1 00:00:00,000 --> 00:00:02,200 All right, diving right in today, 2 00:00:02,200 --> 00:00:04,440 we're talking all about the future of AI, 3 00:00:04,440 --> 00:00:07,240 and we've got some seriously insightful interviews to unpack. 4 00:00:07,240 --> 00:00:08,720 Really fascinating stuff, 5 00:00:08,720 --> 00:00:12,040 especially our chat with Jensen Huan, the CEO of NVIDIA. 6 00:00:12,040 --> 00:00:14,840 Yeah, he's leading a company that's right at the heart 7 00:00:14,840 --> 00:00:17,120 of this whole AI explosion. 8 00:00:17,120 --> 00:00:19,440 It's incredible to see the role they're playing. 9 00:00:19,440 --> 00:00:21,320 Definitely, and what's interesting is a lot of people 10 00:00:21,320 --> 00:00:25,600 think of NVIDIA as just a GPU company. 11 00:00:25,600 --> 00:00:27,300 Right, like they just make the graphics cards 12 00:00:27,300 --> 00:00:28,140 for gaming and stuff. 13 00:00:28,140 --> 00:00:31,120 Exactly, but it goes so much deeper than that. 14 00:00:31,120 --> 00:00:32,800 What we uncover in this deep dive 15 00:00:32,800 --> 00:00:36,080 is this fascinating moat around their business, 16 00:00:36,080 --> 00:00:39,680 this kind of hidden layer of competitive advantage. 17 00:00:39,680 --> 00:00:40,800 Okay, I'm intrigued. 18 00:00:40,800 --> 00:00:41,920 Break it down for us. 19 00:00:41,920 --> 00:00:44,960 What makes NVIDIA's position so unique 20 00:00:44,960 --> 00:00:47,080 in this whole AI landscape? 21 00:00:47,080 --> 00:00:49,340 So imagine you're building a house, right? 22 00:00:49,340 --> 00:00:50,560 You need all the right materials, 23 00:00:50,560 --> 00:00:52,560 you need the lumber, the nails, the concrete, 24 00:00:52,560 --> 00:00:53,400 all of it, right? 25 00:00:53,400 --> 00:00:54,240 Yeah, makes sense. 26 00:00:54,240 --> 00:00:55,880 Gotta have the right tools for the job. 27 00:00:55,880 --> 00:00:57,960 And individually, those things are important, 28 00:00:57,960 --> 00:01:00,240 but they're not that impressive on their own, right? 29 00:01:00,240 --> 00:01:02,680 NVIDIA's got all those pieces for AI, 30 00:01:02,680 --> 00:01:06,320 they have the hardware, the GPUs, the CPUs, you name it. 31 00:01:06,320 --> 00:01:08,600 But their real advantage comes from 32 00:01:08,600 --> 00:01:09,920 how they fit it all together, 33 00:01:09,920 --> 00:01:12,940 how they've mastered not just the ingredients, 34 00:01:12,940 --> 00:01:14,520 but the whole recipe. 35 00:01:14,520 --> 00:01:17,440 Jensen actually calls it their combinatorial advantage. 36 00:01:17,440 --> 00:01:19,880 Interesting, so it's not just about selling the bricks, 37 00:01:19,880 --> 00:01:22,040 it's about selling the entire construction company, 38 00:01:22,040 --> 00:01:23,620 having the whole package deal. 39 00:01:23,620 --> 00:01:24,740 You got it. 40 00:01:24,740 --> 00:01:28,440 And a big part of that is the layers they've built. 41 00:01:28,440 --> 00:01:29,640 They have the hardware, yeah, 42 00:01:29,640 --> 00:01:33,080 but then they have CUD, their programming platform, 43 00:01:33,080 --> 00:01:34,880 that makes it easier for developers 44 00:01:34,880 --> 00:01:37,000 to actually use that hardware. 45 00:01:37,000 --> 00:01:38,000 And on top of that, 46 00:01:38,000 --> 00:01:40,640 they've got these crazy optimized software libraries 47 00:01:40,640 --> 00:01:42,360 and even their own AI algorithms, 48 00:01:42,360 --> 00:01:45,160 all fine-tuned to work together seamlessly. 