The physical AI industry is no longer in the fundamental research stage. It has entered a crucial "advanced engineering" phase between R&D and mass production. The focus is now on solving the subcomponent and reliability problems required to productionize existing technologies.

AI in engineering is not a "black box" that outputs a single perfect design. It generates a wide space of viable options. The core role of the human engineer remains crucial: to navigate the complex trade-offs between performance, cost, aesthetics, and other business-level constraints.

Designing a chip is not a monolithic problem that a single AI model like an LLM can solve. It requires a hybrid approach. While LLMs excel at language and code-related stages, other components like physical layout are large-scale optimization problems best solved by specialized graph-based reinforcement learning agents.

Designers should consider the human operators and machines that will assemble their product. By making choices that simplify manufacturing—providing clear instructions and avoiding known difficulties—the process becomes smoother and more efficient, akin to 'riding a bike downhill.'

To ensure a smooth transition from development to production, an operations or manufacturing SME must be part of the design process from the start. Otherwise, products are developed without manufacturability in mind, leading to expensive, reactive fixes and subjective quality control during scale-up.

Unconventional AI operates as a "practical research lab" by explicitly deferring manufacturing constraints during initial innovation. The focus is purely on establishing "existence proofs" for new ideas, preventing premature optimization from killing potentially transformative but difficult-to-build concepts.

Unlike text-based AI that relies on descriptive prompts, some advanced design tools for physical components work in reverse. The user defines 'no-go' zones and constraints, and the AI then generates numerous optimized design possibilities within those boundaries.

Full automation in electronics manufacturing doesn't require robotics breakthroughs. The existing robots are sufficient. The challenge is designing circuit boards that are 100% compatible with current automation, eliminating the 20% of manual labor caused by non-standard components. AI can create these constrained, manufacturable designs.

The physical separation between US designers and overseas factories has weakened the crucial skill of designing for manufacturability (DFM). AI can rebuild this atrophied muscle by programmatically enforcing manufacturing constraints during the design phase. An AI agent can tirelessly iterate a design until it meets hundreds of DFM checks, a task a human designer might skip.