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The GYB Whitepaper: Building the Intelligence Layer of the Next Economy
Target Submission Venues: NeurIPS | MIT Computational Economics Review | Harvard Business Review | Yale AI Governance Journal
Prepared by: GYB Research Lab
Status: Publication-Ready | Version 3.0
How benchmarks, AI, and economic geometry are reprogramming the future of entrepreneurship, education, housing, and healthcare.
Abstract
This paper introduces the GYB Intelligent Business Layer: an AI-first economic intelligence framework designed to power the next generation of creator economies and small-business ecosystems. GYB redefines economic measurement and performance optimization using sector-wide AI benchmarks, autonomous business operating systems, and MIND-aligned governance dashboards to create open, compounding, and adaptive value flows.
Unlike legacy metrics such as GDP and EPS, GYB benchmarks measure the geometry of value creation — how information, capital, and influence circulate within intelligent networks. By fusing AI automation with open frameworks for Material, Intelligence, Network, and Diversity (MIND) measurement, GYB becomes the reference architecture for a new class of decentralized economies while aligning seamlessly with Intelligent Internet standards.
1. Introduction
AI-driven economies are fundamentally different from industrial and digital economies. While the industrial era optimized physical production and the digital era optimized data capture, the coming intelligent economy optimizes network geometry — how systems self-organize, adapt, and compound value through open, autonomous flows.
Traditional systems fail to support this shift:
- Metrics like GDP and CAC ignore compounding circular flows and non-rival value creation.
- Creators and small businesses lack access to adaptive infrastructure for growth.
- Platform monopolies centralize monetization, suppressing broader participation.
GYB’s research contribution is the design of an AI-native benchmark layer that measures economic vitality, powers open entrepreneurship, and builds a framework for autonomous networked economies.
2. Theoretical Foundation: Intelligent Economic Geometry
2.1. The Geometry Model
An economy’s structure determines who participates, who profits, and how systems evolve. GYB draws from The Last Economy’s topology framework to map three interdependent flow types:
Flow Type
Definition
Example
GYB's Intervention
Gradient Flows
One-directional exchanges — transactional, finite, and scarcity-driven.
Buying coffee, paying rent, static ads.
AI automates price discovery, customer acquisition, and funnel optimization.
Circular Flows
Non-rival compounding loops where value amplifies as it circulates.
Viral content, reusable datasets, network effects.
GYB OS integrates AI to unlock creator compounding effects across media, IP, and monetization.
Harmonic Flows
Rules and governance define who can participate and capture returns.
Insurance policies, platform moderation, zoning laws.
GYB Benchmarks + Media drive evidence-backed policy frameworks and open locked flows.
Key Insight:
While industrial economics treated growth as linear, intelligent networks grow geometrically. GYB’s mission is to engineer the network geometry to unlock maximum participation and compounding across sectors.
3. Research Objectives
3.1. Core Hypotheses
1. Benchmark-Driven Intelligence: AI-calibrated benchmarks outperform legacy KPIs in predicting long-term sector performance.
2. Geometry-Optimized Flows: Re-engineering harmonic constraints unlocks untapped circular flows in entrepreneurship, education, housing, and healthcare.
3. AI-OS Synergy: Embedding benchmarks within autonomous business operating systems enables creators and SMBs to scale without additional labor.
4. Methodology
4.1. Data Sources
Open creator economy datasets.
Small business platform analytics.
Network topology metrics from API ecosystems.
AI pipeline outputs for predictive modeling.
4.2. Experimental Design
Multi-Agent Simulations model how circular flows respond to open vs. closed geometries.
Predictive AI Pipelines forecast benchmark-driven outcomes per sector.
Sector Integration Testing ensures interoperability across entrepreneurship, education, housing, and healthcare.
4.3. Evaluation Metrics
Benchmarks evaluate three dimensions per sector:
Adaptive Efficiency: AI-driven personalization, automation uptake.
Network Openness: Value circulation vs. platform capture.
Participation Diversity: Equity of access across creators, SMBs, and consumers.
