Physical AI at the Edge: Building the Full Stack for Real-World Deployment
For years, AI progress was measured by model benchmark scores. The real test is different: does it work when deployed in a vehicle, a factory, a drone, or a cell tower — under real constraints, without a cloud fallback?
That question exposes a gap most organizations don’t see until something fails in production. The models are ready. The stack underneath them often isn’t.
This white paper examines what it actually takes to deploy AI in the physical world:
- Why this transition is a structural shift in computing — and why software alone won’t close it
- The five domains where the hard problems are concentrated: autonomous vehicles, unmanned aerial systems, computer vision at scale, semiconductor software stacks, and telecom edge infrastructure
- Why organizations staffing these capabilities in silos keep rebuilding the same expertise — and paying for it in production
- What simultaneous competency across compilers, embedded systems, ML inference, and safety certification looks like — and why it’s rare
- Why integrated, cross-domain capability is the only practical foundation for programs deploying AI into the physical world
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