Ford just paid twice for the same lesson
Ford believed the pitch. Feed the design requirements into the machine, let artificial intelligence carry the quality work, and thank the veteran engineers for their service on the way out. Then the quality numbers told a different story, and Ford did something quietly remarkable: it rehired around three hundred veteran engineers. Not to replace the AI, but to teach it. The result, by Ford's own account, was its best initial-quality showing since 2010.
Read that sequence again, because most hiring plans I see this year have the order backwards. The company cut the expertise first, discovered the tools were only as good as the judgment that trains them, and then bought the same expertise back at re-hiring prices, with interest paid in product quality along the way.
AI did not make the veteran engineer cheaper. It made the veteran engineer the most leveraged hire in the building.
This is what I mean when I say the market is repricing senior judgment. Every company deploying serious AI tooling now needs a small number of people whose knowledge is deep enough to be worth encoding. Those people were scarce before the machines arrived. Now every one of them can multiply themselves across a toolchain. The companies that let them walk are not saving a salary. They are exporting their training data to a competitor.
For hiring leaders, the practical read is simple. Budget for fewer, more senior technical hires, and hold onto the veterans whose intuition your systems will one day run on. The cheapest time to secure that expertise is before your own version of the Ford moment, because the second time you buy it, the market sets the price.
Planning a senior AI or data hire and want the market view first? We share it either way.
Send us the brief →