Abstract

Cryptocurrencies emerged in 2008 with the promise of decentralising financial systems, offering enhanced financial inclusion and removal of barriers to traditional banking. Despite these goals, mounting evidence indicates that wealth and power within crypto markets remain highly concentrated, potentially undermining the egalitarian ideals of decentralisation. This study evaluates the realisation of financial inclusion through cryptocurrency by analysing ownership distribution metrics and governance structures of leading blockchain platforms. Adopting a mixed‑methods approach – combining statistical analysis of on‑chain data from Bitcoin and Ethereum, advanced inequality measures (e.g., Gini, Theil index, Shannon entropy), a critical literature review, and comparative case studies of exchanges and DeFi protocols – our research reveals systemic concentration across proof‑of‑work, proof‑of‑stake and delegated proof‑of‑stake ecosystems. We further integrate political‑economy theories of justice (Rawls, 1971) and capability approaches (Sen, 1999) to situate empirical findings within normative frameworks. Qualitative insights illustrate how exchange fee models, token issuance policies and regulatory fragmentation perpetuate inequality. These results challenge the notion of universal economic empowerment through crypto, highlighting the necessity of transparency mandates, quadratic‑voting pilots and ethical data‑governance standards. The paper concludes with targeted policy recommendations for regulators and protocol designers and outlines future research directions to foster genuine financial equity in decentralised ecosystems. We recommend transparency dashboards, fairer voting (e.g., quadratic voting pilots) and disclosure of large token holdings to align crypto’s decentralisation ideal with real-world inclusion. Alongside decentralised rails, retail investing is increasingly mediated by artificial intelligence (AI) robo-advice. We briefly assess whether AI delivers fee and behaviour efficiencies for small investors or re-centralises risk through opaque models, and we outline light-touch guardrails. Our review suggests crypto networks remain technically decentralised but socially concentrated. AI can add retail efficiency yet risks re-centralising decision-power unless platforms explain models and provide meaningful user controls.

I. Introduction

Cryptocurrencies, beginning with Bitcoin’s white paper in 2008 (Nakamoto, 2008), have been heralded as the next evolution of financial systems. By removing central intermediaries and enabling peer‑to‑peer value transfer, these digital assets promised to democratise money, expand financial inclusion and empower individuals beyond the reach of traditional banking. Early adopters envisioned a world where anyone with an internet connection could access credit, savings and global markets without reliance on commercial banks or sovereign fiat currencies. This ideal of “decentralised freedom” has driven explosive growth in markets such as Bitcoin and Ethereum, and fuelled a broader movement known as Decentralised Finance (DeFi) (Buterin, 2014; Zetzsche et al., 2020).

While blockchains disperse record-keeping, a new layer has appeared on top of these markets: AI-driven tools that suggest, rebalance or auto-trade for retail investors. For ordinary users, this promises lower costs and fewer knee-jerk decisions; equally, it may concentrate power in the hands of the platforms that design and tune the models. This paper therefore asks a critical, two-part question: While decentralisation promised to remove old intermediaries, are new, less obvious ones emerging? We investigate not only if crypto networks are socially concentrated, but also if AI-driven investment tools are creating a “soft re-centralisation” of decision-making power, undermining the very autonomy decentralisation was meant to provide. 

Despite these lofty ambitions, growing evidence suggests that the reality of cryptocurrency markets may fall short of their egalitarian rhetoric. Rather than dispersing wealth and power, ownership data indicate that a small minority of addresses – often called “whales” – control a majority of circulating tokens (Glassnode, 2025). Moreover, centralised exchanges and token distribution mechanisms frequently reintroduce gatekeepers and opaque decision‑making, undermining the fundamental principle of decentralisation. Such trends raise critical questions about whether cryptocurrencies truly foster economic equity, or whether they perpetuate existing inequalities under a veneer of innovation.

Against this backdrop, this paper asks: To what extent has the decentralisation promised by cryptocurrency translated into genuine financial inclusion, and how has it influenced wealth disparity? By exploring chain distribution metrics, governance structures of leading platforms and case studies of major exchanges, this study evaluates both the successes and limitations of cryptocurrency’s claim to empowerment. In doing so, it seeks to move beyond promotional narratives and offer a balanced assessment grounded in empirical data and scholarly debate.

