WEBVTT 00:00:00.000 --> 00:00:02.399 For a long time, it really felt like the global 00:00:02.399 --> 00:00:05.320 AI story was, well, mostly being written by one 00:00:05.320 --> 00:00:09.119 side. American tech giants, huge resources, these 00:00:09.119 --> 00:00:11.380 big groundbreaking models dominating all the 00:00:11.380 --> 00:00:14.539 headlines. Open AI, Google, Anthropic, you know 00:00:14.539 --> 00:00:16.940 the names, always making waves, setting the pace. 00:00:18.039 --> 00:00:20.239 And Chinese AI efforts often felt like they were 00:00:20.239 --> 00:00:22.359 playing catch up, sometimes honestly dismissed 00:00:22.359 --> 00:00:25.179 as just copying. But then things started to shift. 00:00:25.519 --> 00:00:27.539 A new player kind of stepped onto the global 00:00:27.539 --> 00:00:31.199 stage. Quietly at first but with immense impact 00:00:31.199 --> 00:00:34.200 Kimi k2 arrived and this isn't just another model 00:00:34.200 --> 00:00:36.859 Not just a competitor this thing from China's 00:00:36.859 --> 00:00:38.960 moonshot AI lab. It's like definitive proof the 00:00:38.960 --> 00:00:41.420 whole AI landscape It's fundamentally shifted 00:00:41.420 --> 00:00:43.820 China isn't just keeping pace anymore in some 00:00:43.820 --> 00:00:47.179 really profound ways They're now leading Welcome 00:00:47.179 --> 00:00:49.520 to the deep dive. Yeah, today we are really immersing 00:00:49.520 --> 00:00:51.579 ourselves in one of the most significant developments 00:00:51.579 --> 00:00:54.719 in AI this year, Kimi K2, for moonshot AI in 00:00:54.719 --> 00:00:56.740 China. It's a fascinating story, really. Right, 00:00:56.820 --> 00:00:58.399 we've all seen the headlines, heard the buzz 00:00:58.399 --> 00:01:02.539 around it, but what exactly makes Kimi K2 such 00:01:02.539 --> 00:01:05.219 a profound game changer? What are the specific, 00:01:05.219 --> 00:01:07.680 you know, underlying breakthroughs that let it 00:01:07.680 --> 00:01:10.140 challenge, maybe even redefine the dominance 00:01:10.140 --> 00:01:12.819 we've seen from Western models like GPT and Gemini? 00:01:12.920 --> 00:01:14.579 Yeah, that's really the core of what we want 00:01:14.579 --> 00:01:16.420 to explore today. We'll peel back the layers 00:01:16.420 --> 00:01:19.359 on its, frankly, innovative architecture. We'll 00:01:19.359 --> 00:01:21.640 dissect how it fundamentally redefines the whole 00:01:21.640 --> 00:01:25.079 process of AI training. And maybe most importantly, 00:01:25.480 --> 00:01:27.700 understand why this isn't just a cool technical 00:01:27.700 --> 00:01:29.879 achievement. This is deeply geopolitical. It 00:01:29.879 --> 00:01:33.459 really forces us to ask, is the West maybe... 00:01:33.469 --> 00:01:36.150 losing a race, it actually started itself. Okay, 00:01:36.170 --> 00:01:38.969 so let's start unpacking this. Kimi K2. It isn't 00:01:38.969 --> 00:01:41.409 just hype. The numbers, the real -world performance 00:01:41.409 --> 00:01:43.549 metrics are genuinely impressive. It's definitely 00:01:43.549 --> 00:01:47.370 making waves. Absolutely. So at its heart, Kimi 00:01:47.370 --> 00:01:49.890 K2 is built on what's called a mixture of experts 00:01:49.890 --> 00:01:53.280 architecture. MoE for short. Now, you can imagine 00:01:53.280 --> 00:01:57.060 it like this vast specialized council of top 00:01:57.060 --> 00:01:59.659 -tier experts. And while the model theoretically 00:01:59.659 --> 00:02:02.019 boasts a trillion parameters, which is just an 00:02:02.019 --> 00:02:04.859 absolute massive scale and I, only a tiny fraction, 00:02:05.099 --> 00:02:08.460 something like 3 .2 % or about 32 billion of 00:02:08.460 --> 00:02:11.159 those parameters are actually active at any given 00:02:11.159 --> 00:02:13.580 moment when it's processing a request. OK, so 00:02:13.580 --> 00:02:15.580 it's kind of like having access to this colossal 00:02:15.580 --> 00:02:18.159 library, right? OK. Millions of specialized books. 00:02:18.479 --> 00:02:21.099 But when you ask a specific question, only the 00:02:21.099 --> 00:02:23.219 most relevant librarians, the real experts for 00:02:23.219 --> 00:02:25.180 your query, get activated to find the answer 00:02:25.180 --> 00:02:27.419 efficiently. Is that kind of the idea? That's 00:02:27.419 --> 00:02:29.419 a perfect analogy, yeah. And the real power is 00:02:29.419 --> 00:02:31.479 how those specific librarians, those experts, 00:02:31.599 --> 00:02:33.520 are intelligently routed to the right request. 