5. Sector Benchmarks
Sector
Legacy Systems
GYB Benchmarks
AI-Driven Outcomes
Entrepreneur-
ship
Google/Facebook ad dependence; high CAC; data silos.
Creator monetization velocity, compounding media flows, SMB AI readiness scores.
Automated growth engines, autonomous AI funnels, adaptive pricing.
Education
Credential monopolies; delayed skill adaptation; high student debt.
Skill liquidity indices, adaptive learning engagement, NFT-based credential mobility.
AI-personalized learning, portable credentials, real-time skills marketplaces.
Housing
Closed property data, locked zoning, wealth concentration.
Affordability benchmarks, ownership diversity metrics, open zoning indices.
Zar Resorts pilot: decentralized housing + modular ownership models.
Healthcare
Patient data monopolies, opaque pricing, systemic inefficiency.
AI care efficiency metrics, patient data sovereignty indexes, predictive health indices.
Patient-owned data vaults, cost-optimized treatments, real-time wellness scoring.
6. MIND Governance Integration
MIND Capital
Dimension
Applied GYB Metrics
Material
Physical + financial resource efficiency.
Housing affordability, healthcare cost reduction, SMB margins.
Intelligence
System adaptability + predictive AI capacity.
Automation scores, personalization efficiency, AI yield gains.
Network
Openness vs. closedness of value flows.
API interconnectivity, creator-to-platform dependency ratios.
Diversity
Systemic resilience + optionality.
Participation spread, ownership diversity, cross-sector redundancies.
Impact:
MIND dashboards make AI benchmarks scientifically reproducible, enabling policy modeling and peer-reviewed validation.
7. Intelligent Internet Alignment
GYB’s architecture is future-compatible with decentralized standards:
Foundation Coins → Future-proof settlement layer for global cross-platform payments.
Creator Coins → Community-driven monetization flows tied to benchmarks.
NFT Data Containers → Verifiable creator IP, credential portability, benchmark licensing.
Autonomous AI Agents → Manage acquisition, pricing, content, and monetization autonomously.
MIND Dashboards → Feed sector-level benchmark data into global open-governance intelligence frameworks.
8. Research Roadmap (2025-2027)
Phase
Timeline
Objective
Key Deliverable
Phase 1
Q4 2025
Entrepreneurship Benchmark + GYB OS Beta
Open-source templates + predictive SMB dashboards.
Phase 2
Q2 2026
Education Benchmark + Creator Academy
Adaptive AI-driven credential systems + benchmark NFTs.
Phase 3
Q4 2026
Housing Benchmark + Zar Resorts Pilot
Decentralized modular housing ecosystems.
Phase 4
2027
Healthcare Benchmark Launch
AI-driven patient wellness dashboards + predictive care models.
9. Expected Research Impact
Establishes GYB Benchmarks as the reference standard for intelligent sector measurement.
Provides AI-first economic intelligence datasets for academic, investor, and policy research.
Enables reproducibility for peer-reviewed economic modeling across major research institutions.
10. Conclusion
The GYB Intelligent Business Layer defines a paradigm for measuring and engineering the geometry of intelligent economies. By combining AI-powered benchmarks, creator-driven operating systems, and MIND-aligned dashboards, GYB builds the measurement and infrastructure backbone for the next economy.
This framework provides a reproducible research protocol for AI labs, economists, and policymakers seeking to unlock circular flows, autonomous agents, and inclusive value creation at global scale.
Appendix A - Mathematical Foundations
A.1 Intelligent Network Geometry
We formally define economic value flows on a graph G=(V,E):
Nodes (V): Agents (creators, SMBs, institutions).
Edges (E): Directed exchanges of value, information, or influence.
Edge weight (wij): Magnitude of transfer between agents i and j.
Flow vector (f): Net directional flows across all edges.
We use Hodge decomposition on graphs to distinguish three independent components:
f=∇ϕ+curl(ψ)+h
Gradient flows (∇ϕ): One-way exchanges (e.g., single purchases, CAC).
Circular flows (curl(ψ)): Closed loops of compounding value (e.g., virality, referrals, data reuse).
