A project shaped by experimentation, trust and a genuinely wonderful collaboration. Thank you to the Kimi team for believing in the process and being such a pleasure to create with! #KIMI# #KIMIK3# #MOONSHOTAI# #K3# #BTS#

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#

Moonshot AI accessed a trove of Nvidia’s most advanced chips this summer, according to documents filed with Beijing regulators, highlighting the Chinese AI champion’s reliance on American hardware despite US export restrictions

Moonshot AI accessed a trove of Nvidia’s most advanced chips this summer, according to documents filed with Beijing regulators, highlighting the Chinese AI champion’s reliance on American hardware despite US export restrictions

Moonshot AI didn't have to do this stupid Kimi K3.1 pro launch campaign the model was okay and we would have praised it anyway but doing illegal marketing campaign before they IPO is such a bad move from them. Congratulations you played yourself.

Moonshot AI, the Chinese upstart that had its own DeepSeek moment over the summer, released the first major upgrade to the seminal model that thrust the $50 billion company into the frontier

Moonshot AI Targets $5 Billion IPO at $50 Billion Valuation According to DIGITIMES, Chinese AI startup Moonshot AI, developer of Kimi, is targeting a $5 billion Hong Kong IPO in Q1 2027, following a private valuation increase to $50 billion The company reportedly generates $1 billion in annual recurring revenue, projected to reach $2 billion by December 2026, implying a valuation of 25x projected ARR However, Moonshot faces an ongoing ownership dispute involving claims potentially exceeding $700 million, alongside Chinese data security scrutiny that could complicate its IPO The company is backed by Alibaba, Tencent, Meituan, and China Mobile

MOONSHOT AI EYES EARLY 2027 HONG KONG IPO AFTER VALUATION HITS $50B Chinese AI startup Moonshot AI has completed its final private funding round at a valuation of about $50B and is preparing for a potential Hong Kong IPO in Q1 2027. The company is considering raising as much as $5B and could begin early investor meetings as soon as this month. Moonshot’s ARR is currently around $1B and is expected to reach $2B by December, up from roughly $300M in June. The company, known for its Kimi AI models, was valued at $31.5B in a previous funding round this summer. Bank of America is serving as overall coordinator for the IPO alongside CICC, Deutsche Bank and Goldman Sachs. Moonshot has reportedly already filed confidentially for the listing. Alibaba, Tencent and 5Y Capital are among its existing backers. The IPO timing could still change, with Moonshot also reportedly facing a Chinese regulatory data-security probe alongside DeepSeek. Source: Bloomberg