00:02:33.860 --> 00:02:36.180 It minimizes wasted effort. This design lets 00:02:36.180 --> 00:02:38.020 it tap into this huge breadth of knowledge, but 00:02:38.020 --> 00:02:40.889 at the same time, stay incredibly nimble and 00:02:40.889 --> 00:02:43.250 crucially computationally efficient. It's all 00:02:43.250 --> 00:02:45.610 about smart activation, you know, not just brute 00:02:45.610 --> 00:02:48.509 force. And Moonshot AI, the creators, they've 00:02:48.509 --> 00:02:50.490 actually put out two different versions. There's 00:02:50.490 --> 00:02:53.530 KimiK2 base. That's more of a foundational model, 00:02:53.870 --> 00:02:55.770 really designed for researchers, other labs to 00:02:55.770 --> 00:02:58.469 build on, fine -tune it for specific stuff. Then 00:02:58.469 --> 00:03:01.020 there's KimiK2 instruct. And this one's purpose 00:03:01.020 --> 00:03:03.860 -built, optimized for what they call agentic 00:03:03.860 --> 00:03:07.300 chat experiences. So that's more ready to use, 00:03:07.719 --> 00:03:10.360 aimed at interacting directly with users for 00:03:10.360 --> 00:03:13.039 complex tasks. And if you look at the benchmarks, 00:03:13.419 --> 00:03:15.360 Keenie K2 is really standing out. It's widely 00:03:15.360 --> 00:03:17.360 recognized, especially in the open source world, 00:03:17.719 --> 00:03:19.300 as maybe the best model out there for coding 00:03:19.300 --> 00:03:22.379 tasks. In a lot of tests, it even rivals, sometimes 00:03:22.379 --> 00:03:25.000 beats. some of the top proprietary models. Well, 00:03:25.000 --> 00:03:27.060 that's significant. It really is. And its skill 00:03:27.060 --> 00:03:29.379 in tool use is a particular bright spot that's 00:03:29.379 --> 00:03:31.860 absolutely critical for these agentic AI tasks 00:03:31.860 --> 00:03:34.319 we mentioned. When I say tool use, what we mean 00:03:34.319 --> 00:03:37.300 is its ability to seamlessly integrate and effectively 00:03:37.300 --> 00:03:40.280 use external tools, link APIs, search engines 00:03:40.280 --> 00:03:43.060 to get complex jobs done. It's not just spitting 00:03:43.060 --> 00:03:45.800 out text. It's acting. It's doing things. Plus, 00:03:45.819 --> 00:03:47.979 it shows genuinely deep knowledge in natural 00:03:47.979 --> 00:03:50.479 sciences. And here's a real surprise. It got 00:03:50.479 --> 00:03:53.120 the highest score ever recorded. on an emotional 00:03:53.120 --> 00:03:56.219 intelligence or EQ benchmark. So it's not just 00:03:56.219 --> 00:03:57.759 about understanding language structure, it's 00:03:57.759 --> 00:03:59.860 about picking up on and responding to the subtleties 00:03:59.860 --> 00:04:02.180 of human emotion. That's key for more natural 00:04:02.180 --> 00:04:03.780 interaction. Right, but it's probably important 00:04:03.780 --> 00:04:06.219 to clarify something here. Yeah. KBK2 is often 00:04:06.219 --> 00:04:08.560 classed as a non -reasoning model. Now, that 00:04:08.560 --> 00:04:11.139 doesn't mean it's not intelligent. It just achieves 00:04:11.139 --> 00:04:13.719 its deep thinking, if you like. Right. In a fundamentally 00:04:13.719 --> 00:04:17.100 different way than models like, say, GPT -403 00:04:17.100 --> 00:04:20.120 or Gem9 2 .5 Pro, those models are designed to 00:04:20.120 --> 00:04:22.500 explicitly show their work. Right, generate complex 00:04:22.500 --> 00:04:25.100 step -by -step chains of thought. Chimikki 2 00:04:25.100 --> 00:04:27.800 doesn't necessarily reason in that explicit linear 00:04:27.800 --> 00:04:31.060 way, but it's agentic training, lest it show 00:04:31.060 --> 00:04:33.579 these profound problem -solving skills acting 00:04:33.579 --> 00:04:36.060 intelligently in real -world scenarios. It's 00:04:36.060 --> 00:04:37.980 less about showing the work, maybe, and more 00:04:37.980 --> 00:04:39.879 about just doing the work really effectively. 00:04:40.579 --> 00:04:43.519 So, the key question then is... How does Kimi 00:04:43.519 --> 00:04:46.540 K2 manage this? How does it get such high performance, 00:04:46.899 --> 00:04:49.139 show such deep capabilities, while activating 00:04:49.139 --> 00:04:51.279 only this tiny fraction of its total parameters? 00:04:51.560 --> 00:04:53.540 It really comes down to that Council of Experts 00:04:53.540 --> 00:04:55.779 MOE approach we talked about. It activates only 00:04:55.779 --> 00:04:58.420 the relevant specialists, giving it both vast 00:04:58.420 --> 00:05:00.939 knowledge and high processing speed. Okay, this 00:05:00.939 --> 00:05:03.060 is where it gets really interesting for me. Kimi 00:05:03.060 --> 00:05:05.360 K2 isn't just about impressive performance numbers. 00:05:05.800 --> 00:05:08.759 It's about challenging. quite directly, the very 00:05:08.759 --> 00:05:11.660 foundations, the sort of widely accepted rules 00:05:11.660 --> 00:05:14.899 of modern AI development. It's questioning the 00:05:14.899 --> 00:05:17.860 playbook. Exactly. Historically, AI progress 00:05:17.860 --> 00:05:20.139 has pretty much followed two main scaling laws. 00:05:20.420 --> 00:05:23.399 First, the pre -training scaling law. Bigger 00:05:23.399 --> 00:05:25.939 models, chained on more data, get better results. 00:05:26.220 --> 00:05:29.500 Simple correlation. Powerful. Then there's test 00:05:29.500 --> 00:05:32.220 time training. This idea suggests that models 00:05:32.220 --> 00:05:34.480 allowed to think longer, maybe break down problems 00:05:34.480 --> 00:05:37.019 step by step, tend to produce better outcomes. 00:05:37.720 --> 00:05:39.339 So reasoning models often use things like chain 00:05:39.339 --> 00:05:41.540 of thought, reinforcement learning, but usually 00:05:41.540 --> 00:05:44.379 on problems with clear, verifiable answers, like 00:05:44.379 --> 00:05:47.339 math or logic puzzles. Right. But that approach, 00:05:47.459 --> 00:05:49.420 even though it worked great for structured problems 00:05:49.420 --> 00:05:52.319 like math, it can hit real limits. Especially 00:05:52.319 --> 00:05:54.100 when you get into more creative stuff or strategic 00:05:54.100 --> 00:05:56.560 thinking, or areas where there just isn't one 00:05:56.560 --> 00:05:59.139 single right answer. The real world is messy. 00:05:59.310 --> 00:06:03.029 Precisely. So Kimi -K2, facing these constraints, 00:06:03.569 --> 00:06:05.870 not being able to just outscale everyone with 00:06:05.870 --> 00:06:08.670 raw compute power, they chose a completely different 00:06:08.670 --> 00:06:11.779 path. a novel path. Instead of forcing the model 00:06:11.779 --> 00:06:14.379 to think longer on abstract math proofs or logic 00:06:14.379 --> 00:06:17.379 games, Moonshot AI trained it really extensively 00:06:17.379 --> 00:06:20.100 in these real -world, agentic settings, which 00:06:20.100 --> 00:06:23.240 means it learned by doing, by actively navigating 00:06:23.240 --> 00:06:25.759 and solving practical, multi -step scenarios. 00:06:26.019 --> 00:06:27.740 Like those examples that have been floating around, 00:06:27.980 --> 00:06:29.779 planning a whole three -day trip to Da Nang, 00:06:30.100 --> 00:06:32.379 right? Finding flights, booking a hotel, suggesting 00:06:32.379 --> 00:06:34.939 a detailed itinerary, or taking a Q2 revenue 00:06:34.939 --> 00:06:37.420 report file, analyzing it, summarizing the key 00:06:37.420 --> 00:06:39.279 points into a PowerPoint, and then drafting a 00:06:39.279 --> 00:06:41.060 professional email to management. I mean, these 00:06:41.060 --> 00:06:42.899 aren't simple questions. They're complex, multi 00:06:42.899 --> 00:06:44.660 -step tasks needing real -world interaction. 00:06:44.839 --> 00:06:47.579 Yes, exactly. And this is crucial. They gathered 00:06:47.579 --> 00:06:50.319 this truly massive data set, thousands upon thousands 00:06:50.319 --> 00:06:53.300 of these complex, real -world scenarios. Then, 00:06:53.300 --> 00:06:55.600 by using reinforcement learning on this rich 00:06:55.600 --> 00:06:58.379 data set, KimiK2 learned through continuous self 00:06:58.379 --> 00:07:01.199 -reflection through experimentation. And critically, 00:07:01.319 --> 00:07:03.600 it wasn't just rewarded for completing the final 00:07:03.600 --> 00:07:06.360 task, like booking the flight. It was also rewarded 00:07:06.360 --> 00:07:09.269 for the efficiency. logic, the coherence of its 00:07:09.269 --> 00:07:12.329 entire problem -solving process. How it got there 00:07:12.329 --> 00:07:14.790 mattered. And the profound result of training 00:07:14.790 --> 00:07:17.350 like this. You get a model that naturally thinks 00:07:17.350 --> 00:07:19.910 deeper. Its average token sequence length, basically 00:07:19.910 --> 00:07:22.089 how much it says and responds, is three times 00:07:22.089 --> 00:07:24.870 longer than typical non -reasoning models. It's 00:07:24.870 --> 00:07:27.370 not being forced to solve abstract puzzles, it's 00:07:27.370 --> 00:07:29.769 just naturally responding to the inherent complexity, 00:07:29.889 --> 00:07:31.990 the multi -step nature of real -world problems. 