The problem is that you still need a human in the loop to determine if you're in the 13% success bucket, or 87% failure bucket, and the time it takes to make that determination is still a significant fraction of just solving the problem.
So the actual value here is not "13% of all issues fixed for the cost of compute," but more like "a discount on human time for 13% of the issues". But you also have to factor in the time taken on the 87% of issues where leading you down a wrong path can be adding time versus human only. It's not clear to me how it all shakes out, and would require large-sample experiments with humans to determine. I would bet the final margins are small though.
You raise a good point - AI + human review might end up being more time than just a human doing everything. I can see a certain subset of issues could be simple enough to done by AI and a quick review - like changing a button color or fixing clearly defined bugs. Time will tell how much work gets shifted over to AI + human review, but I'm betting on most of it.
So the actual value here is not "13% of all issues fixed for the cost of compute," but more like "a discount on human time for 13% of the issues". But you also have to factor in the time taken on the 87% of issues where leading you down a wrong path can be adding time versus human only. It's not clear to me how it all shakes out, and would require large-sample experiments with humans to determine. I would bet the final margins are small though.