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Evaluating Validator Decentralization: Geographic and Infrastructure Distribution in Proof-of-Stake Networks

Jun 1, 2023 ⋅  23 min read

The open-source data collection tools featured in this report were built in collaboration with @theSamPadilla and open for contributions on Github. The repo contains scripts to measure the validator and stake distribution of prominent PoS Layer-1 networks across node hosting infrastructure and geographic location. It currently includes tools for Avalanche, Cardano, Flow, NEAR, Solana, and Aptos. Builders are welcome to contribute to or build upon the project to further the monitoring and analysis of L1 operational decentralization.

Key Insights

  • Operational decentralization of Proof-of-Stake networks can be evaluated by the distribution of validators and stake across geographic jurisdictions, node hosting infrastructure, and basic software components.
  • A well-distributed and decentralized network across these variables is more resilient against buggy code, political and corporate hostility, and physical infrastructure failures.
  • Standardized metrics like the Nakamoto coefficient aid in measuring operational decentralization at a given moment in time. But to understand the full picture, it is important to have a qualitative analysis of the factors that contribute to the concentration of stake and a network’s ability to recover in the face of mass infrastructure failure.
  • Across all networks analyzed, Avalanche, Cardano, NEAR, Solana, and Aptos, there is substantial room for improvement to promote long-term reliability and resilience.

A Proof-of-Stake network is most resilient against unexpected failures or adversarial attacks when its validators are well-distributed and decentralized. This necessitates the distribution of a sufficient number of non-trusting operators across non-correlated environments. “Environments” encompass both a validator’s physical location and the hardware and software components used to connect to a network. A common infrastructure failure across any of these variables that affects a significant number of validators could disrupt and, with increasing severity, even compromise a network entirely.

Operational decentralization refers to the distribution of network validators and stake across geographic jurisdictions, node hosting infrastructure, and basic software components. A well-distributed and decentralized network across these variables offers several critical benefits.

First, it enhances security by minimizing the risk of a single vulnerability or attack vector affecting a significant portion of the network. Second, it improves the network's robustness by reducing the impact of localized issues, such as regulatory actions or infrastructure failures. Lastly, it promotes reliability. It showcases that a network is capable of withstanding potential challenges and threats, thus contributing to its credibility.

Multiple general-purpose Layer-1s have thus emerged with distinct architectural designs, each attempting to strike the balance between security, usability, and scalability. However, each differing design inevitably faces tradeoffs that influence its degree of decentralization. These design differences make a comparative analysis of Layer-1 decentralization at face value inherently challenging. To evaluate decentralization, one must look to standardized models and control for each network’s unique parameters.

Early work by Balaji and Lee set the standard for quantifying decentralization with the Nakamoto coefficient, a method to measure the minimum number of entities required to compromise a blockchain network’s subsystems. But the initial calculations and parameters for the Nakamoto coefficient need to be updated to account for the consensus rules of Proof-of-Stake (PoS)-based networks and to hone in on operational decentralization specifically. Moreover, there are exogenous factors that contribute to the concentration of stake, as well as a network’s ability to recover in the face of mass infrastructure failure.

This report seeks to set new standards for measuring validator and stake distribution across infrastructure components. With its findings, the report aims to provide actionable recommendations to support the operational decentralization of public PoS networks.

The Role of a Validator

To measure operational decentralization in a PoS system, one must first understand the core concepts of how validators function, as well as the factors that influence how and where they operate.

Validators serve as the backbone of a global computing platform, executing, validating, and recording transactions for a permissionless network of users. They reach consensus on the state of a chain of blocks, which represent a network’s history of transactions. To add a block to the chain, a threshold of validators must agree. This ensures Byzantine Fault Tolerance, where a network functions correctly even if some of nodes behave dishonestly or fail. However, the liveness, finality, and safety thresholds of each network ultimately vary depending on their specific consensus algorithms and implementations.

To contribute to network operations, validators lock up, or “stake,” the native asset of the network. This bond serves as a security measure that can be deducted (“slashed”) to discourage actions that are not in the network’s best interest. Such actions include poor performance, such as extended downtime, or malicious intent, such as attempting to produce invalid blocks. Validators are rewarded for their service with a portion of user fees and network inflation, depending on the network.

