If you'd built a "tool" that stupid, why would you advertise the fact? (svpow.com)

A researcher received a promotional email from a scholarly site claiming an AI had “turned” their 2013 paper on sauropod neural spine bifurcation into an “easy to understand analogy.” The purported analogy (spine bifurcation likened to river-delta branching) was nonsensical for the original ontogenetic and phylogenetic analysis, and the full AI-generated text was behind a paid upgrade. The author called out the transformation as both misleading and insulting, using the episode to illustrate how automated, surface-level rewrites can distort scientific meaning and be monetized. This incident highlights a broader technical and ethical problem with current generative systems: LLMs can produce fluent but semantically shallow or hallucinatory outputs that misrepresent domain-specific content. For the AI/ML community it underlines the need for provenance, human-in-the-loop validation, domain-aware models, and fidelity metrics when automating scholarly transformations. It also raises product-design questions about transparency and commercialization—advertising AI “enhancements” that degrade scientific accuracy damages trust. While LLMs are useful for many developer tasks, this example is a cautionary reminder that without stricter guardrails and evaluation, automated summarization or analogy-building can do more harm than good.