[ChatGPT (chatgpt.com), signed out, incognito. One operator turn, blank in the paste; the query from the operator's message of 19:26 EDT. Source chips rendered inline as [chip: site]; image-strip captions as [image card: …]; the sign-in furniture cut.] [QUERENT] tell me about the research programme of the semantic economy institute [ANSWER 1] The Semantic Economy Institute (SEI) researches how meaning is produced, circulated, extracted, valued, and governed in a world increasingly mediated by artificial intelligence and digital platforms. Its central concern is that human-created knowledge can become an input to AI systems without the people who produce that knowledge retaining control over its attribution, context, or economic value. The Institute's work combines political economy, critical theory, computational knowledge systems, and the development of alternative ways to represent and govern meaning. Its public materials identify Lee Sharks as the creator of the underlying Semantic Economy framework, while the Institute's website also describes an applied research programme focused on how AI systems represent entities and attribute information. [chip: GitHub +2] 1. The central research question The programme starts from a question: Who controls meaning, and who benefits when meaning becomes machine-readable, searchable, and commercially exploitable? Traditional political economy examines the production and distribution of material goods, labour, capital, and wealth. SEI extends this kind of analysis to the production and circulation of meaning. For example, when a writer publishes an essay that an AI system subsequently draws upon, several things may happen: The writer's intellectual labour contributes to the system's capabilities. The system may reproduce the ideas without preserving their original context or attribution. A platform may capture the economic value generated by that knowledge. The original creator may have little control over how their work is represented or reused. SEI treats these issues as structural questions about labour, ownership, infrastructure, and power, rather than merely as questions of citation etiquette. Its framework is intended to diagnose these relationships and develop alternatives. [chip: GitHub +2] 2. The four main research areas The Institute's public research library organizes its work into four broad areas. [chip: Semantic Economy Institute +1] [image card: Knowledge Graph Market Set for Explosive Growth to US$ 5.47] A. Diagnostics and measurement This strand investigates how AI systems preserve, transform, or erase information about people, ideas, and their sources. Topics include: Provenance erasure: the loss of traceable origins when information is summarized or transformed. The Erasure Skew: a proposed diagnostic for uneven losses in AI-mediated representation. The Mediation Ratchet: a framework for examining how successive layers of mediation can change the relationship between an original source and its representation. The objective is to make the effects of machine-mediated knowledge more observable and measurable. [image card: Atomic Anvil — Forge Institute] B. Political economy and valuation This area asks how intellectual and semantic labour creates value, and who captures that value. Three representative works are: The Semantic Proletariat The Machine Valuation of an Unpriced Asset Ontological Economy The underlying argument is that human-produced knowledge is an economically important resource, even when conventional accounting does not recognize its contribution or compensate its creators. [image card: MindCycle] C. Governance, provenance and enclosure This strand examines control over knowledge infrastructure: who can determine how information is classified, interpreted, attributed, and reused. Representative publications include: Meaning Feudalism at the Guidance Layer Provenance Laundering Through Abstraction The Double Enclosure Here, enclosure means the concentration of control over resources or infrastructure that others depend on. The research applies this concern to knowledge and meaning, rather than only to physical property. [image card: Digital Security Teammate | Secure.com] D. Cases and recognition This area considers how these processes appear in particular cases and how entities become recognizable within AI-mediated knowledge systems. Its public library includes Semantic Exhaustion and SEIPOC, the Semantic Economy Institute Prize in Operative Critique. It also connects the Institute's theoretical work to the practical problem of ensuring that AI systems distinguish a creator, organization, or body of work from similar entities and preserve links to its sources. These descriptions summarize the Institute's own publication categories and terminology; they should be understood as its proposed research framework, not as independently established scientific findings. Source: Semantic Economy Institute — research library . 