00:07:32.410 --> 00:07:34.269 This process creates a model that's inherently 00:07:34.269 --> 00:07:36.910 designed to act, to be a true agent navigating 00:07:36.910 --> 00:07:39.610 the digital world with purpose. Whoa. I mean, 00:07:39.730 --> 00:07:42.449 just pause and think about that. Imagine AI systems 00:07:42.449 --> 00:07:44.230 learning to navigate the world with that kind 00:07:44.230 --> 00:07:47.069 of nuanced, contextual intelligence. Not just 00:07:47.069 --> 00:07:49.410 fetching answers from a database, but actively 00:07:49.410 --> 00:07:52.370 solving problems, adapting, and maybe even reflecting 00:07:52.370 --> 00:07:54.970 on their actions. It feels like moving from teaching 00:07:54.970 --> 00:07:57.889 a robot to solve a math problem to teaching it 00:07:57.889 --> 00:08:00.589 to genuinely live in problem solve in a messy, 00:08:00.850 --> 00:08:02.970 unpredictable world. This isn't just about efficiency. 00:08:03.069 --> 00:08:04.529 It feels like a different kind of intelligence 00:08:04.529 --> 00:08:06.769 emerging. That's a really powerful image. It 00:08:06.769 --> 00:08:10.339 makes you wonder. Have we been teaching AI mainly 00:08:10.339 --> 00:08:12.660 to pass tests? Well, China's been teaching theirs 00:08:12.660 --> 00:08:15.449 to, well... thrive in the real world. Is that 00:08:15.449 --> 00:08:17.870 a fair way to frame it? Or am I maybe overstating 00:08:17.870 --> 00:08:20.269 the philosophical shift here? No, I honestly 00:08:20.269 --> 00:08:22.250 don't think you're overstating it. It really 00:08:22.250 --> 00:08:24.310 does feel like a philosophical shift. And this 00:08:24.310 --> 00:08:26.189 whole approach also directly challenges what's 00:08:26.189 --> 00:08:28.730 often called the bitter lesson in AI. You know, 00:08:28.769 --> 00:08:31.089 that pervasive idea that just raw computational 00:08:31.089 --> 00:08:33.830 power, just scaling up models bigger and bigger 00:08:33.830 --> 00:08:37.029 will always eventually win out over clever algorithmic 00:08:37.029 --> 00:08:40.389 tricks. Chinese labs, constrained by those significant 00:08:40.389 --> 00:08:42.509 US GPU sanctions, they simply couldn't play that 00:08:42.509 --> 00:08:44.570 game. They couldn't compete on raw compute alone. 00:08:44.710 --> 00:08:46.409 They were forced to innovate somewhere else. 00:08:46.789 --> 00:08:49.830 And necessity, it really seems, became the mother 00:08:49.830 --> 00:08:52.230 of invention here. They didn't just scale. They 00:08:52.230 --> 00:08:54.330 innovated at a really fundamental, algorithmic 00:08:54.330 --> 00:08:57.029 level, specifically with the optimizer. Now, 00:08:57.070 --> 00:08:59.470 for, gosh, over a decade, this technique called 00:08:59.470 --> 00:09:02.690 AdamW has been the undisputed king, used everywhere 00:09:02.690 --> 00:09:05.070 in pretty much every leading large language model. 00:09:05.450 --> 00:09:08.450 But Kimi K2, truly groundbreaking. It's the first 00:09:08.450 --> 00:09:10.649 really large -scale model to completely ditch 00:09:10.649 --> 00:09:14.370 AdamW. it uses a new, pretty sophisticated algorithm 00:09:14.370 --> 00:09:16.929 called Muon. An optimizer, just for our listeners. 00:09:17.330 --> 00:09:19.990 It's basically the unsung hero working behind 00:09:19.990 --> 00:09:22.330 the scenes, right? It's the algorithm that helps 00:09:22.330 --> 00:09:25.509 the AI model update its millions, billions of 00:09:25.509 --> 00:09:28.370 parameters, constantly adjusting itself to minimize 00:09:28.370 --> 00:09:31.870 errors during training. So yeah, this might sound 00:09:31.870 --> 00:09:34.149 like getting deep in the weeds, but it's absolutely 00:09:34.149 --> 00:09:37.029 crucial to how a model actually learns and gets 00:09:37.029 --> 00:09:39.929 better. Precisely. And MUON, which was proposed 00:09:39.929 --> 00:09:42.090 by Killer Jordan, it uses something called second 00:09:42.090 --> 00:09:44.830 -order information. Think of it like this. It's 00:09:44.830 --> 00:09:47.269 not just knowing if you're going uphill or downhill 00:09:47.269 --> 00:09:50.110 on a learning curve, but also understanding how 00:09:50.110 --> 00:09:52.879 steeply that slope is changing. the curvature. 00:09:53.259 --> 00:09:55.679 This deeper understanding helps the AI training 00:09:55.679 --> 00:09:58.899 find a smoother, much more stable path to learning. 00:09:59.000 --> 00:10:01.399 It minimizes errors more precisely, more efficiently. 00:10:01.779 --> 00:10:03.940 And the real breakthrough is that this kind of 00:10:03.940 --> 00:10:05.879 stability during training, it's incredibly rare, 00:10:05.960 --> 00:10:07.559 especially for a model this massive. You just 00:10:07.559 --> 00:10:09.580 don't often see such a consistently stable loss 00:10:09.580 --> 00:10:11.980 curve. And that stability allows for much faster, 00:10:12.080 --> 00:10:14.039 much more effective training in the end. So, 00:10:14.179 --> 00:10:16.000 OK, we put all these pieces together, the new 00:10:16.000 --> 00:10:18.299 optimizer, the stable training, the efficiency. 