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REVIEW 4 major objections 6 minor 41 references

Are Crypto Ecosystems (De)centralizing? A Framework for Longitudinal Analysis

T0 review · 4 major / 6 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read Crypto has largely decentralized over 15 years, but recent trends show centralization in Bitcoin's consensus, NFT marketplaces, and developers.

desk verdict Useful longitudinal framework, but Bitcoin consensus centralization rests on an unvalidated reward-address proxy. read the letter →

arxiv 2506.02324 v1 pith:R2LTAO4U submitted 2025-06-02 cs.CR

Harang Ju , Ehsan Valavi , Madhav Kumar , Sinan Aral This is my paper
classification cs.CR
keywords blockchaindecentralizationShannonentropycryptoecosystemsconsensuslayerNFTmarketplacesDeFideveloperlongitudinalanalysis
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper proposes a general, longitudinal framework for measuring decentralization in crypto ecosystems and applies it to seven blockchains across five subsystems for more than fifteen years. Its central finding is that crypto has largely become more decentralized over time, but recent trends show a shift toward centralization in Bitcoin's consensus layer, NFT marketplaces, and developer communities. The framework defines entities and contributions for each subsystem and uses Shannon entropy as a standardized measure of how evenly control is distributed, which allows direct comparison across consensus, development, exchanges, DeFi, and NFT markets. If the framework is right, regulators and practitioners have a repeatable way to track whether a blockchain ecosystem is becoming more or less concentrated.

What carries the argument

The central object is Shannon entropy, $H(X) = -\sum_x p(x)\log_2 p(x)$, computed on daily or monthly distributions of contributions across entities in each subsystem. It carries the argument because it jointly captures the number of active entities and the evenness of their contributions, allowing trends to be compared across consensus, development, exchanges, DeFi, and NFT marketplaces. The framework's entity-contribution mappings—reward-receiving addresses for validators and miners, GitHub commits for developers, volume for exchanges and NFT marketplaces, and total value locked for DeFi—are the inputs to this measure.

What would settle it

One decisive check is to compare Bitcoin's entropy computed from block-reward recipients against entropy computed from an independent measure of mining control, such as the distribution of hash rates across known mining pools, over 2021-2024. If the hash-rate-based entropy does not also decline after late 2021, the claimed centralization of Bitcoin's consensus layer is an artifact of how reward addresses are attributed.

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Extended reading notes

Core claim

The paper's central claim is that crypto ecosystems have largely become more decentralized over their lifetimes, but this trend has recently reversed in specific subsystems: Bitcoin's consensus layer has become more centralized since late 2021, NFT marketplaces were effectively monopolized by OpenSea until Blur entered in May 2022, and developer activity has concentrated since 2020-2022. This is established by measuring the Shannon entropy of contribution distributions across five subsystems for seven blockchains, including both permissionless and permissioned systems, over more than fifteen years. The paper also compares entropy with other metrics—number of nodes, Gini coefficient, Nakamoto coefficient, HHI, and Renyi entropy—and argues that entropy is the most generally applicable for longitudinal and cross-subsystem comparisons.

Load-bearing premise

The paper's conclusions rest on the assumption that observable contribution data—reward-receiving addresses for miners and validators, GitHub commits for developers, exchange volumes, and TVL—faithfully represent who actually controls each subsystem; if those mappings are wrong, the measured entropy trends and the centralization findings could be measurement artifacts.

Editorial extensions

If this is right

  • Bitcoin's consensus layer has become more centralized since late 2021, implying a heightened risk of collusion or fault concentration in the leading cryptocurrency's security.
  • NFT marketplaces were a near-monopoly dominated by OpenSea with average entropy below 1 bit before 2022, and only the entry of Blur in May 2022 pushed the market past the 1-bit threshold toward competition.
  • DeFi decentralization followed an S-curve and has plateaued since late 2021, while governance token distributions across protocols converged to between 3 and 4.5 bits regardless of initial distribution strategy.
  • Exchange decentralization remained range-bound between 2.24 and 3.48 bits over the sample period, suggesting network effects and low barriers to entry roughly balance in the exchange market.
  • Developer decentralization has declined for Bitcoin since mid-2020 and for other chains since 2022, especially in permissioned chains like BNB and Ronin.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Because the paper explicitly does not assume Sybil resistance and treats mining pools as single entities, the entropy values it reports are best read as upper bounds on decentralization; real control could be more concentrated.
  • The convergence of DeFi governance entropy across protocols with very different token distributions suggests that airdrop and token-allocation strategies have limited long-run power to shape decentralization, a hypothesis that could be tested in other token-governed systems.
  • The contrast between NFT marketplaces and DeFi suggests a testable generalization: platforms trading unique, hard-to-compare assets will tend toward monopoly, while platforms trading fungible, easily comparable assets will tend toward competition.
  • The framework's subsystem-by-subsystem view implies that a single 'is crypto decentralized?' answer is misleading; policy and security assessments should target the least decentralized subsystem, which changes over time.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 6 minor

