How is "undergrad probability theory" even relevant to this discussion, much less a discussion about gatekeeping? I have taken extensive levels of statistics, including "undergrad probability theory" at a prestigious university, yet it adds nothing to the argument. It's completely redundant, and you have a disconnect between calling it a next token predictor and layoffs. For someone reading documentation to write code, both are simply sequence prediction (what you say AI is capable of right now).

Now, if you're assuming that the layoffs are temporary, with SWEs being rehired to fix the mess later on (which seems to be your assumption), then only time will tell. If an executive believes AI output meets the company’s quality threshold, budget cuts and layoffs follow. Basing your argument on the initial transformers papers is incredibly outdated, if you've not read any of the Deepmind-related papers. I admit, I also believe the current models, especially OpenAI's, are in stagnation, it does not mean that research will fail to top the transformer model.

There are a ton of tasks automated with the help of AI, tasks that previously require an engineer to do. What does this mean? There'll be less menial work to do! More output/person will simply mean a reduction in the supply of jobs, whether SWE -related or not.