1 00:00:00,000 --> 00:00:01,480 Welcome to another deep dive. 2 00:00:01,480 --> 00:00:05,000 And this one is taking us straight into the heart 3 00:00:05,000 --> 00:00:06,420 of the AI world. 4 00:00:06,420 --> 00:00:08,820 Specifically, we're talking about DeepSeek R1. 5 00:00:09,920 --> 00:00:11,640 This model has been making waves, 6 00:00:11,640 --> 00:00:14,240 even turning heads at some of the big tech companies. 7 00:00:14,240 --> 00:00:17,440 You know, the usual suspects like NVIDIA and Microsoft. 8 00:00:17,440 --> 00:00:19,960 And Mark Andreessen, well, 9 00:00:19,960 --> 00:00:23,240 he called it China's AI Sputnik moment. 10 00:00:23,240 --> 00:00:24,280 Pretty bold statement, right? 11 00:00:24,280 --> 00:00:25,720 Yeah, definitely got people's attention. 12 00:00:25,720 --> 00:00:27,760 So we're diving deep, trying to figure out if a model 13 00:00:27,760 --> 00:00:30,440 built for, get this under $6 million, 14 00:00:30,440 --> 00:00:34,720 can actually go toe to toe with the AI giants out there. 15 00:00:34,720 --> 00:00:37,120 And of course, what that means for the whole future of AI, 16 00:00:37,120 --> 00:00:38,240 you know, where things are headed. 17 00:00:38,240 --> 00:00:40,200 It's definitely shaking things up, that's for sure. 18 00:00:40,200 --> 00:00:42,120 And yeah, that comparison to Sputnik, it's interesting. 19 00:00:42,120 --> 00:00:43,520 It kind of highlights, I think, 20 00:00:43,520 --> 00:00:46,400 how disruptive DeepSeek R1 could be, you know, 21 00:00:46,400 --> 00:00:48,000 how it could change the game. 22 00:00:48,000 --> 00:00:49,480 Okay, so first things first. 23 00:00:49,480 --> 00:00:50,320 Yeah. 24 00:00:50,320 --> 00:00:51,800 What exactly is DeepSeek R1? 25 00:00:51,800 --> 00:00:53,800 It's being called a distilled language model. 26 00:00:53,800 --> 00:00:55,120 But what does that even mean? 27 00:00:55,120 --> 00:00:57,640 So imagine you have these massive AI models, 28 00:00:57,640 --> 00:01:00,520 like GPT-4 for example, 29 00:01:00,520 --> 00:01:03,320 they're trained on a ridiculous amount of data, right? 30 00:01:03,320 --> 00:01:07,000 Well, DeepSeek R1, it uses this technique called distillation. 31 00:01:07,000 --> 00:01:10,240 Basically, it learns from these giants, these huge models, 32 00:01:10,240 --> 00:01:12,800 but without needing to store all that data themselves, 33 00:01:12,800 --> 00:01:15,680 instead of copying the entire dataset, 34 00:01:15,680 --> 00:01:18,280 it focuses on replicating the outputs, you know, 35 00:01:18,280 --> 00:01:21,000 how the big models respond to different tasks. 36 00:01:21,000 --> 00:01:22,840 So it's more about learning the how, 37 00:01:22,840 --> 00:01:24,520 than memorizing every single detail. 38 00:01:24,520 --> 00:01:26,440 Okay, that makes sense, especially when you consider 39 00:01:26,440 --> 00:01:27,720 how much cheaper it was to develop. 40 00:01:27,720 --> 00:01:28,560 Exactly. 41 00:01:28,560 --> 00:01:31,080 And speaking of cost, DeepSeek R1's developers 42 00:01:31,080 --> 00:01:34,080 say they pull this off for under $6 million. 43 00:01:34,080 --> 00:01:36,680 That's peanuts compared to what Western companies 44 00:01:36,680 --> 00:01:39,280 are pouring in, you know, places like OpenAI, 45 00:01:39,280 --> 00:01:41,440 Anthropic, or talking billions. 46 00:01:41,440 --> 00:01:42,680 Wow, that's a huge difference. 