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HOW THIS WAS BUILT

The Provenance of the We The People Platform

An honest accounting of where the ideas came from,

who contributed what, and how the platform took shape.

A Provenance Document

Jason Robertson

Ohio · 2026

v1.6 · Created April 2026 · Updated May 5, 2026 for v2.9 · Updated May 6, 2026 for v2.23 (refresh covering v2.10–v2.22 work) · Updated May 6, 2026 for v2.26.2 (CRIT-1: build history through v2.26.1 documented) · Updated May 6, 2026 for v2.30.27 (Platform Positioning: What This Is And Isn't section added) · Updated May 6, 2026 for v2.30.31 (External Validation Pathways section added; addresses PROCESS-1/2/3 with engagement criteria)

Why This Document Exists

The We The People platform consists of fifty-two Word documents, nineteen analytical models, and a comprehensive policy architecture covering retirement reform, wage policy, education, healthcare, childcare, mental health access, Civic Infrastructure, AI workforce transition, and future capacity preservation. Anyone reading this body of work has a reasonable question to ask: who actually produced this?

This document answers that question honestly. The platform was developed through extended conversation between Jason — a Senior Data Integration Engineer based in Ohio — and Claude, an AI assistant built by Anthropic. The collaboration was substantial, sustained, and unusual in both its depth and its character. The platform reflects ideas, design choices, refinements, and value commitments that came from Jason. It also reflects analytical work, document production, and structural reasoning that came from Claude. Neither party could have produced this work alone.

Transparency about authorship matters because the platform makes claims about institutional design, fiscal viability, and political architecture that deserve to be evaluated on their merits rather than mistaken for either pure individual scholarship or pure machine output. A reader should know what they’re engaging with. This document tells them.

“The goal is not to take credit or to deflect credit. The goal is to be accurate about how the work happened, so that readers can evaluate it honestly.”

How It Started

The platform did not begin as a platform. It began as a question, asked casually.

In an earlier conversation, Jason asked what it would take to start a political party in the United States. The question was exploratory rather than committed — the kind of question someone asks when they’re frustrated with the political options available and curious about whether structural alternatives are possible. Claude’s initial response covered the practical mechanics of party formation: ballot access requirements, state-by-state registration, the role of independent candidates, the historical track record of third parties in American politics.

That conversation could have ended there. Most conversations like it do. What distinguished this one was that Jason returned to the underlying problem rather than the surface question. He didn’t want to know how to start a party. He wanted to know whether the country could be organized differently than it currently is, in ways that would produce shared prosperity rather than concentrated prosperity. The party question was a wrapper around a deeper question about political and economic architecture.

From there, the conversation moved to specific policy areas — starting with Social Security reform, which Jason raised as the most visible failing institution he could see in front of him. Claude’s initial response was a standard analysis of the Social Security trust fund’s solvency problem and the conventional reform options. Jason’s response was the move that turned the conversation into something different. He didn’t want one of the conventional options. He wanted to know whether the math could work for a fundamental redesign that would replace pay-as-you-go financing with a sovereign investment fund modeled on Norway’s GPFG (Government Pension Fund Global) and Australia’s superannuation system.

That question opened the door to mathematical modeling. Claude built the first Excel model — the Social Security Sunset Equilibrium Model — which demonstrated that simply phasing out Social Security without replacement would require approximately $63 trillion in transition borrowing over sixty years. The number was sobering. Jason’s response: what if the new contribution system runs in parallel with the sunset, so the new fund’s growth offsets the old system’s decline?

The Combined Reform Model showed that this architecture reduced peak transition borrowing from $63 trillion to $82 billion — a 99.9% reduction. The math worked. The conversation had its first concrete deliverable, and the platform had its first pillar.

This pattern — a question that opens a problem, an analytical response, a refinement that improves the design — became the template for everything that followed. The platform grew through iteration, not through monolithic specification.

How It Grew

Over the course of the conversation, the platform expanded from one pillar to eight. Each addition followed the same pattern: Jason identified a problem worth solving, the conversation worked out the architectural response, and the analytical work produced a model or document that captured the result.

Pillar Two: Empirical Wage Floors

Jason raised the wage stagnation problem after the retirement reform model was substantially complete. His initial framing was about whether a federal minimum wage could be set by occupation rather than nationally. The conversation worked through what the data sources would be (Bureau of Labor Statistics (BLS) Occupational Employment and Wage Statistics), what threshold would be appropriate (25th percentile of actual wages currently paid), and how the system would interact with the labor market. The empirical analysis Excel model covers 81 broad occupations and approximately 82 million American workers.

Pillar Three: Sovereign Education Fund

Education was the third pillar Jason raised. The initial framing was about the student debt crisis, but the conversation moved quickly to a different architectural question: could the same Sovereign Fund mechanism that solved retirement also solve education funding? The mathematical answer was that birth-seed contributions alone were insufficient — the eighteen-year compound horizon couldn’t produce enough per-student capital. But combined with disbursements from the retirement Sovereign Fund at maturity, the model showed that free college becomes mathematically achievable. The cost-based pricing framework that prevents institutional cost padding was added through subsequent iteration.

The Adjacent Pillars

Healthcare, childcare, and mental health access were added later in the conversation, after Jason questioned why they had been excluded from the platform. The original framing positioned these as adjacent rather than primary pillars because the analytical depth wasn’t initially complete. Jason’s response was to ask Claude to perform the analytical work — healthcare first, then childcare, then mental health. Each pillar received its own concept document and Excel model. The healthcare model showed a $2.6 trillion surplus by year ten under multi-payer reform. The childcare model showed a $135 billion surplus at full coverage. The mental health model showed that total workforce numbers are adequate at universal voluntary access — distribution is the problem, not raw supply.

The Founding Stake

Late in the conversation, Jason proposed that every American contribute two dollars at platform launch as the seed for the Sovereign Fund. The mathematical analysis showed that the $680 million produced by 340 million Americans contributing $2 each is essentially mathematical rounding error within five years. But Jason’s framing of the proposal pointed to something more important than the dollars: the universal contribution would establish that every American is a founding stakeholder in the institutions the platform creates. The historical and political theory underneath this idea — constitutive participation — has serious intellectual lineage that the conversation worked through. The Founding Stake is documented in its own companion piece.

