MARKET POWER AND GOVERNANCE POWER
NEW TOOLS FOR ANTITRUST ENFORCEMENT IN THE
DECENTRALIZED GIG ECONOMY
Seth C. Oranburg1
ABSTRACT This Article advances a new — ramework — or digital-age antitrust en — orcement by integrating measures o — market concentration (such as the Her — indahl-Hirschman Index) with novel metrics o — governance concentration (the Nakamoto coe —
icient and Gini coe —
icient). Focusing on the rise o — decentralized autonomous organizations (DAOs) and gig economy plat — orms, it demonstrates that traditional antitrust tools are insu —
icient — or assessing competitive risk and organizational power in markets characterized by algorithmic management and decentralized coordination. Through detailed theoretical development, case studies o —
representative gig DAOs, and the construction o
a dual-metric “matrix” mapping
our distinct organizational types, the Article shows how both economic scale and governance structure must be evaluated to determine when intervention is warranted. The — ramework reveals that organizations combining scale with genuinely dispersed control, named “Decentralized Titans” and “Commons Ideals,” should bene — it — rom structural de — enses against regulatory intervention. But DAOs that are practically controlled by — ew people, named “Algorithmic Leviathans” and “Captive Plat — orms,” require targeted remedies to address concentrated power and labor-side harms. Policy and practice recommendations show how agencies and courts can use this dual-metric approach to calibrate remedies. This approach — avors governance re — orm, transparency, and interoperability over blunt structural breakups to balance competitive markets with worker protection. By bridging economic and organizational analysis, this Article o —
ers a quanti — iable, — act-driven, adaptable roadmap — or antitrust law to meet the challenges o — the digital, gig, and DAO era.
JEL Classi
ications: K21 (Antitrust Law); L40 (Antitrust Issues & Policy); L86 (In — ormation & Internet Services); D02 (Institutions: Design, Formation & Operations); J42 (Monopsony).
1 Pro — essor o — Law, University o — New Hampshire Franklin Pierce School o — Law; Director, Program on Organizations, Business and Markets at NYU Law’s Classical Liberal Institute; JD, University o — Chicago; BA, University o — Florida. 2 MARKET POWER AND GOVERNANCE POWER
INTRODUCTION The central conceptual tool o — antitrust law, the de — inition o — “single entity” or “ — irm,” is — acing an acute identity crisis as algorithmic gig plat — orms and decentralized autonomous organizations (“DAOs”) disrupt traditional corporate boundaries. Notably, courts have long recognized that “[a] parent and its wholly owned subsidiary have a complete unity o — interest” such that “they act as a single entity — or antitrust purposes,”2 but have also cautioned that — ormal corporate separateness does not always provide clear guidance — or complex business arrangements.3 In American Needle, the Supreme Court explained that “substance, not — orm, determines whether a[n] arrangement is concerted action” and that antitrust analysis must — ocus on whether entities “pursue separate economic interests” and are capable o — competition,4 an inquiry rendered more challenging as digital plat — orms supplant conventional organizational hierarchies with algorithmic governance and blockchain-based coordination.5 Contemporary competition authorities and scholars now con — ront the reality that algorithms may “create incentives and mechanisms to collude that would not exist otherwise,”6 and that DAOs — urnish decentralized — rameworks — or economic coordination outside — amiliar legal structures, raising “important antitrust implications”7 — or global en — orcement regimes. As a result, competition analysis must be retooled to address how law, not only economics, struggles to de — ine and police boundaries o — market power in the digital age. To address the analytical de — icit in existing competition law, I propose that courts and regulators evaluate both market concentration and decentralization when assessing market power in digitally organized ecosystems. Speci — ically, established measures such as the Her — indahl-Hirschman Index (HHI) could be deployed alongside a decentralization metric—like the Nakamoto coe —
icient—which captures how control is distributed across core decision-makers in blockchain,
2 Copperweld Corp. v. Independence Tube Corp., 467 U.S. 752, 771 (1984) (establishing “single entity” doc- trine — or antitrust). 3 Copperweld, 467 U.S. at 774 (“The logic underlying the Court’s decisions has not always been easy to ap- ply in the context o — complex business arrangements. The Court itsel — has acknowledged that it is not easy to determine when two entities are su —
iciently independent to constitute separate entities — or §1 purposes.”) (emphasizing di —
iculty in drawing boundaries o — a single entity). 4 American Needle, Inc. v. Nat’l Football League, 560 U.S. 183, 195 (2010) (“Substance, not — orm, deter- mines whether a[n] arrangement is concerted action”; “The NFL teams do not possess either the unitary deci- sionmaking quality or the single aggregation o — economic power characteristic o — independent action.”) (clar- i — ying ‘ — irm’ de — inition is context-dependent — or antitrust). 5 OECD, Algorithms and Collusion: Competition Policy in the Digital Age 11–12 (2017) (“The OECD con- cludes that algorithms may create incentives and mechanisms to collude that would not exist otherwise . . . algorithms can concur to make collusive outcomes more likely and stable over time.”) (digital-plat — orm gov- ernance challenges antitrust — rameworks). 6 Id. at 12. 7 Thibault Schrepel, An Introduction to Blockchain Antitrust, at 1 (Kluwer 2023) (“Blockchain raises new competition issues.… [I]t should come as no surprise that blockchain has important antitrust implications.”) (articulating DAOs’ relevance to antitrust law). Seth C. Oranburg 3
DAO, or algorithmically managed plat
orms.8 By mapping both economic concentration and governance centralization, authorities can distinguish genuinely decentralized networks — rom plat — orms with hidden, algorithmic, or coded concentrations o — power.9 This dual-metric methodology re — lects recent calls in the competition literature — or multi-dimensional standards in digital markets10 and aligns with emerging international norms that emphasize — unction over — orm in antitrust review.11 When systematically applied, such a — ramework enables law to capture both the risks and bene — its o — new organizational — orms, ensuring competitive — airness even where economic and organizational boundaries are blurred by technology. This Article begins by setting out the problem o — measuring power in gig and digital plat — orm markets, critiques the limitations o — traditional antitrust metrics, and then proposes a dual-metric approach that jointly analyzes market and governance concentration. Applying this — ramework illustratively to representative gig DAOs, it presents a — our-quadrant matrix identi — ying distinct risk pro — iles and policy implications. The Article concludes by o —
ering tailored recommendations
or regulators and courts, demonstrating how the proposed model could enable more precise, e —
ective, and just antitrust oversight in the Gig DAO era.