47 00:01:42,680 --> 00:01:44,000 Okay, so we have a model 48 00:01:44,000 --> 00:01:47,040 that's potentially way more efficient, cost-wise. 49 00:01:47,040 --> 00:01:48,720 But can it actually perform? 50 00:01:48,720 --> 00:01:51,800 Can this distilled model keep up with the big leagues, 51 00:01:51,800 --> 00:01:53,680 the ones that have been dominating the field? 52 00:01:53,680 --> 00:01:55,480 Well, that's the million dollar question, 53 00:01:55,480 --> 00:01:57,360 or should I say the $6 million question. 54 00:01:57,360 --> 00:01:59,400 And it's what's causing all this buzz. 55 00:01:59,400 --> 00:02:02,320 Early tests, they suggest DeepSeek R1 can match 56 00:02:02,320 --> 00:02:04,480 and sometimes even beat the performance 57 00:02:04,480 --> 00:02:07,280 of those top American models on certain benchmarks. 58 00:02:07,280 --> 00:02:08,120 Hmm, interesting. 59 00:02:08,120 --> 00:02:10,720 So how does this distillation process actually work? 60 00:02:10,720 --> 00:02:11,720 I mean, in practice. 61 00:02:11,720 --> 00:02:13,560 Okay, so picture this. 62 00:02:13,560 --> 00:02:15,600 A panel of AI experts, 63 00:02:15,600 --> 00:02:18,640 each one a specialist in their own area. 64 00:02:18,640 --> 00:02:22,360 DeepSeek R1, it's like, it's learning from this whole group, 65 00:02:22,360 --> 00:02:24,640 picking up the best tricks and strategies 66 00:02:24,640 --> 00:02:26,360 from each expert. 67 00:02:26,360 --> 00:02:28,680 This is what we call multi-model training. 68 00:02:28,680 --> 00:02:29,520 Okay. 69 00:02:29,520 --> 00:02:32,600 And it allows DeepSeek to be more adaptable, more robust. 70 00:02:32,600 --> 00:02:34,720 It's drawing on the combined knowledge 71 00:02:34,720 --> 00:02:36,880 of its teachers, so to speak. 72 00:02:36,880 --> 00:02:39,160 So it's almost like it's learning to think critically, right? 73 00:02:39,160 --> 00:02:41,440 By considering all these different perspectives. 74 00:02:41,440 --> 00:02:43,520 It's not just about brute force memorization. 75 00:02:43,520 --> 00:02:45,360 It's about understanding the underlying logic. 76 00:02:45,360 --> 00:02:47,480 Exactly, and you know, in one of those sources you shared, 77 00:02:47,480 --> 00:02:49,480 there was this retired software engineer, 78 00:02:49,480 --> 00:02:50,720 Dave, I think his name was. 79 00:02:50,720 --> 00:02:53,000 He actually tested DeepSeek R1 himself. 80 00:02:53,000 --> 00:02:54,520 He ran all sorts of tests. 81 00:02:54,520 --> 00:02:56,720 Even trying the model on different hardware, 82 00:02:56,720 --> 00:02:58,160 like even a Raspberry Pi. 83 00:02:58,160 --> 00:02:59,520 Way to Raspberry Pi, 84 00:02:59,520 --> 00:03:01,720 that little computer that hobbyists mess around with. 85 00:03:01,720 --> 00:03:03,200 Yeah, that's the one. 86 00:03:03,200 --> 00:03:04,920 And this is a big deal, you see. 87 00:03:04,920 --> 00:03:08,040 It highlights another key aspect of DeepSeek R1. 88 00:03:08,040 --> 00:03:10,800 It's designed to run on consumer grade hardware. 89 00:03:10,800 --> 00:03:12,600 So it's not just about matching performance. 90 00:03:12,600 --> 00:03:15,160 It's about making powerful AI accessible 91 00:03:15,160 --> 00:03:16,320 to pretty much anyone. 92 00:03:16,320 --> 00:03:18,520 Exactly, think about the possibilities. 93 00:03:18,520 --> 00:03:21,640 We could have AI models tailored to specific industries. 94 00:03:21,640 --> 00:03:25,160 Running on local hardware, which would be great for privacy. 