Commons Collective GitHub

Workshop provenance / source-to-research crosswalk

The directions
behind the research.

The supplied photograph, each distinct printed card, and the research areas they inform. Transcription, editorial interpretation, and untested proposals are kept separate.

AI-assisted visual transcription of the printed cards visible in the photograph, not verified spoken quotations or evidence of endorsement. The brief heading and each printed attribution are represented separately from statement body text. Printed line wrapping is normalized to spaces; wording, capitalization, and parenthetical text are retained. Any unresolved reading is marked [unclear] and described in the card's uncertainties. Internal enlargements were used only for inspection, not to claim recovery of additional detail. The published full-frame image and crops retain original pixel dimensions; they were generated from decoded pixels without copying hidden source metadata. Context supplied by user; not independently inferred from image metadata.
User-provided workshop photograph with the central Collective Bargaining and Institutions brief and attributed direction cards
User-provided photograph of the Collective Bargaining & Institutions workshop whiteboard, containing one group brief and six individually attributed printed cards. Published at the user's request. Copyright in the photograph and depicted material remains with its respective rights holders. No photographer attribution or Creative Commons license is established by the supplied image. [S97]

Overall group brief / source transcription

Collective Bargaining & Institutions

Full crop of the printed card attributed to No individual attribution printed
Attribution as printed: No individual attribution printed. Open the crop to inspect it.

There appears to be growing consensus that some form of collective management or bargaining institution is crucial to a sustainable data economy. Institutions that let dispersed data creators (individual workers, users of platforms, small publishers) bargain collectively with AI labs are largely absent. User-agents, such as browsers, currently don't mediate on this. There is a wide range of both terminology and substance around what such an ecosystem would look like. There is also some history about how those groups (who do represent data creators) have operated in the past in ways that seem opposed to the members that they are designed to serve. What is the right way to describe this ecosystem? What is the optimal scale of such institutions? How should they relate to each other? Can a government body serve the role effectively and efficiently? To what extent should they be novel to the AI data environment or connected to pre-existing institutions such as labor unions, collective management organizations, etc.? What is in the way of their formation: public awareness, policy, entrepreneurship, etc.?

This records a printed card, not a verified spoken quotation or endorsement. [S97]

Editorial alignment and coverage

The overall institutional brief: describe the ecosystem, identify legitimate representation, choose scale and relationships, compare existing and new institutions, and explain how they form.

The brief is now reproduced in section 1. Its questions are traced across institutional comparison, regulatory options, the research program, and the federation blueprint; these remain research questions rather than demonstrated outcomes.

Photographed direction / source transcription

Zoe Cullen (HBS).

Full crop of the printed card attributed to Zoe Cullen (HBS).
Attribution as printed: Zoe Cullen (HBS).. Open the crop to inspect it.

Make concrete the notion of a data trust that holds sensitive work information (corporate email, Slack channels) which is high-value for AI training but currently locked inside firms. The trust would collectively secure the data and collectively negotiate its uses, and would persist across firm insolvency so that rights survive the corporate entity that generated them.

This records a printed card, not a verified spoken quotation or endorsement. [S97]

Editorial alignment and coverage

A workplace-data trust for collective protection and negotiation, with a specific continuity question when the originating firm becomes insolvent.

Private and relational data, scoped mandates, and continuity were already discussed. The workplace-data and insolvency aspiration is now an explicit follow-on question; survival of rights is not assumed to be legally guaranteed.

Photographed direction / source transcription

Tomicah Tillemann (Project Liberty).

Full crop of the printed card attributed to Tomicah Tillemann (Project Liberty).
Attribution as printed: Tomicah Tillemann (Project Liberty).. Open the crop to inspect it.

Population-scale digital cooperatives as ownership structures that distribute AI value more broadly than the traditional corporate form allows.

This records a printed card, not a verified spoken quotation or endorsement. [S97]

Editorial alignment and coverage

Population-scale digital cooperatives as a route to broader ownership and distribution of AI value.

