I think what’s really interesting is that the first version of db9.ai is done by a single person by commanding AI. There must be tons of invaluable lessons learned.

> Review by a senior is one of the biggest "silver bullet" illusions managers suffer from

Especially in a big co like Amazon, most senior engineers are box drawers, meeting goers, gatekeepers, vision setters, org lubricants, VP's trustees, glorified product managers, and etc. They don't necessarily know more context than the more junior engineers, and they most likely will review slowly while uncovering fewer issues.

I was not talking about theoretical foundations like Analysis or measure theory, but just basics in college-level math class. There can be other examples. The point is that many people didn’t have intuitive understanding of what they use everyday — in a way they are like AI, only slower and know less than AI

> It has no real world model, no ability to learn in any but superficial ways

I also think so, and in the meantime I have to admit a lot of people don't learn deeply either. Take math for example, how many STEM students from elite universities truly understood the definition of limit, let alone calculus beyond simple calculation? Or how many data scientists can really intuitively understand Bayesian statistics? Yet millions of them were doing their job in a kinda fine way with the help of the stackexchange family and now with the help of AI.

> when does AI flip from "powerful automation with humans propping it up" to autonomous output?

Another scenario of economics is that AI does not not necessarily output autonomously, but does output so much so fast that companies will require fewer workers, as the economy does not scale as fast to consume the additional output or to demand more labor for the added efficiency.