Could AI slow science?

AI leaders have predicted that it will enable dramatic scientific progress: curing cancer, doubling the human lifespan, colonizing space, and achieving a century of progress in the next decade. Given the cuts to federal funding for science in the U.S., the timing seems perfect, as AI could replace the need for a large scientific workforce. It’s a common-sense view, at least among technologists, that AI will speed science greatly as it gets adopted in every part of the scientific pipeline — summarizing existing literature, generating new ideas, performing data analyses and experiments to test them, writing up findings, and performing “peer” review. But many early common-sense predictions about the impact of a new technology on an existing institution proved badly wrong. The Catholic Church welcomed the printing press as a way of solidifying its authority by printing Bibles. The early days of social media led to wide-eyed optimism about the spread of democracy worldwide following the Arab Spring. Similarly, the impact of AI on science could be counterintuitive. Even if individual scientists benefit from adopting AI, it doesn’t mean science as a whole will benefit. When thinking about the macro effects, we are dealing with a complex system with emergent properties. That system behaves in surprising ways because it is not a market. It is better than markets at some things, like rewarding truth, but worse at others, such as reacting to technological shocks. So far, on balance, AI has been an unhealthy shock to science, stretching many of its processes to the breaking point. Any serious attempt to forecast the impact of AI on science must confront the production-progress paradox. The rate of publication of scientific papers has been growing exponentially, increasing 500 fold between 1900 and 2015. But actual progress, by any available measure, has been constant or even slowing. So we must ask how AI is impacting, and will impact, the factors that have led to this disconnect. Our analysis in this essay suggests that AI is likely to worsen the gap. This may not be true in all scientific fields, and it is certainly not a foregone conclusion. By carefully and urgently taking actions such as those we suggest below, it may be possible to reverse course. Unfortunately, AI companies, science funders, and policy makers all seem oblivious to what the actual bottlenecks to scientific progress are. They are simply trying to accelerate production, which is like adding lanes to a highway when the slowdown is actually caused by a toll booth. It’s sure to make things worse. 1. Science has been slowing — the production-progress paradox 2. Why is progress slowing? Can AI help? 3. Science is not ready for software, let alone AI 4. AI might prolong the reliance on flawed theories 5. Human understanding remains essential 6. Implications for the future of science The total number of published papers is increasing exponentially, doubling every 12 years. The total number of researchers who have authored a research paper is increasing even more quickly. And between 2000 and 2021, investment in research and development increased fourfold across the top seven funders (the US, China, Japan, Germany, South Korea, the UK, and France).1 But does this mean faster progress? Not necessarily. Some papers lead to fundamental breakthroughs that change the trajectory of science, while others make minor improvements to known results. Genuine progress results from breakthroughs in our understanding. For example, we understood plate tectonics in the middle of the last century — the idea that the continents move. Before that, geologists weren’t even able to ask the right questions. They tried to figure out the effects of the cooling of the Earth, believing that that’s what led to geological features such as mountains. No amount of findings or papers in older paradigms of geology would have led to the same progress that plate tectonics did. So it is possible that the number of papers is increasing exponentially while progress is not increasing at the same rate, or is even slowing down. How can we tell if this is the case? One challenge in answering this question is that, unlike the production of research, progress does not have clear, objective metrics. Fortunately, an entire research field — the "science of science", or metascience — is trying to answer this question. Metascience uses the scientific method to study scientific research. It tackles questions like: How often can studies be replicated? What influences the quality of a researcher's work? How do incentives in academia affect scientific outcomes? How do different funding models for science affect progress? And how quickly is progress really happening? Strikingly, many findings from metascience suggest that progress has been slowing down, despite dramatic increases in funding, the number of papers published, and the number of people who author scientific papers. We collect some evidence below; Matt Clancy reviews many of these findings in much more depth. 1) Park et al. find that "disruptive" scientific work represents an ever-smaller fraction of total scientific output. Despite an exponential increase in the number of published papers and patents, the number of breakthroughs is roughly constant. 2) Research that introduces new ideas is more likely to coin new terms. Milojevic collects the number of unique phrases used in titles of scientific papers over time as a measure of the “cognitive extent” of science, and finds that while this metric increased up until the early 2000s, it has since entered a period of stagnation, when the number of unique phrases used in titles of research papers has gone down. 3) Patrick Collison and Michael Nielsen surveyed researchers across fields on how they perceived progress in the most important breakthroughs in their fields over time — those that won a Nobel prize. They asked scientists to compare Nobel-prize-winning research from the 1910s to the 1980s. They found that scientists considered advances from earlier decades to be roughly as important as the ones from more recent decades, across Medicine, Physics, and Chemistry. Despite the vast increases in funding, published papers, and authors, the most important breakthroughs today are about as impressive as those in the decades past. 4) Matt Clancy complements this with an analysis of what fraction of discoveries that won a Nobel Prize in a given year were published in the preceding 20 years. He found that this number dropped from 90% in 1970 to 50% in 2015, suggesting that either transformative discoveries are happening at a slower pace, or that it takes longer for discoveries to be recognized as transformative. 5) Bloom et al. analyze research output from an economic perspective. Assuming that economic growth ultimately comes from new ideas, the constant or declining rate of growth implies that the exponential increase in the number of researchers is being offset by a corresponding decline in the output per researcher. They find that this pattern holds true when drilling down into specific areas, including semiconductors, agriculture, and medicine (where the progress measures are Moore’s law, crop yield growth, and life expectancy, respectively). Of course, there are shortcomings in each of the metrics above. This is to be expected: since progress doesn't have an objective metric, we need to rely on proxies for measuring it, and these proxies will inevitably have some flaws. For example, Park et al. used citation patterns to flag papers as "disruptive": if follow-on citations to a given paper don't also cite the studies this paper cited, the paper is more likely to be considered disruptive. One criticism of the paper is that this could simply be a result of how citation practices have evolved over time, not a result of whether a paper is truly disruptive. And the metric does flag some breakthroughs as non-disruptive — for example, AlphaFold is not co…