MARKET POWER AND THE SINGLE ENTITY DOCTRINE The cornerstone o — antitrust analysis is market power, a concept used both to identi — y harm to competition and to di —
erentiate between unilateral and coordinated conduct in economic markets. The “single entity” doctrine, — ormalized by the Supreme Court in Copperweld, holds that a parent and its wholly owned subsidiary “have a complete unity o — interest,” and there — ore cannot conspire under Section 1 o — the Sherman Act.12 This principle re — rames the — irm as a unit o — economic
unction, not merely a legal structure. Nevertheless, courts have — requently noted
8 See Lin William Cong et al., Decentralized Governance in Blockchain-Based Organizations, OXFORD RE- VIEW OF ECONOMIC POLICY 36 (2), 2020 (“The Nakamoto coe —
icient o —
ers a quanti — iable measure o — decen- tralization, indicating the number o — entities needed to control a blockchain’s consensus.”) (introducing gov- ernance metrics — or digital organizations). 9 OECD, Arti — icial Intelligence, Data and Competition 16 (2024) (“Multi-dimensional approaches to compe- tition analysis—including metrics — or data concentration and decentralized governance—will be essential in assessing market power in AI and plat — orm environments.”) (calling — or multi- — actor tests — or digital markets). 10 Thibault Schrepel, Collusion by Blockchain and Smart Contracts, 33 HARV. J.L. & TECH. 117 (2019) (ad- vocating — or multi-dimensional standards). 11 Jacques Crémer, Yves-Alexandre de Montjoye & Heike Schweitzer, Competition Policy — or the Digital Era 7, 59–60, 83, 127 (Eur. Comm’n, Directorate-Gen. — or Competition 2019), https://doi.org/10.2763/407537 (urging regulatory support where ongoing intervention is needed, e.g., to “impose and allow — or e —
ective in- teroperability” (p. 7); explaining protocol/data/ — ull-protocol interoperability as pro-competitive instruments that can share network e —
ects and address concentration tendencies (pp. 59–60); distinguishing portability
rom broader data-access/interoperability regimes and recognizing competition-law-based data access, espe- cially — or dominant — irms (p. 83); and stressing that in digital markets innovation e —
ects o — ten outweigh price e —
ects and must be systematically integrated into competition analysis (p. 127)).) 12 Copperweld Corp. v. Independence Tube Corp., 467 U.S. 752, 771 (1984) (“A parent and its wholly owned subsidiary have a complete unity o — interest.”). 4 MARKET POWER AND GOVERNANCE POWER
the complexity o
delineating — irm boundaries, especially in multi — aceted or dynamically structured business arrangements.13 American Needle v. NFL advanced the doctrine by emphasizing that “substance, not — orm, determines whether a[n] arrangement is concerted action,”14 thus directing antitrust analysis to the actual economic interests and autonomy o — the entities involved. The — ramework established by these precedents has shaped both agency en — orcement and judicial decision-making, serving to distinguish collaboration — rom monopoly in traditional commercial contexts, where corporate identity and boundaries are usually well de — ined. As recent case law and merger en — orcement actions underscore, doctrinal clarity on market power and single entity status remains essential, even as competitive realities and analytical tools continue to evolve.15 In doctrinal practice, market power is also quanti — ied through the Her — indahl- Hirschman Index (HHI), which aggregates the squares o —
irms’ market shares to produce a numerical measure o — concentration. U.S. merger review now rests on these — igures pursuant to the 2023 DOJ/FTC Merger Guidelines, which classi — y markets as “highly concentrated” at a post-merger HHI above 1,800. Notably, this Biden-era policy re — lects a signi — icant lowering — rom the 2,500-point threshold in earlier standards.16 Under the revised Guidelines, a transaction is presumptively unlaw — ul i — it results in an HHI increase o — 100 points or more in such a market, or i — the merged — irm would hold over 30% market share with a quali — ying HHI increase.17 These quantitative rules re — lect increased regulatory vigilance, as recent en — orcement actions and cases such as United States v. Google reveal active judicial engagement with structural presumptions and analytic screens based on concentration metrics.18 Internationally, the European Commission and other global authorities employ similar concentration ratios, adapting threshold tests and policy instruments to ensure that competitive harms can be detected across a spectrum o — traditional and digitally mediated markets.19 Judicial interpretation o — market power and agency guidelines has evolved in tandem with both legislative change and shi — ting economic realities. Courts today
13 Id. at 774 (“The logic underlying the Court’s decisions has not always been easy to apply . . . it is not easy to determine when two entities are su —
iciently independent to constitute separate entities — or §1 purposes.”). 14 American Needle, Inc. v. Nat’l Football League, 560 U.S. 183, 195 (2010) (“Substance, not — orm, deter- mines whether a[n] arrangement is concerted action.”). 15 E.g., United States v. Google LLC, No. 1:20-cv-03010 (D.D.C. 2025) (applying single entity doctrine in digital market context); RealPage, Inc. Antitrust Litigation (DOJ 2024). 16 DOJ & FTC, 2023 Merger Guidelines §II.A (2023) (“A market is considered highly concentrated i — the post-merger HHI is greater than 1,800.”). 17 Id. (“Mergers resulting in an increase o — the HHI o — 100 or more, or creating — irms with more than 30% share, are presumed unlaw — ul.”). 18 United States v. Google LLC, No. 1:20-cv-03010 (D.D.C. 2025); see also DOJ/FTC, Recent En — orcement Actions, https://www. — tc.gov/legal-library/browse/cases-proceedings/recent-merger-cases. 19 European Commission, Guidelines on the Assessment o — Horizontal Mergers ¶19–22 (2004) (EU thresh- olds — or market concentration); see Baker Botts, Key Takeaways on EU Merger Control: Global Antitrust Hot Topics (2025). Seth C. Oranburg 5
regularly apply structural presumptions
rom the latest Merger Guidelines, striking down or subjecting to close scrutiny mergers that tip HHI above 1,800 or aggregate market shares above 30 percent.20 In United States v. Google, district court analysis closely tracked agency guidelines to address Google’s entrenched position in search and advertising while re — erencing both concentration metrics and
unctional tests derived — rom Copperweld and American Needle.21 En — orcement actions such as the RealPage litigation — urther signal a renewed willingness to scrutinize algorithmic coordination and hub-and-spoke arrangements — or competitive harm, demonstrating that the boundaries o — the “single entity” doctrine and traditional market share tests remain — ront-and-center in legal analysis even as new market con — igurations arise.22 Recent EU and UK merger cases have similarly exempli — ied this trend, with courts pressing both economic and — unctional tests — or dominance and competitive e —
ect.23 The complexities o — market de — inition and power assessment have become especially pronounced in cases involving multi-sided plat — orms. The Supreme Court’s decision in Ohio v. American Express Co. established that antitrust analysis
or such plat — orms must consider all sides o — a transaction, recognizing interdependent user groups and indirect network e —
ects.24 Lower courts have implemented AmEx in subsequent technology litigation, notably Epic Games, Inc. v. Apple Inc., where the Ninth Circuit used AmEx’s multi-sided market — ramework to evaluate the competitive dynamics o — app distribution and payment processing.25 Scholarship by Kacyn H. Fujii — inds that AmEx and its progeny have created procedural and substantive ambiguities — or plat — orm cases, with courts struggling to articulate coherent standards — or market de — inition and harm assessment in technology markets.26 Herbert Hovenkamp observes that antitrust remedies — or plat — orms should prioritize restructuring and interoperability mandates over breakups, and stresses that market power metrics — or plat — orms require — lexibility to accurately account — or multi-sided relationships and network e —