00:10:18.879 --> 00:10:21.000 What does this all mean for the bigger picture? 00:10:21.179 --> 00:10:23.379 For the AI landscape, what's the core takeaway? 00:10:24.059 --> 00:10:26.639 Token efficiency, that's the big one. At a level 00:10:26.639 --> 00:10:29.419 we haven't really seen widely before, KimiK2 00:10:29.419 --> 00:10:31.600 learns significantly more from the same amount 00:10:31.600 --> 00:10:33.500 of data, and it converges, it learns faster. 00:10:33.600 --> 00:10:37.000 Get this. It was trained with only 15 trillion 00:10:37.000 --> 00:10:39.620 tokens. Now, to put that in perspective, that's 00:10:39.620 --> 00:10:42.120 a tiny fraction of what many Western models of 00:10:42.120 --> 00:10:44.639 similar size have consumed. And that number, 00:10:44.779 --> 00:10:46.659 that specific statistic, it really makes you 00:10:46.659 --> 00:10:49.360 pause and wonder, maybe this fear that we're 00:10:49.360 --> 00:10:51.480 running out of high quality training data isn't 00:10:51.480 --> 00:10:54.139 the whole story. Maybe, just maybe, Our current 00:10:54.139 --> 00:10:56.039 models have simply been incredibly wasteful with 00:10:56.039 --> 00:10:58.539 the data we have, and China has found a way to 00:10:58.539 --> 00:11:00.820 squeeze every last drop of insight out of theirs. 00:11:01.220 --> 00:11:03.139 It's efficiency born from necessity, and it's 00:11:03.139 --> 00:11:05.620 turning a perceived weakness those compute constraints 00:11:05.620 --> 00:11:08.179 into a pretty formidable strength on the global 00:11:08.179 --> 00:11:10.740 stage. You know, I still wrestle with prompt 00:11:10.740 --> 00:11:13.039 drift myself sometimes when I'm trying to fine 00:11:13.039 --> 00:11:16.000 -tune models. You know, where AI kind of suddenly 00:11:16.000 --> 00:11:18.259 veers off track from your original instructions 00:11:18.259 --> 00:11:21.779 over time, it happens. So this idea of optimizing 00:11:21.779 --> 00:11:25.100 the very core process, making it inherently more 00:11:25.100 --> 00:11:28.759 stable and efficient. Wow. That's not just a 00:11:28.759 --> 00:11:30.519 technical tweak. It feels like it fundamentally 00:11:30.519 --> 00:11:33.179 changes what's possible. It's almost mind -bending 00:11:33.179 --> 00:11:35.580 thinking about the implications for everyday 00:11:35.580 --> 00:11:38.519 AI users and developers. It's a profound shift. 00:11:38.820 --> 00:11:41.240 Absolutely. And that Moe architecture we discussed, 00:11:41.360 --> 00:11:44.000 while it's complex to engineer, complex to manage, 00:11:44.600 --> 00:11:46.899 it's also a strategic masterstroke when you're 00:11:46.899 --> 00:11:49.039 facing those compute constraints like the Chinese 00:11:49.039 --> 00:11:51.879 labs are. Right. Just to clarify again for everyone 00:11:51.879 --> 00:11:54.279 listening, a dense transformer model, like an 00:11:54.279 --> 00:11:57.139 older GPT -3 maybe, it would activate every single 00:11:57.139 --> 00:11:58.740 one of its parameters for every single token 00:11:58.740 --> 00:12:01.720 it processes. That's like asking a librarian 00:12:01.720 --> 00:12:03.440 to read every single book in the entire library 00:12:03.440 --> 00:12:05.220 just to answer one simple question. Yeah, it's 00:12:05.220 --> 00:12:07.580 incredibly thorough, sure, but astronomically 00:12:07.580 --> 00:12:09.860 expensive, computationally, energy -wise, time 00:12:09.860 --> 00:12:12.960 -wise. Exactly. Whereas MoE, by contrast, is 00:12:12.960 --> 00:12:15.940 precisely those specialized librarians. There's 00:12:15.940 --> 00:12:18.139 this clever routing network inside the model 00:12:18.139 --> 00:12:21.269 that quickly, efficiently figures out which specific 00:12:21.269 --> 00:12:23.629 expert or knowledge pathway is best for the task 00:12:23.629 --> 00:12:26.129 at hand and only those relevant pathways get 00:12:26.129 --> 00:12:28.269 activated. So you get access to this absolutely 00:12:28.269 --> 00:12:31.110 vast pool of knowledge but at a significantly 00:12:31.110 --> 00:12:34.049 lower computational cost. Yeah. It offers a clear 00:12:34.049 --> 00:12:37.169 scalable way to expand knowledge much more efficiently 00:12:37.169 --> 00:12:39.970 and that's a key advantage. Okay let's refocus 00:12:39.970 --> 00:12:43.070 this slightly. How does Kimi K2's agentic training 00:12:43.070 --> 00:12:45.509 approach fundamentally differ from that traditional 00:12:45.509 --> 00:12:47.929 reasoning model training? What's the core distinction 00:12:47.929 --> 00:12:50.750 there? In essence, it trains on complex, messy, 00:12:50.870 --> 00:12:53.409 real -world