Faqir-Rhazoui et al.
A Comparative Analysis of the Platforms for
Decentralized Autonomous Organizations in the
, Javier Arroyo1,2*
and Samer Hassan1,2,3
2Institute of Knowledge
Complutense de Madrid, Spain
Full list of author information is
available at the end of the article
Blockchain technology has enabled a new kind of distributed systems. Beyond its
early applications in Finance, it has also allowed the emergence of novel new
ways of governance and coordination. The most relevant of these are the
so-called Decentralized Autonomous Organizations (DAOs). DAOs typically
implement decision-making systems to make it possible for their online
community to reach agreements. As a result of these agreements, the DAO
operates automatically by executing the appropriate portion of code on the
blockchain network (e.g., hire people, delivers payments, invests in ﬁnancial
products, etc). In the last few years, several platforms such as Aragon, DAOstack
and DAOhaus, have emerged to facilitate the creation of DAOs. As a result,
hundreds of these new organizations have appeared, with their communities
interacting mediated by blockchain. However, the literature has yet to
appropriately explore empirically this phenomena. In this paper, we aim to shed
light on the current state of the DAO ecosystem. We review the three main
platforms nowadays (Aragon, DAOstack, DAOhaus) which facilitate the creation
and management of DAOs. Thus, we introduce their main diﬀerences, and
compare them using quantitative metrics. For such comparison, we retrieve data
from both the main Ethereum network (mainnet) and a parallel Ethereum
network (xDai ). We analyze data from 72,320 users and 2,353 DAO communities
in order to study the three ecosystems across four dimensions: growth, activity,
voting system and funds. Our results show that there are notable diﬀerences
among the DAO platforms in terms of growth and activity, and also in terms of
voting results. Still, we consider that our work is only a ﬁrst step and that further
research is needed to better understand these communities, and evaluate their
level of accomplishment in reaching decentralized governance.
Keywords: Blockchain; DAO; Decentralized Autonomous Organization;
Distributed systems; Ethereum; xDai; Governance; Online Community;
Quantitative Research; Voting
Blockchain technology has enabled the emergence of a new kind of distributed sys-
tem. It provides mechanisms in which decentralized transactions and operations are
secure, without the need to trust a mediating third-party as it is common in server-
Faqir-Rhazoui et al. Page 2 of 21
centric centralized systems [1,2]. Due to its origins linked to cryptocurrencies,
blockchain has been mostly applied to ﬁnancial applications. However, in recent
years it is increasingly applied to other ﬁelds . A specially interesting applica-
tion is the emergence of new forms of decentralized governance which are mediated
by a blockchain. These blockchain-enabled organizations are known as Decentral-
ized Autonomous Organizations (DAOs), and take beneﬁt of the aﬀordances of
blockchain infrastructure to enable e.g. transparent decision processes, formalized
rules, automation of certain operations, or alleged decentralization of power .
The blockchain ﬁeld has attracted a broad range of experts and enthusiasts ,
currently with a majority belonging to the ﬁelds of Computing and Finance, and
focused on new ﬁnancial applications, e.g. the booming DeFi ﬁeld. Some of these
projects chose to rely on DAOs for their governance. Thus, the project’s online com-
munity may use the DAO embedded decision-making mechanisms to vote proposals
and organize their tasks. In order to meet this demand, several platforms have re-
cently appeared to provide DAOs as-a-service, that is, deploying DAOs in a public
blockchain and facilitating community interactions through them. These platforms
have reduced the technical knowledge required to operate through a DAO, and thus
thousands of people are now interacting within hundreds of DAO communities.
This new phenomenon can be followed on the Internet, particularly, through ‘grey
literature’ including technical reports, blogs, social media posts, etc. Research liter-
ature has covered it mostly with theoretical works [6,7,4], although some empirical
works have been slowly emerging. We can highlight qualitative research such as an
ethnographic account of the ﬁrst popular DAO , a comprehensive study under-
standing the imaginaries behind DAOs , or a content analysis of grey literature on
three popular DAOs to understand how are they governed . In  we can ﬁnd
an overview of DAOs, DAO platforms and DAO visualization tools, and a analysis
of the evolution of one popular DAO looking at the time series of metrics such as
the number of users and actions performed in the DAO. Recently, a study analyzed
how aﬀected on DAO activities the increases in the costs of using the Ethereum
blockhain that took place in the second half of 2020 .
In this paper, we will contribute to the growing stream on literature on the topic
by providing a statistical analysis of three of the main DAO platforms (Aragon,
DAOstack and DAOhaus) in terms of growth, activity, voting system and funds.
Revisar cuando reestructuraci´on hecha The article proceeds as follows: Sec-
tion 2introduces the main concepts related to blockchain, Ethereum and DAOs.
In Section 3, we review the three DAO platforms that we are going to analyze in
this work. Section 4compares the three main DAO platforms in terms of growth,
activity, voting system and funds. Section 5proceeds to discuss the main ﬁndings,
while section 6ﬁnishes with some concluding remarks, including the limitations of
Note that trust is displaced to other components, like the need to trust the algo-
rithms and cryptography used, or the developers creating such algorithms.
DeFi means Decentralized Finance, typically blockchain-enabled
Faqir-Rhazoui et al. Page 3 of 21
2 Blockchain and DAOs: The ﬁeld in a nutshell
Blockchain is a distributed ledger, which can be understood as a distributed
append-only database with a synchronization mechanism. Like the Internet, a pub-
lic blockchain is an open infrastructure, not owned or controlled by one central
authority. Generally, the ledger database is replicated in each of the network nodes,
and thus can be viewed by all its users [13,2]. Thus, we consider transactions and
operations in public blockchains to be transparent, since they can be tracked and
seen by any participant in the network.
The ledger is a sequence of blocks (hence block-chain) that contains a set of
transactions already performed. Each block points to the previous block in the
ledger, forming a chain. When a user wants to add a new transaction to the ledger,
the transaction data is veriﬁed by the so-called miners. If there is consensus on
the new block validity, it is added to the chain in a decentralized process [1,2].
