For AI to become a superhuman hardware engineer, it has to learn from the physical world, and real-world data is hard to get: - It's non-uniform and constantly changing - It's sparse: you can't measure everything all the time - It's expensive to collect - Physical processes take time, and can only be sped up so far - Some of it is qualitative: do users actually like the product? "Data limitations like these are massive drivers in the spikiness of AI capability development today. The more quantifiable and machine-readable the results of a test, the faster the models can climb the hill." Full piece from a16z's Christian Keil on why the physical world is so hard for AI to learn from: https://www.a16z.news/p/superhuman-hardware-design-is-the