Open Knowledge Compensation Plan A strategic plan for a Truly Connected Community of Results pursuing open, AI-mediated compensation for knowledge contributions, replacing artificial scarcity with genuine reward. Developed collaboratively by Owen Ambur with AI assistance (Claude Sonnet 4.6, Anthropic) as a seed document for voluntary adoption and refinement by any community of results sharing these goals. Inspired by a LinkedIn dialogue on the Internet Archive's Wayback Machine, Stewart Brand's observation that information wants to be both free and expensive, and the potential for AI to enable fair compensation for knowledge contributions without artificial scarcity. This plan represents a third path beyond the false binary of copyright restriction versus uncompensated openness. It is intended to be machine-readable, openly shared, and freely adapted under StratML (ISO 17469) conventions. Truly Connected Community of Results TcCoR urn:uuid:5c8e2a1d-3f6b-4c7e-9a0d-1b4f2e7c8a3d Convene knowledge contributors, AI developers, beneficiaries, and governance institutions in voluntary coordination to build open, machine-readable, AI-mediated infrastructure for fair knowledge compensation. Knowledge Contributors Journalists, researchers, academics, creators, and others who produce original knowledge and make it available to the public, whose compensation the system is designed to ensure. Contributor and Beneficiary Performer Beneficiary AI Developers Companies and individuals who train AI systems on public knowledge and derive substantial value from the collective store of human contributions. Beneficiary and Funder Performer Beneficiary Public Beneficiaries Citizens, students, and institutions who benefit from open access to knowledge and from AI systems trained on the public record. Beneficiary Beneficiary Digital Preservationists Organizations such as the Internet Archive that maintain the historical record of digital knowledge and provide the infrastructure upon which attribution and accountability depend. Partner and Contributor Performer Governance Institutions Standards bodies, government agencies, and policy organizations that establish and enforce frameworks for knowledge compensation, intellectual property, and machine-readable transparency. Regulator and Partner Performer Policy Advocates Organizations such as the Electronic Frontier Foundation that advocate for open access, digital rights, and equitable knowledge compensation in the public interest. Advocate and Partner Performer Achieve a world in which all people benefit equitably from our collective store of knowledge, and those who enrich it most are rewarded most generously for doing so -- because they share, not despite it. urn:uuid:9a2f4c8e-7b1d-4e3a-b5f6-0c2d8e4a7b9f To foster voluntary coordination to establish open, machine-readable, AI-mediated systems that compensate knowledge contributors in proportion to the value they provide to the public, replacing artificial scarcity with genuine reward. urn:uuid:3d7b1e5f-9c2a-4d6e-8f0b-1a3c5e7d9f2b Openness Embrace transparent, freely accessible knowledge as the foundation for human progress, democratic accountability, and AI-mediated attribution. Fair Compensation Ensure that those who contribute valuable knowledge receive rewards proportional to the value and reach of their contributions, with greater reward for greater contribution. Accountability Maintain machine-readable, auditable records of all attribution, valuation, and compensation decisions, closing the gap between stated intent and measurable outcomes. Voluntary Coordination Pursue shared goals through mutual agreement and incentive alignment rather than coercion or mandate, enabling a truly connected community of results. Structural Transparency Build transparency into the infrastructure of knowledge systems by design, so that accountability does not depend on any single heroic actor or nonprofit archivist. Knowledge Expand the openly accessible, machine-readable store of human knowledge available for public benefit, democratic accountability, and AI-mediated attribution. urn:uuid:1e3a5c7e-9b1d-4e3a-5c7e-9b1d3e5a7c9b 1 Knowledge Commons ~ Expand and Sustain the Open Knowledge Commons: Ensure that the digital historical record remains accessible, preservable, and structurally transparent. The news organizations blocking the Wayback Machine are solving the wrong problem; the right response is