Building AI Capability Through Cumulative Adaptation

Countries outside the artificial-intelligence frontier face a difficult choice because reproducing frontier models may exceed their financial and organizational capacity, while buying access through closed interfaces leaves limited control over the underlying technology. This article examines an intermediate route based on the repeated and lawful adaptation of externally developed open-weight models to local conditions and calls the associated mechanism learning through cumulative adaptation. Extending the distinction between learning by doing and learning by using, the concept locates learning in adaptation episodes whose methods, outcomes, and diagnoses are retained in ways that inform later decisions. The qualifier cumulative refers to reusable institutional competence rather than uninterrupted performance improvement, while know-to denotes the task-level ability to obtain a desired result from a system without fully understanding its internal mechanisms and is distinguished from technical know-how and organizational control capability. Brazilian evidence gives empirical content to the proposed account of tropicalization with local competence. Sabiá documents adaptation and evaluation, the Plano Brasileiro de Inteligência Artificial (PBIA) specifies planned enabling conditions, and Rio 3.5 records public-sector entry into model-level operations within a municipal technology company. Across these institutional levels, the cases provide complementary evidence of the technical, policy, and organizational preconditions for a credible intermediate route toward catch-up. A contextual comparison with national and supranational strategies situates the Brazilian configuration within a wider range of capability-building pathways and shows how technological autonomy may be developed selectively across layers.