A new study is pushing back on some of the most anxiety-inducing narratives around AI-driven job loss. Companies embracing artificial intelligence are actually hiring more people—including entry-level workers—according to a paper released at the end of June from financial operations platform Ramp and workforce data company Revelio Labs.

The analysis, which examined records for more than 21,000 firms in the United States, found that headcount grew by 10.2% in the two years following AI adoption. But the headline number masks an important nuance: nearly all of that growth is concentrated among what the researchers call "high-intensity adopters." These are companies that make sustained, long-term investments in AI, rather than short-term experiments or offering basic chatbot subscriptions.

"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."

High-intensity vs. low-intensity adopters

The study distinguishes between companies that merely dabble in AI and those that embed it deeply into their operations. High-intensity adopters spend an average of $33.67 per person per month on AI services—including coding agents, large language models, GPU cloud computing, API tokens, model serving, and inference. By contrast, low-intensity adopters spend only $2.78 per person monthly.

These high-intensity firms are often venture-backed, engineering-focused, and plugged into networks where AI adoption is common. They are not just experimenting; they are building AI into their core processes. The data suggests that joining a company that heavily uses AI may actually be safer for job security than working at a less AI-forward firm. "That's the one that's going to grow faster," Kharazian said.

Entry-level jobs: a surprising bright spot

Perhaps the most striking finding is the fate of entry-level positions. Contrary to widespread fears—including past warnings from Anthropic CEO Dario Amodei that half of entry-level white-collar jobs could disappear—the study found that high-intensity adopters grew entry-level headcount by 12%. The theory is that companies are actively seeking recent graduates who already know how to use AI tools effectively.

"Young people, especially, are very well positioned to show that they can introduce these new technologies and apply them effectively to the workplace," Kharazian explained. This suggests that AI literacy is becoming a valuable skill for job seekers, and that companies view new hires as potential internal evangelists for AI adoption.

The findings come at a time of intense debate over AI's impact on employment. Consultancy Forrester projects that AI will replace about 6% of U.S. jobs by 2030—roughly 10.4 million positions. The Boston Consulting Group estimates a broader range of 10% to 15%. While these numbers are significant, the Ramp-Revelio study indicates that AI adoption is not a zero-sum game: companies that invest heavily in AI are also creating new roles.

What drives hiring growth?

The study stops short of pinpointing exactly which practices drive the hiring boom. The report mentions several possibilities, including product acceleration, sales productivity, and faster internal analysis. But Kharazian acknowledges that more research is needed to understand the specific mechanisms. He also wants to explore what types of candidates are getting hired and for which roles, as well as whether the patterns hold beyond white-collar work.

One clear implication is that AI is not simply automating away jobs across the board. Instead, it appears to be reshaping the mix of skills that employers value. For example, a company that deploys AI for customer service might need fewer traditional agents but more data scientists, prompt engineers, and AI trainers. The net effect, at least for high-intensity adopters, has been positive headcount growth.

A wake-up call for small businesses

The report also sounds an alarm for smaller businesses. Smaller firms are far less likely to be high-intensity adopters. They lack the venture capital backing, engineering talent, and network connections that facilitate deep AI integration. "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 noted.

The risk for these companies is being outcompeted—or even unseated—by newer, AI-native startups. As AI becomes a competitive necessity, small businesses that delay adoption may struggle to keep up with larger, more tech-savvy rivals that can deliver products and services faster and more cheaply. The report implicitly urges small business owners to invest not just in AI tools, but in the human capital and organizational changes needed to use them effectively.

Historical context and the evolving AI job debate

The Ramp-Revelio study adds a data-driven perspective to an often emotional debate. Earlier waves of automation, such as the rise of PCs and the internet, also sparked fears of mass unemployment, but ultimately led to new job categories. AI follows a similar pattern, albeit with a faster pace of change.

In 2025, Anthropic CEO Dario Amodei warned that half of entry-level white-collar jobs could disappear, though he later modified his comments. The new study suggests that while some roles may be altered or eliminated, companies that embrace AI are creating new opportunities. The key difference may be that the skills required are shifting: fluency in AI tools is becoming as fundamental as basic computer literacy was two decades ago.

Universities and training programs are beginning to respond. Many now offer courses in prompt engineering, AI ethics, and machine learning fundamentals. Graduates who can demonstrate practical AI skills may have a competitive edge in the job market, especially at high-intensity adopter firms.

Methodology and limitations

The study linked Ramp card and bill pay data to Revelio workforce records, allowing researchers to correlate AI spending with hiring patterns over time. By focusing on the first three months after AI adoption, the authors aimed to capture early-stage impacts. However, the study does not account for longer-term displacement effects that may emerge after the two-year window.

Furthermore, the data is limited to firms that use Ramp's financial platform, which may skew toward tech-forward businesses. Still, with over 21,000 firms in the sample, the findings offer a robust counterpoint to purely negative narratives about AI and employment.

The report's authors emphasize that the numbers represent averages and may hide significant variation across industries. For example, manufacturing and logistics firms might experience different outcomes than software companies or professional services. Future research will need to break down the data by sector to provide more granular guidance.

Despite these limitations, the core message is clear: AI adoption does not automatically mean job losses. For workers and job seekers, the most prudent path may be to pursue roles at companies that are making serious, long-term bets on AI, as these appear to be the ones with the strongest hiring momentum.

Source: ZDNET News