1. For readers who’ve never heard of it: what is R, and what does it actually do?
R started as an open-source alternative to a proprietary statistics language, “S,” and it has grown into one of the most important tools in data science. Long before the current AI boom, R became the environment where statisticians, researchers, economists, and data scientists developed many of the methods, workflows, and validation frameworks that modern AI now builds upon. R may not be a household name, but it sits underneath many of the systems and decisions people rely on every day. Today, it’s used by researchers, economists, healthcare organizations, and AI developers around the world to analyze data, build models, and make sense of complex information. In many ways, it’s become part of the foundation that modern data-driven decision-making, and increasingly AI as we know it, relies on.
2. AI has evolved dramatically over the last decade. What role does R play in today’s AI landscape, medicine, economics, etc. and why is it still relevant?
While most attention goes to the AI models themselves, those systems were built on decades of openly shared statistical knowledge. The R community played a major role in creating and distributing the code, datasets, and analytical approaches that helped make modern AI possible. Modern generative AI systems like Anthropic, Google, and OpenAI actually achieved their analytical mastery by training on R’s decades of openly shared code, datasets, and computational workflows.
R also plays a role in many of the world’s most consequential decisions. It is accepted by the U.S. Food and Drug Administration (FDA) for reviewing clinical trials, powers major genomics and cancer research initiatives through Bioconductor, previously supported the Johns Hopkins COVID-19 dashboard, and is used by institutions including the Bank of England and European Central Bank for economic modeling and forecasting. The software has been cited over 343,000 times in academic literature.
As AI adoption accelerates, model performance is no longer the only challenge. Organizations increasingly need confidence that systems are accurate, reproducible, explainable, and validated. R remains one of the primary environments where statistical testing, benchmarking, validation, and model evaluation occur before technologies are deployed in high-stakes settings.
3. Statistics underpins everything from medicine and economics to modern AI, yet the field often operates behind the scenes. Why does recognition like the Rousseeuw Prize matter, especially for R?
The Rousseeuw Prize for Statistics is one of the most prestigious awards in statistics. Established by statistician Peter Rousseeuw, it recognizes innovations that have had a major impact on the field and on society more broadly. This year’s award is especially notable because it honors five members of the R Core Team instead of a single researcher: Brian D. Ripley (University of Oxford), Martin Maechler (ETH Zurich), Kurt Hornik (WU Vienna University of Economics and Business), Peter Dalgaard (Copenhagen Business School), and Luke Tierney (University of Iowa).
This year’s award highlights a reality that often goes unnoticed: some of the world’s most important technology is maintained not by large corporations, but by small groups of dedicated people working behind the scenes. In terms of global impact on data science and AI, R has become every bit as foundational as the technologies developed by companies like NVIDIA, yet it has been built and maintained largely as a public-interest project. Over the last 26 years, these five individuals have collectively contributed nearly 29,000 hours of unpaid work maintaining and improving R, helping ensure that researchers, governments, healthcare organizations, and AI developers around the world can rely on it every day.
The award recognizes that modern innovation is not driven solely by breakthrough discoveries but also by the ongoing stewardship of critical infrastructure. In R’s case, that infrastructure has become foundational to scientific research, medicine, economics, and modern AI. The prize shines a light on the often invisible work required to keep those systems running and acknowledges the extraordinary public impact of a volunteer-driven project.
4. Most people associate modern AI and machine learning with Python. What role did R play in shaping the tools and workflows AI developers actually use today?
When people hear about R they assume it’s just another programming language, but in reality it has helped shape how modern data science is done. R’s conceptual innovations were so foundational that they served as the direct blueprint for Python’s data science ecosystem, including pandas, the tool most AI developers use today. Many of the ideas, workflows, and best practices that underpin today’s AI and analytics ecosystem were developed, tested, or popularized within the R community before becoming industry standards.
5. How did removing the cost barrier of proprietary software affect global AI and data science research?
By releasing R under the free GNU General Public License, the creators dismantled the wealth barriers of expensive, proprietary software. This democratization meant that public health workers in rural India, students in Nairobi, and researchers in Latin America suddenly had free access to the exact same advanced modeling frameworks used by elite universities and tech corporations. This global accessibility allowed statistical capability to scale universally, unconstrained by geographic or economic boundaries. For example, when a new statistical method or machine learning technique is developed, it can often be packaged and shared with the global community almost immediately. That allows researchers, healthcare organizations, governments, and enterprises to test, refine, and deploy new approaches much faster than would be possible in a closed ecosystem.
