Heavy AI spenders see 10.2% job growth while light adopters stagnate, new study reveals

The AI jobs debate just got messier

AI-related job loss fears grow each time another company announces a round of layoffs. Through May of 2026, companies announced that close to 90,000 job cuts were tied to AI, and, by some accounts, up to 15% of U.S. jobs are projected to be eliminated by AI over the next five years. Promises from the tech industry that AI will also create new jobs does little to ease fears, especially for the generation wondering if anyone will be hiring when they graduate. A recent report from Ramp and Revelio Labs, which track enterprise AI spend and workforce records from nearly 22,000 companies, respectively, complicates that gloomy narrative. The report found that companies spending heavily on AI are growing headcount faster, even in the entry-level roles that many fear are doomed. According to the report, "high-intensity adopters" -- firms that spend on average $30 per employee per month on AI in the first three months -- saw headcount increase 10.2%. Headcount also rose across 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 includes software, internet, media, and tech-adjacent firms. Despite these positive signals, the data isn't as rosy as it seems. It skews heavily towards tech-forward, knowledge-work firms -- ones that might have VC-backing and are growing fast anyway, making it difficult to say whether AI is contributing to the hiring or just showing up at companies that are expanding anyway. "This paper does not show that AI universally creates jobs," the paper's authors admit, "but it does counter claims that AI will lead to broad job losses." It also counters claims that AI is killing all junior jobs. Recent research from Goldman Sachs found that 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 of the burden. But in tech-forward firms, the report finds that entry-level headcount actually rose by 12%. So what can we take away from this? Perhaps that AI isn't always a tool for labor substitution, but that it can be a tool for firm-expansion instead. "For software and technology firms, AI can make core output cheaper or faster to produce: writing code, debugging, building internal tools, producing technical documentation, and supporting product development," the report reads. "Lower production costs in these workflows can raise the return to expanding the whole firm, not just the engineering team." But companies that buy subscriptions and run pilots, yet did not go on to make sustained investments, don't tend to see any gains in headcount, per the report. That sets up the potential for a widening gap between firms that have the resources -- like capital, technical staff, founder networks, and management bandwidth -- to turn AI adoption into actual business gains and those that are stuck experimenting with subscriptions. In other words, this report suggests that firms that already have the resources are the ones who will see the largest gains. The paper's authors speculate such a divide may continue to grow, saying: "Firms without those channels may fall behind."

Companies that add more AI also add more people

AI leads to job losses, or so the conventional wisdom goes. But a new survey of over 21,000 US firms implies the exact opposite: When companies invest in AI, they add positions, but not immediately. According to Ramp, an AI finance biz, and Revelio Labs, an HR biz, companies making a significant financial commitment to AI add jobs at a higher rate than low-intensity adopters. But job gains don't appear until six to 12 months later. One might be tempted to interpret this as the amount of time it takes to assess the resources required to clean up after AI mistakes, but the Ramp study argues that the lag reflects the time required for best practices to filter through organizations. "Firms that adopt AI grow headcount 10.2 percent over the two years following adoption, but these gains are entirely driven by high-intensity adopters," Ramp's report on the subject claims. "Low-intensity adopters see no statistically significant change." High-intensity adopter here means average per-employee AI spending of about $33.67 per month in the first three months of adoption (and rising over time), compared to low-intensity adopters spending just $2.78 per employee. That's far less than the roughly $86,000 in severance and restructuring charges Oracle incurred for each of the 21,000 employees laid off last year as a wage-shedding counterbalance to its AI capex costs. In a social media post, Ara Kharazian, lead economist at Ramp, cautioned that some skepticism is warranted because companies adopting AI are already faster growing. But he insists that the analysis accounts for that by comparing early adopters against firms that haven't adopted yet, where the growth trajectory is assumed to be more similar. "Entry-level headcount grows even faster, 12 percent over two years," said Kharazian. "This is our first evidence that high-AI-adopting firms are hiring different kinds of employees. "We believe they are selecting for a new set of skills, specifically, people who know how to use AI and use it well. Entry-level workers, especially recent graduates and college students, are a natural place to look." That may be the case at the companies surveyed, but other sources suggest that the trend hasn't really improved the lot of those entering the job market. The unemployment rate for recent college graduates in March 2026 was 5.6 percent, compared to 4.3 percent for all workers, according to the Federal Reserve Bank of New York. According to the US Bureau of Labor Statistics, the US unemployment rate remained essentially flat since May, when it was 4.3 percent. "Both total nonfarm payroll employment (+57,000) and the unemployment rate (4.2 percent) changed little in June," the Labor Department said. While Ramp's data may suggest some upside to investing in AI, some businesses appear to be having second thoughts, based on concerns about cost and control. In a recent CNBC interview, Palantir CEO Alex Karp argued that military and private sector enterprises share similar skepticism about the way frontier model companies like OpenAI and Anthropic do business. Technical customers, Karp said, want "control over their compute, their models, their data stack, and their (investment) alpha. They want to know they own the means of production." Karp argues that the AI industry needs to rebuild trust, which will require answers to basic questions like who owns the data, where it is stored, and whether prompts are secure. Karp acknowledges that's a self-interested argument because Palantir is pushing a combination of mobile, application layer, and compute. But he's also correct in identifying an unresolved problem with frontier model providers. Government organizations and enterprises can't afford to be beholden to a capricious service provider, particularly if its AI models may not be available due to government restrictions, if its AI model may refuse to respond to what's asked of it, or if the price becomes excessive. When companies invest in AI, they add a job for model providers - make AI available, controllable, affordable, and worthwhile. That work still needs to be done. ®

