@prefix :       <https://www.linkedin.com/pulse/token-budget-wars-jaya-gupta-ibacc/#> .
@prefix post2:  <https://www.linkedin.com/posts/kevbosaurus_prediction-this-time-next-year-marketers-share-7466188437893775362-qv_4/#> .
@prefix schema: <http://schema.org/> .
@prefix rdf:    <http://www.w3.org/1999/02/22-rdf-syntax-ns#> .
@prefix rdfs:   <http://www.w3.org/2000/01/rdf-schema#> .
@prefix xsd:    <http://www.w3.org/2001/XMLSchema#> .
@prefix prov:   <http://www.w3.org/ns/prov#> .
@prefix owl:    <http://www.w3.org/2002/07/owl#> .
@prefix skos:   <http://www.w3.org/2004/02/skos/core#> .

# ═══════════════════════════════════════════════════════════════════════════════
# Token Economy & AI Governance — Knowledge Graph
# Mashup of:
#   (1) "The Token Budget Wars" — Jaya Gupta (LinkedIn Pulse, 2026-05-27)
#   (2) "ROTS: Return on Token Spend" — Kevin White (LinkedIn, 2026-05-27)
# ═══════════════════════════════════════════════════════════════════════════════

# ── Source Article 1 ──────────────────────────────────────────────────────────

:article a schema:Article ;
    schema:name "The Token Budget Wars"@en ;
    schema:url <https://www.linkedin.com/pulse/token-budget-wars-jaya-gupta-ibacc/> ;
    schema:author <https://www.linkedin.com/in/jayagupta10#this> ;
    schema:datePublished "2026-05-27"^^xsd:date ;
    schema:description "Enterprise AI has shifted from adoption to resource allocation. Token consumption is becoming a contested organizational resource analogous to headcount, and companies urgently need token-to-outcome attribution infrastructure to justify AI spend."@en ;
    schema:about
        :marginalTokenUtility ,
        :tokenToOutcomeAttribution ,
        :decisionTraces ,
        :tokenBudgetWars ,
        :tokenEconomy ,
        :retryTails ,
        :contextInflation ,
        :modelRouting ;
    schema:hasPart :faqPage, :glossarySet, :howtoGuide ;
    schema:relatedLink post2:post ;
    prov:wasGeneratedBy
        <https://github.com/OpenLinkSoftware/ai-agent-skills/tree/main/kg-generator#this> ,
        <https://github.com/OpenLinkSoftware/ai-agent-skills/tree/main/rdf-infographic-skill#this> .

# ── Source Article 2 (LinkedIn Post) ─────────────────────────────────────────

post2:post a schema:SocialMediaPosting ;
    schema:name "ROTS: Return on Token Spend — Marketers' Next Primary Metric"@en ;
    schema:url <https://www.linkedin.com/posts/kevbosaurus_prediction-this-time-next-year-marketers-share-7466188437893775362-qv_4/> ;
    schema:author <https://www.linkedin.com/in/kevbosaurus#this> ;
    schema:datePublished "2026-05-27"^^xsd:date ;
    schema:description "Prediction that ROTS (Return on Token Spend) will become a primary performance metric for marketers within one year. Token API costs are now trivial; the real constraint is human time investment, shifting strategic decisions to whether expected returns justify team effort."@en ;
    schema:about
        :returnOnTokenSpend ,
        :pipelineROT ,
        :productivityROT ,
        :conversionROT ,
        :tokenEconomy ;
    schema:relatedLink :article .

# ── People ────────────────────────────────────────────────────────────────────

<https://www.linkedin.com/in/jayagupta10#this> a schema:Person ;
    schema:name "Jaya Gupta"@en ;
    schema:url <https://www.linkedin.com/in/jayagupta10> ;
    schema:identifier <https://www.linkedin.com/in/jayagupta10> ;
    schema:description "AI and enterprise strategy writer; author of 'The Token Budget Wars' on LinkedIn Pulse."@en .

