Ford rehires 350 engineers after AI fails to preserve expertise or train juniors

Interestingly, there were no consequences for the execs that made this 'mistake'. There seems to be almost unlimited cover for execs cargo culting on using AI as a pretext for layoffs. If it doesn't implode almost immediately, they get massive bonuses, if it blows up in their face, oh well they had the courage to 'take a bold strategic decision'

In other words, they don't really have a plan, but they are happy playing with people's lives via layoffs, since it's the 'in' thing to do. The incentives are huge on the upside and zero on the downside for them.

> The return of the veteran engineers at Ford cuts against the prevailing wisdom — and fear — that AI will replace all kinds of knowledge workers. But Ford found the machines couldn’t replace experience.

I'm not sure this story is illustrative of that, when you have a VP of engineering saying “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.”

He's saving face while almost certainly trying to figure out how to make the new systems work so that next time he won't need to rehire engineers.

> 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.

Clearly a lot of careful thought went into their strategy of using AI and firing engineers.

Ford has hired 350 engineers over the last 3 years which happened alongside short comings in using AI inspection tooling.

This has nothing to do with LLMs and instead is almost certainly about their MAIVIS and AiTriz pilots, which use old school CNNs on custom IBM hardware to do visual inspections.

For those of us who lived through the "Offshoring" Craze of the mid-2000s, this has the exact same arc.

Corp CEOs / CFOs golf buddies coouldn't stop yapping about how much they saved paying people less by offshoring. So step 1, they fire a bunch of people and send work overseas, driving up their financial metrics for 5-6 quarters until their staff and their organization finally break at stage 2. Turns out cultural and communication barriers are things we haven't really figured out how to communicate across efficiently, and that only a handful of people are truly rockstars at it; others just aren't cut out for it. Stage 3 anyone that is competent to get another job already left, leaving a smoldering shell of company that dies by attrition at stage 5.

There are two kinds of knowledge. There is explicit knowledge which can be codified easily in markdown files or a wiki. Then there is tacit knowledge which is mostly encoded in the experience of an organization's individuals. Explicit knowledge is like the tip of a giant institutional knowledge iceberg.

I have a simple mind. I think of a company with 100 employees building a dozen houses at a time. That company could replace a six-person framing crew with a two-person, one-robot team as an experiment. They could do various experiments to see if there was a better option here. It would be at the expense of four employees.

A company with 1000 employees that builds 100 houses at a time might cut a dozen employees to create three robot crews. A 10,000-employee company that builds 1000 houses at a time would still only need to experiment with a handful of crews, affecting only 20-30 or so employees.

I marvel that a company has let themselves grow so out of touch with their business that they can't understand the impact of changes without carnage at this scale.

Setting aside how shortsighted it is to fire your employees to replace them with AI, Ford also screwed up by firing the wrong employees. LLMs work best in the hands of experienced senior engineers who can work at a high level of abstraction because they already understand all the pieces underneath.

In a sense, using an LLM agent is like providing instructions to a very smart, very quick junior who despite being brilliant has some blind spots and lacks institutional knowledge. That's something that seniors excel at, so by firing your seniors you've fired the people best positioned to make full use of LLMs.