Fears that AI is driving a white-collar jobs apocalypse may be overstated, according to new research showing that the companies spending most heavily on AI are hiring faster than their peers, not slower. The study, published by the fintech company Ramp and the workforce analytics firm Revelio Labs, linked AI spending data to workforce records at nearly 22,000 US companies between January 2021 and February 2026. It found that the biggest AI adopters tended to grow their head count after rolling out the technology, including in entry-level jobs often assumed to be the most exposed to automation.
The headline numbers are striking. Among what the authors call high-intensity adopters, defined as firms spending an average of about $30 per employee each month on AI in their first three months, head count rose 10.2% over the two years after adoption, and entry-level hiring grew 12%.
The growth spanned functions including engineering, sales, administration, customer service, finance, marketing and scientific roles, with the strongest gains in the information sector, which covers software, internet, media and tech-adjacent companies. The authors’ interpretation is that AI is not only a tool for replacing labor but can be a tool for expansion: when AI makes core work cheaper to produce, the return on growing the whole company rises. They offered blunt advice to young job seekers, saying that between similar firms, choose the one using AI.
The findings land amid a notable cooling of apocalyptic rhetoric from tech leaders themselves. Nvidia chief executive Jensen Huang recently called it “lazy” for executives to blame layoffs on AI, and OpenAI’s Sam Altman said in May he does not expect the kind of jobs apocalypse some in the industry predict. The shift in tone comes as companies try to manage a growing AI backlash, particularly among Gen Z workers who fear being shut out at the entry level. The study authors were pointed on this, writing that readers should be skeptical when CEOs blame layoffs on AI.
The Caveats
The data comes with serious limits that the authors acknowledge. Heavy AI spenders skew toward larger, venture-backed, fast-growing firms that may have been hiring anyway, making it hard to separate AI’s effect from underlying company strength. Much of the growth was concentrated in tech-adjacent sectors rather than thinner-margin industries, so the report captures what happens where AI adoption is already strong, not the wider economy.
Notably, firms that merely bought subscriptions and ran pilots without deeper investment saw no head count gains, which the authors warn could open a widening gap between companies that can turn AI into growth and those stuck experimenting. Heavy adoption has also not reliably translated into higher productivity, a “productivity paradox” in which faster task completion has yet to show up consistently in revenue or profit.
A Contested Picture
The study cuts against, but does not settle, a genuinely mixed body of evidence. Revelio’s own earlier research found that US postings for entry-level jobs fell about 35% over 18 months, attributing much of it to AI, and that highly AI-exposed entry-level roles have dropped more than 40% since early 2023.
Other tallies counted roughly 90,000 job cuts this year linked at least partly to AI. Reconciling these views, the likeliest reading is that AI is reshaping work rather than simply destroying or creating jobs: expanding head count at well-resourced firms that use it to scale, while hollowing out routine junior tasks elsewhere. The net effect on the labor market remains an open and hotly debated question, and a single dataset weighted toward tech winners cannot answer it alone.
