New Data Shows Heavy AI Users Are Hiring More Especially at Entry Level

A fresh batch of data is complicating the familiar story that artificial intelligence mostly destroys jobs, especially for new graduates.

A joint report from Ramp, which tracks enterprise AI spending, and workforce analytics firm Revelio Labs examined records from nearly 22,000 companies. It found that organisations spending the most aggressively on AI are expanding their workforces faster than others including in the junior roles many people assume are first on the chopping block.

High-intensity AI adopters are growing headcount

The study focuses on “high-intensity adopters,” defined as companies that spend an average of $30 per employee per month on AI tools during their first three months of adoption. Among that group, total headcount rose by 10.2%.

That hiring growth wasn’t confined to a single department. These high-intensity adopters increased staff across a range of functions, including:

• Engineering
• Sales
• Administration
• Customer service
• Finance
• Marketing
• Scientist roles

The report highlights the information sector which covers software, internet, media and other tech-adjacent businesses as seeing the strongest job growth among the heavy AI spenders.

One of the most striking findings is at the bottom of the ladder. While much of the public debate has focused on AI wiping out internships and junior positions, the data from these firms points in the opposite direction: entry-level headcount at high-intensity adopters rose by 12%.

The report lands at a time when AI-linked layoffs are fuelling anxiety across the labour market. Through May 2026, companies had announced close to 90,000 job cuts explicitly tied to AI. Some projections suggest that up to 15% of U.S. jobs could be eliminated by AI over the next five years.

Separate research has also painted a bleak picture for younger workers. Goldman Sachs has estimated that AI has already erased roughly 16,000 net jobs per month over the past year, with Gen Z and entry-level employees bearing a disproportionate share of the impact.

Against that backdrop, the Ramp–Revelio findings offer a partial rebuttal. The authors state that their work does not demonstrate that AI “universally creates jobs,” but argue that it does push back against claims that AI will drive broad-based job losses across the board, or that it is inherently hostile to junior roles.

Read more: Big Tech Layoffs hit 100,000 Jobs in 2025 Amid AI Automation

At least within tech-forward firms that are deeply investing in AI, the pattern looks more like expansion than replacement.

The report suggests one explanation: in software and technology companies, AI can reduce the cost and time required for core activities such as writing and debugging code, building internal tools, creating technical documentation and supporting product development. By making these workflows cheaper and faster, AI can increase the returns to growing the overall business not just trimming or restructuring existing teams.

That framing casts AI less as a direct substitute for labour and more as a lever for scaling. If a company can produce more output with slightly augmented teams, hiring more people to capture that opportunity can be rational, particularly in sectors where demand is still growing.

However, the authors are careful to flag the limits of what their dataset can say. The companies that spend heavily on AI tend to be tech-forward, knowledge-work-heavy firms, often with venture backing or existing high growth trajectories. Those characteristics make it hard to cleanly separate cause and effect: are these businesses hiring because AI is boosting productivity, or were they already in expansion mode and simply more inclined to invest in new tools?

In other words, the report captures what is happening where AI adoption is already strong, not necessarily what will happen across the wider economy, including sectors with thinner margins and less digital infrastructure.

It also distinguishes between firms that make sustained AI investments and those that simply test the waters. Companies that only buy subscriptions and run pilots, but do not follow through with deeper integration or continued spend, do not generally see gains in headcount, according to the data.

That divergence sets up a potential widening gap. Organisations with access to capital, technical talent, founder networks and management bandwidth appear better positioned to turn AI tools into tangible business gains. Those without those advantages may find themselves stuck in perpetual experimentation, paying for AI subscriptions without the scale or strategy needed to translate them into growth.

The authors speculate that this divide could deepen over time, warning that “firms without those channels may fall behind.” If that proves true, the impact of AI on jobs may be shaped as much by a company’s capacity to adopt and integrate the technology as by the capabilities of the tools themselves.

For workers, especially those entering the job market, the message is mixed. On one side, headline-grabbing AI-related layoffs and projections of large-scale job elimination are fuelling understandable concern. On the other, among companies leaning hardest into AI, there is evidence of overall hiring and even faster growth in entry-level roles.

What is clear from this report is that the AI jobs story cannot be reduced to a simple narrative of automation-led job destruction or effortless job creation. Outcomes so far appear to depend heavily on sector, company strategy and the depth of AI adoption.