Member governance, buyer/supplier organization, and benefit return were already central. The ownership-versus-governance distinction and the trade-off between population scale and accountable representation are now made explicit.

Photographed direction / source transcription

Erika Yorio (Helena).

Full crop of the printed card attributed to Erika Yorio (Helena).
Attribution as printed: Erika Yorio (Helena).. Open the crop to inspect it.

Investigate whether collective licensing or other intermediary structures can give individual data contributors meaningful bargaining power against AI companies, and whether the design of these structures can avoid creating a new layer of gatekeepers. Second thread: explore how litigation and emerging regulatory standards can begin to establish practical norms on data rights, compensation, and permissible use.

This records a printed card, not a verified spoken quotation or endorsement. [S97]

Editorial alignment and coverage

Collective licensing and intermediaries should improve real bargaining power without becoming new gatekeepers; litigation and emerging regulation can be studied as routes to practical norms.

Licensing, competition, rights, and gatekeeper risks were covered. A dated litigation-and-standards research track is added without asserting unverified case outcomes or universal new compensation rights.

Photographed direction / source transcription

Nicholas Vincent.

Full crop of the printed card attributed to Nicholas Vincent.
Attribution as printed: Nicholas Vincent.. Open the crop to inspect it.

Three parallel interests: (i) applying technical data valuation across data markets, business models, market design, and policy; (ii) designing effective collective bargaining institutions for data creators; (iii) creating better long-run incentives for data creators, e.g. equity stakes in data or AI companies.

This records a printed card, not a verified spoken quotation or endorsement. [S97]

Editorial alignment and coverage

Connect data valuation and market design to effective contributor institutions and long-term incentives, including the possibility of equity participation.

Valuation, business models, and collective bargaining already have dedicated analysis. Creator equity is now distinguished from cash, royalties, and the separate public sovereign-fund proposal, with unresolved governance and legal trade-offs.

Photographed direction / source transcription

Beth Goldberg (Humanity AI).

Full crop of the printed card attributed to Beth Goldberg (Humanity AI).
Attribution as printed: Beth Goldberg (Humanity AI).. Open the crop to inspect it.

Public deliberations on AI data use, so that consequential decisions are not made only by elites in closed-door gatherings; participatory co-design in benefit-sharing arrangements.

This records a printed card, not a verified spoken quotation or endorsement. [S97]

Editorial alignment and coverage

Public deliberation and participatory design should shape consequential AI-data decisions and benefit-sharing arrangements, rather than leaving decisions to closed elite forums.

Affected-person participation, paid design research, member governance, and commons budgeting were covered. Their connection to public co-design beyond invited participants is now explicit.

Photographed direction / source transcription

Jaron Lanier (Microsoft).

Full crop of the printed card attributed to Jaron Lanier (Microsoft).
Attribution as printed: Jaron Lanier (Microsoft).. Open the crop to inspect it.

What is the role of intermediaries in achieving widely distributed economic influence while avoiding centralisation and capture, and while preserving human worth, Autonomy, and creativity? Points to MIDs (mediator intermediaries) as proposed with Glen Weyl in HBR.

This records a printed card, not a verified spoken quotation or endorsement. [S97]

Editorial alignment and coverage

Assess intermediaries by distributed economic influence and protection of human worth, autonomy, and creativity, while resisting centralization and capture; retain the card's reference to MIDs and related work.

Anti-capture governance, intermediaries, shared benefits, and the pro-human stack were covered. The photographed MID reference is preserved and connected to these tests without treating an acronym as proof of effectiveness.

Where the directions appear

This crosswalk assesses the supplied photograph, not the completeness of the conference or any unreviewed supplemental document. Mapping and coverage notes are editorial interpretation; the transcriptions preserve the printed cards.

