According to Ethan Mollick (@emollick) on X, viral content that floods the internet with near-duplicate butternut squash images can lead to future image generators becoming “squashmaxxed,” overfitting to squash visuals and underperforming elsewhere. As reported by academic literature on dataset contamination and model collapse, generative models trained on web-scale data risk amplifying overrepresented motifs, degrading diversity and generalization (according to Stanford HAI and arXiv preprints on model autophagy disorder). According to platform-facing AI practitioners cited by The Verge and MIT Technology Review, this bias can raise inference costs for businesses through more retries and prompt engineering, depress creative variety for media workflows, and distort e-commerce imagery ranking. According to industry guidance from LAION and Common Crawl maintainers, mitigation strategies include source de-duplication, distribution-aware sampling, and classifier-based reweighting to keep category balance during training.