Moonshot AI Responds to Distillation Allegations
Moonshot AI team member Randy Xia responded that Fable was released on July 1, and K3 went live on July 15.
According to the timeline, Moonshot completed model research, distillation, training, and release in just 15 days.
Randy Xia Jokes
Randy Xia stated, "We trained a brand new cutting-edge model in just 15 days, which is practically a Guinness World Record."
Market Mechanism
Driven by controversies in AI model development, the market is focusing on the independence and technical paths of Chinese large models. Short-term capital is flowing out of related Chinese AI stocks for risk aversion, but the long-term outlook is optimistic about the impact of low-cost, high-performance models on global pricing power. Moonshot and others benefit from efficiency narratives, while some U.S.-based closed-source models face pressure.
Source: Public Information
ABAB AI Insight
Moonshot AI was previously known for Kimi's long context and efficient MoE architecture. K3 is accused of having 28 trillion parameters, excelling in front-end code and multimodal performance. It had previously accelerated iteration through legitimate distillation practices via a paid API. This rapid response directly points out the inconsistency in the timeline.
In terms of capital pathways, Moonshot mobilizes internal platform and GB300 server resources to quickly absorb cutting-edge capabilities and convert them into low-cost deployments. The motivation is to capture the developer market through price wars and performance benchmarks, strategically shifting from following to reshaping the generative AI service landscape with cost-effectiveness.
Similar to OpenAI's early reliance on massive computation and data for rapid iteration, or other Chinese labs refining capabilities through public APIs, Moonshot is currently in a fierce competitive phase of transitioning AI models from high-cost closed-source to efficient open-source/semi-open-source.
Structural Judgment
This essentially involves a transfer of pricing power. Moonshot utilizes extreme efficiency and MoE design to rapidly commercialize high-performance generative AI capabilities, originally dominated by U.S. companies like Anthropic, through legitimate or controversial pathways. The mechanism is a cost collapse in training paradigms shifting from zero-training to distillation + post-training, lowering the thresholds for computing power and data, and transferring power from a few resource monopolists to more capable newcomers.
ABAB News · Cognitive Laws
The timeline is the best falsifier; true innovation cannot withstand a 15-day miracle.
Behind the distillation controversy, pricing power shifts from computing power to execution speed.
Cutting-edge models are not about who trains first, but who delivers value at low cost first.