accessible products 294 00:10:21,720 --> 00:10:22,320 and services. 295 00:10:22,320 --> 00:10:23,600 OK, that's a good point. 296 00:10:23,600 --> 00:10:25,440 I've also been hearing a lot about how 297 00:10:25,440 --> 00:10:30,920 deep seek could really democratize access to these AI tools, 298 00:10:30,920 --> 00:10:34,840 making them available to smaller businesses, startups, 299 00:10:34,840 --> 00:10:35,840 individuals. 300 00:10:35,840 --> 00:10:36,240 Yeah. 301 00:10:36,240 --> 00:10:37,520 What are your thoughts on that? 302 00:10:37,520 --> 00:10:40,520 Honestly, I think that's one of the most exciting aspects 303 00:10:40,520 --> 00:10:41,520 of all of this. 304 00:10:41,520 --> 00:10:44,680 Imagine anyone with a laptop and a good idea 305 00:10:44,680 --> 00:10:47,160 can harness the power of AI. 306 00:10:47,160 --> 00:10:48,760 They can create something amazing. 307 00:10:48,760 --> 00:10:49,260 Yeah. 308 00:10:49,260 --> 00:10:51,520 It reminds me of the early days of the internet. 309 00:10:51,520 --> 00:10:54,920 Suddenly, anybody could build a website and share their ideas. 310 00:10:54,920 --> 00:10:56,560 Right, right. 311 00:10:56,560 --> 00:10:57,520 Exactly. 312 00:10:57,520 --> 00:11:00,900 And just like the internet, the democratization of AI 313 00:11:00,900 --> 00:11:03,080 could lead to this incredible explosion 314 00:11:03,080 --> 00:11:04,920 of creativity and innovation. 315 00:11:04,920 --> 00:11:05,600 Yeah. 316 00:11:05,600 --> 00:11:07,560 New applications and solutions emerging 317 00:11:07,560 --> 00:11:09,040 from all corners of the globe. 318 00:11:09,040 --> 00:11:11,880 That's an inspiring thought, but let's not get too carried away. 319 00:11:11,880 --> 00:11:12,240 Yeah. 320 00:11:12,240 --> 00:11:14,360 It's important to acknowledge that there are also 321 00:11:14,360 --> 00:11:19,200 potential downsides to this rapid democratization of AI. 322 00:11:19,200 --> 00:11:19,840 Oh, totally. 323 00:11:19,840 --> 00:11:25,360 I mean, we need to be mindful of the misuse, the bias, 324 00:11:25,360 --> 00:11:27,240 the unintended consequences. 325 00:11:27,240 --> 00:11:31,200 It's crucial that we develop ethical frameworks and safeguards, 326 00:11:31,200 --> 00:11:33,120 especially as this technology gets 327 00:11:33,120 --> 00:11:34,960 more powerful and ubiquitous. 328 00:11:34,960 --> 00:11:36,280 For sure. 329 00:11:36,280 --> 00:11:38,280 And speaking of unintended consequences, 330 00:11:38,280 --> 00:11:40,600 one of the biggest concerns I keep hearing about 331 00:11:40,600 --> 00:11:43,800 is AI's impact on jobs. 332 00:11:43,800 --> 00:11:47,480 If machines can do more and more of the work that humays do, 333 00:11:47,480 --> 00:11:49,720 what does that mean for the future of employment? 334 00:11:49,720 --> 00:11:50,800 I mean, that's a big one. 335 00:11:50,800 --> 00:11:52,600 That's the question that's been on everybody's mind 336 00:11:52,600 --> 00:11:55,160 since the dawn of the Industrial Revolution, right? 337 00:11:55,160 --> 00:11:55,660 Yeah. 338 00:11:55,660 --> 00:11:58,360 That's a really complex issue, no easy answers. 339 00:11:58,360 --> 00:11:59,720 So is this time different? 340 00:11:59,720 --> 00:12:03,840 I mean, is AI going to lead to mass unemployment? 341 00:12:03,840 --> 00:12:07,320 Or are we just going to see a shift in the types of jobs 342 00:12:07,320 --> 00:12:08,160 that are in demand? 343 00:12:08,160 --> 00:12:09,560 It's probably going to be a bit of both. 344 00:12:09,560 --> 00:12:11,120 Some jobs are going to be automated. 345 00:12:11,120 --> 00:12:12,600 That's inevitable. 346 00:12:12,600 --> 00:12:14,880 But new jobs will also be created, 347 00:12:14,880 --> 00:12:18,520 especially in areas that require creativity, 348 00:12:18,520 --> 00:12:21,280 critical thinking, emotional intelligence, 349 00:12:21,280 --> 00:12:24,200 those skills that are hard for machines to replicate. 350 00:12:24,200 --> 00:12:26,800 So the key is to adapt, to learn new skills. 351 00:12:26,800 --> 00:12:27,560 Absolutely. 352 00:12:27,560 --> 00:12:30,720 We all need to embrace lifelong learning, 353 00:12:30,720 --> 00:12:32,040 develop the skills that are going 354 00:12:32,040 --> 00:12:35,480 to be needed in this new, rapidly evolving landscape. 355 00:12:35,480 --> 00:12:37,000 So the future of work is going to be 356 00:12:37,000 --> 00:12:41,200 about staying agile, adaptable, constantly learning 357 00:12:41,200 --> 00:12:41,880 and evolving. 358 00:12:41,880 --> 00:12:42,480 Precisely. 359 00:12:42,480 --> 00:12:44,720 The folks who can embrace that mindset, 360 00:12:44,720 --> 00:12:47,040 they're the ones who will thrive in the age of AI. 361 00:12:47,040 --> 00:12:47,760 OK. 362 00:12:47,760 --> 00:12:49,640 So we've covered a lot here. 363 00:12:49,640 --> 00:12:51,600 The big picture, the risks and rewards, 364 00:12:51,600 --> 00:12:54,200 the impacts on industries, on individuals. 365 00:12:54,200 --> 00:12:55,920 But there's one more piece of this puzzle 366 00:12:55,920 --> 00:12:57,640 that I think we need to explore. 367 00:12:57,640 --> 00:13:00,160 The ethical considerations surrounding deep seek. 368 00:13:00,160 --> 00:13:02,440 And really, the future of AI as a whole. 369 00:13:02,440 --> 00:13:04,720 It feels like we're in uncharted territory here, you know? 370 00:13:04,720 --> 00:13:05,240 Yeah. 371 00:13:05,240 --> 00:13:08,440 With AI becoming so powerful, so accessible. 372 00:13:08,440 --> 00:13:10,360 I mean, we've got to be asking some tough questions. 373 00:13:10,360 --> 00:13:14,360 How do we make sure it's used ethically, responsibly? 374 00:13:14,360 --> 00:13:16,800 It's not even just about preventing it from being 375 00:13:16,800 --> 00:13:18,040 used for bad things. 376 00:13:18,040 --> 00:13:20,600 We have to think about the unintended consequences too. 377 00:13:20,600 --> 00:13:24,000 Even when AI is designed with the best intentions. 378 00:13:24,000 --> 00:13:25,000 OK, give me an example. 379 00:13:25,000 --> 00:13:26,800 What kind of unintended consequences? 380 00:13:26,800 --> 00:13:29,000 Let's say AI-powered hiring tools. 381 00:13:29,000 --> 00:13:29,400 OK. 382 00:13:29,400 --> 00:13:31,400 They're supposed to make hiring more efficient, 383 00:13:31,400 --> 00:13:33,360 remove human bias. 384 00:13:33,360 --> 00:13:33,680 Right? 385 00:13:33,680 --> 00:13:34,080 Right. 386 00:13:34,080 --> 00:13:38,240 But what if the data that's used to train these systems, 387 00:13:38,240 --> 00:13:40,400 what if that data is biased itself? 388 00:13:40,400 --> 00:13:41,840 Ah, I see you're saying. 389 00:13:41,840 --> 00:13:45,160 So the AI could end up perpetuating those biases, 390 00:13:45,160 --> 00:13:48,880 maybe even amplifying them without us even realizing it. 391 00:13:48,880 --> 00:13:49,600 Exactly. 392 00:13:49,600 --> 00:13:50,720 And that's just one example. 393 00:13:50,720 --> 00:13:52,480 We've got to be really careful about the data 394 00:13:52,480 --> 00:13:54,680 that we're using to train these AI systems. 395 00:13:54,680 --> 00:13:55,160 Right. 396 00:13:55,160 --> 00:13:57,440 We need to be constantly monitoring 397 00:13:57,440 --> 00:14:00,640 for any signs of bias, any unintended consequences. 398 00:14:00,640 --> 00:14:03,120 It sounds like a whole new level of responsibility here. 399 00:14:03,120 --> 00:14:05,480 Like, it's not just about writing the code anymore. 400 00:14:05,480 --> 00:14:08,800 It's about understanding the impact of that code on society. 401 00:14:08,800 --> 00:14:10,680 It's a whole new way of thinking for sure. 402 00:14:10,680 --> 00:14:12,280 And it needs to be a team effort. 403 00:14:12,280 --> 00:14:14,640 You know, the developers, the ethicists, the policymakers, 404 00:14:14,640 --> 00:14:16,200 even the public. 405 00:14:16,200 --> 00:14:21,360 We need to create guidelines, frameworks, you know, 406 00:14:21,360 --> 00:14:23,720 for developing and deploying AI ethically. 407 00:14:23,720 --> 00:14:25,080 So this isn't just a tech issue. 408 00:14:25,080 --> 00:14:26,800 It's a societal issue. 409 00:14:26,800 --> 00:14:28,920 And we need to have these conversations now 410 00:14:28,920 --> 00:14:30,840 before it's too late to course correct. 