AI-studie avslöjar: Claude påskyndar vetenskapen – men inte där det behövs mest
2026-11-06 • Kategori: Forskning
TL;DR: Ny studie visar att AI som Claude kan fördubbla forskningsresultat genom att underlätta dataanalys och textskrivning, men används mindre för idégenerering trots att forskare anser det viktigast.
Johannes Hermle*, Ben Jones*, Lukas Althoff*, Ryan Heller, Thomas Lyttelton, Carolyn Stein, Nathan Wilmers, Maxim Massenkoff, Peter McCrory*Lead authors of the reportSummaryWhat we measure. AI could speed up scientific discovery, but by how much depends on what AI can do and how scientists use it. Using privacy-preserving methods, we study 207,000 Claude and Claude Code research sessions to understand how Claude is used across scientific fields and research tasks. We are conducting this research to help the public better understand the state of AI for science today and assess the impact that AI may have on scientific discovery in the future.Where scientists use Claude. Relative to the number of researchers in each field, Claude is used more in the formal sciences, such as computer science, mathematics, and statistics, and less in health and lab-based physical sciences. Scientists primarily use Claude to analyze data and to write. Data analysis alone accounts for about one-third of sessions on Claude and nearly half of sessions on Claude Code. Scientists use it less for idea generation and data gathering, even though, in an accompanying survey, they rank those tasks as the most important for scientific discovery. That said, specialized AI tools designed for scientific work could narrow the gap between the research tasks scientists use AI for and the ones they consider most important. On Claude Science, for instance, idea generation makes up about twice the share of use that it does on Claude.How Claude use tracks AI capability. We create a measure of how well AI can, in theory, perform each research task. The tasks AI is best suited to (the top 20% on this measure) get two to four times as much Claude use as the time researchers spend on them would predict. The tasks AI is least suited to (the bottom 20%), which include most physical work, get very little. We observe the same pattern across fields: fields where AI can perform many tasks well, such as computer science and statistics, use Claude more.What this could mean for the speed of scientific discovery. Using a simple model, we explore scenarios for how much AI could speed up research. If existing AI capabilities are widely adopted, they could double research output in some fields. If AI could carry out all non-physical research work, output in fields such as computer science and statistics could rise as much as 15-fold, while output in bench sciences such as chemistry and biology, which are held back by physical bottlenecks, would rise three- or fourfold. In our survey of researchers who actively use AI, about 40% expect AI models to carry out full research projects on their own by 2030.IntroductionScience has been described as an “endless frontier” (Bush 1945). But how fast we explore that frontier has historically been limited by a scarce resource: the time and talent of human researchers. Artificial intelligence could ease that limit by amplifying what researchers can do, transforming both the process of discovery and the pace of exploration (Wang et al. 2023; Amodei 2024). Few applications of AI could matter more for human welfare. Given the highly cognitive nature of research compared with most sectors of the economy, AI’s effects may be particularly large and early in this domain.Recent examples show AI playing a growing role in scientific discovery. In structural biology, AlphaFold predicted the three-dimensional shapes of over 200 million proteins (Jumper et al. 2021; Varadi et al. 2024), and its creators shared the 2024 Nobel Prize in Chemistry. In materials science, GNoME predicted 380,000 stable new crystals (Merchant et al. 2023). In mathematics, researchers have used AI systems to help resolve decades-old Erdős problems, produce a potential proof for a Millennium Prize problem, and make progress on other major problems, from open conjectures to verifying landmark proofs (Anthropic 2026a, 2026b; OpenAI 2026a, 2026b; Tsoukalas et al. 2026).Beyond these developments, most scientific progress involves many specialized, interlinked research tasks, which can vary substantially across fields. How much AI accelerates scientific discovery depends not only on AI’s capabilities, but also on how it is adopted across these tasks. In this report, we use privacy-preserving infrastructure to study the scientific use of Claude, Claude Code, and Claude Science. We document how usage varies across fields and tasks, identify what explains these usage patterns, and outline scenarios for how AI could accelerate research.Claude use in science varies by field. Relative to their number of researchers, formal sciences such as computer science, mathematics, and statistics use Claude more heavily, while health and lab-based