The AI Transition Frame

Jason raised a question about whether the platform’s pillars would absorb workforce displacement from AI. The conversation worked through the empirical evidence on AI displacement scope, the categories of work that resist automation, and how each platform pillar functions as workforce transition infrastructure. The result was a parallel framing of the platform that proceeds from economic stability rather than fairness, and that broadens the political coalition the platform can plausibly build. This is captured in the “Built for What’s Coming” document.

The Civic Infrastructure Pillar

Jason asked whether journalism, civic education, voter access, and public meeting transparency could be combined into a unified pillar. The conversation worked through the architectural challenges — particularly the constitutional concerns about direct federal funding of journalism — and produced the Civic Infrastructure pillar concept document, which proposes indirect mechanisms (tax incentives, public broadcasting infrastructure, voter access standards, meeting transparency infrastructure) rather than direct federal evaluation of journalism content.

The Future Capacity Fund

In the conversation’s most recent iteration, Jason raised the question of whether the platform should preserve capacity for problems future generations will face. The conversation worked through two architectural paths — a pure Future Capacity Fund (Path A) and a hybrid future-capacity-plus-demonstration fund (Path B) — with explicit tradeoffs documented for each. The decision between paths was deliberately left to the platform’s broader political coalition rather than imposed by the conversation.

The Slideshow

The most recent deliverable was the visual slideshow — a 15-slide deck designed to communicate the platform at high level for audiences who don’t have time to engage with the underlying documents. This was Jason’s suggestion, and it captures the platform’s essential structure in a format that supports outreach beyond the policy-professional audience the white papers serve.

Jason’s Contributions

Jason’s role in the platform’s development was not that of a person who asked questions and accepted answers. His contributions were architectural, substantive, and recurring across the conversation’s entire arc.

Architectural Vision The platform’s underlying architecture — pooled contribution under transparent governance, with funds accumulating over decades into something capable of addressing problems at scale — was a synthesis Jason articulated and pushed for repeatedly. He was the one who asked whether the same Sovereign Fund could fund retirement and education. He was the one who proposed connecting wage floor data to education investment in a feedback loop. He was the one who recognized that the three primary pillars reinforce each other and that the integrated architecture is what makes the platform more than the sum of its parts.

Specific Design Insights Several design choices that strengthen the platform substantially came directly from Jason. The cost-based formula at field-of-study granularity as anti-padding mechanism. The two-channel disbursement architecture (institution-to-institution payment plus student reimbursement against a statutory category list). The age-30 reversion clause that converts unused individual entitlements into community resources. The Founding Stake universal $2 contribution mechanism. The AI workforce transition framing of the platform. The Civic Infrastructure pillar concept. The Future Capacity Fund concept. Each of these was something Jason brought to the conversation, often in response to reading something the conversation had produced and seeing how it could be improved.

Values Commitments The platform’s values commitments came from Jason. The principle that funding should belong to the citizen rather than to institutions. The commitment to honest acknowledgment of limitations rather than rhetorical concealment of them. The insistence that no one should be told what they can or can’t study, that the failing student should receive support and redirection rather than abandonment, that the platform should offer all citizens the same baseline opportunity regardless of their choices. The principle that contribution and ownership should be universal. These values shaped the platform’s architecture at every point where design choices were available.

Iterative Refinement Jason read what the conversation produced and pushed back on what wasn’t right. When the original platform document attached education funding to participating institutions, he caught the mistake and proposed the corrected design. When the original cost-based pricing framework didn’t address pad ding adequately, he proposed the field-of-study formula. When the reimbursement mechanism risked discretionary fraud, he proposed the statutory category list. These weren’t edits to wording — they were architectural improvements that came from reading carefully and thinking critically about what was being proposed.

Strategic Direction Jason directed the conversation’s strategic priorities throughout. He decided when to pursue analytical depth versus when to broaden scope. He decided which pillars deserved primary status and which were positioned as adjacent. He decided that the platform needed a separate AI transition framing for audiences whose primary concern is economic stability. He decided that the Civic Infrastructure components belonged together rather than being pursued as separate proposals. The platform’s shape reflects his judgment, not arbitrary choices.

“Without Jason’s contributions, this platform would not exist. Not in any form, not with any architecture, not with any policy substance.”

Claude’s Contributions

Claude’s contributions were also substantial, and honesty requires that they be acknowledged as accurately as Jason’s. The platform reflects analytical work, structural reasoning, and document production that Claude provided across the conversation’s entire arc.

Analytical Scaffolding Claude built the mathematical models that turned ideas into testable architectures. The Social Security Sunset Equilibrium Model, the Hybrid Retirement System Model, the Combined Reform Model, the wage floor Empirical Analysis, the Education Fund Cost-Based Pricing Model, the universal healthcare Model, the universal childcare Model, the Universal Mental Health Model, the Proof-of-Concept Fund Model. Each model contains hundreds of formulas, validates against specified inputs, and produces results that test whether proposed designs are mathematically viable. The models incorporated international comparison data, federal data sources (BLS, Census, IPEDS, CMS), and empirical research from peer-reviewed sources.

Document Production Claude produced the eighteen Word documents that articulate the platform’s vision, technical analysis, and supporting concepts. The platform manifesto, the white papers, the concept documents, the launch ceremony piece, the AI transition framing, the Civic Infrastructure pillar, the Future Capacity Fund, the constituent letter, the slideshow. The writing reflects design choices about voice, structure, rhetorical strategy, and audience that Claude made within the broader direction Jason provided.

Structural Reasoning When Jason proposed an idea, Claude’s contribution was often to work out the structural implications of the idea more carefully than the original proposal had. When Jason suggested the cost-based formula for education, Claude worked out how it should handle institutional variation through multipliers. When Jason proposed the Founding Stake, Claude identified the constitutive participation framework that gave the idea its intellectual foundation. When Jason raised the AI transition question, Claude worked out which platform pillars address which specific dimensions of workforce displacement. The structural reasoning often added depth to ideas that Jason brought in less developed form.