Heavy corporate AI spenders add staff faster than peers

Companies investing most heavily in AI are adding workers faster than their peers, according to new research that challenges predictions of broad AI-driven job losses. White-collar worker numbers increased 10.2 per cent overall at companies that used generative AI most intensely in the first two years after they first adopted the technology, the research found, with gains across occupation types and seniority levels. Entry-level employment increased by 12 per cent. However, among organisations that adopted AI but to a lesser degree -- defined as the bottom two-thirds of spending per worker -- there was no significant change in worker numbers compared with a control group. The findings run counter to claims that AI adoption will spur widespread job losses, even as tech groups including Oracle and Atlassian have cited AI investment when announcing lay-offs. Instead, they suggest the technology may be associated with faster hiring -- but only for companies investing heavily enough to realise productivity gains. The study, co-authored by researchers at US tech start-ups Ramp and Revelio Labs, covered almost 22,000 US companies and is the first to combine organisation-level headcount and AI spending data. Ara Kharazian, chief economist at Ramp and co-author of the paper, said the data shows companies that use AI are growing faster but the benefits are unevenly distributed. "There's clearly some kind of learning curve -- the [headcount] gains don't show up for at least six to 12 months -- and it's subject to a minimum threshold. You only get these gains if you are a high-intensity adopter, and that typically comes with a decent amount of investment, beyond a couple of dollars a month on ChatGPT." The research linked data from Ramp, a payment processor, showing how much companies paid to AI vendors, with workforce records compiled by Revelio Labs from online public profiles such as LinkedIn. Because AI adopters tended to be more technical, higher-paying and more likely to have received venture capital backing than non-adopters, the study compared early AI adopters with companies that adopted the technology later. One labour economist told the FT that the results, while interesting, should be interpreted with caution, particularly as the groups using AI most in the sample tended to be smaller. "'Intense AI adopters grow faster' and 'small fast-growing start-ups buy a lot of AI quite early' seems hard to separate here," he said. Kharazian at Ramp warned almost all headcount gains were among companies in the tech sector and the study covered only white-collar workers. Academic research has painted a mixed picture of AI's effect on the labour market. Stanford research published in November found a 16 per cent reduction in early-career employment in jobs exposed to AI. However, a paper by Harvard economists last year, covering 280,000 companies, found declines in junior employment among AI adopters, while senior roles were largely unaffected. Oracle said last week it had cut 21,000 jobs over the past year and warned its investment in, and use of, AI could lead to further reductions. Snap, Block and Cisco are among other tech companies to recently link thousands of job cuts to AI.