scenarios and lots of tool use rather 00:12:53.409 --> 00:12:55.830 than focusing on abstract step -by -step problems 00:12:55.830 --> 00:12:58.250 like math. It's a profound philosophical difference 00:12:58.250 --> 00:13:00.470 in approach. Midroll sponsor, Read. Okay, so 00:13:00.470 --> 00:13:04.350 we've just peeled back the layers on Kimi K2's 00:13:04.350 --> 00:13:06.610 really groundbreaking technical side, its efficient 00:13:06.610 --> 00:13:08.389 architecture, the revolutionary training, the 00:13:08.389 --> 00:13:11.629 optimization methods. But now... It's absolutely 00:13:11.629 --> 00:13:13.529 crucial we understand that these aren't just 00:13:13.529 --> 00:13:15.850 fascinating technical achievements stuck in research 00:13:15.850 --> 00:13:18.090 papers or labs. This is where those technical 00:13:18.090 --> 00:13:22.210 shifts spill right onto the global stage. Kimi 00:13:22.210 --> 00:13:25.049 K2 isn't just an academic curiosity. It's a strategic 00:13:25.049 --> 00:13:27.809 move. It's a geopolitical shockwave, potentially 00:13:27.809 --> 00:13:30.529 redefining the whole global AI race. That's spot 00:13:30.529 --> 00:13:33.049 on. Yeah. Moonshot AI's decision to open source 00:13:33.049 --> 00:13:36.019 Kimi K2. That wasn't random generosity, not at 00:13:36.019 --> 00:13:38.500 all. It was a deeply strategic move, a calculated 00:13:38.500 --> 00:13:41.440 maneuver. While we see leading US labs increasingly 00:13:41.440 --> 00:13:43.480 choosing to close source their models, often 00:13:43.480 --> 00:13:45.840 to protect commercial advantages, protect proprietary 00:13:45.840 --> 00:13:48.980 research, China is consciously and very effectively 00:13:48.980 --> 00:13:51.379 using open source as a geopolitical tool. Right. 00:13:51.600 --> 00:13:53.940 They're essentially neutralizing what's been 00:13:53.940 --> 00:13:58.159 a major US advantage, raw compute power. By releasing 00:13:58.159 --> 00:14:00.539 a top -tier model like this into the open -source 00:14:00.539 --> 00:14:03.200 community, they're allowing developers, researchers, 00:14:03.399 --> 00:14:06.059 anyone around the world, really, to build sophisticated 00:14:06.059 --> 00:14:08.980 stuff, do cutting -edge research, without needing 00:14:08.980 --> 00:14:11.980 American -owned infrastructure, or, crucially, 00:14:12.360 --> 00:14:14.580 without needing massive access to those high 00:14:14.580 --> 00:14:16.879 -end US GPUs that are under export controls. 00:14:17.700 --> 00:14:20.580 Precisely. And this strategy, it works on multiple 00:14:20.580 --> 00:14:22.860 levels. First, it steers global research and 00:14:22.860 --> 00:14:24.820 development more and more towards Chinese core 00:14:24.820 --> 00:14:26.899 technology, towards their architectural ideas. 00:14:27.259 --> 00:14:30.159 Second, it wins goodwill globally. It positions 00:14:30.159 --> 00:14:33.059 China as promoting collective progress, accessibility, 00:14:33.500 --> 00:14:35.919 quite a contrast to the closed proprietary approach 00:14:35.919 --> 00:14:38.340 of many Western firms. And third, it puts real 00:14:38.340 --> 00:14:40.559 economic pressure on U .S. proprietary models. 00:14:40.659 --> 00:14:43.440 Why? Because it offers a free, powerful, and 00:14:43.440 --> 00:14:45.919 increasingly competitive alternative. Why pay 00:14:45.919 --> 00:14:48.200 for an API when a comparable, maybe even better, 00:14:48.279 --> 00:14:50.659 model can be run locally for free or very cheaply. 00:14:50.840 --> 00:14:53.659 And it's not just moonshot AI doing this in isolation, 00:14:53.679 --> 00:14:56.039 is it? What we seem to be seeing is this stark 00:14:56.039 --> 00:14:59.360 contrast between China's synergy, their coordination, 00:14:59.600 --> 00:15:01.759 and America's often fragmented approach. I mean, 00:15:01.779 --> 00:15:04.460 consider this. Kimi K2's underlying architecture 00:15:04.460 --> 00:15:07.360 is remarkably similar, almost identical, to DeepSeek 00:15:07.360 --> 00:15:10.220 V3, another big Chinese model. That really signals 00:15:10.220 --> 00:15:12.919 deep collaboration, doesn't it? A unified strategic 00:15:12.919 --> 00:15:15.279 vision across their industry. Yes, it's a really 00:15:15.279 --> 00:15:17.559 striking contrast. In China, you see labs like 00:15:17.559 --> 00:15:20.399 Moonshot AI, DeepSeek, others, often sharing 00:15:20.399 --> 00:15:22.379 foundational architectures, research findings, 00:15:22.539 --> 00:15:24.740 even datasets. They seem to operate, to a large 00:15:24.740 --> 00:15:27.820 extent, like a cohesive team, driven by national 00:15:27.820 --> 00:15:30.779 interest, by a long -term, strategic vision that's 00:15:30.779 --> 00:15:33.039 explicitly laid out by their government. Meanwhile, 00:15:33.399 --> 00:15:35.539 Silicon Valley. Well, it's often a different 00:15:35.539 --> 00:15:38.840 picture. Intense talent wars, fierce competition 00:15:38.840 --> 00:15:41.620 for market share, very public spats playing out 00:15:41.620 --> 00:15:43.799 on social media, Musk versus Altman, things like 00:15:43.799 --> 00:15:46.220 that. And often you see lobbying efforts aimed 00:15:46.220 --> 00:15:49.139 at shaping regulations to protect existing monopolies. 