This is what a lot of skeptics fail to appreciate imo: That yes the current generation of models are not threatening, and on their own they probably never will be. But there will likely come a point where they can be used to recursively self-improve, and that can potentially lead to very dangerous scenarios.
Even if there was a way to gain efficiency through self modification rather than losing it, it would likely hit a plateau of diminishing returns, like most things in the world do.
Microsoft has published a few papers about their research in using LLMs to train new LLM models. They have used training data consisting of essentially recorded chatGPT interactions which consist of the system prompt, user request and model response[1] to successfully train a smaller model that outperforms Vicuna-13B. The other paper[2] demonstrates using a combination of curated web data and machine generated textbooks and exercises (using GPT-3.5) and they produced a small model (1.3B) that outperforms several larger models on programming tasks.
The point here is that the simple presence of machine generated content in the dataset is not an immediate detriment to the model being trained on it. It all hinges on the quality of the data regardless of whether it is of human or machine origin. Think about it, if the information is correct then there is no difference whether it came from a human or a machine. Machine generated data is not secretly cursed or other such nonsense. It's either correct or incorrect, nothing more.
[0] https://arxiv.org/pdf/2305.17493v2.pdf
And maybe you're fine with betting your life on that, but the people who are more-hesitant on this are not crazy.