AI Effect Showing Up in US Employment Numbers | PYMNTS.com

A drop in financial services and IT payrolls -- two sectors where artificial intelligence (AI) adoption has been quickest -- accelerated this year to an average of 28,000 per month, Bloomberg News reported Wednesday (July 1), citing government data. The report contends that this weakness is notable amid an otherwise strong job market, which created 113,000 in the first five months of the year, with that number dragged down somewhat by the banking and tech industries. Payroll data for June, due to be released Thursday (July 2), is projected to show additional gains, the report added. Tech companies have invested heavily in AI and are now increasingly citing it as a factor in job cuts, Bloomberg added. Executives at lenders such as JPMorgan Chase, Citigroup, and Goldman Sachs have also said AI will lead to some layoffs. "It's certainly making an impact as we speak in a way that no technology has before," John Challenger, CEO at Challenger, Gray & Christmas, told Bloomberg. His company, which monitors layoff plans, found nearly 102,000 announced job cuts attributed to artificial intelligence thus far in 2026, the report said, with the tech sector accounting for a third of this year's announced layoffs. "Finance might be the next big sector that's most affected," Challenger added. The report also points to research which suggests the impact of AI on labor hinges on how companies use the tech. For example, Stanford University's Digital Economy Lab found employment has weakened in roles where the technology automates tasks, while remaining strong in roles where AI helps workers do their jobs. Bloomberg's report comes one day after new research showing that companies spending the most on generative AI are expanding their staff faster than businesses spending the least. "A New Look at AI's Impact on Jobs," a study by corporate card firm Ramp and workforce analytics firm Revelio Labs, measured AI vendor spending against workforce records for 21,559 American companies between January 2021 through February of this year. "AI adopters saw headcount rise 10.2% over the two years following adoption, gains the study attributed entirely to high-intensity spenders," PYMNTS wrote. "Low-intensity adopters saw no statistically significant change in headcount over the same period. Within high-intensity adopters, the entry-level headcount grew 12%."

Heavy AI Spenders Are Adding Workers, Not Cutting Them | PYMNTS.com

"A New Look at AI's Impact on Jobs," a study by corporate card firm Ramp and workforce analytics firm Revelio Labs, tracked AI vendor spending against workforce records for 21,559 companies in the United States from January 2021 through February 2026. AI adopters saw headcount rise 10.2% over the two years following adoption, gains the study attributed entirely to high-intensity spenders. Low-intensity adopters saw no statistically significant change in headcount over the same period. Within high-intensity adopters, entry-level headcount grew 12%. A New Layer of Jobs Is Forming Between AI Models and Customers The pattern emerging across Google, Box and IBM isn't primarily displacement, PYMNTS reported June 3. It's a new organizational layer sitting between foundation models and business operations, staffed by roles that didn't exist three years ago. Google is hiring hundreds of forward-deployed engineers to help customers move its AI products from pilot into production, and Google Cloud CEO Thomas Kurian said demand for engineers who can drive agent development is growing. Box CEO Aaron Levie said AI has created 13 new job categories at his company, including model evaluators, a role that exists because models themselves aren't interchangeable and choosing between them carries real operational weight. IBM said it will triple entry-level hiring in the U.S. in 2026, even as AI reshapes the tasks traditionally assigned to new graduates. IBM Chief Human Resources Officer Nickle LaMoreaux said the company overhauled entry-level job descriptions for software developers, since work performed by junior hires two to three years ago can now largely be done by AI. Junior developers at IBM now spend less time on routine coding and more time working directly with customers. Dropbox, meanwhile, is planning to expand its internship and new graduate programs by 25% for the same reason, citing younger workers' comfort with AI tools. The Divide Is Between Companies Funding Growth and Companies Stuck in Pilots A separate, larger dataset pointed to the same mechanism. PwC's 2026 Global AI Jobs Barometer, drawn from more than 1 billion job postings across 27 countries, found that companies most able to use AI grew headcount 52% in 2025 against a 2018 baseline, compared with 36% for the least AI-exposed companies. Wages at those same companies rose 24% compared to 17% for lighter-spending peers. The divide sharpens at entry level. The PwC study found that, based on an analysis of 2.4 million U.S. entry-level job postings, AI-exposed junior roles are seven times more likely than less-exposed roles to require traditionally senior skills, such as leadership and judgment. Postings for those "seniorized" entry-level roles grew 35% since 2019, while postings for other entry-level roles shrank 10% over the same period. PwC's separate AI Performance study, based on a survey of 1,217 senior executives, found that 74% of AI's economic value is being captured by just 20% of organizations. Top-performing companies were roughly two to three times more likely than their peers to use AI to pursue new growth opportunities, rather than simply layering AI tools onto existing workflows. For all PYMNTS AI coverage, subscribe to the daily AI Newsletter.