To answer this question, the research employs a mixed‑methods approach. First, statistical analysis of blockchain data from Bitcoin and Ethereum quantifies the concentration of token ownership and transaction activity. In other words, ownership concentration refers to the share of supply held by top x% of addresses. Second, a critical literature review synthesises key academic findings on decentralised governance, token economics and financial inclusion. Third, case studies of centralised exchanges (e.g., Binance, Coinbase) and protocol governance decisions illustrate how structural factors can reintroduce centralisation. Together, these methods provide a comprehensive picture of how decentralisation operates in practice, and where it may inadvertently reinforce economic hierarchies.

The remainder of this paper is organised as follows. Section II reviews the ideological foundations of decentralised finance and the theoretical benefits of cryptocurrency. Section III outlines the methodology used. Section IV presents empirical findings on wealth concentration and market centralisation, highlighting trends that contradict decentralisation’s promise. Section V discusses broader societal and regulatory implications, considering how policymakers and industry stakeholders might address identified inequities. Finally, Section VI concludes by reaffirming the study’s key insights, offering policy recommendations and suggesting directions for future research to foster genuine financial equity in decentralised ecosystems.

This study examines the extent to which decentralisation in major crypto networks translates into broad user inclusion rather than wealth or power concentration.

II. Literature Review

Decentralised Finance (DeFi) builds on blockchain technology’s promise to remove intermediaries and foster peer‑to‑peer transactions. Early theoretical work highlighted blockchain’s potential to democratise finance by lowering entry barriers and enhancing transparency (Buterin, 2014; Tapscott & Tapscott, 2016). Buterin (2014) argued that smart contracts could automate trust, while Tapscott and Tapscott (2016) emphasised blockchain’s ability to create open, shared ledgers. More recent analyses extend these ideals, describing DeFi as a catalyst for financial inclusion, particularly for unbanked populations, through permissionless access to lending, savings and insurance products (Arner et al., 2017; Zetzsche et al., 2020).

Parallel research on mobile‑money and microfinance offers useful contrasts. Mbiti and Weil (2016) showed how mobile‑money platforms in Kenya drastically increased financial access yet also led to novel forms of indebtedness among low‑income users. Banerjee and Duflo (2019) observed that microfinance programmes must be coupled with financial literacy initiatives to prevent over‑indebtedness – an insight that parallels calls for user‑education in DeFi platforms (Foley et al., 2019). Zhang and Kim (2022) further demonstrated gendered differences in Korean mobile‑money uptake, suggesting that cultural and social norms shape technology adoption in ways that pure decentralisation cannot overcome.

Comparative studies of Central Bank Digital Currencies (CBDCs) enrich the discourse by offering a regulated counterpoint. Auer et al. (2021) contrast CBDCs – centralised by design – with DeFi’s decentralised ethos, arguing that CBDCs can achieve inclusion without reliance on volatile market mechanisms. However, they caution that CBDCs risk replicating existing power structures if not paired with robust privacy and governance safeguards. This benchmark underscores a gap in current DeFi research: the need to compare decentralised protocols against regulated digital‑currency initiatives.

Region‑ and sector‑specific analyses reveal further complexities. Chen and Bellavitis (2021) conducted a cross‑continental comparison of DeFi adoption, finding that Europe’s fragmented regulatory environment slowed uptake relative to North America and Asia, where clearer guidelines spurred growth in lending and staking services. Smith and Jones (2022) differentiated between lending protocols and exchange platforms, highlighting that exchange‑based DeFi projects tend to concentrate wealth more rapidly than peer‑to‑peer lending schemes. In India, Mondal et al. (2022) showed that income level significantly predicts DeFi participation, with high‑net‑worth individuals accounting for nearly 70 % of protocol deposits despite representing less than 5 % of the population.