95 00:03:25,160 --> 00:03:27,960 And maybe even integrated into our everyday devices, 96 00:03:27,960 --> 00:03:29,400 like smartphones. 97 00:03:29,400 --> 00:03:32,400 So instead of being limited to these massive data centers, 98 00:03:32,400 --> 00:03:35,560 the power of AI could be in everyone's hands. 99 00:03:35,560 --> 00:03:38,240 Kind of like what happened with personal computers, right? 100 00:03:38,240 --> 00:03:40,880 Powerful tech became available to the masses. 101 00:03:40,880 --> 00:03:44,760 And it sparked a whole revolution in innovation and accessibility. 102 00:03:44,760 --> 00:03:45,640 You got it. 103 00:03:45,640 --> 00:03:46,760 That's a great comparison. 104 00:03:46,760 --> 00:03:49,760 DeepSeek R1 has that same potential, you know, 105 00:03:49,760 --> 00:03:52,320 to democratize AI in a similar way. 106 00:03:52,320 --> 00:03:53,840 That's exciting for sure. 107 00:03:53,840 --> 00:03:56,200 But before we get carried away, let's talk about the limitations. 108 00:03:56,200 --> 00:03:58,000 I mean, we've talked about all the potential benefits, 109 00:03:58,000 --> 00:04:01,120 but are there any trade-offs we need to think about? 110 00:04:01,120 --> 00:04:04,800 Is this distilled approach all it's cracked up to be? 111 00:04:04,800 --> 00:04:05,760 Yeah, that's a good point. 112 00:04:05,760 --> 00:04:07,960 It's not all sunshine and rainbows right. 113 00:04:07,960 --> 00:04:10,400 There are some potential downsides to consider. 114 00:04:10,400 --> 00:04:12,040 Like what? Give me the rundown. 115 00:04:12,040 --> 00:04:14,600 Well, one concern is that smaller models, 116 00:04:14,600 --> 00:04:16,680 they can be more prone to errors. 117 00:04:16,680 --> 00:04:18,440 You know, sometimes they generate responses 118 00:04:18,440 --> 00:04:20,560 that sound confident but are actually wrong. 119 00:04:20,560 --> 00:04:23,080 So like a student who crammed for a test, 120 00:04:23,080 --> 00:04:24,120 they might know the basics, 121 00:04:24,120 --> 00:04:26,280 but get tripped up when things get more complex. 122 00:04:26,280 --> 00:04:27,080 Exactly. 123 00:04:27,080 --> 00:04:29,160 And then there's the question of scalability, 124 00:04:29,160 --> 00:04:31,360 whether this distilled approach can keep up 125 00:04:31,360 --> 00:04:33,560 with how fast AI is advancing. 126 00:04:33,560 --> 00:04:35,560 Will DeepSeek R1 be able to handle 127 00:04:35,560 --> 00:04:38,120 the increasingly complex tasks and datasets 128 00:04:38,120 --> 00:04:40,440 we're going to be throwing at it in the future? 129 00:04:40,440 --> 00:04:42,320 So we're in this wait-and-see mode. 130 00:04:42,320 --> 00:04:45,640 It's exciting, but there are still a lot of unknowns. 131 00:04:45,640 --> 00:04:46,760 Yeah, exactly. 132 00:04:46,760 --> 00:04:49,680 But one thing's for sure, DeepSeek R1 has changed the game. 133 00:04:49,680 --> 00:04:51,760 It's making us rethink our assumptions 134 00:04:51,760 --> 00:04:54,400 about AI development, about who can access it, 135 00:04:54,400 --> 00:04:57,360 and even about the global balance of power in the field. 136 00:04:57,360 --> 00:05:00,000 And that brings us to the elephant in the room, 137 00:05:00,000 --> 00:05:02,640 the impact on American AI. 138 00:05:02,640 --> 00:05:04,960 DeepSeek R1 has definitely put some pressure 139 00:05:04,960 --> 00:05:07,960 on companies like OpenAI, Google, Microsoft. 