49 00:01:45,160 --> 00:01:47,440 So they've streamlined the entire process 50 00:01:47,440 --> 00:01:48,260 from the ground up. 51 00:01:48,260 --> 00:01:51,280 Precisely, it's like they've created this whole ecosystem 52 00:01:51,280 --> 00:01:53,880 that's way more powerful than just the sum of its parts. 53 00:01:53,880 --> 00:01:55,240 It's not just raw processing power, 54 00:01:55,240 --> 00:01:57,680 it's the way everything works together so smoothly. 55 00:01:57,680 --> 00:01:59,440 And that's where this flywheel concept 56 00:01:59,440 --> 00:02:00,640 comes into play, right? 57 00:02:00,640 --> 00:02:02,280 Yeah, I've heard Jensen talk about that, 58 00:02:02,280 --> 00:02:04,200 this idea that they're constantly building momentum. 59 00:02:04,200 --> 00:02:05,520 Exactly. 60 00:02:05,520 --> 00:02:08,320 The flywheel analogy is all about how they accelerate 61 00:02:08,320 --> 00:02:10,780 the entire cycle of AI development. 62 00:02:10,780 --> 00:02:11,880 Think about it. 63 00:02:11,880 --> 00:02:15,000 They make data curation and preparation faster, 64 00:02:15,000 --> 00:02:18,080 they speed up the training process of those AI models, 65 00:02:18,080 --> 00:02:21,040 and get this, they even accelerate inference, 66 00:02:21,040 --> 00:02:23,600 which is how those trained models are then used 67 00:02:23,600 --> 00:02:24,700 in the real world. 68 00:02:24,700 --> 00:02:27,500 So they're involved in every step of the process, 69 00:02:27,500 --> 00:02:30,200 making each one faster and more efficient. 70 00:02:30,200 --> 00:02:32,760 And because of that constant improvement across the board, 71 00:02:32,760 --> 00:02:35,240 they just keep gaining this incredible advantage. 72 00:02:35,240 --> 00:02:36,760 It snowballs on itself, 73 00:02:36,760 --> 00:02:38,600 making their platform even more attractive, 74 00:02:38,600 --> 00:02:40,880 bringing in more users, generating more data, 75 00:02:40,880 --> 00:02:43,240 which then fuels even more development. 76 00:02:43,240 --> 00:02:45,400 It's a really smart cycle they've got going. 77 00:02:45,400 --> 00:02:48,020 It's like this self-perpetuating loop of innovation. 78 00:02:48,020 --> 00:02:49,820 They make it faster and cheaper for everyone else 79 00:02:49,820 --> 00:02:51,560 to build and use AI, 80 00:02:51,560 --> 00:02:54,040 which in turn makes them even stronger. 81 00:02:54,040 --> 00:02:55,760 And here's the other thing. 82 00:02:55,760 --> 00:02:58,880 They don't just abandon their older technology. 83 00:02:58,880 --> 00:03:01,400 They keep updating the software, the algorithms, 84 00:03:01,400 --> 00:03:03,160 which means even their older hardware 85 00:03:03,160 --> 00:03:04,640 stays relevant and valuable. 86 00:03:04,640 --> 00:03:06,880 Like, remember that story about OpenAI 87 00:03:06,880 --> 00:03:08,820 decommissioning their Volta system? 88 00:03:08,820 --> 00:03:09,960 Oh yeah, and then they realized 89 00:03:09,960 --> 00:03:11,560 they could still use it for other tasks 90 00:03:11,560 --> 00:03:14,000 because Nvidia had kept the software up to date. 91 00:03:14,000 --> 00:03:16,120 They basically got a second life out of that investment. 92 00:03:16,120 --> 00:03:17,000 Exactly. 