The rise of generative artificial intelligence (AI) has been a hot topic in tech circles. But new research from the Federal Reserve Bank of St. Louis, Vanderbilt University, and Harvard Kennedy School reveals the true extent of generative AI’s infiltration into everyday work life—and the results are eye-opening. According to the paper, The Rapid Adoption of Generative AI, the technology has taken hold faster than previous transformative technologies like the personal computer (PC) or the internet.

Here are five surprising takeaways from the study, which surveyed thousands of U.S. workers to gauge the adoption of generative AI at work and at home.

1. Generative AI is already more widely adopted than PCs were at this stage

Generative AI is spreading faster than anyone could have predicted. Just two years after the public release of ChatGPT, 39.4% of Americans aged 18-64 reported using generative AI, with 28% using it at work. To put that in perspective, it took three years for PCs to hit a 20% adoption rate.

“Generative AI has been adopted at a faster pace than PCs or the internet,” the researchers write. “This is driven by faster adoption of generative AI at home compared with the PC, likely because of differences in portability and cost.” The ease of access to tools like ChatGPT and Google Gemini has played a crucial role in this faster uptake.

The data from the survey shows generative A.I. reaching nearly 40 percent adoption just two years after introduction, far outpacing the early adoption rates of PCs and the internet. (Credit: Federal Reserve Bank of St. Louis)
2. Generative AI is being used by everyone—not just tech workers

While you might expect generative AI to be used mostly by software developers or data scientists, the research shows that adoption is widespread across industries. In fact, one in five “blue-collar” workers—those in construction, installation, repair, and transportation—regularly use generative AI on the job.

“Generative AI adoption is most common in management, business, and computer occupations, with usage rates exceeding 40%,” the paper says. “Still, one in five ‘blue-collar’ workers and one in five workers without a college degree use generative AI regularly on the job as well.”

This shows that AI is no longer reserved for high-skilled or specialized roles. From writing reports to generating creative ideas, generative AI is being used in a surprising variety of tasks across the occupational spectrum.

Generative A.I. usage across occupations, showing its reach beyond tech fields. While computer and management professionals lead adoption, even blue-collar workers report significant use, highlighting A.I.’s broad impact on diverse workplaces. (Credit: Federal Reserve Bank of St. Louis)
3. AI adoption mirrors the trend of rising workplace inequality

Just as the PC revolution led to greater workplace inequality, with computers complementing high-skilled workers while automating routine tasks, the adoption of generative AI could accelerate this trend. The study found that younger, more educated, and higher-income workers are more likely to use AI on the job.

Notably, workers with a bachelor’s degree or higher are twice as likely to use AI as those without one (40% vs. 20%). The researchers warn that this could exacerbate existing inequalities in the labor market.

“Generative AI usage is more common among younger, more educated, and higher-income workers. This is notable because the PC revolution was followed by rising labor market inequality,” the authors write.

Disparities in generative A.I. adoption across demographic groups reveal potential new dimensions of workplace inequality. Men, younger workers, and those with advanced degrees show higher usage rates, while adoption among those without college education lags significantly. (Credit: Federal Reserve Bank of St. Louis)
4. AI Is already saving time on a variety of tasks

When it comes to specific tasks, workers are using generative AI for more than just coding or technical work. The most common uses of AI at work include writing, administrative tasks, and interpreting text or data. In fact, 57% of those using AI at work reported using it to help with writing tasks, and 49% said they used it for searching for information.

The researchers note that “usage rates at work exceeded 25% for all ten tasks in our list,” underscoring just how broadly helpful generative AI has become across job functions. Whether it’s summarizing reports or generating new ideas, AI is already saving employees significant time.

A breakdown of generative A.I. usage by task in the workplace, revealing its widespread application. Writing tops the list at nearly 57%, while even less expected tasks like generating new ideas see significant adoption. The data underscores A.I.’s broad impact on diverse work activities. (Credit: Federal Reserve Bank of St. Louis)
5. AI could boost U.S. labor productivity—but it’s still early days

Perhaps the most exciting finding of the study is that generative AI could provide a notable boost to labor productivity. Based on current usage patterns, the researchers estimate that between 0.5% and 3.5% of all U.S. work hours are currently being assisted by generative AI. They further estimate that this could result in a labor productivity increase of between 0.125% and 0.875%.

“If we assume that generative AI increases task productivity by 25%—the median estimate across five randomized studies—this would translate to an increase in labor productivity of between 0.125 and 0.875 percentage points at current levels of usage,” the study explains.

However, the authors caution that these estimates are speculative, given the early adoption stage of generative AI. While the technology’s potential is immense, its long-term impact on the economy will depend on how deeply it becomes embedded in everyday workflows.

Daily time spent using generative A.I. at work, showing varied adoption intensity. While most users engage with A.I. for 15-59 minutes per day, over a quarter use it for more than an hour daily, suggesting its growing integration into workplace routines. (Credit: Federal Reserve Bank of St. Louis)