Share of AI-engaged Scientific Publications

A significant proportion of forecasters predict low numbers because they believe it will be difficult to determine AI-engagement by abstracts alone: “AI will become so prevalent that its use will be assumed--like the use of a computer--and not mentioned in abstracts.” But several high-forecast respondents point out that “major publishers (e.g., Nature, Elsevier) increasingly require AI/tool disclosure,” even if “the details of AI tools are still discussed in the main body of the papers.”

Many high-forecast respondents emphasize that the academic fields under study attained a combined increase of 1293% since 1985 (Duede et al. 2024), that this “rapid increase is mostly associated with the seven years between 2015 and 2022,” and “large increases in the percentage of AI-engaged have surely already occurred by Q3 of 2025 compared with the results of the 2022 study.” Low-forecast respondents often question the wisdom of anchoring off these historical trends.

High-forecast respondents frequently emphasize that “AI will become a ubiquitous research tool” due to domain-specific models, agentic systems, and improved AI literacy among scientists. Low-forecast respondents tend to suspect that unreliability and a lack of interpretability will be a bottleneck. Some argue that to be truly useful in all aspects of research, a breakthrough in the underlying architecture is probably required.

A few low-forecast respondents consider that older researchers may be reluctant to adopt AI, and the introduction of AI-literate researchers into these fields will likely be gradual: “Integrating AI into fields that have barely engaged with it in the past will be naturally slow, both because of the lack of interdisciplinary knowledge and natural resistance to change from the existing body of researchers in these fields.”

Many high-forecast respondents highlight that extensive AI use is likely in data-rich, math-heavy fields of study where AI's mathematical and computational capabilities will be helpful, in particular: particle physics, astrophysics, high-energy physics, quantum systems, anomaly detection, and large-scale simulations. Some low-forecast respondents, however, express skepticism that theory-heavy subfields will see much engagement by 2030: “AI is way less useful for theoretical papers as there is black box problem of not being able to test the hypothesis against causal empirical findings.” Another writes that, in addition to lacking the requisite capabilities, “physics has a conservative publishing culture -- theorists in particular won't add AI to their abstracts unless the method is clearly central.”

As with physics, high-forecast respondents often emphasize that AI is well-suited to assist with data-rich, math-heavy subfields like molecular design and discovery, computational chemistry, energy storage, inverse design, high-throughput screening, and simulation AI coupling. One notes: “Materials science is...the one with the highest potential to be AI-engaged in the next decade due to its large dependence on knowledge coming from sophisticated combinatorics from a fixed set of elements.” Some also stress that industrial demand for accelerated discovery, and the financial rewards that might follow, will provide a strong push. One low-engagement forecaster stressed that “materials science likely [won’t] benefit from vast troves of data crossing disciplines unless new methods of AI accessible data collection are developed.”

High-forecast respondents commonly note that AI is already used in imaging, diagnostics, genomics, drug discovery, medical natural language processing, and personalized medicine design: “Medicine shows the most rapid uptake, fueled by the pervasive use of AI in medical imaging diagnostic algorithms, and clinical decision support systems.” One emphasizes that “AI could enable the utilization of the enormous amount of medical data contained in electronic health records (EHRs).” Low-forecast respondents focus more on the possibility that regulatory, data privacy, validation, and ethical concerns could limit use, and that the number of observational clinical case studies and trials, which are unlikely to involve AI, will keep the percentage low: “A significant portion of papers are observational, often reporting causal effects. There isn't much room for AI in these sorts of papers, as current statistical methods are more reliable and bias-free, compared to AI.”