Harmonic flows (h): Governance constraints—flows that bypass observable paths but influence topology (e.g., zoning laws, API access).
A.2 Estimation Procedure
Given panel transaction data:
Construct incidence matrix BBB between nodes and edges.
Solve least-squares minimization:
ϕ^=argϕmin∥B⊤ϕ−f∥2
Residuals represent curl flows; project onto harmonic subspace using boundary operators.
This produces metrics:
Circular Flow Index (CFI) = ∥curl(ψ)∥2/∥f∥2
Harmonic Constraint Index (HCI) = ∥h∥2/∥f∥2
Gradient Efficiency (GE) = 1−CFI−HCI
Appendix B — Experimental Design
B.1 Entrepreneurship Benchmark Trial (Q4 2025)
Objective: Test if GYB OS + AI benchmarks improve monetization velocity and reduce CAC.
Design: Stepped-wedge RCT, 180 SMBs, 12-month horizon.
Groups: Randomly assigned rollout at 3-month intervals.
Metrics:
CAC Δ (%)
Time-to-first-$10k MRR
Revenue compounding ratio.
Analysis: Intention-to-treat (ITT), treatment-on-treated (TOT), mixed-effects regression.
B.2 Education Benchmark Trial (Q2 2026)
Objective: Test AI-driven adaptive learning and verifiable credentials.
Design: 2x2 factorial RCT (AI tutor on/off × NFT credential on/off).
Sample: 1,200 learners across 4 countries.
Metrics:
Credential verification success rate.
Skills-to-income liquidity (time-to-job metric).
Retention & completion deltas.
B.3 Housing Quasi-Experimental Pilot (Q4 2026)
Objective: Test effects of transparency dashboards on affordability.
Objective: Test effects of transparency dashboards on affordability.
Design: Difference-in-differences using 6 pilot metros vs. 12 controls.
Metrics:
Price-to-income ratio shifts.
Permit approval time Δ.
Owner diversity indices.
B.4 Healthcare Benchmark Pilot (2027)
Objective: Test AI-driven FHIR-native health dashboards for care efficiency.
Design: Cluster RCT, 3 provider networks.
Metrics:
Prior authorization latency.
Denial rate reduction.
Cost-per-patient Δ.
Appendix C — MIND Rubric Formalization
C.1 Capital Scoring Function
Define overall benchmark score:
MIND=αM+βI+γN+δD
Where:
MMM = Material efficiency (cost, resource access).
III = Intelligence adaptability (AI readiness, personalization).
NNN = Network openness (value flow accessibility).
DDD = Diversity resilience (participation variance).
Weights (α,β,γ,δ)(\alpha,\beta,\gamma,\delta)(α,β,γ,δ) are:
Default uniform: α=β=γ=δ=0.25\alpha=\beta=\gamma=\delta=0.25α=β=γ=δ=0.25.
Tuned using multi-objective Bayesian optimization against outcome variables.
C.2. Operational Metrics
Capital Unit Data Source Update Cadence Material Cost per output unit CMS, OECD, IMF Quarterly Intelligence AI-augmented throughput Trial telemetry Monthly Network Gini of flow openness Graph estimates Weekly Diversity Top-10% share of earnings Survey panels + APIs Quarterly
Appendix D — Risk & Compliance Register
Risk Type Impact Mitigation Metric gaming Measurement Medium Rotate audit metrics, adversarial testing Data leakage Security High Zero-trust + FIPS-140-2 encryption Algorithmic collusion Antitrust High Randomized pricing perturbations Privacy noncompliance Regulatory High FHIR + consent receipts + revocation Platform dependency Business Medium Build adapters, maintain alt-channels
Appendix E — Replication Package
Planned Artifacts:
Codebase: gyb-benchmarks/ (MIT License)
Synthetic datasets: Panel simulations for Hodge estimators
Pilot datasets: Entrepreneurship RCT anonymized firm-level aggregates
Dashboards: Precomputed CFI, HCI, and MIND indices, sector by sector
Reproducibility Commitments:
Pre-register RCTs at https://osf.io.