Furthermore, the blockchain grants immutability of its past records: nobody can
delete and alter the data of the block placed within the validated chain .
The ﬁrst implementation of the blockchain technology was Bitcoin, which is a
”crypto-currency”, i.e. decentralized digital currency validated through cryptogra-
phy . After that, thousands of new cryptocurrencies have emerged with their
own features .
The second wave of blockchain was prompted by the advent of Ethereum in 2013
. Ethereum provides a distributed computing platform and a programming lan-
guage, Solidity . Solidity addressed several limitations of the Bitcoin ’s scripting
language, like the lack of Turing-completeness . This has enabled multiple types
of decentralized applications (Dapps) and the so called ”smart contracts”, compu-
tational agreements between parties which may be self-executed and self-enforced.
Dapps have been applied in many ﬁelds [20,21], specially on Finance. Thus,
we may highlight examples such as banking services  or cryptocurrency pay-
ments , leading to the surge of Decentralized Finance (DeFi), a form of ﬁnance
that does not rely on central ﬁnancial intermediaries used to get crypto-savings,
crypto-loans, or trade with them . Beyond Finance, we may mention IoT, using
blockchain as a common communication layer , or supply chains, facilitating
traceability and desintermediation .
In the context of this article, the most relevant ﬁeld where blockchain and smart
contracts have had an impact is in enabling new forms of decentralized governance,
such as Decentralized Autonomous Organizations (DAOs), where decision-making
is distributed or delegated away from a central authority.
2.2 Decentralized Autonomous Organizations
A DAO is a blockchain-based system that enables people to coordinate and self-
govern themselves mediated by a set of self-executing rules deployed on a public
In cryptocurrencies, each block holds transactions, i.e. movements of cryptocur-
rency between accounts. In other more general applications such as Ethereum-based
apps (and DAOs), blocks contain operations, akin to typical instructions in a com-
puter program, that need to be executed.
Faqir-Rhazoui et al. Page 4 of 21
blockchain, and whose governance is decentralized, that is, independent from central
DAOs are organizations in the sense that they mediate the interactions of a group
of people, typically an open community that joins as members. In some DAOs, mem-
bers are token holders of a certain token that enables DAO participation, similar
to corporation shares.
DAOs are considered autonomous because, unless its code explicitly says so, they
are independent from their creators. Their operations follow the rules embedded in
its code, together with the (human) governance of its members. Moreover, being
deployed on a public blockchain, they are censorship-resistant, since there is no
central controller that may turn oﬀ the DAO and its provided service. Thus, as long
as there are members willing to execute their code, DAOs will continue operating,
e.g. providing services, purchasing/selling resources or hiring people.
DAOs are considered decentralized ﬁrst because of relying on a server-less decen-
tralized infrastructure (a public blockchain). Second, because they rely on certain
decentralized governance mechanisms, so the decision-making process relies on the
collective agreement of its members. This process typically relies on some form of
voting, in which the DAO members can participate. Note that such decisions may
refer to e.g. the allocation of the DAO resources (e.g. funding projects or payments
to members), but also may refer to changes in the DAO code. That is, upon the
agreement of its members, a DAO may be updated to operate diﬀerently, with a
new set of encoded rules. This may be critical to ﬁx a bug in the code, but also
enables it to adapt to community needs and demands .
DAOs are deeply related with Ethereum, the most important general-purpose
public blockchain [29,30]. In Ethereum, every operation performed implies a cost,
i.e. a commission to be paid by the user, for the miners to perform the requested
operation. In practice, validating and performing those operations require a certain
amount of computational work performed by miners. The amount of computa-
tion required by an operation is named gas, and it is paid in cryptocurrency; in
Ethereum, with its token Ether. For the user approach, gas ultimately translates
into money and the amount of gas depends on the size and type of each operation.
Hence, the Ethereum blockchain can be seen as a costly and secure distributed
DAO activity is typically recorded into the blockchain. This fact conditions the
type of data that a DAO stores in the blockchain, since blockchains are not designed
for massive storage. For example, DAO members typically use other complementary
oﬀ-chain tools for their communication, such as forums like DAOtalk, since DAO
software does not usually oﬀers interactive communication tools. Consequently, this
technological aspect surely aﬀects the behavior of DAO communities and could make
it substantially diﬀerent from other online communities. See for example how the
increases in the Ethereum cost aﬀected the DAO activity in 2020 .
User actions are recorded as speciﬁc software operations into the blockchain. Op-
erations are named ”transactions”, given the origin of the blockchain as a ledger for
Faqir-Rhazoui et al. Page 5 of 21
The ﬁrst popular DAO implementation received the (confusing) name of TheDAO,
launching in April 2016 within the Ethereum blockchain. TheDAO was a sort of
hedge fund, in which contributors could directly vote proposed projects. It became
the most successful investment crowdfunding in history at that time, raising $150M,
and concentrating the 14% of all ether tokens issued at the time. In June 2016,
due to an error in TheDAO code, an attacker stole $50M . The impact of the
event stirred debate, resulting in the Ethereum community deciding to ”hard fork”
the Ethereum blockchain and return the stolen funds to the original TheDAO
investors. However, the concept of immutability of the ledger past records was
damaged due to this event, and part of the community continued operating under
the old rules, in a blockchain named Ethereum Classic (in which the funds were
stolen and not reverted) .
This event was somehow traumatic for the blockchain community, and had mul-
tiple implications. Still, the endeavor of creating decentralized organizations to op-
erate in the blockchain persisted. However, it was widely recognized that the com-
plexities of blockchain programming make the task of creating a DAO from scratch
a highly risky project, even for specialists . As a result, new solutions emerged to
facilitate templates and tools to dramatically reduce both the risks and the technical
knowledge required to deploy DAOs.
3 DAOs enabled by a platform
The platforms that provide DAO deployment as-a-service enable users to create
their own DAO using a template that typically can be customized. The main plat-
forms are Aragon,DAOstack,DAOhaus and Colony . All are free/open-source
projects in development, in diﬀerent maturity stages.