Printed attributionEditorial direction summaryPrimary research placement
Zoe Cullen (HBS).A workplace-data trust for collective protection and negotiation, with a specific continuity question when the originating firm becomes insolvent. 20. Follow-on research: a Commons Collective Framework Agreement
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Tomicah Tillemann (Project Liberty).Population-scale digital cooperatives as a route to broader ownership and distribution of AI value. 5. From digital Costco to a bargaining federation
Read the aligned card
Erika Yorio (Helena).Collective licensing and intermediaries should improve real bargaining power without becoming new gatekeepers; litigation and emerging regulation can be studied as routes to practical norms. 13. Regulatory framework: present rules and proposals
Read the aligned card
Nicholas Vincent.Connect data valuation and market design to effective contributor institutions and long-term incentives, including the possibility of equity participation. 7. Business model and economic discipline
Read the aligned card
Beth Goldberg (Humanity AI).Public deliberation and participatory design should shape consequential AI-data decisions and benefit-sharing arrangements, rather than leaving decisions to closed elite forums. 16. Research program and falsifiable hypotheses
Read the aligned card
Jaron Lanier (Microsoft).Assess intermediaries by distributed economic influence and protection of human worth, autonomy, and creativity, while resisting centralization and capture; retain the card's reference to MIDs and related work. 23. Institution blueprint: a Commons Collective Federation
Read the aligned card

Review of the supplied conference-note excerpt

Source: Blueprint for a Collective Bargaining Institution for Data in the Public Interest (2026-09-18). Supplied excerpt reviewed 2026-09-19.

Author line as supplied: Yorke Rhodes, Anna Tumadottir, Nick Vincent, Beth Goldberg, Tomicah Tilleman, Sonam Jindal, Ilan.

Reviewed the text excerpt supplied directly by the user. The authenticated Google Doc and its other tabs were not independently accessed. The author line and attributed suggestions belong to the supplied note, not automatically to this Commons Collective publication. Historical project links in the excerpt are superseded by the current site navigation. Drafting placeholders are not filled in as if they were original text.

Coverage assessment: Most major themes were already covered. The additions make organizing asymmetry, safe trace contribution and shareback, the intervention/funding modes, the two-CMO example, and public quality-signal funding explicit. Strong empirical premises remain qualified rather than adopted as facts. [S99]

Download the supplied-excerpt review

compensation and data types

Attribution in the supplied note: Shared notes; no individual attribution.

Source cue (abridged): Fair compensation & accountability; private data attribution & visibility; professional data licensing; synthetic / AI generated data.

Editorial response: The report already separates access, rights, origin, sensitivity, and use. Existing commons funding models mean that 'no systems' is too broad; payments and accountability remain uneven. Synthetic data is not assumed universally harmful.

4. A spectrum, not a single data commodity · 27. Private institutions and the commons service model

contribution withholding

Attribution in the supplied note: Shared notes; no individual attribution.

Source cue (abridged): contribution is chill, can join many coops, easy; strike/withhold is hard requires discipline, major organizing issue

Editorial response: Added the distinction between low-friction participation and sustained, lawful bargaining commitments. Multiple memberships can support choice while complicating mandate coordination. Existing open permissions cannot be retracted by calling an action a strike.

5. From digital Costco to a bargaining federation · 23. Institution blueprint: a Commons Collective Federation

mixed traces

Attribution in the supplied note: Shared notes; no individual attribution.

Source cue (abridged): Note that traces can contain all of these

Editorial response: Made trace-level mixture explicit: a single agent log can contain public, private, professional, and synthetic material with different rights and sensitivities. A user's possession of a trace does not clear every component for sharing.

4. A spectrum, not a single data commodity · 26. Open-agent incentives and a Commons Collective Agency Starter Kit

node forks

Attribution in the supplied note: Shared notes; no individual attribution.

Source cue (abridged): Human input -> protocols -> browser / agent -> example.com -> CDN -> database

Editorial response: The node-by-node diagram already covers this conceptual flow and its countervailing powers. The forks are design possibilities, not proof that every centralized component is captured or every open component distributes power.

11. New knowledge objects and technical architecture · 25. A pro-human stack: working definition and test

intervention modes

Attribution in the supplied note: Tomicah, as attributed in the supplied notes.

