Simon Willison's coverage of this quirky evaluation underscores a critical point for AI practitioners: the need for rigorous, systematic testing to separate anecdotal oddities from genuine model capabilities or biases. The apparent absence of 'pelicanmaxxing' suggests that, for now, the most memorable AI-generated images are likely the product of random chance amplified by social media, not hidden developer agendas. The real takeaway may be a reminder that the quest for 'explainability' in generative AI often leads us to impose narratives on statistical noise, a tendency that could distract from addressing more substantive issues like data provenance and output consistency.