Colony will not be covered in this article due to its early development stage, with,
to the best of our knowledge, just two DAOs deployed in the platform so far, and
no APIs to retrieve the data. It is worth mentioning that Colony DAOs break with
the typical proposal-driven schema of functioning, where each action of the DAO
must be voted. In Colony, DAOs are task-driven, which means tasks are published,
and members accept them for a payout . Thus, the mechanics of task-driven
vs proposal-driven DAOs may add complexities when pursuing comparisons in the
Aragon  is by far the largest DAO platform, with currently 1700 DAOs collectively
managing $900M. Aragon aims to extend the use of DAOs as a free and open-source
technology to allow the creation and management of decentralized organizations 
A fork occurs when a blockchain diverges into two paths forward, due to a change
of the encoded rules: a blockchain path follows the old rules while the new path
follows the new rules. Typically, only one path is considered the ”valid” path; how-
ever, both can still be used. A ”soft fork” maintains some sort of compatibility
(backwards-compatible) and thus just requires a majority of the network to agree
on it. A ”hard fork” is a more radical change, which requires all the network to
follow the new rules, making the blocks following the old rules invalid.
Faqir-Rhazoui et al. Page 6 of 21
under diﬀerent forms including companies, cooperatives, nonproﬁts, or open-source
Aragon provides a static template to make your own DAO, but it also allows
you to create a customized one. Customization is enabled through ”apps” (sets of
smart contracts), which can be installed or removed from DAOs via voting. The
purpose of apps varies widely, including: a Finance app to allocate the DAO’s
funds, an Agent app to interact with other Ethereum smart contracts, a Token
app to manage the membership, or a Vote app used as a decision-making system
[35,36]. In addition, Aragon provides a SDK  to create and deploy smart contracts,
apps, and organization templates (i.e. a set of predeﬁned apps and a customized
conﬁguration for the template purpose).
In this article, we will focus on the Vote app, since voting is the main action in
most DAOs. Furthermore, the default Aragon voting app is one of the most used
apps in Aragon DAOs. Its decision-making system works as follows. The app
deﬁnes two conditions that any voting must fulﬁll to be approved:
1 The majority required: From all cast votes, the percentage of positive cast
votes must be greater than or equals to the required percentage of support.
2 The minimum participation required: The minimum acceptance quorum pa-
rameter states the minimum percentage of votes cast from all possible votes
in the DAO.
Note both parameters may be changed via voting.
However, voting in Aragon goes beyond the Voting app, since there are other
apps for voting and decision-making. For instance, there is an app, currently in
development, which implements the decision-making system of the DAOstack plat-
form, Holographic Consensus  (explained below). Another notable example is
the Dandelion voting app which implements the decision-making system of Moloch
implemented in the DAOhaus platform (explained below). Furthermore, the Dot-
Voting app adds the possibility to vote with more than two answers instead of the
typical binary answer (yes/no).
Another ambitious decision-making system implemented as an Aragon app is
Conviction Voting (CV) , which derives from the work on Social Sensor Fusion
. CV aims to represent the aggregated preference of individuals on proposals,
expressed continuously, and not just in a punctual ”voting” window. Thus, individ-
uals express their preference (vote) on certain proposals, and the longer they keep
the preference on a certain proposal, the longer their ”conviction” on such proposal
will grow. Individuals may change their preference (vote) at any given time. In
DAOs, members represent their preference allocating their limited tokens to one or
more proposals. The longer they keep them there, the more conviction the proposal
will accumulate and the higher the chances it will reach the threshold to pass. Note
such threshold is dynamic, and dependent on the DAO treasury funds [38,40]. CV
was tested through simulations, and Aragon makes it possible to deploy it in a
real environment .
See ”Installed apps” chart in: http://dao-analyzer.science/aragon
Faqir-Rhazoui et al. Page 7 of 21
There are other ways to change the decision-making processes in Aragon, changing
not just the voting app, but how the organization works. For instance, the Commit-
tee template [42,43], which facilitates creating diﬀerent committees (sub-groups)
within a DAO. The DAO community may delegate certain decisions or tasks to those
sub-groups, which may operate autonomously. The idea is to facilitate scalability
in decision-making processes, reducing the number of people involved.
DAOstack is a platform that aims to tackle the governance scalability problem.
Matan Field, co-founder of DAOstack, states that the bigger a DAO is, the harder
it is to manage it , which mimics the classical issues of governance in groups. In
principle, we can specify DAOs where all decisions are taken by voting and a 51%
majority is expected for a proposal to pass. Such model is feasible for small DAO
communities, where the number of proposals does not escalate further than what
the number of members can study and decide on. However, the higher the number of
members, and thus the number of proposals, the more proposals need to be reviewed
by each member in order to participate. A naive solution to this matter could be
to reduce the required quorum (i.e., pass proposals with a relative majority), but
this introduces new ﬂaws. For example, an attacker could spam requesting the DAO
funds, i.e. send plenty of proposals in a small time frame. Thus, it may overwhelm
the community, making it easier to get the funds using a lower quorum. Thus,
increasing the number of DAO members may reduce the DAO resilience.
To face this problem, DAOstack proposes the Holographic Consensus (HC)
decision-making system [45,46]. In HC, DAO members send and vote for proposals,
which pass by absolute majority (51%). However, there is an alternative method for
passing proposals. The idea is to create a prediction market as a middle layer: com-
munity members may ”bet” if a certain proposal will pass or not pass, staking
a certain amount of their tokens (cryptocurrencies). If a proposal receives enough
stakes, reaching a threshold, it may skip the requirement of absolute majority vot-
ing and be passed with a relative majority. Afterwards, stakers may resolve their
bets, depending if they guessed correctly (earning tokens) or not (losing tokens). If
HC works correctly, it will act as a ﬁlter for the community, which may focus on the
proposals that attract attention from stakers. Stakers thus ﬁlter out bad proposals,
enabling a better scalability for large DAO communities. And the DAO may rely
on stakers since they are incentivized to be aligned with the DAO overall opinions,
since they need to guess if the voted proposals will eventually pass or be rejected.
