A good friend of mine works in hardware engineering—the kind of work that deals with chipsets, firmware, and the delicate black magic of embedded systems. For years, he’s lived in a world of cryptic error codes, obscure manuals, and field devices that sometimes decide to die for no apparent reason. Recently, over a late-night conversation, he told me that ChatGPT has become one of the most useful tools in his toolkit.

He wasn’t talking about software or automation scripts. He meant hardware—the real-world stuff. Boards, drivers, and firmware that used to require hours of detective work to debug. Now, when a diagnostic log throws up a string of gibberish or a cryptic error code, he copies it straight into ChatGPT. Within seconds, he gets the context, possible causes, and even a translation of what the system is trying to say. Sometimes it’s a missing dependency. Sometimes it’s an undocumented timing issue. The AI doesn’t replace his expertise—it amplifies it.

He shared one example. A prototype failed during a firmware update. The screen went dark, and the system stopped responding. Normally, that’s a lost day. But he fed the bootloader log into ChatGPT and got an explanation: the watchdog timer was interfering during flash. The fix was to disable a specific register sequence. He did, and the board came back to life in minutes. The thing that would have taken hours of searching forums and documentation was solved by pattern recognition and instant context.

That story stuck with me. Because it isn’t just about convenience—it’s about a shift in how humans and machines communicate. For decades, we’ve written code to talk to machines. Now, with AI acting as translator, the machines can talk back in our own language. The debugging process becomes more like conversation than interrogation.

My friend said morale in his department has gone up. Not because anyone’s afraid of being replaced, but because the friction is fading. The repetitive pain points are disappearing. Engineers aren’t drowning in log files; they’re solving real problems faster. And there’s something deeply human about that—the relief of not fighting your tools anymore.

We keep hearing stories about AI taking jobs or reshaping industries. And yes, that’s happening in some fields. But in others, like this one, AI isn’t taking anything away. It’s giving time back. It’s removing bottlenecks. It’s helping people perform at their best by letting them skip the drudgery.

The real impact of AI in engineering may not come from the headlines or big corporate transformations. It’ll come from nights like that—an exhausted engineer, a bricked device, and an AI that explains the problem clearly enough to make the impossible fixable.

It’s easy to dismiss stories like these as small victories. But they add up. Every recovered hour of work is a quiet revolution. Every time a person learns something faster than before, a system improves. We’re not building smarter machines just for efficiency. We’re building translators that finally let us understand what the machines have been trying to tell us all along.

That, to me, is worth celebrating.

Jorge Luis de la Torre. I put the C in GRC. I bring compliance to the table.