Series
World Models
A deep dive into the systems engineering challenges of Physical AI. This series covers the evolution from text-based LLMs to multimodal systems that intuitively understand the physics of our reality.
We cover the Data Ingestion Wall, deterministic networking, edge-vs-cloud tradeoffs, and the data storage architectures required for continuous learning in robotics and autonomous systems.
Part 1
Part 3
Part 3: The Compute Split - Edge vs Cloud in Physical AI
Physical AI cannot rely on cloud latency for real-time control. Explore how edge inference and cloud simulation divide the compute, data, and learning workloads.
Read Part 3 →Part 4
Part 4: Deterministic Networks for Physical AI
Why best-effort networking is not enough for Physical AI, and how Deterministic Ethernet (TSN) solves the temporal synchronization problem.
Read Part 4 →Part 5
Part 5: The Memory Wall — Storing Reality for World Models
How to curate, store, and process continuous multimodal reality streams for World Model learning and simulation.
Read Part 5 →