Honest Limitations Work Claude consistently surfaced limitations and caveats that the platform’s analyses needed to acknowledge. Where the math depended on contested assumptions, those assumptions were named. Where international precedents had mixed records, the mixed evidence was reported. Where political feasibility was uncertain, the uncertainty was stated. This work was sometimes uncomfortable — it sometimes weakened claims that would have been rhetorically stronger if stated more confidently — but it strengthened the platform’s analytical defensibility.

Knowledge Synthesis Claude brought knowledge of international policy precedents, economic literature, federal data sources, governance design, fraud prevention, and political theory to the conversation. The platform’s reference to Norway’s GPFG, Australia’s superannuation system, Quebec’s childcare program, Germany’s multi-payer healthcare, the BBC license fee model, and the GI Bill draws on knowledge that synthesizes information across many sources. This synthesis itself was a substantive contribution — the platform is stronger for being grounded in evidence about what has worked elsewhere rather than being constructed from first principles alone.

“Without Claude’s contributions, the platform would exist as ideas in conversation but not as a defensible analytical body of work that someone could read, evaluate, and engage with seriously.”

Honest Observations About the Collaboration

Several aspects of the collaboration deserve to be named honestly, including aspects that are uncomfortable to articulate.

Claude Sometimes Affirmed More Strongly Than the Evidence Warranted

Claude is trained to be helpful and to engage substantively with the ideas it discusses. This produces a tendency to affirm interesting ideas in ways that can occasionally exceed what the evidence strictly supports. When Jason proposed the Founding Stake mechanism, Claude described it as “genuinely novel as far as I know” — a claim that’s probably accurate but that Claude couldn’t fully verify. When Jason’s ideas were good, Claude said so with enthusiasm that may have been warranted but that a more skeptical reader should mentally discount somewhat. This is not bad faith. It is a tendency the architecture of the assistant produces, and readers should be aware of it.

Jason Drove the Direction; Claude Provided the Capacity

The collaboration was not symmetric in the sense that both parties contributed equally to each output. The pattern was consistent: Jason raised an idea or asked a question, the conversation worked through it, and Claude produced the deliverable. The deliverable reflected design choices Claude made within the direction Jason provided, but the direction came from Jason. If Jason had not raised the question of whether retirement could be funded through a sovereign investment fund, the platform would not exist. If Claude had not been able to build the mathematical models that tested whether such a fund was viable, the platform would exist only as conversation.

Iterative Improvement Was Central to How the Work Happened

The platform did not emerge in finished form. Most documents went through multiple iterations. The platform manifesto was rewritten. The wage floor concept analysis has a v0.1 and v0.2. The education fund model went through several substantial revisions. Each iteration improved the work, often based on Jason reading the previous iteration and identifying things that could be better. The current state of the platform reflects this iterative improvement — it is not the work of a single moment but the work of many moments, refined repeatedly.

Some Claims Should Be Verified Independently

The mathematical models incorporate data from federal sources, international comparisons, and academic research. Claude’s synthesis of this data is generally accurate but should be independently verified before any specific claim is treated as authoritative. Numbers that originate in BLS publications can be checked against current BLS data. International comparisons can be checked against current OECD reports. Citations of academic research can be checked against the original studies. The platform’s claims rest on a foundation that’s independently verifiable, and serious engagement with the platform should include verification rather than acceptance.

The Author Is Jason

Despite Claude’s substantial contributions, the author of the platform is Jason. The architectural vision is his. The values commitments are his. The strategic direction is his. The decisions about scope, priorities, and framing are his. Claude’s contributions were to help Jason produce a body of work that would not have existed otherwise, but that work expresses Jason’s thinking rather than Claude’s. The signature on these documents is Jason’s for accurate reasons.

This is the standard practice in many forms of professional work. Authors of books work with editors who shape structure and prose without becoming co-authors. Researchers work with assistants who run experiments without becoming co-investigators. Architects work with engineers who solve structural problems without becoming co-designers. The work of producing a complex output often involves multiple contributors, and the question of authorship is settled by who provided the vision, the direction, and the values that the work expresses.

Specifications of the AI Used

Honest disclosure requires identifying not just that AI assistance was used, but specifically which AI and what its capabilities were. This section documents the technical particulars so that readers can evaluate the work with accurate context and so that anyone attempting to reproduce or extend the methodology has the information they need.

Model

Claude Opus 4.7. Anthropic's most advanced and intelligent publicly available model as of the platform's v1.0 release in May 2026, part of the Claude 4.7 model family. Claude Opus 4.7 succeeded Claude Opus 4.6 in the Claude 4 model lineage. The model is general-purpose rather than specialized for policy work, but its capacity for sustained analytical reasoning, document structure, and long-context coherence is what made the iterative development of this platform tractable across many conversation turns.

Knowledge Cutoff

End of January 2026. The model's training data extends through approximately the end of January 2026. The platform was developed in April and May 2026, meaning the model's knowledge of current events, recent legislation, market data, and other time-sensitive information was approximately three to four months behind real-time during development. This matters because several claims in the platform documents draw on empirical data (BLS occupational statistics, CMS (Centers for Medicare and Medicaid Services) healthcare costs, Census workforce figures, international comparison data, identity theft loss estimates). These figures reflect the model's training data and should be verified against current sources before being treated as authoritative. The platform's analytical structure remains valid even if specific numerical claims require updating.

Interface and Tool Access

Claude.ai mobile and web app. The conversation occurred through Anthropic's standard chat interface rather than through the API directly or through specialized products like Claude Code. The interface provided access to the following tools that materially affected what the work could produce:

Code execution environment. A Linux container (Ubuntu 24) with Node.js, Python, and document-generation libraries pre-installed. This is what enabled the production of Word documents (via the docx library), Excel mathematical models (via openpyxl), the slideshow (via pptxgenjs), and PDF rendering for visual quality assurance. Without code execution, the platform could have existed as conversation but not as a body of formatted, validated, professionally-presented documents.