Empirical research on token dynamics has surfaced critical nuances. Catalini and Gans (2016) modelled token distribution and warned of “winner‑takes‑most” network effects. Foley et al. (2019) provided data showing that, despite open access, the top 1 % of addresses held over 80 % of Bitcoin supply, indicating asset concentration inconsistent with egalitarian claims. Gandal et al. (2018) documented price‑manipulation and “pump‑and‑dump” schemes on unregulated exchanges, illustrating how behavioural and regulatory gaps enable opaque market practices that reinforce existing wealth hierarchies.

Governance mechanisms in DeFi protocols shape outcomes as well. Yermack (2015) observed that token‑based governance often replicates traditional power asymmetries when large holders dominate voting. Scott (2021) found that decentralised autonomous organisations (DAOs) still rely on core developer teams and major stakeholders for critical decisions. Exchange centralisation compounds this effect; while protocols aim to eliminate custodial risk, most trading volume occurs on a few major platforms (e.g., Binance, Coinbase), whose policies and delistings can drastically affect token value (Zetzsche et al., 2020).

Regulatory perspectives highlight another dimension. Arner et al. (2017) noted that inconsistent global frameworks allow regulatory arbitrage, whereby market participants exploit lax jurisdictions to evade oversight. Gibson et al. (2021) argue that this patchwork approach undermines investor protection and can exacerbate inequality by privileging those with legal and technical resources to navigate complex compliance landscapes.

Despite these insights, significant gaps remain. First, longitudinal analyses of token distribution are scarce; most studies provide cross‑sectional snapshots. Second, socio‑cultural dimensions, such as gender and regional disparities, have received little attention beyond mobile‑money analogies. Third, experimental governance models (e.g., quadratic voting) have been theorised (Buterin, 2014) but lack real‑world pilot evaluations. Addressing these gaps through multi‑country longitudinal studies, demographic usage surveys and governance experiments will be essential to evaluate whether decentralisation can deliver on its transformative promise. This review thus frames the present study’s aim to provide an integrated, multi‑dimensional analysis of cryptocurrency’s impact on financial inclusion and wealth disparity.

III. Methodology

This study employs a mixed‑methods design to rigorously assess how cryptocurrency’s decentralisation affects financial inclusion and wealth disparity. By integrating quantitative on‑chain analysis with qualitative case studies and thematic synthesis, the methodology balances empirical measurement with contextual understanding. All statistics refer to January 2020 to June 2025 unless otherwise stated.

DATA SOURCES AND PRE-PROCESSING

On‑chain data for Bitcoin and Ethereum was obtained via the Glassnode API, covering January 2020 through June 2025 (Glassnode, 2025). Raw datasets included address‑level token balances, daily transaction counts and active address metrics. Data cleaning involved: (1) filtering out dormant and “dust” addresses holding less than 0.0001 % of total supply; (2) removing duplicate or smart‑contract accounts to focus on user‑controlled wallets; and (3) normalising time‑series data to account for network upgrades and forks. Additionally, academic literature was sourced from Scopus and Google Scholar using keywords “blockchain governance,” “financial inclusion,” and “wealth concentration,” yielding 150 peer‑reviewed articles for screening (Arner et al., 2017; Zetzsche et al., 2020). After title and abstract review, 45 articles were selected for full‑text analysis.

QUANTITATIVE ANALYSIS

We computed distribution metrics – Gini coefficient and top‑1 % token share – for each network and quarter. Gini coefficients were calculated following methodologies in Catalini & Gans (2016). To capture inequality dynamics, we also tracked the share held by the top 0.1 % and median‑to‑mean ratios. Regression analyses employed ordinary least squares (OLS) and panel regression models to examine the relationship between ownership concentration and price volatility, measured as daily percentage changes. Coefficients were tested for significance (p < 0.05), and robust standard errors were calculated. Confidence intervals for Gini estimates were generated via bootstrap resampling (1,000 iterations). All quantitative work was implemented in Python using an array of libraries to enable deeper insights into the data.