140 00:05:07,960 --> 00:05:10,000 But before we get into that, let's take a quick break. 141 00:05:10,000 --> 00:05:12,520 We'll be right back to unpack the implications 142 00:05:12,520 --> 00:05:17,080 of DeepSeek R1 on the AI landscape. 143 00:05:17,080 --> 00:05:20,200 OK, so we're back and ready to dive into how DeepSeek R1 144 00:05:20,200 --> 00:05:22,800 is shaking things up in the AI world, 145 00:05:22,800 --> 00:05:24,880 especially for American AI companies. 146 00:05:24,880 --> 00:05:26,480 I mean, this feels like a pretty big deal. 147 00:05:26,480 --> 00:05:27,880 Oh, yeah, definitely. 148 00:05:27,880 --> 00:05:30,920 DeepSeek R1 has kind of thrown a wrench in the works, 149 00:05:30,920 --> 00:05:34,240 challenging the whole power dynamic we've been seeing in AI. 150 00:05:34,240 --> 00:05:36,560 For a while there, it seemed like companies like OpenAI, 151 00:05:36,560 --> 00:05:38,840 Google, Microsoft, they were calling the shots. 152 00:05:38,840 --> 00:05:42,680 But now with DeepSeek R1 on the scene, 153 00:05:42,680 --> 00:05:43,960 what does this mean for them? 154 00:05:43,960 --> 00:05:45,440 What happens to their business models, 155 00:05:45,440 --> 00:05:47,040 their whole dominance in the field? 156 00:05:47,040 --> 00:05:48,240 Well, one of the biggest challenges 157 00:05:48,240 --> 00:05:50,200 is to how they make money, their revenue models. 158 00:05:50,200 --> 00:05:53,480 A lot of these US AI companies, they depend on subscriptions, 159 00:05:53,480 --> 00:05:57,040 APIs, essentially charging people for access to their models. 160 00:05:57,040 --> 00:05:58,880 But DeepSeek R1, it's open source, 161 00:05:58,880 --> 00:06:00,800 which means it's free to use, free to adapt, 162 00:06:00,800 --> 00:06:01,920 you don't got to pay for it. 163 00:06:01,920 --> 00:06:05,240 So it's like offering a free buffet 164 00:06:05,240 --> 00:06:07,400 when you're running a fancy five-star restaurant. 165 00:06:07,400 --> 00:06:09,040 You're going to have some serious competition. 166 00:06:09,040 --> 00:06:09,520 Yeah. 167 00:06:09,520 --> 00:06:11,680 But it's not just about market share, is it? 168 00:06:11,680 --> 00:06:13,880 It feels like there's a bigger shift happening, 169 00:06:13,880 --> 00:06:17,440 a potential power shift in who controls the future of AI. 170 00:06:17,440 --> 00:06:18,400 Yeah, you're right. 171 00:06:18,400 --> 00:06:20,080 That's a really important point. 172 00:06:20,080 --> 00:06:22,480 Because DeepSeek R1 being open source, 173 00:06:22,480 --> 00:06:26,440 it could really accelerate AI adoption globally. 174 00:06:26,440 --> 00:06:28,240 And that could mean US developed models, 175 00:06:28,240 --> 00:06:30,720 they might not be as dominant as they once were. 176 00:06:30,720 --> 00:06:32,280 OK, so what about the stock market? 177 00:06:32,280 --> 00:06:35,480 Investors, they tend to freak out when there's 178 00:06:35,480 --> 00:06:36,960 a disruption like this. 179 00:06:36,960 --> 00:06:38,960 What are they saying about the potential impact 180 00:06:38,960 --> 00:06:41,440 on companies tied to AI, especially those that 181 00:06:41,440 --> 00:06:44,440 depend on AI licensing and infrastructure? 