Source cue (abridged): identify whether each fork requires a policy solution for a market solution for a competitive marketplace OR a non-market solution supported by philanthropic capital

Editorial response: Added a ten-node matrix separating market leverage, policy/legal instruments, non-market coordination, and philanthropic startup capacity. These modes can combine, and grants do not prove commercial demand or grant governing authority.

11. New knowledge objects and technical architecture · 15. A philanthropic strategy relevant to Humanity AI

motivation and standards

Attribution in the supplied note: Shared notes; no individual attribution.

Source cue (abridged): incentives are all about fear and/or greed; Protocols: IETF capture by big companies

Editorial response: Treat concentration concerns and expected gains as possible motivators, not an exhaustive psychology or a reason to manufacture fear. The IETF claim is a proposed governance case study, not an established finding; examine participation resources, influence, and conflicts.

11. New knowledge objects and technical architecture · 22. Participation incentives and the future AI data club

nontechnical distribution

Attribution in the supplied note: Shared notes; no individual attribution.

Source cue (abridged): a single repository of all the prompts and downloads stewarded by a trusted institution to enable distribution to the non-technical public

Editorial response: The starter-kit proposal already supplies component passports and eligibility-first selection. Clarified accessible distribution, safe defaults, independent review, portability, and competing mirrors so one convenient interface does not become an unchallengeable gatekeeper.

26. Open-agent incentives and a Commons Collective Agency Starter Kit · 25. A pro-human stack: working definition and test

trace donation and shareback

Attribution in the supplied note: Shared notes; no individual attribution.

Source cue (abridged): you could donate all of your agent traces/logs to another institution; Shareback remixed data

Editorial response: Replaced bulk-donation assumptions with a proposed local selection, authority review, minimization, recipient/purpose/retention approval, and separate training assessment. Share back only permission-safe corrections, methods, and approved derived artifacts; routing through an agent is not inherently safer than a browser.

11. New knowledge objects and technical architecture · 26. Open-agent incentives and a Commons Collective Agency Starter Kit

sector guilds and energy

Attribution in the supplied note: Shared notes; no individual attribution.

Source cue (abridged): Specialized knowledge guilds; Data Unions / Knowledge Guilds by Industry; Redirect today's Data Center organizing / bargaining toward AI data commons

Editorial response: The six-sector replication pack and energy-community analysis already address these areas. Coalitions can share capacity, but data-commons funding must not replace local environmental duties or the host community's own priorities.

14. Energy, data centers, and community bargaining · 29. Replicable sector collectives: blueprint and templates

two cmo collaboration

Attribution in the supplied note: Nick, as attributed in the supplied notes.

Source cue (abridged): a book CMO and a music CMO want to join forces; how to share internal assessments of data value / appraisal

Editorial response: Added an illustrative Alice/Bob collaboration. It separates shared appraisal methods and permission-safe interfaces from sensitive prices and customer terms; a clean room does not itself create competition-law immunity.

12. Alice and Bob: end-to-end flows · 20. Follow-on research: a Commons Collective Framework Agreement

quality signals

Attribution in the supplied note: Ilan, as attributed in the supplied notes.

Source cue (abridged): pay for a quality /preference signal that the crawler could only see if they paid - ideally it'd be a public good

Editorial response: Added a public quality-signal core with separately priced assurance, integration, freshness, and service guarantees, never payment for favorable ranking. The notes do not identify a specific Ethereum deployment; ERC-8004 remains a researched comparator, not confirmation of that implementation.

21. Reputation, validation, and quality ranking · 27. Private institutions and the commons service model

quasi commons premise

Attribution in the supplied note: Shared notes; no individual attribution.

Source cue (abridged): More of humanity is (unwittingly) contributing to the quasi-commons of the claude code weights than ever before

Editorial response: Already addressed with account-specific policy distinctions, context versus weight updates, and limits on comparing AI users with Wikimedia contributors. Neither universal involuntary training nor exclusive benefit to one provider is adopted as a verified fact.

28. The quasi-commons tension: contribution without control · 24. Commons-authored RL rewards and regulatory design