Artificial intelligence is transforming U.S. workplaces. AI chatbots like ChatGPT and Claude are bringing new efficiencies to certain work tasks and automating others. These technologies are still relatively new, and it’s too early to tell if they will lead to job loss or job growth in the long run. But there are early signs of a gender gap: Employment growth has been slower for women than for men in occupations with more exposure to AI.
| AI exposure score | Men | Women | Total |
|---|---|---|---|
| 0 | -4.9 | -0.1 | -2.55 |
| 3.5 | 17.8 | 0.5 | 9.3 |
We measured employment growth for men and for women in occupations with different levels of exposure to AI, based on government data. We compared the years before generative AI became widely available (2017-19) to the years since (2023-26).
Employment growth is the percent change in the number of people employed in an occupation. Within each occupation, we compared growth for men to growth for women. We did not compare occupations to each other, and we are not suggesting some occupations are inherently “men’s work” or “women’s work.”
AI exposure measures how much AI can do or help with the tasks required in a particular occupation.
From 2017-19 to 2023-26, men saw substantially more job growth than women in occupations with higher AI exposure.
Different occupations have different levels of AI exposure. In construction labor, AI can do very little of the core work, whereas in accounting, it can meaningfully help with much of the work. Occupations with some level of exposure to AI tend to include tasks that involve reading, writing or working with information.
This analysis finds that for each 1-point increase in an occupation’s AI exposure score, men’s employment grew by 6.3%, while women’s employment barely changed (0.2%).
Because men’s employment grew steadily with each additional point of AI exposure while women’s barely changed, the gender gap in employment growth widened step by step from the least to the most exposed occupations. Men’s employment grew about 24% more in the most AI-exposed occupations than in the least exposed, while women’s employment growth was about the same across the scale.
At the low end of the AI exposure scale, the pattern reverses: In the least AI-exposed occupations, women’s employment grew faster than men’s.
It’s important to note that the relationship between AI exposure and job growth is not necessarily direct. This analysis compares employment before and after generative AI became widely available, but other changes during this period may have also affected employment.
Related: The Gender Gap in AI
Measuring AI exposure
| AI exposure | Exposure score | Occupation |
|---|---|---|
| Very low | 0.49 | Driver/sales workers and truck drivers |
| Very low | 0.59 | Maids and housekeeping cleaners |
| Very low | 0.70 | Construction laborers |
| Low | 0.80 | Carpenters |
| Low | 1.11 | Retail salespersons |
| Low | 1.14 | Home health aides |
| Medium | 1.54 | Medical assistants |
| Medium | 1.64 | Tellers |
| Medium | 1.66 | Audiologists |
| High | 2.14 | Court, municipal and license clerks |
| High | 2.31 | Travel agents |
| High | 2.36 | Proofreaders and copy markers |
| Very high | 3.09 | Accountants and auditors |
| Very high | 3.10 | Software developers, applications and systems software |
| Very high | 3.20 | Financial managers |
We scored 480 occupations on how exposed each is to generative AI, using ratings from the U.S. Department of Labor’s Occupational Information Network (O*NET) and the Current Population Survey (CPS).
Each occupation received an exposure score ranging from zero to 3.5. A score of zero does not mean an occupation has no exposure to AI. Instead, it means it’s the least exposed occupation in our data.
Higher scores mean generative AI can do or assist with a larger share of the occupation’s most important tasks.
Occupations with low AI exposure include construction laborers, carpenters, home health aides and retail sales workers. Occupations with high AI exposure include customer service representatives, accountants, software developers and financial managers.
Differences by gender and education
The gender gap in AI-exposed job growth varies widely by education. As AI exposure increased, the gap between men’s and women’s employment widened for workers without a bachelor’s degree, but not for those with one.
| Women | Men | Grouping | |
|---|---|---|---|
| Total | .2% | 6.3% | 1 |
| High school or less | -9.5 | 8.8 | 2 |
| Some college | -6 | 3.6 | 2 |
| Bachelor’s+ | 0.7 | 0.8 | 2 |
Among workers with a high school diploma or less, men’s employment grew by 8.8% for each 1-point increase in AI exposure, while women’s fell by 9.5%.
For workers with some college education, the gap was smaller but still significant.
For workers with a bachelor’s degree or higher, neither men’s nor women’s employment changed significantly as AI exposure increased.
To be sure, men without a college degree are not heavily represented in occupations with high levels of AI exposure. Instead, they tend to work in occupations with low AI exposure, such as truck driving, construction labor and moving freight. So the job growth we found for men without a degree in more AI-exposed occupations applies to a relatively small share of these men.
Unlike men without a four-year college degree, women with this level of education are often working in occupations like secretaries, receptionists and customer service representatives. These roles are closer to the middle to upper part of the AI exposure scale, so tools like chatbots and automated support have a larger role in their work.