182 00:06:44,440 --> 00:06:47,440 There's definitely a lot of caution out there right now. 183 00:06:47,440 --> 00:06:49,960 Companies that rely heavily on AI licensing, 184 00:06:49,960 --> 00:06:53,040 cloud infrastructure, even hardware like Nvidia Chips, 185 00:06:53,040 --> 00:06:54,800 which are crucial for AI development, 186 00:06:54,800 --> 00:06:56,520 they could be in for a bumpy ride. 187 00:06:56,520 --> 00:07:00,360 It's wild how one model developed for comparatively 188 00:07:00,360 --> 00:07:03,000 very little money could have such a ripple effect 189 00:07:03,000 --> 00:07:05,240 across the entire AI ecosystem. 190 00:07:05,240 --> 00:07:08,760 It really shows you the power of open source tech, 191 00:07:08,760 --> 00:07:11,600 how it can shake things up, even in industries 192 00:07:11,600 --> 00:07:13,800 that seem really established. 193 00:07:13,800 --> 00:07:15,600 And speaking of shaking things up, 194 00:07:15,600 --> 00:07:18,880 there's this theory going around about that crazy low development 195 00:07:18,880 --> 00:07:19,840 cost we were talking about. 196 00:07:19,840 --> 00:07:22,000 Oh, yeah, you mean the whole scop theory thing? 197 00:07:22,000 --> 00:07:23,520 Yeah, yeah, that's the one. 198 00:07:23,520 --> 00:07:25,240 Some people think there might be more to the story. 199 00:07:25,240 --> 00:07:27,600 Right, it's not so much a conspiracy theory, 200 00:07:27,600 --> 00:07:29,880 but more of a different way of looking at things. 201 00:07:29,880 --> 00:07:31,840 Basically, it suggests that China might 202 00:07:31,840 --> 00:07:34,800 be downplaying the resources and costs that actually 203 00:07:34,800 --> 00:07:37,000 went into developing DeepSeek R1. 204 00:07:37,000 --> 00:07:39,120 So it's like they're playing poker, 205 00:07:39,120 --> 00:07:40,840 and they've just shown a really strong hand, 206 00:07:40,840 --> 00:07:42,840 and everyone's wondering if they're bluffing, 207 00:07:42,840 --> 00:07:45,080 or if they have an even better hand hidden away. 208 00:07:45,080 --> 00:07:46,160 Exactly. 209 00:07:46,160 --> 00:07:48,920 The idea is that they're trying to disrupt the Western AI 210 00:07:48,920 --> 00:07:52,240 landscape, maybe create a false sense of security 211 00:07:52,240 --> 00:07:54,800 while they continue to pour money into AI development 212 00:07:54,800 --> 00:07:56,120 behind the scenes. 213 00:07:56,120 --> 00:07:58,600 That's a pretty bold strategy, if it's true. 214 00:07:58,600 --> 00:08:01,720 So we're left with a lot of questions about DeepSeek R1. 215 00:08:01,720 --> 00:08:04,040 Is it truly a groundbreaking achievement 216 00:08:04,040 --> 00:08:06,440 made on a shoestring budget? 217 00:08:06,440 --> 00:08:08,920 Or is it a calculated move to shake up 218 00:08:08,920 --> 00:08:11,000 the whole global AI order? 219 00:08:11,000 --> 00:08:12,240 I guess only time will tell. 220 00:08:12,240 --> 00:08:15,800 But one thing's for sure, DeepSeek R1 221 00:08:15,800 --> 00:08:18,000 has made us rethink our assumptions 222 00:08:18,000 --> 00:08:20,600 about AI development, about how accessible it is, 223 00:08:20,600 --> 00:08:23,240 and about the balance of power in this field. 224 00:08:23,240 --> 00:08:25,360 But beyond all the geopolitical stuff, 225 00:08:25,360 --> 00:08:28,040 I think it's important to look at the practical impact 226 00:08:28,040 --> 00:08:29,400 DeepSeek R1 could have. 227 00:08:29,400 --> 00:08:30,440 Yeah, that's a good point. 228 00:08:30,440 --> 00:08:32,080 We've been focusing on the big picture. 229 00:08:32,080 --> 00:08:36,000 But what about the real benefits this technology could bring, 230 00:08:36,000 --> 00:08:38,520 regardless of who created it or why? 231 00:08:38,520 --> 00:08:41,000 Right, let's talk about that democratization aspect 232 00:08:41,000 --> 00:08:42,200 we mentioned earlier. 