Preliminary research shows that HC works as intended .
DAOhaus is a platform which enables the creation of DAOs mimicking the be-
havior of the Moloch DAO. Moloch DAO was a grassroots response to coordination
problems in funding Ethereum 2 and other community grants.
Scalability in terms of growing a DAO membership and its operations (i.e., votes,
Although other people, external to the DAO, may be allowed to stake as well,
depending on the implementation.
Faqir-Rhazoui et al. Page 8 of 21
DAOhaus DAOs implement a straightforward voting system, which is basically
a non-quorum system, where always a relative majority is enough to approve a
proposal. This way to proceed simpliﬁes development and testing processes  of
their voting system. A key aspect from these DAOs is the ”rage quit” mechanism
that makes it possible to exit a DAO with your portion of the DAO resources if
you do not agree with the result of a voting. After the voting outcome is achieved,
there is a ’grace’ period, when DAO members can quit if they do not agree with
the outcome. Additionally, if there are more than ≈30% of rage quits, then the
vote will be automatically rejected . The idea is similar to the right to fork in
free/open source communities: just the fear of fork makes communities more prone
to consensus, promoting sustainability and facilitating governance .
This voting system has two main attributes to consider: shares and tributes.
Shares refer to an amount of resources that each DAO member has, independently
of the cryptocurrencies the DAO has. And tributes refer to an amount of shares
the proposal applicant pays to the DAO. Thus, in a proposal, the applicant can
request shares and/or pay a tribute, which helps deﬁne the kind of proposal. For
instance, if a proposal has just a request of shares, it typically belongs to a project
proposal (performing a certain task for the community in exchange of the shares).
If a proposal has just tributes, it represents a donation to the DAO .
Nowadays, DAOhaus DAOs split into two groups. Those created in the early stage
of DAOhaus (Moloch v1 DAOs), and those created with new features of Moloch v2.
Some changes introduced in Moloch v2, include the ability to expel a DAO member
from the community. It also includes some changes like the ability to send proposals
by non-DAO members, or some changes related to its voting system, described below
Moloch v2 introduces the sponsorship, which slightly changes the voting system.
Now, when a proposal is sent, it requires the sponsorship of a DAO member. Such
sponsorship is performed when any DAO member makes a deposit conﬁrming that
it is trustful. All the proposals need to be sponsored before moving on to the reg-
ular queue, where the voting starts . When the voting ends, independently of
the outcome, the sponsor will get a portion of her deposit back. In this way they
intend to avoid attackers to spam plenty of proposals to exploit the non-quorum
characteristic of the Moloch voting system.
4 Quantitative comparison of the three main DAO platforms
We will compare the three main DAO platforms introduced in the previous section,
i.e., Aragon, DAOstack, and DAOhaus, using two DAO visualization tools: DAO-
Analyzer  for the adoption and activity metrics, and DeepDAO for the
fund statistics. The data used in this comparison covers, from the start of activity
of each platform to November 30, 2020. The data collection process is described
in the Appendix.
The comparison will tackle four dimensions that will help us to better under-
stand the DAO phenomenon: growth, activity, use of the voting system, and the
The Aragon platform started in October 2018, DAOhaus in February 2019, and
the DAOstack platform in April 2019.
Faqir-Rhazoui et al. Page 9 of 21
funds owned by DAOs. Growth statistics will help us to analyze the adoption of
DAOs: how many of them are and, how many people is involved. Activity metrics
will help us to determine how many DAOs are operative and how many users are
involved, because it may happen that DAOs are abandoned, as happens in other
online projects, such as wikis , or that some members may abandon the project
or barely participate. Since one of the most prominent features of DAOs are their
voting systems, we will also analyze them to see how they are used by means of
participation statistics, and percentages of proposals approved and positives votes.
Finally, we will have a look at the cryptotokens used by DAOs, particularly, we will
analyze their adoption and the funds managed for the richest DAOs.
It is important to remark that in our analysis, we will include both the DAOs
deployed in the Ethereum mainnet and the DAOs deployed in the xDai network.
The use of the Ethereum mainnet implies the payment of a fee (the gas cost of
the computation), and this fee is tied to the network’s use. In mid-2020, the use
of the Ethereum mainnet spiked, increasing dramatically the fees to process any
transaction (e.g. voting, DAO creation). As a result, DAO platforms searched for
alternatives to avoid such expensive prices. One of the most successful solutions was
the case of the xDai network. xDai is a blockchain designed for fast and inexpensive
transactions. It has a bridge with Ethereum mainnet (it is a sidechain) facilitating
the move of tokens from each other. Table 1, shows the cost to create a DAO
(summon) and to vote in the DAOhaus ecosystem, both in the Ethereum mainnet
and xDai. As the table shows, the mainnet is orders of magnitude more expensive
(and slow) than xDai. Note xDai, in turn, is less decentralized than Ethereum and
it is dependant on it.
Summon Vote Speed
mainnet $80 $5 5 tps
xDai $0.01 $0.001 70 tps
Table 1: Comparison of the prices of two DAOhaus operations and the average
speed, in both the Ethereum mainnet and xDAI networks in October 2020.
Before analyzing the four aforementioned dimensions, we will examine the use of
xDai and mainnet. Table 2shows the number of DAOs, users, and proposals per
platform and by network (mainnet and xDai). Aragon is by far the most important
platform in terms of DAOs, users and proposals. Regarding the number of DAOs
, DAOhaus comes second with more than 200 DAOs in mainnet and xDai, while
DAOstack is more populated than DAOhaus.