93 00:03:17,000 --> 00:03:19,640 That ongoing compatibility and support 94 00:03:19,640 --> 00:03:21,560 across their product range is huge, 95 00:03:21,560 --> 00:03:23,400 and it's a big part of why Nvidia 96 00:03:23,400 --> 00:03:26,240 holds such a powerful position in the AI world. 97 00:03:26,240 --> 00:03:27,560 Okay, so for our listeners 98 00:03:27,560 --> 00:03:30,860 who are maybe hearing all this for the first time, 99 00:03:30,860 --> 00:03:32,360 what's the key takeaway here? 100 00:03:32,360 --> 00:03:35,560 Why does this moat around Nvidia really matter 101 00:03:35,560 --> 00:03:36,920 in the grand scheme of things? 102 00:03:36,920 --> 00:03:38,640 Because they're not just providing the tools. 103 00:03:38,640 --> 00:03:40,920 They're essentially shaping the future of AI. 104 00:03:40,920 --> 00:03:43,480 That level of influence, that reach they have, 105 00:03:43,480 --> 00:03:44,780 it's going to be hard to replicate. 106 00:03:44,780 --> 00:03:46,880 So whether you're building AI, using it, 107 00:03:46,880 --> 00:03:48,960 or just trying to wrap your head around it all, 108 00:03:48,960 --> 00:03:51,440 Nvidia's influence is going to be everywhere. 109 00:03:51,440 --> 00:03:53,120 It's kind of wild when you think about it, right? 110 00:03:53,120 --> 00:03:57,200 Nvidia went from powering our favorite video games 111 00:03:57,200 --> 00:03:59,720 to building the foundation for artificial intelligence. 112 00:03:59,720 --> 00:04:00,920 It's a huge jump. 113 00:04:00,920 --> 00:04:02,320 Really speaks to their ability 114 00:04:02,320 --> 00:04:03,800 to kind of see where things are headed 115 00:04:03,800 --> 00:04:06,240 and even create those markets from scratch. 116 00:04:06,240 --> 00:04:07,080 Totally. 117 00:04:07,080 --> 00:04:09,920 It's like they invented the whole 3D gaming PC market, 118 00:04:09,920 --> 00:04:13,800 and now they're doing it again, but this time it's AI. 119 00:04:13,800 --> 00:04:16,940 And the scale of it is, well, Jensen actually predicts 120 00:04:16,940 --> 00:04:20,160 that every single workload is going to be touched 121 00:04:20,160 --> 00:04:21,600 by this acceleration. 122 00:04:21,600 --> 00:04:24,160 Not most, not some, but every single one. 123 00:04:24,160 --> 00:04:25,240 Every single one, yeah. 124 00:04:25,240 --> 00:04:26,800 It's a bold statement for sure. 125 00:04:26,800 --> 00:04:28,200 It really is. 126 00:04:28,200 --> 00:04:30,020 But what does that even look like in the real world? 127 00:04:30,020 --> 00:04:31,400 Give us the lay of the land. 128 00:04:31,400 --> 00:04:33,960 So picture this, everything from scientists 129 00:04:33,960 --> 00:04:35,800 crunching massive data sets 130 00:04:35,800 --> 00:04:38,840 to businesses trying to streamline their operations. 131 00:04:38,840 --> 00:04:42,000 Even the apps on our phones are becoming powered by AI. 132 00:04:42,000 --> 00:04:45,200 And all of those things require serious processing power. 133 00:04:45,200 --> 00:04:46,480 Which is where Nvidia comes in. 134 00:04:46,480 --> 00:04:47,880 They're building the infrastructure 135 00:04:47,880 --> 00:04:50,480 for this AI powered future we've been talking about. 136 00:04:50,480 --> 00:04:51,520 Spot on. 137 00:04:51,520 --> 00:04:53,760 But here's the really mind blowing part. 138 00:04:53,760 --> 00:04:55,520 Jensen throws out these numbers, 139 00:04:55,520 --> 00:04:58,200 talks about trillions of dollars being spent 140 00:04:58,200 --> 00:05:00,240 to replace outdated data centers 141 00:05:00,240 --> 00:05:03,760 with this new AI optimized infrastructure. 