Simply using the most powerful model to generate synthetic data for a smaller model often fails. Effective distillation requires matching the 'teacher' model's token probabilities to the 'student' model's base architecture and training data, making it a complex research problem.

China is gaining an efficiency edge in AI by using "distillation"—training smaller, cheaper models from larger ones. This "train the trainer" approach is much faster and challenges the capital-intensive US strategy, highlighting how inefficient and "bloated" current Western foundational models are.

The public-facing models from major labs are likely efficient Mixture-of-Experts (MOE) versions distilled from much larger, private, and computationally expensive dense models. This means the model users interact with is a smaller, optimized copy, not the original frontier model.

Fears of a single AI company achieving runaway dominance are proving unfounded, as the number of frontier models has tripled in a year. Newcomers can use techniques like synthetic data generation to effectively "drink the milkshake" of incumbents, reverse-engineering their intelligence at lower costs.

US officials and AI labs allege Chinese firms are engaged in industrial-scale IP theft. They reportedly use fraudulent accounts to extract capabilities from US models like Claude to train their own, creating a facade of domestic innovation.

Leading Chinese AI models like Kimi appear to be primarily trained on the outputs of US models (a process called distillation) rather than being built from scratch. This suggests China's progress is constrained by its ability to scrape and fine-tune American APIs, indicating the U.S. still holds a significant architectural and innovation advantage in foundational AI.

The next frontier of competitive advantage in AI may not be public models, but proprietary 'bootleg skills'—custom markdown files—shared within trusted circles. Gatekeeping these unique, highly effective prompts and workflows could become a significant personal or corporate moat in a world of commoditized AI.

Chinese firms are closing the AI capability gap by using "distillation" to replicate the intelligence of leading US models. This creates a strategic vulnerability, as copying software models is easier than replicating China's hardware manufacturing prowess.

Treat AI skills not just as prompts, but as instruction manuals embodying deep domain expertise. An expert can 'download their brain' into a skill, providing the final 10-20% of nuance that generic AI outputs lack, leading to superior results.