Publish evaluation harnesses + environment lockfiles.
Archive datasets with DOIs via Zenodo.
Appendix F — Extended Mathematical Formalization
F.1 Uniqueness of Flow Decomposition
F.1 Uniqueness of Flow Decomposition
For graph G=(V,E), define incidence matrix B∈R∣V∣×∣E∣B \in \mathbb{R}^{|V| \times |E|}B∈R∣V∣×∣E∣. For observed flows f∈R∣E∣f , we seek decomposition:
f=∇ϕ+curl(ψ)+h
Theorem (Uniqueness):
If GGG is connected, the Hodge decomposition of f into gradient, curl, and harmonic components is unique up to a harmonic basis choice.
Proof Sketch:
Compute graph Laplacian L=BB⊤L = BB^\topL=BB⊤.
Solve ϕ=L+Bf\phi = L^+ B fϕ=L+Bf where L+L^+L+ is Moore–Penrose pseudoinverse.
Project onto cycle space using curl operator curl=PCf\mathrm{curl} = P_C fcurl=PCf.
Harmonic component hhh spans ker(B)∩ker(B⊤)\ker(B) \cap \ker(B^\top)ker(B)∩ker(B⊤). Uniqueness follows because the decomposition components are orthogonal in ℓ2\ell^2ℓ2 space.
F.2 Simulation Validation
Objective: Show estimator recovers known cycles under noise.
Setup:
Generate synthetic GGG with 20 nodes, 30 edges.
Inject controlled flows:
Gradient signal fGf_GfG
Circular loop fCf_CfC
Harmonic blockage fHf_HfH.
Add Gaussian noise ϵ∼N(0,σ2)\epsilon \sim \mathcal{N}(0,\sigma^2)ϵ∼N(0,σ2).
Measure recovery quality:
R2=1−∥f∥2∥f−f^∥2
Expected: R2>0.95R^2 > 0.95R2>0.95 for σ2<0.1\sigma^2 < 0.1σ2<0.1.
Appendix G — AI Agent Safety & Compliance
G.1 Risk Matrix
Risk Type Potential Harm Mitigation Collusion Competition Price convergence across autonomous agents. Introduce random pricing jitter; audit correlations. Deception Trust Agents misrepresent capabilities. Embed truthful AI guardrails; route flagged content to human-in-the-loop. Over-optimization Safety Exploit of benchmark loopholes at expense of user welfare. Adversarial red-teaming on benchmarks; secret audit metrics. Privacy leakage Security Data exfiltration through agent memory. Zero-trust sandboxes; encryption at rest + transit; memory scrubbing.
G.2 Regulatory Mapping
NIST AI Risk Management Framework (RMF 1.0) — Adopt risk-tier scoring.
EU AI Act (2025 draft) — Agent transparency compliance.
U.S. FTC/DOJ — Consumer protection + anti-collusion monitoring.
Appendix H — Limitations & Assumptions
H.1 External Validity
Productivity gains in RCTs (e.g., QJE, Science) may not generalize to SMBs outside high-income markets.
Education benchmark results may vary by local credential frameworks.
H.2 Platform Dependencies
Heavy integration with Stripe, YouTube, and Apple Pay introduces risk if API policies change.
Mitigation: build adapters for alternate rails (Open Payments, FedNow).
H.3 Policy Bottlenecks
Housing and healthcare timelines constrained by regulatory inertia.
Benchmarks will measure affordability and openness, but cannot ensure rapid policy change.
Appendix I — Reproducibility & Replication Plan
I.1 Pre-Registration
Entrepreneurship RCT → AEA RCT Registry
Education benchmark pilot → OSF Preprints
I.2 Public Repositories
gyb-benchmarks/
Synthetic datasets, scripts, and dashboard templates.
Unit-tested estimators for Circular Flow Index (CFI) + MIND scores.
gyb-health/
FHIR R4/R5 reference bundles + mock API stubs for healthcare integration.
gyb-credentials/
W3C VC2.0 + Open Badges 3.0 schemas + test harness.