00:15:49.500 --> 00:15:51.639 American labs, generally speaking, are primarily 00:15:51.639 --> 00:15:54.019 fighting for investor profits for individual 00:15:54.019 --> 00:15:56.320 market dominance. And that can sometimes hinder 00:15:56.320 --> 00:15:58.259 collective progress, hinder broader innovation. 00:15:58.379 --> 00:16:00.259 Not saying one is morally better, but strategically, 00:16:00.679 --> 00:16:03.179 China's unified front is a very different and 00:16:03.179 --> 00:16:06.320 potent challenge. So Kimi K2 is really just the 00:16:06.320 --> 00:16:09.340 visible tip of this much larger coordinated iceberg, 00:16:10.220 --> 00:16:12.779 China's big new generation artificial intelligence 00:16:12.779 --> 00:16:15.500 development plan. That's not just a policy paper. 00:16:15.679 --> 00:16:18.279 It actively mobilizes state funding, sets very 00:16:18.279 --> 00:16:21.200 clear tech priorities, and fosters deep institutional 00:16:21.200 --> 00:16:23.440 collaboration across companies, universities, 00:16:23.759 --> 00:16:26.539 government research groups. It's a truly synergistic 00:16:26.539 --> 00:16:29.120 national ecosystem they're building. And this 00:16:29.120 --> 00:16:32.679 flood of powerful, free, open source models like 00:16:32.679 --> 00:16:35.940 KiniK2, it's a huge market disruptor. For countless 00:16:35.940 --> 00:16:38.159 businesses, for startups, even for entire developing 00:16:38.159 --> 00:16:39.919 nations, the question becomes pretty simple. 00:16:40.360 --> 00:16:43.659 Why keep paying for expensive API access to closed 00:16:43.659 --> 00:16:46.500 proprietary models when a highly capable, maybe 00:16:46.500 --> 00:16:48.980 even superior open source alternative can be 00:16:48.980 --> 00:16:51.399 run locally for a fraction of the cost? Or even 00:16:51.399 --> 00:16:54.120 for free? This really democratizes access to 00:16:54.120 --> 00:16:56.200 cutting edge AI. It empowers smaller players, 00:16:56.639 --> 00:16:59.350 unlocks massive opportunities But, we have to 00:16:59.350 --> 00:17:01.649 acknowledge, this also brings significant social 00:17:01.649 --> 00:17:04.789 risks. We can't just gloss over those. Uncensored, 00:17:04.930 --> 00:17:08.009 powerful, open -source models. They undeniably 00:17:08.009 --> 00:17:11.009 make it easier for bad actors, easier to generate 00:17:11.009 --> 00:17:13.450 sophisticated misinformation, create malicious 00:17:13.450 --> 00:17:16.230 code, and gate in other harmful stuff at scale. 00:17:16.460 --> 00:17:19.359 With open source, a lot of the responsibility 00:17:19.359 --> 00:17:22.339 for ethical safe use shifts directly onto the 00:17:22.339 --> 00:17:24.480 end user. It's definitely a double -edged sword. 00:17:24.839 --> 00:17:26.859 Absolutely. So the question becomes, what can 00:17:26.859 --> 00:17:29.740 the U .S. do in response? Policy shifts are definitely 00:17:29.740 --> 00:17:31.960 on the table. Maybe escalating export controls 00:17:31.960 --> 00:17:34.279 not just on hardware, but on advanced AI software 00:17:34.279 --> 00:17:36.769 too. Or maybe dramatically increasing federal 00:17:36.769 --> 00:17:39.289 funding for domestic AI research, trying to accelerate 00:17:39.289 --> 00:17:41.170 innovation at home. On the corporate side, you 00:17:41.170 --> 00:17:43.069 might see leading companies double down on their 00:17:43.069 --> 00:17:46.309 closed models, emphasizing safety, reliability, 00:17:46.609 --> 00:17:48.430 seamless integration as their value proposition 00:17:48.430 --> 00:17:51.849 to justify the cost. Or maybe the U .S. could 00:17:51.849 --> 00:17:54.569 meet the challenge head on. fight fire with fire, 00:17:54.690 --> 00:17:57.690 so to speak. That would mean championing, investing 00:17:57.690 --> 00:18:00.369 heavily in its own open source giants, like Metis 00:18:00.369 --> 00:18:02.849 Llama series, really competing vigorously for 00:18:02.849 --> 00:18:05.109 that global developer community, making sure 00:18:05.109 --> 00:18:07.430 the open source ecosystem doesn't become totally 00:18:07.430 --> 00:18:10.109 dominated by Chinese models. It's a strategic 00:18:10.109 --> 00:18:12.170 choice to be made. It really brings to mind a 00:18:12.170 --> 00:18:13.970 lesson from history, doesn't it? Think about 00:18:13.970 --> 00:18:17.299 the internet. or GPS, these were groundbreaking 00:18:17.299 --> 00:18:19.599 American innovations. They started as matters 00:18:19.599 --> 00:18:22.279 of national security, public investment, profit 00:18:22.279 --> 00:18:24.380 was secondary, maybe even irrelevant initially. 00:18:24.720 --> 00:18:26.880 Then once they were established, private companies 00:18:26.880 --> 00:18:29.380 built trillions of dollars of value on top. It's 00:18:29.380 --> 00:18:31.339 becoming pretty clear which superpower is applying 00:18:31.339 --> 00:18:33.559 that foundational philosophy to AI development 00:18:33.559 --> 00:18:37.200 right now. And currently it doesn't seem to be 00:18:37.200 --> 00:18:39.759 the United States. So thinking purely from a 00:18:39.759 --> 00:18:42.079 business angle, how does Kimi K2's open source 00:18:42.079 --> 00:18:44.200 release fundamentally hit the business models 00:18:44.200 --> 00:18:47.039 of of those big Western AI companies? Well, it 00:18:47.039 --> 00:18:48.940 directly disrupts their main revenue streams, 00:18:48.940 --> 00:18:52.339 doesn't it? By offering this powerful free alternative 00:18:52.339 --> 00:18:55.259 to their proprietary paid API access models, 00:18:55.619 --> 00:18:58.420 it forces them to compete differently. So what 00:18:58.420 --> 00:19:00.880 does this all truly mean then? Looking ahead, 00:19:00.920 --> 00:19:03.460 thinking about the future of AI, Kimi K2 feels 00:19:03.460 --> 00:19:06.180 like a profound shock to the system. It's compelling 00:19:06.180 --> 00:19:09.759 proof that resource constraints can kind of paradoxically 00:19:09.759 --> 00:19:12.680 drive radical innovation. It directly challenges 00:19:12.680 --> 00:19:15.289 that deeply ingrained compute will always win, 00:19:15.769 --> 00:19:18.349 philosophy that shaped AI for so long? Yeah, 00:19:18.529 --> 00:19:20.690 the message coming from China is unmistakable 00:19:20.690 --> 00:19:22.609 now. They aren't just catching up. They're actively 00:19:22.609 --> 00:19:25.329 pushing the frontier forward, reshaping the rules 00:19:25.329 --> 00:19:27.490 of the game itself. They're turning what many 00:19:27.490 --> 00:19:29.650 saw as weaknesses, like compute limits, into 00:19:29.650 --> 00:19:32.230 formidable strengths. And they're cleverly exploiting 00:19:32.230 --> 00:19:34.490 the strategic cracks, the fragmentation in the 00:19:34.490 --> 00:19:37.009 West's current approach, the global open source 00:19:37.009 --> 00:19:40.190 AI ecosystem. It's undeniably being shaped more 00:19:40.190 --> 00:19:42.390 and more by Chinese models. This isn't really 00:19:42.390 --> 00:19:45.430 a subtle shift anymore, is it? The diplodocus, 00:19:45.569 --> 00:19:48.869 as some called it. It's not an elephant quietly 00:19:48.869 --> 00:19:52.069 hiding in the room. It's here. It's enormous. 00:19:52.410 --> 00:19:55.609 It's profoundly influential. And if America continues 00:19:55.609 --> 00:19:58.549 down its current path fragmented, mostly profit 00:19:58.549 --> 00:20:01.430 focused, the playing field hasn't just tilted 00:20:01.430 --> 00:20:04.250 a bit. It feels like it has fundamentally, maybe 00:20:04.250 --> 00:20:06.960 irrevocably shifted. We really hope this deep 00:20:06.960 --> 00:20:09.440 dive has given you, our listeners, a much clearer, 00:20:09.539 --> 00:20:12.619 maybe more nuanced picture of this rapidly evolving 00:20:12.619 --> 00:20:15.279 AI landscape. It's moving fast. And this brings 00:20:15.279 --> 00:20:17.059 us to an important question, something for you 00:20:17.059 --> 00:20:18.779 to consider maybe long after you finish listening. 00:20:19.359 --> 00:20:21.859 As AI becomes increasingly powerful, increasingly 00:20:21.859 --> 00:20:24.839 accessible to everyone, what responsibility do 00:20:24.839 --> 00:20:27.000 each of us as individuals actually bear in how 00:20:27.000 --> 00:20:28.900 this transformative technology is developed, 00:20:29.259 --> 00:20:32.140 deployed, and shaped globally? What kind of AI 00:20:32.140 --> 00:20:34.299 ecosystem do you ultimately want to see flourish 00:20:34.299 --> 00:20:36.490 in the years ahead? It's definitely a question 00:20:36.490 --> 00:20:38.509 worth pondering. Thank you so much for joining 00:20:38.509 --> 00:20:40.670 us for this deep dive. Until next time, keep 00:20:40.670 --> 00:20:42.230 exploring. How's your old music?