The models took on tasks cumulatively worth hundreds of thousands of dollars on Upwork, but they were only able to fix surface-level software issues, while remaining unable to actually find bugs in larger projects or find their root causes. These shoddy and half-baked "solutions" are likely familiar to anyone who's worked with AI — which is great at spitting out confident-sounding information that often falls apart on closer inspection.
Though all three LLMs were often able to operate "far faster than a human would," the paper notes, they also failed to grasp how widespread bugs were or to understand their context, "leading to solutions that are incorrect or insufficiently comprehensive."