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.
"It would be nice for people to still carry on doing [manufacturing], rather than get just some robot, like they do in Japan, just to assemble it all, because it's the human touch," he said.
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Bettenhausen建议投资者提前布局，以应对2027年的上行周期。他指出，PulteGroup在未来两年有充足的空间减少利润率压缩的激励措施，且其可选地块储备充裕，可能成为回购股票的积极公司。