ects.27 The 2023 DOJ/FTC Merger Guidelines acknowledge these analytical complexities, highlighting the need — or — unctional de — initions and economic rigor when evaluating
20 DOJ & FTC, 2023 Merger Guidelines §II.A (2023); see also DOJ/FTC Recent En — orcement Actions. 21 United States v. Google LLC, No. 1:20-cv-03010 (D.D.C. 2025) (“Market power and competitive e —
ects
indings were guided by merger guideline thresholds and — unctional market de — initions.”). 22 United States v. RealPage, Inc., No. 3:23-cv-03847 (N.D. Tex. 2024) (algorithmic price-setting and hub- and-spoke antitrust liability). 23 European Commission, Revised Market De — inition Notice (2024); Baker Botts, Key Takeaways on EU Mer- ger Control: Global Antitrust Hot Topics (2025). 24 Ohio v. American Express Co., 138 S. Ct. 2274, 2287 (2018) (“In two-sided markets, courts must analyze both sides o — the plat — orm to assess market power and competitive e —
ects.”). 25 Epic Games, Inc. v. Apple Inc., 67 F.4th 946, 961–63 (9th Cir. 2024); see also Daniel J. Hemel, How Epic v. Apple Operationalizes Ohio v. Amex, YALE J. REG. BULLETIN (2024). 26 Kacyn H. Fujii, The Impact o — Amex and Its Progeny on Technology Plat — orms, 120 MICH. L. REV. 691 (2022). 27 Herbert Hovenkamp, Antitrust and Plat — orm Monopoly, 130 YALE L.J. 1952, 1960–77 (2021). 6 MARKET POWER AND GOVERNANCE POWER
competition in plat
orm settings.28 European and Asian regulators have likewise revised their guidance, converging on a more adaptive approach to plat — orm market analysis that addresses both doctrinal and practical challenges.29 The rise o — gig plat — orms like Uber, Ly — t, and DoorDash has — oregrounded new challenges — or antitrust en — orcement in labor markets. Gig economy — irms increasingly exert substantial market power over workers through plat — orm control, algorithmic wage-setting, and contractual restrictions, o — ten classi — ying drivers and service providers as independent contractors to avoid labor protections.30 The FTC and DOJ have responded with heightened en — orcement, issuing joint guidelines in 2025 clari — ying that antitrust law applies to business practices a —
ecting gig workers, including wage suppression, deceptive earnings claims, and exclusionary conduct.31 Recent settlements with Ly — t and Amazon Flex address misleading wage representations and illegal withholding o — tips, demonstrating both the breadth o —
agency oversight and the vulnerability o
plat — orm workers to exploitation.32 Legal scholars argue that antitrust remedies should enable collective bargaining — or gig workers, extending labor exemptions to cover independent contractors—a controversial but increasingly relevant re — orm.33 Courts have begun to grapple with these issues directly: in Con — ederación Hípica de Puerto Rico, Inc. v. Con — ederación de Jinetes Puertorriqueños, Inc., the First Circuit cautiously
28 DOJ & FTC, 2023 Merger Guidelines §III.D (2023). 29 See European Commission, Market De — inition Notice (2024), https://competition-policy.ec.europa.eu/mer- gers/legislation/notices-and-guidelines_en (codi — ies analytic adjustments — or multisided and digital markets, emphasizing — unctional and evidence-based market de — inition); WilmerHale, EU Merger Control: What You Need to Know From 2024 to Navigate 2025 (Jan. 30, 2025), https://www.wilmerhale.com/en/insights/publi- cations/20250131-eu-merger-control-what-you-need-to-know- — rom-2024-to-navigate-2025 (“Recent trends in EU merger review point to expanded qualitative and quantitative assessment — or dominant plat — orms, with particular attention to market structure and network e —
ects.”); Baker Botts, Key Takeaways on EU Merger Control: Global Antitrust Hot Topics (Sept. 2, 2025), https://www.bakerbotts.com/thought-leadership/publi- cations/2025/october/key-takeaways-on-eu-merger-control-global-antitrust-hot-topics (“EU competition au- thorities increasingly employ sector-speci — ic guidelines and economic tools to supplement traditional concen- tration metrics.”); Scott Morton et al., “Digital Plat — orm Regulation: Making Markets Work — or People,” Yale School o — Management, https://som.yale.edu/sites/de — ault/ — iles/2025-05/SCOTT-MORTON_Digital_Plat-
orm_Regulation_pages.pd — (providing comparative analysis o — digital market re — orms and regulatory adapta- tion); Financier Worldwide, “Post-Amex – Market De — inition and Anticompetitive E —
ects” (Dec. 31, 2024), https://www. — inancierworldwide.com/post-amex-market-de — inition-and-anticompetitive-e —
ects (“Asian au- thorities, particularly in Japan and South Korea, have issued new guidelines addressing multi-market coordi- nation and cross-plat — orm competitive e —
ects.”). 30 Human Rights Watch, The Gig Trap: Algorithmic, Wage and Labor Exploitation in Plat — orm Work in the US, (May 11, 2025), https://www.hrw.org/report/2025/05/12/the-gig-trap/algorithmic-wage-and-labor-exploi- tation-in-plat — orm-work-in-the-us; Len Sherman, Why The FTC Needs To Investigate Uber’s Anti-Competi- tive Business Practices, FORBES (Sept. 6, 2024). 31 FTC & DOJ, Joint Guidelines on Business Practices Impacting Workers (Apr. 14, 2025), https://www. — tc.gov/news-events/news/press-releases/2025/01/ — tc-doj-jointly-issue-antitrust-guidelines-busi- ness-practices-impact-workers. 32 FTC, FTC Takes Action to Stop Ly — t — rom Deceiving Drivers with Misleading Earnings Claims (July 29, 2025), https://www. — tc.gov/news-events/news/press-releases/2024/10/ — tc-takes-action-stop-ly — t-deceiving- drivers-misleading-earnings-claims; FTC Policy Statement on En — orcement Related to Gig Work (2022). 33 Marina Lao, Workers in the ‘Gig’ Economy: The Case — or Extending the Antitrust Labor Exemption, 51 UC DAVIS L. REV. 1543 (2018); Ioana Marinescu & Eric Posner, Why Has Antitrust Law Failed Workers?, 105 CORNELL L. REV. 1343 (2020) Seth C. Oranburg 7
extended labor antitrust exemptions to certain gig workers, suggesting a new doctrinal pathway — or labor organizing.34 Meanwhile, the “gig trap”—plat — orms capturing enormous revenues while workers see stagnating wages and eroded bargaining power—has drawn criticism — rom human rights organizations and labor economists, prompting ongoing litigation and regulatory inquiry into algorithmic exploitation and market concentration across U.S., European, and Asian jurisdictions.35 Taken together, these doctrinal developments reveal both the strengths and the growing limitations o — traditional competition law tools in the — ace o — economic and technological change. Whether the challenge is measuring market power in conventional corporate mergers, deciphering competitive e —
ects in complex multi- sided plat — orms, or protecting gig workers — rom algorithmic wage suppression and monopsony practices, authorities have adapted by re — ining existing doctrines and embracing new en — orcement priorities. Yet, as the boundaries o — “market power” grow increasingly — luid, and as digital, decentralized, and algorithmic — orms o —
governance become more pervasive, it is clear that orthodox tests, whether rooted in the single entity doctrine or structural concentration metrics like HHI, no longer su —
ice as the sole arbiters o — competitive harm and economic authority. The next section sets out a new analytic approach, introducing — unctional metrics — or decentralization and network structure to supplement classic indicators—laying the groundwork — or a more capable, — uture-oriented — ramework to analyze competition and governance in dynamically evolving markets.
