Does progress in AI safety require progress in AI capabilities?

Executive Summary Leaders of frontier AI labs argue that building more capable models is a prerequisite for progress on AI safety. Critics argue that the two are largely decoupled, and that capability progress is net-negative. Despite the importance of the disagreement, it has rarely been tested against data. This report tests it directly. I assembled a dataset of 521 AI-safety contributions (papers and blog posts) published between 2019 and 2025, drawn from the 2023, 2024, and 2025 shallow review posts, and used an LLM classifier with a documented rubric to score how strongly each contribution's central result depended on access to frontier-model capability. 477 contributions received a score; 44 had insufficient evidence to classify and were left unscored. The data points to five conclusions: Frontier dependence is rare. Only 2.7% (13 of 477) of scored contributions were judged to require frontier-specific capability or access for their central result. 64.4% (307) of results were established either without models at all or on non-frontier models. Capability assistance is common. 32.9% (157) of contributions were capability-assisted: stronger models materially strengthened the work, but the central phenomenon or method was also established below the frontier. Influential work is more capability-dependent. Among the top quartile of papers by age-adjusted citations, the median capability-dependence score rises from 2 to 3, and the frontier-dependent share rises from 2.7% to 7.3%. Capability dependence varies by AI safety subfield. For example, Evaluations & Benchmarks has a median score of 3; Theory & Agent Foundations has a median of 1, with 100% of its papers in the capability-independent or non-frontier range. The field is not becoming more frontier-dependent. The frontier-dependent share was 6.5% in 2023 and 1.9% in 2025. What has changed is a shift from low scores toward the intermediate capability-assisted score. Headline conclusion AI capabilities progress and more capable models often benefited AI safety research contributions. They made safety techniques more effective, showed the behaviours under study more often and more realistically, and did much of the experimental work of scoring, classifying, and generating data. But frontier models were rarely necessary: In this dataset the core phenomenon or method was usually implemented using non-frontier models. These results contradict the strong form of both the capability-enabled (View 1) and capability-independent (View 2) hypotheses, and points to a middle position that varies sharply by AI-safety subfield: Subfields like Evaluations & Benchmarks have a relatively high median capability-dependence score of 3. In contrast, Theory & Agent Foundations and Interpretability have a median capability-dependence score of 1 and 2. Disclaimer: Every result in this report is descriptive. It shows which view the observable publication record more closely resembles. It does not estimate the causal effect of capabilities progress. Introduction and the two hypotheses Several leaders of frontier AI labs have argued that AI capabilities progress is beneficial because it is a prerequisite for progress on AI safety, or is at least highly correlated with it. Critics respond that this mindset is an excuse to build increasingly capable AI for profit, status, and personal gain and believe that AI safety and AI capabilities are uncorrelated, so capabilities progress without simultaneous safety progress is irresponsible and net-negative for the world. View 1: The capability-enabled safety hypothesis (CESH) The first view is that making advances in AI safety requires AI capabilities progress and the ability to work on frontier models. Anthropic's 2023 Core Views on AI Safety post states that a major reason the organization exists is the belief that safety research must be done on frontier systems: A major reason Anthropic exists as an organization is that we believe it's necessary to do safety research on "frontier" AI systems. This requires an institution which can both work with large models and prioritize safety. Sam Altman's 2023 post Planning for AGI and beyond makes a related argument: that separating safety from capabilities is a false dichotomy, and that OpenAI's best safety work has come from its most capable models: Importantly, we think we often have to make progress on AI safety and capabilities together. It’s a false dichotomy to talk about them separately; they are correlated in many ways. Our best safety work has come from working with our most capable models. That said, it’s important that the ratio of safety progress to capability progress increases. Summarized, this view holds that: many concerning risks such as deception arise only in frontier models and cannot be studied in smaller ones; some advanced safety techniques only work at frontier scale; and safety research far behind the frontier therefore has limited value. The capability-enabled safety