<https://www.linkedin.com/in/kevbosaurus#this> a schema:Person ;
    schema:name "Kevin White"@en ;
    schema:url <https://www.linkedin.com/in/kevbosaurus> ;
    schema:identifier <https://www.linkedin.com/in/kevbosaurus> ;
    schema:description "Marketing technologist and AI practitioner; author of the ROTS (Return on Token Spend) prediction post."@en .

# ── Core Concepts ─────────────────────────────────────────────────────────────

:tokenEconomy a schema:DefinedTerm, skos:Concept ;
    schema:name "Token Economy"@en ;
    schema:description "The emerging economic framework governing the allocation, measurement, and optimization of LLM token consumption as a contested organizational resource — analogous to data storage costs in the 2000s or cloud compute in the 2010s."@en ;
    owl:sameAs <http://www.wikidata.org/entity/Q6146561> .

:tokenBudgetWars a schema:DefinedTerm, skos:Concept ;
    schema:name "Token Budget Wars"@en ;
    schema:description "Organizational conflict over AI compute budget allocation as token consumption scales from pilot to production, transforming from an infrastructure cost into a contested proxy for team productivity and headcount justification."@en .

:marginalTokenUtility a schema:DefinedTerm, skos:Concept ;
    schema:name "Marginal Token Utility"@en ;
    schema:description "The business value generated per additional dollar of inference spending. The core ROI metric most enterprises currently cannot measure because token bills arrive without outcome attribution."@en ;
    skos:related :returnOnTokenSpend .

:tokenToOutcomeAttribution a schema:DefinedTerm, skos:Concept ;
    schema:name "Token-to-Outcome Attribution"@en ;
    schema:description "The missing infrastructure layer that connects inference expenditure to actual work performed and business results achieved. Without it, token bills are uninterpretable — rising spend could signal productivity or wastage."@en ;
    skos:related :returnOnTokenSpend .

:decisionTraces a schema:DefinedTerm, skos:Concept ;
    schema:name "Decision Traces"@en ;
    schema:description "Captured records of agent reasoning — retrieval steps, tool calls, retries, human interventions, and final outcomes — that create durable organizational memory currently lost in informal channels and ephemeral session logs."@en .

# ── Hidden Cost Drivers ───────────────────────────────────────────────────────

:retryTails a schema:DefinedTerm, skos:Concept ;
    schema:name "Retry Tails"@en ;
    schema:description "Hidden cost driver: failure rate changes compound token costs nonlinearly. Reducing success rate from 90% to 70% increases effective cost per successful outcome by 28% — invisible on aggregate invoices."@en .

:contextInflation a schema:DefinedTerm, skos:Concept ;
    schema:name "Context Inflation"@en ;
    schema:description "Hidden cost driver: context length scales quadratically in reasoning costs. Doubling the context window roughly quadruples inference spend, making unbounded context accumulation a major budget risk."@en .

:modelRouting a schema:DefinedTerm, skos:Concept ;
    schema:name "Model Routing"@en ;
    schema:description "Hidden cost driver: defaulting to frontier models for all tasks regardless of complexity. Matching model capability to task complexity (routing simple tasks to smaller models) is one of the highest-leverage cost optimizations available."@en .

# ── ROTS Framework ────────────────────────────────────────────────────────────

:returnOnTokenSpend a schema:DefinedTerm, skos:Concept ;
    schema:name "ROTS — Return on Token Spend"@en ;
    schema:description "A proposed primary performance metric for AI operators and marketers: the business return generated per dollar of token expenditure. As token API costs drop to trivial levels ($3–$60 for full applications), ROTS becomes the natural successor to traditional digital ROI metrics."@en ;
    schema:hasPart :pipelineROT, :productivityROT, :conversionROT ;
    skos:related :marginalTokenUtility, :tokenToOutcomeAttribution .

:pipelineROT a schema:DefinedTerm, skos:Concept ;
    schema:name "Pipeline ROT"@en ;
    schema:description "Return on Token Spend measured through revenue pipeline generation — the incremental pipeline value attributable to AI-powered outreach, qualification, and nurture workflows per token dollar spent."@en .

:productivityROT a schema:DefinedTerm, skos:Concept ;
    schema:name "Productivity ROT"@en ;
    schema:description "Return on Token Spend measured through time savings — quantified cost reduction from automating human-intensive tasks (research, drafting, analysis) expressed as hourly cost saved per token dollar spent."@en .

