  In this work, the case is made for a wholistic top-down re-envisioning of the
system stack from the programming language level down through the system
architecture to bridge this complexity gap. The key goal of our design is to
address the critical need for the programmer to articulate solutions with
higher level abstractions at the problem level while having the runtime system
stack subsume and hide a broad scope of diffuse sub-applications and
inter-machine resources. This work also presents the design of a
production-grade realization of such a system stack architecture called Jaseci,
and corresponding programming language Jac. Jac and Jaseci has been released as
open source and has been leveraged by real product teams to accelerate
developing and deploying sophisticated AI products and other applications at
scale. Jac has been utilized in commercial production environments to
accelerate AI development timelines by ~10x, with the Jaseci runtime automating
the decisions and optimizations typically falling in the scope of manual
engineering roles on a team such as what should and should not be a
microservice and changing those dynamically.
