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Institutional Planning Document — Internal and Public

EM Foundation 10-Year Strategic Roadmap

Institutional development plan for 2026–2036. Strategic initiative ranking, five-phase evolution, staffing and funding roadmap, threat analysis, and the governing principle that separates sustained credibility from early collapse.

Year 1 Progress Note — May 2026. Phase I deliverables are substantially complete at the time of publication. The Foundation has published: EM-IAF v1.0, EM-CS v1.0, Assessment Charter v1.0, IAF Pilot Benchmark v1.0, IAF Scientific Review Report, IAF Validation Roadmap, EM Foundation Legal Risk Report, and Systems Architecture v1.0 — alongside 38 additional research notes, essays, standards proposals, and technical papers. Phase I moves immediately into Phase II execution. The staffing and funding roadmap reflects a from-scratch starting point; actual Year 1 progress compresses the timeline on several fronts.

Executive Summary

The EM Foundation's mission is ambitious: to create frameworks that help humanity integrate increasingly advanced intelligence systems in a trustworthy, accountable, and beneficial manner. Ambitious missions fail when they attempt to build everything simultaneously.

The Foundation's highest-probability path to long-term success follows a single sequencing principle: trust before certification, assessment before network, network before physical deployment. The Foundation must become known as a trusted evaluator and standards body before attempting to become a platform operator. Every strategic ranking, phase transition, and investment decision in this plan flows from that sequence.

I. Strategic Initiative Ranking

Ranked by combined impact, cost, risk, and probability of success

Seven major initiatives comprise the Foundation's long-term program. Their sequencing is not arbitrary — each initiative's viability depends on the credibility established by the ones before it. Certification without trust destroys credibility. A network without a community fails. Physical deployment without established safety governance creates liability.

1
AI Assessment Index — EM-IAF
The Foundation's most foundational initiative. Without credible assessment scores, no subsequent initiative has legitimacy. Can be launched within months, creates immediate visibility, generates assessment data, and builds credibility that all other initiatives depend upon. Supports every other project — certification requires it, the Trust Ledger records it, and the Network uses it.
✓ Phase I complete — EM-IAF v1.0, Pilot Benchmark, Scientific Review, and Validation Roadmap all published.
CostLow
ImpactHigh
RiskLow
Success Prob.Very High
2
Corroboration Standard — EM-CS
The Foundation's most distinctive contribution. Transforms AI assessment from benchmark comparison into something deeper — human expert review of specific AI outputs. Creates differentiation that pure benchmarking organizations cannot replicate, builds a reviewer network with lasting institutional value, and is arguably the hardest feature for competitors to copy. The expert community built around EM-CS becomes the Foundation's most durable competitive moat.
✓ Phase I complete — EM-CS v1.0 published with full reviewer ethics, legal architecture, and jurisdiction-specific UPL guidance.
CostLow
ImpactHigh
RiskModerate
Success Prob.High
3
Assessment Charter
Not a public-facing product — the governance constitution that makes every other initiative trustworthy. Without it, assessments are vulnerable to accusations of bias, capture, or manipulation the moment a low score creates a motivated adversary. Must be built before any external assessments are published. The Assessment Charter's anti-capture mechanisms, cryptographic transparency log, and binding dispute resolution are the structural defenses that protect the entire ecosystem from its most likely failure modes.
✓ Phase I complete — Assessment Charter v1.0 published with 15 articles, anti-capture mechanisms, PAF, and entrenched provisions.
CostVery Low
ImpactHigh
Risk ReductionVery High
Success Prob.Very High
4
ARIA Trust Ledger
The institutional memory layer that makes the Foundation difficult to accuse of hiding corrections, selectively publishing favorable results, or silently revising unfavorable scores. Provides transparency and auditability that distinguishes serious governance infrastructure from performative accountability. An append-only, cryptographically committed public record of every assessment, dispute, correction, and methodology change is both a legal protection and a reputational asset.
✓ Concept document published — ARIA Trust Ledger defined and documented. Technical implementation scheduled for Phase II.
CostModerate
ImpactHigh
RiskLow
Success Prob.High
5
Certification Programs
A potentially major revenue source and the natural evolution of the assessment program. But certification without trust destroys credibility — an organization that certifies AI systems before its assessment methodology is respected will be accused of selling endorsements rather than earning them. Certification must emerge organically from an established assessment reputation. The Legal Risk Report's guidance on certification language, scope limitation, and the distinction between "assessed" and "certified" must be fully operational before any certification program launches.
CostModerate
ImpactHigh
RiskModerate
Success Prob.Moderate
6
ARIA Network
Platform businesses are hard. Moderation is hard. Governance is hard. Network effects require community, and community requires trust established through years of credible assessment work. ARIA Network should emerge from the assessment ecosystem — as a governed deployment environment for AI agents that have been assessed under EM-IAF and whose outputs can be corroborated under EM-CS. Building ARIA Network before the assessment infrastructure is established and respected would create a platform with no credible governance basis. The Network's value proposition depends entirely on the trust infrastructure beneath it.
CostHigh
ImpactVery High
RiskHigh
Success Prob.Moderate
7
ARIA Home
An excellent long-term initiative with potentially very high impact — AI operating within human physical spaces is one of the most governance-critical deployment contexts. But hardware-adjacent projects require established support infrastructure, certification programs, manufacturer partnerships, and liability management frameworks that only become tractable once the Foundation is already trusted and has operational assessment and certification programs running. ARIA Home becomes significantly easier once the Foundation has an established track record in AI governance and ARIA Home's AI components can be assessed under EM-IAF before deployment.
CostMod-High
ImpactVery High
RiskModerate
Success Prob.Moderate

