If you spend any time on hardware and tech subreddits, you'll notice a pattern.
A founder posts about an AI tool built for hardware engineers: design review automation, intelligent BOM purchasing, schematic analysis. The comments are brutal. Naive. Doesn't understand the complexity. This will never work in a real fab environment.
I get the skepticism. Hardware is unforgiving. But here's the thing: human hardware engineers make mistakes too, and a bad PCB spin costs $50k and six weeks. If AI tooling can catch even a fraction of those errors earlier in the design cycle, that's not a gimmick. That's leverage.
The Software Déjà Vu
Not long ago, software engineers were saying the exact same things about AI-generated code. It hallucinates. It doesn't understand architecture. No serious engineer would ship this. Senior developers were vocal about it on the same forums, the same comment sections.
Fast forward to today, and I'm regularly hearing from those same senior devs that they haven't written a line of code by hand in six months.
The resistance didn't stop adoption. It just delayed acknowledgment of it.
Why Hardware Might Be Slower
So why might hardware be different, or at least slower? A few things stand out.
The Reddit crowd skews toward engineers at smaller shops and startups, where there's no institutional pressure to adopt new tooling. At large HW companies like your Intels, Broadcoms, and defense primes, the calculus is different. If AI can compress a design cycle by even 10%, the ROI conversation happens at the executive level whether the engineers want it to or not.
There's also a product feedback loop worth noting. As more hardware integrates AI, the engineers building that hardware will be forced to think in AI-native ways. You can't design a chip with an NPU on it and remain philosophically opposed to the tools that help you build it.
The Real Technical Barrier
But there's a structural challenge here that's genuinely different from software. MCAD and ECAD data is messy, proprietary, and doesn't lend itself to LLM interpretation the way source code does. And the generative AI models that transformed software — largely diffusion-based — don't map cleanly onto hardware design outputs. This isn't just skepticism. It's a real problem that hardware AI startups have to solve before widespread adoption is even possible.
Where the Ceiling Is
The honest question I keep coming back to is not just whether hardware will catch up to software, but how far that catchup can realistically go.
AI in software reached a point where it could own entire chunks of the creative work. It's hard to imagine the same happening in hardware. A senior HW engineer saying they haven't made a new design in months? That feels like a stretch, at least for the foreseeable future. The design intent, the physical intuition, the systems-level tradeoffs: those still live in the engineer's head.
Maybe the ceiling for AI in hardware is augmentation, not automation. And maybe that's enough to matter.
Will hardware adoption follow the same curve as software, or will it plateau somewhere short of that? Would love to hear from people on both sides.