142 00:05:03,760 --> 00:05:05,400 Trillions. Trillions of dollars. 143 00:05:05,400 --> 00:05:06,560 Okay, now my head's spinning. 144 00:05:06,560 --> 00:05:07,880 That's hard to even imagine. 145 00:05:07,880 --> 00:05:09,720 It's a massive number, no doubt. 146 00:05:09,720 --> 00:05:13,280 But when you really think about it, our current systems, 147 00:05:13,280 --> 00:05:15,760 they just weren't built for the demands of AI. 148 00:05:15,760 --> 00:05:18,440 Right, they weren't designed for these giant data sets 149 00:05:18,440 --> 00:05:21,960 or the really complex algorithms that AI needs to run. 150 00:05:21,960 --> 00:05:25,180 Exactly, it's like trying to run the latest, 151 00:05:25,180 --> 00:05:28,360 super immersive video game on a computer from the 90s. 152 00:05:28,360 --> 00:05:29,400 It just ain't happening. 153 00:05:29,400 --> 00:05:30,480 Total system meltdown. 154 00:05:30,480 --> 00:05:31,320 Pretty much. 155 00:05:31,320 --> 00:05:33,920 And that's exactly why companies are racing to upgrade. 156 00:05:33,920 --> 00:05:36,440 And it's creating this huge opportunity for, 157 00:05:36,440 --> 00:05:39,200 well you guessed it, companies like Nvidia. 158 00:05:39,200 --> 00:05:41,000 And this isn't just some theoretical thing, right? 159 00:05:41,000 --> 00:05:41,920 Look at Elon Musk. 160 00:05:41,920 --> 00:05:45,440 He built that massive supercomputer for X.AI 161 00:05:45,440 --> 00:05:47,040 in like, what, less than three weeks? 162 00:05:47,040 --> 00:05:48,720 19 days, it's mind blowing. 163 00:05:48,720 --> 00:05:50,640 Seriously, it shows you the urgency, right? 164 00:05:50,640 --> 00:05:51,480 Yeah. 165 00:05:51,480 --> 00:05:53,200 These big players are betting big on AI 166 00:05:53,200 --> 00:05:55,440 and they need the infrastructure to support it. 167 00:05:55,440 --> 00:05:56,280 No doubt. 168 00:05:56,280 --> 00:05:59,160 And as AI keeps getting more integrated into, 169 00:05:59,160 --> 00:06:01,960 well everything, that demand is only gonna go up. 170 00:06:01,960 --> 00:06:04,500 We're not talking about some far off future. 171 00:06:04,500 --> 00:06:06,540 This is happening right now. 172 00:06:06,540 --> 00:06:09,000 So our listeners are probably starting to realize 173 00:06:09,000 --> 00:06:11,640 the world's about to get a whole lot smarter. 174 00:06:11,640 --> 00:06:13,480 But we're also at this crucial point 175 00:06:13,480 --> 00:06:17,480 where the companies that supply those building blocks, 176 00:06:17,480 --> 00:06:19,720 they have a huge influence on how this all plays out. 177 00:06:19,720 --> 00:06:20,840 Couldn't agree more. 178 00:06:20,840 --> 00:06:22,620 Understanding those forces at play, 179 00:06:22,620 --> 00:06:25,280 especially the role of a company like Nvidia, 180 00:06:25,280 --> 00:06:27,760 it's key for anyone who wants to be ready 181 00:06:27,760 --> 00:06:28,640 for what's coming. 182 00:06:28,640 --> 00:06:29,680 Absolutely. 183 00:06:29,680 --> 00:06:30,840 Okay, so we've been talking a lot 184 00:06:30,840 --> 00:06:32,520 about training these AI models, 185 00:06:32,520 --> 00:06:34,260 which is obviously important, 186 00:06:34,260 --> 00:06:35,680 but there's this other side of the coin 187 00:06:35,680 --> 00:06:37,440 that often gets overlooked. 188 00:06:37,440 --> 00:06:38,720 And that's inference. 