physical sciences use it less. Across research tasks, scientists use Claude more for analyzing data and writing, and less for gathering data or generating ideas and hypotheses. This is consistent with self-reported AI use from an accompanying survey of scientists.To understand these differences, we develop a measure of theoretical AI capability. Claude use is higher in tasks and fields that score higher on this measure. These also tend to be more cognitive in nature and better suited to reinforcement learning, in which AI learns from feedback on repeated attempts. In short, scientists appear to use AI where it works best.Based on these findings, we outline scenarios for how AI could accelerate scientific progress. With current capabilities, research output in some fields could as much as double, primarily from task-level augmentation. Researchers in our survey expect much greater capabilities to come. In a hypothetical scenario where AI fully automates all non-physical work, highly cognitive fields such as computer science or statistics could see up to a 15-fold increase in output. Because the fields with the largest potential for acceleration include those that produce AI itself, these gains could create an additional feedback loop, in which faster AI research produces more capable AI, which in turn could further accelerate research.While faster scientific discovery could bring substantial benefits to humanity, AI-driven research also carries risks and uncertainties. Less human oversight could lead to the pursuit of dangerous or unethical research. AI could also reshape researchers’ careers, making research work more or less rewarding, and alter the sense of purpose associated with the human pursuit of knowledge.How we conducted this researchOur goal with this research is to measure the different ways scientists use Claude while protecting the details of their work. To do this, we use privacy-preserving infrastructure to analyze the scientific use of Claude on consumer plans. A language model, used here as a “classifier,” reads each session transcript and labels it with a fixed set of categories, such as the field and the research task it involves, according to a predefined taxonomy (SciNet taxonomy by Alamos and Althoff 2026). The resulting labels, such as “chemistry” or “data cleaning,” are the only information from these conversations retained and analyzed by the research team for this report. No classifier employed in this study extracts and stores open-ended information or details on research content, such as research datasets, hypotheses, experimental methods, early findings, or unpublished papers. Results are reported only in aggregate and only for cells that meet a minimum number of distinct accounts. All classifiers introduced in this study are listed in the Appendix.Our measurement involves two steps. A first classifier identifies whether a session transcript involves scientific work. A second classifier then assigns each of these transcripts to a field, subfield, and task in the SciNet taxonomy. SciNet contains 7,262 tasks, spanning 34 fields and 318 subfields. Some tasks in the taxonomy, such as drafting manuscripts, are universal to all fields. Others are specific to individual fields or subfields. For instance, in the taxonomy, the task of predicting optical absorption and emission spectra is specific to materials science. Together, the tasks cover the full process of producing a new research finding.Our main sample consists of 207,049 sessions on consumer plans from August 2 to September 30, 2026, which were assigned a scientific field and a research task: 128,914 from Chat and Cowork, two general-purpose interfaces for conversation and knowledge work, hereafter referred to as “Claude,” and 78,135 from Claude Code, an agentic coding tool that works directly with a user’s files, writing and running code. We also study a supplemental sample of Claude Science sessions. For details on sample construction, see the Appendix.Combining usage data and a structured taxonomy of research tasks allows us to study which fields adopt Claude and for which parts of the research process. This research contributes to a growing body of work attempting to measure the adoption of AI technology for scientific discovery (Kobak et al. 2025; Liang et al. 2025; Hao et al. 2026; Codreanu et al. 2026).How Claude use varies across scientific fieldsWe begin by describing how Claude use varies across scientific fields. Figure 1 compares a field’s share of sessions against the share of global researchers in that field, giving us a sense of relative use per scientific field.1 On Claude, the three largest fields by session share are computer science (11.9%), engineering (8.4%), and medicine (7.7%), which are also among the largest fields by global researcher counts.2 Overall, fields with a larger share of researchers also tend to account for a larger share of Claude sessions (log-log correlation of 0.79).However, Claude use per field cannot be explained by the number of researchers