233 00:08:42,200 --> 00:08:45,120 Imagine a world where small businesses, researchers, 234 00:08:45,120 --> 00:08:47,880 and developing countries, even individual innovators, 235 00:08:47,880 --> 00:08:50,840 they all have access to powerful AI tools 236 00:08:50,840 --> 00:08:53,480 without the cost or infrastructure barriers. 237 00:08:53,480 --> 00:08:56,000 I mean, that can unlock a wave of creativity and problem 238 00:08:56,000 --> 00:08:58,040 solving we haven't even begun to think about. 239 00:08:58,040 --> 00:09:00,720 It's like everyone suddenly gets a superpower. 240 00:09:00,720 --> 00:09:04,400 Instead of AI being controlled by just a few powerful players, 241 00:09:04,400 --> 00:09:06,480 it becomes a shared resource that anyone 242 00:09:06,480 --> 00:09:08,960 can use to tackle all sorts of challenges. 243 00:09:08,960 --> 00:09:10,880 Like health care, education, sustainability, 244 00:09:10,880 --> 00:09:12,240 the possibilities are endless. 245 00:09:12,240 --> 00:09:12,960 Exactly. 246 00:09:12,960 --> 00:09:15,560 With DeepSeek R1 running on affordable hardware, 247 00:09:15,560 --> 00:09:17,440 readily available hardware, we could 248 00:09:17,440 --> 00:09:21,320 see AI-powered diagnostic tools reaching remote communities. 249 00:09:21,320 --> 00:09:23,960 We could see personalized learning experiences tailored 250 00:09:23,960 --> 00:09:26,800 to each student, and maybe even innovative solutions 251 00:09:26,800 --> 00:09:30,120 to problems like climate change and resource management. 252 00:09:30,120 --> 00:09:32,080 Making this technology more accessible, 253 00:09:32,080 --> 00:09:34,080 it could really accelerate progress 254 00:09:34,080 --> 00:09:36,720 in addressing some of the biggest challenges we face. 255 00:09:36,720 --> 00:09:38,800 It's a good reminder that technology itself 256 00:09:38,800 --> 00:09:41,600 isn't good or bad, it's just a tool. 257 00:09:41,600 --> 00:09:44,400 And it's up to us to decide how we use it, what kind of impact 258 00:09:44,400 --> 00:09:45,720 it has on the world. 259 00:09:45,720 --> 00:09:47,160 DeepSeek R1 has definitely started 260 00:09:47,160 --> 00:09:49,560 an important conversation about the role of AI, 261 00:09:49,560 --> 00:09:51,320 its potential benefits and risks, 262 00:09:51,320 --> 00:09:54,000 and the global forces shaping its development. 263 00:09:54,000 --> 00:09:55,920 But let's not forget about the individual. 264 00:09:55,920 --> 00:09:57,640 How can our listener, someone who's clearly 265 00:09:57,640 --> 00:10:00,880 interested in this topic, explore DeepSeek R1 further, 266 00:10:00,880 --> 00:10:03,200 and maybe even become a part of shaping its future? 267 00:10:03,200 --> 00:10:04,760 That's a great question. 268 00:10:04,760 --> 00:10:06,840 And the beauty of open source technology 269 00:10:06,840 --> 00:10:09,400 is that it encourages participation. 270 00:10:09,400 --> 00:10:13,200 The source code, documentation, even tutorials for DeepSeek R1, 271 00:10:13,200 --> 00:10:15,040 it's all available online. 272 00:10:15,040 --> 00:10:17,000 And there are these active online communities 273 00:10:17,000 --> 00:10:18,960 where people are sharing their experiences, 274 00:10:18,960 --> 00:10:21,000 talking about the implications, and collaborating 275 00:10:21,000 --> 00:10:22,040 on new projects. 