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At this scale (10T+ params), pre-training doesn't just average—model capacity explodes, letting rare signals carve out distinct subspaces in the latent space without dilution. Novel ideas in data (e.g., a fresh paper or edge-case insight) get encoded via the predictive objective if they cohere predictably with context, even if infrequent. Emergence kicks in: the model starts recombining latent patterns into outputs that feel "new" because no single training example had them exactly. It's not invention from void—it's hyper-efficient compression revealing unseen connections in the data distribution. Post-pretrain fine-tuning or prompting amplifies it further.

이게 무슨일이야. 나 지금 잠을 잘 수가 없어. 갑자기 일본어, 영어, 스페인어 계정들의 포스팅이 막 한국어로 보이는거야 ㅋㅋㅋ이건 신세계야. 더 신나는건 뭐냐면, 이렇게 온 세상이 실시간으로 소통하게 되면 공산주의자들의 프로파간다가 더 이상 먹히지 않을거란 말이지. 일론! 정말 대단해!