Compute grows much faster than data . Our current scaling laws require proportional increases in both to scale . But the asymmetry in their growth means intelligence will eventually be bottlenecked by data, not compute. This is easy to see if you look at almost anything other than language models. In robotics and biology, the massive data requirement leads to weak models, and both fields have enough economic incentives to leverage 1000x more compute if that led to significantly better results. But they can't, because nobody knows how to scale with compute alone without adding more data. The solution is to build new learning algorithms that work in limited data, practically infinite compute settings. This is what we are solving at Q Labs: our goal is to understand and solve generalization.
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b. 但 Minimax API 接口等企业服务则从前 9 个月的 160% 加速增长到了 278%，单季超过 1055 万美金。考虑到海外整体收入并未加速，这里可以合理推测，API 收入应该国内外企业客户都在加速调用。