infrastructure that makes open sharing more remunerative than withholding. Registry Establish a publicly accessible, machine-readable registry of knowledge contributions with stable, dereferenceable identifiers enabling attribution, valuation, and compensation. urn:uuid:3a5c7e9b-1d3e-4a5c-7e9b-1d3e5a7c9b1d 1.1 Knowledge Contributors Contributor Performer Beneficiary Digital Preservationists Partner Performer Contribution Registry ~ Establish a Publicly Accessible, Machine-Readable Registry of Knowledge Contributions: Provide the foundational infrastructure for attribution and compensation by ensuring every contribution has a persistent, citable identity. StratML (ISO 17469) conventions offer a proven model for this kind of machine-readable registry at scale. 1.1.1 Contributions Number Status urn:uuid:5c7e9b1d-3e5a-4c7e-9b1d-3e5a7c9b1d3e 2027-01-01 2031-12-31 1000000 Registered Knowledge contributions are registered with stable, dereferenceable identifiers 2026-01-01 2026-12-31 TBD To be determined 1.1.2 Organizations Number Status urn:uuid:7e9b1d3e-5a7c-4e9b-1d3e-5a7c9b1d3e5a 2027-01-01 2031-12-31 500 Publishing Organizations publish machine-readable contribution records using open standards 2026-01-01 2026-12-31 TBD To be determined Preservation Sustain and strengthen digital preservation infrastructure that maintains the historical record of knowledge contributions on which attribution, accountability, and compensation all depend. urn:uuid:9b1d3e5a-7c9b-4d3e-5a7c-9b1d3e5a7c9b 1.2 Digital Preservationists Beneficiary and Partner Performer Beneficiary Governance Institutions Partner and Funder Performer Preservation Support ~ Sustain and Strengthen Digital Preservation Infrastructure: Reduce dependence on any single nonprofit's heroic effort by building broad institutional and financial support for organizations like the Internet Archive, whose work currently stands between the public and the erasure of the digital record. 1.2.1 Institutions Number Status urn:uuid:b1d3e5a7-c9b1-4d3e-5a7c-9b1d3e5a7c9b 2027-01-01 2031-12-31 100 Contributing Institutional partners contribute financially or technically to digital preservation efforts 2026-01-01 2026-12-31 TBD To be determined Open Standards Adopt and promote machine-readable open standards for knowledge contribution metadata, enabling interoperability across platforms, AI systems, and jurisdictions. urn:uuid:d3e5a7c9-b1d3-4e5a-7c9b-1d3e5a7c9b1d 1.3 Governance Institutions Partner Performer AI Developers Contributor and Beneficiary Performer Beneficiary Adopt and Promote Machine-Readable Open Standards for Knowledge Contribution Metadata: Standards like StratML (ISO 17469) demonstrate that machine-readable transparency is technically feasible at institutional scale; the same approach governs knowledge contribution records across the ecosystem. 1.3.1 Platforms Number Status urn:uuid:e5a7c9b1-d3e5-4a7c-9b1d-3e5a7c9b1d3e 2027-01-01 2031-12-31 50 Implementing Platforms implement open knowledge contribution metadata standards 2026-01-01 2026-12-31 TBD To be determined Compensation Reward knowledge contributors in proportion to the value and reach of their contributions, replacing artificial scarcity with genuine, AI-mediated compensation that rewards sharing rather than withholding. urn:uuid:2f4b6d8e-0a2c-4f4b-6d8e-0a2c4f6b8d0a 2 Fair Compensation ~ Reward Knowledge Contributors in Proportion to Value: Replace copyright restriction as the primary revenue lever with direct, proportional compensation. The current regime uses artificial scarcity as its only lever, imposing enormous social costs -- paywalls, litigation, and the erosion of the knowledge commons -- while failing to compensate most contributors fairly. Criteria Define measurable, publicly auditable criteria for assessing the value and downstream impact of knowledge contributions, from original investigations to foundational datasets. urn:uuid:4b6d8e0a-2c4f-4b6d-8e0a-2c4f6b8d0a2c 2.1 Knowledge Contributors Contributor Performer Beneficiary Governance Institutions Partner Performer Value Metrics ~ Define Measurable, Publicly Auditable Value Assessment Criteria: Establish criteria that resist gaming, account for compounding downstream value, and are transparent enough to command broad trust. The lesson of academic citation metrics -- that measurable proxies attract optimization -- must inform the design. 