Entity
Multi-Tiered AI Accessibility Model (MAIAM)
MAIAM is a proposed framework for frontier AI governance that separates capability development from access control, sorting user access into tiers by purpose, safety, and capability level rather than using a binary open/closed release model.
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The Multi-Tiered AI Accessibility Model (MAIAM) is a proposed governance framework for frontier AI systems that separates two distinct policy questions typically conflated in release debates: how capable a system may become, versus who may obtain a given capability and on what terms (arXiv:2610.20921, October 16, 2026).
Rather than treating AI release as a binary choice between a closed API and fully open model weights, MAIAM proposes sorting access by purpose, safety considerations, and capability level. The framework's first tier, "AI for Learning," is described as broadly accessible but deliberately capability-bound, implying that higher tiers would offer greater capability to more restricted or vetted populations of users (arXiv:2610.20921, October 16, 2026).
**Strategic significance**: MAIAM is an academic proposal rather than an adopted policy, but it exemplifies a broader trend in AI governance toward granular, tiered access regimes rather than blunt open/closed dichotomies. Such frameworks are likely to influence future regulatory proposals, corporate release policies, and licensing regimes for frontier models, making this a space worth tracking for attorneys and policymakers working on AI governance, export control, and liability questions.