1.Key Points of the Report

1.1 Core Investment Logic

• Balancing Act: Quilibrium seeks to find a “balance” between the computing power of the traditional internet and the decentralization of blockchain. To achieve this, it has designed a unique decentralized cloud computing architecture.
• Developer-Friendly: Quilibrium has built an operating system based on a database, offering a development experience closer to traditional software. This approach may attract more traditional software developers and facilitate Web3 developers in building more complex encrypted applications.
• Security and Privacy: Quilibrium’s design emphasizes security and privacy, making it highly attractive to enterprises that wish to use encryption technology without exposing sensitive data. For individuals, the initial success of Farcaster also demonstrates the long-term potential of decentralized applications in acquiring users and generating revenue.
• Experienced Leadership: Founder and CEO Cassie Heart, a former senior engineer at Coinbase and developer of Farcaster, leads the team. The team boasts extensive experience, stable delivery capabilities, and a distinctive personality.

1.2 Major Risks

• Early Stage: The project is in a very early stage; the mainnet has not been released yet, and the project’s complexity means that the technical feasibility and market demand have not been verified.
• Competition: In the short term, Quilibrium may face competition from better-known projects like Arweave AO in terms of user mindshare and developer attention.
• Token Model: There is no fixed token model, and the token release rate may be unstable, posing a certain risk to investors.

1.3 Valuation

• Attractive Market Cap: Due to Quilibrium being at a very early stage, it is currently impossible to derive an accurate valuation for the project. However, compared to other market players with overlapping concepts, Quilibrium’s current market cap presents a certain level of attractiveness.

2. Business Analysis

Quilibrium positions itself as a “decentralized internet layer protocol providing the convenience of cloud computing without sacrificing privacy or scalability” and a “decentralized PaaS solution.” This section will explore Quilibrium’s business by addressing the following questions:

• What are the problems with traditional internet cloud computing?
• Why do we need another decentralized computer?
• How does Quilibrium differ from current mainstream blockchain designs?

Source: Cassie Heart’s Farcaster account

2.1 Business Positioning

2.1.1 Starting with Computing

In both Web2 and Web3, “computing” is a crucial concept, driving application development, execution, and scaling. In traditional internet architecture, computing tasks are usually performed by centralized servers. The advent of cloud computing has increased scalability, accessibility, and cost efficiency, gradually replacing traditional computing to become mainstream.

In terms of services, large cloud service providers typically offer cloud service models that can be divided into three categories:

• Infrastructure as a Service (IaaS)
• Platform as a Service (PaaS)
• Software as a Service (SaaS)

These models correspond to different needs and capabilities, providing varying levels of control over resources. End users are generally more familiar with SaaS, while PaaS and IaaS are mainly targeted at developers.

Source: Lydia @ Mint Ventures

Source: S2 Lab, Lydia @ Mint Ventures

In mainstream blockchains like Ethereum, computing is usually performed by decentralized nodes. This method does not rely on centrally controlled servers; each node performs computing tasks locally and ensures data correctness through consensus mechanisms. However, the computing power and processing speed of decentralized computing typically cannot match traditional cloud services.

Quilibrium aims to strike a “balance” between the computing power and scalability of the traditional internet and the decentralization of blockchain, opening new possibilities for application development.

Source: Cassie Heart’s live screen recording

2.1.2 The Centralization Problem in Computer Systems

For most end users, the centralization problem of computer systems is not easily perceived. This is because end users mainly interact with the hardware layer of computer systems. Our PCs, smartphones, and other devices are distributed worldwide and run independently under individual control. This distributed physical presence means that computer systems are not necessarily centralized at the hardware level.

In contrast, existing computer systems are significantly more centralized at the network architecture and cloud computing service levels. Amazon AWS, Microsoft Azure, and Google Cloud collectively held over 67% of the cloud service market share in Q1 2024, significantly outpacing later entrants.

Source: Synergy Research Group

Moreover, as the “water carriers” of the AI wave, the trend of strengthening among major cloud service providers seems to be continuing. Microsoft Azure, as the exclusive cloud service provider for OpenAI, has seen accelerated growth in the past year. In Microsoft’s Q3 FY 2024 financial report (i.e., Q1 2024), Azure and other cloud services’ revenue grew by 31%, exceeding the market expectation of 28.6%.