GOVERNANCE POWER AND DECENTRALIZED AUTONOMOUS ORGANIZATIONS The prior Part has shown how doctrines and quantitative metrics such as the HHI in — orm the analysis o — market power and concentration in both traditional and digitally mediated industries. In this Part, the analytic lens turns inward to address a new dimension: the distribution o — governance power within DAOs and, in particular, gig DAOs now operating at scale in labor and plat — orm markets. To rigorously evaluate governance power, antitrust analysts are increasingly turning to two conceptual metrics: the Nakamoto coe —
icient (“NMC”) and the Gini coe —
icient. The Nakamoto coe —
icient is de — ined as the minimum number o —
entities required to control over hal
o — a network’s critical resources.36 The Gini coe —
icient, borrowed — rom economic inequality analysis, measures the
34 Con — ederación Hípica de Puerto Rico, Inc. v. Con — ederación de Jinetes Puertorriqueños, Inc., 30 F.4th 306 (1st Cir. 2022); Josh Jacob, Avenues — or Gig Worker Collective Action a — ter Jinetes, 123 Colum. L. Rev. 208 (2023). 35 Human Rights Watch, supra note [#]; Administrative Law Review, The Gig Worker Question, 76 ADMIN. L. REV. 945, 947 (2024); ACCC, Merger Guidelines (Australia, 2024). 36 Low NMC values—typically under 10—signal that a hand — ul o — actors dominate decision-making, whereas high values indicate broader distribution and resilience against capture. See Cong et al., supra note [#], at 10 (“The Nakamoto coe —
icient measures e —
ective decentralization by counting the smallest coalition needed to control >50% o — governance power.”); Arxiv, Analysis o — Voting Power in Decentralized Governance. 8 MARKET POWER AND GOVERNANCE POWER
distributional equality o
governance rights, ranging — rom 0.0 (per — ect equality) to 1.0 (total concentration; one actor rules all).37 Although many such organizations present as “decentralized,” recent evidence—including high Gini coe —
icients recorded across major DAOs— demonstrates that the appearance o — decentralization o — ten masks signi — icant concentrations o — control.38 Thus, to understand real market power in digitally organized ecosystems, antitrust analysis must be sensitive to both the external structure o — competition and the internal allocation o — decision-making power. As the — ollowing sections detail, mapping governance power and recognizing the risks o — the “decentralization illusion” are critical preconditions to synthesizing outward and inward metrics — or modern antitrust en — orcement. DAOs, and especially gig DAOs, are no longer theoretical constructs. DAOs are now established actors in plat — orm and labor markets, coordinating everything
rom — reelance sta —
ing to payroll and bene — its — or independent workers.39 Distinguished by blockchain-based protocols, tokenized voting, and transparent smart contracts, these organizations are designed to distribute governance rights and responsibilities among members rather than concentrate authority in a traditional managerial hierarchy.40 Yet, despite their promise o — democratized control, recent empirical research shows that high Gini coe —
icients are common within major DAOs, revealing o — ten persistent, and sometimes severe, concentrations o — governance power even in the absence o — a centralized management structure.41 As such, any meaning — ul antitrust analysis in the plat — orm economy must look beyond the — ormal trappings o —
decentralization and examine how governance power is actually distributed within these organizations. This necessity gives rise to a — undamental methodological
37 Bitquery, supra note [#] (“Gini coe —
icient reveals striking inequality within DAOs: values o — 0.95 and higher indicate e —
ective centralization.”). 38 See Lin William Cong et al., Decentralized Governance in Blockchain-Based Organizations,(August 2025 dra — t), available at https://perma.cc/H3DC-ZHJ9; BITQUERY, Understanding Wealth Distribution with Gini and Nakamoto Coe —
icient (Nov. 16, 2023), available at https://perma.cc/Q23P-3ZJK. 39 See Braintrust, The world’s — irst user-owned talent network, trans — orming global hiring through decentral- ized governance, https://www.usebraintrust.com/governance/ (last visited Nov. 3, 2025) (outlining DAO structure — or labor market coordination), [https://perma.cc/8KJA-HX6F]; Opolis, Employment Commons White Paper, https://opolis.co/wp-content/uploads/2021/01/White-paper.pd — (detailing DAO-managed em- ployment in — rastructure), [https://perma.cc/7P64-PGBG]; Burnett Specialists, 2025 Gig Economy Trends, https://burnettspecialists.com/blog/gig-economy-trends- — or-2025-what-job-seekers-and-employers-need-to- know/ (summarizing scalability and impact o — gig DAOs on contemporary employment markets), [https://perma.cc/CPB6-XQYY]. 40 Schrepel & Gal, Algorithmic Antitrust, supra note[#], at 117–18 (2019) (“DAOs rely on ‘smart contract- based governance’ which replaces the centralized control o — conventional corporations with ‘member-driven decision-making.’”); GreenAppleX, Roles and Use Cases o — DAOs in 2025, https://greenap- plex.com/blog/roles-and-use-cases-o — -daos-in-2025 (quoting, “DAOs empower their communities to make key product, hiring, and strategic decisions”), [https://perma.cc/HZ6D-4TJV]. 41 Cong et al., Decentralized Governance in Blockchain-Based Organizations, supra note [#] (“…recent cal- culations reveal Gini coe —
icients between 0.90 and 0.98 — or governance tokens in leading DAOs, a signature o — acute concentration”); Bitquery, supra note [#] (explaining, “Gini coe —
icient reveals inequality—values exceeding 0.90 suggest near-total concentration at the top”). Seth C. Oranburg 9
shi
t: in the DAO era, understanding real market power requires mapping not only market share, but also the reality o — internal governance power dynamics. The concentrations o — governance power empirically observed within major DAOs are not a mere theoretical curiosity. They have concrete consequences — or how antitrust law should reach and evaluate these new organizational — orms. DAOs, regardless o — their sur — ace-level decentralization, retain the operational capacity to coordinate pricing, restrict entry, or allocate market resources in ways that may harm competition42 This presents acute challenges — or en — orcement, since algorithmic coordination and “smart contracts” can automate cartel-like behavior while obscuring who, in — act, holds control because DAOs can — unctionally replicate both single-entity and cartel characteristics, which complicates en — orcement by con — using the boundaries o — the — irm.43 Antitrust doctrine hinges on the classi — ication o — organizations as either “single entities” or collaborations among competitors.44 A DAO with heavily concentrated governance (e.g., low NMC, high Gini) may — unctionally operate as a classic — irm, wielding uni — ied strategic control. By contrast, a DAO with dispersed control may resemble a multi-actor joint venture, where coordinated action is less likely and legal exposure under Section 1 o — the Sherman Act rises.45 Importantly, highly decentralized governance (high NMC, low Gini) can serve as a structural check against exclusionary or exploitative conduct, — unctioning as rebuttal evidence in competition inquiries.46 However, these metrics require quali — ication due to the problem o — delegation. In most DAOs, token holders o — ten delegate their votes to highly visible representatives or “delegates,” who then accrue outsized control in practice.47 While intended to increase participation and e —
iciency, delegation typically results in a power law distribution that concentrates voting rights, rein — orcing oligarchy even where — ormal decentralization is present.48 Recent developments in the gig economy and digital labor markets provide concrete illustrations o — the relationship between governance structure and antitrust analysis. Braintrust and Opolis, two o — the most prominent gig DAOs, demonstrate
