Asklemmy

I'm not really losing any sleep over this myself. Current approach to machine learning is really no different from a Markov chain. The model doesn't have any understanding in a meaningful sense. It just knows that certain tokens tend to follow certain other tokens, and when you have a really big token space, then it produces impressive looking results.

However, a big part of the job is understanding what the actual business requirements are, translating those to logical steps, and then code. This part of the job can't be replaced until we figure out AGI, and we're nowhere close to doing that right now.

I do think that the nature of work will change, I kind of look at it as sort of doing a pair programming session. You can focus on what the logic is doing, and the model can focus on writing the boilerplate for you.

As this tech matures, I do expect that it will result in less workers being needed to do the same amount of work, and the nature of the job will likely shift towards being closer to a business analyst where the human focuses more on the semantics rather than implementation details.

We might also see new types of languages emerge that leverage the models. For example, I can see a language that allows you to declaratively write a specification for the code, and to encode constraints such as memory usage and runtime complexity. Then the model can bang its head against the spec until it produces code that passes it. If it can run through thousands of solutions in a few minutes, it's still going to be faster than a human coming up with one.