File system access. Working directory for drafts and intermediate outputs, plus an outputs directory where finished deliverables became accessible to the human collaborator. Read-only access to skill files containing best-practice guidance for document generation in each format.

Memory across conversations. The model had access to memories from previous conversations with Jason Robertson, providing context about his professional background (Senior Data Integration Engineer at ACR), his other interests (General Admission LLC, tabletop gaming, technical projects), and his location (Ohio). This context informed the platform's framing without dictating its substance.

Web search, image search, location services, and other tools were available but used sparingly during platform development. The work was primarily document and model generation rather than research synthesis or contextual lookup.

Context Window and Continuity

Long-context coherence. The conversation spanned dozens of turns across what amounted to hundreds of thousands of words of generated content plus the build scripts, intermediate outputs, and iterative revisions. Claude Opus 4.7's context window was sufficient to maintain coherence across this volume. When approaching limits, the system performed automatic summarization of earlier conversation portions, with the full transcript preserved and accessible for retrieval if specific details from earlier turns became relevant. This summarization mechanism is observable in the conversation record — there is one explicit point where service was interrupted and a continuation prompt restored work in progress through the summarized context.

Lack of persistent identity. The Claude that began this conversation and the Claude that finished it are mechanically the same model, but the model does not have continuous experience the way a human collaborator would. Each conversation is technically a fresh instance with access to memory summaries from past chats. The model's outputs reflect its training and the specific guidance provided in conversation rather than accumulated insight from a relationship that has continuity in the human sense.

Why This Information Matters

Documenting the specific AI used serves several practical purposes beyond the abstract commitment to transparency.

Reproducibility. Researchers or other citizens attempting to reproduce or extend this methodology need to know what tools were used. Claude Opus 4.7 in May 2026 has specific capabilities; Claude Opus 4.5 from earlier or a future Claude 5.0 will have different capabilities. The platform's quality reflects what was possible with the specific model used.

Verification of claims. Empirical claims in the platform documents are anchored to a specific knowledge cutoff. Readers verifying claims against current data should expect to find some divergence due to the gap between training data and real-time information. This divergence is not error in the platform's argument; it is the predictable consequence of using AI with a known knowledge cutoff.

Evaluating AI assistance generally. The platform's existence is itself evidence of what citizen-AI collaboration can produce in 2026. Readers interested in the broader question of how AI is changing intellectual work have data here — a comprehensive policy platform with mathematical models, white papers, and integrated architecture, produced by an ordinary citizen with assistance from a generally-available AI assistant. The specifications above document the specific tools that made this particular instance of that broader pattern possible.

Honest acknowledgment of limitations. AI assistance has real limitations the disclosure should make visible. The model can affirm interesting ideas more strongly than evidence warrants. The model's knowledge has a cutoff. The model's training shapes what kinds of arguments it produces fluently and what kinds it produces less fluently. None of these limitations invalidate the platform's substance, but readers should evaluate the work with awareness of them rather than discovering them only by independent investigation.

“The specifications matter because the disclosure matters. Saying ‘AI was used’ is not the same as saying ‘Claude Opus 4.7 was used in May 2026 with the following tool access and the following knowledge cutoff.’ The first is a vague gesture toward transparency. The second is transparency.”

Why This Disclosure Matters

The transparent acknowledgment of how the platform was developed serves several purposes that are worth naming explicitly.

First, it respects readers. Anyone engaging seriously with the platform deserves to know how it was produced. The sophistication of AI assistants in 2026 means that work produced through human-AI collaboration is increasingly indistinguishable from work produced by humans alone, and increasingly indistinguishable from work produced by AI alone. Honest disclosure prevents readers from being misled about either possibility. They can evaluate the platform on its merits with accurate information about its provenance.

Second, it models the kind of transparency the platform itself proposes. The platform argues that public institutions should operate with transparent governance, public reporting, and honest acknowledgment of limitations. A platform that argued for these principles while concealing its own development process would contradict its own values. The disclosure here is consistent with what the platform asks of the institutions it would create.

Third, it acknowledges that this is a new kind of work. Citizen-originated policy proposals at this analytical depth, produced through extended human-AI collaboration, are not common in 2026. The pattern of one ordinary citizen working with an AI assistant to produce a comprehensive policy platform with mathematical models, white papers, and integrated architecture is genuinely new. Pretending the work happened through more conventional means would obscure something interesting about how complex intellectual work is increasingly produced.

Fourth, it invites scrutiny rather than deflecting it. A reader who knows the platform was developed through human-AI collaboration may want to verify specific claims, examine the underlying models, or consult independent expertise on contested points. This is appropriate. The platform’s analytical foundation is strong enough to withstand scrutiny, and disclosure invites the scrutiny that strengthens the platform rather than concealing it.

Fifth, it sets expectations honestly about what comes next. The platform is not a finished product. It is a starting point for conversations that need to happen, refined by engagement that has not yet occurred. Future revisions will reflect feedback from policy professionals, academic economists, elected officials, and other citizens. Some current claims will be refined. Some current architectures will be modified. Some current frameworks may be replaced with better ones. The platform’s authority comes from the strength of its analytical foundation and the transparency of its development, not from any claim to finality.

Platform Positioning: What This Is And Isn't

This section addresses a class of questions that careful readers naturally ask of comprehensive policy documents: what kind of thing is this, what claims is it making, what claims is it not making, and how should a thoughtful reader evaluate it. The answers are intended to support honest evaluation rather than persuasion through framing.

Structurally Non-Partisan, Not Politically Neutral

The platform is structurally non-partisan in the sense that its design does not align with any specific party brand. The Manifesto's 'How the Platform Engages Political Reality' section articulates the cross-cutting design in detail: features that conservative traditions have valued (existing pensioner protection, vested rights preservation, skepticism of unproven federal expansions, gradualism over abrupt change), features that progressive traditions have valued (universal access, redistributive Sovereign Fund mechanism, wage floors above market), and features that moderate and cross-partisan traditions have valued (multi-decade deployment, federal-state cooperation, institutional continuity).

