GM, Ford, and Stellantis have cut 20,000 white-collar jobs. AI is about to accelerate the trend.

General Motors, Ford, and Stellantis have together eliminated more than 20,000 US salaried jobs from their recent employment peaks this decade, a 19% reduction of their combined white-collar workforces, according to public filings and employment data analysed by CNBC. The cuts accelerated this week when GM laid off between 500 and 600 IT workers in Texas and Michigan, partially due to changing workforce needs related to artificial intelligence.

The numbers tell a story of contraction that predates the current AI moment but is being sharpened by it. Combined white-collar employment across the three automakers peaked at roughly 102,000 jobs in 2022. By the end of last year, it had fallen to 88,700. GM led the reductions, cutting approximately 11,000 salaried positions from a 2022 peak of 58,000, driven by the wind-down and eventual discontinuation of its Cruise robotaxi division, rolling workforce evaluations under CEO Mary Barra, and now AI-related restructuring. Ford scaled back by roughly 5,300 workers from its 2020 peak to approximately 30,700. Stellantis dropped from 15,000 to about 11,000 in the same period.

The disconnect between the cuts and the hiring tells you where the industry thinks the value is moving. The three automakers currently have more than 2,000 open US positions, of which nearly 400 involve AI. GM alone is seeking more than 250 AI-related roles. The company is simultaneously eliminating the workers who maintained legacy IT systems and recruiting the people who will build the AI systems that replace them.

Ford CEO Jim Farley has been the most explicit about the trajectory. “Artificial intelligence is going to replace literally half of all white-collar workers in the US,” Farley said in July at the Aspen Ideas Festival. The statement is more provocative than most CEO prognostications, but it aligns with the direction of his own company’s workforce decisions. Salesforce CEO Marc Benioff made a similar point last week, revealing that his company cut support staff from 9,000 to 5,000 after deploying AI agents that now handle half of all customer interactions. The auto industry is arriving at the same conclusion from a different direction: the white-collar workforce built to support internal combustion vehicles, legacy IT infrastructure, and manual business processes is larger than AI-augmented operations require.

A veteran programmer and data scientist at GM who was laid off this week told CNBC, anonymously: “They’re going to push AI for everyday work and everything else. I’ve seen it firsthand. It can make you much more productive, as a programmer. It can really help you get more work done, but AI isn’t going to do you any good if you don’t know the business.” The observation captures the tension at the heart of AI-driven workforce restructuring: the technology makes individual workers more productive, but that productivity gain reduces the number of workers required, and the institutional knowledge that departing workers take with them cannot be replicated by a language model.

Boston Consulting Group forecasts that 10% to 15% of US jobs could be eliminated as AI proliferates over the next five years, with 50% to 55% of jobs being reshaped within two to three years. Gregory Emerson, managing director at BCG, warned that “those who cut their workforce beyond AI’s ability to replace it will see productivity drop, institutional knowledge disappear, and critical talent walk away.” The risk is not that automakers adopt AI too aggressively but that they mistake cost reduction for capability building.

The broader US auto manufacturing sector has not experienced the same scale of white-collar decline. Bureau of Labor Statistics data shows motor vehicle manufacturing jobs dropped only 0.2% from 2022 through last year, to 285,800 workers, a figure that includes both salaried and hourly employees. Toyota reported a roughly 31% increase in its US white-collar workforce from 2020 through 2025, reaching approximately 47,500 people. The Detroit Three’s reductions are not an industry-wide pattern but a company-specific one, shaped by their particular exposure to legacy cost structures, EV transition losses, and competitive pressure from Asian and European manufacturers that have been investing in AI-native processes from earlier in the cycle.

Stellantis CEO Antonio Filosa, who is leading a global turnaround, has said the company still plans to add more than 2,000 white-collar jobs in North America, a counterpoint to the net reduction narrative. Lenny LaRocca, lead of KPMG’s automotive practice in the Americas, cautioned that “I don’t know necessarily if it’s just to reduce headcounts. I think the focus is more on how do they do their job better and how to be more innovative and move quicker.” The distinction between using AI to eliminate roles and using AI to transform roles is one that will define whether the Detroit Three emerge from this transition with a leaner, more capable workforce or a hollowed-out one.

Chinese automakers are entering Canada with vehicles that cost a fraction of Detroit’s products, built by companies that are investing in AI-native manufacturing and software-defined vehicle architectures without the legacy workforce overhead that GM, Ford, and Stellantis are now trying to shed. In the US, Congress is racing to ban Chinese connected vehicles, but the competitive pressure that drives the ban is the same pressure driving the layoffs: Chinese manufacturers can build vehicles more cheaply, in part because they never had 102,000 white-collar workers to begin with.

Barra framed the workforce philosophy succinctly at an Automotive Press Association meeting in January: “Sometimes the people who got you to ‘point A’ aren’t necessarily people who are going to get you to ‘point B.’” For the 20,000 salaried workers who have already been cut, that distinction is not a management philosophy. It is a termination notice. For the workers who remain, the question is whether they are at point B or simply on the way to it.