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ZDNET’s key takeaways
- A new study presents another perspective on AI job fears.
- Companies investing in AI are hiring more people.
- There may also be hope for entry-level jobs.
A new study is pushing back on some of the most anxiety-inducing narratives around AI-driven job loss.
Companies embracing AI grew headcount by 10.2% in the two years following adoption, according to a paper out at the end of June from financial operations platform Ramp and workforce data company Revelio Labs.
“If you’re a consumer of information today, you’re getting a lot of mixed messages,” Ara Kharazian, Ramp’s lead economist, told ZDNET. “[If] you are on the job market, you are simultaneously hearing that you must learn AI, or you’ll get left behind. And yet, AI is also going to be the technology that will likely lead to your layoff.”
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The report aims to shed some light on these contradictions and guide job seekers as they navigate a rocky job landscape.
That 10.2% figure, for example, isn’t just happening at any company using AI. Kharazian said nearly all of the growth is coming from what the paper described as “high-intensity adopters.” These are companies that aren’t merely offering chatbot subscriptions to employees or spinning up short-term pilots — long-term investment is the clincher. These companies also tend to spend more on AI per person monthly, on average, $33.67, compared to $2.78 among low-intensity adopters.
Although it might feel as though joining a company that’s heavily using AI could lead to a layoff, Kharazian said it’s actually the safer bet.
“That’s the one that’s going to grow faster,” he said.
The study’s findings come at a time when job seekers are deluged with headlines heralding layoffs in the name of AI, and facing a potentially dwindling future job pool. There’s no clear consensus on AI’s effect on jobs. Consultancy Forrester projects that AI will replace about 6% of jobs by 2030 in the US (about 10.4 million). The Boston Consulting Group pins that figure in the 10-15% range. And although he’s modified his comments, in 2025, Anthropic CEO Dario Amodei warned that half of entry-level white-collar jobs could disappear.
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The fate of entry-level jobs is yet another key finding from the report. Contrary to fears that jobs for young folks and recent graduates are drying up, those high-intensity adopters grew entry-level headcount by 12%.
One theory is that companies are looking for recent graduates who know how to use AI tools.
“Young people, especially, are very well positioned to show that they can introduce these new technologies and apply them effectively to the workplace,” Kharazian said.
To put together the report, Ramp and Revelio looked at records for more than 21,000 firms in the US, linking Ramp card and bill pay data to Revelio workforce records. In other words, Kharazian and team could look at how much companies spent on AI services like coding agents, large language models, GPU cloud, API tokens, model serving and inference, and much more, per employee, per month, over the first three months after adoption.
A wake-up call for small businesses
Aside from the future of jobs, Kharazian noted that the report highlights that smaller businesses are less likely to be high-intensity adopters. They’re less likely to be VC-backed, engineering-focused, or plugged into the types of networks where AI adoption is common.
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“So much of your usage of AI, and how you use it and whether or not you use it well, is also driven by who you know and where you can hire from and the networks you’re connected to,” Kharazian.
The risk for smaller businesses is getting outcompeted or even unseated by new businesses that know how to use AI effectively.
Further questions
The study also acknowledges that there’s room for more investigation. While the numbers show growth, they don’t reveal exactly which practices drive it, though the report’s conclusion mentions product acceleration, sales productivity, and faster internal analysis as a few possibilities.
Kharazian also wants to look into the types of candidates getting hired and for which roles, as well as whether these patterns hold true beyond white-collar work.
