All the talk about the low quality of code got me thinking: if humans aren’t reading the code, then the only thing that matters is correctness. Correctness may be an easier problem to solve than readability and refactorability.
Consumer level robotics, drones, and battery tech is close to a level that manual labor, delivery, and such tasks can be automated.
Maybe we should be preparing for post-scarcity. At the very least, the idea of "jobs" is going to have to transform. The 40 hour work week isn't going to make much sense in the near future.
The part of dev that you get paid to do is not writing the code. It's the part where you define what the code should do. That's where all the value in development resides. Getting AI to write the syntax is 100% upside for the tech industry.
I guess they felt the same ways when the first compilers were introduced. Or interpreters.
> the lack of available developers at a cost-effective price.
There's already developers on the market at an incredibly low price. Good luck getting code that compiles out of them, much less that is correct. I'm afraid we'll see more and more of these, since now they'll be able to cycle through completions and try until it seems to pass all tests.
I've been a dev for almost 25 years, and in my experience how much someone is paid has very little correlation to how good their code is. I've worked with outsourced developers in India, Poland, and Vietnam who are brilliant at writing clean, robust, well-designed code and earn the equivalent of low-two-digits thousands of dollars per year, as well as former FAANG engineers who earn three-digit thousands of dollars and write untested, untestable spaghetti code.
I'm afraid we'll see more and more of these, since now they'll be able to cycle through completions and try until it seems to pass all tests.
In which case the value will lie in defining the tests, and the people who do that will earn the most. That's fine.
That has not been my experience at all.
There's this myth of the genius dropout in our industry, or that FAANG is basically luck, and yet it always fails to materialize.
These tools are effectively less then a year old in production, but we're already seeing the potential for huge disruption in lots of markets based on relatively straightforward uses of the tech.
I can't wait to see what a skillful and artfully sophisticated use will be. I don't think we've even scratched the surface.
Eh. That deep learning networks is like the brain is like saying that cars are like cheetahs. Sure, they go really fast by converting some kind of fuel into kinetic energy, and they move by exerting force on the ground, but that's about it.
Brains don't have ReLU units. Brains have lots of different types of topology, not just an uniform network, and can handle some of that topology arbitrarily being shut off due to damage. Brains use global chemical changes for (otherwise) out-of-band signaling purposes. Brains don't use gradient descent. Etc...
Reproducing high level code seems like a human step that could be removed.
I suspect it's the opposite: It's rather easy to write code that looks good but isn't exactly correct.