6. How did the ability to easily share statistical packages change the pace of scientific breakthroughs in modern genomics and healthcare?
R transformed the development pipeline so that a new statistical method could be packaged, automatically validated, and globally distributed within weeks, effectively eliminating the lag between theoretical innovation and actual application. This supported the rise of platforms like Bioconductor, which currently hosts over 2,200 packages dedicated to computational biology and bioinformatics. Because researchers could share code frictionlessly, R became the foundational infrastructure for high-throughput genomics, the Human Genome Project’s downstream analysis, and single-cell RNA-seq workflows, recording nearly 99 million package downloads in 2025 alone.
7. What could other AI developers learn from how the R community maintains reliability?
AI developers, who often face challenges maintaining complex software environments, could learn a lot from R’s approach to stability and quality control. With more than 23,000 packages contributed by researchers and developers worldwide, the R ecosystem has earned a reputation for reliability and trust. That stability stems from rigorous review processes, strong community oversight, transparent code, and a culture that prioritizes reproducibility and scientific validation. At a time when concerns about software supply-chain risk and dependency vulnerabilities are growing, R offers a compelling example of how large open-source communities can scale without sacrificing trust.
8. What would happen to the multi-billion-dollar AI industry if this free software maintained by volunteer communities were to suddenly vanish?
R is deeply embedded in many research, analytics, healthcare, finance, and academic workflows, and thousands of organizations rely on it for statistical analysis, data preparation, visualization, and model evaluation. If R were to disappear, many of those workflows would need to be rebuilt or migrated to other tools, which would be costly and time-consuming. More importantly, the community knowledge, packages, and decades of accumulated expertise built around R would be difficult to replace. This highlights the importance of continuity, stewardship, and investing in the people who maintain the foundations that modern technology depends upon. This is another reason why the R Core Team winning the Rousseeuw Prize provides important recognition and support for a small group of volunteers who have helped maintain software relied on by governments, researchers, businesses, and AI developers around the world.
9. As enterprise attention shifts from building models to evaluating them, what role does classical statistical computing play in proving AI systems actually work, can be validated, and are ready for real-world use?
Classical statistical computing platforms like R are critical for proving that AI models are safe, accurate, and compliant in high-stakes environments. Because R is stable, reproducible, and transparent, it is officially trusted for regulatory workflows by the FDA for clinical trials, the European Central Bank for macroeconomic modeling, major pharmaceutical companies, and for cancer research initiatives through Bioconductor. As AI moves into regulated real-world use, R provides the auditable “ground truth” standard required for compliance.
10. If the R story proves that a handful of volunteers can build infrastructure rivaling commercial tech giants, what’s the lesson for how we fund and sustain scientific software going forward?
One of the most interesting aspects of this award is that it challenges traditional ideas about who deserves recognition. Historically, many major scientific prizes have focused on celebrating individual stars. The Rousseeuw Prize takes a different view. By awarding $1 million to a team of relatively unknown volunteers, it recognizes that some of the most important contributions to modern society come from collaborative, long-term efforts rather than from any single individual.
The story of R illustrates just how much of today’s innovation depends on shared, community-driven infrastructure. For more than 25 years, the R Core Team has maintained software that now supports research, healthcare, finance, government decision-making, and AI development around the world. Yet, work like this often receives far less attention and funding than more visible breakthroughs.
Looking ahead, we need to think of scientific software as infrastructure. Just as we invest in laboratories, research facilities, and computing resources, we should invest in the software ecosystems that make discovery possible. That doesn’t necessarily mean commercializing every project. It may involve a mix of public funding, institutional support, industry partnerships, and community contributions. The broader lesson is that many of the essential innovations of the future may come from people and teams we’ve never heard of, and we need systems that recognize and support their work.
About David Donoho:
David Donoho, Ph.D., is a professor of statistics at Stanford University and one of the world’s leading voices in data science and statistical computing, a MacArthur Fellow (AKA “Genius Grant” recipient), and a member of the National Academy of Sciences. As a preeminent statistician and expert on the R ecosystem, he has studied and documented the software’s massive global footprint. Prof. Donoho and collaborators compiled the quantitative research proving how R transformed from a niche academic project into a multi-million-dollar infrastructure powering modern AI, pharmaceutical research, and global macroeconomic forecasting.