276 00:10:22,040 --> 00:10:24,200 So it's not just about watching from the sidelines, 277 00:10:24,200 --> 00:10:26,480 it's about getting involved, experimenting, 278 00:10:26,480 --> 00:10:28,280 and joining the conversation. 279 00:10:28,280 --> 00:10:30,400 If this deep dive has piqued your interest, 280 00:10:30,400 --> 00:10:32,440 I definitely encourage you to delve deeper 281 00:10:32,440 --> 00:10:35,360 into the world of DeepSeek R1 and see where it takes you. 282 00:10:35,360 --> 00:10:35,920 Absolutely. 283 00:10:35,920 --> 00:10:39,720 And as you explore, keep asking yourself that so what question. 284 00:10:39,720 --> 00:10:42,720 What does this technology mean for your life, your community, 285 00:10:42,720 --> 00:10:44,080 the world as a whole? 286 00:10:44,080 --> 00:10:46,880 How can we use AI to solve problems, make lives better, 287 00:10:46,880 --> 00:10:49,200 create a more just and sustainable future? 288 00:10:49,200 --> 00:10:50,600 We've covered a lot in this deep dive, 289 00:10:50,600 --> 00:10:53,840 from the technical nuts and bolts of DeepSeek R1 290 00:10:53,840 --> 00:10:57,040 to its potential impact on the global AI scene. 291 00:10:57,040 --> 00:10:58,880 But as we wrap things up, I want to leave you 292 00:10:58,880 --> 00:11:01,240 with one final thought to consider. 293 00:11:01,240 --> 00:11:03,480 If DeepSeek R1 is just the beginning, 294 00:11:03,480 --> 00:11:05,680 what's next for open source AI? 295 00:11:05,680 --> 00:11:09,560 How will this shift towards accessibility and collaboration 296 00:11:09,560 --> 00:11:12,200 shape the next generation of AI models? 297 00:11:12,200 --> 00:11:15,640 And what role will you play in this exciting new frontier? 298 00:11:15,640 --> 00:11:18,400 We've been exploring the potential of DeepSeek R1 299 00:11:18,400 --> 00:11:21,720 and all the waves it's making in the AI world. 300 00:11:21,720 --> 00:11:24,960 But earlier, we talked about some potential limitations 301 00:11:24,960 --> 00:11:26,320 with this distilled approach. 302 00:11:26,320 --> 00:11:28,600 Or are these just growing pains, things 303 00:11:28,600 --> 00:11:31,320 that will work themselves out as the tech gets better? 304 00:11:31,320 --> 00:11:35,080 Or could they hold DeepSeek R1 back in the long run? 305 00:11:35,080 --> 00:11:36,520 That's a really important question. 306 00:11:36,520 --> 00:11:38,880 And honestly, it's something the whole AI community is 307 00:11:38,880 --> 00:11:40,000 trying to figure out right now. 308 00:11:40,000 --> 00:11:42,280 The early performance is exciting, don't get me wrong. 309 00:11:42,280 --> 00:11:44,800 But we need to see how DeepSeek R1 handles 310 00:11:44,800 --> 00:11:47,200 the messy real world stuff over time. 311 00:11:47,200 --> 00:11:50,040 It's like, think of it like watching a rookie athlete 312 00:11:50,040 --> 00:11:51,200 with tons of potential. 313 00:11:51,200 --> 00:11:52,520 The real test is consistency. 314 00:11:52,520 --> 00:11:53,640 Can they keep delivering? 315 00:11:53,640 --> 00:11:55,640 So are we saying it's too early to call it, 316 00:11:55,640 --> 00:11:58,520 whether this distilled AI is the future? 317 00:11:58,520 --> 00:12:00,280 It's not about making predictions, I think. 318 00:12:00,280 --> 00:12:03,520 It's more about understanding how all the pieces fit together. 