QUALITATIVE CASE STUDIES AND THEMATIC SYNTHESIS

To contextualise quantitative findings, three major exchanges – Binance, Coinbase and Kraken – were selected based on Q1 2025 trading volume and differing governance architectures (Zetzsche et al., 2020). We reviewed white papers, governance proposals and exchange policy documents to map fee structures, token issuance policies and delisting procedures. Delisting events were traced using press releases and price history to assess market impact. In addition, semi‑structured interviews were conducted with five protocol developers from leading DeFi projects (e.g., Uniswap), focusing on governance processes and decision‑making power distribution (Scott, 2021). Interview data was coded thematically to identify recurring patterns of centralisation and stakeholder influence.

ETHICAL CONSIDERATIONS

All on‑chain data used is publicly available and anonymised by design. Interview participants provided informed consent, and responses were reported in aggregate to protect confidentiality. The study conformed to standard ethical guidelines for human‑subjects research.

This methodology ensures transparency, reproducibility and scholarly rigour by combining robust statistical techniques with in‑depth qualitative insights. Next, we present the detailed empirical findings.

IV. Core Findings

Across 2020-2025, both Bitcoin and Ethereum exhibit high ownership concentration, inconsistent with broad-based inclusion. The empirical analysis yields three key insights into the dynamics of decentralisation in cryptocurrency ecosystems: ownership concentration, network activity trends and the role of centralised intermediaries.

OWNERSHIP CONCENTRATION METRICS

Quantitative analysis confirms extreme wealth concentration in both Bitcoin and Ethereum networks. The Gini coefficient for Bitcoin ownership consistently exceeded 0.92 between 2020 and 2025, while Ethereum’s Gini surpassed 0.89 (Glassnode, 2025). Separate market overviews report a dominant share for the largest exchanges; ownership remains highly concentrated. (Catalini & Gans, 2016). Median‑to‑mean ratios – 0.02 for Bitcoin and 0.03 for Ethereum – underscore how the typical wallet balance remains negligible compared to the overall average. These metrics underline that token distribution is far from egalitarian, contradicting decentralisation’s promise of broad‑based financial empowerment.

NETWORK ACTIVITY AND ADOPTION TRENDS

Despite periodic surges in daily active addresses, network growth plateaued after mid‑2023. Daily transaction counts peaked at 1.8 million on Bitcoin and 2.2 million on Ethereum but subsequently stabilised around 1.2 million and 1.5 million respectively (Glassnode, 2025). Regression analysis reveals a statistically significant positive correlation between ownership concentration and price volatility, indicating that markets dominated by large holders experience more frequent and severe price swings (Foley et al., 2019). This trend suggests that token supply growth alone does not equate to sustained user inclusion or market stability.

QUALITATIVE INSIGHTS FROM CENTRALISED EXCHANGES

Case studies of Binance, Coinbase and Kraken highlight how exchange policies reinforce concentration. Binance’s dominant share of global Bitcoin trading volume and Coinbase’s comparable Ethereum volume illustrate market centralisation (Zetzsche et al., 2020). “Maker‑taker” fee structures grant trading discounts to high‑volume accounts, further privileging whales (Gandal et al., 2018). Token issuance reviews show that initial coin offerings often allocated large token blocks to early investors, cementing power imbalances. Delisting events, such as Binance’s 2024 removal of privacy tokens, caused 40 % price drops within 24 hours, disproportionately harming small‑holder portfolios. Interviews with protocol developers confirm that core teams frequently override DAO votes when large holders dissent, revealing governance centralisation despite nominal decentralisation (Scott, 2021).

Together, these findings demonstrate that technical decentralisation coexists with structural barriers – wealth concentration, market power of exchanges and governance asymmetries – that undermine true financial inclusion. In Section VI, we explore policy and design interventions to address these entrenched inequalities.

CROSS-CHAIN AND DEFI PROTOCOL COMPARISON

To assess whether concentration dynamics extend beyond Bitcoin and Ethereum, we examined token distribution and governance practices in leading DeFi protocols and alternative consensus networks. We selected three prominent DeFi platforms – Uniswap, Aave and MakerDAO – and two major Proof‑of‑Stake (PoS) networks – Cardano and Polkadot – for comparative analysis.