Summary. The manuscript develops a longitudinal framework for measuring decentralization in crypto ecosystems, operationalizing decentralization as the Shannon entropy of contribution distributions across entities. It applies this framework to five subsystems (consensus, client development, exchanges, DeFi TVL and governance, and NFT marketplaces) for seven blockchains (Bitcoin, Ethereum, BNB, Solana, Tron, TON, Ronin), using daily or monthly public data. The authors compare Shannon entropy with alternative metrics and report that most subsystems have become more decentralized over time, but that Bitcoin's consensus layer, NFT marketplaces, and developer activity have shown recent centralization tendencies. The paper also contributes public code, SQL queries, aggregated data, and a live dashboard.

Significance. If the entity and contribution mappings are valid, the framework is a useful and broadly applicable measurement tool: it spans multiple subsystems and ecosystems, uses a single non-parametric metric, and makes data and code public, which supports replication. The paper is transparent about key limitations: it does not assume Sybil resistance, and Appendix A.1 states that measured decentralization is an upper bound on true decentralization. The manuscript has no fitted parameters and its claims are falsifiable, which are strengths. Its significance for the field is therefore conditional on addressing the entity-attribution and statistical-inference gaps identified below; with those addressed, the paper would provide a credible empirical baseline for decentralization research.

major comments (4)
  1. [Section 3.1 / Appendix A.1] The headline claim that Bitcoin's decentralization has diminished since late 2021 is computed from entropy over reward-receiving addresses, with pools treated as single entities unless they pay participants directly in coinbase transactions. This makes the entropy series sensitive to payout mechanics rather than hash-power control: a pool that splits coinbase outputs is counted as many entities, while a pool that consolidates payouts is counted as one. The manuscript does not validate this mapping against an independent measure such as public pool hashrate shares, nor does it report a sensitivity analysis. Because this Bitcoin result is the principal evidence for the abstract's consensus-layer centralization claim, this is a load-bearing gap that must be fixed before the headline claim can be accepted.
  2. [Section 3 / Figure 1] The central temporal claims, including Bitcoin consensus entropy declining since late 2021, developer entropy downtrending since mid-2020 for Bitcoin and since 2022 for other blockchains, and NFT marketplace centralization before 2022 with a shift after Blur, are based on visual inspection of entropy series without confidence intervals, significance tests, or structural break estimation. Shannon entropy is a nonlinear summary of a count distribution, and its day-to-day variation has no reported sampling uncertainty. The authors should add trend regressions, bootstrap confidence intervals, or breakpoint tests to support the specific 'since' dates and the direction of change; otherwise the abstract's summary is stronger than the evidence presented.
  3. [Section 2.2.2 / Sections 3.3 and 3.5] Raw Shannon entropy values are compared across subsystems with very different numbers of possible entities. For instance, the claim that NFT marketplaces were 'quite centralized with an average entropy of 0.29 bits' and that exchanges remained 'range-bound between 2.24 and 3.48 bits' mixes entity count with concentration, since the maximum entropy for a subsystem scales as log2(N). The paper should either normalize entropy by the number of possible entities or report the effective number of entities (exp(H)) when making cross-subsystem comparisons. This does not invalidate within-subsystem trends, but it undermines the comparative statements in the text.
  4. [Appendix A.3] Ethereum post-PBS proposer labels are partially based on manually labeled builder transfer lists and third-party address lists, with no validation or error analysis, and the paper notes that some builders use alternate addresses. Since the Ethereum consensus trend is part of the results, the authors should quantify how many blocks rely on inferred versus directly observed proposers and check whether the entropy trend is robust to excluding manually labeled addresses.
minor comments (6)
  1. [Section 3.4] The sentence 'Despite differences in the initial and ongoing distributions of tokens across protocols... the protocols quickly stabilized between 3 and 4.5 bits (Figure 1E)' appears to cite the wrong panel; governance entropy is shown in Figure 1C, so please correct the cross-reference.
  2. [Section 1 / Appendix A.4] The 'live dashboard at the Crypto Decentralization Dashboard' and the Deepnote code link do not provide direct URLs or persistent identifiers; include explicit links or DOIs so the replication claim is checkable.
  3. [Section 3.5] 'An entropy below 1 essentially implies a monopoly' is not strictly accurate: two entities with unequal shares can have entropy below 1 without being a monopoly, so please rephrase and state the distributional condition.
  4. [Appendix A.1 / Section 3.1] The caveat that Bitcoin's early pubkey transactions are marked 'Unknown' and thereby underestimate early decentralization should be repeated next to the first discussion of Bitcoin's consensus trend in Section 3.1, not only in the appendix.
  5. [Figure 1] The six panels in Figure 1 are too small to distinguish the recent trends described in the text; please provide larger panels or zoomed insets for the 2021-2024 period.
  6. [Section 2.1] The footnote markers in Section 2.1 appear duplicated ('45' and similar) and should be cleaned up.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the paper's results are direct entropy measurements from public data, with no fitted parameters or load-bearing self-citations.