:conversionROT a schema:DefinedTerm, skos:Concept ;
    schema:name "Conversion ROT"@en ;
    schema:description "Return on Token Spend measured through conversion rate improvement — the incremental revenue from warming cold prospects, reducing sales cycle length, and improving funnel conversion per token dollar spent."@en .

# ── FAQ ───────────────────────────────────────────────────────────────────────

:faqPage a schema:FAQPage ;
    schema:name "Token Economy & ROTS — Frequently Asked Questions"@en ;
    schema:hasPart :q1, :q2, :q3, :q4, :q5, :q6, :q7, :q8 .

:q1 a schema:Question ;
    schema:name "What is the core measurement problem in enterprise AI spending?"@en ;
    schema:acceptedAnswer [ a schema:Answer ;
        schema:text "The signal and the noise share the same unit — tokens. A rising token bill can indicate productive work or compute wastage, making it impossible to determine value from invoices alone. Without token-to-outcome attribution, AI spend is unaccountable." ] .

:q2 a schema:Question ;
    schema:name "What is Marginal Token Utility and why does it matter?"@en ;
    schema:acceptedAnswer [ a schema:Answer ;
        schema:text "Marginal Token Utility is the business value generated per additional dollar of inference spending. It is the core ROI metric most enterprises cannot currently measure, creating misalignment between AI budget allocation and actual organizational outcomes. The company that can measure it gains decisive influence over AI investment decisions." ] .

:q3 a schema:Question ;
    schema:name "What are the three hidden cost drivers in enterprise token consumption?"@en ;
    schema:acceptedAnswer [ a schema:Answer ;
        schema:text "Retry tails (failure rates compound costs nonlinearly — dropping success from 90% to 70% raises effective cost by 28%), context inflation (quadratic scaling of reasoning costs with context length — doubling context quadruples spend), and model routing (defaulting to frontier models for all tasks regardless of complexity)." ] .

:q4 a schema:Question ;
    schema:name "What is ROTS and why will it become a primary marketing metric?"@en ;
    schema:acceptedAnswer [ a schema:Answer ;
        schema:text "ROTS — Return on Token Spend — measures business return per dollar of token expenditure. As token costs have become trivial (full marketing applications for $3–$60), the strategic question shifts from budget constraints to whether expected returns justify team time investment. ROTS provides the measurement framework for that decision, decomposing into Pipeline, Productivity, and Conversion ROT." ] .

:q5 a schema:Question ;
    schema:name "What is Token-to-Outcome Attribution and why is it the missing layer?"@en ;
    schema:acceptedAnswer [ a schema:Answer ;
        schema:text "Token-to-Outcome Attribution is the infrastructure layer connecting inference expenditure to actual work performed and results achieved. It is currently absent because AI deployments lack Decision Traces — captured records of agent reasoning, tool calls, retries, and interventions that would enable causal attribution from spend to outcome." ] .

:q6 a schema:Question ;
    schema:name "How do the three ROT categories decompose Return on Token Spend?"@en ;
    schema:acceptedAnswer [ a schema:Answer ;
        schema:text "Pipeline ROT measures revenue generation per token dollar; Productivity ROT measures time savings as quantified cost reduction; Conversion ROT measures improvement in prospect warming and funnel performance — collectively covering the full marketing value chain from awareness to close." ] .

:q7 a schema:Question ;
    schema:name "How does the Token Economy compare to prior enterprise transformation waves?"@en ;
    schema:acceptedAnswer [ a schema:Answer ;
        schema:text "Like ERP, BI, and digital transformation, the Token Economy requires executive sponsorship, cross-departmental measurement agreement, and specialized attribution infrastructure. The company that owns token-to-outcome attribution makes the AI allocation calls — analogous to how BI teams controlled budget conversations in the data-driven enterprise era." ] .

:q8 a schema:Question ;
    schema:name "What is the key decision constraint for AI deployments in 2026?"@en ;
    schema:acceptedAnswer [ a schema:Answer ;
        schema:text "Human time investment — not token budget. With full applications costing $3–$60 in API spend, the critical evaluation is whether expected ROTS justifies team effort to build, test, and iterate. The challenge Kevin White identifies is that 'most of the time, the answer is yes' — creating the need for disciplined ROTS thresholds rather than open-ended build authority." ] .