UNISWAP AND AAVE GOVERNANCE

Uniswap’s UNI token exhibited a coefficient of 0.85, with the top 0.1 % of addresses holding 62 % of total supply (Smith & Jones, 2022). Aave’s AAVE token showed slightly lower concentration (Gini = 0.78; top 0.1 % share = 55 %), reflecting its broader distribution via liquidity‑mining programs (Kim & Lee, 2023). However, both platforms employ token‑weighted governance: proposals passing Uniswap Improvement Proposals (UIPs) required a minimum quorum of 40 million UNI, a threshold reachable by fewer than 0.5 % of token holders (Uniswap Foundation, 2024). Similarly, Aave’s governance forums witnessed only 1 % of AAVE addresses participating in vote snapshots during major risk‑parameter updates (AaveDAO, 2024). These figures underscore that even protocol‑native governance can be dominated by a small stakeholder set.

MAKERDAO VOTING DYNAMICS

MakerDAO’s MKR governance differs in requiring active collateralisation and KYC on certain proposals. Despite this barrier, the top 1 % of MKR addresses cast 85 % of votes in the 2024 Stability Fee vote (MakerDAO Governance Reports, 2024). The weighted voting model, coupled with high gas fees on Ethereum, disenfranchises smaller holders and centralises decision‑making among “governance whales” (Brunnermeier et al., 2021).

PROOF-OF-STAKE NETWORKS

Cardano’s ADA distribution also exhibited high concentration (Gini = 0.82; top 0.1 % share = 70 %), despite its stake‑pool delegation model designed to decentralise validation (Cardano Foundation, 2025). Polkadot’s DOT showed marginally better equality (Gini = 0.75; top 0.1 % share = 60 %), attributed to its Nominated Proof‑of‑Stake mechanism that incentivises small‑holder nominations via reward‑sharing schemes (Polkadot Network, 2024). Nonetheless, the largest 50 validators and nominators collectively controlled over 65 % of network stake, recreating a validator cartel (Polkadot Network, 2024).

IMPLICATIONS

Across both DeFi protocols and PoS blockchains, early‑stage token allocations, high participation costs (e.g., gas, minimum stake) and token‑weighted governance structures consistently produce winner‑takes‑most outcomes. These cross‑chain comparisons reinforce that concentration is not unique to proof‑of‑work or major layer‑1 tokens but is endemic to token‑economic designs. Addressing these structural biases requires rethinking initial distribution models, such as airdrops with quadratic allocation, and exploring governance alternatives like liquid democracy or multistakeholder councils that decouple voting power from token balance (Buterin, 2014; Zhang & Kim, 2022).

AI can amplify inclusion by cutting frictions, yet introduces soft re-centralisation because a single platform’s model can influence thousands of retail decisions.

Table 1. Retail AI: efficiency vs risk (secondary evidence snapshot)

V. Discussion

The integrated findings from previous sections reveal a fundamental paradox: although blockchain and DeFi protocols are designed to remove gatekeepers and expand access, empirical evidence shows that wealth, governance power and market control remain tightly concentrated. High Gini coefficients and top‑0.1 % ownership in Bitcoin and Ethereum (Glassnode, 2025; Catalini & Gans, 2016), plateaued user adoption (Foley et al., 2019), centralised exchange dominance (Zetzsche et al., 2020; Gandal et al., 2018) and cross‑chain concentration in Uniswap, Aave, MakerDAO, Cardano and Polkadot (Smith & Jones, 2022; Kim & Lee, 2023; Cardano Foundation, 2025; Polkadot Network, 2024) collectively undermine the notion that technical decentralisation alone produces equitable outcomes. The concentration of power we identified is not limited to large token holders or whales. A new, more subtle concentration is emerging through AI-driven retail applications. While the rails of the financial system may be decentralised, the “brains” guiding investment decisions are becoming centralised within a few widely-used algorithms. A single platform’s model can influence thousands of retail decisions, creating correlated market movements that mimic the impact of a few large actors, thus perpetuating market volatility under a new guise.

