But neither approach engages with a more fundamental question\u00a0warranted\u00a0by\u00a0a technology that is rapidly transforming human activity at an unprecedentedly intimate layer of cognition:\u00a0Is\u00a0generative AI being deployed in ways that\u00a0maximize\u00a0human interests?\u00a0<\/p>\n
Cognitive agency and\u00a0\u2018context\u2019\u00a0<\/p>\n
This working paper argues that generative AI should be assessed\u00a0by\u00a0whether it supports\u00a0cognitive agency\u2014or the capacity for people to think and act with AI in ways that increase their control, efficacy, and mastery. This account combines the foundational work in\u00a0development economics<\/a>\u00a0(that\u00a0identifies\u00a0agency as central to human and sustainable development outcomes) with\u00a0the\u00a0latest research on socio-cognitive and informational agency to capture\u00a0an\u00a0emerging cognitive partnership<\/a>\u00a0between people and AI systems.\u00a0<\/p>\n Current evidence suggests that dominant proprietary AI deployment architectures are associated with\u00a0erosion of cognitive agency<\/a>\u00a0rather than its expansion.\u00a0By default,\u00a0or by design, vendor-controlled AI platforms capture user-AI interaction data (also known as \u201ccontext\u201d) while\u00a0limiting user control<\/a>\u00a0over how that context is stored, organized, or reused. In\u00a0a growing parallel body of research<\/a>, however, evidence suggests that generative AI use can increase cognitive agency when users exercise greater control over\u00a0their\u00a0context. These contrasting signals motivate further research into the relationship between AI\u00a0deployment\u00a0architectures\u00a0and cognitive agency.\u00a0<\/p>\n Introducing context-maxxing\u00a0<\/p>\n In January 2026,\u00a0OpenClaw<\/a>\u2014a viral open-source software that allows users to interact with AI models within their own computing environments\u2014introduced a fundamentally different deployment paradigm for generative AI and a glimpse of what it could mean to center people\u2019s interests within it. Using\u00a0OpenClaw\u00a0and other open-source agent harnesses, millions of people globally\u2014including many non-technical users\u2014are now exercising greater control over the information that they bring to\u00a0their\u00a0interactions with AI.\u00a0<\/p>\n This paper codifies common elements of this emerging AI deployment paradigm as\u00a0\u201ccontext-maxxing,\u201d\u00a0or using self-managed hardware and software to maximally control user-generated context\u00a0in\u00a0interactions with generative AI. The paper\u00a0identifies\u00a0two preliminary, mutually reinforcing components of context-maxxing:\u00a0<\/p>\n Five user-controlled digital infrastructure building blocks:(1) An open-source agent harness; (2) LLM access\u00a0to multiple providers\u00a0via\u00a0Application Programming Interface\u00a0(API); (3) a \u201ccontext web\u201d of human- and machine-readable information distributed across users\u2019 knowledge management applications; (4) security protocols; and (5) persistent hosting (see Figure 1).\u00a0 Figure 1.\u00a0Two generative AI deployment infrastructures in 2026<\/p>\n Note:\u00a0Figure depicts proprietary platform user interfaces (left) versus context-maxxing\u00a0(right). In context-maxxing, an open-source harness allows users to\u00a0more meaningfully control and accumulate context within a self-managed computing environment (blue space). Proprietary platform user interfaces (grey space) typically limit user control over context\u00a0by\u00a0default\u00a0or by design. Dotted-line boxes denote features of the AI deployment infrastructure that users can reliably control. Arrows denote flow of information between infrastructure building blocks. Proprietary platform user interfaces increasingly offer direct integration with user-controlled applications (e.g., email, storage, calendar), denoted by the dotted arrow lines.<\/p>\n Figure 2.\u00a0Three reinforcing competencies of context-maxxing<\/p>\n Paper contributions\u00a0<\/p>\n The working paper is structured in three parts. It begins by analyzing existing deployment paradigms and cognitive agency (Part 1), before presenting a context-maxxing\u00a0playbook for policymakers\u00a0or decisionmakers working on or with generative AI (Part 2).\u00a0Focus areas for ongoing research and\u00a0public\u00a0policy development are outlined in Part 3.\u00a0<\/p>\n Implications and future directions\u00a0<\/p>\n This work suggests that computing environments that prioritize user control over context could lay the foundation for entirely new human\u00a0expertise\u00a0and capabilities for shared problem-solving. This in turn raises the question\u00a0about\u00a0how\u00a0public investment and regulation can lower barriers to context-maxxing\u00a0for people everywhere\u2014including in sustainable development contexts where the\u00a0infrastructure and resources<\/a>\u00a0to do so remain unevenly distributed.\u00a0<\/p>\n This paper adopts the language of \u201ccontext-maxxing\u201d not only to meet an emerging industry discourse on its own terms, but also as a deliberate gesture to the need to complement traditional public policy research and analysis with\u00a0more agile approaches<\/a>\u00a0that are at once technically rigorous and culturally and politically legible. In this spirit, the OpenClaw movement motivates innovation in policymaking itself\u2014a shift from analysis and synthesis toward experimental prototyping, whereby policymakers are now more able than ever to directly cultivate and test working models of the interventions they seek to endorse\u2014and grow them at the edge of the social systems where generative AI is being deployed.\u00a0<\/p>\n
\nThree reinforcing competencies for cognitive efficacy and mastery with AI: Specification (codifying domain knowledge into reusable context assets), Orchestration (deploying that context through structured human-AI workflows optimized for integrity and verifiability), and Exploration (reinvesting efficiency gains into new frontiers of value creation; see Figure 2).\u00a0<\/p>\n![\"\"](\"https:\/\/www.europesays.com\/ai\/wp-content\/uploads\/2026\/05\/Figure-1.-Two-generative-AI-deployment-infrastructures-in-2026.png\")
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