411 00:14:30,840 --> 00:14:31,400 Absolutely. 412 00:14:31,400 --> 00:14:34,080 We're at this, like, pivotal point. 413 00:14:34,080 --> 00:14:37,360 The decisions we make today about AI, 414 00:14:37,360 --> 00:14:39,400 they're going to echo for generations. 415 00:14:39,400 --> 00:14:39,640 OK. 416 00:14:39,640 --> 00:14:40,800 So let's say we get it right. 417 00:14:40,800 --> 00:14:43,440 We manage to steer AI in the right direction. 418 00:14:43,440 --> 00:14:45,120 What does that future look like? 419 00:14:45,120 --> 00:14:46,160 Paint me a picture. 420 00:14:46,160 --> 00:14:48,760 Imagine a world where AI helps us, 421 00:14:48,760 --> 00:14:51,560 where it augments our abilities, helps us solve problems 422 00:14:51,560 --> 00:14:54,360 we could never solve before, helps us make better decisions. 423 00:14:54,360 --> 00:14:54,760 I like it. 424 00:14:54,760 --> 00:14:55,720 Give me some specifics. 425 00:14:55,720 --> 00:14:56,960 What kind of changes? 426 00:14:56,960 --> 00:14:58,200 Think about health care. 427 00:14:58,200 --> 00:15:00,800 AI could help us develop personalized medicine, 428 00:15:00,800 --> 00:15:03,360 you know, to take diseases early, maybe even 429 00:15:03,360 --> 00:15:04,880 discover new cures faster. 430 00:15:04,880 --> 00:15:05,960 That would be incredible. 431 00:15:05,960 --> 00:15:07,120 What about other fields? 432 00:15:07,120 --> 00:15:08,320 Education. 433 00:15:08,320 --> 00:15:10,880 AI could give every student a truly personalized learning 434 00:15:10,880 --> 00:15:12,720 experience tailored to their needs. 435 00:15:12,720 --> 00:15:13,920 Wow. 436 00:15:13,920 --> 00:15:15,040 That's a game changer. 437 00:15:15,040 --> 00:15:17,600 And what about, like, the environment? 438 00:15:17,600 --> 00:15:20,440 AI could help us optimize our energy consumption, 439 00:15:20,440 --> 00:15:23,800 cut down on waste, you know, develop more sustainable solutions 440 00:15:23,800 --> 00:15:24,680 to climate change. 441 00:15:24,680 --> 00:15:26,160 The possibilities are endless. 442 00:15:26,160 --> 00:15:28,440 It's almost like a little overwhelming to think about. 443 00:15:28,440 --> 00:15:29,400 It is. 444 00:15:29,400 --> 00:15:31,760 But it's important to remember, AI is a tool, right? 445 00:15:31,760 --> 00:15:32,560 Right. 446 00:15:32,560 --> 00:15:34,920 It ends up to us how we use it, how we shape it, 447 00:15:34,920 --> 00:15:38,680 how we make sure it's working for humanity, not against it. 448 00:15:38,680 --> 00:15:41,080 This deep-seek moment, it's a wake-up call. 449 00:15:41,080 --> 00:15:43,720 It's telling us that the future isn't set in stone. 450 00:15:43,720 --> 00:15:45,280 We're creating it right now. 451 00:15:45,280 --> 00:15:46,320 That's a great way to put it. 452 00:15:46,320 --> 00:15:48,160 And AI is going to play a huge role. 453 00:15:48,160 --> 00:15:49,040 It is. 454 00:15:49,040 --> 00:15:51,600 This is a call to action for everybody, you know? 455 00:15:51,600 --> 00:15:54,080 Engage the conversation, stay informed, 456 00:15:54,080 --> 00:15:55,720 make your voices heard. 457 00:15:55,720 --> 00:15:58,720 The future of AI, it's in our hands. 458 00:15:58,720 --> 00:16:01,160 So if deep-seek is our sputnik moment, 459 00:16:01,160 --> 00:16:04,240 the big question now is what will be our space race? 460 00:16:04,240 --> 00:16:06,040 What kind of future will we choose to build 461 00:16:06,040 --> 00:16:07,840 with this incredible technology? 462 00:16:07,840 --> 00:16:09,880 It's something for all of us to think about. 463 00:16:09,880 --> 00:16:12,480 I hope this deep-dyes gave everyone a better understanding 464 00:16:12,480 --> 00:16:15,080 of just how complex this all is. 465 00:16:15,080 --> 00:16:17,360 You know, deep-seek, the future of AI, 466 00:16:17,360 --> 00:16:19,320 it's changing so fast. 467 00:16:19,320 --> 00:16:20,440 It is, it is. 468 00:16:20,440 --> 00:16:22,560 So keep those brain gears turning. 469 00:16:22,560 --> 00:16:43,320 And join us again next time for another deep dive.