GPT-6 Astra, PCB layout in KiCad: a 15-second demo that engineers can't verify yet
OpenAI's GPT-6 Astra demo shows PCB layout in KiCad from a schematic. EEBench experts weigh what's credible, what's unproven, and what real evals would add.
Vlad Makarovreviewed and published
When OpenAI published GPT-6 Astra on September 3, the launch post led with a 15-second clip of the model laying out a printed circuit board in KiCad — placing components and routing copper connections from a schematic. OpenAI chose a PCB demo for a flagship moment; the internet chose a verdict within hours. An r/singularity post titled "GPT-6 Astra is actually nuts for electrical engineering" drew roughly 890 points on day one, while the engineers who grade AI hardware answered: impressive, unproven, worth benchmarking.
What the 15 seconds actually show
OpenAI describes the clip as "a 15-second condensed playback of GPT-6 Astra performing printed circuit board (PCB) layout in KiCad, turning an electronic schematic into a manufacturable PCB by placing components and routing copper connections." The embedded video is labeled "BLDC," so the board is a brushless-motor controller. The demo runs under the "world's best computer use model" section, next to a Blender-to-Unreal walkthrough and a playable game — agentic computer use, not a benchmark row. OpenAI's rationale: PCB layout "is a manual task and common source of latency in the electronics design process."
The timing matters: the clip predates general availability — Astra went first to approved Daybreak cyber-defense organizations, with the rest of the rollout "over the coming days" (spec sheet and rollout). For now the electronics claim rests on the footage itself.
EEBench: impressed, and already measuring
The expert counterweight came from EEBench, which runs simulation-backed evals of AI circuit design atop its atopile hardware-description language. "We got pretty excited yesterday," its blog said of the launch-page demo — then it got specific. Models know more than their GUI output shows: "current models know much more about electronics than their output in conventional design tools tends to show," because they absorbed textbooks, datasheets, application notes, and code. The bottleneck is the interface: an agent driving a graphical CAD tool burns context on "coordinates, menus and application state."
That is why EEBench grades in atopile, where the circuit is declarative code an agent can edit, simulate, and re-run. One public task asks for a capacitor bank specified as "22uF +/- 20%" at "10V..25V," X5R dielectric, 0805 package; another simulates a residential energy meter that must hold its processor rail above 3.0 V for 20 ms when mains power drops, with real ceramic parts that derate under bias and cost that matters. Grading is deterministic: the harness rebuilds the design, runs SPICE at worst-case tolerance corners, and checks voltages, recovery, cost.
| OpenAI's demo footage | EEBench V1 | |
|---|---|---|
| Task | Schematic to PCB layout in KiCad | Analog and digital circuit design in code |
| Toolchain | KiCad GUI, driven by Astra | atopile with SPICE harness |
| Evidence | 15-second condensed clip (OpenAI's own footage) | Public leaderboard, 13 tasks |
| Independent eval | None public yet | Runs published by EEBench, Sept 1 |
The engineers who do this for a living squint
The r/singularity post was an image thread — a screenshot of the demo — whose author said he is "genuinely kind of blown away by this," that Astra is "an AI that can start doing engineering stuff," and that the chip-design potential "is what really has me interested." The title spread to r/ChatGPT and r/generativeAI, and the clip reached r/ElectricalEngineering as a crosspost titled "Thoughts on OpenAI's claim for circuit design?" — aimed at engineers who do layout by hand.
The replies did not all share the awe. A commenter who ran "the full component-to-layout pipeline" in KiCad reported the models were "quite limited" at meaningful Altium layout. Another picked at the rendered board: "From a glance there's silkscreen being routed to a bunch of pads which is just nonsensical." A third noted routing is deterministic-algorithm work an LLM can accelerate, not reinvent. The sharpest recurring doubt targets GUI operation — "AI cannot interact with GUI interface at all. It cannot fine tune the position of things, and there is a lot to fine tune in PCB" — the exact layer EEBench removes by testing code, not clicks.
Why hardware is the frontier, and what would settle it
Electrical engineering has been a stubborn weak spot for language models for the same reason it is now a frontier: the work is multimodal (schematics, datasheets, board imagery), tool-dependent, long-horizon, and governed by trade-offs between performance, cost, and supply that no textbook exercise captures. OpenAI betting a launch demo on it — and xAI publishing EEBench in the Grok 4.6 model card under "engineering acceleration" — is the clearest sign that labs treat electronics as a category worth claiming.
EEBench concedes its V1 benchmark grades design through simulation, not the physical finish line: it "does not yet tell us whether a model can lay out, manufacture and bring up a complete product." On the scope it does grade, Astra has not run: the September 1 leaderboard tops out at Claude Opus 5's 61.6% and Grok 4.6's 57.1%, OpenAI's own prior models trail at 42.3% (GPT-5.5) and 39.4% (GPT-5.6 Sol). "We do not have a GPT-6 Astra result yet," it wrote — "we would really like to find out." Its bottom line cuts both ways: "For a useful and growing set of circuit problems, we think the answer is already yes," but "we still would not ask it to design a pacemaker and blindly install the result." The group discloses that EEBench is built and funded by the atopile team, which pays for the public runs and does not sell scores.
What would settle it: general availability, so EEBench or any independent lab can put Astra through the 13 tasks, and a third party re-running the KiCad workflow end-to-end. If Astra scores where its predecessors sat — 39-42% — the demo was a trailer; near the top of the leaderboard, PCB layout stops being a launch clip and becomes a repeatable agent use-case. Until then, EEBench's framing holds: some AI can already design circuit boards, but the 15-second version of that claim is OpenAI's word, and the measured version has yet to be scheduled. The AGI-era framing and critical-cyber rollout are in our launch-day coverage; the model's page shows no parameter count — and no independent electronics score either.