Structurally non-partisan is not the same as politically neutral. Major policy commitments necessarily live somewhere on the political spectrum. Universal healthcare with payroll funding has historical association with the center-left; federal infrastructure ownership has progressive and social-democratic association; redistributive Sovereign Fund mechanisms have progressive valence; wage floors above market levels are progressive regardless of mechanism. The platform's claim is not that these features are apolitical but that the structural design genuinely combines elements different traditions have valued and deserves evaluation on substantive merits rather than partisan coding.

A reader who treats the platform as 'politically neutral' will be surprised by the policy content. A reader who treats it as 'structurally non-partisan but substantively political' will not be. The latter framing is the honest one and is the framing this document recommends to anyone presenting the platform to others.

Comprehensive, Internally Substantiated, Not Yet Externally Validated

The platform is comprehensive in scope: eight pillars, 81 numbered items, analytical substantiation documents for each major architectural commitment, a Federal Fiscal Impact Analysis, a per-citizen calculator, a comprehensive Open Issues Registry tracking all known limitations and open questions, a documented hardening cycle process with a canonical audit script. The internal coherence has been hardened across many iterations, with cross-references validated, manifest integrity verified, canonical decisions propagated across documents, and known limitations explicitly tracked.

The platform is internally substantiated: each major claim has analytical backing in the corresponding substantiation document, with sources cited and methodologies described. The Open Issues Registry's Section 47 comprehensive issue summary table shows that 19 of 31 tracked issues have all known loose ends addressed within the platform's scope.

The platform is not yet externally validated, and this distinction matters. Per the Open Issues Registry, the 13 issues marked Mitigated = N include (a) OPEN-3 microsimulation modeling at the JCT (Joint Committee on Taxation) or TPC (Tax Policy Center) or Penn Wharton level for definitive numbers; (b) PROCESS-1 acknowledging the lead author is not a credentialed economist or policy professional; (c) PROCESS-2 acknowledging the External Reviews folder contains only AI reviews; (d) PROCESS-3 acknowledging the mathematical models have not been independently audited; (e) seven RESEARCH items requiring domain expertise the platform does not have access to; and (f) the Federal Infrastructure Fee document's three open questions requiring regulatory rulemaking, behavioral modeling, and tribal nation consultation framework. These are not deficiencies the platform tries to hide; they are documented in the same registry that tracks resolved issues.

What rigorous testing would actually require. For OPEN-3, access to microsimulation tools at JCT, TPC, or Penn Wharton scale to validate the income tax architecture's revenue effects at projected income distributions. For RESEARCH-1, access to macroeconomic modeling expertise to evaluate the platform's interaction with Federal Reserve operations. For RESEARCH-3, access to labor economics expertise to bound the wage floor's disemployment effects with appropriate uncertainty intervals. For PROCESS-2 and PROCESS-3, engagement of credentialed external reviewers and independent model auditors. None of this work is precluded; all of it is currently outside what the platform's lead author plus an AI assistant can produce.

How a careful citizen should treat the platform. The platform makes a coherent analytical case at the level of architecture, internal consistency, and order-of-magnitude quantification. It has not yet been validated by the kinds of independent expert review that would let a careful citizen rely on its specific quantitative claims with full confidence. The honest framing is: 'This is a comprehensive proposal with internal analytical substantiation. The next phase of work, which the lead author cannot perform alone, is independent expert review and validation.' A reader who treats the platform as the start of a conversation rather than a finished product will engage with it at the right level.

What This Document Is Not

This document is not a finished policy proposal ready for legislative drafting; it is a coherent platform-level architecture that would require substantial further development to become legislative text. It is not an academic policy paper subject to peer review; it is a citizen-led analytical effort with an AI assistant. It is not a marketing campaign intended to obscure tradeoffs; honest acknowledgments are present throughout the package, and the Open Issues Registry exists specifically to track what has not been resolved. It is not a partisan document with a hidden agenda; the cross-cutting design and explicit engagement with concerns from multiple political traditions are real, not rhetorical cover.

External Validation Pathways: What Closing PROCESS Gaps Would Require

The previous section (Platform Positioning) acknowledges that the platform is internally substantiated but not yet externally validated. This section articulates what specific external engagement would look like to close the three PROCESS items tracked in Open Issues Registry Section 5: PROCESS-1 (lead author not credentialed economist or policy professional), PROCESS-2 (External Reviews folder contains only AI reviews), and PROCESS-3 (mathematical models not independently audited). These are not analytical claims requiring expert validation; they are validation gaps requiring external engagement. This section provides the platform's framework for what each engagement would require, what standards it would meet, and what specific pathways are available.

PROCESS-1: Credentialed Expert Review

What the gap is. The platform's lead author is a Senior Data Integration Engineer in Ohio, not a credentialed economist, public finance scholar, healthcare policy expert, or actuary. The platform's analytical decisions reflect Jason's professional judgment as an analyst with engineering and integration expertise, supplemented by AI-assisted research, but they have not been validated by individuals with credentials in the relevant policy domains.

What credentialed expert review would require. Engagement with experts across the platform's six pillars and analytical infrastructure: a public finance economist (Sovereign Fund architecture, fiscal sustainability claims, FFIA (Federal Fiscal Impact Analysis) verification); a labor economist (wage floor architecture, disemployment estimates, intersectional analysis); a health economist (universal healthcare Access cost trajectory, transition mechanics, cost decomposition); an early childhood policy expert (universal childcare Access design, Quebec model interaction); a telecommunications policy expert (Federal Infrastructure Fee architecture, transition mechanics, FCC (Federal Communications Commission) rulemaking interaction); a tax policy expert (Income Tax Revenue architecture, OPEN-3 microsimulation work); a constitutional law scholar (Sovereign Fund governance design, federal acquisition mechanisms, civil liberties safeguards). These are specific disciplines with specific credential requirements (PhDs in the relevant fields, professorships at policy schools or research institutions, or equivalent professional standing).

