You can’t make this stuff up!
Ford CEO Jim Farley last October made headlines prognosticating that “artificial intelligence is going to replace literally half of all white-collar workers in the U.S.” He opined that “AI will leave a lot of white-collar people behind.”
Given that Farley’s mistaken embrace of electric vehicles ultimately resulted in $19.5 billion in write offs, one might expect the media wouldn’t rush to embrace and publicize his dire AI warnings. But business journalists today, particularly corporate media automotive writers, view themselves as stenographers and treat CEO wisdom as sacred scripture.
Yet while Farley was making those sweeping prognostications to the public, Ford had already come to the realization that firing experienced “gray beard” quality assurance and mechanical engineers was a colossal mistake.
Bloomberg reported today that over the last three years Ford has hired 350 veteran engineers, many of them former employees and others from suppliers, to help address seemingly intractable quality woes that have cost the automaker billions. Turns out, AI couldn’t do their jobs after all.
“Artificial intelligence is a fantastic tool, but it’s only as good as the information you use to train it,” Charles Poon, Ford’s vice president of vehicle hardware engineering, told reporters. “Over prior years, we didn’t pay as much attention as we should have to the experience of our most knowledgeable engineers that have been with us through many product cycles.”
Added Poon: “Mistakenly we thought that by just introducing artificial intelligence and ingesting the design requirements that we had, that that would produce a high-quality product,” Poon said. But “we recognized that for us to enhance some of our automation and machine learning and artificial intelligence tools we needed to ensure that they were trained by the most experienced individuals.”
One might have expected Ford had already learned the perils of firing the company’s most experienced engineers.
Years ago, the company was successfully sued for age discrimination. In an attempt to slash costs and impress Wall Street, Ford targeted for elimination salaried workers with flawless performance records who had been promoted, rewarded and cited for excellence but were then deemed expendable because of their age and proximity to key retirement milestones, according to a federal age discrimination lawsuit filed by eight Ford employees.
I’ve read speculation over the years that Ford’s record recall problems stemmed from the loss of its most experienced engineers.
But let’s not just pick on Farley, who received $27.5 million in compensation last year. There’s a mounting gallery of obscenely paid CEOs who went gaga over AI and wound up with AI egg on their faces. Some discovered AI couldn’t replace experienced employees. Others discovered they weren’t nearly as good at managing AI as they imagined. Dare I say it, they have been frequent targets of this blog.
That would include Starbucks’ Brian Niccol, whose leadership brilliance is such that he can manage the Seattle-based company from a luxe remote office in Southern California with a $14,000 espresso machine.
Reuters recently reported that Starbucks terminated an AI program workers used to automate certain inventory counts nine months after deploying it across its North American stores. The tool was part of Niccol’s efforts to fix the coffee chain’s persistent product shortages that he blamed for hurting sales.
The app frequently miscounted and mislabeled items, confusing similar milk types or missing them altogether.
It’s telling that it took Starbucks nine months to figure out its AI solution wasn’t working. I’d wager a slew of on-the-ground baristas promptly identified the problem, but perhaps they didn’t know how to reach Niccol on his frequent Gulfstream travels. Niccol last year received $31 million in compensation, which includes use of the company jet for personal use.
Let’s not forget Dara Khosrowshahi’s Uber, another frequent target of this blog.
Uber reportedly exhausted its entire 2026 artificial intelligence budget by April, four months into the calendar year, after Anthropic’s Claude Code spread across roughly 5,000 engineers faster than the company’s finance models had anticipated.
One possible reason: Uber ranked its engineers on their AI usage. Unfortunately for Uber’s Chief Technology Officer, Praveen Neppalli Naga, Claude’s tech is priced like a text message plan from 2002, charged by the unit, and Uber’s tech management seemingly didn’t quite understand the billing structure. Naga himself managed to blow $1,200 in a single two-hour demo.
Khosrowshahi last year received $36 million in compensation for his supposedly visionary leadership, which included abandoning autonomous taxi development, an area where Uber under founder Travis Kalanick was an early pioneer.
Then there is Meta and multibillionaire Mark Zuckerberg, the company’s founder and weasel-in-chief, who last month laid off 10% of his workforce just months after committing up to $135 billion toward AI development this year.
Meta was recently forced to pause an internal AI training program after sensitive data became accessible across the company. A screenshot obtained by Business Insider showed the leak exposed employees’ private conversations, performance data, and transcriptions.
The incident was classified as a SEV 2 on a scale of 0 to 5, with 0 being the most severe.
The leak was the latest in a string of recent security incidents for Meta. Last month, a flaw in its AI chatbot allowed people to hijack multiple Instagram accounts, while a rogue AI agent also reportedly caused a severe incident in March.
Of course, I cherry-picked AI debacles involving some of my favorite CEO targets. They are hardly alone.
In fact, companies firing and then rehiring employees because management was out of its depth with AI have become so commonplace there’s even a name for it: the AI Boomerang.
Forrester Research reported last year that 55% of employers regretted their decision to lay off staff because of artificial intelligence. A Gartner prediction published earlier this year claims that 50% of companies replacing customer service or operational employees with AI will be forced to restaff those roles under different titles by next year.
According to research from the consulting firm Robert Half, reviewed and cited by Fast Company, nearly a third (32%) of hiring managers say their organizations eliminated a role or let someone go primarily because of productivity gains from AI or automation, only later to rehire for that exact role.
One might assume CEOs looking for guidance on AI integration would turn to Accenture, the global IT juggernaut whose CEO, Julie Sweet, presumably possesses superior insight into AI implementation. Sweet embraced AI so enthusiastically that she pinned Accenture’s future growth on “Generative AI Transformation,” restructuring the company’s legacy businesses into a single “Reinvention Services” model.
Sweet isn’t looking so AI-savvy these days. Accenture last week reported a 2% decline in new bookings and revenue that fell short of expectations. Shares plunged nearly 20% and have shed more than 50% of their value over the past year.
Some companies are so out of their depth when it comes to understanding and integrating AI that Gartner predicts 40% of agentic AI projects will fail by 2027 because of cost, governance issues, or inaccuracies.
Apologies for fearmongering, but Gartner also warns that by 2028, misconfigured AI will shut down national critical infrastructure in a G20 nation.
If I had to bet, China won’t be the country.