Source: Microsoft, Lydia @ Mint Ventures

Apart from market competition considerations, the privacy and security issues brought by centralized computer systems are also receiving increasing attention. Each outage of a major cloud service provider can have widespread impacts. Data shows that between 2010 and 2019, AWS experienced 22 unexpected failures, with an average of 2.4 failures per year. These outages affected not only Amazon’s own e-commerce business but also the network services of companies using AWS, such as Robinhood, Disney, Netflix, and Nintendo.

2.1.3 The Proposal of Decentralized Computers

In this context, the necessity of decentralized computers has been repeatedly proposed. With centralized cloud service providers increasingly adopting distributed architectures to avoid single points of failure by replicating data and services across multiple locations, and using edge storage to enhance performance, the narrative of decentralized computing has shifted towards data security, privacy, scalability, and cost-effectiveness.

We first analyze several concepts of decentralized computers proposed by different projects, all sharing the common feature of building a global distributed computing platform through decentralized data storage and processing, supporting the development of decentralized applications.

• World Computer: Generally refers to Ethereum, providing a global smart contract execution environment, with its core function being decentralized computing and unified execution of smart contracts globally.
• Internet Computer: Usually refers to ICP developed by the Dfinity Foundation, aiming to extend the functionality of the internet to enable decentralized applications to run directly on the internet.
• Hyper Parallel Computer: Typically refers to the AO protocol proposed by Arweave, a distributed computing system running on the Arweave network, characterized by high parallelism and high fault tolerance.

It’s worth noting that ICP, AO, and Quilibrium are not traditional blockchains. They do not rely on a linear block arrangement structure but maintain the core principles of blockchain such as decentralization and immutability of data. They can be seen as natural extensions of blockchain technology. Although ICP has yet to realize its grand vision, the emergence of AO and Quilibrium indeed brings new possibilities that could impact the future of Web3.

The table below compares the technical features and application directions of the three, aiming to help readers understand “Will Quilibrium repeat ICP’s mistakes?” and, as a frontier solution for decentralized computing, what are the differences between Quilibrium and AO, which is dubbed the “Ethereum killer.”

2.2 Consensus Mechanism

In traditional blockchains, the consensus mechanism is an abstract and core component that defines how the network reaches agreement, processes, and verifies transactions and other operations. The choice of consensus mechanism affects the network’s security, speed, scalability, and degree of decentralization.

Quilibrium’s consensus mechanism is called “Proof of Meaningful Work” (PoMW), where miners are required to complete tasks that are practically meaningful to the network, such as data storage, data retrieval, and network maintenance. The PoMW consensus mechanism integrates multiple fields, including cryptography, multiparty computation, distributed systems, database architecture, and graph theory, aiming to reduce dependency on a single resource (such as energy or capital), ensure the degree of decentralization, and maintain security and scalability as the network expands.

The incentive mechanism is crucial to ensuring the smooth operation of the consensus mechanism. Quilibrium’s incentive distribution is not static but dynamically adjusts according to the network state to ensure that incentives match demand. Quilibrium also introduces a multi-proof mechanism, allowing a node to verify multiple data fragments, ensuring the network can continue to operate even when nodes and core resources are insufficient.

We can understand miners’ final earnings with a simplified formula, where the unit reward dynamically adjusts based on the network scale:

Earnings = Score × Unit Reward

The calculation of the score is based on a variety of factors. The specific formula is as follows:

The parameters are defined as follows:

• Time in Mesh for Topic: Longer participation time and higher stability lead to a higher score.
• First Message Deliveries for Topic: More first-time message deliveries result in a higher score.
• Mesh Message Delivery Rate/Failures for Topic: Higher delivery rates and lower failure rates lead to higher scores.
• Invalid Messages for Topic: Fewer invalid message deliveries result in a higher score.

The weighted sum of these parameters will have a topic cap (TC) to limit the value within a certain range, preventing unfair scoring due to excessively large parameters.

• Application-Specific Score: Defined by specific applications.
• IP Collocation Factor: Fewer nodes from the same IP address lead to a higher score.

Source: Quilibrium Dashboard

Quilibrium currently operates over 60,000 nodes, with the actual earnings of nodes possibly fluctuating depending on the parameter weights between different versions. From version 1.4.19 onwards, miners can view their earnings in real-time, but payouts will only be available after the mainnet launch.

2.3 Network Architecture

Quilibrium’s core business is decentralized PaaS (Platform as a Service) solutions. Its network architecture mainly consists of communication, storage, data query and management, and the operating system. This section will focus on how its design differs from mainstream blockchains. For those interested in technical details and implementation, please refer to the official documentation and white paper.