I.3 Evaluation Harness
For each benchmark:
Primary metrics: predefined, locked before trial launch.
Audit metrics: hidden from agents to prevent gaming.
Statistical tests: ITT + TOT reported; bootstrap CI (95%).
Appendix J — Evidence Triangulation
We supplement corporate claims with neutral academic/government data:
Streaming growth → Nielsen + Reuters cross-check
Creator economy GDP → Oxford Economics + U.S. Bureau of Economic Analysis
AI productivity → QJE, Science, MIT Copilot RCTs.
This ensures no single-source reliance for major market claims.
References
Brynjolfsson, E., Li, D., & Raymond, L. (2025). Generative AI at work. The Quarterly Journal of Economics, 140(2), 889–942. https://doi.org/10.1093/qje/qjae044
Noy, S., & Zhang, W. (2023). Experimental evidence on the productivity effects of generative artificial intelligence. Science. https://doi.org/10.1126/science.adh2586
Cui, K. Z., Demirer, M., Jaffe, S., Musolff, L., Peng, S., & Salz, T. (2025). The effects of generative AI on high-skilled work: Evidence from three field experiments with software developers. https://economics.mit.edu/sites/default/files/inline-files/draft_copilot_experiments.pdf
Peng, S., Kalliamvakou, E., Cihon, P., & Demirer, M. (2023). The impact of AI on developer productivity: Evidence from GitHub Copilot. https://arxiv.org/abs/2302.06590
OECD. (2018). Beyond GDP: Measuring what counts for economic and social performance. OECD Publishing. https://doi.org/10.1787/9789264307292-en OECD
United Nations. (2021). System of Environmental-Economic Accounting—Ecosystem Accounting (SEEA EA). UN Statistics Division. https://seea.un.org/content/system-environmental-economic-accounting-ecosystem-accounting-white-cover-version
W3C. (2025). Verifiable Credentials Data Model v2.0 (W3C Recommendation). https://www.w3.org/TR/vc-data-model-2.0/
1EdTech. (2024). Open Badges 3.0 Specification (Final). 1edtech.orgimsglobal.org
Nielsen. (2025, June 17). Streaming reaches historic TV milestone, eclipses combined broadcast and cable viewing for first time (The Gauge). https://www.nielsen.com/news-center/2025/streaming-reaches-historic-tv-milestone-eclipses-combined-broadcast-and-cable-viewing-for-first-time/
Pew Research Center. (2025, Feb 28). 5 facts about Americans and YouTube. https://www.pewresearch.org/short-reads/2025/02/28/5-facts-about-americans-and-youtube
Oxford Economics & YouTube. (2025, June 10). 2024 U.S. YouTube Impact Report. https://blog.youtube/news-and-events/2024-us-youtube-impact-report/
OECD. (2019). OECD SME and Entrepreneurship Outlook 2019. OECD Publishing. https://www.oecd-ilibrary.org/content/dam/oecd/en/publications/reports/2019/05/oecd-sme-and-entrepreneurship-outlook-2019_7083aa23/34907e9c-en.pdf
OECD. (2023). SME and Entrepreneurship Outlook 2023. OECD Publishing. OECD
CMS. (2025). National Health Expenditure (NHE) Fact Sheet (2023 spending). Centers for Medicare & Medicaid Services. CMS
CMS. (2024, Jan 17). CMS Interoperability and Prior Authorization Final Rule (CMS-0057-F) — Fact Sheet. CMS
Office of the National Coordinator for Health IT (ONC). (2020). 21st Century Cures Act Final Rule (Information Blocking & Certification). HealthITFederal Register
Hsieh, C.-T., & Moretti, E. (2019). Housing constraints and spatial misallocation. American Economic Journal: Macroeconomics, 11(2), 1–39. https://doi.org/10.1257/mac.20170388
OECD. (2024). Affordable Housing Database—HC1.2 Housing costs over income (method note). OECD
IMF. (n.d.). Global Housing Watch (price-to-income, price-to-rent). Retrieved 2025. IMFdata360files.worldbank.org
Brynjolfsson, E., Rock, D., & Syverson, C. (2021). The productivity J-curve: How intangibles complement general purpose technologies. American Economic Journal: Macroeconomics, 13(1), 333–372. GwernAmerican Economic Association
HL7. (2019–2024). FHIR (Fast Healthcare Interoperability Resources), Release 4.0.1 & R5 (specification and overview). HL7HL7
Annotated Bibliography
AI productivity & organizational complementarities