How Network Design Impacts Operational Decentralization

While there are many nuances that influence operational decentralization, some of the key factors are:

  • Hardware & Upfront Capital Requirements: The cost of hardware to host a node and the minimum stake amount required to (profitably) operate a validator.
  • Ongoing Operational Expenses: The cost of ongoing resource consumption and validator maintenance.
  • Built-in Stake Delegation Mechanisms: Whether tokenholders can contribute stake to existing validators.
  • Active Validator Set Caps: A network constraint that limits block production to validators with the highest stakes.

To illustrate, costly capital requirements and resource-intensive physical infrastructure can act as barriers to entry, reducing the diversity of operators but ensuring performant operations. Resource-intensive networks like Solana, with its high transaction processing capabilities enabled by Proof of History, and NEAR, which employs sharding, may require heavier infrastructure. As resource requirements increase, validator operators may favor the convenience of running nodes on hosted server solutions instead of procuring and managing dedicated infrastructure locally.

To broaden staking participation, most networks feature a delegated staking mechanism, allowing users to commit stake to existing validators instead of running their own. A larger amount of stake increases a validator’s likelihood of producing blocks and earning rewards for themselves and their delegators. However, the more stake a validator operator attracts, the larger the concentration of risk to the network should the validator fail. Networks like Cardano implement a pool saturation mechanism whereby validators that have attracted over 70 million ADA begin to lessen their potential rewards. This, however, does not stop operators from running multiple validators, often using the same infrastructure. While Ethereum doesn’t offer in-protocol delegated staking, this is similar to how operators by definition run multiple validators in increments of 32 ETH.

Further, networks with a capped active validator set limit active validators to those with the greatest stake weight. This is meant to ensure that only the most performant validators participate in network operations, but in practice, it often leads to a monopoly of active validators that have acquired social capital, making operating a validator unprofitable or unfeasible for others. To participate in the active validator set of the Cosmos Hub, for example, a validator must own or attract nearly 110,000 ATOM tokens, equivalent to $1.2 million at today’s prices.

Operational decentralization is thus highly nuanced; numerous factors contribute to who runs validators, where, and how. Some networks can be highly decentralized in one vertical and less so in others. Standardized metrics like the Nakamoto coefficient aid in aggregating the degree of operational decentralization across validator infrastructure and operational variables.

Beyond Nakamoto: Measuring Operational Decentralization


Infrastructure concentration, validator distribution, and stake distribution all play crucial roles in evaluating a network’s degree of operational decentralization. To establish a standardized base measurement, both stake and validator distribution is assessed across the following essential variables or "subsystems":

  • Node Software (Client)
  • Node Hosting Infrastructure
  • Geographic Location

In the context of PoS-based consensus algorithms, the requirement for finality, safety, and liveness typically refers to a percentage of the total stake, rather than the number of validators. Thus, the Nakamoto coefficient for each subsystem is measured by stake weight.

The Nakamoto coefficient defines an “operative threshold” as the percentage of failed stake required to compromise a network. In most PoS-based systems, when over 33.3% of stake is compromised, it impacts a network’s robustness and ability to tolerate faults. It will suffer instability and in most cases halt, thereby losing liveness and finality (except for Ethereum which maintains liveness up to 50% of faulty validators). It is important that a network operates consistently at over ⅔ of valid stake to ensure resilience against potential safety issues and maintain overall network stability. Thus, 33.3% is used as the standard PoS-network operative threshold.

Subsystems with a stake concentration exceeding 33.3% in a specific area have a Nakamoto coefficient of 1, as the failure of just one element can compromise the entire system. The higher the operational Nakamoto coefficient, the more resilient the network should be against potential infrastructure failures.

For Avalanche, Cardano, NEAR, Solana, and Aptos, the aggregated “operational Nakamoto coefficient” will be computed based on the staking distribution of each network across software clients, node hosting infrastructure, and geographic location. For other prominent networks, general observations will be made where applicable. The operational Nakamoto coefficient acts as a base measurement of the essential subsystems that represent operational decentralization, but additional analysis is required to account for other interconnected variables.

Qualitative Considerations

There are various additional variables that contribute to a network’s degree of operational and overall decentralization. Operational decentralization doesn’t exist in a vacuum. These variables include the distribution of token ownership, operator stake, and developer activity, among others.