189 00:06:38,720 --> 00:06:39,840 Right, inference, yeah. 190 00:06:39,840 --> 00:06:42,200 It doesn't get as much hype, but it's essential. 191 00:06:42,200 --> 00:06:43,960 Training is all about building the model, 192 00:06:43,960 --> 00:06:45,440 teaching it how to think, 193 00:06:45,440 --> 00:06:48,160 but inference is how we actually put those trained models 194 00:06:48,160 --> 00:06:50,040 to work in the real world. 195 00:06:50,040 --> 00:06:51,880 So if training is like writing the software, 196 00:06:51,880 --> 00:06:53,800 inference is like actually running it. 197 00:06:53,800 --> 00:06:55,040 Perfect analogy. 198 00:06:55,040 --> 00:06:56,840 Every time you ask Siri a question, 199 00:06:56,840 --> 00:06:59,960 every time you use facial recognition to unlock your phone, 200 00:06:59,960 --> 00:07:01,600 or Netflix recommends a show 201 00:07:01,600 --> 00:07:04,000 that's all inference happening behind the scenes. 202 00:07:04,000 --> 00:07:06,520 And Jensen had this pretty amazing prediction about inference. 203 00:07:06,520 --> 00:07:10,080 He said it's gonna be a billion times larger than training. 204 00:07:10,080 --> 00:07:11,160 A billion. 205 00:07:11,160 --> 00:07:12,880 That's not just a little bit bigger, 206 00:07:12,880 --> 00:07:14,320 that's a whole different universe. 207 00:07:14,320 --> 00:07:15,160 It is. 208 00:07:15,160 --> 00:07:17,180 And it speaks to this fundamental shift 209 00:07:17,180 --> 00:07:20,020 in how we'll be interacting with AI in the future. 210 00:07:20,020 --> 00:07:21,880 As we move toward more complex, 211 00:07:21,880 --> 00:07:25,800 more interactive applications, the demand for inference, 212 00:07:25,800 --> 00:07:27,200 it's gonna be off the charts. 213 00:07:27,200 --> 00:07:28,320 You know, it's easy to get lost 214 00:07:28,320 --> 00:07:30,320 in all the technical stuff with AI, 215 00:07:30,320 --> 00:07:32,600 but we gotta talk about the human side of all this too. 216 00:07:32,600 --> 00:07:36,360 Like, what happens to our jobs when AI can do so much? 217 00:07:36,360 --> 00:07:37,960 It's a question a lot of folks are asking. 218 00:07:37,960 --> 00:07:38,800 Yeah, absolutely. 219 00:07:38,800 --> 00:07:39,620 And it's a big one. 220 00:07:39,620 --> 00:07:42,800 It's something that Jensen Wong addresses head on, actually. 221 00:07:42,800 --> 00:07:46,320 And his vision isn't about AI replacing us, you know. 222 00:07:46,320 --> 00:07:48,760 It's about AI boosting our capabilities, 223 00:07:48,760 --> 00:07:50,480 making us even better at what we do. 224 00:07:50,480 --> 00:07:52,280 So instead of taking our jobs, 225 00:07:52,280 --> 00:07:55,800 AI becomes a tool to actually improve them. 226 00:07:55,800 --> 00:07:56,920 Right. 227 00:07:56,920 --> 00:07:57,800 Think about it. 228 00:07:57,800 --> 00:08:00,680 What if AI could help doctors diagnose diseases 229 00:08:00,680 --> 00:08:02,440 way more accurately, 230 00:08:02,440 --> 00:08:05,780 or help architects design eco-friendly cities, 231 00:08:05,780 --> 00:08:09,440 or even help teachers personalize lessons for each student? 232 00:08:09,440 --> 00:08:11,120 We're talking about a whole new level 233 00:08:11,120 --> 00:08:12,840 of efficiency and possibility. 