Aragon DAOhaus DAOstack
mainnet xDai mainnet xDai mainnet xDai
Number of DAOs 1,744 325 169 56 22 37
Number of Users 41,021 17,660 1,180 265 6,645 5,549
Number of Proposals 10,246 - 1,668 554 1,954 463
Table 2: Comparison of the three DAO ecosystems in terms of their number of
DAOs, users and proposals
Faqir-Rhazoui et al. Page 10 of 21
Regarding xDai adoption, in Aragon xDai DAOs represent 15.71% of the total
number of DAOs, while xDai users are 30.1% of the total. Aragon started using
xDai from July 2020. Note there is no available data about the proposals in xDai
In the case of DAOhaus, xDai DAOs are 24.89% from its total, while xDai users
are 18.34%, and the xDai proposals represent 24.93% of all proposals. Similar to
Aragon, DAOhaus uses xDai since July 2020.
Finally, in DAOstack the DAOs in xDai are 62.71% from its total, xDai users are
45.51%, and the number of xDai proposals are 19.16% from the total. The adoption
platforms. This may be explained by the fact that DAOstack started using xDai
earlier, since February 2020.
These ﬁgures illustrate the importance of the xDai network in the DAO platforms.
Hence, we will include xDAI in the comparisons in the following sections.
4.1 Growth over time
Given DAOs are an early ﬁeld relying on a novel technology, growth by early
adopters is critical for the future mainstream adoption. In fact, observing the growth
over time of an online community, we may observe e.g. if the growth of the platform
is healthy, or if it has stalled.
For the comparison of the platforms concerning growth over time, we will use
two metrics: the number of DAOs and the number of users. However, the times-
tamp of the DAO creation currently is not available for DAOstack DAOs, while the
timestamp of the user registration is not available for Aragon DAOs.
Figure 1: Total number of DAOs of Aragon and DAOhaus, diﬀerentiating
DAOs just in mainnet, and in both mainnet and xDai networks.
Faqir-Rhazoui et al. Page 11 of 21
Figure 1shows the evolution in the number of DAOs in Aragon and DAOhaus.
It shows that the growth of Aragon in the Ethereum mainnet was constant and it
even seems boosted by the xDai DAOs. On the other hand, the growth exhibited
by DAOhaus was more modest. The new DAOs created in xDai can be brand new
DAOs or DAOs that ’migrated’ from mainnet DAOs; however, the DAO migration
implies the creation of a new DAO with a diﬀerent id and account. Thus, our data
does not reﬂect when a xDai DAO is new or the result of a migration process.
Figure 2: Total number of users in DAOstack and DAOhaus, diﬀerentiating
users just in mainnet, and in both mainnet and xDai networks.
Figure 2shows the evolution of the number of users in the DAOs of DAOstack
and DAOhaus. It shows the number of users of DAOhaus and DAOstack. We
can see two steps in the DAOstack series. The ﬁrst step took place in June 2019
when 5,397 new users joined the project and almost 5,000 joined to the same DAO
. In February 2020, the second step (2,822 new users) was due to the launch of
the xDai network. That is clear in the ﬁgure, since the gap between the dark and
light green lines corresponds to the new xDai DAOs. Besides these two remarkable
increases, the user growth was steadier and much more modest.
By contrast, the user growth in DAOhaus had no signiﬁcant increases. Still, the
growth is more pronounced since April 2020, even if the scale of the plot makes
diﬃcult to appreciate it. In addition, as we previously explained in Section 3.3,
DAOhaus users can easily quit from a DAO (rage-quitting). Thus, the number of
users could be higher, because 311 people used the ’rage quit’ option during the
period analyzed and such option is not available in other platforms, where users
just abandon their accounts.
Due to the lack of a DAO creation timestamp in the API, DAOstack is omitted
in this ﬁgure.
Due to the lack of a user creation timestamp in the Aragon API, that DAO
platform is omitted in this ﬁgure.
Faqir-Rhazoui et al. Page 12 of 21
4.2 Activity over time
Growth is highly relevant, and yet it typically does not provide the full picture.
Similarly to other online communities or online platforms, it is diﬀerent to mention
the number of users vs the number of active users. Thus, in this context it is highly
relevant to also diﬀerentiate between DAOs and active DAOs, or users and active
users. This will allow us to better compare the platforms use, focusing on the users
and DAOs that operate in a certain period.
First, we need to deﬁne what means ‘active’ for both a DAO and a user. We will
follow the deﬁnition in  that considers that a DAO or a user were active in
a given month if at least they performed an action in that month. The available
actions to be performed depend on the platform. For DAOstack we will consider
the following activities as actions: to create a proposal, vote a proposal, and stake
in a proposal. In the case of DAOhaus, we will consider: to create a proposal, vote
a proposal, and quit a DAO. Finally, due to the customization of Aragon DAOs,
it is diﬃcult to homogenize the actions because of the multiple possible apps to
install. So, in the case of Aragon we will just consider data, ﬁrst from the basic
Voting app (create a proposal and cast a vote), and second from the Transaction
app, used for donations or payments, where we will consider transactions as actions.
However, for Aragon xDai actions, we can only consider data from the Transaction
app because the API does not provide data from the Voting app in xDai. The
approach followed may result in a highly conservative estimation in Aragon, where
DAOs can be customized with diﬀerent apps, and, hence, exhibit other types of
Figure 3shows that Aragon has the highest number of active DAOs, even consid-
ering the limitations mentioned, so the number of active DAOs should be higher.
Still the number seems small (around 100) considering the number of DAOs reg-
istered (over 2,000 considering both mainnet and xDai). We can also observe a
negative trend since May 2020.
DAOhaus apparently follows an increasing trend that also has beneﬁted from the
xDai network, greatly increasing its active DAOs to forty per month. Finally, the
number of active DAOs in DAOstack is more modest (around 10) and xDai did not
mean a sensible increase. Still, it seems to remain stable during the last year and a
half except for an activity surge in February 2020 due to the adoption of xDai.
Figure 4shows the activity in terms of active users. Again Aragon has substan-
tially higher number than the other two platforms. The number of active users is
increasing, but the last three months show a volatile behavior. There is a peak in
October 2020, which we believe it could be due to a migration to xDai.