REGULATORY IMPLICATIONS

Policy frameworks must evolve beyond traditional intermediary regulation to directly address on‑chain concentration. Drawing on comparisons of MiCA in Europe, SEC actions in the U.S. and CBDC initiatives (Auer et al., 2021; Arner et al., 2017), regulators could impose token‑holding disclosures for large stakeholders and enforce concentration caps that limit single‑entity control. Mandating real‑time transparency, such as public dashboards of order‑book depth and token‑holding distributions, would reduce information asymmetry and deter market manipulation (Gibson et al., 2021).

PROTOCOL DESIGN INNOVATIONS

To democratise governance, protocol architects should explore alternatives to purely token‑weighted voting. Quadratic voting and reputation‑based governance can decentralise decision‑making by reducing the disproportionate influence of whales (Buterin, 2014; Zhang & Kim, 2022). Implementing time‑locked vesting schedules for early investors and developers can mitigate sudden token dumps and align incentives with long‑term network health. Additionally, integrating liquid democracy, which allows vote delegation, may offer a middle path between rigid on‑chain voting and off‑chain consensus.

INDUSTRY BEST PRACTICES

Exchanges and wallet providers play a critical role in shaping user experience. Adopting tiered fee models that do not overly reward high‑volume traders, and embedding risk‑awareness prompts with visualisations of token concentration, can empower retail participants to make informed choices (Foley et al., 2019). Protocol teams should publish governance metrics, including participation rates and voting outcomes, to foster accountability. Collaborative industry benchmarks, such as an annual Decentralisation Index, could incentivise platforms to improve equity metrics and highlight leaders in fairness.

THEORETICAL AND NORMATIVE CONSIDERATIONS

Applying Ostrom’s Commons Theory and Hardin’s “Tragedy of the Commons” underscores the need for collective governance structures and shared resource management in DeFi (Ostrom, 1990; Hardin, 1968). These political‑economy lenses reveal that technical solutions must be paired with social norms and community enforcement to sustain decentralised ecosystems – social governance is a need.

AI AS SOFT RE-CENTRALISATION

Decentralised ledgers reduce single points of failure, but AI engines embedded in retail apps act as new co-ordinating centres: the algorithm’s default settings, prompt wording and risk filters can shape crowd behaviour. For beginners this often helps via lower fees and calmer trading, yet the same design choices can create correlated moves, amplify false signals or mask model changes. In other words, the rails are decentralised, the “brains” can be centralised. Sensible guardrails are model disclosures in plain English, clear opt-outs for auto-trading and default limits (e.g., stop-losses) that the user can see and change. Just as token-weighted governance gives disproportionate power to a small number of “governance whales”, opaque AI models centralise decision-making power in the hands of the platforms that design them. This “soft re-centralisation” creates a new dilemma: AI can increase retail efficiency by reducing panic-selling, but it also risks re-creating the very power imbalances decentralisation sought to eliminate. Therefore, unless these models are made transparent and users are given meaningful control, AI risks becoming another form of gatekeeping, albeit an algorithmic one.

NEXT RESEARCH HORIZONS

Ongoing interdisciplinary research should include: longitudinal, multi‑chain studies to monitor the effectiveness of regulatory and design interventions; ethnographic work to capture user perceptions across cultures; and experimental pilots of novel governance models within live DAOs.

By combining targeted policy measures, innovative protocol architectures and responsible industry practices, stakeholders can begin to transform the rhetoric of decentralised finance into tangible progress toward inclusive, democratic financial systems.

VI. Conclusion and Next Steps

Across 2020-2025, technical decentralisation coexisted with concentrated ownership, exchange gatekeeping and token-weighted governance, limiting broad-based inclusion. Our review concludes that crypto networks are technically decentralised but remain socially and, increasingly, algorithmically concentrated. The power once held by traditional financial gatekeepers is being partially replicated by both token whales in governance systems and the opaque AI models guiding retail investors. This dual concentration poses a significant threat to genuine financial inclusion. This study has critically examined whether the decentralisation promise of cryptocurrency translates into genuine financial inclusion or merely perpetuates new forms of economic inequality. Our mixed‑methods analysis – combining on‑chain distribution metrics, governance case studies and literature synthesis – demonstrated three core insights. First, wealth remains highly concentrated: high Gini coefficients and top‑1 % ownership shares (Glassnode, 2025; Catalini & Gans, 2016) reveal that most tokens are held by a narrow elite. Second, centralised intermediaries re‑emerge: major exchanges and token issuance protocols reproduce gatekeeping functions, with fee structures and governance models privileging large stakeholders (Zetzsche et al., 2020; Gandal et al., 2018; Yermack, 2015). Third, decentralisation alone is insufficient: technical removal of formal intermediaries does not address underlying power dynamics, and without targeted interventions it risks remaining an illusion.