For example, should a network halt and a validator client require maintenance in order to restart, operational decentralization would be defined by how many individuals are able (permissioned) and equipt (experienced) to resolve the problem. Their locations also contribute to operational decentralization in respect to the availability to respond in the event of a network compromise. The concentration of stake by operator then determines an individual entity's degree of influence over network operations. It can directly impact infrastructure concentration if dominant operators host large amounts of stake using the same infrastructure.

Additional essential variables and factors that contribute to operational decentralization are considered when analyzing the operational Nakamoto coefficient of each network.


For the present analysis, the methodology used offers a momentary snapshot of each network at a given point in time. While under normal circumstances validator infrastructure does not tend to change rapidly, it naturally shifts over time in response to in-protocol and exogenous factors. Additionally, one must also consider that sentry or private node architectures, the use of VPNs, or other IP mixing services by operators may obscure the true location of a validator. As such, the accuracy of the data on hosting location and geographic distribution would be affected.

Lastly, a network’s ability to bounce back following a critical infrastructure failure remains unmeasured. For networks with simpler operating requirements, for example, the migration of stake may be easier to execute in response to such an event.


Client Diversity

A validator client is the specific software that a node operator runs to contribute to network operations. It is effectively an application that connects an operator to the network. Multiple implementations can be developed, each with different architectures and features. A diverse client base can make a blockchain more accessible and user-friendly, catering to different preferences. This concept is similar to email services like Gmail and Outlook, which utilize different clients to connect to email protocols such as SMTP, IMAP, and POP3.

In a probabilistic world, the more diversity at the client level, the smaller the chance of a single code error or malicious attack affecting a significant number of validators. Additionally, different implementations offer varying levels of performance optimization, enabling node operators to choose a client that best suits their needs and hardware capabilities. This can lead to a more efficient and faster network overall.

Most networks have a single point of failure at the basic software level, with Avalanche, Cardano, the Cosmos Hub, Flow, NEAR, and Aptos only having one validator client implementation. Ethereum separates execution and consensus into two separate clients, which have a multitude of implementations developed and used each. Solana presently has two validator clients with a third in development. Despite the optionality for these networks, there is still substantial concentration.

For Ethereum, the majority of validators use Geth as an execution client, accounting for approximately 62% of execution clients. Lighthouse and Prysm are the predominant consensus clients, each representing around 38% of consensus clients, respectively. Historically, Prysm has been the dominant consensus client by a larger margin, so consensus client diversity has improved over time. Client concentration has caused network instability for PoS Ethereum, both prior to The Merge and more recently when Prysm and Teku contributed to a loss of beacon chain finality for several epochs over the span of two days. Ethereum is unique compared to most other networks, as its liveness persists despite the loss of finality at over 33.3% of compromised validators.

On the other hand, Solana has halted and experienced significant downtime on several occasions in part due to relying on its singular validator client at the time, developed by Solana Labs. In response, the Solana community forked the Solana Labs client to create the self-maintained Jito Labs client. Jump Crypto is also in the process of developing a third client, Firedancer. Notably, these clients will have a built-in failover mechanism, allowing validators to switch to another client if one is not responding. This feature should contribute to greater network reliability for Solana going forward.

Client Operational Nakamoto

The operational Nakamoto coefficient for client distribution among all networks analyzed is 1, indicating that over 33.3% of stake is operated on a single validator client for each network.

Hosting Distribution

Across nearly all networks analyzed, the vast majority of validators and stake are run on hosted server solutions. This can be attributed to the convenience of professionally managed infrastructure and the difficulty of self-hosting validators due to high infrastructure and operating requirements. Validator operators prioritize cost-efficiency by running nodes on hosted servers, where they can leverage economies of scale and lower operational costs.

While this approach is practical for many networks, concentrating nodes and stake on a limited number of infrastructure providers increases the susceptibility to single points of failure and heightens the risks of vendor lock-in. Presently, the dominant cloud providers AWS, OVH Cloud, Hetzner, and Google Cloud represent the majority of stake for most networks analyzed. Considering the current distribution of stake by providers when spinning up a validator can help mitigate the risk of concentration.