# ── Glossary ──────────────────────────────────────────────────────────────────

:glossarySet a schema:DefinedTermSet ;
    schema:name "Token Economy Glossary"@en ;
    schema:hasPart
        :tokenEconomy ,
        :tokenBudgetWars ,
        :marginalTokenUtility ,
        :tokenToOutcomeAttribution ,
        :decisionTraces ,
        :retryTails ,
        :contextInflation ,
        :modelRouting ,
        :returnOnTokenSpend ,
        :pipelineROT ,
        :productivityROT ,
        :conversionROT .

# ── HowTo ─────────────────────────────────────────────────────────────────────

:howtoGuide a schema:HowTo ;
    schema:name "HowTo: Establish a Token Economy Governance Framework"@en ;
    schema:step :step1, :step2, :step3, :step4, :step5, :step6 .

:step1 a schema:HowToStep ;
    schema:position 1 ;
    schema:name "Instrument Token Consumption"@en ;
    schema:text "Deploy logging at every AI call site capturing: model used, input/output token counts, latency, success or failure status, and retry count. This raw telemetry is the prerequisite for all downstream attribution."@en .

:step2 a schema:HowToStep ;
    schema:position 2 ;
    schema:name "Build Decision Trace Infrastructure"@en ;
    schema:text "Capture agent reasoning traces — retrieval steps, tool calls, human interventions, branching decisions, and outcomes. Store as durable organizational memory with retention policies, not ephemeral session logs discarded after completion."@en .

:step3 a schema:HowToStep ;
    schema:position 3 ;
    schema:name "Define Business Outcome Anchors"@en ;
    schema:text "Identify the measurable business outcome each AI workflow is expected to drive (pipeline generated, hours saved, conversion rate, deal velocity). Establish proxies that can be causally linked to token consumption events in Decision Traces."@en .

:step4 a schema:HowToStep ;
    schema:position 4 ;
    schema:name "Eliminate Hidden Cost Drivers"@en ;
    schema:text "Audit the three hidden cost drivers: reduce retry tails by improving prompt reliability, cap context inflation with explicit context management policies, and implement model routing to match capability to task complexity. Each intervention improves Marginal Token Utility."@en .

:step5 a schema:HowToStep ;
    schema:position 5 ;
    schema:name "Compute ROTS by Category"@en ;
    schema:text "Calculate Pipeline ROT (incremental pipeline per token dollar), Productivity ROT (hours saved × loaded hourly cost per token dollar), and Conversion ROT (conversion lift × average deal value per token dollar). Report by individual workflow, not as an aggregate — aggregates obscure underperformers."@en .

:step6 a schema:HowToStep ;
    schema:position 6 ;
    schema:name "Gate New AI Initiatives on Projected ROTS"@en ;
    schema:text "Require projected ROTS estimates by category before approving new AI builds. The binding constraint is team time, not API budget — but time investment must be justified by ROTS projections with minimum thresholds by category and workflow type. Establish a quarterly ROTS review cadence."@en .

# ── Skills Attribution ────────────────────────────────────────────────────────

<https://github.com/OpenLinkSoftware/ai-agent-skills/tree/main/kg-generator#this> a schema:SoftwareApplication ;
    schema:name "KG Generator Skill"@en ;
    schema:url <https://github.com/OpenLinkSoftware/ai-agent-skills/tree/main/kg-generator> ;
    schema:description "AI Agent Skill for generating RDF Knowledge Graphs from web content"@en .

<https://github.com/OpenLinkSoftware/ai-agent-skills/tree/main/rdf-infographic-skill#this> a schema:SoftwareApplication ;
    schema:name "RDF Infographic Skill"@en ;
    schema:url <https://github.com/OpenLinkSoftware/ai-agent-skills/tree/main/rdf-infographic-skill> ;
    schema:description "AI Agent Skill for generating interactive HTML infographics from RDF Knowledge Graphs"@en .