alone. In Figure 1, fields above the 45-degree line show higher usage relative to their size, while fields below the line show lower usage. Relative usage is especially high for the formal sciences, including computer science, mathematics, and statistics. Usage is also high for the social sciences. By contrast, physical sciences such as chemistry, where work often happens in a lab, tend to show lower relative usage. So does medicine, the third-largest field by session volume, where researchers spend much of their time with patients.On Claude Code, usage is less strongly related to field size (log-log correlation with researcher counts of 0.62) and concentrates in more quantitative fields. Despite making up only 4.3% of the world’s researchers, computer science alone accounts for 32.7% of Claude Code sessions.3 Other formal sciences, such as mathematics and statistics, also show high relative usage. Relative usage is low in the arts and humanities and in the social sciences, with the exception of economics, a comparatively quantitative field.We observe similar patterns when comparing usage across 318 subfields within those 34 fields (Figure 2).4 This more granular subfield view reveals that artificial intelligence and machine learning stands out, with session shares of 5.0% for Claude and 15.2% for Claude Code, despite making up only 1.2% of the world’s researchers. More generally, regardless of the broad scientific domain, computational subfields are among the strongest adopters. These subfields include computational biology, computational linguistics, and computational neuroscience. Robotics also shows high relative usage, likely because it builds heavily on computational methods despite its focus on physical systems.How Claude use varies across research tasksBeyond understanding which fields use Claude, we also want to understand what scientists use it for. Research involves many different tasks, from generating hypotheses and analyzing data to writing up findings in a research paper. To better discern how Claude supports scientific discovery, we study how usage varies across these tasks. To do so, we assign each session to one of the 7,262 tasks in SciNet. These tasks can be universal to all fields, such as drafting manuscripts, or specific to fields or subfields, such as performing differential gene expression analysis to compare tumor subtypes, which is associated with cancer biology. The taxonomy distinguishes between research tasks, which researchers perform to produce new scientific findings, and non-research tasks, such as teaching, peer review, and service to the department or field.5 We limit our analysis to research tasks spanning eight categories: (1) data analysis, (2) writing and communication, (3) data gathering, (4) reading and knowledge acquisition, (5) theoretical analysis, (6) design and development (of software, materials, and creative works), (7) ideation and hypothesis generation, and (8) other, such as administering research.For each task category, we calculate its share of Claude and Claude Code sessions separately. To assess whether usage in a category is high or low, we consider the share of their time that researchers typically spend on that category, as estimated by SciNet. These time estimates are based on an LLM classifier, validated using both wet-lab research protocols that record researcher time spent per task and the frequency of tasks across thousands of academic papers (see the Appendix). This provides a benchmark to assess whether the share of Claude use across different task categories follows or deviates from how researchers spend their time.Usage by task category. Data analysis tasks have the highest usage in absolute terms and are overrepresented relative to researcher time. Data analysis accounts for 33.7% of sessions on Claude and 48.1% on Claude Code, compared with 27.6% of researcher time. Writing and communication tasks also show high usage, especially on Claude (32.0%, compared with 16.0% of researcher time).6 Data gathering tasks have low relative usage, accounting for 28.6% of researcher time but only 11.5% of sessions on Claude and 11.9% on Claude Code. The underrepresentation of data gathering is particularly strong for tasks with physical barriers, such as the collection of clinical or biochemical data. Data gathering tasks that can rely on more digital processes, such as retrieving archival documents or text, are less underrepresented, while purely digital data collection tasks, such as collecting online data, can be overrepresented. Ideation and hypothesis generation tasks also have low usage, accounting for 4.2% of sessions on Claude and 2.1% on Claude Code, compared with 7.1% of researcher time.Comparing observed usage to survey evidence. Along with our analysis of Claude and Claude Code sessions, we conducted a survey of 528 scientists, whose self-reported AI usage lines up with the patterns we observed in those sessions. The sample consists of researchers primarily in the life and health sciences who are active