Of the networks analyzed, Cardano is the least reliant on hosted server solutions, presumably due to its lower upfront costs to run a validator making it more affordable for at-home validator operators. Notably, 452 validators representing 17.5% of Cardano validators are self-hosted connecting to the network through 183 distinct Internet Service Providers (ISPs). Although we did not identify a representative sample of Ethereum validators, we identified at least 1,679 self-hosted Ethereum validators distributed across more than 300 distinct ISPs.

In terms of stake distribution, Cardano’s 17.5% of self-hosted validators only account for roughly 6% of stake. Nevertheless, it is still the network with the greatest amount of self-hosted stake of the networks analyzed. It is also the network with the largest variety of non-dominant node hosting providers with 143 identified providers representing over 25% of stake.

Solana is the network with the lowest reliance on the dominant server solutions, with over 70% of stake hosted on non-dominant providers. This is in contrast to all other networks which rely on AWS, OVH Cloud, Hetzner, and Google Cloud for between ~55% and 80% of stake, respectively. This may be attributed to Solana's use of high-performance bare-metal servers, which require dedicated hardware to achieve higher throughput compared to traditional cloud solutions where resources are shared with other users. Notably, Solana nodes were once heavily hosted on Hetzner, but all active Solana validators migrated from the service following its announcement to ban crypto operations in August 2022. Hetzner is still a notable hosting solution for NEAR, Cardano, and Aptos, representing 16.5%, 7.7%, and 6.2% of stake, respectively, for these networks despite its hostility towards crypto.

The concentration of stake across Avalanche, Cardano, and NEAR is much more pronounced as compared to validator distribution. Aside from Avalanche and Aptos with over 40% of validators hosted on AWS, most other providers including AWS don’t exceed 25% of validators per network. Overall, AWS accounts for the greatest amount of stake for each of these networks, representing 67.4% of staked AVAX, 43.2% of staked APT, 35% of staked NEAR, and 30.1% of staked ADA. This is in contrast to Solana where AWS represents only 2.3% of validators and ~15% of stake. For Avalanche, the network’s use of AWS may in part be attributed to its partnership to promote blockchain adoption in enterprises, institutions, and governments using the server solution.

Hosting Operational Nakamoto

Avalanche, NEAR, and Aptos have an operational Nakamoto coefficient for hosting infrastructure of 1, considering that more than 33.3% of stake is hosted on AWS across each network. Cardano has a hosting Nakamoto coefficient of 2 by a small margin with 30.1% of stake hosted on AWS and with four other providers representing over 5% of stake, respectively. Lastly, Solana has a hosting Nakamoto coefficient of 3 as just over 33.3% of stake is hosted across TeraSwitch, AWS, and a third provider.

Geographic Distribution

A well-distributed network across multiple regions reduces the risk of localized issues, such as natural disasters or infrastructure failures. Moreover, it ensures that the network remains resistant to potential censorship or geopolitical pressures that may arise in specific jurisdictions.

Across the board, every network analyzed has a significant presence in Europe and North America. Asia is generally represented, but by a lesser degree, and Oceania, Africa, and South America represent less than 4% of validators and 9% of stake for all networks analyzed. For certain countries in underrepresented regions, this may represent a lack of physical infrastructure or ability to cover capital costs. It could also indicate a preference for hosting on servers in different locations to take advantage of better pricing and the network effects of running validators close to each other, especially for chains with significant MEV activity. Generally, the default hosting location, where most users are situated, tends to be the cheapest. It costs nearly three times more to host a validator on AWS in South Africa as opposed to the default U.S.-East data centers, for example.

Avalanche has the greatest presence across underrepresented regions at 8.2% of stake, followed by Cardano at 2.6% and Aptos at 0.6%. We identified no active stake for NEAR and only one active validator for Solana across these regions. Overall, Avalanche has the greatest stake distribution by continent and Solana the poorest.

In terms of countries, the United States and Germany are the geographic locations with the greatest number of validators and stake across all networks. The United States is the most represented country for validator hosting accounting for the greatest number of validators for Avalanche, Cardano, and Solana. NEAR and Aptos have slightly more validators hosted in Germany.

In terms of stake, Avalanche, NEAR, and Solana are more heavily represented in the United States, and NEAR and Aptos in Germany. What’s clear across all networks is that both validators and stake are concentrated in a handful of dominant countries. The vast majority of network operations occur in just 10-15 countries per network out of the 69 total countries we identified, most being the same countries across all networks analyzed.