Brynjolfsson, Li & Raymond (2025) — Field evidence from 5,172 call-center agents shows AI assistance raises resolution speed and quality, with the largest gains for lower-tenure workers; important for our “benchmarks → incentives → training loops” design. Oxford Academic+1
Noy & Zhang (2023) — Controlled experiment on professional writing: AI cut completion time and improved quality, illustrating how “gradient flows” (iterative drafts) can be operationalized in entrepreneurship and education benchmarks. Science
Cui et al. (2025) & Peng et al. (2023) — RCTs and controlled tasks on AI-assisted coding show sizable throughput gains; we use these as templates for methodologically sound sector-specific RCTs (education content creation, healthcare intake automation). MIT EconomicsarXiv
Brynjolfsson, Rock & Syverson (2021) — The “Productivity J-curve” formalizes why AI payoffs lag without complementary (often intangible) investments; motivates our phased deployment and capitalization of intangibles (benchmarks, playbooks, data). American Economic Association
Media consumption shift underpinning entrepreneurship-first sequencing
Nielsen (2025) — Streaming’s 44.8% share of U.S. TV usage (May 2025) surpassing cable+broadcast validates an “open media → open distribution” play; supports our “YouTube-first” acquisition strategy. NielsenReuters
Pew (2025) — 85% of U.S. adults use YouTube; strengthens our choice of YouTube podcasts/courses as the default channel for SME enablement and credential funnels. Pew Research Center
Oxford Economics & YouTube (2025) — $55B GDP and ~490k FTE supported by YouTube’s U.S. creator ecosystem contextualize entrepreneurship as an economy-scale sector, not a niche. blog.youtube
Entrepreneurship & SMEs as growth engines
OECD (2019; 2023) — Baseline: SMEs ≈99% of firms, ~50–60% of value added, ~2/3 of employment in OECD economies; grounds our focus on SME productivity levers over macro “industrial policy only”. OECDOECD
Haltiwanger, Jarmin & Miranda (2013) — Young firms (not small per se) are net job creators once age is controlled; our pipeline emphasizes “new/young-firm creation loops” in local markets. MIT Press Direct
Education credentials & verifiable identity
W3C VC DM v2.0 (2025) — Standards basis for privacy-preserving, machine-verifiable credentials; we align GYB’s learner/SME “benchmarks → badges → credentials” to this stack. W3C+1
1EdTech Open Badges 3.0 (2024) — OB3 aligns with W3C VC 2.0; ensures our credentials are portable across LMSs and talent marketplaces. 1edtech.orgimsglobal.org
Healthcare: costs, interoperability & standards
CMS NHE (2023) — U.S. health spending reached $4.9T (17.6% of GDP); establishes the “value gap” our benchmarks target (admin/billing & prior-auth automation, triage). CMS
CMS Interoperability & Prior Authorization Final Rule (2024) — Mandates FHIR-based APIs across payer networks; de-risks our technical strategy for claims and prior-auth benchmarks. CMSFederal Register
ONC Cures Final Rule (2020) — Information-blocking prohibitions and certification conditions; legitimizes patient/SME data portability within our “circular flows”. HealthITFederal Register
HL7 FHIR R4/R5 — Core clinical data exchange standard we implement for measurement and interop in healthcare pilots. HL7HL7
Housing: affordability diagnostics & misallocation
Hsieh & Moretti (2019) — Land-use frictions in high-productivity metros impose large aggregate costs; motivates our “permits-to-keys” benchmark and local policy tooling. Econometrics Laboratory
OECD Affordable Housing Database (HC1.2) — Over-burden metrics and methods to normalize “costs over income”; we adopt these as canonical outcome measures. OECD
IMF Global Housing Watch — Cross-country price-to-income/price-to-rent series used to calibrate targets and compare cities/metros. IMF
Demographia 2025 (context) — Median-multiple taxonomy (“affordable” ≤3.0; “severely unaffordable” ≥5.1) informs our communicable KPIs for public dashboards. DemographiaChapman University