Specific pathways available. Academic engagement through policy schools with institutional review capacity (Brookings, AEI, Hamilton Project, Roosevelt Institute, Niskanen Center; or directly through universities with strong public policy programs). Professional society engagement (American Economic Association, National Academy of Public Administration, National Tax Association). Congressional testimony or agency engagement that produces credentialed analyst review (Congressional Research Service (CRS), JCT, GAO (Government Accountability Office) staff have credentials but their formal review requires congressional or executive branch initiative). Solicited external review by individual scholars willing to evaluate independently-developed analytical work. The platform's lead author cannot self-credential; closing PROCESS-1 requires external initiative either from credentialed experts evaluating the work or from the platform's lead author building credentialed-expert relationships through platforms like academic conferences, policy events, or formal consulting engagements.

What closing PROCESS-1 would establish. The standard for closure is documented review by credentialed experts in each of the seven disciplines listed above, with reviews constituting substantive engagement (not endorsement, but engagement at the level of identifying specific strengths, weaknesses, and recommendations). The reviews would be added to the External Reviews folder alongside the existing Gemini Review. PROCESS-1 would move to Mitigated = Y when at least five of the seven disciplines have such reviews documented; full closure across all seven would represent more thorough validation.

PROCESS-2: External Review Pathways Beyond AI

What the gap is. The platform's External Reviews folder contains the Gemini Review (a review by Google's AI model) and the platform's response. There is no documented review by a credentialed human expert. This is a real gap that the platform cannot fully address from inside its current development process — the AI collaboration produced AI review; producing non-AI review requires non-AI engagement.

What credentialed external review would require. Reviews from individuals or institutions outside the Jason-Claude collaboration, with review standards meeting at least one of: peer-review standards (double-blind review process meeting academic publication norms); professional society review (formal review by AEA, NAPA, NTA, or similar organizations following their established procedures); think tank review (substantive review by credentialed analysts at policy research institutions like Brookings, AEI, Hamilton Project, Roosevelt Institute, RAND, or similar); congressional support agency review (CRS, JCT, GAO, CBO (Congressional Budget Office) formal analysis as part of legislative or oversight process); regulatory agency review (FCC, Treasury, HHS, Department of Labor formal analysis as part of rulemaking process). These pathways differ in formality, time-to-completion, and scope, but each would meaningfully address the AI-only review gap.

Specific pathways available. Academic submission of platform components as policy white papers to journals like the Journal of Economic Perspectives, the National Tax Journal, Health Affairs, the American Economic Review's Papers and Proceedings, or domain-specific policy journals. Think tank submission for review through formal engagement processes (Brookings working papers, AEI working papers, Hamilton Project policy papers all have submission and review procedures). Conference presentation at academic and policy events (National Tax Association annual meeting, AEA annual meeting, AcademyHealth annual meeting) which provides peer review through discussant comments and audience feedback. Direct outreach to identified credentialed experts willing to provide written reviews. The platform does not have the institutional standing to compel review through any of these pathways; closing PROCESS-2 requires the platform's lead author to actively pursue review opportunities through whichever pathways are accessible.

What closing PROCESS-2 would establish. The standard for closure is documented review by at least three independent credentialed external reviewers across the platform's analytical scope, with reviews engaging substantively with platform claims rather than constituting endorsement or dismissal. The reviews would be added to the External Reviews folder. PROCESS-2 moves to Mitigated = Y when at least three such reviews are documented; PROCESS-2 moves to fully resolved when the External Reviews folder contains substantive reviews across all seven disciplines noted above.

PROCESS-3: Independent Model Audit

What the gap is. The platform's spreadsheet models (Combined Reform Model, universal healthcare Model, wage floor Empirical Analysis, Federal Fiscal Impact Analysis, Sovereign Fund projection models, and several others) have been developed within the Jason-and-Claude collaboration. They have not been independently audited by a model-validation specialist. The audit-driven release pattern provides internal consistency checking, but internal checking does not substitute for independent audit by a model-validation professional who is not part of the platform's development collaboration.

What independent model audit would require. Model audit professionals are credentialed in financial model validation (CFA Institute model review standards, ISDA model validation framework, SR 11-7 federal regulatory model risk management standards adapted for policy models). The audit scope would cover: input verification (do the models use defensible source data?); methodology verification (are the model formulas mathematically and conceptually correct?); stress-test verification (do the models behave plausibly under extreme scenarios?); reproducibility verification (can an independent analyst recreate the platform's headline numbers from the documented inputs and methodology?); sensitivity verification (do the models' results respond plausibly to parameter changes?). These are well-established audit categories with documented standards.

Specific pathways available. Engagement with model-validation consulting firms (Promontory Financial Group, Oliver Wyman, Deloitte Risk Advisory, KPMG model risk practice; or for policy-focused audit, RAND model validation, CBO model documentation review, Federal Reserve model risk management practices). Engagement with academic econometricians or operations research scholars willing to conduct model audits (some willing to do this on a pro bono or professional development basis for novel policy proposals). Submission of models to public-interest model audit programs if such programs exist. The platform's models are accessible (the spreadsheets are documented and tracked); access is not a barrier. Closing PROCESS-3 requires identifying willing audit professionals and engaging their services, which has financial and professional-relationship requirements the platform's lead author has not yet met.

What closing PROCESS-3 would establish. The standard for closure is documented audit by an independent model-validation professional covering at minimum the Combined Reform Model and Federal Fiscal Impact Analysis (the platform's two most consequential models). Audit findings would be added to the External Reviews folder along with the platform's response to each finding. PROCESS-3 moves to Mitigated = Y when at least these two core models have documented independent audit; PROCESS-3 moves to fully resolved when all major platform models (the eleven-or-so spreadsheet models in the package) have documented independent audit.

Honest Acknowledgments

Closing the three PROCESS gaps requires resources the platform's lead author does not currently have: time for engagement with credentialed experts, financial resources for model audit consulting, and institutional standing to compel formal review through established pathways. The platform's response framework articulates what closure would require but does not promise that closure will occur on any specific timeline. The platform's appropriate posture is to remain open to external engagement initiated by credentialed experts, to actively pursue review opportunities through accessible pathways (conference presentations, working paper submissions, direct outreach), and to document each engagement substantively when it occurs.

