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.
Instead of training models on scarce circuit board data, Diode Computers built a compiler that makes hardware design look like a Python program. This allows powerful language models, which are expert coders, to design physical hardware by leveraging their existing capabilities, bypassing the data bottleneck.
The biggest obstacle to fully AI-driven hardware design is the absence of a large, public training dataset. Unlike software code, circuit board designs are proprietary and siloed within companies like Apple and SpaceX. Until this data is generated or aggregated, model capability will be constrained, regardless of architectural breakthroughs.
The construction industry's fragmented, risk-averse incentive structure stifles technology adoption. To overcome this, AI firm Unlimited Industries vertically integrates design and engineering, owning a larger part of the value chain. This allows them to offer a complete solution rather than trying to sell a point product into a broken system.
For physical design, simulation shouldn't just be a final verification step. Instead, it should be a tool used during model training to build the AI's intuition or "taste." This allows the model to generate high-quality designs quickly at inference time, mirroring how expert human engineers develop their skills.
For complex physical-world AI, deep domain expertise is paramount. Construction AI firm Unlimited Industries prioritizes hiring multidisciplinary engineers (civil, mechanical) and training them on AI tools. They find this more effective than teaching AI experts the intricate, nuanced physics and regulations of a field like construction.
To succeed in traditional industries, sell what customers already buy (e.g., a finished circuit board), not the novel tool used to make it. Diode Computers positions its AI as an internal implementation detail that provides speed and cost advantages, fitting seamlessly into existing procurement workflows without forcing customers to adopt new software.
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.