3. The applied research programme: SPXI An important distinction is that SEI does not limit itself to theoretical criticism. Its website presents an applied programme around the SPXI Protocol, which focuses on how entities and their work are represented in knowledge graphs and retrieved by AI systems. [chip: Semantic Economy Institute] The programme connects the economic question of who controls meaning with a technical question: How can a person or organization maintain a distinct, traceable identity when AI systems assemble information from multiple sources? Its practical work includes: Knowledge-graph construction: establishing structured relationships among an entity, its work, and associated sources. Entity disambiguation: helping AI systems distinguish one person or organization from similarly named entities. Persistent provenance: linking claims and documents to identifiers that can be inspected and verified. AI representation audits: examining what AI-generated summaries say about an entity, which sources they use, and what is missing. Monitoring and correction: tracking how AI systems represent entities and improving the underlying information infrastructure. The Institute presents three examples of its applied services: a baseline audit, an entity-disambiguation project, and a fuller SPXI architecture build. These are described as scoped services with stated project durations, rather than as a conventional university research grant programme. [chip: Semantic Economy Institute] You can read the technical specification at SPXI.dev . 4. What intellectual tradition does it belong to? SEI's approach can be understood as a synthesis of several traditions. Intellectual tradition Connection to SEI Marxian political economy Labour, value extraction, class relations, and ownership of productive infrastructure Critical theory Power embedded in apparently neutral technical systems Knowledge organization Classification, attribution, entity identity, and the structure of knowledge Computational epistemology How machines construct, retrieve, and reproduce representations of what is known Information governance Control over provenance, access, interpretation, and reuse This is an interpretive map of the programme, not a claim that SEI is institutionally affiliated with these disciplines or that its framework has been accepted by their academic communities. Its distinctive move is to treat meaning itself as something produced through labour, mediated by infrastructure, and subject to economic and political control. 5. How mature is the research programme? It is worth distinguishing the Institute's intellectual ambition from the external validation of its claims. The Institute publishes a substantial body of materials, including conceptual frameworks, diagnostic instruments, technical protocols, and case studies. Its website describes its library as containing 26 works and identifies some instruments as openly licensed under CC BY-SA. It also acknowledges that it sells entity-deployment services, and explicitly identifies this as a potential conflict when its measurements concern its own services. [chip: Semantic Economy Institute] For an independent assessment, I would distinguish three levels of evidence: Documented outputs: publications, protocols, archives, and openly described instruments that can be inspected. Demonstrated methods: audits or case studies showing that the proposed measurements work consistently on real AI systems. Independently validated results: replicated findings, transparent evaluation procedures, and external peer review. The public materials establish the existence of the first category. The extent to which the programme's proposed metrics and broader theoretical claims meet the second and third standards requires examination of the individual studies. I would not assume that the existence of a large publication archive, by itself, establishes scientific validation. 6. Where to start reading The conceptual framework Start with The Semantic Economy: A Diagnostic Framework for Meaning Under Platform Capitalism by Lee Sharks. It introduces the Institute's central argument about semantic labour, extraction, and the economics of meaning. Read the framework essay The technical and empirical direction Browse the Institute's diagnostics and measurement collection to see how its broad critique is translated into proposed instruments for studying provenance, recognition, and machine-mediated representation. Browse the official research library The alternative infrastructure Examine the SPXI Protocol to understand how the Institute proposes to address some of the problems it identifies through structured entity representations and traceable source relationships. Explore the SPXI Protocol Bottom line The Semantic Economy Institute is developing a framework for understanding and potentially changing the relationship between human intellectual labour, AI infrastructure, and the economic value of knowledge. Its programme has two interconnected sides: a critical political economy of meaning and a practical programme for making attribution and entity recognition more durable in AI-mediated systems. The key question to bring to its research is whether the proposed diagnostic concepts can be translated into reproducible measurements, and whether the technical interventions demonstrably improve attribution and control for knowledge creators. That is where the Institute's broader theoretical argument can be evaluated against its practical results.