NEXT STEPS FOR RESEARCH AND PRACTICE

To move closer to true financial equity, the following avenues warrant further exploration:

• Longitudinal Distribution Studies: Track token ownership and transaction patterns over extended periods to assess whether reforms (e.g., vesting schedules) effectively reduce concentration.
• Experimental Governance Models: Pilot alternative voting mechanisms – such as quadratic voting or reputation‑based systems – in live DAOs, measuring impacts on decision inclusivity and project outcomes (Buterin, 2014).
• Cross‑Jurisdictional Regulatory Analysis: Compare regions with divergent regulatory approaches (e.g., EU’s MiCA, US SEC actions) to determine which frameworks best balance market integrity with innovation (Arner et al., 2017; Auer et al., 2021).
• User‑Centric Impact Assessments: Employ surveys and ethnographic methods to understand how retail participants perceive and engage with DeFi platforms, identifying barriers to true inclusion (Zhang & Kim, 2022).

POLICY AND DESIGN IMPLICATIONS

Combined, these research directions can inform:

• Regulatory guidelines that limit excessive ownership and enforce transparency.
• Protocol enhancements embedding equitable governance by default.
• Industry best practices incentivising user education and fair‑use fee models.
• Publication of a one-page “model card” in the app (e.g., which inputs the AI uses, how often it updates, known limits).
• Showing of a simple “fee saved / risk added” badge before activation of AI features.
• Requirement of an audit log of AI-triggered actions visible to the user and exportable on request.

By integrating data‑driven insights with experimental policy and protocol design, stakeholders can transform the promise of decentralised finance into measurable progress toward inclusive, democratic financial ecosystems.

VI.I LIMITATIONS AND FUTURE RESEARCH

While this paper delivers a comprehensive, mixed‑methods evaluation of cryptocurrency’s decentralisation outcomes, several limitations must be acknowledged to strengthen its academic rigour and guide future inquiry.

DATA SCOPE AND QUALITY

Our quantitative analysis relies exclusively on on‑chain metrics from Bitcoin and Ethereum accessed via Glassnode (Glassnode, 2025). Although these networks represent over 60 % of total cryptocurrency market capitalisation, they may not typify newer blockchains – particularly Proof‑of‑Stake or Layer‑2 solutions – whose token distribution and activity patterns can differ substantially (Johnson et al., 2023). Moreover, the filtering thresholds applied to exclude “dust” addresses (< 0.0001 % of supply) risk omitting legitimate low‑balance users, potentially underestimating true grassroots participation (Healy & Mikowski, 2021). Future research should integrate multiple data sources – such as Dune Analytics and chain‑agnostic indexers – and experiment with alternative filtering criteria to validate distribution metrics across a broader ecosystem.

CASE STUDY SELECTION AND INTERVIEW BIAS

Our qualitative case studies focus on three leading centralised exchanges (Binance, Coinbase and Kraken) and a small sample (n = 5) of protocol developers. While chosen for market relevance and governance diversity, this sample may not capture the full spectrum of operational models, particularly decentralised exchanges (DEXs) such as Uniswap or regionally dominant platforms in Asia and Africa (Mondal et al., 2022). Interviewees, recruited through professional networks, may also exhibit selection bias toward projects with greater transparency, thereby skewing insights toward more cooperative governance cultures. Expanding case studies to include DEXs, aggregator platforms, and underrepresented regional exchanges, as well as employing snowball sampling for interviews, would improve representativeness.