While the number of active users in DAOhaus shows ﬁrst an increase and then a
decrease in the last few months, in DAOstack they have been decreasing since July
2019. According to this metric, the impact of xDai has not boosted the activity of
4.3 Voting system
Arguably the most critical feature of DAOs is that they enable new models of gover-
nance. Internal processes and specially the decision-making processes typically rely
Proposals are known as ”votes” in Aragon
Faqir-Rhazoui et al. Page 13 of 21
Figure 3: Number of active DAOs in Aragon, DAOhaus and DAOstack,
diﬀerentiating DAOs just in mainnet, and in both mainnet and xDai networks.
Figure 4: Number of active users of the three platforms, diﬀerentiating users
just in mainnet, and in both mainnet and xDai networks.
on new instruments like tokens. The diﬀerent decision-making methods explained
in Section 3such as holographic consensus, conviction voting, or dandelion voting,
reﬂect a nascent diversity of options that are being both theoretically explored and
experimented in practice within this ﬁeld. Thus, studying the DAO voting systems
renders crucial to understand the diﬀerences in the use of DAOs across platforms.
Faqir-Rhazoui et al. Page 14 of 21
DAOstack DAOhaus Aragon
total mainnet xDai total mainnet xDai total mainnet xDai
Users who vote 4.5% 6.3% 2.1% 38.37% 39.5% 24.32% - 6.18% -
Votes per voter 4.6 4.64 3.64 4.26 3.96 7.28 - 4.08 -
Approved prop. 74% 74% 76% 92% 93% 87% - 81% -
Positive votes 86% 86% 95% 91% 90% 98% - 94% -
Table 3: Voting statistics by platform and network
The three platforms considered are proposal driven, but each of them has their
own governance and voting system, as explained in Section 3. In the case of Aragon,
DAOs may have multiple voting systems. However, for the sake of simplicity, we
just retrieved data from the standard voting app.
In order to compare these decision-making systems, we have chosen four metrics:
•The percentage of users who vote, which may enable us to observe the en-
gagement of the DAO community.
•The number of cast votes per voter, which will show how active voters are in
terms of participation.
•The percentage of proposals that are approved, which can show how the voting
system may inﬂuence the results.
•The percentage of positive votes among those cast.
As we did for the previous metrics, we will consider both the mainnet and the
Table 3shows the voting statistics for the three platforms. Interestingly, both in
DAOstack and Aragon the percentage of users that vote is less than 10%, while in
DAOhaus is close to 40%. In the case of DAOstack, the cause of such low percentage
may be the inactivity of a DAO with around 4,000 users, while in the case of Aragon
the high number of inactive DAOs. It is worth noting that the percentage of users
who vote in xDai is smaller than in mainnet, even if it is cheaper to do so. A potential
explanation may be that xDai is a younger alternative, specially in DAOhaus.
Regarding the ratio of votes per voter, in all the platforms has a value around 4.
The main diﬀerence that we observe is that in DAOhaus the ratio in xDai almost
doubles that in mainnet, which could mean that xDai boosted participation. In fact,
according to Figure 4, this eﬀect cannot come from an increase of active users, since
the number of active users in xDai decreased since the beginning of its adoption.
Hence, it may correspond to an activity increase in the users that are active in xDai.
Regarding the percentages of approved proposals, the values are high for all plat-
forms. It might be due to the fact that DAO members mainly present proposals
that they believe that can be approved. This may respond to discussions held oﬀ-
chain, before proposing on-chain. This behavior may also be a response to avoid
the cost (in gas, reputation and time) of presenting proposals that are likely to be
rejected. Still, there are important diﬀerences in the percentage of approved pro-
posals across platforms. DAOstack has the lowest values (around 75%), followed by
Aragon (81%) and then DAOhaus (around 90%). The diﬀerent voting systems may
cause such diﬀerences, we present our hypotheses below.
In the case of the DAOstack voting system, the lower number of proposals passed
might be explained because it requires either an absolute majority (51%), or enough
staking for a proposal to be ”boosted” and thus able to be approved by relative
Faqir-Rhazoui et al. Page 15 of 21
majority. Thus, non-boosted proposals are more likely to be rejected. According to
the analysis in , such behavior mainly happens in larger DAOs (those with more
than 23 members), which are those that may have a greater need for holographic
consensus to facilitate the approval of a proposal.
The high number of approved proposals in DAOhaus may be explained because
the voting system requires no quorum, which makes proposals easier to be passed,
since a relative majority is always enough to approve a proposal. Moreover, in
DAOhaus v2, DAO proposals require a sponsorship of a community member, which
acts as a preliminary ﬁlter of potentially rejectable proposals.
In the case of Aragon, we ﬁnd a percentage of approved proposals between
DAOstack and DAOhaus. Since the Aragon standard voting app requires a quo-
rum to approve a proposal, it makes sense that the approval rate is below to that
from DAOhaus. The aggregated results show that the Aragon voting system leads
to lower rejection rates than the DAOstack system, which in some cases requires a
majority voting to pass a proposal.
Note that we are not stating that a voting system is more eﬀective than others, we
are just interpreting the inﬂuence of those decision-making systems on the general
ﬁgures. Nevertheless, our conclusions should be validated through further studies.
Given the importance of crypto assets within the blockchain ecosystem, studying
the funds accumulated by each DAO is an essential aspect. In fact, one of the main
features of DAOs is to enable collective management of funds through transparent
open accounting. Multiple DAOs employ people or fund proposed projects that may
beneﬁt the community (e.g. programming or event organizing). And of course, given
the rise of DeFi, DAOs are also being used to facilitate investment and ﬁnancial
In the following study, we focus on the DAOs accumulated cryptocurrencies. For
that purpose, we have used the DeepDAO web service. However, DeepDAO does
not provide information from all the DAOs in the considered ecosystems. Instead,
it focuses on the most important ones and still covering a large number of them,
and thus our analysis will rely on their available data.
