Sakana AI 在东京举办研讨会，宣布从头训练智能体原生 LLM Sakana Sawara

Schmidhuber's argument that only half of his AI vision — autonomous artificial scientists and Physical AI — has reached scale positions these areas as openings for smaller labs rather than Big Tech. Japan is framed as a promising base for that remaining half, giving regional researchers and companies a potential partner outside the US-China duopoly. Teams evaluating non-US foundation models can watch for Sawara Small's 2027 release as an early test of Japan's pretraining capability.

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A from-scratch Japanese LLM effort is notable because nearly all frontier models now trace back to a handful of US and Chinese labs, and Sakana argues the next openings lie outside them. Teams tracking model ecosystem diversity, or researchers interested in Japan's AI ambitions, should watch the spring 2027 release and the event videos for technical direction.

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Sakana's shift signals that agent-first labs are re-entering base-model development, betting that small teams with AI-managed experiment pipelines can compete on limited compute. Japanese-language post-training (Namazu) and orchestration (Fugu) offer a template for non-English markets building sovereign AI stacks. Teams weighing whether to build or fine-tune models can benchmark Sakana's three-pillar framing against single-model strategies.

官方账号Sakana AI@SakanaAILabsSource