The much deeper problem in science is not these obvious errors. It is the theories and claims that are wrong - built on experimental artifacts, logical fallacies, or wildly overstated interpretations - yet they remain in the conversation for years or decades because they were published in prestigious journals or asserted by influential authorities and never seriously challenged. Those studies become the foundations for entire research ecosystems. Thousands of grants get funded. Careers are built around them, including senior administrative roles. Universities spend millions recruiting people based on those narratives. This is where the reproducibility crisis lies - not in easily detected image issues, but in influential ideas that continue shaping the scientific system long after their foundations should have been questioned. These kinds of gotcha moment retractions over image issues are among the easiest problems to detect and correct. They typically appear in smaller journals with limited editorial and reviewing resources, where people already suspect much of the work resembles papermill output and treat it accordingly. AI can now detect most of these issues almost instantly.