AI-assisted coding creates a Ghost Intent problem where the software works perfectly but no human knows why it was written that way.
April 23, 2026
Original Paper
Ghost Intent: An Effect of Traceability Collapse in GenAI-Assisted SDLCs
SSRN · 6348599
The Takeaway
The original engineering rationale for code disappears when developers use generative AI to write large chunks of software. This leads to a debug cost inversion where it takes longer to understand and fix AI code than it would have taken to write it from scratch. The immediate speed gains of AI are eventually wiped out by the massive cost of maintaining black box systems. While the code passes tests, the lack of human intent makes it nearly impossible to upgrade or refactor safely. Engineering teams must find a way to capture the why before they lose control of their own products.
From the abstract
As Generative AI (GenAI) shifts the human role in software development from creative authorship to review-centric oversight, the foundational assumptions of software traceability are undergoing a systemic failure. This paper defines Ghost Intent-the experiential manifestation of Authorless Traceability Collapse (ATC)-a structural condition where engineering rationale evaporates postgeneration, leaving only behavioral remnants embedded in artifacts. We argue that Ghost Intent invalidates long-sta