234 00:08:12,840 --> 00:08:14,920 Yeah, it's a much more, I don't know, 235 00:08:14,920 --> 00:08:16,400 optimistic way to look at it 236 00:08:16,400 --> 00:08:18,880 than that whole robots are gonna steal our jobs 237 00:08:18,880 --> 00:08:19,880 thing you always hear. 238 00:08:19,880 --> 00:08:20,700 For sure. 239 00:08:20,700 --> 00:08:23,260 And it goes hand in hand with this idea Jensen talks about, 240 00:08:23,260 --> 00:08:25,420 this idea of people becoming like CEOs 241 00:08:25,420 --> 00:08:27,040 of their own AI agents. 242 00:08:27,040 --> 00:08:28,920 Imagine having super smart tools 243 00:08:28,920 --> 00:08:30,360 that can help manage your schedule, 244 00:08:30,360 --> 00:08:32,040 take care of all those little tasks, 245 00:08:32,040 --> 00:08:34,240 even help generate creative ideas. 246 00:08:34,240 --> 00:08:35,080 It's not- 247 00:08:35,080 --> 00:08:35,920 It's not interesting. 248 00:08:35,920 --> 00:08:36,760 It's interesting. 249 00:08:36,760 --> 00:08:37,600 It's ours. 250 00:08:37,600 --> 00:08:38,800 So working smarter, not harder, 251 00:08:38,800 --> 00:08:41,120 using AI to multiply our own skills and talents. 252 00:08:41,120 --> 00:08:42,000 Exactly. 253 00:08:42,000 --> 00:08:43,320 And the cool thing is, 254 00:08:43,320 --> 00:08:45,760 this isn't some far off sci-fi scenario. 255 00:08:45,760 --> 00:08:48,120 Jensen himself, he uses AI tools 256 00:08:48,120 --> 00:08:49,640 every single day in his own work, 257 00:08:49,640 --> 00:08:51,640 whether he's doing research, brainstorming, 258 00:08:51,640 --> 00:08:53,320 making big decisions, you name it. 259 00:08:53,320 --> 00:08:54,720 He's like a walking example 260 00:08:54,720 --> 00:08:56,780 of how we can integrate this tech into our lives 261 00:08:56,780 --> 00:08:58,160 and do incredible things. 262 00:08:58,160 --> 00:08:59,240 I like that. 263 00:08:59,240 --> 00:09:00,240 Feels like we're at this point 264 00:09:00,240 --> 00:09:03,000 where we can choose to be scared of AI, 265 00:09:03,000 --> 00:09:06,440 or we can choose to embrace what it could help us create. 266 00:09:06,440 --> 00:09:07,280 Totally. 267 00:09:07,280 --> 00:09:08,760 It's about shifting our perspective, right? 268 00:09:08,760 --> 00:09:10,880 Moving from this fear of the unknown 269 00:09:10,880 --> 00:09:12,960 to like a collaborative spirit. 270 00:09:12,960 --> 00:09:15,240 The real magic of AI, 271 00:09:15,240 --> 00:09:18,040 it lies in its ability to work with us, 272 00:09:18,040 --> 00:09:20,360 to make us smarter and stronger. 273 00:09:20,360 --> 00:09:21,760 Okay, now before we wrap up, 274 00:09:21,760 --> 00:09:23,280 we gotta touch on this whole debate 275 00:09:23,280 --> 00:09:25,600 that's been swirling around the AI world. 276 00:09:25,600 --> 00:09:28,040 Open source versus closed source models. 277 00:09:28,040 --> 00:09:29,120 What's the deal with that? 278 00:09:29,120 --> 00:09:30,420 It's a big one, for sure. 279 00:09:30,420 --> 00:09:31,820 Lots of different viewpoints. 280 00:09:31,820 --> 00:09:34,580 So on the one hand, you have closed models, right? 281 00:09:34,580 --> 00:09:37,500 Like what OpenAI does, keeping their code under wraps. 282 00:09:37,500 --> 00:09:39,560 This allows them to protect their inventions, 283 00:09:39,560 --> 00:09:41,800 maybe even develop even more advanced systems. 284 00:09:41,800 --> 00:09:43,360 But then there's that whole other side that says, 285 00:09:43,360 --> 00:09:45,840 hey, if we make these models open source, 286 00:09:45,840 --> 00:09:47,840 if everyone can access the code, 287 00:09:47,840 --> 00:09:50,500 innovation will skyrocket and more people will benefit. 