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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<title>FIL+ Governance Framework — Case Study | Tanisha Katara</title>
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/* ─── Challenge ─── */
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/* ─── Key Design Decisions (Timeline) ─── */
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/* ─── Footer ─── */
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/* ─── Utility ─── */
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</style>
</head>
<body>
<!-- ─── Nav ─── -->
<nav id="nav">
<div class="nav-inner">
<div class="nav-logo">Tanisha Katara</div>
<ul class="nav-links">
<li><a href="#challenge">Challenge</a></li>
<li><a href="#approach">Approach</a></li>
<li><a href="#insights">Insights</a></li>
<li><a href="#impact">Impact</a></li>
</ul>
</div>
</nav>
<!-- ─── Hero ─── -->
<section class="hero">
<div class="hero-label">Case Study</div>
<h1>FIL+ Governance Framework: A Multi-Track Approach to Funding Allocation</h1>
<p class="hero-sub">
Designing credible, capture-resistant governance for Filecoin's storage incentive program
across public-goods data, enterprise clients, and treasury allocation.
</p>
<p class="hero-author"><strong>Tanisha Katara</strong> — Senior Governance Consultant, Filecoin</p>
</section>
<!-- ─── Background Primer ─── -->
<div class="primer">
<div class="primer-inner">
<span class="primer-label">Background</span>
<p class="primer-text">
<strong>Filecoin</strong> is a decentralised storage network that pays storage providers
block rewards for reliably holding data over time. <strong>FIL+</strong> is Filecoin's
verified-storage program: it boosts block rewards for providers who store data that has
been vouched for as useful — originally public datasets, and increasingly enterprise
workloads. A set of <strong>badgeholders</strong> attests to the value of incoming data,
and a <strong>treasury</strong> funds subsidies that make verified deals economically
competitive. As the program grows, deciding <em>who</em> gets to attest, <em>how</em>
subsidies are prioritised, and <em>how much</em> goes to public-goods versus commercial
storage become the central governance questions.
</p>
</div>
</div>
<!-- ─── The Challenge ─── -->
<section id="challenge">
<div class="section-inner">
<p class="section-label">The Challenge</p>
<h2 class="section-title">Scaling FIL+ Without Fragmenting Governance</h2>
<div class="challenge-body">
<div class="challenge-text">
<p>
FIL+ sits at the intersection of public-goods data and enterprise-driven storage demand.
As the program scales, the core challenge is no longer <em>whether</em> to support datasets,
but <em>how</em> to allocate incentives and funding credibly across fundamentally different use cases.
</p>
<p>
A single governance model risks either over-indexing on commercial viability
or under-serving the public-goods mission. The framework needed to answer three
questions simultaneously: how to split treasury funds, how to evaluate public datasets
at scale, and how to assess enterprise proposals with the nuance they require.
</p>
<p>
This exploration tested whether combining AI signals, expert judgment, and structured
governance could improve FIL+ funding outcomes without increasing capture risk.
</p>
</div>
<div class="challenge-img">
<img src="images/ecosystem.png" alt="FOC market architecture diagram showing the relationship between public open data, enterprise storage, and treasury allocation pools" />
</div>
</div>
</div>
</section>
<!-- ─── The Approach ─── -->
<section id="approach">
<div class="section-inner wide">
<p class="section-label">The Approach</p>
<h2 class="section-title">Three Tracks, Three Models</h2>
<p class="section-desc">
The framework is built on three validated assumptions, each of which maps directly
to a governance track and a recommended decision model.
</p>
<!-- Assumptions -->
<div class="assumptions">
<div class="assumption-card">
<div class="num">1</div>
Public open data is transparent and machine-verifiable — well-suited to AI-assisted evaluation.
</div>
<div class="assumption-card">
<div class="num">2</div>
Enterprise data requires commercial judgment (SLAs, market impact, viability) — better evaluated by domain-expert juries.
</div>
<div class="assumption-card">
<div class="num">3</div>
Treasury allocation benefits from mixed representation to prevent capture by either commercial or public-goods factions.
</div>
</div>
<!-- Track 1 -->
<div class="track">
<div class="track-header">
<span class="track-badge purple">Track 1</span>
<h3>Treasury Allocation</h3>
</div>
<div class="track-body">
<div class="track-card">
<span class="model-tag purple">Admissions Committee</span>
<h4>How funds are split between tracks</h4>
<p>
Treasury allocation is a meta-governance decision that shapes the overall direction
of FIL+ incentives. A standing committee with representatives from both the Public
Open Data and Enterprise tracks proposes allocation percentages, which are then
reviewed by a mixed jury and ratified by badgeholder voting.
