Heavy AI Adopters Are Hiring More, Not Less, New Study Finds

A landmark research paper from Ramp Economics Lab, published June 30, challenges the prevailing narrative that AI adoption leads to widespread job displacement. By linking firm-level AI spending data to workforce records across 21,599 U.S. companies, the study finds that businesses making the heaviest investments in artificial intelligence are expanding their workforces — including entry-level positions — rather than shrinking them.

The Research at a Glance

The study, conducted by Ramp Economics Lab in partnership with Revelio Labs, analyzed AI spending from Ramp’s corporate card and bill pay platform alongside workforce data from January 2021 through February 2026. The results show that firms classified as “high-intensity AI adopters” — those spending approximately $30 per employee per month on AI tools — grew their overall headcount by 10.2% over the two years following adoption.

Entry-level hiring grew even faster, surging 12% at these same companies. According to NBC News, high-intensity adopters also increased their workforce share of entry-level workers by 1.15 percentage points compared to the control group.

A Nuanced Picture

Crucially, the gains are not universal. Low-intensity adopters — firms that experimented with AI subscriptions but did not make sustained investments — saw no statistically significant change in headcount. The benefits are concentrated among companies that commit meaningfully to the technology.

“The research until now has relied on datasets that are available but not appropriate for these questions, resulting in the general public getting unreliable answers on how AI will actually affect our economy,” said Ara Kharazian, Lead Economist at Ramp, in a press release. “Our research shows that firms that invest more in AI also hire more following adoption, including in entry-level roles.”

Why the Gains Take Time

The research reveals a significant learning curve. Hiring gains do not appear immediately — firms do not increase headcount until 6 to 12 months following adoption, and those gains compound over time. This suggests that organizations need time to integrate AI into workflows before it drives enough growth to affect hiring decisions.

Headcount rose across a wide range of functions, including engineering, sales, administration, customer service, finance, marketing, and scientist roles. The strongest job growth among high-intensity adopters was in the information sector, which encompasses software, internet, media, and tech-adjacent firms.

The Uneven Distribution Problem

While the findings offer a counterpoint to alarmist predictions, the paper’s authors are careful to note limitations. AI adopters are already larger, more engineering-intensive, more likely to be venture-backed, and faster-growing than non-adopters. As TechCrunch reported, this sets up the potential for a widening gap between firms with the resources to turn AI adoption into business gains and those stuck experimenting.

“This paper does not show that AI universally creates jobs,” the authors acknowledge, “but it does counter claims that AI will lead to broad job losses.”

Small businesses present a particular concern. They are less likely to adopt AI in the first place, but when they do, they tend to adopt more intensively and see higher marginal impact. Kharazian noted that “there are likely many small businesses that would benefit from AI but do not use it today, missing out on meaningful growth.”

Contrasting Evidence

The findings arrive amid a heated debate. TechCrunch notes that Goldman Sachs research found AI has already erased about 16,000 net jobs per month over the past year, with Gen Z and entry-level workers taking the brunt. Through May 2026, companies announced close to 90,000 job cuts tied to AI, and some projections suggest up to 15% of U.S. jobs could be eliminated by AI over five years.

The Ramp study complicates this picture by showing that in tech-forward firms — precisely where AI adoption is highest — the opposite dynamic is playing out.

What Comes Next

The paper, co-authored by Lisa Simon of Revelio Labs and Ryan Stevens of Ramp, is available in full at ramp.com/data/ai-jobs-impact. Ramp intends to update the results as more data becomes available and test additional outcomes, including wage effects and longer-term workforce composition changes.

For now, the research offers a data-driven middle ground in a debate often dominated by extremes. AI may not universally create jobs, but the evidence increasingly suggests that for companies willing to invest seriously, it can be a powerful engine of growth — and hiring.