Table 4shows the Top 10 cryptocurrencies in terms of DAO adoption, that is, by
the number of DAOs that use them. Ether and Dai are used by 50 and 51 DAOs,
respectively, but Ether has more capitalization (close to 15 million dollars versus
over 6 million dollars). Regarding the USD capitalization, it is important to bear
in mind that the funds of a DAO are dynamic as it has inﬂows and outﬂows. The
fund data was retrieved on the 1st of December 2020.
Interestingly, many of those crypto-currencies are stablecoins (DAI, SAI, USDC,
or USDT). Stablecoins are designed to maintain a stable value, typically pegged
to a ﬁat currency such as the dollar (e.g. 1 DAI = 1 dollar), to avoid volatile
market periods and reduce transaction fees. We may split them into two groups.
On one hand, the ﬁat-collateralized type (e.g. USDC, USDT) are the most common
stablecoins, and they usually rely on centralized institutions. On the other hand, the
case of the crypto-collateralized stablecoins (e.g. DAI, SAI) that do not depend on
traditional ﬁnance infrastructure, and use crypto assets as collateral. For example,
Faqir-Rhazoui et al. Page 16 of 21
Token name Token acronym #DAOs #USD in DAOs
Dai stablecoin DAI 51 6,229,754$
Ether ETH 50 14,714,446$
Sai stablecoin v1.0 SAI 21 15,013$
USD Coin USDC 20 5,878,148$
Wrapped Ether WETH 18 9,303,476$
Aragon ANT 15 12,824,896$
Panvala pan PAN 11 20,552$
DAOstack GEN 9 37,553$
Tether USD USDT 8 1,158,129$
Balancer BAL 6 331,744$
Table 4: Top 10 crypto-currencies by DAO adoption, including number of DAOs
that use them, and the accumulated funds converted to USD, as of 1st December
DAI and SAI cryptocurrencies are created by MakerDAO, a DAO created before
the emergence of DAO platforms. Typically, in order to acquire these stablecoins,
anyone may exchange Ethereum’s cryptocurrency (ether) for them .
Ethereum’s cryptocurrency, Ether (ETH), is one of the most used cryptocurren-
cies, despite its market volatility. However, not all DAOs can use ETH as an asset,
especially because it does not comply with the popular Ethereum fungible token
standard ERC20. That is the case of DAOhaus’s DAOs, which cannot use non-
ERC20 cryptocurrencies. Due to that, there are solutions like WETH, that wraps
ETH in an ERC20 smart contract.
There are other cryptocurrencies like ANT or GEN, which are speciﬁc tokens for
Aragon and DAOstack ecosystems, respectively. The ANT token is used for the
governance of the Aragon platform, while the GEN token is used in DAOstack’s
proposal boosting process. Besides that, some DAOs have their own crypto, for ex-
ample, PieDAO has DOUGH, a coin with 44,291,262 USD of market capitalization,
but owned only by this DAO.
Table 5shows the Top 10 DAOs with more cryptofunds in USD. Most of those
DAOs belong to the Aragon ecosystem. Interestingly, most of them have a small
number of registered members (less than 10). We may describe some of these DAOs:
mStable is a DAO which provides autonomous and non-custodial stablecoin in-
frastructure to exchange stablecoins without additional fees. PieDAO  is focused
on bringing market accessibility and economic empowerment, facilitating the au-
tomation of tokenized ”wealth creation” strategies (e.g. proﬁtable investments). In
the case of dxDAO , it is a DAOstack DAO that obtains revenues from its DeFi
services they have and/or develop. MetaCartel Ventures  is a for-proﬁt DAOhaus
DAO created for investing into early-stage Decentralized Applications (DApps).
Ether was created before the ERC20 standard was established. The Ethereum
community is currently working to update Ether to comply with their own standard.
Faqir-Rhazoui et al. Page 17 of 21
DAO name DAO platform #Funds in USD #Members
PieDAO Aragon 73,829,906$ 2,881
mStable Aragon 38,263,266$ 8
dxDAO DAOstack 17,581,208$ 444
Airalab Aragon 13,263,696$ 11
Aragon Trust Aragon 7,015,477$ 5
Aragon Network Budget Aragon 5,903,309$ 3
MetaCartel Ventures DAOhaus 5,619,718$ 99
Aavegotchi Aragon 5,059,662$ 3
API3 DAOv1 Aragon 2,991,833$ 30
Aragon Network Aragon 2,932,121$ 5
Table 5: Top 10 DAOs by a total of cryptocurrencies in USD, as of 1st Decem-
We have compared the three main DAO ecosystems using four dimensions: growth,
activity, voting, and funds. According to our quantitative analysis, Aragon is clearly
the largest and most active platform. Still, the diﬀerence with the other platforms is
signiﬁcantly less than what a superﬁcial exploration may indicate. The initial DAO
numbers, which are typically observed (and advertised) for each platform (shown in
Table 2), would reveal that the size of Aragon is a 10 times larger than DAOhaus
and a 79 times larger than DAOstack. However, from its 1,700+ DAOs (2,000+
including xDai DAOs) and 41,000+ users (68,000 including xDai), Aragon has just
100 DAOs and 330 users which are active each month. This is noteworthy, since
the gap across platforms in practice, counting only active DAOs and active users,
is signiﬁcantly smaller. Thus, according to active DAOs, Aragon is 3 times more
active than DAOhaus, and 11 times than DAOstack; according to active users,
Aragon is 27 times more active than DAOhaus, and 5 than DAOstack.
Still, this does not diminish the Aragon platform in any way. The participation
of a minority of a community and the abandonment of the project, it is typical in
online communities such as wikis [54,55,52], and it seems that this aspect also
holds for the case of DAOs. Besides, Aragon shows a steady growth in the number
of DAOs, at least an order of magnitude higher than the other platforms. Moreover,
eight of the top ten wealthiest DAOs rely on the Aragon platform. And its ability
for customizing DAOs may reveal essential to exploit the potential of DAOs .
Still, since May 2020 we can observe a decline in the number of active DAOs within
Aragon, which is worth more research.