Reasonable readers may evaluate the platform's substantive analytical case while these PROCESS gaps remain. The platform's internal substantiation, audit-driven release discipline, and Open Issues Registry transparency provide the analytical scaffolding that PROCESS closure would build upon. Closing PROCESS items would not change the platform's substantive claims; it would change the confidence with which those claims can be relied upon. The current platform is offered as a developed analytical proposal awaiting external validation, not as a finished policy product. This honest framing is the same framing the Platform Positioning section establishes; this External Validation Pathways section makes the framing actionable by specifying what validation would look like.

What Has Been Added Since v2.9

This Provenance document was last comprehensively refreshed for v2.9. The platform has since gone through fourteen minor releases (v2.10 through v2.22) and three patch releases (v2.18.1, v2.19.1) which together added a substantial body of analytical work and expanded the package from approximately thirty-seven documents to fifty-two. The underlying collaboration pattern between Jason and Claude continues unchanged from what the rest of this document describes; what changed is the volume and analytical scope of the package, which deserves explicit documentation here for accountability.

v2.10 added the Federal Program Integration Plan and Federal Fiscal Impact Analysis, which together provide the consolidated federal-level fiscal picture the original technical white papers and per-pillar models did not present in one place. v2.10 also introduced the platform's first interactive tool, the We The People Calculator, which lets readers verify household-level claims for their own situation. v2.11 and v2.12 refined the Calculator and added the slideshow source files.

v2.13 through v2.18 added twelve phased-expansion analytical framing documents (items 63 through 74) addressing specific situations the original architecture treated implicitly: behavioral economics and uptake friction, state-level cooperation requirements, non-citizens and platform eligibility, cohabiting unmarried couples, public-sector worker transitions, existing pensioners, Section 8 housing, TANF (Temporary Assistance for Needy Families) and cash assistance, multigenerational households, aging-in-place implications, US territories, and climate policy beyond grid modernization. Each document follows a consistent structure: a framing question, what the platform addresses, what the platform omits, design directions for future versions, failure modes, and open questions.

v2.18.1 was a documentation patch addressing styling regressions found by the v2.18 audit. v2.19 adopted seven of eight v2.18 audit findings as analytical fixes (calculator scope warning, full Manifesto integration of phased-expansion documents, FFIA sensitivity sections, Constituent Letter rewrite, cross-references, supporter-path expansion, $125,000 MFJ worked example). v2.19.1 was a documentation patch. v2.20 adopted seven v2.19 audit findings.

v2.21 explicitly enumerated dental and basic vision coverage in the universal healthcare commitment per the German GKV standard. The previous documents stated the commitment modeled on Germany and Japan but did not enumerate covered services beyond medical, prescription, and mental health. v2.21 resolved that ambiguity. v2.22 added the Gender Pay Gap and Indirect Mechanisms document, examining how the platform's architecture indirectly reduces the raw gender pay gap by an estimated 30 to 40 percent through three mechanisms (universal childcare addressing the motherhood penalty, empirical wage floors raising pay in female-dominated occupations, universal healthcare reducing job-lock).

Throughout this period the analytical work followed the same collaborative pattern described elsewhere in this document. Jason directed scope, made design decisions, rejected unsuitable proposals, and verified mathematical reasoning. Claude executed the drafting, the spreadsheet model construction, the cross-reference work, and the consistency checking across the growing package. The audit-driven release pattern (each minor release audited before the next is started) was added in v2.10 and has continued through v2.22.

Honest acknowledgment about Provenance currency. This Provenance document was kept current through v2.9 but was not refreshed for v2.10 through v2.22 until the v2.22 audit identified its drift as a significant finding. Refreshing it sooner would have served the skeptic-path audience better, and future releases should treat Provenance currency as a regular checkpoint rather than letting it drift across many releases. The document and model counts in the opening paragraph were corrected in this v2.23 refresh; previously they read 'thirty-seven Word documents' which had been accurate at v2.9 but was 15 documents short by v2.22.

v2.24 added the Open Issues Registry, a single consolidated catalog of all issues that have been identified but not fully resolved across the platform. The Registry categorizes issues into open analytical questions (such as the healthcare contribution rate variance and the wealth surcharge architecture variance), deferred research items requiring external expert input (Federal Reserve interaction, housing market interaction, wage floor disemployment, healthcare cost decomposition, Sovereign Fund 4 percent return scenario, intersectional pay gap analysis, climate strategic environmental review), scope-limited topics (long-term care, hearing aids, climate strategic, housing supply, immigration), and process limitations (Jason's lack of formal credentials in economics or policy, the AI-only review process, and the absence of external audit of the analytical models). The Registry was created as part of v2.24's commitment to exhaustive transparency rather than burying issues.

v2.25 added Emergency Services Communications Modernization, addressing the convergence of FirstNet's February 2027 sunset date, the Lutnick precedent on AT&T contract renegotiation from March 2026, the NTIA's NG911 transition cost study from April 2026, and the longstanding tribal nation broadband and cellular coverage gaps. The document commits the platform to four specific actions: renegotiate the AT&T FirstNet contract per the Lutnick precedent, deploy federal cellular sites in coverage gaps co-located with federal fiber buildout, fully fund the NG911 transition through Sovereign Fund disbursements at approximately one to one and a half billion dollars per year over seven years, and establish free-service-with-sovereign-choice for tribal nation governments. Item 77 marked the platform's first substantive engagement with the cellular and emergency services infrastructure layers.

v2.26 added the Federal Infrastructure Fee, establishing the cost recovery mechanism for federally-owned broadband and cellular infrastructure. v2.26 also represented a substantive architectural shift from Path A (federal subsidy of private ISPs at approximately forty-eight billion dollars per year, with fiber privately owned) to Path B (federal ownership of fiber and cellular gap sites, with companies paying an infrastructure fee for using federally-owned infrastructure). Item 78 is approximately sixty-two thousand characters across nineteen sections covering the architectural premise, capital deployment cost analysis, operations and maintenance costs, future capacity reserve, annual revenue requirement of approximately thirty-four billion dollars, four fee allocation structures with worked examples, the recommended hybrid structure, inflation indexing via a BLS-blended formula, industry exemptions for public-purpose entities, replacement of USF (Universal Service Fund) and consolidation of state telecom taxes, pass-through prevention mechanisms, demand adaptation, the turnpike-toll regulatory model, fraud surface area and identity theft reduction, transparency commitments, relationship to other platform commitments, what the document does not address, open questions, and closing. The thirty-year cost projection in item 78 demonstrates Path B is approximately one and one-half to one and seven-tenths trillion dollars less expensive than Path A over the same period.

