Do scientific discoveries slow down?

Is science short of steam? An increasing set of research suggests that disruptive breakthroughs – the type that fundamentally redefines whole fields – can occur less frequently. An article in 2023 in Nature reported that scientific articles and patents are, on average, less “disruptive” than they were in the middle of the 20th century. The study aroused intense interest and considerable controversy, covered in a recent news element entitled provocative “Do revolutionary scientific discoveries become more difficult to find?”

Before weighing, however, it is worth questioning a more fundamental question: what do we mean when we call “disruptive” science? And is it, in fact, the appropriate reference index for progress?

The study in question, led by Entrepreneurship Scholar Russell FunkUse a metric based on the quote known as the Disolidation Disruption Index (CD). The tool tries to quantify whether the new research moves previous work – a disturbance signal – or is built directly, thus reinforcing the existing paradigms. It represents a notable contribution to our understanding of scientific change. Their conclusion, according to which the disturbances have decreased between the disciplines, even if the volume of scientific production has widened, triggered a debate among scientists, academics and decision -makers.

Innovation can become more difficult, but also deeply

At the structural level, science becomes more complex as it matures. In a certain sense it is has To slow down. The simplest questions are often the first to answer, and what remains are challenges that are more subtle, more interdependent and more difficult to resolve. The law of the decrease in marginal yields, familiar for a long time in economics, finds a natural corollary in research: at a given moment, the intellectual “fruit” has been widely harvested.

However, this does not necessarily imply stagnation. In fact, science itself evolves. I think that exposed drops in disturbance do not reflect an impoverishment of ideas, but a transformation of the conduct and culture of research itself. Quote practices have changed. Publication incentives have changed. The availability of digital data and resources has exploded. Compare the behavior of contemporary quotation to that of previous decades is not simply orange apples; It’s more like comparing ecosystems separated by tectonic time.

More deeply, we could ask ourselves if the paradigm changes – in particular those in the Kuhnian sense – are really the milestones that we should attribute above all the others. A large part of innovation that stimulates societal progress and economic productivity does not emerge from revolutions in thought, but from the subtle extension and the application of existing knowledge. In the fields as varied as biomedicine, agriculture and climate science, incremental refinement has given transformer impact results.

Science today is more sophisticated and more efficient

Scientists publish more than ever today. Critics of contemporary science attribute this to the metric culture of “cutting of salamis”, in which ideas are fragmented in “the minimum publication unit” so that scientists can accumulate a constantly increasing number of publications to guarantee career viability in a published or perished environment. But such criticisms neglect the extraordinary gains in the efficiency of research that has occurred in recent decades, which, I think, is a much more convincing explanation for the massive production of scientific research today.

Since the 1980s, personal IT has transformed almost all dimensions of the scientific process. The preparation of the manuscript, once the province of writing machines and repaired projects, has become transparent. The acquisition of data now involves automated sensors and real -time surveillance. Analytical tools like Python and R allow researchers to carry out modeling and sophisticated statistics at an unprecedented speed. Communication is instantaneous. Knowledge sharing platforms and free access magazines have dismantled many old barriers to the entrance.

Indeed, one wonders if the criticisms recently read a research document of the 1930s or 1970s. Methodological rigor, the analytical depth and the interdisciplinary scope of modern research are, according to almost all standards, much more advanced.

The horizon has extended

In the only biology, broadband technologies – part of the broader “ommal” revolution catalyzed by innovations such as the chain reaction by polymerase (PCR), which allowed a rapid amplification of DNA and supported the possible success of the human genome project – contains to utter discovery at an astonishing rate.

When the criticisms deplore the apparent decline of the “blockbusters” of Nobel caliber, they neglect that the border of science has extended – not narrowed. If we consider scientific knowledge as a volume, it is delimited by an outer edge where the discovery occurs. In Euclidean geometry, as the radius of a sphere increases, the surface (scale with the square of the radius) grows more slowly than the volume (which evolves with the cube). Although the volume of knowledge increases more quickly – encompassing the theories and established tools which continue to produce applications – the surface also develops, and it is along this extended border, where the known meets the unknown, that innovation occurs.

Rethink investment returns

The modern belief that science must provide measurable economic yields is, historically, a relatively recent development. Before the Second World War, scientific research was not widely considered as an engine of productivity. Economist Daniel Susskind argued that even the concept of economic growth as a central political objective is an invention of the mid -20th century.

After the war, it radically changed. Governments have started to see research as essential to national development, security and public health. However, even if expectations have increased, public investment in science has paradoxically decreased, despite the fact that fundamental scientific research is a massive accelerator of economic productivity and effectively self -financing. Although absolute funding has increased, public spending on science as a GDP share has decreased in the United States and many other countries. Given the scale and complexity of the challenges we are now facing, we can underestimate in the very company that could provide solutions. Recent proposals to Cut funding for NIH and NSF By certain estimates, could cost the United States of tens of billions of people with loss of productivity.

There is convincing evidence suggesting that the significant increase in R&D expenses – dining or even triples them – would have given rise Strong and supported feedback.

AI and the next wave of scientific efficiency

Looking towards the future, artificial intelligence offers potential not only to rationalize research, but also to increase the innovation process itself. AI tools – large language models as chatgpt with specialized engines for data exploration and synthesis – have researchers to cross disciplines, identify models and generate new hypotheses at a remarkable speed.

The ability to navigate large bodies of scientific literature – once reserved for those who have access to elite research libraries and amply time for reading – has been radically democratized. Today, scientists can access digitized benchmarks, annotate articles with precision tools, manage bibliographies with software and instantly trace the intellectual line of ideas. The tools fueled by AI support researchers to pass through and synthesize equipment between disciplines, identify models, highlight connections and put in view of under-explored ideas. For researchers like me – an environmentalist who often draws inspiration from non -linear dynamics, statistical physics and cognitive psychology – these technologies work as accelerators of thought rather than substitutes. They support the process of discovering latent analogies and assembly of new constellations of insight, the type of cognitive recombination which underlies the real creativity. Although in -depth understanding always requires a sustained intellectual commitment – reading, interpretation and critical analysis – these tools lower the barrier to discovery and widen the range of intellectual possibilities.

By improving interdisciplinary thinking and reducing latency between the idea and the investigation, AI could well revive the type of scientific innovation which, according to some, slips from the scope.

Science as a cultural effect

Finally, it should be noted that the value of science is not only, or even mainly, economic. Like the arts, literature or philosophy, science is a cultural and intellectual enterprise. It is an expression of curiosity, a vehicle of collective understanding and a means of locating us in the universe.

From my point of view and that of many colleagues, the current landscape of discovery seems more fertile than ever. The questions we ask are more ambitious, the tools at our disposal more refined and the connections that we are able to make more multidimensional.

If the disturbance signal seems to adapt, it may only be because the spectrum of science has become too wide for a single wavelength dominates. Rather than deploring an apparent slowdown, we could ask a more constructive question: will we measure good things? And do we create the conditions that allow the most vital forms of science – creative, integrative and with the potential to transform human society for the best – to flourish?