Concerning DAOhaus, it has shown a clear and steady growth in the last months
analyzed, in number of DAOs, active DAOs and user activity. Its voting system
seems to be the easiest to approve a proposal, reaching a surprising 92% of proposals
passed. Despite of DAOhaus being born recently, in 2019 , it has managed to
position itself as an active platform with positive trends.
Concerning DAOstack, the numbers of activity and adoption show signs of stagna-
tion, even after the adoption of xDai. A potential explanation may be the problems
in Genesis DAO, a DAO dedicated to promote the use of DAOs through DAOstack
. Still, some of its communities remain loyal and active, including the third
wealthiest DAOs overall, dxDAO. It is worth noting that DAOstack has more DAOs
in xDai than in mainnet, which may facilitate a venue of recent growth which we
could not monitor in Figure 1(due to API limitations, as explained in Section 4.1).
Faqir-Rhazoui et al. Page 18 of 21
Due to the surges in gas price during 2020, the platforms facilitated the possibility
to operate in the xDai network. However, our analysis does not show a strong eﬀect
on the platform’s activity, as we may have expected. In any case, xDai solutions are
temporary solutions until the arrival of Ethereum 2.0, which is expected to clearly
mitigate the problems of gas cost.
6 Concluding Remarks
In this work we have reviewed the three main platforms that nowadays facilitate
the creation and management of DAOs: Aragon, DAOstack, DAOhaus. For such
comparison, we retrieve data from both the main Ethereum network (mainnet ) and
a parallel Ethereum network (sidechain xDai ). We analyze data from 72,320 users
and 2,353 DAO communities in order to study the three ecosystems across four
dimensions: growth, activity, voting and funds. Our results, discussed in Section
5, show that there are notable diﬀerences across the DAO platforms in terms of
growth, activity, and voting results.
In general, we need to remark that the conclusions drawn from the retrieved statis-
tics must be taken with caution. First, because we are looking at general statistics
(counts, rates and general trends) without looking into the individual communities.
Given the current relatively small number of DAOs and the diversity of DAOs,
these ﬁgures may be misleading in some cases. Second, because the ﬁgures reﬂect
the activity of the early-adopters interacting with a new technology. Thus, in some
cases, some of these early adopters may have already abandoned the technology, or
are using it purely for testing their capabilities.
We believe that further research could explore this phenomenon both quantita-
tively and qualitatively, deepening into some of the open questions extracted from
this work, such as the current decline in Aragon active DAOs, its volatile active
user numbers in recent months, the very high percentage of passed proposals in
DAOhaus (and relatively, also in the other platforms), or the reasons behind the
xDai growth in DAOstack.
Despite these shortcomings, we believe it is necessary to advance in the under-
standing of this new form of online organization that it is taking shape in the
blockchain and that is implementing innovative forms of governance. The people
that design DAO mechanisms often do it without prior extensive testing. Thus, as
a result, these ﬁrst organizations can be seen as guinea pigs that are experimenting
with a novel system for the ﬁrst time, while at the same time being the object
of the experiment. Furthermore, these new organizations are strongly inﬂuenced
by the underlying technology, a costly, append-only, decentralized and transparent
database. Hence, their collective behavior could be diﬀerent to organizations that
operate through standard client-server applications deployed on the Internet.
All these aspects make DAOs a challenging research ﬁeld. We particularly consider
important to dive into the voting systems and how they are aﬀected by aspects such
as accumulated funds, reputation, etc. We hope our article stimulates the research
on these novel communities to help them deliver truly eﬀective decentralized and
scalable collective governance.
Faqir-Rhazoui et al. Page 19 of 21
Appendix: Data Collection Process
All data from the studied DAO platforms is publicly available in the Ethereum’s
blockchain (and its sidechain xDai). However, the process to fetch and query this
data is rather tedious, since the data is stored as transactions in the ledger. In order
to ease the query process, diﬀerent solutions have came out. For our purposes we
have used The Graph.The Graph is a protocol which indexes blockchain data in
order to facilitate database queries. Its use is popularizing across Ethereum Dapps
which may use it to facilitating making their data available. The data is served as an
API, and it is fetched with the GraphQL language . This is the case for the three
DAO platforms we have analyzed, providing APIs using The Graph. DAOstack,
and DAOhaus oﬀer all their ecosystem data through the same endpoint for each.
However, Aragon oﬀers an endpoint for each app it has, and thus we have used
diﬀerent endpoints for the voting, DAOs, or tokens.
•Dapp: Decentralized application
•DAO: Decentralized Autonomous Organizations
•DeFi: Decentralized Finance
•CV: Conviction Voting
•HC: Holographic Consensus
•USD: United States Dollar ($)
•ETH: Ether, Ethereum’s cryptocurrency
•SDK: Software Development Kit
•DApps: Decentralized Applications
Availability of data and materials
The datasets generated and/or analysed during the current study are available in the following GitHub repository,
https://github.com/Grasia/DAO-comparison- data. Most of such data was extracted from the DAO-Analyzer
web tool, with its open-licensed code available in the GitHub repository
https://github.com/Grasia/dao-analyzer, and deployed in http://dao-analyzer.science/. The data
concerning cryptocurrency funds was extracted from the DeepDAO web tool, http://deepdao.world. Its source
code is proprietary and therefore not publicly available.
The authors declare that they have no competing interests.
This work was partially supported by the project P2P Models (https://p2pmodels.eu) funded by the European
Research Council (ERC-2017-STG 625 grant no.: 759207), and by the project Chain Community (grant no.:
RTI2018-096820-A-100) funded by the Spanish Ministry of Science and Innovation.
YFR carried out the research, retrieving and analyzing the data metrics for the DAO comparison and implementing
those metrics in DAO-Analyzer. He also wrote the ﬁrst draft of the manuscript. JA planned and supervised the
research, analyzed the data and co-wrote the introduction, the quantitative comparison and the discussion section.
SH planned the research, reviewed, and rewrote all sections of the manuscript.
Faqir-Rhazoui et al. Page 20 of 21
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