  Recently, the U.S. Department of Energy (DOE), Office of Science, Biological
and Environmental Research (BER), and Advanced Scientific Computing Research
(ASCR) programs organized and held the Artificial Intelligence for Earth System
Predictability (AI4ESP) workshop series. From this workshop, a critical
conclusion that the DOE BER and ASCR community came to is the requirement to
develop a new paradigm for Earth system predictability focused on enabling
artificial intelligence (AI) across the field, lab, modeling, and analysis
activities, called ModEx. The BER's `Model-Experimentation', ModEx, is an
iterative approach that enables process models to generate hypotheses. The
developed hypotheses inform field and laboratory efforts to collect measurement
and observation data, which are subsequently used to parameterize, drive, and
test model (e.g., process-based) predictions. A total of 17 technical sessions
were held in this AI4ESP workshop series. This paper discusses the topic of the
`AI Architectures and Co-design' session and associated outcomes. The AI
Architectures and Co-design session included two invited talks, two plenary
discussion panels, and three breakout rooms that covered specific topics,
including: (1) DOE HPC Systems, (2) Cloud HPC Systems, and (3) Edge computing
and Internet of Things (IoT). We also provide forward-looking ideas and
perspectives on potential research in this co-design area that can be achieved
by synergies with the other 16 session topics. These ideas include topics such
as: (1) reimagining co-design, (2) data acquisition to distribution, (3)
heterogeneous HPC solutions for integration of AI/ML and other data analytics
like uncertainty quantification with earth system modeling and simulation, and
(4) AI-enabled sensor integration into earth system measurements and
observations. Such perspectives are a distinguishing aspect of this paper.
