A recent AI scientific research conference showcased compelling demonstrations of AI models assisting scientists as co-researchers in literature synthesis, model-based hypothesis design and lab-scale simulation reasoning. However, several presentations also highlighted failures in interpretability, incorrect causal link inference and hallucinated academic references, especially at higher model parameter densities. Stanford researchers emphasized that while AI has accelerated knowledge scaffolding, the reliability gap, falsifiability inconsistency and epistemic stability remain critical blockers for replacing human scientific reasoning. Industry, academia, and policy groups appear aligned that future breakthroughs must focus on inter-model explainability standardization, robust peer review protocols and scientific evidence traceability.