</p>
<p>
Mixed representation is the key design lever here. By requiring input from both
tracks, treasury decisions reflect the full ecosystem's interests and prevent
capture by either the commercial or public-goods faction.
</p>
</div>
<div class="track-flow-img">
<img src="images/treasury-flow.png" alt="Treasury allocation governance flow: proposal, jury review, badgeholder voting, disbursement" />
</div>
</div>
</div>
<hr class="track-divider" />
<!-- Track 2 -->
<div class="track">
<div class="track-header">
<span class="track-badge green">Track 2</span>
<h3>Public Open Data</h3>
</div>
<div class="track-body">
<div class="track-card">
<span class="model-tag green">Smart Jury + AI</span>
<h4>Evaluating datasets at scale</h4>
<p>
Public data is inherently transparent and machine-readable, making AI evaluation
tractable. The AI can verify dataset integrity, assess completeness, and compare
against existing public datasets — tasks that are significantly harder with
proprietary enterprise data.
</p>
<p>
A jury-based evaluation system is augmented with AI rubric scoring and median jury
alignment. The AI provides an objective assessment layer while human jurors bring
contextual judgment that algorithms may miss. Scores are combined using a consensus
mechanism selected during Phase 1.
</p>
</div>
<div class="track-flow-img">
<img src="images/public-data-flow.png" alt="Public Open Data governance flow: submission, parallel evaluation by jury and AI, consensus logic, decision" />
</div>
</div>
</div>
<hr class="track-divider" />
<!-- Track 3 -->
<div class="track">
<div class="track-header">
<span class="track-badge amber">Track 3</span>
<h3>Enterprise Data</h3>
</div>
<div class="track-body">
<div class="track-card">
<span class="model-tag amber">Simple Jury (Ranked Choice)</span>
<h4>Commercial proposals require human judgment</h4>
<p>
Enterprise proposals involve proprietary information, nuanced commercial judgment,
and relationship context that AI cannot reliably assess. A small, trusted jury of
domain experts evaluates proposals and ranks them using Ranked Choice Voting.
</p>
<p>
Enterprise applicants post a bond to participate. The bond is refunded upon proposal
acceptance and burned upon rejection, creating a direct incentive for high-quality
submissions and positively impacting FIL token value accrual.
</p>
</div>
<div class="track-flow-img">
<img src="images/enterprise-flow.png" alt="Enterprise governance flow: submission with bond, jury evaluation, ranked choice voting, decision with bond resolution" />
</div>
</div>
</div>
</div>
</section>
<!-- ─── Key Design Decisions ─── -->
<section id="insights">
<div class="section-inner">
<p class="section-label">Key Design Decisions</p>
<h2 class="section-title">Four Insights That Shaped the Framework</h2>
<p class="section-desc">
Each decision below surfaced during the design process and had a material impact
on the final governance architecture.
</p>
<div class="timeline">
<!-- (a) Why AI for public, not enterprise -->
<div class="timeline-item">
<div class="timeline-dot"></div>
<h4>AI evaluation is asymmetric by design</h4>
<p>
Public open data is machine-verifiable: dataset integrity, completeness, and
coverage can be assessed algorithmically. Enterprise data is not. Commercial
viability, SLA realism, and competitive positioning require the kind of
contextual, relationship-aware judgment that current AI systems cannot reliably
provide. The decision to apply AI selectively — to Track 2 only —
was deliberate, not a limitation.
</p>
</div>
<!-- (b) AI-Human consensus table -->
<div class="timeline-item">
<div class="timeline-dot"></div>
<h4>Five models for combining AI and human scores</h4>
<p>
The framework does not prescribe a single AI-Human alignment mechanism. Instead,
five well-researched models are candidates for Phase 1 evaluation. The recommended
starting point is <strong>AI as Partner</strong>, where AI and jury scores are
computed independently and then combined.
</p>
<table class="consensus-table">
<thead>
<tr><th>Model</th><th>How it works</th></tr>
</thead>
<tbody>
<tr><td>AI as Checker</td><td>AI validates jury evaluations for internal consistency</td></tr>
<tr><td>AI as Maker</td><td>Jury reviews and refines AI's initial evaluation</td></tr>
<tr><td class="highlight">AI as Partner</td><td class="highlight">Independent scores combined — recommended for Phase 1</td></tr>
<tr><td>AI as Devil's Advocate</td><td>AI stress-tests jury decisions by challenging assumptions</td></tr>
<tr><td>AI as Tie Breaker</td><td>AI weighs in only when jury reaches no clear consensus</td></tr>
</tbody>
</table>
</div>
<!-- (c) Prediction markets + scatter -->
<div class="timeline-item">
<div class="timeline-dot"></div>
<h4>Prediction markets as a feedback loop</h4>
<p>
Traditional governance voting has no feedback loop: jurors have no incentive for
accurate judgment, and there is no mechanism to surface private information.