2.3.1 Communication

As the foundational structure of the network, Quilibrium’s communication is composed of four parts:

a. Key Generation Quilibrium introduces a key generation method based on graph theory called the PCAS (Planted Clique Addressing Scheme). Similar to traditional blockchain technology, PCAS also uses asymmetric encryption—each user has a public key and a private key. The public key can be publicly shared and is used to encrypt information or verify signatures, while the private key is kept secret and is used to decrypt information or generate signatures. The main differences lie in the key generation method, its form, and its application (see table below for details).

b. End-to-End Encryption End-to-end encryption (E2EE) is a crucial component for ensuring secure communication between nodes. Only the communicating parties can see the plaintext data, and even intermediaries facilitating the communication cannot read the content. Quilibrium employs a method called Triple-Ratchet for end-to-end encryption, which provides higher security compared to traditional ECDH schemes. Specifically, while traditional schemes often use a single static key or periodically update the keys, the Triple-Ratchet protocol updates the key after each communication, achieving forward secrecy, post-compromise security, deniability, replay protection, and unordered message delivery. This method is especially suitable for group communication but comes with higher complexity and computational costs.

c. Mix Network Routing Mix networks (Mixnets) act as black boxes, receiving the sender’s information and delivering it to the receiver. External attackers, even if they can access the information outside the black box, cannot link the sender and receiver. Quilibrium uses RPM (Random Permutation Matrix) technology, providing a mix network architecture that is structurally complex and difficult for both external and internal attackers to break, offering advantages in anonymity, security, and scalability.

d. Peer-to-Peer Communication GossipSub is a peer-to-peer message dissemination protocol based on the publish/subscribe model, widely used in blockchain technology and decentralized applications (DApps). Quilibrium’s BlossomSub protocol is an extension and improvement of the traditional GossipSub protocol, aimed at enhancing privacy protection, improving resistance to Sybil attacks, and optimizing network performance.

2.3.2 Storage

Most traditional blockchains use cryptographic hash functions as fundamental tools for data integrity verification and rely on consensus mechanisms to ensure network consistency. However, these mechanisms have two main limitations:

• They usually do not include verification of storage time and lack direct mechanisms to defend against time-based or computational attacks.
• Storage and consensus mechanisms are typically separated, potentially leading to issues with data synchronization and consistency.

Quilibrium’s storage solution uses a Verifiable Delay Function (VDF) design, creating a time-dependent chain structure that integrates storage and consensus mechanisms. The key features of this solution can be summarized as follows:

Input Processing: By using hash functions such as SHA256 and SHAKE128 to process inputs, any minor changes in the data result in significantly different hash values, making the data more resistant to tampering and easier to verify.

Delay Guarantee: The computation process is intentionally set to be time-consuming. The tasks must be executed sequentially, with each step depending on the result of the previous step, preventing acceleration through additional computational resources. This ensures the output is derived from continuous and deterministic calculations over time. Since the generation process cannot be parallelized, any attempt to recompute or alter the already published VDF results would take considerable time, giving network participants ample time to detect and respond.

Fast Verification: The time required to verify a VDF result is much less than the time needed to generate it. Verification typically involves some mathematical checks or auxiliary data to confirm the validity of the result.

Source: Quilibrium White Paper

This chain structure based on time proof does not rely on the generation of blocks in traditional blockchains, and can theoretically reduce MEV attacks and front-running phenomena.

This time-proof chain structure does not rely on the traditional block generation in blockchains and theoretically can reduce MEV (Maximal Extractable Value) attacks and front-running.

2.3.3 Data Query and Management

Traditional blockchains mostly use simple key-value storage or Merkle Tree structures to manage data, which are usually limited in expressing complex relationships and supporting advanced queries. Moreover, most current blockchain systems do not provide built-in privacy protection mechanisms for node queries, which is the context for the emergence of privacy-enhancing technologies such as Zero-Knowledge Proofs.

Quilibrium proposes an “Oblivious Hypergraph” framework, which combines hypergraph structures with Oblivious Transfer technology, enabling support for complex query capabilities while maintaining data privacy. Specifically:

Hypergraph Structure: This structure allows edges to connect multiple vertices, enhancing the capability to express complex relationships. It can directly map various database models, making it possible to express and query any type of data relationship on the hypergraph.

Oblivious Transfer Technology: This technology ensures that even the nodes processing the data cannot know the specific data content being accessed, enhancing privacy protection during data queries.

