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