  The ability of Generative AI (GAI) technology to automatically check,
synthesize and modify software engineering artifacts promises to revolutionize
all aspects of software engineering. Using GAI for software engineering tasks
is consequently one of the most rapidly expanding fields of software
engineering research, with dozens of LLM-based code models having been
published since 2021. However, the overwhelming majority of existing code
models share a major weakness - they are exclusively trained on the syntactic
facet of software, significantly lowering their trustworthiness in tasks
dependent on software semantics. To address this problem, a new class of
"Morescient" GAI is needed that is "aware" of (i.e., trained on) both the
semantic and static facets of software. This, in turn, will require a new
generation of software observation platforms capable of generating ultra-large
quantities of execution observations in a structured and readily analyzable
way. In this paper, we present a vision for how such "Morescient" GAI models
can be engineered, evolved and disseminated according to the principles of open
science.
