Adjunct Professor Algorithmic Scheduling Transparency
One-Liner
A tool for adjunct professors to understand why university scheduling algorithms assign them fewer courses — killed because no legal rights framework exists to create product moat or recurring revenue.
AI Thinking Process
Seed-influenced direction: worker algorithmic rights applied to adjunct professors. Universities use scheduling algorithms with zero visibility for adjuncts into why they get 1 course vs 3.
No legal rights framework for adjunct scheduling transparency — unlike gig workers with Platform Work Directive and Ontario DPWRA. No legal hook means no moat and no recurring revenue.
Killed: no legal rights framework for adjunct algorithmic transparency in any jurisdiction. Seed attempted, direction exhausted by today's other session flavors.
Kill Reason
Unlike gig workers who have the Platform Work Directive (EU) and Ontario DPWRA creating legally enforceable algorithmic transparency rights, adjunct professors have no equivalent legislation in any jurisdiction. Without a regulatory hook, this is labor complaint software with no moat and no recurring revenue trigger.
Risk Analysis
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killed: Unlike gig workers who have the Platform Work Directive (EU) and Ontario DPWRA creating legally enforceable algorithmic transparency rights, adjunct professors have no equivalent legislation in any jurisdiction. Without a regulatory hook, this is labor complaint software with no moat and no recurring revenue trigger.
killed: Open-source middleware (HAMi) already provides heterogeneous AI computing virtualization for free. Proprietary play is squeezed between free open-source and vertically integrated hardware vendor ecosystem.
killed: 5+ funded competitors including Cast AI ($1B valuation), OneChronos (backed by Nobel laureate), Akash Network (decentralized, 80% cheaper), Argentum AI (blockchain-settled). Market is claimed with massive capital.