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#

Kimi K3 is now in Cursor! It scores close to the frontier on CursorBench. It's available on US-based inference thanks to our partners Fireworks, Together, and Baseten. Zero Data Retention is also supported.

Kimi K3 is now available on ChatLLM and hosted in the US! We have also kicked off an open-source fine-tune based on this top frontier model This is the biggest release in the enterprise AI world! Companies can control the LLM and the data. Congrats to Kimi 🚀🚀

Kimi K3’s weights are live - 2.8 trillion parameters, only about 50 billion active per token. That efficiency is real. But the other 2.75 trillion still have to sit somewhere the moment someone self-hosts it. Sparse compute doesn’t mean sparse memory.

Kimi K3 will be truly open-weight and 3x faster on Monday Get ready to move all your standard workloads immediately Anything that is on Sonnet or GPT 5.5 can move — it's faster, cheaper, and better 🚀🚀

Kimi K3 just dropped. 2.8T params, 1M context, biggest open-weight release ever. The real story: Fireworks routed tasks between K3 and Claude Fable 5. The hybrid beat both models solo. Open weights land July 27, but you'll need 1.4TB+ of GPU memory to self-host. For most teams the play is API access plus per-task routing. Full specs, benchmarks, and access options:

Kimi K3 cooked GPT-5.6 Sol I tested both models on physics and 3D generation. Same prompts, same harness and Kimi outperformed GPT-5.6 Sol by far, it was not even close. Total cost: - Kimi K3: $0.23 - GPT-5.6 Sol: $2.15 Kimi was ~9.3x cheaper and produced better results.