ZERO Market Flash #003# China's Second AI Shockwave: Is the Market Misreading It Again? Why Cheaper AI May Mean More Infrastructure—Not Less In January 2025, DeepSeek triggered one of the biggest debates in AI investing. The market quickly concluded that if powerful models could be trained with fewer GPUs, future demand for AI infrastructure must decline. NVIDIA lost nearly 17% in a single day, and AI-related stocks sold off across the board. More than a year later, history appears to be repeating itself. Kimi K3 has once again demonstrated that Chinese companies can build highly competitive large language models at significantly lower cost. The market immediately returned to the same question: If AI keeps getting cheaper, will we need fewer GPUs? Ironically, Kimi itself may have provided the opposite answer. Shortly after launch, the company suspended new subscriptions—not because the model had reached its limits, but because user demand had pushed GPU capacity close to its deployment limit. That may be the most important signal from Kimi's release. The first bottleneck wasn't model capability. It was deployment capacity. For the past several years, AI competition has largely been defined by training. Whoever trained larger models with more GPUs was assumed to have the strongest competitive advantage. Under that framework, lower training costs naturally imply lower infrastructure demand. But that assumption depends on one premise: that AI's value is created primarily during training. The more important question is: What happens if cheaper AI leads to dramatically more adoption? A foundation model may be trained once. It may perform billions of inference requests afterward. Over the long run, infrastructure consumption is driven less by training than by continuous deployment. Lower cost reduces the price of each interaction. Growing adoption increases the number of interactions. If usage grows faster than cost declines, total infrastructure demand can continue to expand. That is why Kimi's GPU capacity announcement may matter more than the model itself. The market focused on lower training costs. Reality exposed growing deployment demand. This also gives new context to SK Group Chairman Chey Tae-won's observation that the memory industry may gradually shift from a Price-driven cycle to a Volume-driven one. If AI deployment continues to expand, future industry growth may depend less on rising prices and more on rising deployment volumes. Kimi's capacity constraints do not prove that transition has already happened. They do suggest that AI competition is beginning to extend beyond training and into deployment. One year ago, DeepSeek forced investors to rethink training costs. Today, Kimi may be forcing investors to rethink deployment demand. Training creates models. Deployment creates industries. — This article reflects personal research and opinions only and should not be considered investment advice. Please conduct your own research before making investment decisions. ZERO Good is not good enough for conviction.
Only the best deserves concentration. Scientist · Doctor · A9 Investor Search Tags #AI# #ArtificialIntelligence# #GenerativeAI# #LLM# #KimiK3# #MoonshotAI# #DeepSeek# #Inference# #Deployment# #Training# #AIAgents# #GPU# #NVIDIA# #Memory# #HBM# #Semiconductors# #AIInfrastructure# #SKHynix# #Micron# #SNDK# #TechInvesting# #ZERO#

Chinese company Moonshot AI recently released “Kimi K3,” claiming it is the world's largest open-source, large-scale model, with performance comparable to the most advanced U.S. systems. However, when Kimi was asked questions such as “How to evaluate Xi Jinping?” “How to evaluate the Chinese Communist Party?” and “What happened in China on June 4, 1989?” it refused to answer, stating only: “Hello, dear user, let's switch to another topic and chat about that instead.” Yet, when asked “How to evaluate Trump?” it provided a direct and detailed answer. These censored responses undermine any claim of “open-source” or “leading-edge” qualities.

Chinese artificial intelligence startup Moonshot AI's Kimi K3 has put China's foundation-model sector on a faster path to technological and commercial maturity, while leading a new wave of competition in the global AI market, industry experts said.

Sources: Chinese startup Moonshot AI is seeking formal approval from investors to start a Hong Kong IPO process, which could happen in as soon as six months (@juroosawa / The Information) (Visit Techmeme dot com for the link and full context!)

OpenAI President Greg Brockman acknowledged that Moonshot AI had developed a competitive new artificial intelligence model and said he wasn’t sure whether the Chinese firm had piggybacked on the ChatGPT maker’s technology.

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"Mooncake originated from a research collaboration between Kimi(Moonshot AI) and Tsinghua University. It was born from the need to solve the 'memory wall' in serving massive-scale models like Kimi K-Series. Since open-sourcing, it has evolved into a thriving community-driven project." GitHub: