Press Space to continue
Curated tech posts from X on AI, startups, and product development—signal without the doomscroll, updated on weekdays.
Press Space to continue
Press Space to continue
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…