Companies Investing in AI Expand Employment, Increasing by 10% with High-Intensity Investment

Contrary to the common perception that AI adoption reduces employment, a new study reveals the opposite. According to a joint study conducted by Ramp, a financial platform company specializing in AI, and Revelio Labs, a human resources analytics firm, companies that actively invest in AI are actually expanding their workforce.

The study analyzed data from over 21,000 companies across the United States. It found that firms with a strong financial commitment to AI adoption (“high-intensity adopters”) are increasing their workforce at a significantly higher rate compared to companies with a lower level of commitment. However, the employment growth effect takes 6–12 months to become evident.

In Ramp’s report, it is stated that “companies adopting AI increase their workforce by 10.2% over two years following adoption. However, this growth is entirely driven by high-intensity adopters, with no statistically significant changes observed among low-intensity adopters.”

“High-intensity adopters” are defined as companies that spend an average of approximately $33.67 per employee per month on AI-related investments during the first three months of implementation. In contrast, low-intensity adopters spend a mere $2.78 per employee per month.

For example, Oracle’s layoff of 21,000 employees last year resulted in severance and restructuring costs of approximately $86,000 per employee. It has been suggested that the company was forced to reduce wages to offset the financial burden of AI-related capital investments. This contrast highlights that the impact of AI investment on employment is not uniform.

Ara Kharazian, Ramp’s chief economist, acknowledged some skepticism on social media but defended the validity of the analytical methods used. He pointed out that companies adopting AI might already be experiencing rapid growth. However, by comparing early adopters to non-adopters, the analysis accounts for similarities in growth trajectories.

What stands out in particular is the faster growth in entry-level employment, which has increased by 12% over two years. Kharazian stated, “High-intensity AI adopters appear to be hiring a different type of employee—specifically, individuals capable of leveraging AI, such as recent graduates and college students.”

However, despite these company-level trends, broader labor market data paints a less optimistic picture. According to data from the Federal Reserve Bank of New York, the unemployment rate for recent college graduates is projected to be 5.6% as of March 2026, higher than the 4.3% unemployment rate for all workers. This suggests a potential mismatch between the skill sets demanded by AI-adopting companies and the skills possessed by recent graduates.

Methodology and Limitations

The study combines financial data from Ramp with employment data from Revelio Labs. AI-related spending was extracted from quarterly financial reports, and subsequent changes in workforce size were tracked. Ramp attributes the 6–12-month lag in employment effects to “the time required for best practices to permeate the organization,” though this interpretation is open to debate.

As Kharazian noted, AI-adopting companies tend to be growth-oriented by nature. While the analysis claims to adjust for growth bias by comparing early adopters to non-adopters, some non-adopters may still adopt AI in the future, making it challenging to fully eliminate bias. Additionally, the study’s focus on U.S. companies limits its generalizability.

Differences Between High-Intensity and

Low-Intensity Adoption

The disparity in spending on AI adoption reflects more than just differences in investment scale. High-intensity adopters ($33.67 per employee/month) are more likely to take a holistic approach, encompassing tool selection, workflow integration, and employee training. In contrast, low-intensity adopters ($2.78 per employee/month) appear to limit their efforts to small-scale experiments or pilot implementations in specific departments.

According to Ramp’s report, low-intensity adopters show no statistically significant changes in workforce size. This suggests that unless AI investments exceed a certain threshold, they may not significantly impact overall organizational processes or lead to changes in employment structure.

Entry-Level Employment and Skill Specialization

The 12% growth in entry-level employment highlights a shift in labor demand driven by AI adoption. Contrary to the conventional belief that AI replaces simple labor, the data reveals a growing demand for “AI-capable talent.” This includes recent graduates and college students who can quickly adapt to new technologies, making them attractive hires for companies.

However, the Federal Reserve Bank of New York’s data showing a 5.6% unemployment rate among recent college graduates indicates that these benefits are not evenly distributed. A divide appears to be emerging between graduates with AI literacy and data analysis skills and those with degrees in more traditional academic fields, which may limit their employment opportunities.

Editorial Opinion

This study is noteworthy as it challenges the simplistic narrative of “AI equals job destruction.” In the short term, the trend of companies actively investing in AI accelerating entry-level hiring is likely to become more pronounced over the next 6–12 months. However, the benefits appear to be confined to individuals with AI-related skills, raising concerns about the deepening of skill mismatches in the labor market. Companies should not only focus on expanding their workforce but also invest in internal training and reskilling programs.

From a long-term perspective, the study suggests that the labor market is on the cusp of structural transformation. Job creation driven by AI adoption is still in its infancy. As the scope of adopting companies expands over the next 1–3 years, the growth gap between high-intensity and low-intensity adopters may widen further. In industries where AI investment is directly tied to competitiveness, the competition for talent is likely to intensify, driving up wages. At the same time, companies that base their AI adoption decisions solely on short-term ROI may risk drawing incorrect conclusions about its potential.

References

• Companies that add more AI also add more people — Published by The Register on July 2, 2026