Comparing the validator and stake distribution charts, there is considerable diversity of a small number of validators across underrepresented regions, especially for Avalanche and Cardano. Stake on the other hand is more concentrated across fewer countries. This indicates that underrepresented regions are more likely to host validators with lesser stakes.

The outlier is Aptos with a highly similar spread of validators and stake. This does not imply a more fair distribution, rather it accounts for Aptos’ planned infrastructure launch partners purposefully distributed across the globe, and that fewer smaller operators may feasibly run Aptos validators as the minimum staking requirement is over $8 million.

Cardano is the network with the greatest number of countries represented of the networks analyzed. However, it is also the network with the greatest stake concentration in the United States and Germany. Cardano validators are distributed across 64 countries, while Avalanche is distributed across 39, Solana 25, Aptos 23, and Near 22. We also identified Ethereum validators across at least 66 different countries.

There is considerable stake concentration in the United States and a considerable amount of that stake is hosted on AWS servers. The most common way to run a validator for Avalanche, Cardano, and NEAR is on an AWS data center located in the United States. While there is stake hosted on AWS servers across more than 15 countries, a notable portion is located in the United States and Ireland. Considering AWS’ present dominance in terms of total stake, greater distribution amongst its servers could help move the needle for geographic decentralization.

The majority of AWS-hosted stake is located in Amazon's U.S.-East data centers. These data centers are well-known for managing a substantial share of internet infrastructure and activity. Across most networks analyzed, this trend appears to be consistent, presumably due to the price benefits of being the default data center and the low-latency benefits of being closer to other network nodes for efficient operations.

Geographic Operational Nakamoto

To compute the operational Nakamoto coefficient for geographic distribution, stake distribution is measured across countries, as this better reflects potential political forces or concentrated infrastructure failures as compared to larger regions. Four out of the five networks have a geographic Nakamoto coefficient of 2, with stake highly concentrated across the United States and Germany. Aptos is narrowly differentiated with a geographic Nakamoto coefficient of 3, with a notable portion of stake in Korea in addition to the United States and Germany.

Final Results

To compute the aggregated operational Nakamoto coefficients for Avalanche, Cardano, NEAR, Solana, and Aptos, the operational variables are assigned a weighted average based on the severity of potential compromise.

  • Node Software (Client) : 0.4
  • Node Hosting Infrastructure 0.3
  • Geographic Location: 0.3

Client diversity is assigned a greater weight considering that concentration at the validator client level can be potentially catastrophic if a dominant validator client suffers a bug or malicious attack. On the other hand, if a country or node hosting provider becomes hostile towards crypto, the immediate risks to network safety are arguably less severe. Historically, miners and validators have been able to migrate to other solutions without causing major network instability, as evidenced by China banning Bitcoin mining in May 2021 and Hetzner banning crypto operations entirely in August 2022.

All networks analyzed have a relatively low aggregated operational Nakamoto coefficient with Avalanche and NEAR achieving a 1.3, Cardano and Aptos a 1.6, and Solana narrowly surpassing the other networks with a 1.9. On average, no single network has reached a state where at least two unrelated points of failure would be required to disrupt the network. The common theme across all networks is operator preference for similar infrastructure, often relying on early-developed clients, AWS, and representation in the United States and Germany. Another general observation is that stake is almost always more concentrated than validators suggesting a smaller number of organizations representing large amounts of stake.

Solana differs from the other networks analyzed in a few ways. First, despite receiving a client operational Nakamoto coefficient of 1, Solana will soon have three different implementations on mainnet. Validators will need to leverage these newer clients in order to improve the network’s client diversity. Second, although a significant portion of stake is hosted using infrastructure provider solutions, of the networks analyzed, Solana relies the least on the dominant providers, AWS, OVH Cloud, Hetzner, and Google Cloud, presumably due to its bare metal server requirements. The Solana Foundation Delegation Program may also contribute to this, as validators are only eligible if they choose a data center that hosts 10% or fewer validators. Finally, of the networks analyzed, Solana is second only to Cardano in terms of the number of countries represented. However, Solana still has room to improve its international presence as only one active validator was identified across Oceania, South America, and Africa.