adopters of AI.7 Consistent with the Claude usage patterns shown in Figure 3, survey participants’ self-reported AI usage is highest for data analysis and writing and communication, and lower for ideation and hypothesis generation and data gathering. Participants also report more shared or delegated use of AI for data analysis and writing and communication (Figure 4).The survey also asked which categories matter most for producing important research results. Respondents rank ideation and hypothesis generation first and data gathering second. Notably, these categories are underrepresented in self-reported usage as well as in Claude sessions. The reasons for this underrepresentation likely differ. Much of data gathering is physical, while ideation is not. For ideation, scientists may prefer generating ideas themselves, or they may perceive AI’s capabilities as limited. Our data may also undercount ideation, since it tends to be woven into many other research tasks and is difficult to classify. But AI may also be genuinely weaker at high-level ideation, such as choosing which research question to pursue and designing a study to answer it.Biological and biomedical use of Claude Science. While Claude and Claude Code are general-purpose interfaces, specialized AI tools for the sciences are increasingly available. To study whether such tools support more specialized and demanding research, we use our privacy-preserving analysis infrastructure to compare biology and biomedical work on Claude Science with that on Claude and Claude Code.Claude Science connects Claude to many research tools and databases that are otherwise fragmented and acts to integrate and accelerate scientific workflows. Scientists must often use different platforms, each with unique data structures or query languages, to combine distinct tools and databases in their research. For example, biologists studying whether existing drugs can act on a specific protein must typically consult separate databases regarding the protein’s function, its structure, compounds already tested against it, and related drug trials or papers. Claude Science combines these into one workflow.Use of Claude Science is concentrated in the life sciences. The top two fields are biomedical sciences and biology, which together comprise over 50% of sessions. Within these fields, we assess which tasks are most overrepresented relative to tasks performed by Claude.We find that researchers use Claude Science for more specialized tasks than Claude (Figure 5). For example, the task “Propose mechanistic models for protein–protein or protein–ligand interactions” is 5.6x more likely to appear on Claude Science than on Claude. This task involves explaining, at the level of individual molecular contacts, how a protein binds to another protein or to a small molecule such as a drug. Claude Science is well positioned for this task, as it can gather protein structures and binding data from multiple connected databases and combine them into a testable model of the interaction. By contrast, Claude Science is used relatively less for general tasks such as drafting manuscripts or coordinating with collaborators. Moreover, we find that ideation and hypothesis generation accounts for 8% of Claude Science sessions, roughly on par with the share of researcher time these tasks take, suggesting that specialized AI tools for science can facilitate more complex and creative work.Focusing on biomedical sciences and biology, and using measures from our economic primitives framework introduced in prior work (Appel et al. 2026), we find that tasks on Claude Science are longer, more sophisticated, and performed more autonomously than those on Claude and Claude Code (Figure 6). This is consistent with specialized AI tools being used for more demanding tasks.8 Sessions on Claude Science contain work that would take nearly twice as many human hours to complete without AI as work on Claude and Claude Code. The content of these sessions is also more advanced. Over 80% of Claude Science use is estimated to require doctoral-level training (20 years of education), compared with about 50–70% on other interfaces. Over 70% of Claude Science sessions are performed with a high degree of AI autonomy, far higher than on Claude (23.7%) and somewhat higher than on Claude Code (64.1%).Outputs of Claude use. Finally, the artifacts produced with Claude show clear differences across fields (Figure 7).9 Written artifacts such as documents, reports, or academic papers are more common in the social sciences and humanities, relative to the natural and formal sciences. Conversely, technical artifacts such as code, software outputs, and data are more frequent in the natural and formal sciences. The health sciences overall are more similar to the social sciences in terms of artifacts produced. Technical artifacts are about twice as common on Claude Code as on Claude, but the broad pattern across fields is similar (see the Appendix).How use relates to capabilitiesDoes AI usage track AI capabilities? Field