Can AI Design Circuit Boards Yet? Engineers Say Not Even Close

EEBench Puts LLMs to the Test Against Real-World Hardware Engineering

EEBench has published a hands-on evaluation examining whether today's large language models can move beyond conversation and actually produce functional circuit board designs. The findings suggest that while models have measurably improved over the past year—handling simple, well-documented circuits with reasonable competence—they collapse when confronted with genuine complexity, datasheet and errata comprehension, and the spatial reasoning required for physical PCB layout. This inquiry is timely: as coding assistants dominate the software world, there is mounting commercial pressure to extend the same 'agentic' promise into hardware and electrical engineering domains. The technological background here is unforgiving, because circuit design fuses symbolic reasoning, precise numeric constraints, and multi-dimensional spatial optimization—a combination that stresses the exact weaknesses of autoregressive text models. EEBench's verdict frames AI as a capable co-pilot for review and organization, but a wholly unreliable autonomous designer.