288 00:09:50,500 --> 00:09:52,720 Exactly, and that's where it gets really interesting 289 00:09:52,720 --> 00:09:54,640 because Jensen Hong, 290 00:09:54,640 --> 00:09:56,680 he doesn't think it has to be one or the other. 291 00:09:56,680 --> 00:09:59,240 He actually argues that we need both. 292 00:09:59,240 --> 00:10:02,340 Closed models, they drive that cutting edge research, 293 00:10:02,340 --> 00:10:03,200 that investment. 294 00:10:03,200 --> 00:10:07,080 But open models, they make AI more accessible, 295 00:10:07,080 --> 00:10:08,520 more democratic. 296 00:10:08,520 --> 00:10:10,340 So it's all about finding that balance, 297 00:10:10,340 --> 00:10:12,120 that sweet spot where everyone wins. 298 00:10:12,120 --> 00:10:13,160 Exactly. 299 00:10:13,160 --> 00:10:15,160 We need a healthy AI ecosystem 300 00:10:15,160 --> 00:10:17,760 and that means both open and closed models 301 00:10:17,760 --> 00:10:19,840 playing their part, even in video 302 00:10:19,840 --> 00:10:21,240 and they're leading the pack, 303 00:10:21,240 --> 00:10:23,640 even they see the value in open source. 304 00:10:23,640 --> 00:10:25,960 They released their own open source model a while back 305 00:10:25,960 --> 00:10:28,280 that's actually designed to help make other models better. 306 00:10:28,280 --> 00:10:29,120 It's pretty cool. 307 00:10:29,120 --> 00:10:29,940 It really is. 308 00:10:29,940 --> 00:10:32,400 So for everyone listening, it's not about taking sides. 309 00:10:32,400 --> 00:10:34,500 It's about understanding both perspectives, 310 00:10:34,500 --> 00:10:36,820 working together and making sure this technology 311 00:10:36,820 --> 00:10:38,920 is developed in a way that benefits everyone. 312 00:10:38,920 --> 00:10:41,720 And hey, maybe that brings us to the big question, 313 00:10:41,720 --> 00:10:43,320 the one we all need to be thinking about. 314 00:10:43,320 --> 00:10:44,800 The billion dollar question, right. 315 00:10:44,800 --> 00:10:48,060 So we've talked about how AI is growing at an insane pace, 316 00:10:48,060 --> 00:10:49,300 all this investment, 317 00:10:49,300 --> 00:10:52,480 billions of these interactions happening every second, 318 00:10:52,480 --> 00:10:53,860 but where do we fit in? 319 00:10:53,860 --> 00:10:54,700 Exactly. 320 00:10:54,700 --> 00:10:56,820 Are we just gonna sit back and watch 321 00:10:56,820 --> 00:10:59,220 as AI changes the world around us? 322 00:10:59,220 --> 00:11:02,440 Or are we gonna jump in, use these tools 323 00:11:02,440 --> 00:11:04,640 and actually shape what this future looks like, 324 00:11:04,640 --> 00:11:06,320 not just for ourselves, but for everyone? 325 00:11:06,320 --> 00:11:08,520 Because let's be real, the future of AI, 326 00:11:08,520 --> 00:11:09,680 it's not set in stone. 327 00:11:09,680 --> 00:11:11,560 It's being written right now. 328 00:11:11,560 --> 00:11:14,840 And every single one of us has the power to grab the pen 329 00:11:14,840 --> 00:11:17,000 and add our own lines to the story. 330 00:11:17,000 --> 00:11:18,840 And that's what this deep dive is all about, 331 00:11:18,840 --> 00:11:21,120 equipping everyone with the insights and the knowledge 332 00:11:21,120 --> 00:11:23,520 to navigate this incredible moment in history. 333 00:11:23,520 --> 00:11:42,520 Thanks for joining us.