In my workplace, we actually did some comparison testing using Claude to identify bugs (and suggest fixes).
We maintain a large and ageing code base that is a mixture of well-written and well-structured code, and what we might euphemistically refer to as "legacy" code which is poorly documented and tightly coupled.
Our findings were that, with the well-written (i.e. relatively modern) code, the "AI" was no quicker than the human (muggins here in this case) at identifying where the cause of a bug lay, and its suggested fix were sensible approximately half of the time. In the older code-base, when trying to identify bugs that had, in one case, laid hidden for over two decades, it was okay at finding the bit of code with the problem (maybe marginally faster), but its suggested fix was woefully wrong.
So really, its skill could be summed up as the ability to read and follow a call stack quickly. As any professional will tell you, that's the easy bit.
What is actually slightly useful is the Copilot PR review in Github, which sometimes spots silly mistakes, and is helpful to fix typos in comments. It can help promote good code hygiene practices. It's hardly a vital and indispensable tool though, and we still require human reviewers on our PRs.