2.1.1 Criteria Number Status urn:uuid:6d8e0a2c-4f6b-4d8e-0a2c-4f6b8d0e2a4c 2027-01-01 2031-12-31 20 Adopted Value assessment criteria are formally adopted and publicly auditable 2026-01-01 2026-12-31 TBD To be determined Licensing Establish scalable, low-friction collective licensing mechanisms to distribute compensation to contributors based on usage, reach, and AI-mediated value metrics. urn:uuid:8e0a2c4f-6b8d-4e0a-2c4f-6b8d0e2a4c6f 2.2 Knowledge Contributors Beneficiary Beneficiary AI Developers Funder Performer Royalty Distribution ~ Establish Scalable Royalty Distribution Mechanisms: Drawing on the ASCAP/BMI model for music performance royalties -- collective licensing that operates at scale without requiring individual litigation -- build knowledge compensation infrastructure with AI-mediated precision that flat licensing fees have historically lacked. 2.2.1 Annual Compensation USD Status urn:uuid:0a2c4f6b-8d0e-4a2c-4f6b-8d0e2a4c6f8b 2027-01-01 2031-12-31 100000000 Distributed Distribute compensation to knowledge contributors through the system 2026-01-01 2026-12-31 TBD To be determined 2.2.2 Contributors Number Status urn:uuid:2c4f6b8d-0e2a-4c4f-6b8d-0e2a4c6f8b0d 2027-01-01 2031-12-31 10000 Compensated Contributors receive compensation through the system 2026-01-01 2026-12-31 TBD To be determined Costs Identify and allocate compensation costs equitably among those who benefit from knowledge contributions, with particular attention to AI developers whose models are trained on the public record. urn:uuid:4f6b8d0e-2a4c-4f6b-8d0e-2a4c6f8b0d2a 2.3 AI Developers Funder Performer Public Beneficiaries Beneficiary Beneficiary Beneficiary Allocation ~ Allocate Compensation Costs Equitably Among Beneficiaries: Ensure costs are borne proportionally by those who derive the most value. Blocking a nonprofit archivist does nothing to stop well-funded AI companies from acquiring data elsewhere; it only stops the public from holding powerful institutions accountable through the historical record. 2.3.1 Categories Number Status urn:uuid:6b8d0e2a-4c6f-4b8d-0e2a-4c6f8b0d2e4a 2027-01-01 2031-12-31 10 Enrolled Formally enroll beneficiary categories in the compensation system 2026-01-01 2026-12-31 TBD To be determined Value Apply AI to trace, measure, and allocate the value of knowledge contributions across the full chain from creation to use, enabling compensation precision that collective licensing systems have historically lacked. urn:uuid:3e5a7c9b-1d3e-4e5a-7c9b-1d3e5a7c9b1d 3 AI Attribution ~ Apply AI to Trace, Measure, and Allocate Knowledge Value: AI capacity for large-scale provenance tracing and pattern recognition makes it possible to move from flat licensing fees to proportional compensation that recognizes foundational contributions, primary sources, and original investigations for their compounding downstream value. Source Influences Develop and deploy AI systems capable of identifying source influences in derived works, AI training datasets, and published outputs at internet scale. urn:uuid:5a7c9b1d-3e5a-4c7e-9b1d-3e5a7c9b1d3e 3.1 AI Developers Contributor and Beneficiary Performer Beneficiary Provenance Tracing ~ Develop AI-Powered Provenance Tracing Systems: Enable reliable identification of which knowledge contributions influenced which outputs, making attribution tractable at internet scale. RAG architectures and citation systems offer early models. 3.1.1 AI Training Datasets Percent Status urn:uuid:7c9b1d3e-5a7c-4b1d-3e5a-7c9b1d3e5a7c 2027-01-01 2031-12-31 50 Traceable Major AI training datasets have traceable attribution metadata 2026-01-01 2026-12-31 TBD To be determined Weightings Apply AI to assign relative value weights to knowledge contributions based on originality, downstream influence, and public benefit, moving beyond flat rates to proportional compensation. urn:uuid:9b1d3e5a-7c9b-4e3a-5c7e-9b1d3e5a7c9b 3.2 AI Developers Contributor Performer Knowledge Contributors Beneficiary Beneficiary Value Weighting ~ Apply AI-Mediated Value Weighting to Knowledge Contributions: Recognize that not all contributions are equal -- a primary source, a foundational dataset, an original investigation have compounding downstream value that flat licensing ignores. AI makes proportional attribution tractable where human adjudication cannot. 