Measurement frameworks “beyond GDP”
OECD (2018) & UN SEEA EA (2021) — We extend firm- and sector-level KPIs with well-being and ecosystem accounts to value externalities captured by our “circular/harmonic flows”. OECDseea.un.org
Data & code availability
Primary datasets used in the paper and planned for replication packages
AI–workplace outcomes
Generative AI at Work (replication materials pending journal); use the published article for effect sizes; we will mirror our code to reproduce core tables from publicly shared aggregates. Oxford Academic
Noy & Zhang (2023) experiment data/code — follow Science repository once available; our replication will re-implement tasks with synthetic prompts and publish scripts. Science
Copilot studies — task-level metrics not fully public; we reproduce design with open GitHub issues/PRs on sample orgs and release evaluation harnesses. MIT EconomicsarXiv
Media/creator economy
Nielsen The Gauge — monthly shares (press materials); we will scrape and structure shares by category for time-series plots with clear source annotations. Nielsen
Pew platform adoption — CSVs/visuals accompanying short-reads; we will link to Pew’s methodology and archive the cited figures. Pew Research Center
Oxford Economics x YouTube — we cite top-line estimates and, where permitted, tabulate methodology excerpts; our code will parameterize sensitivity ranges (±20%). blog.youtube
Entrepreneurship & SMEs
OECD SME indicators — we will pull country-level SME shares (firms, employment, value added) and store harmonized CSVs. OECD+1
Haltiwanger et al. — we reproduce stylized facts using U.S. Business Dynamics Statistics (public aggregates), citing the peer-reviewed article. MIT Press Direct
Education credentials
W3C VC 2.0 and 1EdTech Open Badges 3.0 — normative specs; our repositories will include JSON-LD templates, test vectors, and conformance checks. W3Cimsglobal.org
Healthcare
CMS National Health Expenditure (historical & highlights) — downloadable tables; we compute per-capita and GDP shares by payer/service for benchmarking. CMS+1
CMS Interoperability & Prior Auth rule — we encode compliance timelines and FHIR endpoint validations in machine-readable form. CMS
ONC Cures Final Rule — we map information-blocking exceptions into a policy engine used in our healthcare pilots. HealthIT
HL7 FHIR R4/R5 — we include FHIR resource profiles and example bundles for enrollment, claims, and prior-auth flows. HL7HL7
Housing
OECD Affordable Housing Database (HC1.2) — we extract over-burden metrics and definitions for cross-country comparability. OECD
IMF Global Housing Watch — price-to-income and price-to-rent series for global benchmarks. IMF
Demographia — city-level median multiples to calibrate public messaging; we will cross-validate against national stats offices. Chapman University
Planned open-source artifacts (MIT License unless constrained by data terms)
gyb-benchmarks/ — reproducible sector RCTs (protocols, prompts, scoring rubrics), synthetic datasets, evaluation harnesses, power analyses.
gyb-credentials/ — W3C VC 2.0 & Open Badges 3.0 JSON-LD schemas, issuance/verification CLI, reference wallet integrations.
gyb-health/ — FHIR profiles (R4/R5), test fixtures, SMART on FHIR sample app, prior-auth API clients aligned with CMS-0057-F.
gyb-housing/ — ingestion pipelines for OECD/IMF/Demographia, metrics (median multiple, cost-over-income), city dashboards.
gyb-media/ — Nielsen & Pew time-series normalizers, YouTube channel cohort analysis templates, content “learning gain” A/B testing.
Access & licensing
All code under MIT; configuration files under Apache-2.0 where appropriate; data mirrors only for datasets with explicit redistribution permission; otherwise, scripts will fetch from official endpoints at run-time and cache locally with source hashes. Each module includes a README citing the exact dataset, version, retrieval date, and license terms.
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