The End of AI as Human Replacement? Ford Rehires “Gray Beard” Engineers After AI Quality Fails

The First BigCo Reverses Course on AI as Replacement for Tech Talent. So What Did We Learn?

Remember 10 months ago when I said that AI would become too expensive to use as labor replacement, and then a couple weeks ago it started happening?

OK, well, here we go again.

Remember eight months ago when I said that megacap companies were playing with fire by replacing experienced developers with AI?

Guess what?

Ford, the company founded by the man who may or may not have said that if he asked his customers what they wanted they would have told him “faster horses,” just re-hired not a few, not a handful, but 350 experienced “gray beard” engineers back after “artificial intelligence and automated systems failed to deliver the desired quality level.”

And that sound you’re hearing is the collective panic of thousands of tech industry leaders all at once asking the same question: “Wait, we were supposed to measure the quality? The AI sales guy assured us there would be no math.”

Man, let’s not get all “I told you so.” It’s not a good look. Instead, let’s reflect on what we’ve learned.

Lesson 1: All Tech Has Limits

Over the last couple years, Corporate Tech has been racing to lop off as much headcount as possible. And especially over the last nine months, they’ve been scapegoating AI as the primary reason for the layoffs — reducing human expense as a preventative measure to meet “the new nimble.”

This wasn’t 100 percent true. A lot of the layoffs have been about poor overhiring decisions made during the Great Labor Arms Race of the cheap-money early 2020s. But the CEOs scaped AI’s goat anyway, to maximize the results and minimize the blame. It’s what we seasoned executives call a “two-fer.”

Because see, even if AI isn’t a great one-to-one replacement for talent right now today, the thinking is, give AI a couple months, maybe a year, and this new technology that has evolved at an admittedly startling pace will soon surpass the capacity and productivity of even the most experienced talent, so we might as well clear out the “gray beards” too.

Well, remember the story that my mentor told me about the worst mistake a leader can make is looking at a chart that goes up and to the right and believing it will always go up and to the right? The chart on AI replacement capacity stopped going up and to the right.

The Lesson: New tech always comes out of the gate hot. It looks like magic and the market-chasing loss-leader economics make it seem cheap, nearly free. But all tech has limits. Don’t be the first one to hit them without a workaround in your back pocket.

Lesson 2: New Does Not Always Beat Old

Hey! I am right there with them on this one! Or I used to be, anyway.

Young Me: “The time wasted defending the status quo does far more damage than the mistakes made adopting the new solution.”

Older Wiser Me: “Yeah, but not every time. Also, my back hurts. Also, get a haircut, young me.”

I don’t know any CEO who hasn’t heard the old adage, “measure twice, cut once,” but as the 2020s rolled on, I became more and more exasperated by the lack of forethought going into the human-labor-for-AI replacement theory.

Certain CEOs, like Jack Dorsey at Block, were straight up admitting these layoffs amounted to pre-emptive strikes. Sure, tech leaders collectively said, we don’t know the exact impact of AI on the traditional workforce, but we know that tech eats jobs, so this time, we’re getting ahead of the curve.

Except this turned out to be the one major technical evolution in a couple of generations where it made sense to go against conventional tech wisdom and hang on to the experienced talent instead.

Internet? The geezers don’t get it, let them go. Mobile? The old folks keep squinting, get rid of them. SaaS? Whatever, just fire the senior citizens. Ageism? What’s that? Are you making that word up?

The Lesson: AI is a technology that, out of the box, rewards technical experience and is a threat to those workers with a lack of human, intuitive, decision-making experience.

Lesson 3: Those Who Refuse To Learn From History

I hate when critics try to use the Henry Ford story to bemoan my desire for faster horses at the expense of these shiny but scary auto-mo-biles. With 30 years of tech in my rearview, including 20 years of AI, and a couple years of consulting on it and writing guides for it, I gotta be mean to them:

“Hey. Idiot. These are faster horses.”

What we call AI today is the resultant set of decades of advancements in processing and engineering, plus the payoff of years of the best mathematical and scientific minds having the luxury of focusing on it. Computers are still computers. Chips are still chips. Code is still code.

This is all cyclical, and success requires the same kind of talent that tech has always required.

The Lesson: If coding were just the proper placement of syntax, then everyone would have been ninja coders long ago. AI makes it seem like syntax was the only gap between skilled coder and unskilled coder. But then people also thought Bitcoin was the new money and NFTs were the new art and mobile phones weren’t going to turn us into doomscrolling zombies.

Lesson 4: We Might Want to Stop Calling Them “Gray Beards”

Look, I know. I’ve got gray in my beard.

And I know they’re calling themselves gray beards.

But here’s the thing. The people who got swept aside in the name of AI-as-replacement, they’re not pirates. In fact, they’re not all engineers, they don’t all have beards, and oh, they’re not all men.

I call them babies, because they got thrown out with bathwater. I don’t know, maybe that’s more insulting. I’m not here to discuss verbal semantics, I’m here to say it’s time to buy a large assortment of “I’m Sorry” fruit bouquets,

Because Ford might be the first company to start calling back experienced technical talent, the first we know about anyway, but they are far from the last BigCo that’s going to have to dial some numbers and have some awkward conversations across the spectrum of the tech organization.

And they’re going to have to pay some inflated salaries. Yeah, I said that was coming too, I just said it in private. Now I’m saying it in public.

Big Tech leaders, you’ve got about three to nine months before these lessons hit everyone.

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