Prediction markets address both problems. Jurors who are consistently aligned
with both the AI rubric (on objective criteria) and the median score (on
collective alignment) are identified as more reliable and incentivized accordingly.
Divergences reveal where AI misses context that humans catch.
</p>
<div class="insight-img">
<img src="images/prediction-market.png" alt="Smart Jury Prediction Market scatter plot: AI Rubric score (x-axis) vs. Median Jury Score (y-axis), showing correlation between algorithmic and human judgment" />
</div>
</div>
<!-- (d) Enterprise bond -->
<div class="timeline-item">
<div class="timeline-dot"></div>
<h4>The enterprise bond as a quality signal</h4>
<p>
Enterprise applicants are required to post a bond before their proposal enters
jury review. The bond is refunded if the proposal is accepted, and burned if
rejected. This mechanism does two things: it filters out low-effort submissions
before they consume jury time, and it aligns proposer incentives with proposal
quality. The burn-on-rejection path also contributes to FIL token value accrual,
creating a secondary ecosystem benefit.
</p>
</div>
</div><!-- /timeline -->
</div>
</section>
<!-- ─── Impact ─── -->
<section id="impact">
<div class="section-inner wide">
<p class="section-label">Impact</p>
<h2 class="section-title">Outcomes and Next Steps</h2>
<p class="section-desc">
The framework delivers four concrete governance outcomes and a phased path
to implementation.
</p>
<div class="outcome-grid">
<div class="outcome-card">
<div class="outcome-icon blue"></div>
<div>
<h4>Capture-resistant allocation</h4>
<p>Mixed-track treasury committees prevent either commercial or public-goods factions from dominating funding decisions.</p>
</div>
</div>
<div class="outcome-card">
<div class="outcome-icon purple"></div>
<div>
<h4>AI-augmented evaluation at scale</h4>
<p>Public dataset review scales without proportional jury growth, while preserving human override on edge cases.</p>
</div>
</div>
<div class="outcome-card">
<div class="outcome-icon green"></div>
<div>
<h4>Incentive-aligned enterprise onboarding</h4>
<p>The bond mechanism filters low-quality proposals and creates a direct link between submission quality and token value.</p>
</div>
</div>
<div class="outcome-card">
<div class="outcome-icon amber"></div>
<div>
<h4>Modular, evolvable governance</h4>
<p>Each track operates independently. Consensus mechanisms and jury composition can be tuned per-track without redesigning the system.</p>
</div>
</div>
</div>
<!-- Implementation path -->
<h3 style="font-size: 1.2rem; margin-bottom: 24px;">Implementation Path</h3>
<div class="impl-path">
<div class="impl-step">
<div class="impl-step-num">1</div>
<div>
<h4>Phase 1 — Governance Design & Validation</h4>
<p>Finalize the three-track model. Run structured pilots of AI-Human consensus mechanisms on a representative set of public dataset proposals. Recruit and onboard jury candidates for both Public Open Data and Enterprise tracks.</p>
</div>
</div>
<div class="impl-step">
<div class="impl-step-num">2</div>
<div>
<h4>Phase 2 — FIP Process & Coordination</h4>
<p>Draft and submit the required FIPs covering QAP changes, PoRep market enablement (sector notification), and fee governance via FRC. Coordinate with the broader Filecoin governance community for review and feedback.</p>
</div>
</div>
<div class="impl-step">
<div class="impl-step-num">3</div>
<div>
<h4>Phase 3 — Execution</h4>
<p>Execute treasury allocation under the new model, with monthly reexamination for the first quarter and quarterly cadence thereafter.</p>
</div>
</div>
</div>
</div>
</section>
<!-- ─── Footer ─── -->
<footer>
<div class="footer-name">Tanisha Katara — Senior Governance Consultant, Filecoin</div>
<div class="footer-disclaimer">
<p>
This case study presents exploratory, work-in-progress governance analysis.
It does not represent a final or adopted policy, and may or may not result
in execution or implementation.
</p>
<p>
The content is provided for informational purposes only and does not constitute
legal, financial, or investment advice. No representations or warranties are
made regarding the feasibility or adoption of the proposals described herein.
</p>
<p>Proprietary analysis — all rights reserved.</p>
</div>
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