Is Science Slowing Down?

Forbes

26-05-2025

Basic scientific research is a key contributor to economic productivity.
Is science running out of steam? A growing body of research suggests that disruptive breakthroughs—the kind that fundamentally redefine entire fields—may be occurring less frequently. A 2023 article in Nature reported that scientific papers and patents are, on average, less 'disruptive' than they were in the mid-20th century. The study sparked intense interest and considerable controversy, covered in a recent news feature provocatively titled 'Are Groundbreaking Science Discoveries Becoming Harder To Find?'
Before weighing in, however, it is worth interrogating a more fundamental question: What do we mean when we call science 'disruptive'? And is that, in fact, the appropriate benchmark for progress?
The study in question, led by entrepreneurship scholar Russell Funk, employs a citation-based metric known as the Consolidation–Disruption (CD) index. The tool attempts to quantify whether new research displaces prior work—a signal of disruption—or builds directly upon it, thereby reinforcing existing paradigms. It represents a noteworthy contribution to our understanding of scientific change. Their conclusion, that disruption has declined across disciplines even as the volume of scientific output has expanded, has ignited debate among scientists, scholars and policymakers.
At a structural level, science becomes more complex as it matures. In some sense it has to slow down. The simplest questions are often the first to be answered, and what remains are challenges that are more subtle, more interdependent, and more difficult to resolve. The law of diminishing marginal returns, long familiar in economics, finds a natural corollary in research: at some point the intellectual 'low-hanging fruit' has largely been harvested.
Yet this does not necessarily imply stagnation. In fact, science itself is evolving. I think that apparent declines in disruption reflect not an impoverishment of ideas, but a transformation in the conduct and culture of research itself. Citation practices have shifted. Publication incentives have changed. The sheer availability of data and digital resources has exploded. Comparing contemporary citation behavior to that of earlier decades is not simply apples to oranges; it's more like comparing ecosystems separated by tectonic time.
More profoundly, we might ask whether paradigm shifts—particularly those in the Kuhnian sense—are truly the milestones we should prize above all others. Much of the innovation that drives societal progress and economic productivity does not emerge from revolutions in thought, but from the subtle extension and application of existing knowledge. In fields as varied as biomedicine, agriculture, and climate science, incremental refinement has yielded results of transformative impact.
Brighter green hybrid rice plants (left) help increase yields at this Filipino farm. (Photo by ... More)
Scientists are publishing more today than ever. Critics of contemporary science attribute this to metric-driven culture of 'salami slicing,' in which ideas are fragmented into the 'minimum publishable unit' so that scientists can accrue an ever-growing publication count to secure career viability in a publish-or-perish environment. But such critiques overlook the extraordinary gains in research efficiency that have occurred in the past few decades, which I think are a far more compelling explanation for the massive output of scientific research today.
Since the 1980s, personal computing has transformed nearly every dimension of the scientific process. Manuscript preparation, once the province of typewriters and retyped drafts, has become seamless. Data acquisition now involves automated sensors and real-time monitoring. Analytical tools like Python and R allow researchers to conduct sophisticated modeling and statistics with unprecedented speed. Communication is instantaneous. Knowledge-sharing platforms and open-access journals have dismantled many of the old barriers to entry.
Advances in microcomputer technology in the 1980s and 1990s dramatically accelerated scientific ... More research.
Indeed, one wonders whether critics have recently read a research paper from the 1930s or 1970s. The methodological rigor, analytical depth, and interdisciplinary scope of modern research are, by nearly any standard, vastly more advanced.
In biology alone, high-throughput technologies—part of the broader 'omics' revolution catalyzed by innovations like the polymerase chain reaction (PCR), which enabled rapid DNA amplification and supported the eventual success of the Human Genome Project—continue to propel discovery at an astonishing pace.
Nobel Prize laureate James D. Watson speaks at a press conference to announce that a six-country ... More consortium has successfully drawn up a complete map of the human genome, completing one of the most ambitious scientific projects ever and offering a major opportunity for medical advances, 14 April 2003 at the National Institute of Health in Bethesda, Maryland. The announcement coincides with the 50th anniversary of the publication of the landmark paper describing DNA's double helix by Watson and Francis Crick. AFP PHOTO / Robyn BECK (Photo credit should read ROBYN BECK/AFP via Getty Images)
