I see two possible ways forward, not necessarily mutually exclusive.
* Make AI work with languages with reduced complexity. A lot of language complexity exists to make it (surprise!) easier for humans to deal with the language. For example, generics in a language like Java make it easier because they allow the programmer to write many similar programs just once. And because people are prone to make mistakes when working on repetitive code, generics are useful for people. AI, on the other hand, doesn't get tired. It doesn't care if it needs to implement the same template for a different type a dozen more times. So, if we give AI a language without generics, it will be just as happy, but it will eliminate a whole class of potential errors related to eg. up/down-casting of generic collections. Similarly, we could remove "dangerous" tools like infinite loops, or outlaw some system calls, producing less generic / more purpose-built languages (like PostScript!) Perhaps projects like OMeta could see some revival in this light?
* Replace, as much as possible, the work of testers with proofs, but not AI-generated proofs! The formal specification (which is another way to look at the proof, at least its statement) is where the human being needs to provide input to AI. If it's not possible to read the whole generated code, we need a tool more reliable than a test to convince ourselves that the code does what we want. Formal verification, or, even better, tests generated dynamically from formal specification (there's no need for AI to do that, this can be an entirely deterministic process) should be the way for the human to stay in the loop.