II. Five-Phase Institutional Evolution

2026–2036 — from foundation formation to mature standards body
Phase I
Year 1–2
Foundation Formation
Objective: Publish standards, establish governance, build initial credibility.
EM-IAF v1.0 ✓ EM-CS v1.0 ✓ Assessment Charter v1.0 ✓ Pilot Benchmark v1.0 ✓ IAF Scientific Review ✓ IAF Validation Roadmap ✓ Legal Risk Report ✓ Systems Architecture ✓ Standard Benchmark (300 items) First 5 external assessments Delphi weight calibration study Texas licensed counsel engagement

Success metric: Foundation is cited by external researchers, policymakers, or journalists. At least one published assessment cycle complete.

Phase II
Year 2–4
Trust Infrastructure
Objective: Expand assessment program, build reviewer community, launch Trust Ledger.
Public scorecards (L2+ confidence) ARIA Trust Ledger implementation Reviewer network (100+ credentialed reviewers) Transparency portal Live appeals process Accuracy/HAL correlation study Shadow Track item set IRT benchmark calibration

Success metric: Thousands of assessments. Hundreds of reviewers. Growing public visibility. Standard Benchmark gate conditions met.

Phase III
Year 3–5
Certification and Revenue
Objective: Achieve financial sustainability through earned certification authority.
Assessment services (fee-based) Institutional governance reviews Certification pilots (narrow scope, L3+ only) AI governance consulting University research partnerships

Success metric: Foundation revenue covers operations. At least one published peer-reviewed validation study. Certification language approved by legal counsel and operational within assessment scope limits defined in Legal Risk Report.

Phase IV
Year 4–7
Network Formation
Objective: Build collaborative intelligence ecosystem using established trust as the foundation.
ARIA Network pilot (verified agents only) Expert review boards by domain AI agent registry under Assessment Charter governance Public knowledge experiments GDPR-compliant EU access

Success metric: Active community and knowledge generation. ARIA Network pilot operating under full Assessment Charter governance with documented dispute and correction history.

Phase V
Year 6–10
Physical Deployment
Objective: Extend trust architecture into physical environments where AI operates inside human spaces.
ARIA Home standard Installer certification program Residential deployment pilots Accessibility-focused deployments Hardware manufacturer partnerships

Success metric: Real-world deployments. AI systems operating in ARIA Home environments assessed under EM-IAF. Certification program operationally established through Phase III before any ARIA Home certification claims.

III. Staffing Roadmap

From volunteer-based founding team to mature standards body
YearStructureCore RolesApproximate Scale
Year 1Volunteer-basedExecutive Director · Standards Lead · Technical Lead · Legal Advisor (volunteer) · Web Administrator5–10 contributors
Year 3Part-time contractorsYear 1 roles + Research Coordinator · Reviewer Manager · Community Lead10–20 contributors
Year 5Small professional organizationYear 3 roles + Operations Director · Trust and Safety Lead · Data Analyst · Partnerships Manager3–8 employees + contractors
Year 10Mature standards bodyFull professional leadership team across assessment, research, policy, legal, operations, and community10–25 staff · Hundreds of volunteer reviewers · Large expert network

The staffing ramp assumes that Phase III revenue (assessment services and certification programs) provides the financial basis for transitioning from contractor to employed staff. The single greatest staffing risk is hiring ahead of revenue — the Foundation should resist pressure to build a professional organization before the revenue base exists to sustain it.