When critics lament the apparent decline of Nobel-caliber 'blockbusters' they overlook that the frontier of science has expanded—not narrowed. If we consider scientific knowledge as a volume, then it is bounded by an outer edge where discovery occurs. In Euclidean geometry, as the radius of a sphere increases, the surface area (scaling with the square of the radius) grows more slowly than the volume (which scales with the cube). While the volume of knowledge grows more rapidly—encompassing established theories and tools that continue to yield applications—the surface area also expands, and it is along this widening frontier, where the known meets the unknown, that innovation arises.
The modern belief that science must deliver measurable economic returns is, historically speaking, a relatively recent development. Before the Second World War, scientific research was not broadly viewed as a driver of productivity. Economist Daniel Susskind has argued that even the concept of economic growth as a central policy goal is a mid-20th century invention.
After the war, that changed dramatically. Governments began to see research as critical to national development, security, and public health. Yet even as expectations have grown, relative public investment in science has, paradoxically, diminished, despite the fact that basic scientific research is a massive accelerant of economic productivity and effectively self-financing. While absolute funding has increased, government spending on science as a share of GDP has declined in the US and many other countries. Given the scale and complexity of the challenges we now face, we may be underinvesting in the very enterprise that could deliver solutions. Recent proposals to cut funding for NIH and NSF could, by some estimates, cost the U.S. tens of billions in lost productivity.
There is compelling evidence to suggest that significantly increasing R&D expenditures—doubling or even tripling them—would yield strong and sustained returns.
Looking to the future, artificial intelligence offers the potential to not only streamline research but also to augment the process of innovation itself. AI tools—from large language models like ChatGPT to specialized engines for data mining and synthesis—enable researchers to traverse disciplines, identify patterns, and generate new hypotheses with remarkable speed.
The ability to navigate vast bodies of scientific literature—once reserved for those with access to elite research libraries and ample time for reading—has been radically democratized. Scientists today can access digitized repositories, annotate papers with precision tools, manage bibliographies with software, and instantly trace the intellectual lineage of ideas. AI-powered tools support researchers in sifting through and synthesizing material across disciplines, helping to identify patterns, highlight connections, and bring under-explored ideas into view. For researchers like myself—an ecologist who often draws inspiration from nonlinear dynamics, statistical physics, and cognitive psychology—these technologies function as accelerators of thought rather than substitutes for it. They support the process of discovering latent analogies and assembling novel constellations of insight, the kind of cognitive recombination that underlies true creativity. While deep understanding still demands sustained intellectual engagement—reading, interpretation, and critical analysis—these tools lower the barrier to discovery and expand the range of intellectual possibilities.
By enhancing cross-disciplinary thinking and reducing the latency between idea and investigation, AI may well reignite the kind of scientific innovation that some believe is slipping from reach.
Finally, it bears emphasizing that the value of science is not solely, or even primarily, economic. Like the arts, literature, or philosophy, science is a cultural and intellectual enterprise. It is an expression of curiosity, a vehicle for collective self-understanding, and a means of situating ourselves within the universe.
From my vantage point, and that of many colleagues, the current landscape of discovery feels more fertile than ever. The questions we pose are more ambitious, the tools at our disposal more refined, and the connections we are able to make more multidimensional.
If the signal of disruption appears to be dimming, perhaps it is only because the spectrum of science has grown too broad for any single wavelength to dominate. Rather than lament an apparent slowdown, we might ask a more constructive question: Are we measuring the right things? And are we creating the conditions that allow the most vital forms of science—creative, integrative, and with the potential to transform human society for the better—to flourish?