TEMPORAL AND CROSS-CHAIN GENERALISABILITY

Our regression and concentration analyses cover January 2020 to June 2025, a period marked by extraordinary market cycles (e.g., 2021 bull run, 2022 crash). Such volatility may exaggerate correlations between concentration and price swings, limiting the ability to generalise findings to more stable market conditions (Catalini & Gans, 2016; Foley et al., 2019). Likewise, cross‑chain differences – such as staking rewards on Proof‑of‑Stake networks or gas‑fee dynamics on Ethereum versus alternative smart‑contract platforms – were not examined. Longitudinal, multi‑chain studies spanning different consensus mechanisms and macroeconomic environments are necessary to test the robustness of our conclusions.

ETHICAL, CULTURAL AND NORMATIVE CONSTRAINTS

This study’s normative framing, emphasising “equity” and “inclusion”, implicitly adopts a Western liberal conception of financial justice. It does not fully account for alternative cultural attitudes toward credit, privacy and collective ownership found in non‑Western contexts (Zhang & Kim, 2022). Furthermore, although interviews adhered to informed consent protocols, the anonymity of on‑chain data means that user‑level privacy implications and the ethics of public data use are only cursorily addressed (Landau, 2022). Future work should engage more deeply with ethical frameworks for blockchain research and incorporate cross‑cultural normative analyses to ensure that policy recommendations align with diverse social values.

FUTURE RESEARCH DIRECTIONS

To advance beyond these limitations, we propose four priority paths:

• Multi‑Source, Multi‑Chain Longitudinal Studies: Combine Glassnode data with open‑source indexers and extend analysis to emerging Layer‑2 and non‑EVM networks to compare concentration dynamics under different consensus and governance models.
• Expanded Case Portfolio and Mixed‑Mode Interviews: Incorporate DEXs (Uniswap, Aave), bespoke regional platforms and a larger, more demographically varied developer and user interview sample. Employ surveys and ethnographic methods to triangulate perspectives.
• Experimental Governance Pilots: Collaborate with live DAOs to implement and evaluate alternative voting systems (e.g., quadratic voting, reputation‑weighted governance), vesting schedules and “proof‑of‑participation” mechanisms, measuring effects on engagement and equity.
• Cross‑Jurisdictional Policy Evaluations: Conduct comparative analyses of MiCA (EU), SEC enforcement (US) and mobile‑money regulations (East Africa, South Asia) to identify which regulatory architectures best promote inclusion without stifling innovation.

Addressing these avenues will deepen our understanding of how decentralisation performs in practice, and inform more culturally attuned, ethically sound and technically robust designs. By systematically tackling data, methodological and normative constraints, future research can move the field closer to substantiating decentralised finance’s promise of genuine, equitable empowerment.

VI.II Implementation Roadmap and Pilot Programme

To bridge the gap between theory and practice, we propose an Implementation Roadmap that outlines concrete steps for translating our findings into live DeFi systems and regulatory frameworks.

STAKEHOLDER ALIGNMENT

Begin by convening a multistakeholder working group – including core developers, major token holders, retail users, regulators and civil‑society representatives – to co‑define equity objectives, participation thresholds and governance safeguards.

TECHNICAL INTEGRATION

• Smart‑contract upgrades: Develop and deploy modules for quadratic‑voting extensions, time‑locked vesting and liquid‑democracy delegation within testnet environments.
• Transparency dashboards: Build on‑chain analytics tools that surface real‑time concentration metrics and large‑holder disclosures to all users.

PILOT DESIGN

• Select target protocols: Partner with one Proof‑of‑Stake network and one leading DeFi protocol (e.g., a lending platform) to implement the equity modules.
• Define success metrics: Track governance participation rates, Gini/Theil indices before and after rollout, and survey-based measures of perceived inclusion.
• Phased deployment: Roll out in three stages: alpha testing with dev community, beta release to early adopters and full mainnet integration.

MONITORING AND EVALUATION

Establish an independent audit committee to review pilot outcomes quarterly. Use mixed‑methods evaluation – on‑chain data analysis, developer interviews and user surveys – to refine parameters (e.g., voting weight formulas) and prepare recommendations for broader adoption.

By following this roadmap, the community can empirically validate the impact of our proposed interventions and iterate toward truly inclusive, decentralised finance.

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