2.3.4 Operating System

Operating systems are not a native concept in blockchain. Most traditional blockchains primarily focus on consensus mechanisms and data immutability, usually not providing complex operating system-level functions. For instance, while Ethereum supports smart contracts, its operating system functions are relatively simple, mainly limited to transaction processing and state management.

Quilibrium has designed an operating system based on its hypergraph database, implementing common operating system primitives such as file systems, schedulers, IPC-like mechanisms, message queues, and control key management. This design, directly constructing the operating system on the database, can support the development of complex decentralized applications.

Source: Quilibrium White Paper

2.4 Programming Languages

Quilibrium primarily uses Go as its main programming language, along with Rust and JavaScript. The advantages of Go include its ability to handle concurrent tasks, concise syntax, and an active developer community. According to the Tiobe programming language rankings, Go’s popularity has increased significantly in recent years, reaching the 7th position in the latest June ranking. Other blockchain projects utilizing Go for their core development include Ethereum, Polygon, and Cosmos.

Source: Quilibrium

Source: Tiobe

3. Project Status

3.1 Project History and Roadmap

Quilibrium’s whitepaper was released in December 2022, outlining a roadmap divided into three phases: Dusk, Equinox, and Event Horizon. Currently, Quilibrium is in the very early stages, with the team updating the network bi-weekly. The latest version is v1.4.20. The team has decided to skip the 1.5 phase of the roadmap, moving directly from version 1.4 to version 2.0. The 2.0 version, marking the end of the Dusk phase, is expected to launch in late July, introducing the bridge for $QUIL tokens. According to the provisional plan, the Equinox and Event Horizon phases will support more advanced applications such as streaming and AI/ML model training.

3.2 Team and Funding

Quilibrium was founded by CEO Cassie Heart. Before establishing Quilibrium, she was a senior software engineer at Coinbase with over 12 years of experience in software development and blockchain. Cassie, who opposes centralized social media platforms, is primarily active on Farcaster, both personally and through Quilibrium’s project account. Her Farcaster account has over 310,000 followers, including Ethereum founder Vitalik. Cassie is also a developer for Farcaster. Development on Quilibrium began in April 2023 and has been progressing steadily. The development team consists of 24 members, with Cassie Heart (Cassandra Heart) being the lead developer.

Source: Quilibrium

Quilibrium’s team has yet to disclose its funding history and investment institutions.

3.3 Token Model Analysis

$QUIL is the native token of Quilibrium, adopting a 100% fair launch model, where all tokens are produced through node operation. The team operates a small number of nodes but holds less than 1% of the total tokens.

$QUIL does not have a fixed token model, and its total supply is uncapped. The token release rate dynamically adjusts based on network adoption. When the network expands, more tokens are released to incentivize nodes; if growth slows, the release rate decreases accordingly.

The table below shows the predicted token release schedule by the team and community members. The current circulating supply is 340 million, with an estimated final supply converging around 2 billion, depending on ecosystem development.

Source: @petejcrypto

3.4 Risks

The potential risks for Quilibrium at this stage include:

• The project is in a very early stage, with the mainnet yet to be launched. The project’s complexity means that technical feasibility and market demand validation are still pending.
• In the short term, it may face competition from the better-known Arweave AO in terms of user and developer attention.
• The lack of a fixed token model means the token release rate could be unstable, posing additional risks for investors.

4. Valuation

Valuing infrastructure projects like Quilibrium is inherently complex, involving multiple dimensions such as Total Value Locked (TVL), on-chain active addresses, number of dApps, and developer community. Since Quilibrium is still in a very early stage and Arweave AO’s token $AO is not yet traded, it is currently impossible to provide an accurate valuation of the project.

Below, we list the circulating market cap and fully diluted market cap of projects with a certain degree of conceptual overlap with Quilibrium (data as of June 23, 2024) for reference.

Source: CoinGecko, data as of June 23, 2024

5. Reference content and acknowledgments

The writing of this article requires thanks to Brother Hai (@PleaseCallMeWhy), Brother Lan and Connor for their review and comments.

Disclaimer：

1. This article is reprinted from [Mintventures]. All copyrights belong to the original author [Lydia Wu]. If there are objections to this reprint, please contact the Gate Learn team, and they will handle it promptly.
2. Liability Disclaimer: The views and opinions expressed in this article are solely those of the author and do not constitute any investment advice.
3. Translations of the article into other languages are done by the Gate Learn team. Unless mentioned, copying, distributing, or plagiarizing the translated articles is prohibited.