3.2.1 Domains Number Status urn:uuid:b1d3e5a7-c9b1-4e3a-5a7c-9b1d3e5a7c9b 2027-01-01 2031-12-31 10 Developed & Validated AI value weighting models have been developed and independently validated for knowledge domains 2026-01-01 2026-12-31 TBD Status Tools Develop and publish open-source AI tools for attribution tracing and value weighting, preventing capture of the attribution system by proprietary interests and enabling broad community adoption. urn:uuid:d3e5a7c9-b1d3-4a5c-7e9b-1d3e5a7c9b1d 3.3 Policy Advocates Partner Performer AI Developers Contributor Performer Open AI Tools ~ Develop and Publish Open-Source AI Attribution Tools: Ensure that core attribution tools are openly available, community-auditable, and free from control by the very parties with the greatest financial interest in how value is defined and allocated. 3.3.1 Tools Number Status urn:uuid:f5a7c9b1-d3e5-4c7a-9b1d-3e5a7c9b1d3e 2027-01-01 2031-12-31 5 Released & Maintained Open-source AI attribution tools str publicly released and actively maintained 2026-01-01 2026-12-31 TBD To be determined Administration Establish accountable, machine-readable administration of the open knowledge compensation system, resistant to capture by parties with disproportionate leverage over how value is defined. urn:uuid:4f6b8d0a-2c4e-4f6b-8d0a-2c4e6f8b0d2c 4 Transparent Governance ~ Establish Accountable, Machine-Readable Governance of the Compensation System: Apply StratML principles to the governance of the knowledge compensation system itself. If the system's rules, decisions, and outcomes are not as transparent as the contributions it manages, it will be captured by those with the most leverage -- which is precisely the failure mode it is designed to correct. Plans & Reports Publish all governance plans, performance reports, and administrative decisions for the compensation system in StratML-compliant, machine-readable format at stable, dereferenceable URLs. urn:uuid:6b8d0a2c-4e6f-4b8d-0a2c-4e6f8b0d2c4e 4.1 Governance Institutions Contributor Performer StratML Compliance ~ Publish All Governance Plans and Reports in StratML-Compliant Format: ISO 17469 compliance is the baseline; GPRAMA Section 10 statutory requirements for federal agencies provide the policy precedent that voluntary transparency cannot replicate. 4.1.1 Governance Decisions Percent Status urn:uuid:8d0a2c4e-6f8b-4d0a-2c4e-6f8b0d2c4e6f 2027-01-01 2031-12-31 100 Documented Governance decisions are documented in machine-readable StratML-compliant format 2026-01-01 2026-12-31 TBD To be determined Capture Implement structural safeguards preventing capture of the compensation system by any party with disproportionate leverage, including major AI companies, large publishers, or government actors. urn:uuid:0a2c4e6f-8b0d-4c2e-4f6b-8d0a2c4e6f8b 4.2 Policy Advocates Partner Performer Public Beneficiaries Partner Beneficiary Capture Prevention ~ Implement Structural Safeguards Against System Capture: News outlets blocking the Wayback Machine want asymmetric freedom -- public institutions transparent, private institutions protected. That asymmetry may be defensible but must be argued explicitly and governed transparently, not embedded invisibly in the rules of the compensation system. 4.2.1 Safeguards Number Status urn:uuid:2c4e6f8b-0d2a-4e6f-8b0d-2a4c6e8f0b2d 2027-01-01 2031-12-31 10 Adopted & Audited Structural governance safeguards are formally adopted and independently audited 2026-01-01 2026-12-31 TBD To be determined Transparency & Compensation Advocate for statutory and regulatory frameworks requiring machine-readable transparency and fair compensation in knowledge ecosystems, drawing on GPRAMA Section 10 as a proven policy precedent. urn:uuid:4e6f8b0d-2a4c-4f6b-8d0a-2c4e6f8b0d2a 4.3 Policy Advocates Contributor Performer Governance Institutions Partner Performer Policy Advocacy ~ Advocate for Statutory Frameworks Supporting Open Knowledge Compensation: GPRAMA Section 10 demonstrates that statutory mandates drive machine-readable transparency at federal scale; analogous requirements for knowledge compensation systems would reduce dependence on voluntary adoption and prevent the structural opacity that currently allows powerful institutions to rewrite the historical record. 