42 Cong et al., Decentralized Governance in Blockchain-Based Organizations, supra note [#] (“DAOs with governance concentrations above 0.95 Gini are susceptible to capture by dominant interests, with practical consequences — or exclusion and collusion.”). 43 Schrepel & Gal, Algorithmic Antitrust, supra note [#], 120–22 (2019) (“Smart contracts — acilitate algorith- mic antitrust harms and complicate detection.”). 44 Copperweld, 467 U.S. 752. 45 GreenAppleX, Roles and Use Cases o — DAOs in 2025, supra note [#] (discussing legal ambiguity o — DAOs as both — irm- and cartel-like). 46 Bitquery, supra note [#]; Cong et al., supra note [#] (“High dispersion o — voting power dilutes coordinated exclusion risk”). 47 Appel et al., Decentralized Governance and Digital Asset Prices, https://perma.cc/XV2Y-LCJA (“Dele- gated voting mechanisms o — ten result in highly concentrated outcomes regardless o — initial token disper- sion.”). 48 GreenAppleX, supra note [#] (“Delegate-centric governance perpetuates asymmetric voting power.”). 10 MARKET POWER AND GOVERNANCE POWER
how similar market-
acing business models can yield di —
erent legal characterizations—and risk pro — iles—depending on the internal distribution o —
power. FUDx, while less empirically documented, is representative o
a new wave o — DAOs challenging established delivery plat — orms. Braintrust is a user-owned, Web3-based talent marketplace designed to connect — reelance workers with clients seeking specialized expertise, including legal, — inancial, and technology services. The BTRST token represents both ownership and governance rights, and ownership o — this token con — ers proposal and voting rights. Braintrust allows talent to advertise their availability, set their own rates, and apply directly — or projects sourced — rom Fortune 1000 companies such as Nestlé, Nike, and NASA. Clients have access to a pool o — vetted experts and can select individuals or teams — or a wide variety o — project assignments, ranging in size and complexity.49 The plat — orm operates as a not- — or-pro — it and charges a — lat 10% — ee to clients, which — unds network operations, although current product pages list a 15% client
ee, indicating a pricing update or product-tier variation. Freelancers keep 100% o —
their earnings. Governance o
Braintrust is decentralized, with users—talent and clients—granted governing rights through blockchain tokens. As a competitive talent marketplace, Braintrust’s top competitors include other digital plat — orms such as Turing, Buzzzy, Squadio, and MVP Match, which similarly enable open market matching between — reelance experts and client demand.50 Braintrust’s structure aims to remove traditional sta —
ing intermediaries, thereby promoting direct competition among — reelancers — or project-based work and o —
ering organizations a transparent, on-demand approach to hiring talent. These — eatures place Braintrust in the emerging category o — networked marketplaces that blend market competition with user control, distinguishing it within the broader gig economy landscape.51 Public data su —
iciently to directly measure Braintrust governance power is not available, so no conclusions can be drawn — rom direct evidence. However, circumstantial evidence and analogous case provide grounds — or — urther inquiry. Braintrust uses token-weighted voting (“the more tokens you have, the greater your
49 Joseph B. Fuller, Manjari Raman & Emilie B. Feldman, Building the On-Demand Work — orce, Harv. Bus. Sch. Case No. 20-076, at 6–7 (2020), https://www.hbs.edu/managing-the- — uture-o — -work/Documents/20- 076.pd — [https://perma.cc/Y2JV-Q6HJ]; Braintrust, Braintrust: The Decentralized Talent Network (Whitepa- per) 4, 10–12 (Sept. 2021) [https://perma.cc/73B4-F23U]; Sarah Friedman, This — uturistic gig plat — orm is owned by workers who keep 100% o — earnings, The Hustle (Apr. 4, 2024) [https://perma.cc/T7BM-W93C]; CBInights, Braintrust (last visited Nov. 11, 2025) [https://perma.cc/T84D-98MD]; Braintrust, Terms o — Ser- vice (last updated Feb. 6, 2025; last visited Nov. 11, 2025) [https://perma.cc/2SYT-LSMS]. 50 Messari, State o — Braintrust (Q1 2023) (Apr. 20, 2023), https://messari.io/report/state-o — -braintrust-q1-2023 (“Braintrust takes a — lat 10% — ee … paid by the client; talent keeps — ull billings.”); see also Braintrust, The 10 Biggest Misconceptions About Braintrust (Aug. 11, 2021), https://www.usebraintrust.com/blog/10-biggest- misconceptions-about-braintrust (“zero — ees to Talent … 10% service — ee to clients”) [https://perma.cc/U4FP- 84TJ]. 51 See PR Newswire, Coatue and Tiger Global Purchase $100M Braintrust Tokens to Seed Web3 Network Development Initiative (Dec. 9, 2021) (noting this is a press release provided by Braintrust) [https://perma.cc/W7WB-YFQE]. Seth C. Oranburg 11
voting power”), so its
ormal decentralization can still produce high voting-power concentration, a pattern widely documented across large token-governed DAOs such as Uniswap and Compound, where measured Gini coe —
icients approach 0.95– 0.99.52 Using some realistic assumptions based on typical patterns in crypto holding,53 we can estimate Braintrust’s Gini is probably in the 0.85-0.92 range, representing signi — icant (but not severe) governance power concentration—enough to undermine nominal claims about the truly decentralized nature o — this marketplace. Opolis (“The Employment Commons”) represents an alternative model o —
decentralized labor organization. Legally structured as a Colorado public-bene
it limited cooperative association (LCA), Opolis provides an employer-o — -record service that enables — reelancers and sel — -employed pro — essionals to receive W-2 payroll, tax withholding, and group bene — its while remaining independent contractors in substance.54 Membership is divided into two classes—Employee Members and Coalition Members—each subscribing to a single share o — class- speci — ic common stock and governed by the cooperative’s Bylaws through a Board o — Stewards.55 This structure institutes a near one-member-one-vote rule within each class, replacing token-weighted control with democratic membership voting grounded in cooperative law.
52 Johnnatan Messias and Ayae Ide, Fairness in Token Delegation: Mitigating Voting Power Concentration in DAOs, arXiv (dra — t submitted Oct. 7, 2025), https://arxiv.org/pd — /2510.05830 [https://perma.cc/UTR7- YY94]; Robin Fritsch, Marino Müller & Roger Wattenho — er, Analyzing Voting Power in Decentralized Gov- ernance: Who Controls DAOs? tbl. 2 & § 4.1 (Apr. 3, 2022) (preprint), https://arxiv.org/abs/2204.01176 [https://perma.cc/B9MP-S3SS] (reporting, as o — Mar. 1, 2022, Compound: Gini_delegates 0.987; Gini_dele- gates_voted 0.964; Nakamoto (delegates) 8; Uniswap: Gini_delegates 0.995; Gini_delegates_voted 0.971; Nakamoto (delegates) 11; describing these as “extreme” inequalities and noting concentration o — potential voting power among a hand — ul o — delegates).; see also Appel, supra note [#]. 53 Based on publicly available token holder data — rom November 2025, the top — ive Braintrust (BTRST) ad- dresses collectively control approximately 29.5% o — the total token supply, with the single largest holder con- trolling 7.5%. For the purposes o — this estimate, it is assumed that a — ter the top — ive holders, the next several largest holders each possess between 3% and 4% o — supply, such that the ten largest holders together could control just over 50% o — the outstanding tokens i — voting as a bloc. This produces a back-o — -the-envelope Nakamoto coe —
icient estimate in the range o — 10–12. The remaining “tail” o — token holders—constituting roughly 70% o — supply—is presumed to be distributed among many small, unrelated holders, which neces- sarily limits the impact o — per — ect decentralization assumptions. As a result, even under a best-case (but unre- alistic) scenario o — atomized individual holdings outside the top 10, the estimated Gini coe —