Recommendations to Support Safety and Decentralization

The operational Nakamoto coefficient helps us understand the present state of network vulnerabilities, but it fails to account for a network’s ever-changing ecosystem and development. It's ultimately up to a network’s stakeholders to determine the network's ability to withstand potential infrastructure failures and exogenous threats. Operational decentralization is crucial to ensure the long-term resilience of a network, and all types of stakeholders can contribute to improving and incentivizing the distribution of validators and stake.

Validator Operators

Validator operators should aim to self-host nodes when feasible, switch to a non-dominant server solution, or at the very least when economically feasible run validators on data centers in different geographical locations with the same hosting provider. Doing so will help mitigate risks associated with political or corporate hostility, infrastructure failure, and natural disasters impacting a specific region.

Additionally, operators may consider enhancing infrastructure redundancy using Distributed Validator Technology (DVT). By operating a validator across multiple setups in a fault-tolerant manner, DVT can improve the security and reliability of a network. It increases the number of operators beyond current limits, making it more resistant to technical and social failures and attacks.

As decentralized hosting solutions such as Ankr, Akash, and Pocket become more stable, operators may consider using these platforms. These projects allow individuals and existing infrastructure providers to monetize unused compute. As open marketplaces, their rates are set by market demand as opposed to business-driven decisions. A growing supply side could contribute to lower-cost access to servers in underrepresented regions for validator operators. Considering that providers can permissionlessly join these networks, it will also be important to monitor where compute is coming from to avoid concentration all the same.


Delegators are encouraged to consider multiple operational factors when choosing validators rather than simply yield or stake weight. By supporting validators that contribute to a network's decentralization, delegators can play a role in its long-term viability.

Ecosystem Builders, Researchers, and Infrastructure Providers

Networks will benefit from consistent monitoring of operational decentralization. Persistent visuals like live dashboards can help keep a community informed on a network’s health and areas of improvement. By raising awareness and educating stakeholders on the benefits of operational decentralization, an ecosystem can foster a culture that values and supports a network’s long-term resilience and success. Doing so may incentivize the development of new and redundant infrastructure, like additional validator client implementations, to diversify network risk. Builders are encouraged to continuously improve upon existing protocols and infrastructure by identifying and resolving barriers that may hinder broader network participation.

Parting Thoughts

Operational decentralization can be measured by the distribution of validators and stake across software components, node hosting infrastructure, and geographic location. It ensures that a network is resilient against buggy code, political and corporate hostility, and physical infrastructure failures. In order for a network to be anti-fragile, it must exhibit operational decentralization. Across all networks analyzed (Avalanche, Cardano, NEAR, Solana, and Aptos), there is substantial room for improvement to promote long-term reliability and resilience.

Ongoing monitoring and standardized metrics can help us understand a network’s current state of operational decentralization, but it is ultimately up to a network’s community to build systems that prevent single points of failure. Developing incentives to distribute the power and operations of a public global computing platform may be one of the least appreciated, yet crucial tasks of our time. It will be the key to ensuring the long-term viability of permissionless programmable blockchains. Without sufficient decentralization, a network is susceptible to unforeseen vulnerabilities or the will of bad actors. Thus, success ultimately depends on improving existing systems and future-proofing against potential threats.

On June 5, 2023, the U.S. Securities & Exchange Commission stated that ATOM is a "crypto asset security." Note: (1) Messari does not provide financial or trading advice - our services are for informational purposes only; and (2) Messari's services are impersonal - do your own due diligence. Please refer to our Terms of Use for more info.

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Stephanie graduated with a BSc in Psychology and began her career in startup operations and consultancy. Prior to joining Messari, she specialized in crypto technical communications. She was an early member of Messari's Diligence Team with ownership over product design, project management, and research quality. She produced and oversaw professional cryptoasset due diligence reports and helped scale the team to 20+ members. On the Enterprise Research team, Stephanie is best known for her work on validator decentralization, scaling, and the modular blockchain landscape.

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About the author

Stephanie graduated with a BSc in Psychology and began her career in startup operations and consultancy. Prior to joining Messari, she specialized in crypto technical communications. She was an early member of Messari's Diligence Team with ownership over product design, project management, and research quality. She produced and oversaw professional cryptoasset due diligence reports and helped scale the team to 20+ members. On the Enterprise Research team, Stephanie is best known for her work on validator decentralization, scaling, and the modular blockchain landscape.