With the recent release of its GPT-6 Astra model, OpenAI explicitly pushed the claim that it is capable of designing complete circuit boards in KiCad, starting from a provided schematic and outputting a fully routed PCB that theoretically could be sent off to be manufactured. This of course raises the question whether this is just a nifty party trick that works under strictly controlled conditions like most auto-routing tools, or whether there’s more to it. In a recent [EEBench] blog post, OpenAI’s claims are put to the test.

Back in 2024, we looked at how LLMs handle circuit board design, starting with the schematic. The conclusion was that you might as well just do it all by hand. Tracking progress here, [EEBench] is an electrical engineering agent benchmark that tests how effective these so-called AI agents are at performing useful hardware engineering work. As their methodology already makes clear, creating a populated and routed PCB from a schematic is just one step of many.

Consequently, GPT-6 Astra scores 69.3% (+/- 10%) on their benchmark, roughly in the same ballpark as Claude Opus 5, albeit cheaper and faster. It should be noted that [EEBench] is run by the developers behind Atopile, which is a code-based system for creating PCBs in KiCad with a strong focus on use by such AI agents.

Despite this, the blog post makes it clear that there is still a long way to go before hardware engineers can receive their pink slips and take up another profession. After all, while vibe-coding a quick prototype PCB can be a nice shortcut, for a one-off project PCB, most boards are expected to handle all the edge cases, be fully validated by multiple independent engineers, and have passed stringent testing before committing to a production run.

(Top image: from OpenAI GPT-6 Astra KiCad video)