icient — or voting power would still remain high, plausibly within the 0.85–0.92 range, re — lecting signi — icant inequality in po- tential voting in — luence. These calculations are — or illustrative purposes, using only summary statistics — or the largest known holders. See Gate.com, 2025 BTRST Price Prediction: Analyzing Market Trends and Future Growth o — the Braintrust Token (Sept. 30, 2025) https://www.gate.com/crypto-wiki/article/2025-btrst-price- prediction-analyzing-market-trends-and- — uture-growth-potential-o — -the-braintrust-token [https://perma.cc/KWT3-924D]. 54 Employment Commons LCA—Terms o — Service, Opolis (Oct. 1, 2024), https://opolis.co/terms-o — -service/ [https://perma.cc/LD5B-EHTN] (identi — ying “Employment Commons LCA, a Colorado public-bene — it lim- ited cooperative association” providing W-2 payroll and bene — its administration). 55 Employee Member—Membership Agreement, Opolis (last visited Nov. 11, 2025), https://opolis.co/em- ployee-member/ [https://perma.cc/K5S6-ZQH2] (requiring each Employee Member to purchase one share o —
Class A Common Stock; governance by Bylaws and Board o
Stewards); Stakeholder Economics & Tokeni- zation o — the Employment Commons (Whitepaper), Opolis (last visited Nov. 11, 2025), https://opolis.co/re- sources/downloads/Opolis-White-Paper.pd — [https://perma.cc/VZ4X-Q2D4]. 12 MARKET POWER AND GOVERNANCE POWER
The Opolis cooperative issues a digital token, $WORK, described in public documents as a patronage or rewards instrument that distributes community incentives --- or participation rather than conveying ownership or voting power.56 $WORK tokens are not yet available --- or open market sale, and Opolis has not published quantitative decentralization metrics such as a Gini or Nakamoto coe ---
icient; there — ore, no empirical assessment o — governance concentration can be made at this time. Nonetheless, Opolis’s hybrid design—anchoring on-chain record-keeping to an o —
-chain cooperative charter—places it in what commentators term the emerging “LCA-DAO” class o — legally wrapped decentralized organizations.57 From an antitrust perspective, this cooperative model di —
ers sharply — rom token-weighted gig plat — orms: the locus o — governance power is — ormally dispersed across members rather than accumulated in large token holdings. Opolis thus serves as a comparative baseline — or genuinely democratic decentralized governance in labor markets, even as its empirical distribution o —
control remains to be measured. FUDx is a blockchain-based project proposing a hyperlocal, decentralized hospitality and delivery marketplace, intended to serve restaurants, delivery agents, and consumers by leveraging peer-to-peer networks and smart contracts — or transparency and reduced commission costs.58 The plat — orm aspires to address monopsony and middleman problems in the on-demand delivery sector by issuing its own utility token (“FUDx Coin”) — or payments, rewards, and ecosystem participation. Its architecture includes distributed ledger management, tokenized transactions, and planned support — or autonomous delivery — leets and IoT integration.
Governance details, voting mechanisms, decentralization metrics, and live market operations --- or FUDx remain undeveloped as o --- publication. The FUDx Coin white paper lays out tokenomics with allocations --- or public sale, team, partners, marketing, rewards, and ecosystem reserves, but does not speci --- y projected voting power distribution, governance protocols, or empirical measures o --- concentration (such as Gini or Nakamoto coe ---
icients). As with other emerging gig DAOs, FUDx’s case illustrates the expansion o — decentralized labor plat — orms into sectors
56 WORK Tokens: A Simple Guide to Opolis Rewards, Opolis 1–3 (June 2023), https://opolis.co/wp-con- tent/uploads/2023/06/WORK-Tokens-A-Guide-to-Opolis-Rewards.pd — [https://perma.cc/47PE-BF6K] (“The Opolis WORK token is a digital rewards token received by Members — or engaging in activities valuable to the cooperative”); Opolis Employment Commons Launches $WORK Token, GlobeNewswire (Apr. 22, 2021), https://www.globenewswire.com/news-release/2021/04/22/2215144/0/en/Opolis-Employment-Commons- Launches-WORK-Token.html [https://perma.cc/GYT9-HL5Y] (announcing $WORK as a community pat- ronage utility used inside the Commons). 57 Navigating DAO Legality (written input o — Opolis Employment Commons), U.S. Sec. & Exch. Comm’n, FinHub Comment File 9–10 (Jan. 27, 2024), https://www.sec.gov/ — iles/ct — -written-input-navigating-dao-le- gality-opolis-043025.pd — [https://perma.cc/KB3N-Z63Q] (describing Employment Commons (Opolis) as “a pioneer in the LCA-DAO class,” combining cooperative membership and on-chain coordination). 58 FUDx Coin White Paper, FUDxCoin.com (last visited Nov. 11, 2025), https://www. — udxcoin.com/As- sets/whitepaper.pd — [https://perma.cc/9BGY-W3AU]. Seth C. Oranburg 13
historically dominated by monopolistic intermediaries and highlights the need
or continued legal and economic analysis as this market matures. These examples con — irm that—consistent with the theoretical and doctrinal case built above—the real antitrust classi — ication and en — orcement risk o — a gig DAO hinges as much on the internal distribution o — governance power as on its outward market share. The application o — governance metrics to gig DAOs such as Braintrust and Opolis demonstrates a — undamental methodological imperative — or modern antitrust: analysis must shi — t — rom a single, outward- — acing measure o — market concentration (e.g., HHI or market share) to a dual-axis — ramework that simultaneously maps economic concentration and governance centralization. Only through this two-dimensional lens can competition policy reliably capture the complex realities o — digitally organized ecosystems. Structurally decentralized organizations—like Opolis, — eaturing high NMC and low Gini—provide power — ul structural evidence that anti-competitive coordination is organizationally implausible. In a merger review or conduct investigation, such structural resistance to power capture constitutes robust rebuttal evidence that “no substantial lessening o — competition is threatened,” even where market share appears signi — icant.59 By contrast, plat — orms with high economic competition but sharply concentrated governance—like Braintrust—may — unctionally replicate the risks o — traditional single-entity monopolists, justi — ying scrutiny rooted in established antitrust doctrine.60
MARKET POWER AND GOVERNANCE POWER The descriptive and analytical groundwork o — the preceding sections culminates in a simple, yet power — ul insight: antitrust analysis — or digitally organized — irms must proceed along two axes, mapping both market (economic) concentration and governance (structural) concentration. This dual-metric — ramework enables competition authorities to classi — y plat — orm and DAO risks more precisely, illuminate which organizational — orms truly threaten competition, and choose interventions that are targeted, — lexible, and consistent with the realities o — digital markets. The — ollowing pages operationalize this diagnostic, de — ine its — our core quadrants, and o —
er strategies — or law and policy that re — lect the complexity—and promise—o — the new digital — irm. Building on these analytic — oundations, the proposed dual-axis — ramework can be operationalized as a simple but power — ul matrix. On one axis lies market concentration, measured conventionally by the Her — indahl-Hirschman Index (HHI) or analogous market share assessments—the classic “outward” view o — competitive
59 U.S. Dep’t o — Justice & Fed. Trade Comm’n, 2023 Merger Guidelines §3 (“Merger parties may provide evidence o — … structural — eatures that render anticompetitive e —
ects implausible.”), https://www.jus- tice.gov/atr/2023-merger-guidelines [https://perma.cc/2946-JB35]. 60 Copperweld, 467 U.S. at 769–71 (“substance, not — orm” determines single entity status); Cong et al., supra note [#] (high Gini, low NMC DAOs “e —
ectively recreate classic ‘ — irm’ power structures”). 14 MARKET POWER AND GOVERNANCE POWER
structure. The other axis, newly added
or the digital era, charts governance concentration, captured by metrics such as the Nakamoto coe —
icient and the Gini coe —
icient—a — inely calibrated “inward” view o — how organizational power is distributed. Each digital — irm, protocol, or plat — orm can thus be plotted within this two-dimensional space, and — our distinct zones—or analytical quadrants—emerge. This structured approach enables competition authorities to classi — y organizational risks more precisely and to tailor interventions accordingly.