319 00:12:03,520 --> 00:12:05,960 And that's where DeepSeek R1 being open source, 320 00:12:05,960 --> 00:12:07,280 that's a big advantage. 321 00:12:07,280 --> 00:12:10,120 By making the code and training data available, 322 00:12:10,120 --> 00:12:13,320 they're basically crowdsourcing the development of the model. 323 00:12:13,320 --> 00:12:14,360 That's pretty cool, actually. 324 00:12:14,360 --> 00:12:16,760 So instead of a small team working on their own, 325 00:12:16,760 --> 00:12:20,240 you have this global network of researchers, developers, 326 00:12:20,240 --> 00:12:23,160 hobbyists even, all contributing to the project, 327 00:12:23,160 --> 00:12:26,280 like a giant brain trust, pushing the technology forward. 328 00:12:26,280 --> 00:12:27,320 Exactly. 329 00:12:27,320 --> 00:12:30,240 This open approach, it means they can identify and fix 330 00:12:30,240 --> 00:12:32,840 problems way faster, potentially leading 331 00:12:32,840 --> 00:12:36,120 to much better versions of DeepSeek R1 down the line. 332 00:12:36,120 --> 00:12:38,040 We talked about the impact on big companies. 333 00:12:38,040 --> 00:12:39,600 That's Killscop theory, remember. 334 00:12:39,600 --> 00:12:42,080 But I keep thinking about the democratization aspect, 335 00:12:42,080 --> 00:12:44,840 how DeepSeek R1 is changing, who has access 336 00:12:44,840 --> 00:12:46,360 to this kind of AI? 337 00:12:46,360 --> 00:12:47,800 Yeah, it's a huge shift. 338 00:12:47,800 --> 00:12:48,680 Think about it. 339 00:12:48,680 --> 00:12:50,880 Researchers in developing countries, 340 00:12:50,880 --> 00:12:52,880 small businesses without a lot of money, 341 00:12:52,880 --> 00:12:55,400 even just regular people with good ideas, 342 00:12:55,400 --> 00:12:59,400 they can all experiment with and use powerful AI now. 343 00:12:59,400 --> 00:13:02,520 This has the potential to level the playing field. 344 00:13:02,520 --> 00:13:05,080 And who knows what incredible innovations 345 00:13:05,080 --> 00:13:06,240 will come from that. 346 00:13:06,240 --> 00:13:08,520 It's like opening a door that used to be locked. 347 00:13:08,520 --> 00:13:10,360 And now all these people can walk through it 348 00:13:10,360 --> 00:13:11,640 and see what they can create. 349 00:13:11,640 --> 00:13:12,520 Exactly. 350 00:13:12,520 --> 00:13:14,040 It's what makes this so exciting. 351 00:13:14,040 --> 00:13:16,720 DeepSeek R1, it's not just a new piece of tech. 352 00:13:16,720 --> 00:13:19,760 It's a whole new way of thinking about AI development, 353 00:13:19,760 --> 00:13:22,520 making it more open, more collaborative, more accessible. 354 00:13:22,520 --> 00:13:25,280 It really shows that innovation can come from anywhere. 355 00:13:25,280 --> 00:13:28,680 And that the future of AI, well, it's not set in stone. 356 00:13:28,680 --> 00:13:31,200 We're shaping it right now with every choice we make. 357 00:13:31,200 --> 00:13:34,520 This has been an amazing deep dive into DeepSeek R1. 358 00:13:34,520 --> 00:13:37,600 Thanks for joining us as we explored this cutting edge AI 359 00:13:37,600 --> 00:13:39,320 and how it could change everything. 360 00:13:39,320 --> 00:13:40,520 It's been a pleasure being here. 361 00:13:40,520 --> 00:13:42,080 And remember, this is just the beginning. 362 00:13:42,080 --> 00:13:44,080 Stay curious, stay engaged. 363 00:13:44,080 --> 00:13:45,600 Who knows, maybe you'll be the one 364 00:13:45,600 --> 00:13:47,600 to make the next big AI breakthrough.