4.3.1 Proposals Number Status urn:uuid:6f8b0d2a-4c6e-4b0d-2a4c-6e8f0b2d4a6c 2027-01-01 2031-12-31 5 Principles Adopted Legislative and regulatory proposals formally incorporate open knowledge compensation principles 2026-01-01 2026-12-31 TBD To be determined Attention Accelerate the creative destruction of advertising-based content compensation by demonstrating that AI-mediated direct payment to creators is more efficient, less manipulative, and more equitable for both creators and consumers. urn:uuid:5a7b9c1d-3e2f-4a5b-9c7d-1e3f5a7b9c1d 5 Attention Economy ~ Replace Advertising as the Default Content Compensation Model: The advertising model answered the creator compensation problem in the 20th century by inserting a third party willing to subsidize content in exchange for access to eyeballs. It is structurally misaligned (the advertiser's customer is not the reader), economically wasteful (conversion rates celebrated as successes would be failures in any other communication channel), and increasingly obsolete as AI satisfies information needs without ad-supported intermediaries. The Open Knowledge Compensation Plan offers the viable alternative that advertising filled in the absence of micropayment and attribution infrastructure. Micropayments Establish AI-mediated direct micropayment infrastructure enabling consumers to compensate creators in fractions of a cent per article read, video watched, or insight absorbed, eliminating the need for advertising as an intermediary. urn:uuid:b3c5d7e9-1f2a-4b3c-5d7e-9f1a3b5c7d9f 5.1 Knowledge Contributors Beneficiary Beneficiary Public Beneficiaries Participant Performer Micropayments ~ Establish AI-Mediated Direct Micropayment Infrastructure: The absence of viable micropayment infrastructure is the proximate cause of advertising's dominance. AI attribution and allocation systems make per-use compensation tractable at internet scale for the first time, removing the economic justification for the advertising intermediary. 5.1.1 Platforms Number Status urn:uuid:c5d7e9f1-2a3b-4c5d-7e9f-1a2b3c5d7e9f 2027-01-01 2031-12-31 25 Deployed Content platforms have deployed AI-mediated micropayment infrastructure as an alternative to advertising revenue 2026-01-01 2026-12-31 TBD To be determined Waste Measure and publish the full social cost of advertising-based content compensation, including attention consumed, manipulation imposed, and misalignment between creator incentives and consumer benefit. urn:uuid:d7e9f1a3-5b7c-4d7e-9f1a-3b5c7d9f1a3b 5.2 Policy Advocates Contributor Performer Public Beneficiaries Beneficiary Beneficiary Advertising Waste ~ Measure and Publish the Social Cost of Advertising-Based Compensation: The attention tax imposed on the entire population to produce marginal advertising conversions is enormous and unmeasured. Publishing rigorous estimates of this cost -- time consumed, manipulation imposed, engagement-optimized distortion of content -- builds the public case for the direct compensation alternative. 5.2.1 Studies Number Status urn:uuid:e9f1a3b5-7c9d-4e9f-1a3b-5c7d9f1a3b5c 2027-01-01 2031-12-31 10 Published Peer-reviewed studies quantifying the social cost of advertising-based content compensation are published and publicly accessible 2026-01-01 2026-12-31 TBD To be determined Adoption Grow measurable consumer and creator participation in direct compensation models, demonstrating at scale that the advertising intermediary is neither necessary nor the most efficient path to sustainable content creation. urn:uuid:f1a3b5c7-9d1e-4f1a-3b5c-7d9f1a3b5c7d 5.3 Knowledge Contributors Performer and Beneficiary Performer Beneficiary AI Developers Enabler Performer Adoption ~ Grow Creator and Consumer Participation in Direct Compensation Models: Network effects favor incumbents; demonstrating viability at scale is the critical threshold. AI's disruption of ad-supported search and content discovery creates a natural forcing function -- as AI intermediaries displace ad-supported ones, direct compensation becomes the path of least resistance for creators and consumers alike. 5.3.1 Creators Number Status urn:uuid:a3b5c7d9-1e3f-4a3b-5c7d-9f1e3a5b7c9d 2027-01-01 2031-12-31 100000 Participating Creators receive a majority of their content compensation through direct AI-mediated payment rather than advertising revenue 2026-01-01 2026-12-31 TBD To be determined 5.3.2 Ad Revenue Displaced Percent Status urn:uuid:b5c7d9f1-3e5a-4b5c-7d9f-1e3a5b7c9d1f 2027-01-01 2031-12-31 20 Displaced Twenty percent of content creator revenue previously derived from advertising is now sourced through direct AI-mediated compensation channels 2026-01-01 2026-12-31 TBD To be determined urn:uuid:7f3a9b2c-1d4e-4f5a-8b6c-2e7d9a0b3c4f 2026-01-01 2031-12-31 2026-04-17 https://stratml.us/docs/OKCP.xml Owen Ambur Owen.Ambur@verizon.net