Generative AI is reshaping software development – and fast. A new study published in Science shows that AI-assisted coding is spreading rapidly, though unevenly: in the U.S., the share of new code relying on AI rose from 5% in 2022 to 29% in early 2025, compared with just 12% in China. AI usage is highest among less experienced programmers, but productivity gains go to seasoned developers.
The software industry is enormous. In the U.S. economy alone, firms spend an estimated $600 billion a year in wages on coding-related work. Every day, billions of lines of code keep the global economy running. How is AI changing this backbone of modern life?
In a study published in Science, a research team led by the Complexity Science Hub (CSH) found that by the end of 2024, around one-third of all newly written software functions – self-contained subroutines in a computer program – in the United States were already being created with the support of AI systems.
“We analyzed more than 30 million Python contributions from roughly 160,000 developers on GitHub, the world’s largest collaborative programming platform,” says Simone Daniotti of CSH and Utrecht University. GitHub records every step of coding – additions, edits, improvements – allowing researchers to track programming work across the globe in real time. Python is one of the most widely used programming languages in the world.
REGIONAL GAPS ARE LARGE
The team used a specially trained AI model to identify whether blocks of code were AI-generated, for instance via ChatGPT or GitHub Copilot.
“The results show extremely rapid diffusion,” explains Frank Neffke, who leads the Transforming Economies group at CSH. “In the U.S., AI-assisted coding jumped from around 5% in 2022 to nearly 30% in the last quarter of 2024.”
At the same time, the study found wide differences across countries. “While the share of AI-supported code is highest in the U.S. at 29%, Germany reaches 23% and France 24%, followed by India at 20%, which has been catching up fast,” he says, while Russia (15%) and China (12%) still lagged behind at the end of our study.
Global diffusion of AI-assisted coding and its impact | Left: The share of AI-written Python functions (2019-2024) grows rapidly, but countries differ in their adoption rates. The U.S. leads the early adoption of generative AI, followed by European nations such as France and Germany. From 2023 onward, India rapidly catches up, whereas adoption in China and Russia progresses more slowly. Right: Comparing usage rates for the same programmers at different points in time, generative AI adoption is associated with increased productivity (commits), breadth of functionality (library use) and exploration of new functionality (library entry), but only for senior developers, while early-career developers do not derive any statistically significant benefits from using generative AI.
“It's no surprise the U.S. leads – that's where the leading LLMs come from. Users in China and Russia have faced barriers to accessing these models, blocked by their own governments or by the providers themselves, though VPN workarounds exist. Recent domestic Chinese breakthroughs like DeepSeek, released after our data ends in early 2025, suggest this gap may close quickly,” says Johannes Wachs, a faculty member at CSH and associate professor at Corvinus University of Budapest.
EXPERIENCED DEVELOPERS BENEFIT MOST
The study shows that the use of generative AI increased programmers’ productivity by 3.6% by the end of 2024. “That may sound modest, but at the scale of the global software industry it represents a sizeable gain,” says Neffke, who is also a professor at Interdisciplinary Transformation University Austria (IT:U).
The study finds no differences in AI usage between women and men. By contrast, experience levels matter: less experienced programmers use generative AI in 37% of their code, compared to just 27% for experienced programmers. Despite this, the productivity gains the study documents are driven exclusively by experienced users. "Beginners hardly benefit at all," says Daniotti. Generative AI therefore does not automatically level the playing field; it can widen existing gaps.
In addition, experienced software developers experiment more with new libraries and unusual combinations of existing software tools. "This suggests that AI does not only accelerate routine tasks, but also speeds up learning, helping experienced programmers widen their capabilities and more easily venture into new domains of software development," says Wachs.
ECONOMIC GAINS
What does all of this mean for the economy? “The U.S. spends an estimated $637 billion to $1.06 trillion annually in wages on programming tasks, according to an analysis of about 900 different occupations,” says co-author Xiangnan Feng from CSH. If 29% of code is AI-assisted and productivity rises by 3.6%, that adds between $23 and $38 billion in value each year. “This is likely a conservative estimate,” Neffke points out, “the economic impact of generative AI in software development was already substantial at the end of 2024 and is likely to have increased further since our analysis.”
LOOKING AHEAD
Software development is undergoing profound transformation. AI is becoming central to digital infrastructure, boosting productivity and fostering innovation – but mainly for people who already have substantial work experience.
“For businesses, policymakers, and educational institutes, the key question is not whether AI will be used, but how to make its benefits accessible without reinforcing inequalities,” says Wachs. “When even a car has essentially become a software product, we need to understand the hurdles to AI adoption – at the company, regional, and national levels – as quickly as possible,” Neffke adds.
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ABOUT THE STUDY
The study “Who is using AI to code? Global diffusion and impact of Generative AI” by Simone Daniotti, Johannes Wachs, Xiangnan Feng, and Frank Neffke has been published in Science (doi: 10.1126/science.adz9311).
More information, including a copy of the paper, can be found online at the Science press package at https://www.eurekalert.org/press/scipak/
ABOUT THE COMPLEXITY SCIENCE HUB
The Complexity Science Hub (CSH) is Europe’s research center for the study of complex systems. We derive meaning from data from a range of disciplines – economics, medicine, ecology, and the social sciences – as a basis for actionable solutions for a better world. CSH members are Austrian Institute of Technology (AIT), BOKU University, Central European University (CEU), Graz University of Technology, Interdisciplinary Transformation University Austria (IT:U), Medical University of Vienna, TU Wien, University of Continuing Education Krems, Vetmeduni Vienna, Vienna University of Economics and Business, and Austrian Economic Chambers (WKO).
