A New Look at AI’s Impact on Jobs
Ramp – Revelio: Firm-Level AI Spending and Workforce Adjustment
Abstract
We study how employment changes when firms adopt generative AI using observed AI spending from Ramp card and bill pay data linked to Revelio Labs workforce records for 21,559 firms in the United States. We find that companies that adopt AI tend to grow faster following adoption, but the relationship is driven almost entirely by high-intensity adopters. Firms making the largest AI investments grow employment by roughly 10% following adoption, while low-intensity adopters see no statistically significant change. Entry-level headcount rises 12% for high-intensity adopters. Gains emerge gradually and are broad across roles, including engineering, sales, administration, and customer service. They are also uneven: adopters are already larger, more technical, faster-growing firms, and sector-level gains are concentrated in Information. The results counter predictions that AI adoption will lead to broad job loss.
Key Takeaways
Firms that adopt AI grow headcount 10.2% over the two years following adoption, but these gains are entirely driven by high-intensity adopters. Low-intensity adopters see no statistically significant change.
Entry-level headcount grew even faster. At the companies making the largest AI investments, entry-level headcount grew 12% over the two years following adoption.
AI adoption and the associated gains are unevenly distributed. AI adopters are already larger, more engineering-intensive, more likely to be venture-backed, and faster-growing than non-adopters. These firms then grow faster upon adoption.
Change in headcount after AI adoption: Total Headcount
Effect of AI adoption on firm-level employment by relative month since adoption
View chart data as a table
| Month Relative to Adoption | Effect on High-Intensity Firms (log points × 100) | Low-End Confidence Interval on High-Intensity Firms (log points × 100) | High-End Confidence Interval on High-Intensity Firms (log points × 100) | Effect on Low-Intensity Firms (log points × 100) | Low-End Confidence Interval on Low-Intensity Firms (log points × 100) | High-End Confidence Interval on Low-Intensity Firms (log points × 100) |
|---|---|---|---|---|---|---|
| -12 | -2.74 | -7.11 | 1.64 | -0.39 | -3.40 | 2.63 |
| -11 | -2.07 | -6.07 | 1.94 | -1.07 | -3.97 | 1.83 |
| -10 | -1.97 | -5.82 | 1.88 | -0.67 | -3.66 | 2.32 |
| -9 | -3.50 | -6.93 | -0.07 | -0.62 | -3.18 | 1.94 |
| -8 | -3.07 | -6.60 | 0.47 | -0.29 | -2.55 | 1.97 |
| -7 | -2.17 | -5.30 | 0.96 | -0.18 | -2.16 | 1.80 |
| -6 | -3.83 | -6.56 | -1.10 | -0.35 | -2.16 | 1.46 |
| -5 | -3.01 | -5.38 | -0.65 | -0.18 | -1.72 | 1.36 |
| -4 | -1.19 | -3.43 | 1.05 | -0.12 | -1.29 | 1.04 |
| -3 | -1.50 | -3.09 | 0.08 | -0.12 | -0.93 | 0.69 |
| -2 | -0.84 | -1.93 | 0.24 | -0.09 | -0.56 | 0.38 |
| -1 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| 0 | 0.32 | -0.85 | 1.49 | -0.13 | -0.58 | 0.32 |
| 1 | 0.97 | -0.73 | 2.68 | 0.12 | -0.61 | 0.86 |
| 2 | 1.45 | -0.88 | 3.78 | -0.18 | -1.24 | 0.88 |
| 3 | 2.01 | -0.83 | 4.86 | -1.03 | -2.47 | 0.42 |
| 4 | 3.66 | 0.30 | 7.02 | -1.32 | -3.13 | 0.50 |
| 5 | 5.83 | 1.65 | 10.01 | -1.19 | -3.37 | 0.99 |
| 6 | 7.05 | 2.10 | 11.99 | -1.56 | -4.14 | 1.03 |