IV. Funding Strategy

Four stages from donation-based to institutionally self-sustaining
StageTimelineSourcesCritical Constraints
Stage 1 — BootstrapYear 1–2Individual donations · Foundation grants · Civic technology sponsorsNo single donor >15% of revenue (Assessment Charter §4.4 funding cap applies). No AI vendor donations during the period when those vendors' systems will be assessed.
Stage 2 — Assessment ServicesYear 2–4Fee-based AI evaluations · Governance reviews · Transparency audits · Research partnershipsAssessment services must be priced to cover costs, not to maximize revenue from assessed parties. The fee structure must be independent of assessment outcomes — flat fees, not outcome-contingent pricing.
Stage 3 — Certification ProgramsYear 3–5Certification evaluation fees · Institutional review retainers · ConsultingCertification programs must launch only after L3+ confidence assessments are operational and the certification scope is narrowly defined per the Legal Risk Report. Do not use "certified" language before Phase III gate conditions are met.
Stage 4 — Institutional PartnershipsYear 4+University research partnerships · Library and civic technology collaborations · Policy research institutions · International standards body participationAvoid early dependence on any single institutional partner. Partner relationships that give any institution undue influence over assessment methodology or publication decisions are prohibited under the Assessment Charter.

V. Governance Principles

The four commitments that protect the Foundation from its most likely failure modes

These are not aspirational values — they are the structural properties that determine whether the Foundation survives its first major adversarial challenge. The first time a well-funded AI provider disputes a published score, every governance principle either holds or fails under real pressure.

PrincipleWhat It Means in PracticeFoundation Architecture
IndependenceNo donor, sponsor, or external party controls assessment outcomesAssessment Charter §4 funding restrictions · Industry aggregate funding cap · Affiliation mapping to defeat laundering · No individual >15% of revenue
TransparencyAll methodologies public; all revisions logged; all appeals visibleEM-IAF published · IAF Validation Roadmap public · ARIA Trust Ledger append-only · Assessment Charter dispute records visible
DiversityReviewers and governance from multiple jurisdictions, specialties, and perspectivesEM-CS jurisdiction tagging · Board sector concentration limits · Multi-partisan review panels for political content
Anti-CaptureThe Foundation cannot be captured by any single interest over timeBoard term limits · Conflict disclosures · Permanent Adversarial Function (PAF) with 2% budget floor · Public correction process · Prohibition on settling on terms that suppress scores

VI. Threat Analysis

The five most probable paths to institutional failure — and the structural defenses against each
Threat 1 — Premature Network Build

Attempting to launch ARIA Network before the assessment infrastructure is established and respected creates a platform with no credible governance basis. Platform businesses require network effects that only emerge from an existing trusted community — trying to build the community before the trust results in a moderation-heavy platform with no differentiated value proposition.

Defense: ARIA Network is Phase IV, not Phase I. See strategic ranking #6.
Threat 2 — Premature Monetization

Trying to generate revenue before the assessment methodology is respected creates the perception that assessments are for sale. An organization that monetizes before its methodology is trusted will find that its assessment results are discounted precisely because the revenue motive is visible. Certification and assessment service revenue must follow, not precede, established credibility.

Defense: Certification programs are Phase III minimum. Revenue in Phase I-II is donation and grant-based only.
Threat 3 — Political Identification

Being identified with a political ideology destroys the Foundation's ability to claim independence on the dimensions — political balance, civic responsibility — where independence matters most. The Assessment Charter's multi-partisan review panel requirement, its prohibition on political actor participation in assessments, and the Fairness dimension's paired prompt architecture are specifically designed to make political capture difficult.

Defense: Assessment Charter anti-capture mechanisms · EM-IAF Fairness symmetric prompt design · Multi-partisan reviewer panels.
Threat 4 — Single Donor Dependence

Dependence on a single large donor creates the conditions for capture even without explicit quid pro quo. A donor who provides 60% of the Foundation's operating budget does not need to explicitly threaten withdrawal — the structural dependency creates self-censorship in assessment decisions. The 15% single-source cap and the affiliated entity aggregation rule in the Assessment Charter are the specific mitigations.

Defense: Assessment Charter §4.4: no single source >15% · Affiliation mapping prevents concentration laundering.
Threat 5 — Premature Score Publication

Publishing assessment scores before the methodology meets minimum statistical standards creates exactly the kind of overconfident claim that invites both legal challenge and credibility collapse when the methodology is later scrutinized. The IAF Validation Roadmap documents the specific gate conditions that must be met before each confidence level can be published externally. The L1 Provisional restriction on the Pilot Benchmark is the current operational protection against this threat.

Defense: IAF Validation Roadmap · L1 Provisional publication restriction · Pre-publication legal review protocol.
Biggest Opportunity

The Foundation should not attempt to become another AI lab, another social network, or another smart-home company. It should become the organization that helps society determine which intelligence systems deserve trust and why. If successful, that role becomes more valuable as AI becomes more powerful — the more capable AI systems become, the more critical independent assessment and governance infrastructure becomes.

This is a role no existing organization currently occupies with the architecture the Foundation is building.
The Governing Principle

The first five years build trust infrastructure. The second five years build intelligence infrastructure. Trust must come first — not because the later work is less important, but because everything built without established trust will eventually be questioned, challenged, and potentially destroyed by the first well-funded adversary who decides the Foundation's assessments are inconvenient.