full rationale

The paper is an empirical measurement study rather than a derivation. Decentralization is operationalized as Shannon entropy of contribution distributions (Section 2.2.2), and each subsystem's time series is computed directly from public data (Appendix A.1). The central conclusions, such as 'Bitcoin's decentralization has diminished since late 2021', are summaries of those entropy series, not predictions derived from fitted parameters or from the authors' prior results. No parameter is fitted and then renamed as a prediction; the only self-citation (Ju et al., 2025) is a passing mention of causal inference techniques and is not load-bearing. The appendix limitations (no Sybil resistance; mining pools treated as single entities; manually labeled PBS builder lists) are measurement-validity caveats, not circular steps: the inputs are observable addresses, commits, volumes, and TVL, and the outputs are entropies computed from them. There is no equation or definitional chain in which an output quantity is reused as an input, and no uniqueness theorem or ansatz is imported from the authors' prior work. The claim 'more decentralized' is equivalent to 'higher entropy' by the paper's own operational definition, but that is a transparent measurement choice for an empirical question, not a circular derivation of the kind flagged by the analyzer.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

No free parameters are fitted. The central measurement rests on the assumptions above: entropy is a valid decentralization proxy, observable contributors map to true control, and the public data sources are accurate. There are no invented entities.

assumptions (3)
  • domain assumption Shannon entropy computed from entity-contribution distributions is a valid proxy for decentralization
    The framework centers on entropy as the general metric (Section 2.2.2). The paper justifies this via criteria but does not validate against an external ground truth, and raw entropy is not normalized for the number of possible entities in each subsystem.
  • domain assumption Consensus reward recipients and contributor identities accurately represent control
    Appendix A.1 treats reward-receiving addresses as entities, admits no Sybil resistance, and Appendix A.3 reconstructs Ethereum proposers through manually labeled builder transfer lists. Errors here would directly shift entropy trends.
  • domain assumption Third-party data sources (Dune, DefiLlama, TheBlock, Reservoir, GitHub) are complete and unbiased for each subsystem
    Section A.1 relies on these public sources. Coverage gaps, such as the early Bitcoin 'Unknown' pubkey period, are noted but not corrected, and exchange data is limited to the top 38 exchanges.

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Pith. "Pith review of Are Crypto Ecosystems (De)centralizing? A Framework for Longitudinal Analysis." pith.science (2026). https://pith.science/paper/R2LTAO4U

@misc{pith2026250602324,
  author       = {Pith},
  title        = {Pith review of: Are Crypto Ecosystems (De)centralizing? A Framework for Longitudinal Analysis},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/R2LTAO4U}},
  note         = {Machine review of arXiv:2506.02324}
}
read the original abstract

Blockchain technology relies on decentralization to resist faults and attacks while operating without trusted intermediaries. Although industry experts have touted decentralization as central to their promise and disruptive potential, it is still unclear whether the crypto ecosystems built around blockchains are becoming more or less decentralized over time. As crypto plays an increasing role in facilitating economic transactions and peer-to-peer interactions, measuring their decentralization becomes even more essential. We thus propose a systematic framework for measuring the decentralization of crypto ecosystems over time and compare commonly used decentralization metrics. We applied this framework to seven prominent crypto ecosystems, across five distinct subsystems and across their lifetime for over 15 years. Our analysis revealed that while crypto has largely become more decentralized over time, recent trends show a shift toward centralization in the consensus layer, NFT marketplaces, and developers. Our framework and results inform researchers, policymakers, and practitioners about the design, regulation, and implementation of crypto ecosystems and provide a systematic, replicable foundation for future studies.

Figures

Figures reproduced from arXiv: 2506.02324 by the authors.

Figure 1
Figure 1. Daily entropy across crypto ecosystems. 3.2 Client Development Blockchain clients are the software that powers servers on blockchain networks and are instrumental to the functionality of blockchains. The decentralization of the development of clients is thus pivotal for ensuring blockchain integrity by preventing or fixing code vulnerabilities in the client software.10 To measure decentralization in blockchain clien… view at source ↗
Figure 2
Figure 2. Examples of labeled distributions of contributions by entities: (A) monthly commits by [PITH_FULL_IMAGE:figures/full_fig_p012_2.png] view at source ↗

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