Low Governance High Governance
Concentration Concentration
Low NMC / High Gini) (Low NMC / High Gini) High Market Q1: Decentralized Titan Q2: Algorithmic Concentration Monopolistic & Leviathan (High HHI) Distributed Monopolistic &
Oligarchic Low Market Q3: Commons Ideal Q4: Captive Plat --- orm Concentration Competitive & Competitive & (Low HHI) Distributed Oligarchic
Q1: Decentralized Titan A “Decentralized Titan” is an organization with signi — icant market share or HHI, but internal governance is demonstrably dispersed—evidenced by a high Nakamoto coe —
icient and a low Gini coe —
icient. While such entities may initially raise regulatory concerns due to their size and in — luence, both empirical studies and policy analysis suggest that decentralized governance serves as power — ul rebuttal evidence against anticompetitive risk.61 Recent research con — irms that although most major DAOs display increasing centralization over time, the best-designed (e.g., one-member-one-vote cooperatives or experimental DAOs using quadratic voting or delegation sa — eguards) can sustain relatively low governance concentration.62 For these rare cases, policy — ocus should be on transparency and ongoing monitoring, not structural breakups, because scale e —
iciencies are preserved without managerial dominance or exclusionary conduct.
Q2: Algorithmic Leviathan The “Algorithmic Leviathan” archetype captures the greatest antitrust risk: organizations that are both economically dominant (high HHI/market share) and internally governed by a small elite (low NMC, high Gini). Studies o — leading DeFi,
61 Cong et al., at 2–3, 9–15 (documenting rare instances o — persistent decentralized governance in DAOs with signi — icant participation sa — eguards). 62 Id. at 16–18 (surveying mechanisms such as quadratic voting and delegated sa — eguards as “meaning — ul cor- rectives to the centralization trend”). Seth C. Oranburg 15
DEX, and lending DAOs show that Gini coe
icients routinely exceed 0.93, with the top decile controlling over 75% o — voting power63—mirroring risks — amiliar
rom classic monopolies, but o — ten compounded by automation and algorithmic opacity.64 Here, the potential — or exclusionary conduct, market exploitation, and coordination is at its peak; law and policy should prioritize active conduct remedies, mandated governance restructuring, or, as a last resort, divestiture o — governing rights. Interoperability and transparency requirements, as endorsed in recent antitrust guidance, are especially justi — ied — or Leviathans.65
Q3: Commons Ideal The “Commons Ideal” describes organizations that are both — ragmented in the market (low HHI) and — eature genuinely decentralized internal structures (high NMC, low Gini). Empirical studies identi — y this quadrant with certain cooperatives and small, socially- — ocused DAOs wherein governance and economic power are widely distributed and collective decision-making prevails.66 In these cases, the structure itsel —
unctions as a perpetual barrier to collusion, exclusion, or abuse. Risks o — anticompetitive behavior are lowest,67 and this category serves as the policy benchmark: law should avoid intervention and instead enable or gently encourage the emergence o — such models, which o — ten enhance both voice and market competition.
Q4: Captive Plat
orm “Captive Plat — orms” appear competitive in market terms but hide highly concentrated governance. Token-weighted voting, delegation mechanisms, and vote concentration yield low NMC and high Gini coe —
icients even as the entity’s market share is low. Leading DAOs—including Uniswap, Compound, Aave, and ENS—are paradigmatic cases: studies consistently — ind Gini coe —
icients — or voting power exceeding 0.95 and Nakamoto coe —
icients below 15, indicating high
63 See Chao, A Study o — Uniswap On-Chain Voting: Implications — or Power, Apathy and Ethics, https://www.panewslab.com/en/articles/7c66 — 3 — a-b71d-4 — a0-898d-243c — 083e8a8 [https://perma.cc/MVY5- ER6D] (reporting Gini coe —
icients o — “0.938” and top decile holding >75% o — power). 64 Li Cheng, Algorithmic Monopolization and Antitrust Regulation in the AI Industry, 10(38s) J. In — . Sys. Eng. & Mgmt. 2025, https://jisem-journal.com/index.php/journal/article/download/6941/3217/11596 [https://perma.cc/42VL-C4BS] (describing algorithmic exclusion, opacity, and sel — -pre — erencing in dominant digital plat — orms). 65 US DOJ & FTC, 2023 Merger Guidelines §3 (“Parties may provide evidence o — … structural — eatures that render anticompetitive e —
ects implausible. When the Agencies conclude a merger would cause a lessening o —
competition, they evaluate rebuttal arguments … including market realities.”). 66 Sharma et al., Large Scale Analysis o — Decentralized Autonomous Organizations, arXiv (Oct. 16, 2024) https://arxiv.org/html/2410.13095v1 [https://perma.cc/F98B-SUGX] (2020) (demonstrating statistically sig- ni — icant lower Gini coe —
icients and high decentralization in small, social-good, and public-goods DAOs). 67 Paul Van Vulpen & Slinger Jansen, Decentralized autonomous organization design — or the commons, FRONTIERS (Dec. 6, 2023) https://www. — rontiersin.org/journals/blockchain/arti- cles/10.3389/ — bloc.2023.1287249/ — ull (arguing “commons DAOs” have lowest risk o — coordination — ailure). 16 MARKET POWER AND GOVERNANCE POWER
governance concentration and limited e
ective decentralization.68 Despite their decentralized branding, these structures — unctionally replicate single-entity risks, potentially evading Section 1 scrutiny — or collusion but susceptible to managerial sel — -dealing, manipulation, and user exploitation.69 Legal and regulatory oversight should — ocus on governance re — orms (e.g., — orced decentralization), transparency, and internal neutrality, aligning with recent legal commentary advocating process- based remedies over asset-based ones.70
Summary: Integrating Market and Governance Power This dual-metric — ramework demonstrates why antitrust in digitally organized markets must analyze both outward market structure and inward governance. “Decentralized Titans” and “Commons Ideals” serve as evidence that true decentralization can rebut standard presumptions o — harm—even at scale—by rendering coordination or abuse structurally implausible. By contrast, “Algorithmic Leviathans” and “Captive Plat — orms” expose how concentrated governance perpetuates the risks o — exclusion and exploitation, regardless o — HHI or sur — ace competition. The matrix thus provides the backbone — or decentralized-distributed antitrust analysis: interventions should precisely — it the locus o — actual risk, targeting opaque or concentrated governance but enabling innovation and e —
iciency wherever structural checks already exist.71
POLICY AND PRACTICE IMPLICATIONS FOR ANTITRUST IN THE GIG DAO ERA This Part distills the dual-metric — ramework into actionable guidance — or the next era o — gig economy oversight. By integrating market concentration (HHI) and governance concentration (NMC/Gini), regulators and courts can — inally match remedies and review standards to the actual source o — risk and power in digitally organized gig markets. First, en — orcement agencies should treat low-governance-concentration gig DAOs—those with high Nakamoto coe —
icients, low Gini coe —
icients, and robust transparency—as presenting a strong structural de — ense against antitrust intervention.72 Whether or not a plat — orm has signi — icant market share, a genuinely decentralized decision-making structure rebuts assumptions o — collusion,
68 See Cong et al.; Fritsch et al. 69 Winston & Strawn LLP, Mitigating Antitrust Risk In Decentralized Autonomous Orgs, https://www.win- ston.com/en/insights-news/mitigating-antitrust-risk-in-decentralized-autonomous-orgs [https://perma.cc/DKE7-9RM4] (“DAO voting structure can lead to co-conspirator liability and plat — orm- owner sel — -pre — erencing unless mechanisms prevent governance capture”). 70 Id. (advocating “monitoring, reporting, disciplinary, and restructuring policy tailored to DAO governance
