Research paper 35
Standing Framework: AI Work That Holds Up
AI makes work easier to produce and harder to account for. A task can end with a fluent answer, a changed file, a dashboard, or a passing check, yet still leave the important question unresolved: can anyone safely decide what just happened? Standing Framework builds for that moment. Its software, research, and applied systems are organized around AI work that has to hold up after the run. The work should carry its intent, evidence, uncertainty, approval state, and limits with it, so a person can continue, reject, approve, or hold the result without reconstructing the whole conversation. A selected 24-episode review of company work from March through September 2026 supports a narrow claim: Standing Framework can be understood as an AI systems company for evidence-bearing, authority-aware work. Market acceptance, customer validation, adoption, willingness to pay, product-market fit, publication approval, public release, legal/privacy clearance, source-permission clearance, expert participation, and raw-log publication remain unproven.
- Paper
- 35
- Authors
- A.G. Mauro and C.A. Harris
- Date
- Collection
- Standing Framework Research
Abstract
AI makes work easier to produce and harder to account for. A task can end with a fluent answer, a changed file, a dashboard, or a passing check, yet still leave the important question unresolved: can anyone safely decide what just happened?
Standing Framework builds for that moment. Its software, research, and applied systems are organized around AI work that has to hold up after the run. The work should carry its intent, evidence, uncertainty, approval state, and limits with it, so a person can continue, reject, approve, or hold the result without reconstructing the whole conversation.
A selected 24-episode review of company work from March through September 2026 supports a narrow claim: Standing Framework can be understood as an AI systems company for evidence-bearing, authority-aware work. Market acceptance, customer validation, adoption, willingness to pay, product-market fit, publication approval, public release, legal/privacy clearance, source-permission clearance, expert participation, and raw-log publication remain unproven.
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