I assume this is just speculation on your part? Do you have any reason to make that claim? I personally know multiple people doing this full time at large tech companies.
I can give you some examples of serious safety oriented criticism of large language models to contrast with what plays out in the press and amongst "ethicists".
It's well understood that one can generate so-called "adversarial examples" for image classifiers. These adversarial examples can be chosen so that to a human they look like thing A, but the model classifies them as thing B with high probability. Methods of finding these adversaries are well understood. Methods of preventing them from being problematic are less developed but rapidly advancing.
For language models, the situation is much worse. I don't know of any effective way to prevent a large language model from being searched for adversarial inputs, trivially. That is, an attacker could find inputs from large curated input spaces that cause the model to output a specific, desired sequence. For example, an attacker with access to the model weights could probably find an innocuous looking input that causes the model to output "kill yourself".
Is this a risk that AI researchers are aware of? Yes, of course. But the difference between AI researchers and "ethicists" is that AI researchers understand the implications of the risk and will work on mitigations. "Ethicists" do not care about mitigating risk, and they don't care that the people who build the models already understand them and are comfortable with them.