ailures”). 71 Monopoly Power and Market Power in Antitrust Law, U.S. DOJ (2024), https://www.justice.gov/ar- chives/atr/monopoly-power-and-market-power-antitrust-law [https://perma.cc/NLX3-DDRM] (“antitrust should chart both price and exclusionary power; — ocusing solely on one closes courts’ eyes to potential anti- competitive e —
ects”). 72 See Cong et al. Seth C. Oranburg 17
exclusion, or algorithmic wage-
ixing.73 This extends to gig worker cooperatives and Opolis-type organizations, which use one-member-one-vote or other anti- oligarchic designs as structural assurances o — market and worker — airness.74 For such — irms, policymakers should avoid remedies that would undermine network e —
iciencies. This structural integrity, achieved through anti-oligarchic designs,
unctions as rebuttal evidence—consistent with Section 3 o — the Merger Guidelines—that merger-speci — ic procompetitive e —
iciencies are present and that structural barriers to harm exist, justi — ying lighter-touch oversight, reporting, and agency “sa — e harbors.”75 Second, antitrust agencies and courts should recognize that a low-governance- concentration Gig DAO—demonstrated by high Nakamoto and low Gini coe —
icients—provides a strong legal shield against intervention, even where gig workers collectively coordinate wages or terms. In classical antitrust, collective bargaining by independent contractors is per se unlaw — ul price- — ixing under Section 1 o — the Sherman Act unless exempted. Yet, when gig workers organize under a truly decentralized DAO, dispersed voting power acts as a robust rebuttal, structurally preventing collusion and reducing risk o — exclusion or algorithmic exploitation. This allows both courts and en — orcers a — actual basis to decline intervention, ensuring competitive and labor bene — its are preserved. Lighter-touch oversight or agency sa — e harbors may justly apply in such contexts. Third, — or gig DAOs and plat — orms displaying concentrated governance— empirically evidenced by low NMC and high Gini coe —
icients and ampli — ied by delegation mechanisms that rein — orce a “rich-get-richer” dynamic and ideological misalignment—policy must — ocus on internal plat — orm power, not just output or price. This structure enables algorithmic management with tremendous exploitation potential: ratings-based allocation, dynamic wage-setting (causing arbitrary pay
luctuations and suppressing wages), and exclusionary gating.76 Instances o —
monopsony, sel
-pre — erencing, discrimination, and exclusion should trigger scrutiny and creative, proportional remedies such as — orced decentralization, transparency in delegation, plat — orm neutrality, mandated labor-side representation, and interoperability/pooling.77 Remedies should prioritize internal restructuring and e —
iciency, not asset break-up. Where collective bargaining in gig DAOs raises antitrust questions, the FTC and DOJ should clari — y—via rulemaking—that organizing under credible, decentralized, pro-competitive DAO governance is not 73 U.S. DOJ & FTC, 2023 Merger Guidelines §3. 74 See Opolis, White Paper, supra note [#]. 75 See Vulpen & Jansen, supra note [#]. 76 See AI Now, Arti — icial Power: 2025 Landscape Report (Jun. 3, 2025), https://ainowinstitute.org/wp-con- tent/uploads/2025/06/FINAL-20250602_AINowLandscapeReport_Full.pd — [https://perma.cc/BMH7-CJZE]. 77 Herbert Hovenkamp, Structural Antitrust Relie — Against Digital Plat — orms, 7 J. L. & INNOVATION 57, 69–74 (2024), https://scholarship.law.upenn.edu/context/jli/article/1033/viewcontent/Hovenkamp_Structural_Anti- trust_Relie — _Against_Digital_Plat — orms_Publication_Dra — t_FINAL.pd — ; Annie Soo Yeon Ahn, Antitrust, Sel — -Pre — erencing, and Display o — Search Results,79 Ill. L. Rev. Online (2025) https://illinoislawre- view.org/online/antitrust-sel — -pre — erencing-and-display-o — -search-results/ [https://perma.cc/497D-GFTU]. 18 MARKET POWER AND GOVERNANCE POWER
per se unlaw
ul price- — ixing. This is necessary, as private suits and damages have otherwise chilled organizing, and the labor exemption is still narrowly tied to employment status under — ederal law.78 Finally, the dual-metric — ramework equips antitrust with the agility and depth needed — or the Gig DAO era. En — orcement and policy must reject one-size- — its-all penalties and instead pursue remedies exactly proportional to the structural reality revealed—whether that means — ostering decentralized gig cooperatives by recognizing their organizational resistance to collusion and exclusion, or restructuring concentrated, algorithmic plat — orms to prevent exploitation and internal abuse. By targeting the true locus o — risk—whether external market dominance, inward oligarchy, or their intersection—authorities can unlock e —
iciencies, protect worker and consumer wel — are, and harness the democratic, innovative potential o — DAOs and gig plat — orms.79 This quanti — iable, — act-driven,
unction-over- — orm approach not only realigns antitrust with its empiricist roots, but provides a scalable, actionable roadmap — or competition oversight in digital, decentralized, and labor-intensive industries where old doctrines and blanket remedies no longer su —
ice.80 In sum, the prescription is clear: tailored, data-driven scrutiny according to the dual axes o — market and governance power is essential to e —
ective gig-era competition policy and to the — uture evolution o — digital market law.81
CONCLUSION This Article charts a new course — or antitrust in the Gig DAO era by introducing a dual-metric — ramework that analyzes both market and governance concentration. In doing so, it moves beyond the limits o — traditional tools to provide a more — aith — ul account o — power and risk in digitally organized and labor-intensive markets. When applied to gig plat — orms and decentralized organizations, this — ramework enables regulators and courts to calibrate remedies that are proportional, innovation-
riendly, and just—distinguishing between entities where scale poses little risk and those where hidden concentrations o — governance power enable exclusion, exploitation, or collusion. Antitrust law’s success in the digital age requires an individualized, empiricist approach—one that matches the remedy not to — orm or size, but to the structure and incentives that truly govern economic outcomes. The prescription is clear: only through — lexible, evidence-driven scrutiny, attentive to both outward market share
78 FTC, Rulemaking Petition — or Gig Worker Antitrust Labor Exemption, — iled May 2025; Lina M. Khan, Tes- timony to Congress, Sept. 28, 2025; FTC, Federal Trade Commission En — orcement Policy Statement on Ex- emption o — Protected Labor Activity by Workers — rom Antitrust Liability https://www. — tc.gov/sys- tem/ — iles/ — tc_gov/pd — /p251201laborexemptionpolicystatement.pd — [https://perma.cc/6U4P-XETH]. 79 U.S. DOJ & FTC, 2023 Merger Guidelines §3, supra note [#]; Hovenkamp, supra note [#]. 80 See Cong, supra note [#]; Decentralized autonomous organization design — or the commons, supra note [#]. 81 See Ahn, supra note [#]. Seth C. Oranburg 19
and inward organizational dynamics, can competition law ensure e
icient, dynamic, and — air markets — or workers, consumers, and creators alike. Looking — orward, the tools and concepts developed here supply a roadmap — or addressing emerging legal controversies in labor markets, digital governance, and beyond. As technologies, market — orms, and threats to economic justice evolve, the dual-metric model stands ready to guide — uture policy, judicial interpretation, and empirical inquiry—preserving antitrust’s vital purpose as the law o —
air and open markets in the twenty- — irst century.