| 7 | 8.79 | 3.52 | 14.06 | -1.83 | -4.90 | 1.24 |
| 8 | 9.71 | 3.63 | 15.79 | -2.21 | -5.59 | 1.16 |
| 9 | 11.11 | 4.66 | 17.55 | -2.20 | -5.60 | 1.21 |
| 10 | 13.76 | 6.50 | 21.02 | -1.60 | -6.21 | 3.00 |
| 11 | 17.53 | 10.14 | 24.92 | -0.65 | -5.49 | 4.19 |
| 12 | 18.79 | 10.43 | 27.15 | -0.99 | -6.20 | 4.21 |
| 13 | 19.02 | 10.18 | 27.86 | -1.75 | -7.58 | 4.08 |
| 14 | 20.94 | 11.27 | 30.60 | -1.42 | -7.84 | 5.01 |
| 15 | 20.05 | 10.21 | 29.89 | -0.42 | -6.82 | 5.98 |
| 16 | 20.88 | 9.44 | 32.33 | -0.64 | -8.50 | 7.22 |
| 17 | 22.62 | 10.70 | 34.54 | -0.14 | -9.20 | 8.92 |
| 18 | 27.70 | 14.70 | 40.69 | 0.88 | -9.29 | 11.05 |
| 19 | 30.66 | 15.67 | 45.65 | 2.26 | -8.37 | 12.88 |
| 20 | 31.96 | 14.57 | 49.34 | 1.49 | -10.68 | 13.67 |
| 21 | 35.25 | 16.48 | 54.02 | 3.96 | -9.27 | 17.18 |
| 22 | 35.99 | 16.15 | 55.82 | 5.32 | -10.84 | 21.47 |
| 23 | 36.47 | 15.45 | 57.49 | 4.60 | -11.90 | 21.11 |
| 24 | 45.24 | 21.67 | 68.82 | 3.68 | -13.32 | 20.67 |
Methodology
We link firm-level Ramp transaction data to Revelio Labs workforce records to study employment changes following paid generative AI adoption. Ramp card and bill pay records identify monthly AI vendor spend, including foundation models, GPU cloud, model serving, coding agents, API tools, and AI image, video, search, and research products. Revelio provides monthly firm-level headcount and workforce composition. The linked panel covers 21,559 U.S. firms observed monthly from January 2021 through February 2026.
We define AI adoption as the first month of the earliest three-consecutive-month spell in which a firm records at least $100 of AI vendor spend in every month. This rule screens out one-off experiments and captures sustained organizational use. Among treated firms, adoption intensity is measured as AI spend per baseline employee over the first three post-adoption months, using headcount in the month before adoption as the denominator. High-intensity adopters are firms in the top tercile of this measure; lower-intensity adopters are firms in the bottom two terciles.
Our preferred design is a Callaway-Sant’Anna staggered difference-in-differences estimator. Because permanent non-adopters differ sharply from adopters before treatment, the main comparison is not adopter versus never-adopter. Instead, each adopter is compared to firms in the same eventual intensity group that adopt later but have not yet adopted in that calendar month. Specifications include NAICS sector fixed effects, estimate dynamic effects from 12 months before adoption through 24 months after adoption, and use pre-adoption event-study coefficients as the main diagnostic for parallel trends.
Frequently asked questions
Cite this work
Kharazian, A., Simon, L., & Stevens, R. (2026). A New Look at AI’s Impact on Jobs: Firm-Level AI Spending and Workforce Adjustment. Ramp Economics Lab. https://ramp.com/data/ai-jobs-impact
@techreport{kharazian2026ainewlook,
author = {Kharazian, Ara and Simon, Lisa and Stevens, Ryan},
title = {A New Look at AI's Impact on Jobs: Firm-Level AI Spending and Workforce Adjustment},
year = {2026},
month = {June},
institution = {Ramp Economics Lab},
url = {https://ramp.com/data/ai-jobs-impact}
}You might also like
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