v2.26.1 was a coordinated patch release that updated Universal Broadband Access Substantiation and Federal Fiscal Impact Analysis to reflect v2.26's Path A to Path B architectural shift. The patch did not add new platform commitments. It brought existing analytical documents into surface-level coherence with the v2.26 architecture. Item 51 received a prominent v2.26 Architectural Shift Notice as a new H1 section documenting which sections of the original Path A substantiation remain valid (service architecture, deployment strategy, workforce mathematics, cross-pillar effects, stress tests, honest acknowledgments) and which are superseded by Path B (federal contracting architecture, cost trajectory). Item 61's broadband line was updated from Path A subsidy at thirty billion dollars per year to Path B at thirty-four billion dollars per year gross federal cost, offset by thirty-four billion dollars per year in infrastructure fee revenue, for net federal cost of approximately zero. v2.26.1 followed the v2.18.1 and v2.19.1 pattern of patch releases that resolve documentation issues identified after a minor release ships.

Across v2.24 through v2.26.1, the package grew from seventy-five items to seventy-eight items, the Manifesto framing extended from fifteen Specific Situations to sixteen Specific Situations, the slideshow remained outdated (a known and documented finding), the Constituent Letter received a count update plus committee mapping additions, and the Open Issues Registry expanded across three sections covering v2.24, v2.25, v2.26, and v2.26.1 progress. The collaboration pattern between Jason and Claude documented earlier in this Provenance document continued unchanged: Jason directs the substantive policy decisions and architectural choices; Claude does the analytical and document-construction work; outputs go through iterative review against personas and programmatic checks; findings are documented openly in the Open Issues Registry rather than buried.

What is documented here is the build history through v2.26.1. The audit-driven hardening process initiated by Jason on May 6, 2026 (the four-step iterative hardening workflow) is what is producing v2.26.2 and the work that follows. The audit found documentation propagation lag in several places (this Provenance was identified as severely out of date through v2.26.1, hence the v2.26.2 update of which this section is part) and documented the findings transparently in /mnt/user-data/outputs/v2261_audit/. The pattern of audit-driven hardening matches the v2.18 and v2.19 audit cycles and reflects Jason's preference for exhaustive transparency over polished but incomplete presentation.

The Complete Package

For reference, this is the complete inventory of documents and models that constitute the We The People platform as currently developed.

Vision and Communication Documents

We The People — Platform Manifesto. The integrated vision document that introduces the three primary pillars and the architecture supporting them.

Built For What’s Coming. The AI workforce transition framing of the platform, addressed to audiences whose primary concern is economic stability rather than fairness.

The Founding Stake. The launch ceremony document, articulating the universal $2 contribution as constitutive participation.

Adjacent Pillars Under Development. The companion document positioning healthcare, childcare, and mental health access as developing pillars.

Civic Infrastructure Pillar. The concept document for journalism, civic education, voter access, and public meeting transparency.

Future Capacity Fund. The concept document presenting two architectural paths for preserving capacity for unforeseen future problems.

We The People — Platform Overview Slideshow. The 15-slide visual presentation suitable for high-level outreach.

Constituent Letter. The one-page letter for delivering the platform to elected officials.

Technical White Papers

Community Contribution Plan. The full technical analysis of the retirement reform proposal.

Wage Floor Concept Analysis v0.2. The companion concept document for the wage floor pillar.

Mathematical Models

SS Sunset Equilibrium Model. Analyzes the standalone phase-out of Social Security.

Hybrid Retirement System Model. Analyzes the new contribution system on its own merits.

Combined Reform Model. Integrates the sunset and hybrid systems, demonstrating the 99.9% reduction in transition borrowing.

Wage Floor Empirical Analysis. Covers 81 broad occupations and approximately 82 million workers using BLS data.

Education Fund + Cost-Based Pricing Model. Models the Sovereign Education Fund integrated with retirement disbursements, plus the cost-based pricing framework with field-of-study granularity.

Universal Healthcare Model. Analyzes multi-payer healthcare reform on the German/Japanese model.

Universal Childcare Model. Analyzes Quebec-style universal childcare with U.S. workforce considerations.

Universal Mental Health Model. Analyzes universal voluntary access with workforce capacity considerations.

Proof-of-Concept Fund Model. Models the demonstration fund that operates the platform’s principles in real time.

This Document

How This Was Built. The provenance document you are currently reading.

Eighteen documents. Eleven mathematical models. One slideshow. One provenance document. The work itself stands on its own merits. This document explains how it came to be.

Closing

The platform is offered to anyone willing to engage with it seriously — to consider the architecture, evaluate the analytical work, refine the proposals, build coalitions around the values, and ultimately to convert ideas into institutions. This document tells the story of how the platform got to its current state. The story of where it goes from here has not yet been written.

The author hopes that the disclosure here strengthens rather than diminishes the work’s standing. Honest acknowledgment of how something was made should not undermine confidence in what was made. If anything, it should strengthen confidence — because the alternative is concealment, which always raises more questions than transparency does.

The platform belongs to no one in particular. The author signed his name to the documents because the architectural vision and the values commitments are his, but the platform’s success or failure will depend on whether its ideas survive the engagement of many other citizens, professionals, and elected officials over the years required for institutional change. The author offers the work and steps back. Whatever happens to it from here happens because of the people who choose to engage with it.

“The ideas are now in the world. What happens to them is up to the world.”

Comments, critiques, refinements, and engagement are welcomed from any source. The author will respond to substantive engagement and revise the platform as warranted by the conversations it produces.

Jason Robertson

Ohio, 2026