size explains some of the variation in Claude use, but not all of it. For example, researchers in computer science use Claude far more than the field’s size predicts, while those in nutrition use Claude far less. Similarly, where researchers spend their own time does not fully predict where they use Claude the most. Some broad task categories, such as data analysis and writing, have a much larger share of usage than of research time, while others, such as data gathering and ideation and hypothesis generation, have a much smaller share. Here, we test whether these differences in usage reflect what AI can actually do.To measure task-specific AI capabilities, we first construct capability scores using an LLM classifier. For each task, SciNet provides a list of individual steps that a scientist needs to perform to complete the task. For instance, in chemistry, one task involves purifying reaction mixtures, and one of that task’s steps involves packing a chromatography column. The classifier is shown pairs of steps across the taxonomy and asked which one AI can perform better. We aggregate these comparisons using a Bradley–Terry model—the same model behind the Elo ratings that rank chess players based on head-to-head results. A step’s score rises when it wins comparisons and falls when it loses them. We then average each task’s steps to obtain a task-level score (see the Appendix for details). We label this measure theoretical AI capability. We refer to a task’s specific score as its AI capability score.To compare usage across tasks, we ask whether Claude use is higher or lower for a task, relative to the time researchers spend on that task. For example, a task that takes 5% of researchers’ time but accounts for 10% of Claude sessions would represent twice as much use as expected, a ratio of 2. A ratio of 1 indicates that Claude use is in line with researcher time spent on the task. A ratio below 1 indicates that Claude is used less than expected based on the amount of researcher time the task takes.Figure 8 shows that this use ratio rises sharply with a task’s AI capability score. For tasks in the lowest 20% of capability scores, like purifying reaction mixtures and conducting participant observation, Claude is barely used. At median capability scores, the ratio is roughly 1, so usage is in line with researcher time. For tasks in the top 20% of capability scores, like visualizing data and applying statistical tests, usage is about twice as high on Claude and about four times as high on Claude Code, compared with the typical time researchers spend on those tasks. These patterns suggest that scientists’ use of Claude closely tracks AI capability scores. The steeper gradient for Claude Code reflects its focus on computational work, which has higher capability scores.Theoretical AI capabilities can differ across tasks for many reasons. To understand these differences, we relate each task’s AI capability score to three groups of LLM-derived task characteristics: its suitability for reinforcement learning (RL); whether the work is predominantly cognitive, interpersonal, or physical; and whether regulatory restrictions limit AI use (Figure 9; see the Appendix for definitions and data sources).Consider RL suitability. In RL, an AI model tries a task repeatedly and is rewarded when it succeeds, allowing the model to learn how to perform that task. Tasks are particularly suitable for RL when success is easy to verify, when many examples can be simulated in a computing environment, and when feedback can be delivered quickly (Lambert et al. 2024; Guo et al. 2025). We use task-specific measures from SciNet for these three RL-related components and summarize them in a single index. A task’s capability score is highly correlated with its RL index score, which was classified separately (correlation of 0.78). This suggests that AI is most capable at the research tasks where RL is easiest to apply.Other important task characteristics concern the nature of the work. Some research tasks are fundamentally cognitive, while others require physical actions or interpersonal contact. Physical tasks in general are beyond the reach of purely cognitive machines. There can also be regulatory restrictions that limit AI use. AI capability scores are higher for tasks characterized by cognitive work (correlation of 0.69). By contrast, research tasks that involve more physical work have lower AI capability scores (−0.57). Scores also tend to be lower for research activities that include interpersonal work (−0.31) or that involve regulatory restrictions on AI use (−0.38).The same task characteristics also predict how much researchers use Claude for a given task (Figure 10). Claude is barely used for tasks in the bottom fifth of RL suitability. For tasks in the top fifth, Claude use is disproportionately high relative to the time researchers spend on them: twice as high on Claude, and four times as high on Claude Code. This is similar to the association between Claude use and AI capability scores overa