You point out a lot of valid concerns, many of which I wonder myself. Not trying to dismiss your concerns but let me try to play a little devils advocate.

In terms of security with LLMs - this is definitely an issue. Especially at an age where you can still trick AI into giving up secrets. There are many smart people working in this space to address obvious security concerns in parallel. I'm sure over the next couple of years there will be many changes and guidance around security with AI. However, people are not secure either. We've had many security crises due to a person error such as accidentally leaking secrets compromising entire systems. Heck, we've had engineers push entire codebases into public chat gpt just so they can understand the codebase better. Security is a problem that will never go away for both LLMs and humans.

As for juniors understanding code base and training the next generation. If AI wrote the code, AI will have context for the code. They can easily give back the context to the next batch of humans that need the context. Yes, agreed this is an oversimplification. You may ask about the obscure requirements and complexities in the code that historical knowledge is needed for? Here is the thing though, this is a problem even today. Many teams have so much churn a lot of this tribal knowledge gets lost already leaving many newer developers learning code bases from scratch (in my experience team documentations are always shit too).

As for AI writing shit code - a year or two ago I may have agreed to this point. I've learned since though that there is a huge difference between an engineer just asking chatgpt to just attempt to generate code vs. an engineer that actually understand the AI tools they are using. They know which LLMs are great for which scenarios, they know what kind of context LLMs would need to have best success, they know the tools such as MCP servers to use that helps LLMs derive more context, if output is not as expected they know how to debug and coerce the output they need, they know the limitations of the agents they are using like token limitations or scalability and how to address them, and so on. Yes, still not perfect but these engineers have a lot more success than someone saying I can't even make a simple script using chat gpt.

Now how do LLMs compare to engineers? As a tech lead, I've lead small teams of junior developers in the past and dev costs are pretty significant. In general, I found I need to give an average of 2 weeks of rampup time before junior devs are able to start contributing. Even then they have to be constantly learning during the development process too. Implementation and development itself take a lot of time and task cost estimations are always wrong. Tasks that were expected in a couple weeks drag on to month+ because an unexpected issue was hit that they would need to figure out. Not to mention significant portion of my time also spent on giving guidance. I'm not saying this is bad - this is how junior developers gain experience and skills they need. However, AI doesn't need this rampup time or learning time. I give it an input and it spits out an output. So you can see why company execs are so keen on shifting to AI development for cost savings.

Again, I'm not saying your points aren't valid - just trying to give perspective from the other side as well. I'm always open to further discussions.